U.S. patent application number 17/481622 was filed with the patent office on 2022-08-04 for biomarker identification.
The applicant listed for this patent is ImmuneXpress Pty Ltd. Invention is credited to Richard Bruce BRANDON, Leo Charles MCHUGH.
Application Number | 20220243272 17/481622 |
Document ID | / |
Family ID | |
Filed Date | 2022-08-04 |
United States Patent
Application |
20220243272 |
Kind Code |
A1 |
BRANDON; Richard Bruce ; et
al. |
August 4, 2022 |
BIOMARKER IDENTIFICATION
Abstract
Disclosed are method and apparatus for identifying biomarkers
and in particular for identifying biomarkers for use in making
clinical assessments, such as early diagnostic, diagnostic, disease
stage, disease severity, disease subtype, response to therapy or
prognostic assessments. In one particular example, the techniques
are applied to allow assessments of patients suffering from,
suspected or suffering from, or with clinical signs of SIRS
(Systemic Inflammatory Response Syndrome) being either
infection-negative SIRS or infection-positive SIRS.
Inventors: |
BRANDON; Richard Bruce;
(Boonah, AU) ; MCHUGH; Leo Charles; (Seattle,
WA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
ImmuneXpress Pty Ltd |
Boonah |
|
AU |
|
|
Appl. No.: |
17/481622 |
Filed: |
September 22, 2021 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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16951758 |
Nov 18, 2020 |
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17481622 |
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16184873 |
Nov 8, 2018 |
10975437 |
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16951758 |
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15201431 |
Jul 2, 2016 |
10167511 |
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16184873 |
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14714182 |
May 15, 2015 |
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15201431 |
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PCT/AU2014/050075 |
Jun 18, 2014 |
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14714182 |
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International
Class: |
C12Q 1/6883 20060101
C12Q001/6883; G16B 25/00 20060101 G16B025/00; C12Q 1/689 20060101
C12Q001/689; G16B 25/10 20060101 G16B025/10 |
Foreign Application Data
Date |
Code |
Application Number |
Jun 20, 2013 |
AU |
2013902243 |
Claims
1. A composition comprising a mixture of a DNA polymerase, sample
peripheral blood leukocyte cDNA having a cDNA expression profile
characteristic of a subject with a clinical sign of systemic
inflammatory response syndrome (SIRS), wherein the sample
peripheral blood leukocyte cDNA comprises a Olfactomedin 4 (OLFM4)
cDNA, and at least one oligonucleotide primer that hybridizes to
the OLFM4 cDNA.
2. The composition of claim 1, wherein the sample cDNA expression
profile comprises Granulysin (GNLY) cDNA at a lower level than a
control cDNA expression profile characteristic of a healthy
subject, and each of Toll-like Receptor 5 (TLR5) cDNA, Vanin 1
(VNN1) cDNA, and Matrix Metalloproteinase 9 (MMP9) cDNA at a higher
level than the control cDNA expression profile.
3. The composition of claim 1, further comprising a probe that
hybridizes to OLFM4 cDNA.
4. The composition of claim 1, further comprising
deoxynucleotides.
5. The composition of claim 1, wherein the sample peripheral blood
leukocyte cDNA comprises at least one other cDNA selected from the
group consisting of a Defensin Alpha 4 (DEFA4) cDNA,
Lactotransferrin (LTF) cDNA, Myeloperoxidase (MPO) cDNA, Killer
Cell Lectin Like Receptor F1 (KLRF1) cDNA, Vesicle Associated
Membrane Protein 2 (VAMP2) cDNA, Killer Cell Lectin Like Receptor
D1 (KLRD1) cDNA, Histone Cluster 1 H3 Family Member 3 (HIST1H3J)
cDNA, Histone Cluster 1 H3 Family Member A (HIST1H3A) cDNA, PMS2
C-Terminal Like Pseudogene (PMS2CL) cDNA,
Beta-1,4-Galactosyltransferase 3 (B4GALT3) cDNA and Immunoglobulin
Lambda Variable 1-44 (IGLV1-44), and the composition further
comprises at least one oligonucleotide primer that hybridizes to
the at least one other cDNA.
6. The composition of claim 5, further comprising a probe that
hybridizes to the at least one other cDNA.
7. The composition of claim 5, wherein the sample peripheral blood
leukocyte cDNA comprises DEFA4 cDNA, and the composition comprises
at least one oligonucleotide primer that hybridizes to the DEFA4
cDNA.
8. A composition comprising a mixture of a DNA polymerase, sample
peripheral blood leukocyte cDNA having a cDNA expression profile
characteristic of a subject with a clinical sign of systemic
inflammatory response syndrome (SIRS), wherein the sample
peripheral blood leukocyte cDNA comprises a Arginase 1 (ARG1) cDNA,
and at least one oligonucleotide primer that hybridizes to the ARG1
cDNA.
9. The composition of claim 8, wherein the sample cDNA expression
profile comprises Granulysin (GNLY) cDNA at a lower level than a
control cDNA expression profile characteristic of a healthy
subject, and each of Toll-like Receptor 5 (TLR5) cDNA, Vanin 1
(VNN1) cDNA, and Matrix Metalloproteinase 9 (MMP9) cDNA at a higher
level than the control cDNA expression profile.
10. The composition of claim 8, further comprising a probe that
hybridizes to ARG1 cDNA.
11. The composition of claim 8, further comprising
deoxynucleotides.
12. The composition of claim 8, wherein the sample peripheral blood
leukocyte cDNA comprises at least one other cDNA selected from the
group consisting of Cluster of Differentiation 177 (CD177), VNN1
cDNA, Tudor Domain Containing 9 (TDRD9), (GPR84) cDNA,
15-Hydroxyprostaglandin Dehydrogenase (HPGD) cDNA, Galectin-Related
Protein (HSPC159) cDNA, Folate Receptor Beta (FOLR2) cDNA, Folate
Receptor Gamma (FOLR3) cDNA, Lipocalin 2 (LCN2) cDNA, ATP Binding
Cassette Subfamily A Member 13 (ABCA13) cDNA, Marker Of
Proliferation Ki-67 (MKI67) cDNA, Lactotransferrin (LTF) cDNA,
Resistin (RETN) cDNA, Ankyrin Repeat Domain 28 (ANKRD28) cDNA,
LHFPL Tetraspan Subfamily Member 6 (LHFP) cDNA, Trace Amine
Associated Receptor 1 (TAAR1) cDNA, Defensin Alpha 4 (DEFA4) cDNA,
Secreted Protein Acidic And Cysteine Rich (SPARC) cDNA, Joining
Chain Of Multimeric IgA And IgM (IGJ) cDNA and High Mobility Group
Box 2 (HMGB2) cDNA, and the composition further comprises at least
one oligonucleotide primer that hybridizes to the at least one
other cDNA.
13. The composition of claim 12, further comprising a probe that
hybridizes to the at least one other cDNA.
14. The composition of claim 12, wherein the sample peripheral
blood leukocyte cDNA comprises DEFA4 cDNA, and the composition
comprises at least one oligonucleotide primer that hybridizes to
the DEFA4 cDNA.
Description
RELATED APPLICATIONS
[0001] This application is a divisional of U.S. application Ser.
No. 16/951,758, filed Nov. 18, 2020, which is a divisional of U.S.
application Ser. No. 16/184,873, filed Nov. 8, 2018, now U.S. Pat.
No. 10,975,437, issued on Apr. 13, 2021, which is a divisional of
U.S. application Ser. No. 15/201,431, filed Jul. 2, 2016, now U.S.
Pat. No. 10,167,511, issued on Jan. 1, 2019, which is a
continuation of U.S. application Ser. No. 14/714,182, filed May 15,
2015, now abandoned, which is a continuation of International
Application No. PCT/AU2014/050075 entitled "Biomarker
Identification," filed Jun. 18, 2014, which claims priority to
Australian Provisional Application No. 2013902243, filed Jun. 20,
2013, the subject matter of each of which is hereby incorporated
herein by reference in its entirety.
STATEMENT REGARDING SEQUENCE LISTING
[0002] The Sequence Listing associated with this application is
provided in text format in lieu of a paper copy, and is hereby
incorporated by reference into the specification. The name of the
text file containing the Sequence Listing is
DAVI_036_04US_ST25.txt. The text file is about 2.53 MB, created on
Sep. 21, 2021, and is being submitted electronically via
EFS-Web.
FIELD OF THE INVENTION
[0003] The present invention relates to a method and apparatus for
identifying biomarkers and in particular for identifying biomarkers
for use in making clinical assessments, such as early diagnostic,
diagnostic, disease stage, disease severity, disease subtype,
response to therapy or prognostic assessments. In one particular
example, the techniques are applied to allow assessments of
patients suffering from, suspected of suffering from, or with
clinical signs of SIRS (Systemic Inflammatory Response Syndrome)
being either infection-negative SIRS (inSIRS) or infection-positive
SIRS (ipSIRS).
DESCRIPTION OF THE RELATED ART
[0004] The reference in this specification to any prior publication
(or information derived from it), or to any matter which is known,
is not, and should not be taken as an acknowledgement or admission
or any form of suggestion that the prior publication (or
information derived from it) or known matter forms part of the
common general knowledge in the field of endeavour to which this
specification relates.
[0005] The analysis of gene expression products for diagnostic
purposes is known. Such analysis requires identification of one or
more genes that can be used to generate a signature for use in
distinguishing between different conditions. However, such
identification can require the analysis of many gene expression
products, which can be mathematically complex, computationally
expensive and hence difficult. Much of the biomarker discovery
process is devoted to identifying a subset of the data that may
have relevant import, from which a signature is derived using a
combination of these values to produce a model for diagnostic or
prognostic use.
[0006] WO2004044236 describes a method of determining the status of
a subject. In particular, this is achieved by obtaining subject
data including respective values for each of a number of
parameters, the parameter values being indicative of the current
biological status of the subject. The subject data are compared to
predetermined data that includes values for at least some of the
parameters and an indication of the condition. The status of the
subject, and in particular, the presence and/or absence of the one
or more conditions, can then be determined in accordance with the
results of the comparison.
SUMMARY OF THE INVENTION
[0007] In one aspect, the present invention provides apparatus for
identifying biomarkers, the apparatus including an electronic
processing device that: [0008] uses reference data from a plurality
of individuals to define a number of groups of individuals, the
reference data including measurements of the activity of a
plurality of reference biomarkers; [0009] uses a plurality of
analysis techniques to identify a number of potential biomarkers
from the plurality of reference biomarkers that are potentially
useful for distinguishing the groups of individuals, allowing the
potential biomarkers to be used in generating signatures for use in
clinical assessments.
[0010] Suitably, the electronic processing device, for each
analysis technique: [0011] using the analysis technique, identifies
a number of reference biomarkers that best distinguish the groups
of individuals; [0012] determines if the predictive performance of
the identified reference biomarkers exceeds a predetermined
threshold; and, [0013] in response to a successful determination,
determines the identified reference biomarkers to be potential
biomarkers.
[0014] In some embodiments, the number of reference biomarkers is
at least one of:
[0015] less than 10;
[0016] more than 1;
[0017] between 2 and 8; and,
[0018] 5.
[0019] In some embodiments, the predetermined threshold is at least
one of:
[0020] at least 90%;
[0021] at least 85%; and,
[0022] at least 80%.
[0023] Suitably, the electronic processing device:
[0024] adds potential biomarkers to a potential biomarker
collection; and,
[0025] removes the potential biomarkers from a reference biomarker
collection.
[0026] Suitably, for each of a plurality of analysis techniques the
electronic processing device repeatedly identifies reference
biomarkers as potential biomarkers until the predictive performance
of the identified reference biomarkers falls below the
predetermined threshold.
[0027] The electronic processing device may iteratively identify
potential biomarkers.
[0028] In some embodiments, the electronic processing device uses a
number of iterations including at least one of:
[0029] at least 100;
[0030] at least 500;
[0031] at least 1000;
[0032] at least 2000; and,
[0033] at least 5000.
[0034] The electronic processing device may repeatedly determine
potential biomarkers until a predetermined number of potential
biomarkers are identified.
[0035] Suitably, the predetermined number of potential biomarkers
includes at least one of:
[0036] at least 100;
[0037] less than 500;
[0038] about 200.
[0039] In some embodiments, the analysis techniques include at
least one of:
[0040] regression techniques;
[0041] correlation analysis; and,
[0042] a combination of regression and correlation techniques.
[0043] Suitably, the analysis techniques include:
[0044] sparse PLS;
[0045] random forest; and,
[0046] support vector machines.
[0047] In some embodiments, the electronic processing device:
[0048] removes a validation subgroup from the reference data prior
to determining the potential biomarkers;
[0049] determines the potential biomarkers using the reference data
without the validation subgroup; and,
[0050] uses the validation subgroup to validate at least one of:
[0051] the potential biomarkers; and, [0052] signatures including a
number of the potential biomarkers.
[0053] In some embodiments, the processing system determines the
number of groups by classifying the individuals using at least one
of:
[0054] an indication of a presence, absence, degree, or stage, or
progression of a condition;
[0055] phenotypic traits associated with the individuals;
[0056] genetic information associated with the individuals;
[0057] biomarkers associated with the individuals.
[0058] Suitably, the processing system determines groups at least
in part using input commands from a user.
[0059] The reference data may include time series data indicative
of the progression of a condition.
[0060] In some embodiments, the time series data is indicative of
whether a condition that is at least one of:
[0061] improving;
[0062] worsening; and,
[0063] static.
[0064] The reference data may include for each of the individuals
an indication of at least one of:
[0065] an activity of each of the reference biomarkers;
[0066] a degree of a condition;
[0067] a stage of a condition;
[0068] a presence of a condition;
[0069] an absence of a condition;
[0070] an indication of a condition progression;
[0071] phenotypic information;
[0072] genetic information; and,
[0073] a SOFA score.
[0074] In some embodiments, the electronic processing device
identifies a number of potential biomarkers for use as signature
biomarkers, the signature biomarkers being used in generating the
signatures.
[0075] Suitably, the electronic processing device: determines a
clinical assessment; and, identifies the signature biomarkers for
the clinical assessment.
[0076] Suitably, the electronic processing device:
[0077] determines second groups of individuals relevant to the
clinical assessment;
[0078] using a second analysis technique, identifies a number of
the potential biomarkers that best distinguish the second groups of
individuals;
[0079] determines if the predictive performance of the identified
potential biomarkers exceeds a predetermined threshold; and,
[0080] in response to a successful determination, determines the
identified potential biomarkers to be signature biomarkers.
[0081] In some embodiments, the electronic processing device, in
response to an unsuccessful determination:
[0082] modifies parameters of the second analysis technique;
and,
[0083] uses the second analysis technique to identify alternative
potential biomarkers.
[0084] In some embodiments, the electronic processing device:
[0085] determines if the identified potential biomarkers are to be
excluded; and,
[0086] in response to a successful determination: [0087] removes
the potential biomarkers from a potential biomarker database;
[0088] and, uses the second analysis technique to identify
alternative potential biomarkers for use as signature
biomarkers.
[0089] Suitably, the second analysis technique includes at least
one of:
[0090] ordinal regression and, support vector machines.
[0091] In some embodiments, the signatures are indicative of:
[0092] activities of each of a number of signature biomarkers;
and,
[0093] at least one of: [0094] a SOFA score; and,
[0095] a presence, absence, degree, or stage, or progression of a
condition.
[0096] The signatures may be indicative of a presence, absence,
degree, or stage or progression of at least one of:
[0097] infection-negative SIRS; and,
[0098] infection-positive SIRS.
[0099] In some embodiments, activities of at least some of the
potential biomarkers are indicative of at least one of:
[0100] a presence, absence, degree, or stage, or progression of
SIRS;
[0101] a healthy diagnosis;
[0102] a presence, absence, degree, or stage, or progression of
infection positive SIRS; and,
[0103] a presence, absence, degree, or stage, or progression of
infection negative SIRS.
[0104] Suitably, an activity of biomarkers are indicative of a
level or abundance of a molecule selected from one or more of:
[0105] A nucleic acid molecule;
[0106] A proteinaceous molecule;
[0107] An amino acid
[0108] A carbohydrate;
[0109] A lipid;
[0110] A steroid;
[0111] An inorganic molecule;
[0112] An ion;
[0113] A drug;
[0114] A chemical;
[0115] A metabolite;
[0116] A toxin;
[0117] A nutrient;
[0118] A gas;
[0119] A cell;
[0120] A pathogenic organism; and,
[0121] A non pathogenic organism.
[0122] In another aspect, the present invention provides a method
for determining the likelihood of the presence or absence of a
condition selected from a healthy condition (e.g., a normal
condition or one in which inSIRS and ipSIRS are absent), SIRS
generally (i.e., not distinguishing between inSIRS or ipSIRS),
inSIRS or ipSIRS, or to assess the likelihood of the presence,
absence or risk of development of a stage of ipSIRS (e.g., a stage
of ipSIRS with a particular severity), the method comprising: (1)
correlating a reference Inflammatory Response Syndrome (IRS)
biomarker profile with the presence or absence of a condition
selected from a healthy condition, SIRS, inSIRS, ipSIRS, or a
particular stage of ipSIRS, wherein the reference IRS biomarker
profile evaluates at least one IRS biomarker; (2) obtaining an IRS
biomarker profile of a sample from a subject, wherein the sample
IRS biomarker profile evaluates for an individual IRS biomarker in
the reference IRS biomarker profile a corresponding IRS biomarker;
and (3) determining a likelihood of the subject having or not
having the condition based on the sample IRS biomarker profile and
the reference IRS biomarker profile, wherein an individual IRS
biomarker is an expression product of an IRS biomarker gene
selected from the group consisting of: TLR5; CD177; VNN1; UBE2J1;
IMP3; RNASE2//LOC643332; CLEC4D; C3AR1; GPR56; ARG1;
FCGR1A//FCGR1B//FCGR1C; C11orf82; FAR2; GNLY; GALNT3; OMG; SLC37A3;
BMX//HNRPDL; STOM; TDRD9; KREMEN1; FAIM3; CLEC4E; IL18R1; ACER3;
ERLIN1; TGFBR1; FKBP5//LOC285847; GPR84; C7orf53; PLB1; DSE; PTGDR;
CAMK4; DNAJC13; TNFAIP6;
FOXD4L3//FOXD4L6//FOXD4//FOXD4L1//FOXD4L2//FOXD4L4//FOXD4L5;
MMP9//LOC100128028; GSR; KLRF1; SH2D1B; ANKRD34B; SGMS2;
B3GNT5//MCF2L2; GK3P//GK; PFKFB2; PICALM; METTL7B; HIST1H4C;
C9orf72; HIST1H3I; SLC15A2; TLR10; ADM; CD274; CRIP1; LRRN3;
HLA-DPB1; VAMP2; SMPDL3A; IFI16; JKAMP; MRPL41; SLC1A3; OLFM4;
CASS4; TCN1; WSB2; CLU; ODZ1; KPNAS; PLACE; CD63; HPSE; C1orf161;
DDAH2; KLRK1//KLRC4; ATP13A3; ITK; PMAIP1; LOC284757; GOT2; PDGFC;
B3GAT3; HIST1H4E; HPGD; FGFBP2; LRRC70//IPO11; TMEM144//LOC285505;
CDS2; BPI; ECHDC3; CCR3; HSPC159; OLAH; PPP2R5A//SNORA16B; TMTC1;
EAF2//HCG11//LOC647979; RCBTB2//LOC100131993; SEC24A//SAR1B;
SH3PXD2B; HMGB2; KLRD1; CHI3L1; FRMD3; SLC39A9; GIMAP7; ANAPC11;
EXOSC4; gene for IL-1beta-regulated neutrophil survival protein as
set forth in GenBank Accession No. AF234262; INSIG1; FOLR3//FOLR2;
RUNX2; PRR13//PCBP2; HIST1H4L; LGALS1; CCR1; TPST1; HLA-DRA; CD163;
FFAR2; PHOSPHO1; PPIF; MTHFS; DNAJC9//FAM149B1//RPL26; LCN2;
EIF2AK2; LGALS2; SIAE; AP3B2; ABCA13; gene for transcript set forth
in GenBank Accession No. AK098012; EFCAB2; HIST1H2AA; HINT1;
HIST1H3J; CDA; SAP30; AGTRAP; SUCNR1; MTRR; PLA2G7; AIG1; PCOLCE2;
GAB2; HS2ST1//UBA2; HIST1H3A; C22orf37; HLA-DPA1;
VOPP1//LOC100128019; SLC39A8; MKI67; SLC11A1; AREG; ABCA1;
DAAM2//LOC100131657; LTF; TREML1; GSTO1; PTGER2; CEACAM8; CLEC4A;
PMS2CL//PMS2; RETN; PDE3B; SULF2; NEK6//LOC100129034; CENPK; TRAF3;
GPR65; IRF4; MACF1; AMFR; RPL17//SNORD58B; IRS2; JUP; CD24; GALNT2;
HSP90AB1//HSP90AB3P//HSP90AB2P; GLT25D1; OR9A2; HDHD1A; ACTA2;
ACPL2; LRRFIP1; KCNMA1; OCR1; ITGA4//CERKL;
EIF1AX//SCARNA9L//EIF1AP1; SFRS9; DPH3; ERGIC1; CD300A; NF-E4;
MINPP1; TRIM21; ZNF28; NPCDR1; gene for protein FLJ21394 as set
forth in GenBank Accession No. BC013935; gene for transcript set
forth in GenBank Accession No. AK000992; ICAM1; TAF13;
P4HA1//RPL17; C15orf54; KLHL5; HAL; DLEU2//DLEU2L; ANKRD28;
LY6G5B//CSNK2B; KIAA1257//ACAD9//LOC100132731; MGST3; KIAA0746;
HSPB1//HSPBL2; CCR4; TYMS; RRP12//LOC644215; CCDC125; HIST1H2BM;
PDK4; ABCG1; IL1B; THBS1; ITGA2B; LHFP; LAIR1//LAIR2; HIST1H3B;
ZRANB1; TIMM10; FSD1L//GARNL1; HIST1H2AJ//HIST1H2AI; PTGS1; gene
for transcript set forth in GenBank Accession No. BC008667;
UBE2F//C20orf194//SCLY; HIST1H3C; FAM118A; CCRL2; E2F6; MPZL3;
SRXN1; CD151; HIST1H3H; FSD1L; RFESD//SPATA9; TPX2; S100B;
ZNF587//ZNF417; PYHIN1; KIAA1324; CEACAM6//CEACAM5; APOLD1; FABP2;
KDM6B//TMEM88;
IGK@//IGKC//IGKV1-5//IGKV3D-11//IGKV3-20//IGKV3D-15//LOC440871//LOC652493-
//LOC100291464//LOC652694//IGKV3-15//LOC650405//LOC100291682; MYL9;
HIST1H2BJ; TAAR1; CLC; CYP4F3//CYP4F2; CEP97; SON; IRF1; SYNE2;
MME; LASS4; DEFA4//DEFA8P; C7orf58; DYNLL1; gene for transcript set
forth in GenBank Accession No. AY461701; MPO; CPM; TSHZ2; PLIN2;
FAM118B; B4GALT3; RASA4//RASA4PHRASA4B//POLR2J34//LOC100132214;
CTSL1//CTSLL3; NP; ATF7; SPARC; PLB1; C4orf3; POLE2; TNFRSF17;
FBXL13; PLEKHA3; TMEM62//SPCS2//LOC653566; RBP7; PLEKHF2; RGS2;
ATP6V0D1//LOC100132855; RPIA; CAMK1D; IL1RL1; CMTM5; AIF1; CFD;
MPZL2; LOC100128751; IGJ; CDCl26; PPP1R2//PPP1R2P3; IL5RA;
ARL17P1//ARL17; ATP5L//ATP5L2; TAS2R31; HIST2H2BF//HIST2H3D;
CALM2//C2orf61; SPATA6; IGLV6-57; C1orf128; KRTAP15-1; IFI44;
IGL@//IGLV1-44//LOC96610//IGLV2-23//IGLC1//IGLV2-18//IGLV5-45//IGLV3-25//-
IGLV3-12//IGLV1-36//IGLV3-27//IGLV7-46//IGLV4-3//IGLV3-16//IGLV3-19//IGLV7-
-43//IGLV3-22//IGLV5-37//IGLV10-54//IGLV8-61//LOC651536; gene for
transcript set forth in GenBank Accession No. BC034024; SDHC;
NFXL1; GLDC; DCTN5; and KIAA0101//CSNK1G1
[0123] In some embodiments, the method determines the likelihood
that SIRS or a healthy condition is present or absent in the
subject, and wherein the method comprises: 1) providing a
correlation of a reference IRS biomarker profile with the presence
or absence of SIRS or the healthy condition, wherein the reference
biomarker profile evaluates at least one IRS biomarker selected
from CD177, CLEC4D, BMX, VNN1, GPR84, ARG1, IL18R1, ERLIN1, IMP3,
TLR5, UBE2J1, GPR56, FCGR1A, SLC1A3, SLC37A3, FAIM3, C3AR1, RNASE2,
TNFAIP6, GNLY, OMG, FAR2, OLAH, CAMK4, METTL7B, B3GNT5, CLEC4E,
MMP9, KREMEN1, GALNT3, PTGDR, TDRD9, GK3P, FKBP5, STOM, SMPDL3A,
PFKFB2, ANKRD34B, SGMS2, DNAJC13, LRRN3, SH2D1B, C1orf161,
HIST1H4C, IFI16, ACER3, PLB1, C9orf72, HMGB2, KLRK1, C7orf53, GOT2,
TCN1, DSE, CCR3, CRIP1, ITK, KLRF1, TGFBR1, GSR, HIST1H4E, HPGD,
FRMD3, ABCA13, C11orf82, PPP2R5A, BPI, CASS4, AP3B2, ODZ1, TMTC1,
ADM, FGFBP2, HSPC159, HLA-DRA, HIST1H3I, TMEM144, MRPL41, FOLR3,
PICALM, SH3PXD2B, DDAH2, HLA-DPB1, KPNAS, PHOSPHO1, TPST1, EIF2AK2,
OR9A2, OLFM4, CD163, CDA, CHI3L1, MTHFS, CLU, ANAPC11, JUP, PMAIP1,
GIMAP7, KLRD1, CCR1, CD274, EFCAB2, SUCNR1, KCNMA1, LGALS2,
SLC11A1, FOXD4L3, VAMP2, ITGA4, LHFP, PRR13, FFAR2, B3GAT3, EAF2,
HPSE, CLC, TLR10, CCR4, HIST1H3A, CENPK, DPH3, HLA-DPA1, ATP13A3,
DNAJC9, S100B, HIST1H3J, 110, RPL17, C15orf54, LRRC70, IL5RA,
PLA2G7, ECHDC3, HINT1, LCN2, PPIF, SLC15A2, PMS2CL, HIST1H2AA,
CEACAM8, HSP90AB1, ABCG1, PDGFC, NPCDR1, PDK4, GAB2, WSB2, FAM118A,
JKAMP, TREML1, PYHIN1, IRF4, ABCA1, DAAM2, ACPL2, RCBTB2, SAP30,
THBS1, PCOLCE2, GPR65, NF-E4, LTF, LASS4, B4GALT3, RETN, TIMM10,
IL1B, CLEC4A, SEC24A, RUNX2, LRRFIP1, CFD, EIF1AX, ZRANB1, SULF2,
EXOSC4, CCDC125, LOC284757, ANKRD28, HIST1H2AJ, CD63, PLIN2, SON,
HIST1H4L, KRTAP15-1, DLEU2, MYL9, FABP2, CD24, MACF1, GSTO1, RRP12,
AIG1, RASA4, FBXL13, PDE3B, CCRL2, C1orf128, E2F6, IL1RL1, CEACAM6,
CYP4F3, 199, TAAR1, TSHZ2, PLB1, UBE2F; (2) obtaining a sample IRS
biomarker profile from the subject, which evaluates for an
individual IRS biomarker in the reference IRS biomarker profile a
corresponding IRS biomarker, and (3) determining a likelihood of
the subject having or not having the healthy condition or SIRS
based on the sample IRS biomarker profile and the reference IRS
biomarker profile.
[0124] Suitably, the method determines the likelihood that inSIRS,
ipSIRS or a healthy condition is present or absent in the subject,
wherein the method comprises: 1) providing a correlation of a
reference IRS biomarker profile with the likelihood of having or
not having inSIRS, ipSIRS or the healthy condition, wherein the
reference biomarker profile evaluates at least one IRS biomarker
selected from PLACE, 132, INSIG1, CDS2, VOPP1, SLC39A9, B3GAT3,
CD300A, OCR1, PTGER2, LGALS1, HIST1H4L, AMFR, SIAE, SLC39A8,
TGFBR1, GAB2, MRPL41, TYMS, HIST1H3B, MPZL3, KIAA1257, OMG,
HIST1H2BM, TDRD9, C22orf37, GALNT3, SYNE2, MGST3, HIST1H3I,
LOC284757, TRAF3, HIST1H3C, STOM, C3AR1, KIAA0101, TNFRSF17, HAL,
UBE2J1, GLT25D1, CD151, HSPB1, IMP3, PICALM, ACER3, IGL@,
HIST1H2BJ, CASS4, KREMEN1, IRS2, APOLD1, RBP7, DNAJC13, ERGIC1,
FSD1L, TLR5, TMEM62, SDHC, C9orf72, NP, KIAA0746, PMAIP1, DSE,
SMPDL3A, DNAJC9, HIST1H3H, CDCl26, CRIP1, FAR2, FRMD3, RGS2,
METTL7B, CLEC4E, MME, ABCA13, PRR13, HIST1H4C, RRP12, GLDC, ECHDC3,
IRF1, C7orf53, IGK@, RNASE2, FCGR1A, SAP30, PMS2CL, SLC11A1, AREG,
PLB1, PPIF, GSR, NFXL1, AP3B2, DCTN5, RPL17, IGLV6-57, KLRF1,
CHI3L1, ANKRD34B, OLFM4, CPM, CCDC125, GPR56, PPP1R2, 110, ACPL2,
HIST1H3A, C7orf58, IRF4, ANAPC11, HIST1H3J, KLRD1, GPR84, ZRANB1,
KDM6B, TPST1, HINT1, DAAM2, PTGDR, FKBP5, HSP90AB1, HPGD, IFI16,
CD177, TAS2R31, CD163, B4GALT3, EIF1AX, CYP4F3, HIST1H2AA, LASS4
(where if a gene name is not provided then a SEQ ID NO. is
provided).; (2) obtaining a sample IRS biomarker profile from the
subject, which evaluates for an individual IRS biomarker in the
reference IRS biomarker profile a corresponding IRS biomarker; and
(3) determining a likelihood of the subject having or not having
inSIRS, ipSIRS or a healthy condition the condition based on the
sample IRS biomarker profile and the reference IRS biomarker
profile.
[0125] In some embodiments, the method determines the likelihood
that inSIRS or ipSIRS is present or absent in the subject, wherein
the method comprises: 1) providing a correlation of a reference IRS
biomarker profile with the likelihood of having or not having
inSIRS or ipSIRS, wherein the reference biomarker profile evaluates
at least one IRS biomarker selected from C11orf82, PLACE, 132,
INSIG1, CDS2, VOPP1, SLC39A9, FOXD4L3, WSB2, CD63, CD274, B3GAT3,
CD300A, OCR1, JKAMP, TLR10, PTGER2, PDGFC, LGALS1, HIST1H4L,
AGTRAP, AMFR, SIAE, 200, SLC15A2, SLC39A8, TGFBR1, DDAH2, HPSE,
SUCNR1, MTRR, GAB2, P4HA1, HS2ST1, MRPL41, TYMS, RUNX2, GSTO1,
LRRC70, HIST1H3B, RCBTB2, MPZL3, KIAA1257, AIG1, NEK6, OMG,
HIST1H2BM, TDRD9, GALNT3, ATP13A3, C22orf37, SYNE2, ADM, MGST3,
PDE3B, HIST1H3I, LOC284757, TRAF3, HIST1H3C, STOM, KLHL5, EXOSC4,
C3AR1, KIAA0101, TNFRSF17, HAL, UBE2J1, GLT25D1, CD151, TPX2,
PCOLCE2, HSPB1, EAF2, IMP3, PICALM, ACER3, IGL@, HIST1H2BJ, CASS4,
ACTA2, PTGS1, KREMEN1, IRS2, TAF13, FSD1L, APOLD1, RBP7, DNAJC13,
SEC24A, ERGIC1, FSD1L, TLR5, MKI67, TMEM62, CLEC4A, SDHC, C9orf72,
NP, CLU, ABCA1, KIAA0746, PMAIP1, DSE, CMTM5, SMPDL3A, DNAJC9,
HDHD1A, HIST1H3H, CDC26, ICAM1, LOC100128751, FAR2, CRIP1, MPZL2,
FRMD3, CTSL1, METTL7B, RGS2, CLEC4E, MME, ABCA13, PRR13, HIST1H4C,
RRP12, GLDC, ECHDC3, ITGA2B, C7orf53, IRF1, 268, IGK@, RNASE2,
FCGR1A, UBE2F, SAP30, LAIR1, PMS2CL, SLC11A1, PLB1, AREG, PPIF,
GSR, NFXL1, AP3B2, DCTN5, RPL17, PLA2G7, GALNT2, IGLV6-57, KLRF1,
CHI3L1, ANKRD34B, OLFM4, 199, CPM, CCDC125, SULF2, LTF, GPR56,
MACF1, PPP1R2, DYNLL1, LCN2, FFAR2, SFRS9, IGJ, FAM118B, 110,
ACPL2, HIST1H3A, C7orf58, ANAPC11, HIST1H3J, IRF4, MPO, TREML1,
KLRD1, GPR84, CCRL2, CAMK1D, CCR1, ZRANB1, KDM6B, TPST1, HINT1,
DAAM2, PTGDR, FKBP5, CD24, HSP90AB1, HPGD, CEACAM8, DEFA4, IL1B,
IFI16, CD177, KIAA1324, SRXN1, TAS2R31, CEACAM6, CD163, B4GALT3,
ANKRD28, TAAR1, EIF1AX, CYP4F3, 314, HIST1H2AA, LY6G5B, LASS4
(where if a gene name is not provided then a SEQ ID NO. is
provided); (2) obtaining a sample IRS biomarker profile from the
subject, which evaluates for an individual IRS biomarker in the
reference IRS biomarker profile a corresponding IRS biomarker; and
(3) determining a likelihood of the subject having or not having
inSIRS or ipSIRS based on the sample IRS biomarker profile and the
reference IRS biomarker profile.
[0126] Suitably, the method determines the likelihood that a stage
of ipSIRS selected from mild sepsis, severe sepsis and septic shock
is present or absent the subject, wherein the method comprises: 1)
providing a correlation of a reference IRS biomarker profile with
the likelihood of having or not having the stage of ipSIRS, wherein
the reference biomarker IRS biomarker profile evaluates at least
one IRS biomarker selected from PLEKHA3, PLEKHF2, 232, SFRS9,
ZNF587, KPNA5, LOC284757, GPR65, VAMP2, SLC1A3, ITK, ATF7, ZNF28,
AIF1, MINPP1, GIMAP7, MKI67, IRF4, TSHZ2, HLA-DPB1, EFCAB2, POLE2,
FAIM3, 110, CAMK4, TRIM21, IFI44, CENPK, ATP5L, GPR56, HLA-DPA1,
C4orf3, GSR, GNLY, RFESD, BPI, HIST1H2AA, NF-E4, CALM2, EIF1AX,
E2F6, ARL17P1, TLR5, SH3PXD2B, FAM118A, RETN, PMAIP1, DNAJC9,
PCOLCE2, TPX2, BMX, LRRFIP1, DLEU2, JKAMP, JUP, ABCG1, SLC39A9,
B3GNT5, ACER3, LRRC70, NPCDR1, TYMS, HLA-DRA, TDRD9, FSD1L, FAR2,
C7orf53, PPP1R2, SGMS2, EXOSC4, TGFBR1, CD24, TCN1, TAF13, AP3B2,
CD63, SLC15A2, IL18R1, ATP6V0D1, SON, HSP90AB1, CEACAM8, SMPDL3A,
IMP3, SEC24A, PICALM, 199, CEACAM6, CYP4F3, OLAH, ECHDC3, ODZ1,
KIAA0746, KIAA1324, HINT1, VNN1, C22orf37, FSD1L, FOLR3, IL1RL1,
OMG, MTHFS, OLFM4, S100B, ITGA4, KLRD1, SLC39A8, KLHL5, KLRK1, MPO,
PPIF, GOT2, LRRN3, HIST1H2AJ, CLU, LCN2, 132, CEP97, KLRF1, FBXL13,
HIST1H3B, ANKRD34B, RPIA, HPGD, HIST2H2BF, GK3P (where if a gene
name is not provided then a SEQ ID NO. is provided).; (2) obtaining
a sample IRS biomarker profile from the subject, which evaluates
for an individual IRS biomarker in the reference IRS biomarker
profile a corresponding IRS biomarker; and (3) determining a
likelihood of the subject having or not having the stage of ipSIRS
based on the sample IRS biomarker profile and the reference IRS
biomarker profile.
[0127] In illustrative examples, an individual IRS biomarker is
selected from the group consisting of: (a) a polynucleotide
expression product comprising a nucleotide sequence that shares at
least 70% (or at least 71% to at least 99% and all integer
percentages in between) sequence identity with the sequence set
forth in any one of SEQ ID NO: 1-319, or a complement thereof; (b)
a polynucleotide expression product comprising a nucleotide
sequence that encodes a polypeptide comprising the amino acid
sequence set forth in any one of SEQ ID NO: 320-619; (c) a
polynucleotide expression product comprising a nucleotide sequence
that encodes a polypeptide that shares at least 70% (or at least
71% to at least 99% and all integer percentages in between)
sequence similarity or identity with at least a portion of the
sequence set forth in SEQ ID NO: 320-619; (d) a polynucleotide
expression product comprising a nucleotide sequence that hybridizes
to the sequence of (a), (b), (c) or a complement thereof, under
medium or high stringency conditions; (e) a polypeptide expression
product comprising the amino acid sequence set forth in any one of
SEQ ID NO: 320-619; and (f) a polypeptide expression product
comprising an amino acid sequence that shares at least 70% (or at
least 71% to at least 99% and all integer percentages in between)
sequence similarity or identity with the sequence set forth in any
one of SEQ ID NO: 320-619.
[0128] Evaluation of IRS markers suitably includes determining the
levels of individual IRS markers, which correlate with the presence
or absence of a condition, as defined above.
[0129] In some embodiments, the method of determining the
likelihood of the presence or absence of a condition, as broadly
described above, comprises comparing the level of a first IRS
biomarker in the sample IRS biomarker profile with the level of a
second IRS biomarker in the sample IRS biomarker profile to provide
a ratio and determining a likelihood of the presence or absence of
the condition based on that ratio. In illustrative examples of this
type, the determination is carried out in the absence of comparing
the level of the first or second IRS biomarkers in the sample IRS
biomarker profile to the level of a corresponding IRS biomarker in
the reference IRS biomarker profile. Representative IRS biomarkers
that are useful for these embodiments are suitably selected from
those listed in Example 6 and Tables 16-21.
[0130] In a related aspect, the present invention provides a kit
comprising one or more reagents and/or devices for use in
performing the method of determining the likelihood of the presence
or absence of a condition as broadly described above.
[0131] Another aspect of the present invention provides a method
for treating, preventing or inhibiting the development of inSIRS,
ipSIRS or a particular stage of ipSIRS in a subject, the method
comprising: (1) correlating a reference IRS biomarker profile with
the presence or absence of a condition selected from a healthy
condition, SIRS, inSIRS, ipSIRS, or a particular stage of ipSIRS,
wherein the reference IRS biomarker profile evaluates at least one
IRS biomarker; (2) obtaining an IRS biomarker profile of a sample
from a subject, wherein the sample IRS biomarker profile evaluates
for an individual IRS biomarker in the reference IRS biomarker
profile a corresponding IRS biomarker; (3) determining a likelihood
of the subject having or not having the condition based on the
sample IRS biomarker profile and the reference IRS biomarker
profile, and administering to the subject, on the basis that the
subject has an increased likelihood of having inSIRS, an effective
amount of an agent that treats or ameliorates the symptoms or
reverses or inhibits the development of inSIRS, or administering to
the subject, on the basis that the subject has an increased
likelihood of having ipSIRS or a particular stage of ipSIRS, an
effective amount of an agent that treats or ameliorates the
symptoms or reverses or inhibits the development of ipSIRS or the
particular stage of ipSIRS.
[0132] Yet another aspect of the present invention provides a
method of monitoring the efficacy of a particular treatment regimen
in a subject towards a desired health state (e.g., healthy
condition), the method comprising: (1) providing a correlation of a
reference IRS biomarker profile with the likelihood of having a
healthy condition; (2) obtaining a corresponding IRS biomarker
profile of a subject having inSIRS, ipSIRS or a particular stage of
ipSIRS after treatment with a treatment regimen, wherein a
similarity of the subject's IRS biomarker profile after treatment
to the reference IRS biomarker profile indicates the likelihood
that the treatment regimen is effective for changing the health
status of the subject to the desired health state.
[0133] Still another aspect of the present invention provides a
method of correlating a reference IRS biomarker profile with an
effective treatment regimen for a condition selected from inSIRS,
ipSIRS or a particular stage of ipSIRS, wherein the reference IRS
biomarker profile evaluates at least one IRS biomarker, the method
comprising: (a) determining a sample IRS biomarker profile from a
subject with the condition prior to treatment, wherein the sample
IRS biomarker profile evaluates for an individual IRS biomarker in
the reference IRS biomarker profile a corresponding IRS biomarker;
and correlating the sample IRS biomarker profile with a treatment
regimen that is effective for treating the condition.
[0134] In another aspect, the present invention provides a method
of determining whether a treatment regimen is effective for
treating a subject with a condition selected from inSIRS, ipSIRS or
a particular stage of ipSIRS, the method comprising: (a)
correlating a reference biomarker profile prior to treatment with
an effective treatment regimen for the condition, wherein the
reference IRS biomarker profile evaluates at least one IRS
biomarker; and (b) obtaining a sample IRS biomarker profile from
the subject after treatment, wherein the sample IRS biomarker
profile evaluates for an individual IRS biomarker in the reference
IRS biomarker profile a corresponding IRS biomarker, and wherein
the sample IRS biomarker profile after treatment indicates whether
the treatment regimen is effective for treating the condition in
the subject.
[0135] In a further aspect, the present invention provides a method
of correlating an IRS biomarker profile with a positive or negative
response to a treatment regimen, the method comprising: (a)
obtaining an IRS biomarker profile from a subject with a condition
selected from inSIRS, ipSIRS or a particular stage of ipSIRS
following commencement of the treatment regimen, wherein the IRS
biomarker profile evaluates at least one IRS biomarker; and (b)
correlating the IRS biomarker profile from the subject with a
positive or negative response to the treatment regimen.
[0136] Another aspect of the present invention provides a method of
determining a positive or negative response to a treatment regimen
by a subject with a condition selected from inSIRS, ipSIRS or a
particular stage of ipSIRS, the method comprising: (a) correlating
a reference IRS biomarker profile with a positive or negative
response to the treatment regimen, wherein the reference IRS
biomarker profile evaluates at least one (e.g., 1, 2, 3, 4, 5, 6,
7, 8, 9, 10, etc.) IRS biomarker; and (b) determining a sample IRS
biomarker profile from the subject, wherein the subject's sample
IRS biomarker profile evaluates for an individual IRS biomarker in
the reference IRS biomarker profile a corresponding IRS biomarker
and indicates whether the subject is responding to the treatment
regimen.
[0137] In some embodiments, the method of determining a positive or
negative response to a treatment regimen further comprises:
determining a first sample IRS biomarker profile from the subject
prior to commencing the treatment regimen, wherein the first sample
IRS biomarker profile evaluates at least one IRS biomarker; and
comparing the first sample IRS biomarker profile with a second
sample IRS biomarker profile from the subject after commencement of
the treatment regimen, wherein the second sample IRS biomarker
profile evaluates for an individual IRS biomarker in the first
sample IRS biomarker profile a corresponding IRS biomarker.
BRIEF DESCRIPTION OF THE DRAWINGS
[0138] FIG. 1 shows a box and whiskers plot of PLEKHA3 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0139] FIG. 2 shows a box and whiskers plot of VAMP2 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0140] FIG. 3 shows a box and whiskers plot of ITK gene expression
for healthy subjects and subjects with SIRS, mild sepsis, severe
sepsis.
[0141] FIG. 4 shows a box and whiskers plot of C11orf82 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0142] FIG. 5 shows a box and whiskers plot of PLAC8 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0143] FIG. 6 shows a box and whiskers plot of INSIG1 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0144] FIG. 7 shows a box and whiskers plot of
FCGR1A//FCGR1B//FCGR1C gene expression for healthy subjects and
subjects with SIRS, mild sepsis, severe sepsis.
[0145] FIG. 8 shows a box and whiskers plot of CHI3L1 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0146] FIG. 9 shows a box and whiskers plot of CD177 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0147] FIG. 10 shows a box and whiskers plot of GNLY gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0148] FIG. 11 shows a box and whiskers plot of BMX//HNRPDL gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0149] FIG. 12 shows a box and whiskers plot of TLR5 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0150] FIG. 13 shows a box and whiskers plot of TLR5 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0151] FIG. 14 shows a box and whiskers plot of CD177 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0152] FIG. 15 shows a box and whiskers plot of VNN1 gene
expression for
[0153] FIG. 16 shows a box and whiskers plot of UBE2J1 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0154] FIG. 17 shows a box and whiskers plot of IMP3 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0155] FIG. 18 shows a box and whiskers plot of RNASE2//LOC643332
gene expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0156] FIG. 19 shows a box and whiskers plot of CLEC4D gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0157] FIG. 20 shows a box and whiskers plot of C3AR1 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0158] FIG. 21 shows a box and whiskers plot of GPR56 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0159] FIG. 22 shows a box and whiskers plot of ARG1 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0160] FIG. 23 shows a box and whiskers plot of
FCGR1A//FCGR1B//FCGR1C gene expression for healthy subjects and
subjects with SIRS, mild sepsis, severe sepsis.
[0161] FIG. 24 shows a box and whiskers plot of C11orf82 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0162] FIG. 25 shows a box and whiskers plot of FAR2 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0163] FIG. 26 shows a box and whiskers plot of GNLY gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0164] FIG. 27 shows a box and whiskers plot of GALNT3 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0165] FIG. 28 shows a box and whiskers plot of OMG gene expression
for healthy subjects and subjects with SIRS, mild sepsis, severe
sepsis.
[0166] FIG. 29 shows a box and whiskers plot of SLC37A3 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0167] FIG. 30 shows a box and whiskers plot of BMX//HNRPDL gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0168] FIG. 31 shows a box and whiskers plot of STOM gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0169] FIG. 32 shows a box and whiskers plot of TDRD9 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0170] FIG. 33 shows a box and whiskers plot of KREMEN1 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0171] FIG. 34 shows a box and whiskers plot of FAIM3 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0172] FIG. 35 shows a box and whiskers plot of CLEC4E gene
expression for
[0173] FIG. 36 shows a box and whiskers plot of IL18R1 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0174] FIG. 37 shows a box and whiskers plot of ACER3 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0175] FIG. 38 shows a box and whiskers plot of ERLIN1 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0176] FIG. 39 shows a box and whiskers plot of TGFBR1 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0177] FIG. 40 shows a box and whiskers plot of FKBP5//LOC285847
gene expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0178] FIG. 41 shows a box and whiskers plot of GPR84 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0179] FIG. 42 shows a box and whiskers plot of C7orf53 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0180] FIG. 43 shows a box and whiskers plot of PLB1 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0181] FIG. 44 shows a box and whiskers plot of DSE gene expression
for healthy subjects and subjects with SIRS, mild sepsis, severe
sepsis.
[0182] FIG. 45 shows a box and whiskers plot of PTGDR gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0183] FIG. 46 shows a box and whiskers plot of CAMK4 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0184] FIG. 47 shows a box and whiskers plot of DNAJC13 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0185] FIG. 48 shows a box and whiskers plot of TNFAIP6 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0186] FIG. 49 shows a box and whiskers plot of
FOXD4L3//FOXD4L6//FOXD4//FOXD4L1//FOXD4L2//FOXD4L4//FOXD4L5 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0187] FIG. 50 shows a box and whiskers plot of MMP9//LOC100128028
gene expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0188] FIG. 51 shows a box and whiskers plot of GSR gene expression
for healthy subjects and subjects with SIRS, mild sepsis, severe
sepsis.
[0189] FIG. 52 shows a box and whiskers plot of KLRF1 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0190] FIG. 53 shows a box and whiskers plot of SH2D1B gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0191] FIG. 54 shows a box and whiskers plot of ANKRD34B gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0192] FIG. 55 shows a box and whiskers plot of SGMS2 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0193] FIG. 56 shows a box and whiskers plot of B3GNT5//MCF2L2 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0194] FIG. 57 shows a box and whiskers plot of GK3P//GK gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0195] FIG. 58 shows a box and whiskers plot of PFKFB2 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0196] FIG. 59 shows a box and whiskers plot of PICALM gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0197] FIG. 60 shows a box and whiskers plot of METTL7B gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0198] FIG. 61 shows a box and whiskers plot of HIST1H4C gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0199] FIG. 62 shows a box and whiskers plot of C9orf72 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0200] FIG. 63 shows a box and whiskers plot of HIST1H3I gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0201] FIG. 64 shows a box and whiskers plot of SLC15A2 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0202] FIG. 65 shows a box and whiskers plot of TLR10 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0203] FIG. 66 shows a box and whiskers plot of ADM gene expression
for healthy subjects and subjects with SIRS, mild sepsis, severe
sepsis.
[0204] FIG. 67 shows a box and whiskers plot of CD274 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0205] FIG. 68 shows a box and whiskers plot of CRIP1 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0206] FIG. 69 shows a box and whiskers plot of LRRN3 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0207] FIG. 70 shows a box and whiskers plot of HLA-DPB1 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0208] FIG. 71 shows a box and whiskers plot of VAMP2 gene
expression for
[0209] FIG. 72 shows a box and whiskers plot of SMPDL3A gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0210] FIG. 73 shows a box and whiskers plot of IFI16 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0211] FIG. 74 shows a box and whiskers plot of JKAMP gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0212] FIG. 75 shows a box and whiskers plot of MRPL41 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0213] FIG. 76 shows a box and whiskers plot of SLC1A3 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0214] FIG. 77 shows a box and whiskers plot of OLFM4 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0215] FIG. 78 shows a box and whiskers plot of CASS4 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0216] FIG. 79 shows a box and whiskers plot of TCN1 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0217] FIG. 80 shows a box and whiskers plot of WSB2 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0218] FIG. 81 shows a box and whiskers plot of CLU gene expression
for healthy subjects and subjects with SIRS, mild sepsis, severe
sepsis.
[0219] FIG. 82 shows a box and whiskers plot of ODZ1 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0220] FIG. 83 shows a box and whiskers plot of KPNAS gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0221] FIG. 84 shows a box and whiskers plot of PLAC8 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0222] FIG. 85 shows a box and whiskers plot of CD63 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0223] FIG. 86 shows a box and whiskers plot of HPSE gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0224] FIG. 87 shows a box and whiskers plot of C1orf161 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0225] FIG. 88 shows a box and whiskers plot of DDAH2 gene
expression for
[0226] FIG. 89 shows a box and whiskers plot of KLRK1//KLRC4 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0227] FIG. 90 shows a box and whiskers plot of ATP13A3 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0228] FIG. 91 shows a box and whiskers plot of ITK gene expression
for healthy subjects and subjects with SIRS, mild sepsis, severe
sepsis.
[0229] FIG. 92 shows a box and whiskers plot of PMAIP1 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0230] FIG. 93 shows a box and whiskers plot of LOC284757 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0231] FIG. 94 shows a box and whiskers plot of GOT2 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0232] FIG. 95 shows a box and whiskers plot of PDGFC gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0233] FIG. 96 shows a box and whiskers plot of B3GAT3 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0234] FIG. 97 shows a box and whiskers plot of HIST1H4E gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0235] FIG. 98 shows a box and whiskers plot of HPGD gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0236] FIG. 99 shows a box and whiskers plot of FGFBP2 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0237] FIG. 100 shows a box and whiskers plot of LRRC70//IPO11 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0238] FIG. 101 shows a box and whiskers plot of TMEM144/LOC285505
gene expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0239] FIG. 102 shows a box and whiskers plot of CDS2 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0240] FIG. 103 shows a box and whiskers plot of BPI gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0241] FIG. 104 shows a box and whiskers plot of ECHDC3 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0242] FIG. 105 shows a box and whiskers plot of CCR3 gene
expression for
[0243] FIG. 106 shows a box and whiskers plot of HSPC159 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0244] FIG. 107 shows a box and whiskers plot of OLAH gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0245] FIG. 108 shows a box and whiskers plot of PPP2R5A//SNORA16B
gene expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0246] FIG. 109 shows a box and whiskers plot of TMTC1 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0247] FIG. 110 shows a box and whiskers plot of
EAF2//HCG11//LOC647979 gene expression for healthy subjects and
subjects with SIRS, mild sepsis, severe sepsis.
[0248] FIG. 111 shows a box and whiskers plot of
RCBTB2//LOC100131993 gene expression for healthy subjects and
subjects with SIRS, mild sepsis, severe sepsis.
[0249] FIG. 112 shows a box and whiskers plot of SEC24A//SAR1B gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0250] FIG. 113 shows a box and whiskers plot of SH3PXD2B gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0251] FIG. 114 shows a box and whiskers plot of HMGB2 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0252] FIG. 115 shows a box and whiskers plot of KLRD1 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0253] FIG. 116 shows a box and whiskers plot of CHI3L1 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0254] FIG. 117 shows a box and whiskers plot of FRMD3 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0255] FIG. 118 shows a box and whiskers plot of SLC39A9 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0256] FIG. 119 shows a box and whiskers plot of GIMAP7 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0257] FIG. 120 shows a box and whiskers plot of ANAPC11 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0258] FIG. 121 shows a box and whiskers plot of EXOSC4 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0259] FIG. 122 shows a box and whiskers plot of NA gene expression
for
[0260] FIG. 123 shows a box and whiskers plot of INSIG1 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0261] FIG. 124 shows a box and whiskers plot of FOLR3//FOLR2 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0262] FIG. 125 shows a box and whiskers plot of RUNX2 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0263] FIG. 126 shows a box and whiskers plot of PRR13//PCBP2 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0264] FIG. 127 shows a box and whiskers plot of HIST1H4L gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0265] FIG. 128 shows a box and whiskers plot of LGALS1 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0266] FIG. 129 shows a box and whiskers plot of CCR1 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0267] FIG. 130 shows a box and whiskers plot of TPST1 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0268] FIG. 131 shows a box and whiskers plot of HLA-DRA gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0269] FIG. 132 shows a box and whiskers plot of CD163 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0270] FIG. 133 shows a box and whiskers plot of FFAR2 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0271] FIG. 134 shows a box and whiskers plot of PHOSPHO1 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0272] FIG. 135 shows a box and whiskers plot of PPIF gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0273] FIG. 136 shows a box and whiskers plot of MTHFS gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0274] FIG. 137 shows a box and whiskers plot of
DNAJC9//FAM149B1//RPL26 gene expression for healthy subjects and
subjects with SIRS, mild sepsis, severe sepsis.
[0275] FIG. 138 shows a box and whiskers plot of LCN2 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0276] FIG. 139 shows a box and whiskers plot of EIF2AK2 gene
expression for
[0277] FIG. 140 shows a box and whiskers plot of LGALS2 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0278] FIG. 141 shows a box and whiskers plot of SIAE gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0279] FIG. 142 shows a box and whiskers plot of AP3B2 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0280] FIG. 143 shows a box and whiskers plot of ABCA13 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0281] FIG. 144 shows a box and whiskers plot of NA expression for
healthy subjects and subjects with SIRS, mild sepsis, severe
sepsis.
[0282] FIG. 145 shows a box and whiskers plot of EFCAB2 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0283] FIG. 146 shows a box and whiskers plot of HIST1H2AA gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0284] FIG. 147 shows a box and whiskers plot of HINT1 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0285] FIG. 148 shows a box and whiskers plot of HIST1H3J gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0286] FIG. 149 shows a box and whiskers plot of CDA gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0287] FIG. 150 shows a box and whiskers plot of SAP30 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0288] FIG. 151 shows a box and whiskers plot of AGTRAP gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0289] FIG. 152 shows a box and whiskers plot of SUCNR1 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0290] FIG. 153 shows a box and whiskers plot of MTRR gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0291] FIG. 154 shows a box and whiskers plot of PLA2G7 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0292] FIG. 155 shows a box and whiskers plot of AIG1 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0293] FIG. 156 shows a box and whiskers plot of PCOLCE2 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0294] FIG. 157 shows a box and whiskers plot of GAB2 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0295] FIG. 158 shows a box and whiskers plot of HS2ST1//UBA2 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0296] FIG. 159 shows a box and whiskers plot of HIST1H3A gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0297] FIG. 160 shows a box and whiskers plot of C22orf37 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0298] FIG. 161 shows a box and whiskers plot of HLA-DPA1 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0299] FIG. 162 shows a box and whiskers plot of
VOPP1//LOC100128019 gene expression for healthy subjects and
subjects with SIRS, mild sepsis, severe sepsis.
[0300] FIG. 163 shows a box and whiskers plot of SLC39A8 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0301] FIG. 164 shows a box and whiskers plot of MKI67 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0302] FIG. 165 shows a box and whiskers plot of SLC11A1 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0303] FIG. 166 shows a box and whiskers plot of AREG gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0304] FIG. 167 shows a box and whiskers plot of ABCA1 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0305] FIG. 168 shows a box and whiskers plot of
DAAM2//LOC100131657 gene expression for healthy subjects and
subjects with SIRS, mild sepsis, severe sepsis.
[0306] FIG. 169 shows a box and whiskers plot of LTF gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0307] FIG. 170 shows a box and whiskers plot of TREML1 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0308] FIG. 171 shows a box and whiskers plot of GSTO1 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0309] FIG. 172 shows a box and whiskers plot of PTGER2 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0310] FIG. 173 shows a box and whiskers plot of CEACAM8 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0311] FIG. 174 shows a box and whiskers plot of CLEC4A gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0312] FIG. 175 shows a box and whiskers plot of PMS2CL/PMS2 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0313] FIG. 176 shows a box and whiskers plot of RETN gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0314] FIG. 177 shows a box and whiskers plot of PDE3B gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0315] FIG. 178 shows a box and whiskers plot of SULF2 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0316] FIG. 179 shows a box and whiskers plot of NEK6//LOC100129034
gene expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0317] FIG. 180 shows a box and whiskers plot of CENPK gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0318] FIG. 181 shows a box and whiskers plot of TRAF3 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0319] FIG. 182 shows a box and whiskers plot of GPR65 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0320] FIG. 183 shows a box and whiskers plot of IRF4 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0321] FIG. 184 shows a box and whiskers plot of MACF1 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0322] FIG. 185 shows a box and whiskers plot of AMFR gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0323] FIG. 186 shows a box and whiskers plot of RPL17//SNORD58B
gene expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0324] FIG. 187 shows a box and whiskers plot of IRS2 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0325] FIG. 188 shows a box and whiskers plot of JUP gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0326] FIG. 189 shows a box and whiskers plot of CD24 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0327] FIG. 190 shows a box and whiskers plot of GALNT2 gene
expression for
[0328] FIG. 191 shows a box and whiskers plot of
HSP90AB1//HSP90AB3P//HSP90AB2P gene expression for healthy subjects
and subjects with SIRS, mild sepsis, severe sepsis.
[0329] FIG. 192 shows a box and whiskers plot of GLT25D1 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0330] FIG. 193 shows a box and whiskers plot of OR9A2 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0331] FIG. 194 shows a box and whiskers plot of HDHD1A gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0332] FIG. 195 shows a box and whiskers plot of ACTA2 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0333] FIG. 196 shows a box and whiskers plot of ACPL2 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0334] FIG. 197 shows a box and whiskers plot of LRRFIP1 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0335] FIG. 198 shows a box and whiskers plot of KCNMA1 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0336] FIG. 199 shows a box and whiskers plot of OCR1 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0337] FIG. 200 shows a box and whiskers plot of ITGA4//CERKL gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0338] FIG. 201 shows a box and whiskers plot of
EIF1AX//SCARNA9L//EIF1AP1 gene expression for healthy subjects and
subjects with SIRS, mild sepsis, severe sepsis.
[0339] FIG. 202 shows a box and whiskers plot of SFRS9 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0340] FIG. 203 shows a box and whiskers plot of DPH3 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0341] FIG. 204 shows a box and whiskers plot of ERGIC1 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0342] FIG. 205 shows a box and whiskers plot of CD300A gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0343] FIG. 206 shows a box and whiskers plot of NF-E4 gene
expression for
[0344] FIG. 207 shows a box and whiskers plot of MINPP1 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0345] FIG. 208 shows a box and whiskers plot of TRIM21 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0346] FIG. 209 shows a box and whiskers plot of ZNF28 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0347] FIG. 210 shows a box and whiskers plot of NPCDR1 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0348] FIG. 211 shows a box and whiskers plot of NA gene expression
for healthy subjects and subjects with SIRS, mild sepsis, severe
sepsis.
[0349] FIG. 212 shows a box and whiskers plot of NA gene expression
for healthy subjects and subjects with SIRS, mild sepsis, severe
sepsis.
[0350] FIG. 213 shows a box and whiskers plot of ICAM1 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0351] FIG. 214 shows a box and whiskers plot of TAF13 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0352] FIG. 215 shows a box and whiskers plot of P4HA1//RPL17 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0353] FIG. 216 shows a box and whiskers plot of C15orf54 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0354] FIG. 217 shows a box and whiskers plot of KLHL5 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0355] FIG. 218 shows a box and whiskers plot of HAL gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0356] FIG. 219 shows a box and whiskers plot of DLEU2//DLEU2L gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0357] FIG. 220 shows a box and whiskers plot of ANKRD28 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0358] FIG. 221 shows a box and whiskers plot of LY6G5B//CSNK2B
gene expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0359] FIG. 222 shows a box and whiskers plot of
KIAA1257//ACAD9//LOC100132731 gene expression for healthy subjects
and subjects with SIRS, mild sepsis, severe sepsis.
[0360] FIG. 223 shows a box and whiskers plot of MGST3 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0361] FIG. 224 shows a box and whiskers plot of KIAA0746 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0362] FIG. 225 shows a box and whiskers plot of HSPB1//HSPBL2 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0363] FIG. 226 shows a box and whiskers plot of CCR4 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0364] FIG. 227 shows a box and whiskers plot of TYMS gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0365] FIG. 228 shows a box and whiskers plot of RRP12//LOC644215
gene expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0366] FIG. 229 shows a box and whiskers plot of CCDC125 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0367] FIG. 230 shows a box and whiskers plot of HIST1H2BM gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0368] FIG. 231 shows a box and whiskers plot of PDK4 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0369] FIG. 232 shows a box and whiskers plot of ABCG1 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0370] FIG. 233 shows a box and whiskers plot of IL1B gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0371] FIG. 234 shows a box and whiskers plot of THBS1 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0372] FIG. 235 shows a box and whiskers plot of ITGA2B gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0373] FIG. 236 shows a box and whiskers plot of LHFP gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0374] FIG. 237 shows a box and whiskers plot of LAIR1//LAIR2 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0375] FIG. 238 shows a box and whiskers plot of HIST1H3B gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0376] FIG. 239 shows a box and whiskers plot of ZRANB1 gene
expression for
[0377] FIG. 240 shows a box and whiskers plot of TIMM10 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0378] FIG. 241 shows a box and whiskers plot of FSD1L//GARNL1 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0379] FIG. 242 shows a box and whiskers plot of
HIST1H2AJ//HIST1H2AI gene expression for healthy subjects and
subjects with SIRS, mild sepsis, severe sepsis.
[0380] FIG. 243 shows a box and whiskers plot of PTGS1 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0381] FIG. 244 shows a box and whiskers plot of NA gene expression
for healthy subjects and subjects with SIRS, mild sepsis, severe
sepsis.
[0382] FIG. 245 shows a box and whiskers plot of
UBE2F//C20orf194//SCLY gene expression for healthy subjects and
subjects with SIRS, mild sepsis, severe sepsis.
[0383] FIG. 246 shows a box and whiskers plot of HIST1H3C gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0384] FIG. 247 shows a box and whiskers plot of FAM118A gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0385] FIG. 248 shows a box and whiskers plot of CCRL2 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0386] FIG. 249 shows a box and whiskers plot of E2F6 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0387] FIG. 250 shows a box and whiskers plot of MPZL3 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0388] FIG. 251 shows a box and whiskers plot of SRXN1 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0389] FIG. 252 shows a box and whiskers plot of CD151 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0390] FIG. 253 shows a box and whiskers plot of HIST1H3H gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0391] FIG. 254 shows a box and whiskers plot of FSD1L gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0392] FIG. 255 shows a box and whiskers plot of RFESD//SPATA9 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0393] FIG. 256 shows a box and whiskers plot of TPX2 gene
expression for
[0394] FIG. 257 shows a box and whiskers plot of S100B gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0395] FIG. 258 shows a box and whiskers plot of ZNF587//ZNF417
gene expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0396] FIG. 259 shows a box and whiskers plot of PYHIN1 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0397] FIG. 260 shows a box and whiskers plot of KIAA1324 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0398] FIG. 261 shows a box and whiskers plot of CEACAM6//CEACAM5
gene expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0399] FIG. 262 shows a box and whiskers plot of APOLD1 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0400] FIG. 263 shows a box and whiskers plot of FABP2 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0401] FIG. 264 shows a box and whiskers plot of KDM6B//TMEM88 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0402] FIG. 265 shows a box and whiskers plot of
IGKV3-20//IGKV3D-15//LOC440871//LOC652493//LOC100291464//LOC652694//IGKV3-
-15 gene expression for healthy subjects and subjects with SIRS,
mild sepsis, severe sepsis.
[0403] FIG. 266 shows a box and whiskers plot of MYL9 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0404] FIG. 267 shows a box and whiskers plot of HIST1H2BJ gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0405] FIG. 268 shows a box and whiskers plot of TAAR1 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0406] FIG. 269 shows a box and whiskers plot of CLC gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0407] FIG. 270 shows a box and whiskers plot of CYP4F3//CYP4F2
gene expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0408] FIG. 271 shows a box and whiskers plot of CEP97 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0409] FIG. 272 shows a box and whiskers plot of SON gene
expression for
[0410] FIG. 273 shows a box and whiskers plot of IRF1 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0411] FIG. 274 shows a box and whiskers plot of SYNE2 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0412] FIG. 275 shows a box and whiskers plot of MME gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0413] FIG. 276 shows a box and whiskers plot of LASS4 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0414] FIG. 277 shows a box and whiskers plot of DEFA4//DEFA8P gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0415] FIG. 278 shows a box and whiskers plot of C7orf58 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0416] FIG. 279 shows a box and whiskers plot of DYNLL1 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0417] FIG. 280 shows a box and whiskers plot of NA gene expression
for healthy subjects and subjects with SIRS, mild sepsis, severe
sepsis.
[0418] FIG. 281 shows a box and whiskers plot of MPO gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0419] FIG. 282 shows a box and whiskers plot of CPM gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0420] FIG. 283 shows a box and whiskers plot of TSHZ2 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0421] FIG. 284 shows a box and whiskers plot of PLIN2 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0422] FIG. 285 shows a box and whiskers plot of FAM118B gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0423] FIG. 286 shows a box and whiskers plot of B4GALT3 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0424] FIG. 287 shows a box and whiskers plot of
RASA4//RASA4PHRASA4B//POLR2J4//LOC100132214 gene expression for
healthy subjects and subjects with SIRS, mild sepsis, severe
sepsis.
[0425] FIG. 288 shows a box and whiskers plot of CTSL1//CTSLL3 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0426] FIG. 289 shows a box and whiskers plot of NP gene expression
for healthy subjects and subjects with SIRS, mild sepsis, severe
sepsis.
[0427] FIG. 290 shows a box and whiskers plot of ATF7 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0428] FIG. 291 shows a box and whiskers plot of SPARC gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0429] FIG. 292 shows a box and whiskers plot of PLB1 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0430] FIG. 293 shows a box and whiskers plot of C4orf3 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0431] FIG. 294 shows a box and whiskers plot of POLE2 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0432] FIG. 295 shows a box and whiskers plot of TNFRSF17 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0433] FIG. 296 shows a box and whiskers plot of FBXL13 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0434] FIG. 297 shows a box and whiskers plot of PLEKHA3 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0435] FIG. 298 shows a box and whiskers plot of
TMEM62//SPCS2//LOC653566 gene expression for healthy subjects and
subjects with SIRS, mild sepsis, severe sepsis.
[0436] FIG. 299 shows a box and whiskers plot of RBP7 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0437] FIG. 300 shows a box and whiskers plot of PLEKHF2 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0438] FIG. 301 shows a box and whiskers plot of RGS2 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0439] FIG. 302 shows a box and whiskers plot of
ATP6V0D1//LOC100132855 gene expression for healthy subjects and
subjects with SIRS, mild sepsis, severe sepsis.
[0440] FIG. 303 shows a box and whiskers plot of RPIA gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0441] FIG. 304 shows a box and whiskers plot of CAMK1D gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0442] FIG. 305 shows a box and whiskers plot of IL1RL1 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0443] FIG. 306 shows a box and whiskers plot of CMTM5 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0444] FIG. 307 shows a box and whiskers plot of AIF1 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0445] FIG. 308 shows a box and whiskers plot of CFD gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0446] FIG. 309 shows a box and whiskers plot of MPZL2 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0447] FIG. 310 shows a box and whiskers plot of LOC100128751 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0448] FIG. 311 shows a box and whiskers plot of IGJ gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0449] FIG. 312 shows a box and whiskers plot of CDCl26 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0450] FIG. 313 shows a box and whiskers plot of PPP1R2//PPP1R2P3
gene expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0451] FIG. 314 shows a box and whiskers plot of IL5RA gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0452] FIG. 315 shows a box and whiskers plot of ARL17P1//ARL17
gene expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0453] FIG. 316 shows a box and whiskers plot of ATP5L//ATP5L2 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0454] FIG. 317 shows a box and whiskers plot of TAS2R31 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0455] FIG. 318 shows a box and whiskers plot of
HIST2H2BF//HIST2H3D gene expression for healthy subjects and
subjects with SIRS, mild sepsis, severe sepsis.
[0456] FIG. 319 shows a box and whiskers plot of CALM2//C2orf61
gene expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0457] FIG. 320 shows a box and whiskers plot of SPATA6 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0458] FIG. 321 shows a box and whiskers plot of IGLV6-57 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0459] FIG. 322 shows a box and whiskers plot of C1orf128 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0460] FIG. 323 shows a box and whiskers plot of KRTAP15-1 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0461] FIG. 324 shows a box and whiskers plot of IFI44 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0462] FIG. 325 shows a box and whiskers plot of
IGLV3-25//IGLV3-12//IGLV1-36//IGLV3-27//IGLV7-46//IGLV4-3//IGLV3-16//IGLV-
3-19//gene expression for healthy subjects and subjects with SIRS,
mild sepsis, severe sepsis.
[0463] FIG. 326 shows a box and whiskers plot of NA gene expression
for healthy subjects and subjects with SIRS, mild sepsis, severe
sepsis.
[0464] FIG. 327 shows a box and whiskers plot of SDHC gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0465] FIG. 328 shows a box and whiskers plot of NFXL1 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0466] FIG. 329 shows a box and whiskers plot of GLDC gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0467] FIG. 330 shows a box and whiskers plot of DCTN5 gene
expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
[0468] FIG. 331 shows a box and whiskers plot of KIAA0101//CSNK1G1
gene expression for healthy subjects and subjects with SIRS, mild
sepsis, severe sepsis.
DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS
[0469] An example process for performing biomarker identification
will now be described. For the purpose of this example, it is
assumed that the process is performed at least in part using an
electronic processing device, such as a processor of a computer
system, as will be described in more detail below.
[0470] Furthermore, for the purpose of explanation, different terms
will be used to identify biomarkers at different stages of the
process. For example, the term "reference biomarkers" is used to
refer to biomarkers whose activity has been quantified for a sample
population of reference individuals having different conditions,
stages of different conditions, subtypes of different conditions or
with different prognoses. The different reference biomarkers
measured for the individuals may be referred to as a reference
biomarker collection. The term "reference data" refers to data
measured for the individuals in the sample population, and may
include quantification of the activity of the biomarkers measured
for each individual, information regarding any conditions of the
individuals, and optionally any other information of interest. The
number of reference biomarkers will vary, but is typically more
than 1000 biomarkers.
[0471] The term "potential biomarkers" refers to a subset of the
reference biomarkers that have been identified as being potentially
useful in distinguishing between different groups of individuals,
such as individuals suffering from different conditions, or having
different stages or prognoses. The number of potential biomarkers
will vary, but is typically about 200. The different potential
biomarkers may be referred to as a potential biomarker
collection.
[0472] The term "remaining reference biomarkers" refers to
reference biomarkers remaining in the reference biomarker
collection, once potential biomarkers have been removed.
[0473] The term "signature biomarkers" is used to refer to a subset
of the potential biomarkers that have been identified as being
potentially useful in defining signatures that can be used in
performing a clinical assessment, such as to rule in or rule out a
specific condition, different stages or severity of conditions,
subtypes of different conditions or different prognoses. The number
of signature biomarkers will vary, but is typically of the order of
10 or less, with the different signature biomarkers identified
being referred to as a signature biomarker collection.
[0474] It will be appreciated that the above described terms and
associated definitions are used for the purpose of explanation only
and are not intended to be limiting.
[0475] In this example, at step 100, the process involves using
reference data from a plurality of individuals to define a number
of groups of individuals. The individuals are taken from a
reference population, typically including individuals having a
range of different conditions, or stages of different conditions,
or subtypes of different conditions or with different
prognoses.
[0476] The reference data typically includes measurements of a
plurality of reference biomarkers, the measurements including
information regarding the activity, such as the level or abundance,
of any expression product or measurable molecule, as will be
described in more detail below. The reference data may also include
other additional relevant information such as clinical data
regarding one or more conditions suffered by each individual. This
can include information regarding a presence, absence, degree,
stage, severity or progression of a condition, phenotypic
information, such as details of phenotypic traits, genetic or
genetically regulated information, amino acid or nucleotide related
genomics information, results of other tests including imaging,
biochemical and hematological assays, other physiological scores
such as a SOFA (Sequential Organ Failure Assessment) score, or the
like and this is not intended to be limiting, as will be apparent
from the description below.
[0477] At step 110, a plurality of analysis techniques, such as
statistical analysis or machine learning techniques, are used to
identify a number of potential biomarkers from the plurality of
reference biomarkers that are potentially useful for distinguishing
the groups of individuals, allowing the potential biomarkers to be
used in selecting signature biomarkers for use in generating
signatures for use in clinical assessments.
[0478] The analysis techniques are typically applied in an
iterative fashion, with each iteration being used to identify a
subset of reference biomarkers that might prove suitable for use as
potential biomarkers. In one example, as each iteration is
performed, the predictive performance of the reference biomarkers
in distinguishing the groups is assessed, with reference biomarkers
being identified for use as potential biomarkers only in the event
that they exceed a predetermined predictive performance threshold,
such as at least 90%, at least 85% or more typically, at least 80%.
This threshold may be implemented as accuracy in the case of
classification or a measure of correlation in the case of
continuous outcomes.
[0479] Once reference biomarkers are identified for use as
potential biomarkers, they can be removed from the reference
biomarker collection, allowing the next iteration to be performed
on the remaining reference biomarkers. The number of iterations
will depend on the analysis techniques and associated parameters
used, and can include at least 100, at least 500, at least 1000, at
least 2000 and even at least 5000.
[0480] The process uses a plurality of different analysis
techniques, such as classification, regression and/or machine
learning techniques, allowing a variety of potential biomarkers to
be identified. This is performed as each analysis technique
typically operates slightly differently and as a result will often
identify different potential biomarkers, so using the plurality of
different analysis techniques ensures that as many potentially
useful biomarkers as possible are captured for use as potential
biomarkers.
[0481] The analysis techniques may be performed until the
predictive performance of the remaining reference biomarkers in the
reference biomarker collection falls below the predetermined
threshold and each technique has been used, or may be repeated
until a predetermined number of potential biomarkers, such as at
least 100, less than 500 or more typically about 200, are
identified.
[0482] Following identification of potential biomarkers, at step
120, a subset of the potential biomarkers can be optionally
identified for use as signature biomarkers, to allow signatures for
use in specific clinical assessments to be determined. This can be
achieved in any suitable manner, but in one example, this involves
a further process of identifying specific groups relevant to the
clinical assessment, and then performing a further regression or
other similar statistical analysis to select those potential
biomarkers that can be used as signature biomarkers.
[0483] Accordingly, in one example, the above described process is
used to identify a subset of measured reference biomarkers that can
act as potential biomarkers, before a more in depth analysis is
performed to identify a subset of potential biomarkers for use as
signature biomarkers that can be used in specific clinical
assessments. As a result, the above process can act as a coarse
filter, allowing a relatively large number of potential biomarkers
to be identified that can be used in distinguishing the different
groups of individuals.
[0484] By way of example, many patients suffer from a condition
called Systemic Inflammatory Response Syndrome (SIRS) (M S
Rangel-Frausto, D Pittet, M Costigan, T Hwang, C S Davis, and R P
Wenzel, "The Natural History of the Systemic Inflammatory Response
Syndrome (SIRS). a Prospective Study.," JAMA: the Journal of The
American Medical Association 273, no. 2 (Jan. 11, 1995): 117-123.).
SIRS is an overwhelming whole body reaction that may have an
infectious or non-infectious aetiology, whereas sepsis is SIRS that
occurs during infection. Both are defined by a number of
non-specific host response parameters including changes in heart
and respiratory rate, body temperature and white cell counts
(Mitchell M Levy et al., "2001 SCCM/ESICM/ACCP/ATS/SIS
International Sepsis Definitions Conference," Critical Care
Medicine 31, no. 4 (April 2003): 1250-1256,
doi:10.1097/01.CCM.0000050454.01978.3B.; K Reinhart, M Bauer, N C
Riedemann, and C S Hartog, "New Approaches to Sepsis: Molecular
Diagnostics and Biomarkers," Clinical Microbiology Reviews 25, no.
4 (Oct. 3, 2012): 609-634, doi:10.1128/CMR.00016-12.) To
differentiate these conditions they are referred herein to as SIRS
(both conditions), infection-negative SIRS (SIRS without infection,
hereafter referred to as "inSIRS") and infection-positive SIRS
(sepsis, SIRS with a known or suspected infection, hereafter
referred to as "ipSIRS"). The causes of SIRS are multiple and
varied and can include, but are not limited to, trauma, burns,
pancreatitis, endotoxemia, surgery, adverse drug reactions, and
infections (local and systemic). Using two patient populations of
healthy individuals and individuals having SIRS, a coarse filter
can be used to identify which reference biomarkers can distinguish
these two groups of individuals, thereby allowing potential
biomarkers to be identified. A coarse filter could also be used to
identify which reference biomarkers can separate inSIRS patients
from ipSIRS patients, both groups of patients having SIRS in
common, but each group of patients differing in whether a clinician
has determined the presence of an infection or not.
[0485] Following this, more specific and computationally intensive
analyses could be performed to identify a subset of potential
biomarkers for use as signature biomarkers to answer more specific
clinical questions such as: for patients with ipSIRS which
biomarkers can separate out those with severe sepsis or septic
shock, or provide a prognosis or indication of likely progression
to another stage of disease, or for patients with inSIRS which
biomarkers can separate those with pancreatitis from those
following surgery.
[0486] Thus, if it is desired to make clinical assessments relating
to SIRS, and in particular, inSIRS and ipSIRS, a suite of
biomarkers can be quantified for individuals suffering either one
of these conditions, as well as healthy individuals and used as
reference biomarkers. These data can be used to define first groups
of individuals having one of the two conditions or both, as well as
of healthy individuals. Potential biomarkers can be ascertained
that may be used to distinguish these groups. For example, the
first stage could be used to determine biomarkers that
differentiate healthy individuals and individuals having SIRS.
[0487] Following this, signature biomarkers for specific clinical
assessments within these groups, such as distinguishing inSIRS from
ipSIRS (rule in and rule out ipSIRS), can be determined. In this
case, second groups are defined that relate to individuals having
or not having infection-positive or inSIRS, and then signature
biomarkers are determined from the potential biomarkers.
[0488] It can be complex and computationally difficult to select a
limited number of clinically useful and manageable biomarkers from
a large data set in a single stage. Thus, using a single stage
identification process, potentially useful biomarkers can be easily
overlooked or omitted, so that the resulting signature biomarkers
are not necessarily the best suited for use in a specific clinical
assessment. A particular benefit of the described approach is that
by separating the process into multiple stages, the chances of
overlooking or omitting the discovery of new and clinically useful
biomarkers is greatly reduced.
[0489] The multi-stage approach allows coarse filtering to be used
first so as to limit the number of measured reference biomarkers to
a more manageable number of potential biomarkers, so that more
specific, and computationally intensive, techniques can be used to
identify signature biomarkers for use in specific clinical
assessments. The coarse analysis therefore allows a collection of
potential biomarkers to be established that will be relevant to a
range of different but related clinical assessments. A more focused
analysis can then be performed to identify specific signature
biomarkers, which is less computationally intensive than attempting
to do this for a greater number of biomarkers, and also helps
ensure the best biomarkers for the clinical assessment are
identified by excluding the noise introduced by many uninformative
biomarkers which have been removed from consideration.
[0490] The above approach can therefore allow a large number of
measured reference biomarkers, typically several thousand, to be
used as a basis for the analysis, thereby reducing the likelihood
of new and clinically relevant biomarkers being excluded from the
resulting potential biomarkers, and ultimately signature
biomarkers, hence improving the ability of the signatures to be
clinically useful in assessments.
[0491] In one example, the process is performed at least in part
using a processing system, such as a suitably programmed computer
system. This can be performed on a stand-alone computer, with the
microprocessor executing applications software allowing the
above-described method to be performed. Alternatively, the process
can be performed by one or more processing systems operating as
part of a distributed architecture, an example of which will now be
described.
[0492] In this example, a base station 201 is coupled via a
communications network, such as the Internet 202, and/or a number
of local area networks (LANs) 204, to a number of computer systems
203. It will be appreciated that the configuration of the networks
202, 204 are for the purpose of example only, and in practice the
base station 201, computer systems 203 can communicate via any
appropriate mechanism, such as via wired or wireless connections,
including, but not limited to mobile networks, private networks,
such as an 802.11 networks, the Internet, LANs, WANs, or the like,
as well as via direct or point-to-point connections, such as
Bluetooth, or the like.
[0493] In one example, the base station 201 includes a processing
system 210 coupled to a database 211. The base station 201 is
adapted to be used in analysing reference data, selecting potential
biomarkers, and optionally generating signatures for use in
clinical assessments. The reference data may be stored in the
database 211 and may be received from the computer systems 203, or
other remote devices. The base station 201 may also be adapted to
assist in performing clinical assessments by comparing individual
data relating to a patient or other individual and then comparing
this to the signatures to allow a clinical assessment to be made.
The computer systems 203 are therefore adapted to communicate with
the base station 201, allowing data to be transferred there between
and/or to control the operation of the base station 201.
[0494] Whilst the base station 201 is a shown as a single entity,
it will be appreciated that the base station 201 can be distributed
over a number of geographically separate locations, for example by
using processing systems 210 and/or databases 211 that are provided
as part of a cloud based environment.
[0495] However, the above-described arrangement is not essential
and other suitable configurations could be used. For example, the
process for identifying biomarkers, as well as any subsequent
clinical assessment of individual data could be performed on a
stand-alone computer system.
[0496] An example of a suitable processing system 210 includes at
least one microprocessor 300, a memory 301, an input/output device
302, such as a keyboard and/or display, and an external interface
303, interconnected via a bus 304 as shown. In this example the
external interface 303 can be utilised for connecting the
processing system 210 to peripheral devices, such as the
communications networks 202, 204, databases 211, other storage
devices, or the like. Although a single external interface 303 is
shown, this is for the purpose of example only, and in practice
multiple interfaces using various methods (e.g., Ethernet, serial,
USB, wireless or the like) may be provided.
[0497] In use, the microprocessor 300 executes instructions in the
form of applications software stored in the memory 301 to allow the
biomarker identification process to be performed, as well as to
perform any other required processes, such as communicating with
the computer systems 203. The applications software may include one
or more software modules, and may be executed in a suitable
execution environment, such as an operating system environment, or
the like.
[0498] Accordingly, it will be appreciated that the processing
system 300 may be formed from any suitable processing system, such
as a suitably programmed computer system, PC, web server, network
server, or the like. In one particular example, the processing
system 100 is a standard processing system such as a 32-bit or
64-bit Intel Architecture based processing system, which executes
software applications stored on non-volatile (e.g., hard disk)
storage, although this is not essential. However, it will also be
understood that the processing system could be any electronic
processing device such as a microprocessor, microchip processor,
logic gate configuration, firmware optionally associated with
implementing logic such as an FPGA (Field Programmable Gate Array),
or any other electronic device, system or arrangement.
[0499] In one example, the computer system 203 includes at least
one microprocessor 400, a memory 401, an input/output device 402,
such as a keyboard and/or display, and an external interface 403,
interconnected via a bus 404 as shown. In this example the external
interface 403 can be utilised for connecting the computer system
203 to peripheral devices, such as the communications networks 202,
204, databases 211, other storage devices, or the like. Although a
single external interface 403 is shown, this is for the purpose of
example only, and in practice multiple interfaces using various
methods (eg. Ethernet, serial, USB, wireless or the like) may be
provided.
[0500] In use, the microprocessor 400 executes instructions in the
form of applications software stored in the memory 401 to allow
communication with the base station 201, for example to allow data
to be supplied thereto and allowing results of any clinical
assessment to be displayed to an operator. The computer system 203
may also be used to allow the operation of the base station 201 to
be controlled, for example to allow the biomarker identification
process to be performed remotely.
[0501] Accordingly, it will be appreciated that the computer
systems 203 may be formed from any suitable processing system, such
as a suitably programmed PC, Internet terminal, lap-top, hand-held
PC, smart phone, PDA, web server, or the like. Thus, in one
example, the processing system 100 is a standard processing system
such as a 32-bit or 64-bit Intel Architecture based processing
system, which executes software applications stored on non-volatile
(e.g., hard disk) storage, although this is not essential. However,
it will also be understood that the computer systems 203 can be any
electronic processing device such as a microprocessor, microchip
processor, logic gate configuration, firmware optionally associated
with implementing logic such as an FPGA (Field Programmable Gate
Array), or any other electronic device, system or arrangement.
[0502] Examples of the biomarker identification process, and
subsequent use in a clinical assessment will now be described in
further detail. For the purpose of these examples, it is assumed
that reference data, including the reference biomarker collection,
any potential biomarkers, signature biomarkers or signatures can be
stored in the database 211, and that the biomarker identification
process is performed using the processing system 210 under control
of one of the computer systems 203. Thus, it is assumed that the
processing system 210 of the base station 201 hosts applications
software for performing the biomarker identification process, with
actions performed by the processing system 210 being performed by
the processor 300 in accordance with instructions stored as
applications software in the memory 301 and/or input commands
received from a user via the I/O device 302, or commands received
from the computer system 203.
[0503] It will also be assumed that the user interacts with
application software executed by the processing system 210 via a
GUI, or the like presented on the computer system 203. Actions
performed by the computer system 203 are performed by the processor
401 in accordance with instructions stored as applications software
in the memory 402 and/or input commands received from a user via
the I/O device 403. The base station 201 is typically a server
which communicates with the computer system 203 via a LAN, or the
like, depending on the particular network infrastructure
available.
[0504] However, it will be appreciated that the above-described
configuration assumed for the purpose of the following examples is
not essential, and numerous other configurations may be used. It
will also be appreciated that the partitioning of functionality
between the computer system 203, and the base station 201 may vary,
depending on the particular implementation.
[0505] A second example of a process for determining biomarkers
will now be described.
[0506] In this example, at step 500 reference data is acquired for
a plurality of individuals with the reference data including at
least data regarding a plurality of reference biomarkers, measured
for each individual.
[0507] The reference data may be acquired in any appropriate manner
but typically this involves obtaining gene expression product data
from a plurality of individuals, selected to include individuals
diagnosed with one or more conditions of interest, as well as
healthy individuals. The terms "expression" or "gene expression"
refer to production of RNA message or translation of RNA message
into proteins or polypeptides, or both. Detection of either types
of gene expression in use of any of the methods described herein is
encompassed by the present invention. The conditions are typically
medical, veterinary or other health status conditions and may
include any illness, disease, stages of disease, disease subtypes,
severities of disease, diseases of varying prognoses or the
like.
[0508] In order to achieve this, gene expression product data are
collected, for example by obtaining a biological sample, such as a
peripheral blood sample, and then performing a quantification
process, such as a nucleic acid amplification process, including
PCR (Polymerase Chain Reaction) or the like, in order to assess the
activity, and in particular, level or abundance of a number of
reference biomarkers. Quantified values indicative of the relative
activity are then stored as part of the reference data.
[0509] Example reference biomarkers will be described in more
detail below but it will be appreciated that these could include
expression products such as nucleic acid or proteinaceous
molecules, as well as other molecules relevant in making a clinical
assessment. The number of biomarkers measured for use as reference
biomarkers will vary depending upon the preferred implementation,
but typically include a large number such as, 1000, 5000, 10000 or
above, although this is not intended to be limiting.
[0510] The individuals also typically undergo a clinical assessment
allowing any conditions to be clinically identified, and with an
indication of any assessment or condition forming part of the
reference data. Whilst any conditions can be assessed, in one
example the process is utilized specifically to identify conditions
such as SIRS, including inSIRS and ipSIRS or sepsis. It will be
appreciated from the following, however, that this can be applied
to a range of different conditions, and reference to SIRS or sepsis
is not intended to be limiting.
[0511] Additionally, the reference data may include details of one
or more phenotypic traits of the individuals and/or their
relatives. Phenotypic traits can include information such as the
gender, ethnicity, age, or the like. Additionally, in the case of
the technology being applied to individuals other than humans, this
can also include information such as designation of a species,
breed or the like.
[0512] Accordingly, in one example the reference data can include
for each of the reference individuals an indication of the activity
of a plurality of reference biomarkers, a presence, absence degree,
stage, or progression of a condition, phenotypic information such
as phenotypic traits, genetic information and a physiological score
such as a SOFA score.
[0513] The reference data is typically collected from individuals
presenting at a medical centre with clinical signs relating to
relevant any conditions of interest, and may involve follow-on
consultations in order to confirm clinical assessments, as well as
to identify changes in biomarkers, and/or clinical signs, and/or
severity of clinical signs, over a period of time. In this latter
case, the reference data can include time series data indicative of
the progression of a condition, and/or the activity of the
reference biomarkers, so that the reference data for an individual
can be used to determine if the condition of the individual is
improving, worsening or static. It will also be appreciated that
the reference biomarkers are preferably substantially similar for
the individuals within the sample population, so that comparisons
of measured activities between individuals can be made.
[0514] It will be appreciated that once collected, the reference
data can be stored in the database 211 allowing this to be
subsequently retrieved by the processing system 210 for subsequent
analysis. The processing system 210 also typically stores an
indication of an identity of each of the reference biomarkers as a
reference biomarker collection.
[0515] At step 505, the processing system 210 optionally removes a
validation subgroup of individuals from the reference data prior to
determining the potential biomarkers. This is performed to allow
the processing system 210 to determine the potential biomarkers
using the reference data without the validation subgroup so that
the validation subgroup can be subsequently used to validate the
potential biomarkers or signatures including a number of the
potential biomarkers. Thus, data from the validation subgroup is
used to validate the efficacy of the potential or signature
biomarkers in identifying the presence, absence, degree, stage,
severity, prognosis or progression of any one or more of the
conditions to ensure the potential or signature biomarkers are
effective, as will be described in more detail below.
[0516] In one example, this is achieved by having the processing
system 210 flag individuals within the validation subgroup or
alternatively store these in either an alternative location within
the database 211 or an alternative database to the reference data.
The validation subgroup of individuals is typically selected
randomly and may optionally be selected to include individuals
having different phenotypic traits. When a validation subgroup of
individuals is removed, the remaining individuals will simply be
referred to as reference data for ease throughout the remaining
description.
[0517] At step 510, the individuals remaining within the reference
data (ie excluding the validation subgroup) are classified into
groups. The groups may be defined in any appropriate manner and may
be defined based on any one or more of an indication of a presence,
absence, degree, stage, severity, prognosis or progression of a
condition, phenotypic traits, other tests or assays, genetic
information or measured activity of the reference biomarkers
associated with the individuals.
[0518] For example, a first selection of groups may be to identify
one or more groups of individuals suffering from SIRS, one or more
groups of individuals suffering ipSIRS, one or more groups of
individuals suffering inSIRS, and one or more groups of healthy
individuals. Further groups may also be defined for individuals
suffering from other conditions. Additionally, further subdivision
may be performed based on phenotypic traits, so groups could be
defined based on gender, ethnicity or the like so that a plurality
of groups of individuals suffering from a condition are defined,
with each group relating to a different phenotypic trait.
[0519] It will also be appreciated, however, that identification of
different groups can be performed in other manners, for example on
the basis of particular activities of biomarkers within the
biological samples of the reference individuals, and accordingly,
reference to conditions is not intended to be limiting and other
information may be used as required.
[0520] The manner in which classification into groups is performed
may vary depending on the preferred implementation. In one example,
this can be performed automatically by the processing system 210,
for example, using unsupervised methods such as Principal
Components Analysis (PCA), or supervised methods such as k-means or
Self Organising Map (SOM). Alternatively, this may be performed
manually by an operator by allowing the operator to review
reference data presented on a Graphical User Interface (GUI), and
define respective groups using appropriate input commands.
[0521] Once the groups have been defined, analysis techniques are
utilized in order to identify reference biomarkers that can be
utilized to potentially distinguish the groups. The analysis
technique typically examines the activity of the reference
biomarkers for individuals within and across the groups, to
identify reference biomarkers whose activities differ between and
hence can distinguish groups. A range of different analysis
techniques can be utilized including, for example, regression or
correlation analysis techniques. Examples of the techniques used
can include established methods for parametized model building such
as Partial Least Squares, Random Forest or Support Vector Machines,
usually coupled to a feature reduction technique for the selection
of the specific subset of the biomarkers to be used in a
signature.
[0522] Such techniques are known and described in a number of
publications. For example, the use of Partial Least Squares is
described in "Partial least squares: a versatile tool for the
analysis of high-dimensional genomic data" by Boulesteix,
Anne-Laure and Strimmer, Korbinian, from Briefings in
Bioinformatics 2007 vol 8. no. 1, pg 32-44. Support Vector machines
are described in "LIBSVM: a library for support vector machines" by
Chang, C. C. and Lin, C. J. from ACM Transactions on Intelligent
Systems and Technology (TIST), 2011 vol 2, no. 3, pg 27. Standard
Random Forest in R language is described in "Classification and
Regression by random Forest" by Liaw, A. and Wiener, M., in R news
2002, vol, no. 3, pg 18-22.
[0523] The analysis techniques are implemented by the processing
system 210, using applications software, which allows the
processing system 210 to perform multiple ones of the analysis
techniques in sequence. This is advantageous as the different
analysis techniques typically have different biases and can
therefore be used to identify different potential biomarkers that
can distinguish the groups, thereby reducing the risk of clinically
relevant biomarkers being overlooked.
[0524] At step 515 a next analysis technique is selected by the
processing system 210, with this being implemented at step 520 to
identify the best N reference biomarkers for distinguishing the
groups, where the variable N is a predetermined or algorithmically
derived number of biomarkers whose value may vary depending on the
analysis technique used and the preferred implementation, but is
typically a relatively small number compared to the overall number
of biomarkers, such as less than 10, more than 1, between 2 and 8
and 5. This process typically involves a predictive model to assess
the ability of activities of particular ones of the reference
biomarkers to distinguish between different groups. For example
this can examine the manner in which the activity of reference
biomarkers differ between groups, and/or are relatively similar
within a group. This can be performed iteratively for different
combinations of reference biomarkers until a best N of the
reference biomarkers are identified.
[0525] At step 525, the processing system 210 determines the
predictive performance of the identified best N reference
biomarkers, when used in the model, for in distinguishing the
relevant groups. The predictive performance is typically a
parameter determined as part of the combination of analysis
technique and chosen embodying model, as will be appreciated by
persons skilled in the art. For example, receiver operating
characteristic (ROC) analysis may be used to determine optimal
assay parameters to achieve a specific level of accuracy,
specificity, positive predictive value, negative predictive value,
and/or false discovery rate.
[0526] Optionally, a cross-validation approach may be used whereby
steps 520 and 525 are repeated M times to produce a distribution of
M predictive performance measures, and N.times.M selected reference
biomarkers. It will be appreciated that there may be none, some, or
complete overlap in the sets of selected reference biomarkers for
the M iterations. The union (unique set) of selected reference
biomarkers from all M iterations is the set U.
[0527] At step 530, the predictive performance is compared to a
predetermined threshold, which is typically selected dependent upon
the preferred implementation, but may be a relatively low value
such as 80%. In the case of cross-validation, in which steps 520
and 525 are repeated M times, the predictive performance at step
530 is some property of the M predictive performance measurements
such as the mean, median or maximum.
[0528] By example, ruling in ipSIRS might have a lower threshold
than ruling out ipSIRS since the clinical risk of treating someone
with inSIRS with antibiotics might be considered to be less than
not treating someone with ipSIRS with antibiotics. Thus, it can be
appreciated that the threshold set is influenced by a variety of
factors including clinical utility, patient welfare, disease
prevalence, and econometrics of test use to name a few
examples.
[0529] At step 535, if it is determined that the predictive
performance is above the threshold, the identified N reference
biomarkers are added to a list or collection of potential
biomarkers, an indication of which is typically stored in the
database 211. In the case of a cross-validation approach, where the
set of unique selected biomarkers (U) may be larger than the number
to be selected as potential biomarkers (N), the N most frequently
selected biomarkers during the M iterations are identified as the N
reference biomarkers and are then removed from the reference
biomarker collection before further analysis is performed. The
process then returns to step 520 allowing the same analysis
technique to be performed and the next N reference biomarkers
identified.
[0530] It will therefore be appreciated that this is an iterative
technique that allows reference biomarkers capable of
distinguishing the groups to be progressively identified with the
ability of an additional N reference biomarkers to act as potential
biomarkers being assessed, within each iteration. This process
performs a relatively coarse filtering of reference biomarkers
allowing groups of reference biomarkers with predictive performance
above the threshold to be progressively removed from the reference
biomarker collection and added to the potential biomarker
collection.
[0531] During this process, if it is determined that the predictive
performance of the N identified reference biomarkers is below the
threshold, then the process moves to step 540 when it is determined
by the processing system 210 if all analysis techniques have been
used. If not, the process returns to step 515 allowing a next
analysis technique to be selected.
[0532] Thus, it will be appreciated that the iterative process is
repeated for a number of different analysis techniques allowing
biases between the techniques to identify different potential
biomarkers. Accordingly, this process progressively identifies
reference biomarkers useful as potential biomarkers utilizing a
coarse identification process that can be performed relatively
rapidly, and optionally in parallel, over a large number of
reference biomarkers.
[0533] At this stage, the potential biomarkers may be utilized in
an attempt to classify the validation subgroup of individuals. In
particular, the different activities of the identified potential
biomarkers for individuals within each group are utilized to
attempt to classify individuals in the validation subgroup into the
groups defined at step 510. In the event that classification of the
validation subgroup is successful, potential biomarkers may be
retained, whereas if a validation is unsuccessful potential
biomarkers may optionally be removed from the potential biomarker
collection.
[0534] In one example, the above-described process is performed
over several thousand different reference biomarkers allowing a
collection of several hundred potential biomarkers to be
identified. However, the potential biomarkers may not be ideal for
answering specific clinical assessment questions, such as ruling in
a condition, ruling out a condition, or determining a stage of
progression or likely outcome of a condition or treatment.
[0535] Accordingly, once the potential biomarkers have been
identified, more refined processes are used to allow the processing
system 210 to identify a number of potential biomarkers for use as
signature biomarkers, in turn allowing signatures to be developed
for performing specific clinical assessments.
[0536] In this regard, it will be appreciated that typically
clinicians will want to perform a specific clinical assessment
based on a preliminary diagnosis made using clinical signs, present
in a subject presented to them. Accordingly, a clinician could
potentially only need to answer the question of whether the subject
has ipSIRS, or does not have ipSIRS. As the cost, speed and ability
to perform a diagnostic test will typically be heavily dependent on
the number of biomarkers assessed as part of the test, it is
preferable to be able to identify a minimal number of biomarkers
that are able to answer the specific clinical assessment of
interest. To address this, the process can use more refined
analysis of the potential biomarkers to identify those that are
most useful in performing a particular clinical assessment, and
hence can be used as signature biomarkers.
[0537] Accordingly, at step 545 a next clinical assessment is
determined. This can be achieved in any manner, but usually
involves having the user define the clinical assessment using
appropriate input commands. As part of this, at step 550, the
processing system 210 is used to identify second groups that are
relevant to the clinical assessment, for example, by having the
user identify criteria, such as the relevant conditions associated
with each group, or the stage of progression for the individuals
within the groups. This could include, for example, defining groups
of individuals having ipSIRS and those not having ipSIRS, or those
having mild, major, worsening or improving ipSIRS. Whilst it will
be appreciated that the second groups may be the same as the first
groups previously defined at step 510, more typically the second
groups are more appropriately targeted based on the particular
clinical assessment.
[0538] At step 555, the processing system 210 uses a second
analysis technique to identify a number of the potential biomarkers
that best distinguish the second groups of individuals. In
particular, this will attempt to identify potential biomarkers
whose level of activity for the individuals within the groups, can
be used to distinguish the groups. The nature of the analysis
technique will vary depending upon the preferred implementation and
can include analysis techniques similar to those outlined above.
Alternatively, different analysis techniques can be used such as
ordinal classification, which differs from regular classification
in that the known order of classes is used without assumptions as
to their relative similarity to impose extra constraints in the
model leading to more accurate clarification of borderline cases.
Such ordinal classification is described in "Support vector ordinal
regression" by Chu, W. and Keerthi, S. S., in Neural Computation
2007, vol 19, no. 3, pg 792-815.
[0539] An ordinal SVM for classification consists of the same
fundamental elements of any SVM technique that would be familiar to
anyone skilled in the art. Namely, the objective is to describe a
number of maximally separating hyper-planes in the transformed
hyperspace defined by the kernel function. An ordinal classifier
differs from a regular SVM classifier in that it imposes an ordinal
structure through the use of the cost function. This is implemented
by adding to cost functions a component which penalizes incorrect
ranks during execution, as described "Support vector ordinal
regression" by Chu et al. (2007, supra).
[0540] Typically, the analysis techniques are implemented to
identify a limited overall number of potential biomarkers that can
be used as signature biomarkers, and may therefore use more
stringent criteria than the analysis techniques used in steps 515
to 530 above. Alternatively, the analysis techniques may not be
limited in the number of potential biomarkers identified, and can
instead identify more or less potential biomarkers than the
predetermined number N, above. Additionally, for this reason, only
a single analysis technique is typically required at this stage,
although this is not essential and multiple second analysis
techniques could be used.
[0541] At step 560, the processing system 210 determines if the
predictive performance of the identified potential biomarkers
exceeds a second predetermined threshold.
[0542] Optionally, a cross-validation approach may be used whereby
steps 550 and 560 are repeated M times to produce a distribution of
M predictive performance measures, and N.times.M selected reference
biomarkers. It will be appreciated that there may be none, some, or
complete overlap in the sets of selected reference biomarkers for
the M iterations. The union (unique set) of selected reference
biomarkers from all M iterations is the set U.
[0543] Optionally, a consensus approach may be used, whereby steps
555 and 560 are repeated multiple times, and the predictive
performance measure is some measure of the consensus of the
iterations, such as the average value.
[0544] At step 565, if it is determined that the predictive
performance is not above the second predetermined threshold, the
processing system 210 modifies parameters associated with the
analysis technique at step 570 and the process returns to step 555
allowing the same or alternative potential biomarkers to be
assessed. This process is repeated until a successful determination
occurs when a limited number of potential biomarkers are identified
which provide a predictive performance above the threshold, in
which case the process moves on to step 575.
[0545] It will be appreciated that as this is attempting to
identify a limited number of biomarkers that provide better
predictive performance, the second predetermined threshold is
typically set to be higher that the first predetermined threshold
used at step 530, and as a result of this, the second analysis
technique may be computationally more expensive. Despite this, as
the process is only being performed on the basis of the potential
biomarkers and not the entire set of reference biomarkers, this can
typically be performed relatively easily.
[0546] At step 575, the processing system 210 determines if the
identified potential biomarkers are to be excluded. This may occur
for any one of a number of reasons. For example, a limited number
of say five biomarkers may be identified which are capable of
providing the required clinical assessment outcome. However, it may
not be possible to use some of these biomarkers for legal or
technical reasons, in which case the biomarkers may be excluded. In
this case, the excluded biomarkers are removed from the potential
biomarker database at step 580 and the process returns to step 555
allowing the analysis to be performed.
[0547] It will be appreciated that whilst such excluded biomarkers
may be removed from the reference data at an earlier point in the
process, the ability to identify excluded biomarkers may be
difficult. For example, performing a freedom-to-operate assessment
of potential biomarkers can be an expensive process. It is
therefore unfeasible to do this to the entire collection of
biomarkers within the reference database or even to the entire
collection of potential biomarkers. Accordingly, this assessment is
only typically made once a potential biomarker has been identified
at step 555 to 565 as providing a predictive performance above the
threshold.
[0548] In the event that none of the potential biomarkers are
excluded, the identified potential biomarkers are used as signature
biomarkers, and an indication of the signature biomarkers is
typically stored in a signature biomarker collection in the
database 211. The measured activities from the reference
individuals for the signature biomarkers can then be used to
generate signatures for use in performing the clinical assessment
at step 585. The signatures will typically define activities or
ranges of activities of the signature biomarkers that are
indicative of the presence, absence, degree, stage, or progression
of a condition. This allows the signatures to be used in performing
diagnostic and/or prognostic assessment of subjects.
[0549] For example, an indication of the activity of the signature
biomarkers can be obtained from a sample taken from a test subject,
and used to derive a signature indicative of the health status of
the test subject. This can then be compared to the signatures
derived from the reference data to assess the likely heath status
of the subject.
[0550] Following this, at step 590 the process moves on to
determine whether all clinical assessments have been addressed and
if not, returns to step 545 allowing a next clinical assessment to
be selected. Otherwise, the process ends at step 595.
[0551] Accordingly, it will be appreciated that the above-described
methodology utilizes a staged approach in order to generate
potential biomarkers and optionally, further signature biomarkers,
for use in performing clinical assessments.
[0552] The process utilizes an initial coarse filtering based on a
plurality of analysis techniques in order to identify a limited
number of potential biomarkers. The limited number of potential
biomarkers, which is typically in the region of less than 500, are
selected from a larger database of biomarkers as being those most
capable of distinguishing between different conditions, and/or
different stages or progressions of a condition.
[0553] Following this, in a further stage, specific clinical
assessments are identified with additional analysis techniques
being used to select particular biomarkers from the database of
potential biomarkers with the particular biomarkers being capable
of being use in answering the specific clinical assessments.
[0554] A specific example of the above-described process will now
be described with reference to distinguishing between inSIRS and
ipSIRS.
[0555] A number of patients clinically identified as having
infection negative SIRS and infection positive SIRS had peripheral
blood samples taken (N=141). These samples were run on microarray.
The microarray data was then normalised and quality control (QC)
filtered as per the recommendation of the manufacturer to produce a
list of samples with a corresponding clinical diagnosis of SIRS
with or without an infection (N=141), and a list of reference
biomarkers that passed QC (N=15,989).
[0556] The process of building and testing a model will now be
described. In this example, 10% of the samples are randomly
selected to act as the testing/validation set and are put aside.
The remaining 90% of the samples are the training set, used to
identify the potential biomarkers.
[0557] A feature selection algorithm coupled to a machine learning
model is then applied to the training set, In this example a
Recursive Feature Selection Support Vector Machine, described for
example in "Recursive SVM feature selection and sample
classification for mass-spectrometry and microarray data", by
Xuegong Zhang, Xin Lu, Qian Shi, Xiu-qin Xu, Hon-chiu E Leung,
Lyndsay N Harris, James D Iglehart, Alexander Miron, Jun S Liu and
Wing H Wong from BMC Bioinformatics 2006, 7:197, was used to build
a model with exactly 10 genes as the input.
[0558] Assuming no technical or biological noise and ignoring
sample size considerations, these genes best describe the inherent
variability between inSIRS and ipSIRS samples when using an SVM
model, and therefore provide the best available separation
signature.
[0559] For each sample in the testing set, the model is used to
predict either inSIRS or ipSIRS. If the prediction matches the
clinical record for this sample, it is declared a correct
prediction. The performance of the model in this case is measured
by accuracy, which can be expressed as the percentage of correct
predictions for the testing set.
[0560] Optionally, the building and testing step may be repeated
with a different random testing and training set. This could be
performed any number of times depending on the preferred
implementation, and in one example is performed 100 times. If the
accuracy of the model was not significantly better than the last 2
iterations (1 way ANOVA p-value >0.95), then the selection of
biomarkers was terminated.
[0561] If the accuracy remained significantly better than either of
the last 2 iterations (as described above), then the 10 genes that
were selected in the model (or most frequently appear if repeated
runs were used) are then added to the collection of potentially
useful biomarkers, and were removed from subsequent iterations.
[0562] The biomarker identification process described above and
elsewhere herein has been used to identify 319 biomarker genes
(hereafter referred to as "inflammatory response syndrome (IRS)
biomarker genes"), which are surrogate markers that are useful for
assisting in distinguishing: (1) between SIRS affected subjects
(i.e., subject having inSIRS or ipSIRS) and healthy subjects or
subjects not affected by SIRS; (2) between subjects with inSIRS and
subjects with ipSIRS; and/or (3) between subjects with different
stages of ipSIRS (e.g., sepsis, severe sepsis and septic shock).
Based on this identification, the present inventors have developed
various methods and kits, which take advantage of these biomarkers
to determine the likelihood of the presence or absence of a
condition selected from a healthy condition (e.g., a normal
condition or one in which inSIRS and inSIRS are absent), SIRS
generally (i.e., not distinguishing between inSIRS or ipSIRS),
inSIRS or ipSIRS, or to assess the likelihood of the presence,
absence or risk of development of a stage of ipSIRS (e.g., a stage
of ipSIRS with a particular severity, illustrative examples of
which include mild sepsis, severe sepsis and septic shock). In
advantageous embodiments, the methods and kits involve monitoring
the expression of IRS biomarker genes in blood cells (e.g., immune
cells such as leukocytes), which may be reflected in changing
patterns of RNA levels or protein production that correlate with
the presence of active disease or response to disease.
[0563] As used herein, the term SIRS ("systemic inflammatory
response syndrome") refers to a clinical response arising from a
non-specific insult with two or more of the following measureable
clinical characteristics; a body temperature greater than
38.degree. C. or less than 36.degree. C., a heart rate greater than
90 beats per minute, a respiratory rate greater than 20 per minute,
a white blood cell count (total leukocytes) greater than 12,000 per
mm.sup.3 or less than 4,000 per mm.sup.3, or a band neutrophil
percentage greater than 10%. From an immunological perspective, it
may be seen as representing a systemic response to insult (e.g.,
major surgery) or systemic inflammation. As used herein, "inSIRS"
includes the clinical response noted above but in the absence of a
systemic infectious process. By contrast, "ipSIRS" includes the
clinical response noted above but in the presence of a presumed or
confirmed systemic infectious process. Confirmation of infectious
process can be determined using microbiological culture or
isolation of the infectious agent. From an immunological
perspective, ipSIRS may be seen as a systemic response to
microorganisms be it local, peripheral or a systemic infection.
[0564] The terms "surrogate marker" and "biomarker" are used
interchangeably herein to refer to a parameter whose measurement
(e.g., level, presence or absence) provides information as to the
state of a subject. In various exemplary embodiments, a plurality
of biomarkers is used to assess a condition (e.g., healthy
condition, SIRS, inSIRS, ipSIRS, or a particular stage of ipSIRS).
Measurements of the biomarkers may be used alone or combined with
other data obtained regarding a subject in order to determine the
state of the subject biomarker. In some embodiments, the biomarkers
are "differentially present" in a sample taken from a subject of
one phenotypic status (e.g., having a specified condition) as
compared with another phenotypic status (e.g., not having the
condition). A biomarker may be determined to be "differentially
present" in a variety of ways, for example, between different
phenotypic statuses if the presence or absence or mean or median
level or concentration of the biomarker in the different groups is
calculated to be statistically significant. Common tests for
statistical significance include, among others, t-test, ANOVA,
Kruskal-Wallis, Wilcoxon, Mann-Whitney and odds ratio.
[0565] In some embodiments, the methods and kits involve: (1)
correlating a reference IRS biomarker profile with the presence or
absence of a condition selected from a healthy condition, SIRS,
inSIRS, ipSIRS, or a particular stage of ipSIRS, wherein the
reference IRS biomarker profile evaluates at least one (e.g., 1, 2,
3, 4, 5, 6, 7, 8, 9, 10 etc.) IRS biomarker; (2) obtaining an IRS
biomarker profile of a sample (i.e., "a sample IRS biomarker
profile") from a subject, wherein the sample IRS biomarker profile
evaluates for an individual IRS biomarker in the reference IRS
biomarker profile a corresponding IRS biomarker; and (3)
determining a likelihood of the subject having or not having the
condition based on the sample IRS biomarker profile and the
reference IRS biomarker profile.
[0566] As used herein, the term "profile" includes any set of data
that represents the distinctive features or characteristics
associated with a condition of interest, such as with a particular
prediction, diagnosis and/or prognosis of a specified condition as
taught herein. The term generally encompasses quantification of one
or more biomarkers inter alia nucleic acid profiles, such as for
example gene expression profiles (sets of gene expression data that
represents the mRNA levels of one or more genes associated with a
condition of interest), as well as protein, polypeptide or peptide
profiles, such as for example protein expression profiles (sets of
protein expression data that represents the levels of one or more
proteins associated with a condition of interest), and any
combinations thereof.
[0567] Biomarker profiles may be created in a number of ways and
may be the combination of measurable biomarkers or aspects of
biomarkers using methods such as ratios, or other more complex
association methods or algorithms (e.g., rule-based methods), as
discussed for example in more detail below. A biomarker profile
comprises at least two measurements, where the measurements can
correspond to the same or different biomarkers. Thus, for example,
distinct reference profiles may represent the prediction of a risk
(e.g., an abnormally elevated risk) of having a specified condition
as compared the prediction of no or normal risk of having that
condition. In another example, distinct reference profiles may
represent predictions of differing degrees of risk of having a
specified condition.
[0568] The terms "subject," "individual" and "patient" are used
interchangeably herein to refer to any subject, particularly a
vertebrate subject, and even more particularly a mammalian subject.
Suitable vertebrate animals that fall within the scope of the
invention include, but are not restricted to, any member of the
subphylum Chordata including primates, rodents (e.g., mice rats,
guinea pigs), lagomorphs (e.g., rabbits, hares), bovines (e.g.,
cattle), ovines (e.g., sheep), caprines (e.g., goats), porcines
(e.g., pigs), equines (e.g., horses), canines (e.g., dogs), felines
(e.g., cats), avians (e.g., chickens, turkeys, ducks, geese,
companion birds such as canaries, budgerigars etc), marine mammals
(e.g., dolphins, whales), reptiles (snakes, frogs, lizards, etc.),
and fish. A preferred subject is a primate (e.g., a human, ape,
monkey, chimpanzee).
[0569] IRS biomarkers are suitably expression products of IRS
biomarker genes, including polynucleotide and polypeptide
expression products. The term "gene" as used herein refers to any
and all discrete coding regions of the cell's genome, as well as
associated non-coding and regulatory regions. The term "gene" is
also intended to mean the open reading frame encoding specific
polypeptides, introns, and adjacent 5' and 3' non-coding nucleotide
sequences involved in the regulation of expression. In this regard,
the gene may further comprise control signals such as promoters,
enhancers, termination and/or polyadenylation signals that are
naturally associated with a given gene, or heterologous control
signals. The DNA sequences may be cDNA or genomic DNA or a fragment
thereof. The gene may be introduced into an appropriate vector for
extrachromosomal maintenance or for integration into the host.
[0570] As used herein, polynucleotide expression products of IRS
biomarker genes are referred to herein as "IRS biomarker
polynucleotides." Polypeptide expression products of the IRS
biomarker genes are referred to herein as "IRS biomarker
polypeptides."
[0571] Suitably, individual IRS biomarker genes are selected from
the group consisting of: TLR5; CD177; VNN1; UBE2J1; IMP3;
RNASE2//LOC643332; CLEC4D; C3AR1; GPR56; ARG1;
FCGR1A//FCGR1B//FCGR1C; C11orf82; FAR2; GNLY; GALNT3; OMG; SLC37A3;
BMX//HNRPDL; STOM; TDRD9; KREMEN1; FAIM3; CLEC4E; IL18R1; ACER3;
ERLIN1; TGFBR1; FKBP5//LOC285847; GPR84; C7orf53; PLB1; DSE; PTGDR;
CAMK4; DNAJC13; TNFAIP6;
FOXD4L3//FOXD4L6//FOXD4//FOXD4L1//FOXD4L2//FOXD4L4//FOXD4L5;
MMP9//LOC100128028; GSR; KLRF1; SH2D1B; ANKRD34B; SGMS2;
B3GNT5//MCF2L2; GK3P//GK; PFKFB2; PICALM; METTL7B; HIST1H4C;
C9orf72; HIST1H3I; SLC15A2; TLR10; ADM; CD274; CRIP1; LRRN3;
HLA-DPB1; VAMP2; SMPDL3A; IFI16; JKAMP; MRPL41; SLC1A3; OLFM4;
CASS4; TCN1; WSB2; CLU; ODZ1; KPNAS; PLACE; CD63; HPSE; C1orf161;
DDAH2; KLRK1//KLRC4; ATP13A3; ITK; PMAIP1; LOC284757; GOT2; PDGFC;
B3GAT3; HIST1H4E; HPGD; FGFBP2; LRRC70//IPO11; TMEM144//LOC285505;
CDS2; BPI; ECHDC3; CCR3; HSPC159; OLAH; PPP2R5A//SNORA16B; TMTC1;
EAF2//HCG11//LOC647979; RCBTB2//LOC100131993; SEC24A//SAR1B;
SH3PXD2B; HMGB2; KLRD1; CHI3L1; FRMD3; SLC39A9; GIMAP7; ANAPC11;
EXOSC4; gene for IL-1beta-regulated neutrophil survival protein as
set forth in GenBank Accession No. AF234262; INSIG1; FOLR3//FOLR2;
RUNX2; PRR13//PCBP2; HIST1H4L; LGALS1; CCR1; TPST1; HLA-DRA; CD163;
FFAR2; PHOSPHO1; PPIF; MTHFS; DNAJC9//FAM149B1//RPL26; LCN2;
EIF2AK2; LGALS2; SIAE; AP3B2; ABCA13; gene for transcript set forth
in GenBank Accession No. AK098012; EFCAB2; HIST1H2AA; HINT1;
HIST1H3J; CDA; SAP30; AGTRAP; SUCNR1; MTRR; PLA2G7; AIG1; PCOLCE2;
GAB2; HS2ST1//UBA2; HIST1H3A; C22orf37; HLA-DPA1;
VOPP1//LOC100128019; SLC39A8; MKI67; SLC11A1; AREG; ABCA1;
DAAM2//LOC100131657; LTF; TREML1; GSTO1; PTGER2; CEACAM8; CLEC4A;
PMS2CL//PMS2; RETN; PDE3B; SULF2; NEK6//LOC100129034; CENPK; TRAF3;
GPR65; IRF4; MACF1; AMFR; RPL17//SNORD58B; IRS2; JUP; CD24; GALNT2;
HSP90AB1//HSP90AB3P//HSP90AB2P; GLT25D1; OR9A2; HDHD1A; ACTA2;
ACPL2; LRRFIP1; KCNMA1; OCR1; ITGA4//CERKL;
EIF1AX//SCARNA9L//EIF1AP1; SFRS9; DPH3; ERGIC1; CD300A; NF-E4;
MINPP1; TRIM21; ZNF28; NPCDR1; gene for protein FLJ21394 as set
forth in GenBank Accession No. BC013935; gene for transcript set
forth in GenBank Accession No. AK000992; ICAM1; TAF13;
P4HA1//RPL17; C15orf54; KLHL5; HAL; DLEU2//DLEU2L; ANKRD28;
LY6G5B//CSNK2B; KIAA1257//ACAD9//LOC100132731; MGST3; KIAA0746;
HSPB1//HSPBL2; CCR4; TYMS; RRP12//LOC644215; CCDC125; HIST1H2BM;
PDK4; ABCG1; IL1B; THBS1; ITGA2B; LHFP; LAIR1//LAIR2; HIST1H3B;
ZRANB1; TIMM10; FSD1L//GARNL1; HIST1H2AJ//HIST1H2AI; PTGS1; gene
for transcript set forth in GenBank Accession No. BC008667;
UBE2F//C20orf194//SCLY; HIST1H3C; FAM118A; CCRL2; E2F6; MPZL3;
SRXN1; CD151; HIST1H3H; FSD1L; RFESD//SPATA9; TPX2; S100B;
ZNF587//ZNF417; PYHIN1; KIAA1324; CEACAM6//CEACAM5; APOLD1; FABP2;
KDM6B//TMEM88;
IGK@//IGKC//IGKV1-5//IGKV3D-11//IGKV3-20//IGKV3D-15//LOC440871//LOC652493-
//LOC100291464//LOC652694//IGKV3-15//LOC650405//LOC100291682; MYL9;
HIST1H2BJ; TAAR1; CLC; CYP4F3//CYP4F2; CEP97; SON; IRF1; SYNE2;
MME; LASS4; DEFA4//DEFA8P; C7orf58; DYNLL1; gene for transcript set
forth in GenBank Accession No. AY461701; MPO; CPM; TSHZ2; PLIN2;
FAM118B; B4GALT3; RASA4//RASA4//RASA4B//POLR2J4//LOC100132214;
CTSL1//CTSLL3; NP; ATF7; SPARC; PLB1; C4orf3; POLE2; TNFRSF17;
FBXL13; PLEKHA3; TMEM62//SPCS2//LOC653566; RBP7; PLEKHF2; RGS2;
ATP6V0D1//LOC100132855; RPIA; CAMK1D; IL1RL1; CMTM5; AIF1; CFD;
MPZL2; LOC100128751; IGJ; CDCl26; PPP1R2//PPP1R2P3; IL5RA;
ARL17P1//ARL17; ATP5L//ATP5L2; TAS2R31; HIST2H2BF//HIST2H3D;
CALM2//C2orf61; SPATA6; IGLV6-57; C1orf128; KRTAP15-1; IFI44;
IGL@//IGLV1-44//LOC96610//IGLV2-23//IGLC1//IGLV2-18//IGLV5-45//IGLV3-25//-
IGLV3-12//IGLV1-36//IGLV3-27//IGLV7-46//IGLV4-3//IGLV3-16//IGLV3-19//IGLV7-
-43//IGLV3-22//IGLV5-37//IGLV10-54//IGLV8-61//LOC651536; gene for
transcript set forth in GenBank Accession No. BC034024; SDHC;
NFXL1; GLDC; DCTN5; and KIAA0101//CSNK1G1.
[0572] As used herein, the term "likelihood" is used as a measure
of whether subjects with a particular IRS biomarker profile
actually have a condition (or not) based on a given mathematical
model. An increased likelihood for example may be relative or
absolute and may be expressed qualitatively or quantitatively. For
instance, an increased risk may be expressed as simply determining
the subject's level of a given IRS biomarker and placing the test
subject in an "increased risk" category, based upon previous
population studies. Alternatively, a numerical expression of the
test subject's increased risk may be determined based upon IRS
biomarker level analysis.
[0573] As used herein, the term "probability" refers strictly to
the probability of class membership for a sample as determined by a
given mathematical model and is construed to be equivalent
likelihood in this context.
[0574] In some embodiments, likelihood is assessed by comparing the
level or abundance of individual IRS biomarkers to one or more
preselected or threshold levels. Thresholds may be selected that
provide an acceptable ability to predict diagnosis, prognostic
risk, treatment success, etc. In illustrative examples, receiver
operating characteristic (ROC) curves are calculated by plotting
the value of a variable versus its relative frequency in two
populations in which a first population has a first condition or
risk and a second population has a second condition or risk (called
arbitrarily, for example, "healthy condition" and "SIRS," "healthy
condition" and "inSIRS," "healthy condition" and "ipSIRS," "inSIRS"
and "ipSIRS," "mild sepsis" and "severe sepsis," "severe sepsis"
and "septic shock," "mild sepsis" and "septic shock," or "low risk"
and "high risk").
[0575] For any particular IRS biomarker, a distribution of IRS
biomarker levels for subjects with and without a disease will
likely overlap. Under such conditions, a test does not absolutely
distinguish a first condition and a second condition with 100%
accuracy, and the area of overlap indicates where the test cannot
distinguish the first condition and the second condition. A
threshold is selected, above which (or below which, depending on
how an IRS biomarker changes with a specified condition or
prognosis) the test is considered to be "positive" and below which
the test is considered to be "negative." The area under the ROC
curve (AUC) provides the C-statistic, which is a measure of the
probability that the perceived measurement will allow correct
identification of a condition (see, e.g., Hanley et al., Radiology
143: 29-36 (1982).
[0576] Alternatively, or in addition, thresholds may be established
by obtaining an earlier biomarker result from the same patient, to
which later results may be compared. In these embodiments, the
individual in effect acts as their own "control group." In
biomarkers that increase with condition severity or prognostic
risk, an increase over time in the same patient can indicate a
worsening of the condition or a failure of a treatment regimen,
while a decrease over time can indicate remission of the condition
or success of a treatment regimen.
[0577] In some embodiments, a positive likelihood ratio, negative
likelihood ratio, odds ratio, and/or AUC or receiver operating
characteristic (ROC) values are used as a measure of a method's
ability to predict risk or to diagnose a disease or condition. As
used herein, the term "likelihood ratio" is the probability that a
given test result would be observed in a subject with a condition
of interest divided by the probability that that same result would
be observed in a patient without the condition of interest. Thus, a
positive likelihood ratio is the probability of a positive result
observed in subjects with the specified condition divided by the
probability of a positive results in subjects without the specified
condition. A negative likelihood ratio is the probability of a
negative result in subjects without the specified condition divided
by the probability of a negative result in subjects with specified
condition. The term "odds ratio," as used herein, refers to the
ratio of the odds of an event occurring in one group (e.g., a
healthy condition group) to the odds of it occurring in another
group (e.g., a SIRS group, an inSIRS group, an ipSIRS group, or a
group with particular stage of ipSIRS), or to a data-based estimate
of that ratio. The term "area under the curve" or "AUC" refers to
the area under the curve of a receiver operating characteristic
(ROC) curve, both of which are well known in the art. AUC measures
are useful for comparing the accuracy of a classifier across the
complete data range. Classifiers with a greater AUC have a greater
capacity to classify unknowns correctly between two groups of
interest (e.g., a healthy condition IRS biomarker profile and a
SIRS, inSIRS, ipSIRS, or ipSIRS stage IRS biomarker profile). ROC
curves are useful for plotting the performance of a particular
feature (e.g., any of the IRS biomarkers described herein and/or
any item of additional biomedical information) in distinguishing or
discriminating between two populations (e.g., cases having a
condition and controls without the condition). Typically, the
feature data across the entire population (e.g., the cases and
controls) are sorted in ascending order based on the value of a
single feature. Then, for each value for that feature, the true
positive and false positive rates for the data are calculated. The
sensitivity is determined by counting the number of cases above the
value for that feature and then dividing by the total number of
cases. The specificity is determined by counting the number of
controls below the value for that feature and then dividing by the
total number of controls. Although this definition refers to
scenarios in which a feature is elevated in cases compared to
controls, this definition also applies to scenarios in which a
feature is lower in cases compared to the controls (in such a
scenario, samples below the value for that feature would be
counted). ROC curves can be generated for a single feature as well
as for other single outputs, for example, a combination of two or
more features can be mathematically combined (e.g., added,
subtracted, multiplied, etc.) to produce a single value, and this
single value can be plotted in a ROC curve. Additionally, any
combination of multiple features, in which the combination derives
a single output value, can be plotted in a ROC curve. These
combinations of features may comprise a test. The ROC curve is the
plot of the sensitivity of a test against the specificity of the
test, where sensitivity is traditionally presented on the vertical
axis and specificity is traditionally presented on the horizontal
axis. Thus, "AUC ROC values" are equal to the probability that a
classifier will rank a randomly chosen positive instance higher
than a randomly chosen negative one. An AUC ROC value may be
thought of as equivalent to the Mann-Whitney U test, which tests
for the median difference between scores obtained in the two groups
considered if the groups are of continuous data, or to the Wilcoxon
test of ranks.
[0578] In some embodiments, at least one (e.g., 1, 2, 3, 4, 5, 6,
7, 8, 9, 10, or more) IRS biomarker or a panel if IRS biomarkers is
selected to discriminate between subjects with a first condition
and subjects with a second condition with at least about 50%, 55%
60%, 65%, 70%, 75%, 80%, 85%, 90%, 95% accuracy or having a
C-statistic of at least about 0.50, 0.55, 0.60, 0.65, 0.70, 0.75,
0.80, 0.85, 0.90, 0.95.
[0579] In the case of a positive likelihood ratio, a value of 1
indicates that a positive result is equally likely among subjects
in both the "condition" and "control" groups; a value greater than
1 indicates that a positive result is more likely in the condition
group; and a value less than 1 indicates that a positive result is
more likely in the control group. In this context, "condition" is
meant to refer to a group having one characteristic (e.g., the
presence of a healthy condition, SIRS, inSIRS, ipSIRS, or a
particular stage of ipSIRS) and "control" group lacking the same
characteristic. In the case of a negative likelihood ratio, a value
of 1 indicates that a negative result is equally likely among
subjects in both the "condition" and "control" groups; a value
greater than 1 indicates that a negative result is more likely in
the "condition" group; and a value less than 1 indicates that a
negative result is more likely in the "control" group. In the case
of an odds ratio, a value of 1 indicates that a positive result is
equally likely among subjects in both the condition" and "control"
groups; a value greater than 1 indicates that a positive result is
more likely in the "condition" group; and a value less than 1
indicates that a positive result is more likely in the "control"
group. In the case of an AUC ROC value, this is computed by
numerical integration of the ROC curve. The range of this value can
be 0.5 to 1.0. A value of 0.5 indicates that a classifier (e.g., a
IRS biomarker profile) is no better than a 50% chance to classify
unknowns correctly between two groups of interest, while 1.0
indicates the relatively best diagnostic accuracy. In certain
embodiments, IRS biomarkers and/or IRS biomarker panels are
selected to exhibit a positive or negative likelihood ratio of at
least about 1.5 or more or about 0.67 or less, at least about 2 or
more or about 0.5 or less, at least about 5 or more or about 0.2 or
less, at least about 10 or more or about 0.1 or less, or at least
about 20 or more or about 0.05 or less.
[0580] In certain embodiments, IRS biomarkers and/or IRS biomarker
panels are selected to exhibit an odds ratio of at least about 2 or
more or about 0.5 or less, at least about 3 or more or about 0.33
or less, at least about 4 or more or about 0.25 or less, at least
about 5 or more or about 0.2 or less, or at least about 10 or more
or about 0.1 or less.
[0581] In certain embodiments, IRS biomarkers and/or IRS biomarker
panels are selected to exhibit an AUC ROC value of greater than
0.5, preferably at least 0.6, more preferably 0.7, still more
preferably at least 0.8, even more preferably at least 0.9, and
most preferably at least 0.95.
[0582] In some cases, multiple thresholds may be determined in
so-called "tertile," "quartile," or "quintile" analyses. In these
methods, the "diseased" and "control groups" (or "high risk" and
"low risk") groups are considered together as a single population,
and are divided into 3, 4, or 5 (or more) "bins" having equal
numbers of individuals. The boundary between two of these "bins"
may be considered "thresholds." A risk (of a particular diagnosis
or prognosis for example) can be assigned based on which "bin" a
test subject falls into.
[0583] In other embodiments, particular thresholds for the IRS
biomarker(s) measured are not relied upon to determine if the
biomarker level(s) obtained from a subject are correlated to a
particular diagnosis or prognosis. For example, a temporal change
in the biomarker(s) can be used to rule in or out one or more
particular diagnoses and/or prognoses. Alternatively, IRS
biomarker(s) are correlated to a condition, disease, prognosis,
etc., by the presence or absence of one or more IRS biomarkers in a
particular assay format. In the case of IRS biomarker panels, the
present invention may utilize an evaluation of the entire profile
of IRS biomarkers to provide a single result value (e.g., a "panel
response" value expressed either as a numeric score or as a
percentage risk). In such embodiments, an increase, decrease, or
other change (e.g., slope over time) in a certain subset of IRS
biomarkers may be sufficient to indicate a particular condition or
future outcome in one patient, while an increase, decrease, or
other change in a different subset of IRS biomarkers may be
sufficient to indicate the same or a different condition or outcome
in another patient.
[0584] In certain embodiments, a panel of IRS biomarkers is
selected to assist in distinguishing a pair of groups (i.e., assist
in assessing whether a subject has an increased likelihood of being
in one group or the other group of the pair) selected from "healthy
condition" and "SIRS," "healthy condition" and "inSIRS," "healthy
condition" and "ipSIRS," "inSIRS" and "ipSIRS," "mild sepsis" and
"severe sepsis," "severe sepsis" and "septic shock," "mild sepsis"
and "septic shock," or "low risk" and "high risk" with at least
about 70%, 80%, 85%, 90% or 95% sensitivity, suitably in
combination with at least about 70% 80%, 85%, 90% or 95%
specificity. In some embodiments, both the sensitivity and
specificity are at least about 75%, 80%, 85%, 90% or 95%.
[0585] The phrases "assessing the likelihood" and "determining the
likelihood," as used herein, refer to methods by which the skilled
artisan can predict the presence or absence of a condition (e.g., a
condition selected from healthy condition, SIRS, inSIRS, ipSIRS, or
a particular stage of ipSIRS) in a patient. The skilled artisan
will understand that this phrase includes within its scope an
increased probability that a condition is present or absence in a
patient; that is, that a condition is more likely to be present or
absent in a subject. For example, the probability that an
individual identified as having a specified condition actually has
the condition may be expressed as a "positive predictive value" or
"PPV." Positive predictive value can be calculated as the number of
true positives divided by the sum of the true positives and false
positives. PPV is determined by the characteristics of the
predictive methods of the present invention as well as the
prevalence of the condition in the population analysed. The
statistical algorithms can be selected such that the positive
predictive value in a population having a condition prevalence is
in the range of 70% to 99% and can be, for example, at least 70%,
75%, 76%, 77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%,
88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99%.
[0586] In other examples, the probability that an individual
identified as not having a specified condition actually does not
have that condition may be expressed as a "negative predictive
value" or "NPV." Negative predictive value can be calculated as the
number of true negatives divided by the sum of the true negatives
and false negatives. Negative predictive value is determined by the
characteristics of the diagnostic or prognostic method, system, or
code as well as the prevalence of the disease in the population
analysed. The statistical methods and models can be selected such
that the negative predictive value in a population having a
condition prevalence is in the range of about 70% to about 99% and
can be, for example, at least about 70%, 75%, 76%, 77%, 78%, 79%,
80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%,
93%, 94%, 95%, 96%, 97%, 98%, or 99%.
[0587] In some embodiments, a subject is determined as having a
significant likelihood of having or not having a specified
condition. By "significant likelihood" is meant that the subject
has a reasonable probability (0.6, 0.7, 0.8, 0.9 or more) of
having, or not having, a specified condition.
[0588] The IRS biomarker analysis of the present invention permits
the generation of high-density data sets that can be evaluated
using informatics approaches. High data density informatics
analytical methods are known and software is available to those in
the art, e.g., cluster analysis (Pirouette, Informetrix), class
prediction (SIMCA-P, Umetrics), principal components analysis of a
computationally modeled dataset (SIMCA-P, Umetrics), 2D cluster
analysis (GeneLinker Platinum, Improved Outcomes Software), and
metabolic pathway analysis (biotech.icmb.utexas.edu). The choice of
software packages offers specific tools for questions of interest
(Kennedy et al., Solving Data Mining Problems Through Pattern
Recognition. Indianapolis: Prentice Hall PTR, 1997; Golub et al.,
(2999) Science 286:531-7; Eriksson et al., Multi and Megavariate
Analysis Principles and Applications: Umetrics, Umea, 2001). In
general, any suitable mathematic analyses can be used to evaluate
at least one (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, et.) IRS
biomarker in an IRS biomarker profile with respect to a condition
selected from healthy condition, SIRS, inSIRS, ipSIRS, or a
particular stage of ipSIRS. For example, methods such as
multivariate analysis of variance, multivariate regression, and/or
multiple regression can be used to determine relationships between
dependent variables (e.g., clinical measures) and independent
variables (e.g., levels of IRS biomarkers). Clustering, including
both hierarchical and non-hierarchical methods, as well as
non-metric Dimensional Scaling can be used to determine
associations or relationships among variables and among changes in
those variables.
[0589] In addition, principal component analysis is a common way of
reducing the dimension of studies, and can be used to interpret the
variance-covariance structure of a data set. Principal components
may be used in such applications as multiple regression and cluster
analysis. Factor analysis is used to describe the covariance by
constructing "hidden" variables from the observed variables. Factor
analysis may be considered an extension of principal component
analysis, where principal component analysis is used as parameter
estimation along with the maximum likelihood method. Furthermore,
simple hypothesis such as equality of two vectors of means can be
tested using Hotelling's T squared statistic.
[0590] In some embodiments, the data sets corresponding to IRS
biomarker profiles are used to create a diagnostic or predictive
rule or model based on the application of a statistical and machine
learning algorithm. Such an algorithm uses relationships between an
IRS biomarker profile and a condition selected from healthy
condition, SIRS, inSIRS, ipSIRS, or a particular stage of ipSIRS
observed in control subjects or typically cohorts of control
subjects (sometimes referred to as training data), which provides
combined control or reference IRS biomarker profiles for comparison
with IRS biomarker profiles of a subject. The data are used to
infer relationships that are then used to predict the status of a
subject, including the presence or absence of one of the conditions
referred to above.
[0591] Practitioners skilled in the art of data analysis recognize
that many different forms of inferring relationships in the
training data may be used without materially changing the present
invention. The data presented in the Tables and Examples herein has
been used to generate illustrative minimal combinations of IRS
biomarkers (models) that differentiate between two conditions
selected from healthy condition, SIRS, inSIRS, ipSIRS, or a
particular stage of ipSIRS using feature selection based on AUC
maximisation in combination with support vector machine
classification. Tables 1-15 provide illustrative lists of IRS
biomarkers ranked according to their p value and FIGS. 1-331
illustrate the ability of each IRS biomarker to distinguish between
at least two of the conditions. Illustrative models comprising at
least about 2 IRS biomarkers were able to discriminate between two
control groups as defined above with significantly improved
positive predictive values compared to conventional
methodologies.
[0592] The term "correlating" generally refers to determining a
relationship between one type of data with another or with a state.
In various embodiments, correlating an IRS biomarker profile with
the presence or absence of a condition (e.g., a condition selected
from a healthy condition, SIRS, inSIRS, ipSIRS, or a particular
stage of ipSIRS) comprises determining the presence, absence or
amount of at least one IRS biomarker in a subject that suffers from
that condition; or in persons known to be free of that condition.
In specific embodiments, a profile of IRS biomarker levels,
absences or presences is correlated to a global probability or a
particular outcome, using receiver operating characteristic (ROC)
curves.
[0593] Thus, in some embodiments, evaluation of IRS biomarkers
includes determining the levels of individual IRS biomarkers, which
correlate with the presence or absence of a condition, as defined
above. In certain embodiments, the techniques used for detection of
IRS biomarkers will include internal or external standards to
permit quantitative or semi-quantitative determination of those
biomarkers, to thereby enable a valid comparison of the level of
the IRS biomarkers in a biological sample with the corresponding
IRS biomarkers in a reference sample or samples. Such standards can
be determined by the skilled practitioner using standard protocols.
In specific examples, absolute values for the level or functional
activity of individual expression products are determined.
[0594] In semi-quantitative methods, a threshold or cut-off value
is suitably determined, and is optionally a predetermined value. In
particular embodiments, the threshold value is predetermined in the
sense that it is fixed, for example, based on previous experience
with the assay and/or a population of affected and/or unaffected
subjects. Alternatively, the predetermined value can also indicate
that the method of arriving at the threshold is predetermined or
fixed even if the particular value varies among assays or may even
be determined for every assay run.
[0595] In some embodiments, the level of an IRS biomarker is
normalized against a housekeeping biomarker. The term "housekeeping
biomarker" refers to a biomarker or group of biomarkers (e.g.,
polynucleotides and/or polypeptides), which are typically found at
a constant level in the cell type(s) being analysed and across the
conditions being assessed. In some embodiments, the housekeeping
biomarker is a "housekeeping gene." A "housekeeping gene" refers
herein to a gene or group of genes which encode proteins whose
activities are essential for the maintenance of cell function and
which are typically found at a constant level in the cell type(s)
being analysed and across the conditions being assessed.
[0596] Generally, the levels of individual IRS biomarkers in an IRS
biomarker profile are derived from a biological sample. The term
"biological sample" as used herein refers to a sample that may be
extracted, untreated, treated, diluted or concentrated from an
animal. The biological sample is suitably a biological fluid such
as whole blood, serum, plasma, saliva, urine, sweat, ascitic fluid,
peritoneal fluid, synovial fluid, amniotic fluid, cerebrospinal
fluid, tissue biopsy, and the like. In certain embodiments, the
biological sample contains blood, especially peripheral blood, or a
fraction or extract thereof. Typically, the biological sample
comprises blood cells such as mature, immature or developing
leukocytes, including lymphocytes, polymorphonuclear leukocytes,
neutrophils, monocytes, reticulocytes, basophils, coelomocytes,
hemocytes, eosinophils, megakaryocytes, macrophages, dendritic
cells natural killer cells, or fraction of such cells (e.g., a
nucleic acid or protein fraction). In specific embodiments, the
biological sample comprises leukocytes including peripheral blood
mononuclear cells (PBMC).
[0597] The term "nucleic acid" or "polynucleotide" refers to a
polymer, typically a heteropolymer, of nucleotides or the sequence
of these nucleotides from the 5' to 3' end of a nucleic acid
molecule and includes DNA or RNA molecules, illustrative examples
of which include RNA, mRNA, siRNA, miRNA, hpRNA, cRNA, cDNA or DNA.
The term encompasses a polymeric form of nucleotides that is linear
or branched, single or double stranded, or a hybrid thereof. The
term also encompasses RNA/DNA hybrids. Nucleic acid sequences
provided herein are presented herein in the 5' to 3' direction,
from left to right and are represented using the standard code for
representing the nucleotide characters as set forth in the U.S.
sequence rules, 37 CFR 1.821-1.825 and the World Intellectual
Property Organization (WIPO) Standard ST.25.
[0598] "Protein," "polypeptide" and "peptide" are used
interchangeably herein to refer to a polymer of amino acid residues
and to variants and synthetic analogues of the same.
[0599] Suitably, the levels of individual IRS biomarkers in a
reference IRS biomarker profile are derived from IRS biomarker
samples obtained from one or more control subjects having that
condition (e.g., "healthy control subjects," "SIRS control
subjects," "inSIRS control subjects," "ipSIRS control subjects,"
"control subjects with a particular stage of ipSIRS," illustrative
examples of which include "mild sepsis control subjects," "severe
sepsis control subjects," and "septic shock control subjects,"
etc.), which are also referred to herein as control groups (e.g.,
"healthy control group," "SIRS control group," "inSIRS control
group," "ipSIRS control group," "ipSIRS stage group," illustrative
examples of which include "mild sepsis control group," "severe
sepsis control group," and "septic shock control group," etc.). By
"obtained" is meant to come into possession. Biological or
reference samples so obtained include, for example, nucleic acid
extracts or polypeptide extracts isolated or derived from a
particular source. For instance, the extract may be isolated
directly from a biological fluid or tissue of a subject.
[0600] As used herein the terms "level" and "amount" are used
interchangeably herein to refer to a quantitative amount (e.g.,
weight or moles), a semi-quantitative amount, a relative amount
(e.g., weight % or mole % within class or a ratio), a
concentration, and the like. Thus, these terms encompasses absolute
or relative amounts or concentrations of IRS biomarkers in a
sample, including ratios of levels of IRS biomarkers, and odds
ratios of levels or ratios of odds ratios. IRS biomarker levels in
cohorts of subjects may be represented as mean levels and standard
deviations as shown in the Tables and Figures herein.
[0601] In some embodiments, the level of at least one (e.g., 1, 2,
3, 4, 5, 6, 7, 8, 9, 10 etc.) IRS biomarker of the subject's sample
IRS biomarker profile is compared to the level of a corresponding
IRS biomarker in the reference IRS biomarker profile. By
"corresponding IRS biomarker" is meant an IRS biomarker that is
structurally and/or functionally similar to a reference IRS
biomarker. Representative corresponding IRS biomarkers include
expression products of allelic variants (same locus), homologs
(different locus), and orthologs (different organism) of reference
IRS biomarker genes. Nucleic acid variants of reference IRS
biomarker genes and encoded IRS biomarker polynucleotide expression
products can contain nucleotide substitutions, deletions,
inversions and/or insertions. Variation can occur in either or both
the coding and non-coding regions. The variations can produce both
conservative and non-conservative amino acid substitutions (as
compared in the encoded product). For nucleotide sequences,
conservative variants include those sequences that, because of the
degeneracy of the genetic code, encode the amino acid sequence of a
reference IRS polypeptide.
[0602] Generally, variants of a particular IRS biomarker gene or
polynucleotide will have at least about 40%, 45%, 50%, 51%, 52%,
53%, 54%, 55%, 56%, 57%, 58%, 59% 60%, 61%, 62%, 63%, 64%, 65%,
66%, 67%, 68%, 69% 70%, 71%, 72%, 73%, 74%, 75%, 76%, 77%, 78%,
79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%,
92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or more sequence identity to
that particular nucleotide sequence as determined by sequence
alignment programs known in the art using default parameters. In
some embodiments, the IRS biomarker gene or polynucleotide displays
at least about 40%, 45%, 50%, 51%, 52%, 53%, 54%, 55%, 56%, 57%,
58%, 59% 60%, 61%, 62%, 63%, 64%, 65%, 66%, 67%, 68%, 69% 70%, 71%,
72%, 73%, 74%, 75%, 76%, 77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%,
85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%,
98%, 99% or more sequence identity to a nucleotide sequence
selected from any one of SEQ ID NO: 1-319.
[0603] Corresponding IRS biomarkers also include amino acid
sequence that displays substantial sequence similarity or identity
to the amino acid sequence of a reference IRS biomarker
polypeptide. In general, an amino acid sequence that corresponds to
a reference amino acid sequence will display at least about 50, 51,
52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68,
69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85,
86, 97, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99% or even up
to 100% sequence similarity or identity to a reference amino acid
sequence selected from any one of SEQ ID NO: 320-619.
[0604] In some embodiments, calculations of sequence similarity or
sequence identity between sequences are performed as follows:
[0605] To determine the percent identity of two amino acid
sequences, or of two nucleic acid sequences, the sequences are
aligned for optimal comparison purposes (e.g., gaps can be
introduced in one or both of a first and a second amino acid or
nucleic acid sequence for optimal alignment and non-homologous
sequences can be disregarded for comparison purposes). In some
embodiments, the length of a reference sequence aligned for
comparison purposes is at least 30%, usually at least 40%, more
usually at least 50%, 60%, and even more usually at least 70%, 80%,
90%, 100% of the length of the reference sequence. The amino acid
residues or nucleotides at corresponding amino acid positions or
nucleotide positions are then compared. When a position in the
first sequence is occupied by the same amino acid residue or
nucleotide at the corresponding position in the second sequence,
then the molecules are identical at that position. For amino acid
sequence comparison, when a position in the first sequence is
occupied by the same or similar amino acid residue (i.e.,
conservative substitution) at the corresponding position in the
second sequence, then the molecules are similar at that
position.
[0606] The percent identity between the two sequences is a function
of the number of identical amino acid residues shared by the
sequences at individual positions, taking into account the number
of gaps, and the length of each gap, which need to be introduced
for optimal alignment of the two sequences. By contrast, the
percent similarity between the two sequences is a function of the
number of identical and similar amino acid residues shared by the
sequences at individual positions, taking into account the number
of gaps, and the length of each gap, which need to be introduced
for optimal alignment of the two sequences.
[0607] The comparison of sequences and determination of percent
identity or percent similarity between sequences can be
accomplished using a mathematical algorithm. In certain
embodiments, the percent identity or similarity between amino acid
sequences is determined using the Needleman and Wunsch, (1970, J.
Mol. Biol. 48: 444-453) algorithm which has been incorporated into
the GAP program in the GCG software package (available at
http://www.gcg.com), using either a Blossum 62 matrix or a PAM250
matrix, and a gap weight of 16, 14, 12, 10, 8, 6, or 4 and a length
weight of 1, 2, 3, 4, 5, or 6. In specific embodiments, the percent
identity between nucleotide sequences is determined using the GAP
program in the GCG software package (available at
http://www.gcg.com), using a NWSgapdna.CMP matrix and a gap weight
of 40, 50, 60, 70, or 80 and a length weight of 1, 2, 3, 4, 5, or
6. An non-limiting set of parameters (and the one that should be
used unless otherwise specified) includes a Blossum 62 scoring
matrix with a gap penalty of 12, a gap extend penalty of 4, and a
frameshift gap penalty of 5.
[0608] In some embodiments, the percent identity or similarity
between amino acid or nucleotide sequences can be determined using
the algorithm of E. Meyers and W. Miller (1989, Cabios, 4: 11-17)
which has been incorporated into the ALIGN program (version 2.0),
using a PAM120 weight residue table, a gap length penalty of 12 and
a gap penalty of 4.
[0609] The nucleic acid and protein sequences described herein can
be used as a "query sequence" to perform a search against public
databases to, for example, identify other family members or related
sequences. Such searches can be performed using the NBLAST and
XBLAST programs (version 2.0) of Altschul, et al., (1990, J. Mol.
Biol, 215: 403-10). BLAST nucleotide searches can be performed with
the NBLAST program, score=100, wordlength=12 to obtain nucleotide
sequences homologous to 53010 nucleic acid molecules of the
invention. BLAST protein searches can be performed with the XBLAST
program, score=50, wordlength=3 to obtain amino acid sequences
homologous to 53010 protein molecules of the invention. To obtain
gapped alignments for comparison purposes, Gapped BLAST can be
utilized as described in Altschul et al., (1997, Nucleic Acids Res,
25: 3389-3402). When utilizing BLAST and Gapped BLAST programs, the
default parameters of the respective programs (e.g., XBLAST and
NBLAST) can be used.
[0610] Corresponding IRS biomarker polynucleotides also include
nucleic acid sequences that hybridize to reference IRS biomarker
polynucleotides, or to their complements, under stringency
conditions described below. As used herein, the term "hybridizes
under low stringency, medium stringency, high stringency, or very
high stringency conditions" describes conditions for hybridization
and washing. "Hybridization" is used herein to denote the pairing
of complementary nucleotide sequences to produce a DNA-DNA hybrid
or a DNA-RNA hybrid. Complementary base sequences are those
sequences that are related by the base-pairing rules. In DNA, A
pairs with T and C pairs with G. In RNA, U pairs with A and C pairs
with G. In this regard, the terms "match" and "mismatch" as used
herein refer to the hybridization potential of paired nucleotides
in complementary nucleic acid strands. Matched nucleotides
hybridize efficiently, such as the classical A-T and G-C base pair
mentioned above. Mismatches are other combinations of nucleotides
that do not hybridize efficiently.
[0611] Guidance for performing hybridization reactions can be found
in Ausubel et al., (1998, supra), Sections 6.3.1-6.3.6. Aqueous and
non-aqueous methods are described in that reference and either can
be used. Reference herein to low stringency conditions include and
encompass from at least about 1% v/v to at least about 15% v/v
formamide and from at least about 1 M to at least about 2 M salt
for hybridization at 42.degree. C., and at least about 1 M to at
least about 2 M salt for washing at 42.degree. C. Low stringency
conditions also may include 1% Bovine Serum Albumin (BSA), 1 mM
EDTA, 0.5 M NaHPO.sub.4 (pH 7.2), 7% SDS for hybridization at
65.degree. C., and (i) 2.times.SSC, 0.1% SDS; or (ii) 0.5% BSA, 1
mM EDTA, 40 mM NaHPO.sub.4 (pH 7.2), 5% SDS for washing at room
temperature. One embodiment of low stringency conditions includes
hybridization in 6.times. sodium chloride/sodium citrate (SSC) at
about 45.quadrature..quadrature.C., followed by two washes in
0.2.times.SSC, 0.1% SDS at least at 50.degree. C. (the temperature
of the washes can be increased to 55.degree. C. for low stringency
conditions). Medium stringency conditions include and encompass
from at least about 16% v/v to at least about 30% v/v formamide and
from at least about 0.5 M to at least about 0.9 M salt for
hybridization at 42.degree. C., and at least about 0.1 M to at
least about 0.2 M salt for washing at 55.degree. C. Medium
stringency conditions also may include 1% Bovine Serum Albumin
(BSA), 1 mM EDTA, 0.5 M NaHPO.sub.4 (pH 7.2), 7% SDS for
hybridization at 65.degree. C., and (i) 2.times.SSC, 0.1% SDS; or
(ii) 0.5% BSA, 1 mM EDTA, 40 mM NaHPO.sub.4 (pH 7.2), 5% SDS for
washing at 60-65.degree. C. One embodiment of medium stringency
conditions includes hybridizing in 6.times.SSC at about
45.quadrature..quadrature.C, followed by one or more washes in
0.2.times.SSC, 0.1% SDS at 60.degree. C. High stringency conditions
include and encompass from at least about 31% v/v to at least about
50% v/v formamide and from about 0.01 M to about 0.15 M salt for
hybridization at 42.degree. C., and about 0.01 M to about 0.02 M
salt for washing at 55.degree. C. High stringency conditions also
may include 1% BSA, 1 mM EDTA, 0.5 M NaHPO.sub.4 (pH 7.2), SDS for
hybridization at 65.degree. C., and (i) 0.2.times.SSC, 7% 0.1% SDS;
or (ii) 0.5% BSA, 1 mM EDTA, 40 mM NaHPO.sub.4 (pH 7.2), 1% SDS for
washing at a temperature in excess of 65.degree. C. One embodiment
of high stringency conditions includes hybridizing in 6.times.SSC
at about 45.quadrature..quadrature.C, followed by one or more
washes in 0.2.times.SSC, 0.1% SDS at 65.degree. C.
[0612] In certain embodiments, a corresponding IRS biomarker
polynucleotide is one that hybridizes to a disclosed nucleotide
sequence under very high stringency conditions. One embodiment of
very high stringency conditions includes hybridizing 0.5 M sodium
phosphate, 7% SDS at 65.degree. C., followed by one or more washes
at 0.2.times.SSC, 1% SDS at 65.degree. C.
[0613] Other stringency conditions are well known in the art and a
skilled addressee will recognize that various factors can be
manipulated to optimize the specificity of the hybridization.
Optimization of the stringency of the final washes can serve to
ensure a high degree of hybridization. For detailed examples, see
Ausubel et al., supra at pages 2.10.1 to 2.10.16 and Sambrook et
al. (1989, supra) at sections 1.101 to 1.104.
[0614] Thus, in some embodiments, IRS biomarker levels in control
groups as broadly defined above and elsewhere herein are used to
generate a profile of IRS biomarker levels reflecting difference
between levels in two control groups as described above and
elsewhere herein. Thus, a particular IRS biomarker may be more
abundant or less abundant in one control group as compared to
another control group. The data may be represented as an overall
signature score or the profile may be represented as a barcode or
other graphical representation to facilitate analysis or diagnosis
or determination of likelihood. The IRS biomarker levels from a
test subject may be represented in the same way and the similarity
with the signature score or level of "fit" to a signature barcode
or other graphical representation may be determined. In other
embodiments, the levels of a particular IRS biomarker are analysed
and a downward or an upward trend in IRS biomarker level
determined.
[0615] In some embodiments, the individual level of an IRS
biomarker in a first control group (e.g., a control group selected
from healthy condition control group, SIRS control group, inSIRS
control group, ipSIRS control group, or ipSIRS stage control group)
is at least 101%, 102%, 103%, 104%, 105%, 106%, 107% 108%, 109%,
110%, 120%, 130%, 140%, 150%, 160%, 170%, 180%, 190%, 200%, 300%,
400%, 500%, 600%, 700%, 800%, 900% or 1000% (i.e. an increased or
higher level), or no more than about 99%, 98%, 97%, 96%, 95%, 94%,
93%, 92%, 91%, 90%, 80%, 70%, 60%, 50%, 40%, 30%, 20%, 10%, 5%, 4%,
3%, 2%, 1%, 0.5%, 0.1%, 0.01%, 0.001% or 0.0001% (i.e. a decreased
or lower level) of the level of a corresponding IRS biomarker in a
second control group (e.g., a control group selected from healthy
condition control group, SIRS control group, inSIRS control group,
ipSIRS control group, or ipSIRS stage control group, illustrative
examples of which include "mild sepsis control group, severe sepsis
control group, and septic shock control group, which is different
from the first control group).
[0616] An IRS biomarker profile provides a compositional analysis
(e.g., concentration or mole percentage (%) of the IRS biomarker)
in which two or more, three or more, four or more, five or more,
six or more, seven or more, eight or more, nine or more, ten or
more, twelve or more, fifteen or more, twenty or more, fifty or
more, one-hundred or more or a greater number of IRS biomarkers are
evaluated.
[0617] The IRS biomarker profile can be quantitative,
semi-quantitative and/or qualitative. For example, the IRS
biomarker profile can evaluate the presence or absence of an IRS
biomarker, can evaluate the presence of an IRS biomarker(s) above
or below a particular threshold, and/or can evaluate the relative
or absolute amount of an IRS biomarker(s). In particular
embodiments, a ratio among two, three, four or more IRS biomarkers
is determined (see Example 6 and Tables 16-21 for examples of the
use of 2-gene ratios in separating various inSIRS and ipSIRS
conditions). Changes or perturbations in IRS biomarker ratios can
be advantageous in indicating where there are blocks (or releases
of such blocks) or other alterations in cellular pathways
associated with an IRS condition, response to treatment,
development of side effects, and the like.
[0618] IRS biomarkers may be quantified or detected using any
suitable technique including nucleic acid- and protein-based
assays.
[0619] In illustrative nucleic acid-based assays, nucleic acid is
isolated from cells contained in the biological sample according to
standard methodologies (Sambrook, et al., 1989, supra; and Ausubel
et al., 1994, supra). The nucleic acid is typically fractionated
(e.g., poly A+RNA) or whole cell RNA. Where RNA is used as the
subject of detection, it may be desired to convert the RNA to a
complementary DNA. In some embodiments, the nucleic acid is
amplified by a template-dependent nucleic acid amplification
technique. A number of template dependent processes are available
to amplify the IRS biomarker sequences present in a given template
sample. An exemplary nucleic acid amplification technique is the
polymerase chain reaction (referred to as PCR), which is described
in detail in U.S. Pat. Nos. 4,683,195, 4,683,202 and 4,800,159,
Ausubel et al. (supra), and in Innis et al., (" PCR Protocols",
Academic Press, Inc., San Diego Calif., 1990). Briefly, in PCR, two
primer sequences are prepared that are complementary to regions on
opposite complementary strands of the biomarker sequence. An excess
of deoxynucleotide triphosphates are added to a reaction mixture
along with a DNA polymerase, e.g., Taq polymerase. If a cognate IRS
biomarker sequence is present in a sample, the primers will bind to
the biomarker and the polymerase will cause the primers to be
extended along the biomarker sequence by adding on nucleotides. By
raising and lowering the temperature of the reaction mixture, the
extended primers will dissociate from the biomarker to form
reaction products, excess primers will bind to the biomarker and to
the reaction products and the process is repeated. A reverse
transcriptase PCR amplification procedure may be performed in order
to quantify the amount of mRNA amplified. Methods of reverse
transcribing RNA into cDNA are well known and described in Sambrook
et al., 1989, supra. Alternative methods for reverse transcription
utilize thermostable, RNA-dependent DNA polymerases. These methods
are described in WO 90/07641. Polymerase chain reaction
methodologies are well known in the art.
[0620] In certain advantageous embodiments, the template-dependent
amplification involves quantification of transcripts in real-time.
For example, RNA or DNA may be quantified using the Real-Time PCR
technique (Higuchi, 1992, et al., Biotechnology 10: 413-417). By
determining the concentration of the amplified products of the
target DNA in PCR reactions that have completed the same number of
cycles and are in their linear ranges, it is possible to determine
the relative concentrations of the specific target sequence in the
original DNA mixture. If the DNA mixtures are cDNAs synthesized
from RNAs isolated from different tissues or cells, the relative
abundance of the specific mRNA from which the target sequence was
derived can be determined for the respective tissues or cells. This
direct proportionality between the concentration of the PCR
products and the relative mRNA abundance is only true in the linear
range of the PCR reaction. The final concentration of the target
DNA in the plateau portion of the curve is determined by the
availability of reagents in the reaction mix and is independent of
the original concentration of target DNA. In specific embodiments,
multiplexed, tandem PCR (MT-PCR) is employed, which uses a two-step
process for gene expression profiling from small quantities of RNA
or DNA, as described for example in US Pat. Appl. Pub. No.
20070190540. In the first step, RNA is converted into cDNA and
amplified using multiplexed gene specific primers. In the second
step each individual gene is quantitated by real time PCR.
[0621] In certain embodiments, target nucleic acids are quantified
using blotting techniques, which are well known to those of skill
in the art. Southern blotting involves the use of DNA as a target,
whereas Northern blotting involves the use of RNA as a target. Each
provides different types of information, although cDNA blotting is
analogous, in many aspects, to blotting or RNA species. Briefly, a
probe is used to target a DNA or RNA species that has been
immobilized on a suitable matrix, often a filter of nitrocellulose.
The different species should be spatially separated to facilitate
analysis. This often is accomplished by gel electrophoresis of
nucleic acid species followed by "blotting" on to the filter.
Subsequently, the blotted target is incubated with a probe (usually
labelled) under conditions that promote denaturation and
rehybridisation. Because the probe is designed to base pair with
the target, the probe will bind a portion of the target sequence
under renaturing conditions. Unbound probe is then removed, and
detection is accomplished as described above. Following
detection/quantification, one may compare the results seen in a
given subject with a control reaction or a statistically
significant reference group or population of control subjects as
defined herein. In this way, it is possible to correlate the amount
of a IRS biomarker nucleic acid detected with the progression or
severity of the disease.
[0622] Also contemplated are biochip-based technologies such as
those described by Hacia et al. (1996, Nature Genetics 14: 441-447)
and Shoemaker et al. (1996, Nature Genetics 14: 450-456). Briefly,
these techniques involve quantitative methods for analysing large
numbers of genes rapidly and accurately. By tagging genes with
oligonucleotides or using fixed probe arrays, one can employ
biochip technology to segregate target molecules as high-density
arrays and screen these molecules on the basis of hybridization.
See also Pease et al. (1994, Proc. Natl. Acad. Sci. U.S.A. 91:
5022-5026); Fodor et al. (1991, Science 251: 767-773). Briefly,
nucleic acid probes to IRS biomarker polynucleotides are made and
attached to biochips to be used in screening and diagnostic
methods, as outlined herein. The nucleic acid probes attached to
the biochip are designed to be substantially complementary to
specific expressed IRS biomarker nucleic acids, i.e., the target
sequence (either the target sequence of the sample or to other
probe sequences, for example in sandwich assays), such that
hybridization of the target sequence and the probes of the present
invention occur. This complementarity need not be perfect; there
may be any number of base pair mismatches, which will interfere
with hybridization between the target sequence and the nucleic acid
probes of the present invention. However, if the number of
mismatches is so great that no hybridization can occur under even
the least stringent of hybridization conditions, the sequence is
not a complementary target sequence. In certain embodiments, more
than one probe per sequence is used, with either overlapping probes
or probes to different sections of the target being used. That is,
two, three, four or more probes, with three being desirable, are
used to build in a redundancy for a particular target. The probes
can be overlapping (i.e. have some sequence in common), or
separate.
[0623] In an illustrative biochip analysis, oligonucleotide probes
on the biochip are exposed to or contacted with a nucleic acid
sample suspected of containing one or more IRS biomarker
polynucleotides under conditions favouring specific hybridization.
Sample extracts of DNA or RNA, either single or double-stranded,
may be prepared from fluid suspensions of biological materials, or
by grinding biological materials, or following a cell lysis step
which includes, but is not limited to, lysis effected by treatment
with SDS (or other detergents), osmotic shock, guanidinium
isothiocyanate and lysozyme. Suitable DNA, which may be used in the
method of the invention, includes cDNA. Such DNA may be prepared by
any one of a number of commonly used protocols as for example
described in Ausubel, et al., 1994, supra, and Sambrook, et al., et
al., 1989, supra.
[0624] Suitable RNA, which may be used in the method of the
invention, includes messenger RNA, complementary RNA transcribed
from DNA (cRNA) or genomic or subgenomic RNA. Such RNA may be
prepared using standard protocols as for example described in the
relevant sections of Ausubel, et al. 1994, supra and Sambrook, et
al. 1989, supra).
[0625] cDNA may be fragmented, for example, by sonication or by
treatment with restriction endonucleases. Suitably, cDNA is
fragmented such that resultant DNA fragments are of a length
greater than the length of the immobilized oligonucleotide probe(s)
but small enough to allow rapid access thereto under suitable
hybridization conditions. Alternatively, fragments of cDNA may be
selected and amplified using a suitable nucleotide amplification
technique, as described for example above, involving appropriate
random or specific primers.
[0626] Usually the target IRS biomarker polynucleotides are
detectably labelled so that their hybridization to individual
probes can be determined. The target polynucleotides are typically
detectably labelled with a reporter molecule illustrative examples
of which include chromogens, catalysts, enzymes, fluorochromes,
chemiluminescent molecules, bioluminescent molecules, lanthanide
ions (e.g., Eu.sup.34), a radioisotope and a direct visual label.
In the case of a direct visual label, use may be made of a
colloidal metallic or non-metallic particle, a dye particle, an
enzyme or a substrate, an organic polymer, a latex particle, a
liposome, or other vesicle containing a signal producing substance
and the like. Illustrative labels of this type include large
colloids, for example, metal colloids such as those from gold,
selenium, silver, tin and titanium oxide. In some embodiments in
which an enzyme is used as a direct visual label, biotinylated
bases are incorporated into a target polynucleotide.
[0627] The hybrid-forming step can be performed under suitable
conditions for hybridizing oligonucleotide probes to test nucleic
acid including DNA or RNA. In this regard, reference may be made,
for example, to NUCLEIC ACID HYBRIDIZATION, A PRACTICAL APPROACH
(Homes and Higgins, eds.) (IRL press, Washington D.C., 1985). In
general, whether hybridization takes place is influenced by the
length of the oligonucleotide probe and the polynucleotide sequence
under test, the pH, the temperature, the concentration of mono- and
divalent cations, the proportion of G and C nucleotides in the
hybrid-forming region, the viscosity of the medium and the possible
presence of denaturants. Such variables also influence the time
required for hybridization. The preferred conditions will therefore
depend upon the particular application. Such empirical conditions,
however, can be routinely determined without undue
experimentation.
[0628] After the hybrid-forming step, the probes are washed to
remove any unbound nucleic acid with a hybridization buffer. This
washing step leaves only bound target polynucleotides. The probes
are then examined to identify which probes have hybridized to a
target polynucleotide.
[0629] The hybridization reactions are then detected to determine
which of the probes has hybridized to a corresponding target
sequence. Depending on the nature of the reporter molecule
associated with a target polynucleotide, a signal may be
instrumentally detected by irradiating a fluorescent label with
light and detecting fluorescence in a fluorimeter; by providing for
an enzyme system to produce a dye which could be detected using a
spectrophotometer; or detection of a dye particle or a coloured
colloidal metallic or non metallic particle using a reflectometer;
in the case of using a radioactive label or chemiluminescent
molecule employing a radiation counter or autoradiography.
Accordingly, a detection means may be adapted to detect or scan
light associated with the label which light may include
fluorescent, luminescent, focused beam or laser light. In such a
case, a charge couple device (CCD) or a photocell can be used to
scan for emission of light from a probe:target polynucleotide
hybrid from each location in the micro-array and record the data
directly in a digital computer. In some cases, electronic detection
of the signal may not be necessary. For example, with enzymatically
generated colour spots associated with nucleic acid array format,
visual examination of the array will allow interpretation of the
pattern on the array. In the case of a nucleic acid array, the
detection means is suitably interfaced with pattern recognition
software to convert the pattern of signals from the array into a
plain language genetic profile. In certain embodiments,
oligonucleotide probes specific for different IRS biomarker
polynucleotides are in the form of a nucleic acid array and
detection of a signal generated from a reporter molecule on the
array is performed using a `chip reader`. A detection system that
can be used by a `chip reader` is described for example by Pirrung
et al (U.S. Pat. No. 5,143,854). The chip reader will typically
also incorporate some signal processing to determine whether the
signal at a particular array position or feature is a true positive
or maybe a spurious signal. Exemplary chip readers are described
for example by Fodor et al (U.S. Pat. No. 5,925,525).
Alternatively, when the array is made using a mixture of
individually addressable kinds of labelled microbeads, the reaction
may be detected using flow cytometry.
[0630] In other embodiments, IRS biomarker protein levels are
assayed using protein-based assays known in the art. For example,
when an IRS biomarker protein is an enzyme, the protein can be
quantified based upon its catalytic activity or based upon the
number of molecules of the protein contained in a sample.
Antibody-based techniques may be employed including, for example,
immunoassays, such as the enzyme-linked immunosorbent assay (ELISA)
and the radioimmunoassay (RIA).
[0631] In specific embodiments, protein-capture arrays that permit
simultaneous detection and/or quantification of a large number of
proteins are employed. For example, low-density protein arrays on
filter membranes, such as the universal protein array system (Ge,
2000 Nucleic Acids Res. 28(2):e3) allow imaging of arrayed antigens
using standard ELISA techniques and a scanning charge-coupled
device (CCD) detector. Immuno-sensor arrays have also been
developed that enable the simultaneous detection of clinical
analytes. It is now possible using protein arrays, to profile
protein expression in bodily fluids, such as in sera of healthy or
diseased subjects, as well as in subjects pre- and post-drug
treatment.
[0632] Exemplary protein capture arrays include arrays comprising
spatially addressed antigen-binding molecules, commonly referred to
as antibody arrays, which can facilitate extensive parallel
analysis of numerous proteins defining a proteome or subproteome.
Antibody arrays have been shown to have the required properties of
specificity and acceptable background, and some are available
commercially (e.g., BD Biosciences, Clontech, BioRad and Sigma).
Various methods for the preparation of antibody arrays have been
reported (see, e.g., Lopez et al., 2003 J. Chromatogr. B 787:19-27;
Cahill, 2000 Trends in Biotechnology 7:47-51; U.S. Pat. App. Pub.
2002/0055186; U.S. Pat. App. Pub. 2003/0003599; PCT publication WO
03/062444; PCT publication WO 03/077851; PCT publication WO
02/59601; PCT publication WO 02/39120; PCT publication WO 01/79849;
PCT publication WO 99/39210). The antigen-binding molecules of such
arrays may recognise at least a subset of proteins expressed by a
cell or population of cells, illustrative examples of which include
growth factor receptors, hormone receptors, neurotransmitter
receptors, catecholamine receptors, amino acid derivative
receptors, cytokine receptors, extracellular matrix receptors,
antibodies, lectins, cytokines, serpins, proteases, kinases,
phosphatases, ras-like GTPases, hydrolases, steroid hormone
receptors, transcription factors, heat-shock transcription factors,
DNA-binding proteins, zinc-finger proteins, leucine-zipper
proteins, homeodomain proteins, intracellular signal transduction
modulators and effectors, apoptosis-related factors, DNA synthesis
factors, DNA repair factors, DNA recombination factors and
cell-surface antigens.
[0633] Individual spatially distinct protein-capture agents are
typically attached to a support surface, which is generally planar
or contoured. Common physical supports include glass slides,
silicon, microwells, nitrocellulose or PVDF membranes, and magnetic
and other microbeads.
[0634] Particles in suspension can also be used as the basis of
arrays, providing they are coded for identification; systems
include colour coding for microbeads (e.g., available from Luminex,
Bio-Rad and Nanomics Biosystems) and semiconductor nanocrystals
(e.g., QDots.TM., available from Quantum Dots), and barcoding for
beads (UltraPlex.TM., available from Smartbeads) and multimetal
microrods (Nanobarcodes.TM. particles, available from Surromed).
Beads can also be assembled into planar arrays on semiconductor
chips (e.g., available from LEAPS technology and BioArray
Solutions). Where particles are used, individual protein-capture
agents are typically attached to an individual particle to provide
the spatial definition or separation of the array. The particles
may then be assayed separately, but in parallel, in a
compartmentalized way, for example in the wells of a microtiter
plate or in separate test tubes.
[0635] In operation, a protein sample, which is optionally
fragmented to form peptide fragments (see, e.g., U.S. Pat. App.
Pub. 2002/0055186), is delivered to a protein-capture array under
conditions suitable for protein or peptide binding, and the array
is washed to remove unbound or non-specifically bound components of
the sample from the array. Next, the presence or amount of protein
or peptide bound to each feature of the array is detected using a
suitable detection system. The amount of protein bound to a feature
of the array may be determined relative to the amount of a second
protein bound to a second feature of the array. In certain
embodiments, the amount of the second protein in the sample is
already known or known to be invariant.
[0636] For analysing differential expression of proteins between
two cells or cell populations, a protein sample of a first cell or
population of cells is delivered to the array under conditions
suitable for protein binding. In an analogous manner, a protein
sample of a second cell or population of cells to a second array is
delivered to a second array that is identical to the first array.
Both arrays are then washed to remove unbound or non-specifically
bound components of the sample from the arrays. In a final step,
the amounts of protein remaining bound to the features of the first
array are compared to the amounts of protein remaining bound to the
corresponding features of the second array. To determine the
differential protein expression pattern of the two cells or
populations of cells, the amount of protein bound to individual
features of the first array is subtracted from the amount of
protein bound to the corresponding features of the second
array.
[0637] All the essential materials and reagents required for
detecting and quantifying IRS biomarker expression products may be
assembled together in a kit, which is encompassed by the present
invention. The kits may also optionally include appropriate
reagents for detection of labels, positive and negative controls,
washing solutions, blotting membranes, microtiter plates dilution
buffers and the like. For example, a nucleic acid-based detection
kit may include (i) an IRS biomarker polynucleotide (which may be
used as a positive control), (ii) a primer or probe that
specifically hybridizes to an IRS biomarker polynucleotide. Also
included may be enzymes suitable for amplifying nucleic acids
including various polymerases (Reverse Transcriptase, Taq,
Sequenase.TM., DNA ligase etc. depending on the nucleic acid
amplification technique employed), deoxynucleotides and buffers to
provide the necessary reaction mixture for amplification. Such kits
also generally will comprise, in suitable means, distinct
containers for each individual reagent and enzyme as well as for
each primer or probe. Alternatively, a protein-based detection kit
may include (i) an IRS biomarker polypeptide (which may be used as
a positive control), (ii) an antibody that binds specifically to an
IRS biomarker polypeptide. The kit can also feature various devices
(e.g., one or more) and reagents (e.g., one or more) for performing
one of the assays described herein; and/or printed instructions for
using the kit to quantify the expression of an IRS biomarker
gene.
[0638] In some embodiments, the methods and kits comprise or
enable: comparing the level of at least one (e.g., 1, 2, 3, 4, 5,
6, 7, 8, 9, 10 etc.) IRS biomarker in the subject's sample IRS
profile to the level of a corresponding IRS biomarker in a
reference IRS biomarker profile from at least one control subject
or group selected from a healthy control subject or group
(hereafter referred to as a "reference healthy IRS biomarker
profile"), a SIRS control subject or group (hereafter referred to
as a "reference SIRS IRS biomarker profile"), an inSIRS control
subject or group (hereafter referred to as a "reference inSIRS IRS
biomarker profile"), an ipSIRS control subject or group (hereafter
referred to as a "reference ipSIRS IRS biomarker profile") and a
control subject or group with a particular stage of ipSIRS
(hereafter referred to as a "reference ipSIRS stage IRS biomarker
profile"), wherein a similarity between the level of the at least
one IRS biomarker in the sample IRS biomarker profile and the level
of the corresponding IRS biomarker in the reference healthy IRS
biomarker profile identifies that the subject has an IRS biomarker
profile that correlates with the presence of a healthy condition,
or alternatively the absence of inSIRS, ipSIRS, or a particular
stage of ipSIRS, wherein a similarity between the level of the at
least one IRS biomarker in the sample IRS biomarker profile and the
level of the corresponding IRS biomarker in the SIRS IRS biomarker
profile identifies that the subject has an IRS biomarker profile
that correlates with the presence of inSIRS or ipSIRS, or
alternatively the absence of a healthy condition, wherein a
similarity between the level of the at least one IRS biomarker in
the sample IRS biomarker profile and the level of the corresponding
IRS biomarker in the inSIRS IRS biomarker profile identifies that
the subject has an IRS biomarker profile that correlates with the
presence of inSIRS, or alternatively the absence of a healthy
condition, ipSIRS, or a particular stage of ipSIRS, wherein a
similarity between the level of the at least one IRS biomarker in
the sample IRS biomarker profile and the level of the corresponding
IRS biomarker in the ipSIRS IRS biomarker profile identifies that
the subject has an IRS biomarker profile that correlates with the
presence of ipSIRS, or alternatively the absence of a healthy
condition or inSIRS, and wherein a similarity between the level of
the at least one IRS biomarker in the sample IRS biomarker profile
and the level of the corresponding IRS biomarker in the ipSIRS
stage IRS biomarker profile identifies that the subject has an IRS
biomarker profile that correlates with the presence of a particular
stage of ipSIRS, or alternatively the absence of a healthy
condition or inSIRS.
[0639] A subset of the instantly disclosed IRS biomarkers has been
identified as being useful for assisting in distinguishing between
healthy subjects and unhealthy subjects that have SIRS (i.e., sick
subjects with either inSIRS or ipSIRS). Thus, in some embodiments,
the methods and kits involve determining the likelihood that SIRS
or a healthy condition (e.g., a normal condition or a condition in
which SIRS is absent) is present or absent in a subject. These
methods and kits generally comprise or involve: 1) providing a
correlation of a reference IRS biomarker profile with the presence
or absence of SIRS or the healthy condition, wherein the reference
biomarker profile evaluates at least one (e.g., 1, 2, 3, 4, 5, 6,
7, 8, 9, 10 etc.) IRS biomarker selected from CD177, CLEC4D, BMX,
VNN1, GPR84, ARG1, IL18R1, ERLIN1, IMP3, TLR5, UBE2J1, GPR56,
FCGR1A, SLC1A3, SLC37A3, FAIM3, C3AR1, RNASE2, TNFAIP6, GNLY, OMG,
FAR2, OLAH, CAMK4, METTL7B, B3GNT5, CLEC4E, MMP9, KREMEN1, GALNT3,
PTGDR, TDRD9, GK3P, FKBP5, STOM, SMPDL3A, PFKFB2, ANKRD34B, SGMS2,
DNAJC13, LRRN3, SH2D1B, C1orf161, HIST1H4C, IFI16, ACER3, PLB1,
C9orf72, HMGB2, KLRK1, C7orf53, GOT2, TCN1, DSE, CCR3, CRIP1, ITK,
KLRF1, TGFBR1, GSR, HIST1H4E, HPGD, FRMD3, ABCA13, C11orf82,
PPP2R5A, BPI, CASS4, AP3B2, ODZ1, TMTC1, ADM, FGFBP2, HSPC159,
HLA-DRA, HIST1H3I, TMEM144, MRPL41, FOLR3, PICALM, SH3PXD2B, DDAH2,
HLA-DPB1, KPNAS, PHOSPHO1, TPST1, EIF2AK2, OR9A2, OLFM4, CD163,
CDA, CHI3L1, MTHFS, CLU, ANAPC11, JUP, PMAIP1, GIMAP7, KLRD1, CCR1,
CD274, EFCAB2, SUCNR1, KCNMA1, LGALS2, SLC11A1, FOXD4L3, VAMP2,
ITGA4, LHFP, PRR13, FFAR2, B3GAT3, EAF2, HPSE, CLC, TLR10, CCR4,
HIST1H3A, CENPK, DPH3, HLA-DPA1, ATP13A3, DNAJC9, S100B, HIST1H3J,
110, RPL17, C15orf54, LRRC70, IL5RA, PLA2G7, ECHDC3, HINT1, LCN2,
PPIF, SLC15A2, PMS2CL, HIST1H2AA, CEACAM8, HSP90AB1, ABCG1, PDGFC,
NPCDR1, PDK4, GAB2, WSB2, FAM118A, JKAMP, TREML1, PYHIN1, IRF4,
ABCA1, DAAM2, ACPL2, RCBTB2, SAP30, THBS1, PCOLCE2, GPR65, NF-E4,
LTF, LASS4, B4GALT3, RETN, TIMM10, IL1B, CLEC4A, SEC24A, RUNX2,
LRRFIP1, CFD, EIF1AX, ZRANB1, SULF2, EXOSC4, CCDC125, LOC284757,
ANKRD28, HIST1H2AJ, CD63, PLIN2, SON, HIST1H4L, KRTAP15-1, DLEU2,
MYL9, FABP2, CD24, MACF1, GSTO1, RRP12, AIG1, RASA4, FBXL13, PDE3B,
CCRL2, C1orf128, E2F6, IL1RL1, CEACAM6, CYP4F3, 199, TAAR1, TSHZ2,
PLB1, UBE2F (where if a gene name is not provided then a SEQ ID NO.
is provided); (2) obtaining a sample IRS biomarker profile from the
subject, which evaluates for an individual IRS biomarker in the
reference IRS biomarker profile a corresponding IRS biomarker, and
(3) determining a likelihood of the subject having or not having
the healthy condition or SIRS based on the sample IRS biomarker
profile and the reference IRS biomarker profile.
[0640] In illustrative examples of this type, a reference healthy
condition IRS biomarker profile comprises at least one (e.g., 1, 2,
3, 4, 5, 6, 7, 8, 9, 10 etc.) IRS biomarker that is downregulated
or underexpressed relative to a reference SIRS IRS biomarker
profile, illustrative examples of which include: GNLY, GPR56,
KLRF1, HIST1H2AJ, HIST1H4C, KLRK1, CHI3L1, SH2D1B, PTGDR, CAMK4,
FAIM3, CRIP1, CLC, HLA-DPB1, FGFBP2, HIST1H3J, IMP3, ITK, HIST1H3I,
LRRN3, KLRD1, PHOSPHO1, CCR3, HIST1H4E, MRPL41, HIST1H3A, HLA-DRA,
GIMAP7, KPNA5, CENPK, HLA-DPA1, HINT1, HIST1H4L, GOT2, DNAJC9,
PLA2G7, CASS4, CFD, ITGA4, HSP90AB1, IL5RA, PMAIP1, LGALS2, SULF2,
C1orf128, RPL17, EIF1AX, PYHIN1, S100B, PMS2CL, CCR4, C15orf54,
VAMP2, ANAPC11, B3GAT3, E2F6, NPCDR1, FAM118A, PPIF, 199, JUP,
B4GALT3, TIMM10, RUNX2, RASA4, SON, ABCG1, TSHZ2, IRF4, PDE3B,
RRP12, LASS4 (where if a gene name is not provided then a SEQ ID
NO. is provided).is provided).
[0641] In other illustrative examples, a reference healthy
condition IRS biomarker profile comprises at least one (e.g., 1, 2,
3, 4, 5, 6, 7, 8, 9, 10 etc.) IRS biomarker that is upregulated or
overexpressed relative to a reference SIRS IRS biomarker profile,
non-limiting examples of which include: CD177, ARG1, VNN1, CLEC4D,
GPR84, IL18R1, OLFM4, FCGR1A, RNASE2, TLR5, TNFAIP6, PFKFB2, C3AR1,
TCN1, BMX, FKBP5, TDRD9, OLAH, ERLIN1, LCN2, MMP9, BPI, CEACAM8,
CLEC4E, HPGD, CD274, GK3P, KREMEN1, ANKRD34B, SLC37A3, CD163,
TMTC1, PLB1, UBE2J1, TPST1, B3GNT5, SMPDL3A, FAR2, ACER3, ODZ1,
HMGB2, LTF, SGMS2, EIF2AK2, TMEM144, GALNT3, DNAJC13, IFI16,
C11orf82, ABCA13, CD24, METTL7B, FOLR3, C7orf53, SLC1A3, DAAM2,
HSPC159, OMG, CCR1, TREML1, STOM, CEACAM6, FOXD4L3, C9orf72, GSR,
DSE, THBS1, SH3PXD2B, PDGFC, KCNMA1, PICALM, TLR10, PDK4, ADM, CLU,
C1orf161, NF-E4, HPSE, FFAR2, PPP2R5A, CDA, NA, ATP13A3, ABCA1,
TGFBR1, OR9A2, EFCAB2, EAF2, AP3B2, SLC15A2, ECHDC3, MTHFS, IL1B,
WSB2, SUCNR1, DDAH2, CLEC4A, MACF1, MYL9, IL1RL1, EXOSC4, FBXL13,
LOC284757, PRR13, DPH3, SLC11A1, FRMD3, ACPL2, PLB1, RETN, RCBTB2,
CD63, CYP4F3, SEC24A, ZRANB1, CCDC125, PCOLCE2, JKAMP, LRRFIP1,
GPR65, ANKRD28, LRRC70, AIG1, UBE2F, GAB2, CCRL2, SAP30, DLEU2,
HIST1H2AA, GSTO1, PLIN2, LHFP, KRTAP15-1, TAAR1, FABP2 (where if a
gene name is not provided then a SEQ ID NO. is provided).
[0642] In still other illustrative examples, a reference healthy
condition IRS biomarker profile comprises: (1) at least one IRS
biomarker that is downregulated or underexpressed relative to a
reference SIRS IRS biomarker profile, as broadly described above
and (2) at least one IRS biomarker that is upregulated or
overexpressed relative to a reference SIRS IRS biomarker profile,
as broadly described above.
[0643] The term "upregulated," "overexpressed" and the like refer
to an upward deviation in the level of expression of an IRS
biomarker as compared to a baseline expression level of a
corresponding IRS biomarker in a control sample.
[0644] The term "downregulated," "underexpressed" and the like
refer to a downward deviation in the level of expression of an IRS
biomarker as compared to a baseline expression level of a
corresponding IRS biomarker in a control sample.
[0645] Another subset of the instantly disclosed IRS biomarkers has
been identified as being useful for assisting in distinguishing
between healthy subjects, inSIRS affected subjects and subjects
having ipSIRS. Accordingly, in some embodiments, the methods and
kits are useful for determining the likelihood that inSIRS, ipSIRS
or a healthy condition (e.g., a normal condition or a condition in
which SIRS is absent) is present or absent in a subject. These
methods and kits generally comprise or involve: 1) providing a
correlation of a reference IRS biomarker profile with the
likelihood of having or not having inSIRS, ipSIRS or the healthy
condition, wherein the reference biomarker profile evaluates at
least one (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 etc.) IRS biomarker
selected from PLACE, 132, INSIG1, CDS2, VOPP1, SLC39A9, B3GAT3,
CD300A, OCR1, PTGER2, LGALS1, HIST1H4L, AMFR, SIAE, SLC39A8,
TGFBR1, GAB2, MRPL41, TYMS, HIST1H3B, MPZL3, KIAA1257, OMG,
HIST1H2BM, TDRD9, C22orf37, GALNT3, SYNE2, MGST3, HIST1H3I,
LOC284757, TRAF3, HIST1H3C, STOM, C3AR1, KIAA0101, TNFRSF17, HAL,
UBE2J1, GLT25D1, CD151, HSPB1, IMP3, PICALM, ACER3, IGL@,
HIST1H2BJ, CASS4, KREMEN1, IRS2, APOLD1, RBP7, DNAJC13, ERGIC1,
FSD1L, TLR5, TMEM62, SDHC, C9orf72, NP, KIAA0746, PMAIP1, DSE,
SMPDL3A, DNAJC9, HIST1H3H, CDCl26, CRIP1, FAR2, FRMD3, RGS2,
METTL7B, CLEC4E, MME, ABCA13, PRR13, HIST1H4C, RRP12, GLDC, ECHDC3,
IRF1, C7orf53, IGK@, RNASE2, FCGR1A, SAP30, PMS2CL, SLC11A1, AREG,
PLB1, PPIF, GSR, NFXL1, AP3B2, DCTN5, RPL17, IGLV6-57, KLRF1,
CHI3L1, ANKRD34B, OLFM4, CPM, CCDC125, GPR56, PPP1R2, 110, ACPL2,
HIST1H3A, C7orf58, IRF4, ANAPC11, HIST1H3J, KLRD1, GPR84, ZRANB1,
KDM6B, TPST1, HINT1, DAAM2, PTGDR, FKBP5, HSP90AB1, HPGD, IFI16,
CD177, TAS2R31, CD163, B4GALT3, EIF1AX, CYP4F3, HIST1H2AA, LASS4
(where if a gene name is not provided then a SEQ ID NO. is
provided).; (2) obtaining a sample IRS biomarker profile from the
subject, which evaluates for an individual IRS biomarker in the
reference IRS biomarker profile a corresponding IRS biomarker; and
(3) determining a likelihood of the subject having or not having
inSIRS, ipSIRS or a healthy condition the condition based on the
sample IRS biomarker profile and the reference IRS biomarker
profile.
[0646] In illustrative examples of this type, a reference healthy
condition IRS biomarker profile comprises at least one (e.g., 1, 2,
3, 4, 5, 6, 7, 8, 9, 10 etc.) IRS biomarker that is downregulated
or underexpressed relative to a reference inSIRS IRS biomarker
profile, representative examples of which include: CD177, CLEC4E,
FKBP5, CD163, TPST1, DAAM2, GPR84, FCGR1A, IFI16, RNASE2, TLR5,
ECHDC3, OCR1, MME, LOC284757, 110, C3AR1, HAL, PRR13, ACPL2,
SLC11A1, CYP4F3, SAP30, OLFM4, ZRANB1, GAB2, CCDC125, KREMEN1,
UBE2J1, AREG, FAR2, CPM, PLB1, ERGIC1, RGS2, 132, HPGD, ANKRD34B,
TDRD9, DNAJC13, GALNT3, IRS2, HIST1H2AA, RBP7, KDM6B, ACER3, MPZL3,
KIAA1257, C7orf53, C9orf72, STOM, METTL7B, SMPDL3A, GSR, SYNE2,
OMG, DSE, PICALM, ABCA13, PPP1R2, TGFBR1, AP3B2, FRMD3 (where if a
gene name is not provided then a SEQ ID NO. is provided).
[0647] In other illustrative examples, a reference healthy
condition IRS biomarker profile comprises at least one (e.g., 1, 2,
3, 4, 5, 6, 7, 8, 9, 10 etc.) IRS biomarker that is upregulated or
overexpressed, relative to a reference inSIRS IRS biomarker
profile, illustrative examples of which include: SIAE, FSD1L, GLDC,
HSPB1, HIST1H2BJ, CDS2, CASS4, DCTN5, SLC39A9, CDCl26, LGALS1,
CD151, NP, TYMS, IGLV6-57, TMEM62, CD300A, LASS4, GLT25D1, IRF1,
AMFR, IGL@, NFXL1, SLC39A8, APOLD1, TNFRSF17, KIAA0101, C22orf37,
VOPP1, KLRD1, TRAF3, RRP12, PTGER2, KIAA0746, MGST3, CHI3L1,
TAS2R31, SDHC, IRF4, INSIG1, PPIF, B4GALT3, ANAPC11, PLAC8,
HIST1H2BM, KLRF1, B3GAT3, C7orf58, PMS2CL, PTGDR, RPL17, EIF1AX,
PMAIP1, HIST1H3B, IGK@, HINT1, HSP90AB1, GPR56, HIST1H3H, HIST1H3A,
IMP3, DNAJC9, MRPL41, HIST1H3J, HIST1H3C, HIST1H3I, HIST1H4L,
CRIP1, HIST1H4C (where if a gene name is not provided then a SEQ ID
NO. is provided).
[0648] In still other illustrative examples, a reference healthy
condition IRS biomarker profile comprises: (1) at least one IRS
biomarker that is downregulated or underexpressed relative to a
reference inSIRS IRS biomarker profile, as broadly described above
and (2) at least one IRS biomarker that is upregulated or
overexpressed relative to a reference inSIRS IRS biomarker profile,
as broadly described above.
[0649] In other illustrative examples, a reference inSIRS IRS
biomarker profile comprises at least one (e.g., 1, 2, 3, 4, 5, 6,
7, 8, 9, 10 etc.) IRS biomarker that is downregulated or
underexpressed relative to a reference ipSIRS IRS biomarker
profile, representative examples of which include: OLFM4, PLAC8,
HIST1H4L, HIST1H3C, TDRD9, IGK@, HIST1H3B, HIST1H2BM, HPGD, GPR84,
TLR5, SMPDL3A, CD177, HIST1H3I, C3AR1, DNAJC9, ABCA13, ANKRD34B,
RNASE2, FCGR1A, HIST1H3H, KIAA0746, ACER3, SDHC, CRIP1, IGLV6-57,
PLB1, MRPL41, HIST1H4C, SLC39A8, NP, NFXL1, PTGER2, TYMS, LGALS1,
C7orf58, CD151, KREMEN1, AMFR, METTL7B, TNFRSF17, HSP90AB1, VOPP1,
GLT25D1, GALNT3, OMG, SIAE, FAR2, C7orf53, DNAJC13, HIST1H2BJ,
KIAA0101, HSPB1, UBE2J1, HIST1H3J, CDS2, MGST3, PICALM, HINT1,
SLC39A9, STOM, TRAF3, INSIG1, AP3B2, B3GAT3, CD300A, TGFBR1,
HIST1H3A, PMAIP1, DSE, TMEM62, IGL@, IRF4, GSR, IRF1, EIF1AX,
C9orf72, PMS2CL, C22orf37, FRMD3, IMP3, RPL17, FSD1L, APOLD1,
B4GALT3, DCTN5, PPIF, CDCl26, TAS2R31, RRP12, ANAPC11, GLDC, LASS4
(where if a gene name is not provided then a SEQ ID NO. is
provided)..
[0650] In yet other illustrative examples, a reference inSIRS IRS
biomarker profile comprises at least one (e.g., 1, 2, 3, 4, 5, 6,
7, 8, 9, 10 etc.) IRS biomarker that is upregulated or
overexpressed, relative to a reference ipSIRS IRS biomarker
profile, non-limiting examples of which include: HIST1H2AA, IFI16,
PPP1R2, CCDC125, ZRANB1, SLC11A1, GPR56, 110, KDM6B, GAB2, CYP4F3,
RGS2, KIAA1257, CPM, ACPL2, PRR13, ERGIC1, PTGDR, IRS2, MPZL3,
AREG, SAP30, RBP7, CASS4, FKBP5, SYNE2, KLRD1, 132, KLRF1,
LOC284757, HAL, TPST1, ECHDC3, CD163, CLEC4E, DAAM2, CHI3L1, MME,
OCR1 (where if a gene name is not provided then a SEQ ID NO. is
provided).
[0651] In still other illustrative examples, a reference inSIRS IRS
biomarker profile comprises: (1) at least one IRS biomarker that is
downregulated or underexpressed relative to a reference ipSIRS IRS
biomarker profile, as broadly described above and (2) at least one
IRS biomarker that is upregulated or overexpressed relative to a
reference ipSIRS IRS biomarker profile, as broadly described
above.
[0652] In other illustrative examples, a reference ipSIRS IRS
biomarker profile comprises at least one (e.g., 1, 2, 3, 4, 5, 6,
7, 8, 9, 10 etc.) IRS biomarker that is downregulated or
underexpressed relative to a reference healthy condition IRS
biomarker profile, representative examples of which include: GNLY,
GPR56, CHI3L1, KLRF1, KLRK1, PTGDR, SH2D1B, HIST1H2AJ, FAIM3,
HLA-DPB1, CAMK4, FGFBP2, KLRD1, CLC, PHOSPHO1, HIST1H4C, ITK,
LRRN3, CCR3, CRIP1, IMP3, HIST1H3J, HIST1H4E, HLA-DRA, PLA2G7,
GIMAP7, HLA-DPA1, CASS4, HIST1H3I, KPNAS, CENPK, SULF2, KIAA1324,
HIST1H3A, CFD, C1orf128, RPIA, MRPL41, GOT2, IL5RA, PYHIN1, ITGA4,
HINT1, 200, VAMP2, C15orf54, LGALS2, 199, S100B, HSP90AB1, DNAJC9,
PMAIP1, CCR4, RPL17, RUNX2, NPCDR1, JUP, PMS2CL, ANAPC11, PDE3B,
RASA4, CAMK1D, LY6G5B, 268, FAM118A, PPIF, B4GALT3, B3GAT3, ABCG1,
IRF4, LASS4 (where if a gene name is not provided then a SEQ ID NO.
is provided).
[0653] In yet other illustrative examples, a reference ipSIRS IRS
biomarker profile comprises at least one (e.g., 1, 2, 3, 4, 5, 6,
7, 8, 9, 10 etc.) IRS biomarker that is upregulated or
overexpressed relative to a reference healthy condition IRS
biomarker profile, illustrative examples of which include:
ATP6V0D1, SAP30, GAB2, KRTAP15-1, NEK6, HDHD1A, SLC39A8, HIST1H2AA,
FABP2, CDS2, SRXN1, KLHL5, ACPL2, HS2ST1, HIST1H2BJ, PLIN2, ICAM1,
HSPB1, PRR13, P4HA1, SLC11A1, ECHDC3, TAF13, LGALS1, TAAR1, TPX2,
DLEU2, TRIM21, AGTRAP, PTGS1, LHFP, CEP97, ACTA2, SIAE, GPR65,
IL1RL1, MTHFS, FAM118B, MKI67, LRRFIP1, CCRL2, GALNT2, GSTO1,
LRRC70, MTRR, ANKRD28, DPH3, 110, AIG1, UBE2F, LAIR1, PCOLCE2,
PLB1, CDA, JKAMP, FRMD3, ITGA2B, SEC24A, REIN, THBS1, MYL9, SPARC,
RCBTB2, PLACE, PDK4, PPP2R5A, SH3PXD2B, DAAM2, NF-E4, DDAH2, MACF1,
CD63, CLEC4A, MPO, SUCNR1, EXOSC4, EFCAB2, IL1B, OR9A2, AP3B2,
DYNLL1, WSB2, SLC15A2, EAF2, C1orf161, TGFBR1, ABCA1, FFAR2,
SLC1A3, ATP13A3, CLU, ADM, IFI16, KCNMA1, C9orf72, GSR, DSE,
PICALM, EIF2AK2, HPSE, TLR10, HSPC159, TPST1, ODZ1, STOM, HMGB2,
PDGFC, CCR1, OMG, CD163, SGMS2, TREML1, FOXD4L3, C7orf53, CEACAM6,
FOLR3, METTL7B, TMEM144, DNAJC13, GALNT3, B3GNT5, CLEC4E, SLC37A3,
ABCA13, CD24, C11orf82, FAR2, UBE2J1, GK3P, DEFA4, LTF, ACER3,
TMTC1, SMPDL3A, FKBP5, ERLIN1, PLB1, MMP9, KREMEN1, ANKRD34B, OLAH,
BMX, PFKFB2, HPGD, BPI, CD274, CEACAM8, TDRD9, LCN2, TNFAIP6,
C3AR1, TCN1, IL18R1, CLEC4D, TLR5, RNASE2, FCGR1A, GPR84, OLFM4,
VNN1, ARG1, CD177 (where if a gene name is not provided then a SEQ
ID NO. is provided).
[0654] In yet other illustrative examples, a reference ipSIRS IRS
biomarker profile comprises: (1) at least one IRS biomarker that is
downregulated or underexpressed relative to a reference healthy
condition IRS biomarker profile, as broadly described above and (2)
at least one IRS biomarker that is upregulated or overexpressed,
relative to a reference healthy condition IRS biomarker profile, as
broadly described above.
[0655] Yet another subset of the disclosed IRS biomarkers has been
identified as being useful for assisting in distinguishing between
inSIRS affected subjects and ipSIRS affected subjects. Accordingly,
in some embodiments, the methods and kits are useful for
determining the likelihood that inSIRS or ipSIRS is present or
absent in a subject. These methods and kits generally comprise or
involve: 1) providing a correlation of a reference IRS biomarker
profile with the likelihood of having or not having inSIRS or
ipSIRS, wherein the reference biomarker profile evaluates at least
one (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 etc.) IRS biomarker
selected from C11orf82, PLACE, 132, INSIG1, CDS2, VOPP1, SLC39A9,
FOXD4L3, WSB2, CD63, CD274, B3GAT3, CD300A, OCR1, JKAMP, TLR10,
PTGER2, PDGFC, LGALS1, HIST1H4L, AGTRAP, AMFR, SIAE, 200, SLC15A2,
SLC39A8, TGFBR1, DDAH2, HPSE, SUCNR1, MTRR, GAB2, P4HA1, HS2ST1,
MRPL41, TYMS, RUNX2, GSTO1, LRRC70, HIST1H3B, RCBTB2, MPZL3,
KIAA1257, AIG1, NEK6, OMG, HIST1H2BM, TDRD9, GALNT3, ATP13A3,
C22orf37, SYNE2, ADM, MGST3, PDE3B, HIST1H3I, LOC284757, TRAF3,
HIST1H3C, STOM, KLHL5, EXOSC4, C3AR1, KIAA0101, TNFRSF17, HAL,
UBE2J1, GLT25D1, CD151, TPX2, PCOLCE2, HSPB1, EAF2, IMP3, PICALM,
ACER3, IGL@, HIST1H2BJ, CASS4, ACTA2, PTGS1, KREMEN1, IRS2, TAF13,
FSD1L, APOLD1, RBP7, DNAJC13, SEC24A, ERGIC1, FSD1L, TLR5, MKI67,
TMEM62, CLEC4A, SDHC, C9orf72, NP, CLU, ABCA1, KIAA0746, PMAIP1,
DSE, CMTM5, SMPDL3A, DNAJC9, HDHD1A, HIST1H3H, CDCl26, ICAM1,
LOC100128751, FAR2, CRIP1, MPZL2, FRMD3, CTSL1, METTL7B, RGS2,
CLEC4E, MME, ABCA13, PRR13, HIST1H4C, RRP12, GLDC, ECHDC3, ITGA2B,
C7orf53, IRF1, 268, IGK@, RNASE2, FCGR1A, UBE2F, SAP30, LAIR1,
PMS2CL, SLC11A1, PLB1, AREG, PPIF, GSR, NFXL1, AP3B2, DCTN5, RPL17,
PLA2G7, GALNT2, IGLV6-57, KLRF1, CHI3L1, ANKRD34B, OLFM4, 199, CPM,
CCDC125, SULF2, LTF, GPR56, MACF1, PPP1R2, DYNLL1, LCN2, FFAR2,
SFRS9, IGJ, FAM118B, 110, ACPL2, HIST1H3A, C7orf58, ANAPC11,
HIST1H3J, IRF4, MPO, TREML1, KLRD1, GPR84, CCRL2, CAMK1D, CCR1,
ZRANB1, KDM6B, TPST1, HINT1, DAAM2, PTGDR, FKBP5, CD24, HSP90AB1,
HPGD, CEACAM8, DEFA4, IL1B, IFI16, CD177, KIAA1324, SRXN1, TAS2R31,
CEACAM6, CD163, B4GALT3, ANKRD28, TAAR1, EIF1AX, CYP4F3, 314,
HIST1H2AA, LY6G5B, LASS4 (where if a gene name is not provided then
a SEQ ID NO. is provided); (2) obtaining a sample IRS biomarker
profile from the subject, which evaluates for an individual IRS
biomarker in the reference IRS biomarker profile a corresponding
IRS biomarker; and (3) determining a likelihood of the subject
having or not having inSIRS or ipSIRS based on the sample IRS
biomarker profile and the reference IRS biomarker profile.
[0656] In illustrative examples of thus type, a reference inSIRS
IRS biomarker profile comprises at least one (e.g., 1, 2, 3, 4, 5,
6, 7, 8, 9, 10 etc.) IRS biomarker that is downregulated or
underexpressed relative to a reference ipSIRS IRS biomarker
profile, non-limiting examples of which include: OLFM4, CD274,
PLACE, LCN2, IGJ, HIST1H4L, HIST1H3C, DEFA4, TDRD9, IGK@, HIST1H3B,
CEACAM8, C11orf82, HIST1H2BM, LTF, HPGD, FOXD4L3, PDGFC, CD24,
GPR84, CEACAM6, TLR5, SMPDL3A, CD177, HIST1H3I, C3AR1, TLR10,
DNAJC9, ABCA13, ANKRD34B, RNASE2, FCGR1A, HPSE, HIST1H3H, KIAA0746,
ACER3, SDHC, MTRR, WSB2, CRIP1, IGLV6-57, ATP13A3, CD63, TREML1,
PLB1, MRPL41, HIST1H4C, SLC39A8, NP, NFXL1, MPO, ITGA2B, LAIR1,
PTGER2, EXOSC4, TYMS, LGALS1, C7orf58, SLC15A2, CD151, ADM,
KREMEN1, RCBTB2, PTGS1, AMFR, ABCA1, METTL7B, TNFRSF17, DYNLL1,
HSP90AB1, CLU, MKI67, VOPP1, UBE2F, P4HA1, GLT25D1, IL1B, SUCNR1,
GALNT3, AIG1, CCR1, OMG, MACF1, CLEC4A, SIAE, FAR2, C7orf53,
DNAJC13, HIST1H2BJ, JKAMP, KIAA0101, GSTO1, HSPB1, DDAH2, ICAM1,
UBE2J1, KLHL5, HIST1H3J, EAF2, CDS2, MGST3, FFAR2, TPX2, PICALM,
HINT1, SLC39A9, SEC24A, STOM, TRAF3, INSIG1, AP3B2, PCOLCE2,
B3GAT3, TAF13, CD300A, TGFBR1, HIST1H3A, PMAIP1, AGTRAP, FAM118B,
DSE, NEK6, CMTM5, GALNT2, TMEM62, HS2ST1, IGL@, ACTA2, LRRC70,
IRF4, GSR, IRF1, EIF1AX, C9orf72, PMS2CL, ANKRD28, CTSL1, C22orf37,
FRMD3, HDHD1A, CCRL2, IMP3, RPL17, FSD1L, APOLD1, B4GALT3, FSD1L,
DCTN5, PPIF, CDCl26, TAS2R31, RRP12, SFRS9, TAAR1, ANAPC11, SRXN1,
GLDC, LASS4 (where if a gene name is not provided then a SEQ ID NO.
is provided).
[0657] In other illustrative examples, a reference inSIRS IRS
biomarker profile comprises at least one (e.g., 1, 2, 3, 4, 5, 6,
7, 8, 9, 10 etc.) IRS biomarker that is upregulated or
overexpressed, relative to a reference ipSIRS IRS biomarker
profile, representative examples of which include: HIST1H2AA,
LY6G5B, 268, IFI16, PPP1R2, CCDC125, ZRANB1, LOC100128751, SLC11A1,
GPR56, RUNX2, 110, KDM6B, GAB2, 199, CYP4F3, RGS2, PDE3B, KIAA1257,
CAMK1D, CPM, ACPL2, PRR13, ERGIC1, PTGDR, IRS2, MPZL3, MPZL2, AREG,
SAP30, RBP7, CASS4, FKBP5, SYNE2, SULF2, KLRD1, 132, KLRF1, 314,
LOC284757, HAL, TPST1, ECHDC3, CD163, KIAA1324, PLA2G7, CLEC4E,
DAAM2, 200, CHI3L1, MME, OCR1 (where if a gene name is not provided
then a SEQ ID NO. is provided).
[0658] In still other illustrative examples, an inSIRS IRS
biomarker profile comprises: (1) at least one IRS biomarker that is
downregulated or underexpressed relative to a reference ipSIRS IRS
biomarker profile, as broadly described above and (2) at least one
IRS biomarker that is upregulated or overexpressed relative to a
reference ipSIRS IRS biomarker profile, as broadly described
above.
[0659] Still another subset of the disclosed IRS biomarkers has
been identified as being useful for assisting in distinguishing
between subjects with different stages of ipSIRS selected from mild
sepsis, severe sepsis and septic shock. Accordingly, in some
embodiments, the methods and kits are useful for determining the
likelihood that a stage of ipSIRS selected from mild sepsis, severe
sepsis and septic shock is present or absent in a subject. These
methods and kits generally comprise or involve: 1) providing a
correlation of a reference IRS biomarker profile with the
likelihood of having or not having the stage of ipSIRS, wherein the
reference biomarker IRS biomarker profile evaluates at least one
(e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 etc.) IRS biomarker selected
from PLEKHA3, PLEKHF2, 232, SFRS9, ZNF587, KPNA5, LOC284757, GPR65,
VAMP2, SLC1A3, ITK, ATF7, ZNF28, AIF1, MINPP1, GIMAP7, MKI67, IRF4,
TSHZ2, HLA-DPB1, EFCAB2, POLE2, FAIM3, 110, CAMK4, TRIM21, IFI44,
CENPK, ATP5L, GPR56, HLA-DPA1, C4orf3, GSR, GNLY, RFESD, BPI,
HIST1H2AA, NF-E4, CALM2, EIF1AX, E2F6, ARL17P1, TLR5, SH3PXD2B,
FAM118A, RETN, PMAIP1, DNAJC9, PCOLCE2, TPX2, BMX, LRRFIP1, DLEU2,
JKAMP, JUP, ABCG1, SLC39A9, B3GNT5, ACER3, LRRC70, NPCDR1, TYMS,
HLA-DRA, TDRD9, FSD1L, FAR2, C7orf53, PPP1R2, SGMS2, EXOSC4,
TGFBR1, CD24, TCN1, TAF13, AP3B2, CD63, SLC15A2, IL18R1, ATP6V0D1,
SON, HSP90AB1, CEACAM8, SMPDL3A, IMP3, SEC24A, PICALM, 199,
CEACAM6, CYP4F3, OLAH, ECHDC3, ODZ1, KIAA0746, KIAA1324, HINT1,
VNN1, C22orf37, FSD1L, FOLR3, IL1RL1, OMG, MTHFS, OLFM4, S100B,
ITGA4, KLRD1, SLC39A8, KLHL5, KLRK1, MPO, PPIF, GOT2, LRRN3,
HIST1H2AJ, CLU, LCN2, 132, CEP97, KLRF1, FBXL13, HIST1H3B,
ANKRD34B, RPIA, HPGD, HIST2H2BF, GK3P (where if a gene name is not
provided then a SEQ ID NO. is provided); (2) obtaining a sample IRS
biomarker profile from the subject, which evaluates for an
individual IRS biomarker in the reference IRS biomarker profile a
corresponding IRS biomarker; and (3) determining a likelihood of
the subject having or not having the stage of ipSIRS based on the
sample IRS biomarker profile and the reference IRS biomarker
profile.
[0660] In illustrative examples of this type, a reference mild
sepsis IRS biomarker profile comprises at least one (e.g., 1, 2, 3,
4, 5, 6, 7, 8, 9, 10 etc.) IRS biomarker that is downregulated or
underexpressed relative to a reference severe sepsis IRS biomarker
profile, illustrative examples of which include: OLFM4, CEACAM8,
TCN1, BPI, LCN2, CD24, CEACAM6, NF-E4, HIST1H3B, MKI67, OLAH, TYMS,
DNAJC9, MPO, LOC284757, ODZ1, HSP90AB1, VNN1, ANKRD34B, FBXL13,
TSHZ2, KIAA0746, FOLR3, GSR, IRF4, LRRN3, TPX2, SFRS9, C7orf53,
CYP4F3, IL1RL1, TDRD9, IL18R1, BMX, NPCDR1, GOT2, ATF7, CEP97, ITK,
SEC24A, KIAA1324, FAM118A, 132, SMPDL3A, CD63, ABCG1, TLR5, CAMK4,
CLU, SLC39A9, GK3P, LRRFIP1, AP3B2, SLC15A2, PICALM, HIST1H2AA,
SGMS2, OMG, RETN, FAIM3, EXOSC4, SH3PXD2B, FAR2, 199, C4orf3,
PCOLCE2 (where if a gene name is not provided then a SEQ ID NO. is
provided).
[0661] In other illustrative examples, a reference mild sepsis IRS
biomarker profile comprises at least one (e.g., 1, 2, 3, 4, 5, 6,
7, 8, 9, 10 etc.) IRS biomarker that is upregulated or
overexpressed, relative to a reference severe sepsis IRS biomarker
profile, non-limiting examples of which include: JUP, SLC1A3,
ECHDC3, IMP3, SLC39A8, MTHFS, TGFBR1, FSD1L, HIST2H2BF, HPGD,
FSD1L, PPP1R2, B3GNT5, C22orf37, ACER3, GIMAP7, ATP6V0D1, KLHL5,
PPIF, KLRK1, HINT1, GPR56, LRRC70, S100B, 110, SON, ZNF587, JKAMP,
ITGA4, HLA-DRA, ZNF28, TRIM21, TAF13, HLA-DPA1, ARL17P1, KLRF1,
PMAIP1, RPIA, ATP5L, VAMP2, E2F6, KLRD1, EIF1AX, PLEKHA3, GPR65,
CENPK, CALM2, GNLY, DLEU2, HLA-DPB1, AIF1, KPNA5, EFCAB2, PLEKHF2,
232, RFESD, MINPP1, HIST1H2AJ, POLE2, IFI44 (where if a gene name
is not provided then a SEQ ID NO. is provided).
[0662] In still other illustrative examples, a reference mild
sepsis IRS biomarker profile comprises: (1) at least one IRS
biomarker that is downregulated or underexpressed relative to a
reference severe sepsis IRS biomarker profile, as broadly described
above and (2) at least one IRS biomarker that is upregulated or
overexpressed relative to a reference severe sepsis IRS biomarker
profile, as broadly described above.
[0663] In other illustrative examples, a reference severe sepsis
IRS biomarker profile comprises at least one (e.g., 1, 2, 3, 4, 5,
6, 7, 8, 9, 10 etc.) IRS biomarker that is downregulated or
underexpressed relative to a reference septic shock IRS biomarker
profile, non-limiting examples of which include: HPGD, SLC1A3,
B3GNT5, SMPDL3A, ACER3, RETN, IL18R1, FSD1L, SH3PXD2B, SLC39A8,
EXOSC4, FSD1L, AP3B2, ECHDC3, GPR65, TDRD9, BMX, PCOLCE2, PLEKHF2,
SGMS2, RPIA, GK3P, FAR2, LRRC70, TGFBR1, MTHFS, C4orf3, TLR5, OLAH,
TAF13, JKAMP, POLE2, PICALM, RFESD, ANKRD34B, OMG, VNN1, EIF1AX,
KLHL5, SON, LRRFIP1, HIST1H2AJ, AIF1, SLC15A2, CALM2, CD63,
HIST1H2AA, MINPP1, S100B, DLEU2, PLEKHA3, ODZ1, FOLR3, 232, EFCAB2,
SEC24A, E2F6, SLC39A9, ZNF28, KLRF1, ATP6V0D1, IL1RL1, PPIF (where
if a gene name is not provided then a SEQ ID NO. is provided).
[0664] In yet other illustrative examples, a reference severe
sepsis IRS biomarker profile comprises at least one (e.g., 1, 2, 3,
4, 5, 6, 7, 8, 9, 10 etc.) IRS biomarker that is upregulated or
overexpressed, relative to a reference septic shock IRS biomarker
profile, representative examples of which include: LCN2, CENPK,
C22orf37, PMAIP1, KPNA5, ATP5L, TCN1, 132, CD24, ITGA4, KLRD1,
SFRS9, TRIM21, VAMP2, GSR, LOC284757, PPP1R2, HINT1, 110, IMP3,
C7orf53, ATF7, KIAA0746, GNLY, HLA-DRA, IFI44, ZNF587, CEP97,
GPR56, OLFM4, CLU, KLRK1, GOT2, JUP, HLA-DPA1, NPCDR1, TPX2,
HIST2H2BF, HLA-DPB1, FAM118A, ABCG1, MKI67, MPO, LRRN3, FBXL13,
ARL17P1, CEACAM8, TSHZ2, 199, BPI, HSP90AB1, CYP4F3, TYMS, GIMAP7,
DNAJC9, NF-E4, IRF4, HIST1H3B, CAMK4, FAIM3, CEACAM6, ITK, KIAA1324
(where if a gene name is not provided then a SEQ ID NO. is
provided).
[0665] In still other illustrative examples, a reference severe
sepsis IRS biomarker profile comprises: (1) at least one IRS
biomarker that is downregulated or underexpressed relative to a
reference septic shock IRS biomarker profile, as broadly described
above and (2) at least one IRS biomarker that is upregulated or
overexpressed relative to a reference septic shock IRS biomarker
profile, as broadly described above.
[0666] In other illustrative examples, a reference septic shock IRS
biomarker profile comprises at least one (e.g., 1, 2, 3, 4, 5, 6,
7, 8, 9, 10 etc.) IRS biomarker that is downregulated or
underexpressed relative to a reference mild sepsis IRS biomarker
profile, representative examples of which include: IFI44, HLA-DPB1,
ARL17P1, HIST1H2AJ, MINPP1, GNLY, GIMAP7, HLA-DPA1, POLE2, 232,
KPNA5, GPR56, HLA-DRA, ZNF587, KLRK1, RFESD, VAMP2, CENPK,
KIAA1324, KLRD1, EFCAB2, ATP5L, 110, ITK, FAIM3, TRIM21, PMAIP1,
HIST2H2BF, HINT1, DLEU2, AIF1, E2F6, ITGA4, KLRF1, CALM2, PLEKHA3,
PPP1R2, CAMK4, 199, ZNF28, PLEKHF2, JUP, EIF1AX, PPIF, IMP3,
C22orf37, ATP6V0D1, S100B, SON, GPR65, ABCG1, TAF13, FAM118A, RPIA,
KLHL5, JKAMP, IRF4, CLU, CYP4F3, LRRC70 (where if a gene name is
not provided then a SEQ ID NO. is provided).
[0667] In yet other illustrative examples, a reference septic shock
IRS biomarker profile comprises at least one (e.g., 1, 2, 3, 4, 5,
6, 7, 8, 9, 10 etc.) IRS biomarker that is upregulated or
overexpressed, relative to a reference mild sepsis IRS biomarker
profile, illustrative examples of which include: GOT2, NPCDR1,
CEP97, LRRN3, DNAJC9, TSHZ2, HSP90AB1, TYMS, HIST1H3B, ATF7,
FBXL13, TPX2, TGFBR1, MPO, 132, NF-E4, MTHFS, CEACAM6, C7orf53,
FSD1L, FSD1L, SLC39A9, MKI67, KIAA0746, HIST1H2AA, ACER3, ECHDC3,
SLC15A2, SLC39A8, SEC24A, SFRS9, LRRFIP1, OMG, GSR, C4orf3, CD63,
PICALM, LOC284757, FAR2, PCOLCE2, IL1RL1, B3GNT5, SGMS2, TLR5,
EXOSC4, SH3PXD2B, GK3P, AP3B2, FOLR3, BPI, RETN, ODZ1, CEACAM8,
BMX, HPGD, VNN1, ANKRD34B, SLC1A3, TDRD9, SMPDL3A, CD24, IL18R1,
OLAH, LCN2, TCN1, OLFM4 (where if a gene name is not provided then
a SEQ ID NO. is provided).
[0668] In yet other illustrative examples, a reference septic shock
IRS biomarker profile comprises: (1) at least one IRS biomarker
that is downregulated or underexpressed relative to a reference
mild sepsis IRS biomarker profile, as broadly described above and
(2) at least one IRS biomarker that is upregulated or overexpressed
relative to a reference mild sepsis IRS biomarker profile, as
broadly described above.
[0669] In some embodiments, individual IRS biomarkers as broadly
described above and elsewhere herein are selected from the group
consisting of: (a) a polynucleotide expression product comprising a
nucleotide sequence that shares at least 70% (or at least 71% to at
least 99% and all integer percentages in between) sequence identity
with the sequence set forth in any one of SEQ ID NO: 1-319, or a
complement thereof; (b) a polynucleotide expression product
comprising a nucleotide sequence that encodes a polypeptide
comprising the amino acid sequence set forth in any one of SEQ ID
NO: 320-619; (c) a polynucleotide expression product comprising a
nucleotide sequence that encodes a polypeptide that shares at least
70% (or at least 71% to at least 99% and all integer percentages in
between) sequence similarity or identity with at least a portion of
the sequence set forth in SEQ ID NO: 320-619; (d) a polynucleotide
expression product comprising a nucleotide sequence that hybridizes
to the sequence of (a), (b), (c) or a complement thereof, under
medium or high stringency conditions; (e) a polypeptide expression
product comprising the amino acid sequence set forth in any one of
SEQ ID NO: 320-619; and (f) a polypeptide expression product
comprising an amino acid sequence that shares at least 70% (or at
least 71% to at least 99% and all integer percentages in between)
sequence similarity or identity with the sequence set forth in any
one of SEQ ID NO: 320-619.
[0670] In some embodiments, the methods and kits comprise or
involve: (1) measuring in the biological sample the level of an
expression product of at least one (e.g., 1, 2, 3, 4, 5, 6, 7, 8,
9, 10 or more) IRS biomarker gene and (2) comparing the measured
level or functional activity of each expression product to the
level or functional activity of a corresponding expression product
in a reference sample.
[0671] The present invention also extends to the management of
inSIRS, ipSIRS or particular stages of ipSIRS, or prevention of
further progression of inSIRS, ipSIRS or particular stages of
ipSIRS (e.g., mild sepsis, severe sepsis and septic shock), or
assessment of the efficacy of therapies in subjects following
positive diagnosis for the presence of inSIRS, ipSIRS or particular
stage of ipSIRS (e.g., mild sepsis, severe sepsis and septic shock)
in a subject. The management of inSIRS or ipSIRS conditions is
generally highly intensive and can include identification and
amelioration of the underlying cause and aggressive use of
therapeutic compounds such as, vasoactive compounds, antibiotics,
steroids, antibodies to endotoxin, anti tumour necrosis factor
agents, recombinant protein C. In addition, palliative therapies as
described for example in Cohen and Glauser (1991, Lancet 338:
736-739) aimed at restoring and protecting organ function can be
used such as intravenous fluids and oxygen and tight glycemic
control. Therapies for ipSIRS are reviewed in Healy (2002, Ann.
Pharmacother. 36(4): 648-54) and Brindley (2005, CJEM. 7(4): 227)
and Jenkins (2006, J Hosp Med. 1(5): 285-295).
[0672] Typically, the therapeutic agents will be administered in
pharmaceutical (or veterinary) compositions together with a
pharmaceutically acceptable carrier and in an effective amount to
achieve their intended purpose. The dose of active compounds
administered to a subject should be sufficient to achieve a
beneficial response in the subject over time such as a reduction
in, or relief from, the symptoms of inSIRS, ipSIRS or particular
stages of ipSIRS. The quantity of the pharmaceutically active
compounds(s) to be administered may depend on the subject to be
treated inclusive of the age, sex, weight and general health
condition thereof. In this regard, precise amounts of the active
compound(s) for administration will depend on the judgment of the
practitioner. In determining the effective amount of the active
compound(s) to be administered in the treatment or prevention of
inSIRS, ipSIRS or particular stages of ipSIRS, the medical
practitioner or veterinarian may evaluate severity of any symptom
associated with the presence of inSIRS, ipSIRS or particular stages
of ipSIRS including, inflammation, blood pressure anomaly,
tachycardia, tachypnea fever, chills, vomiting, diarrhoea, skin
rash, headaches, confusion, muscle aches, seizures. In any event,
those of skill in the art may readily determine suitable dosages of
the therapeutic agents and suitable treatment regimens without
undue experimentation.
[0673] The therapeutic agents may be administered in concert with
adjunctive (palliative) therapies to increase oxygen supply to
major organs, increase blood flow to major organs and/or to reduce
the inflammatory response. Illustrative examples of such adjunctive
therapies include non steroidal-anti inflammatory drugs (NSAIDs),
intravenous saline and oxygen.
[0674] Thus, the present invention contemplates the use of the
methods and kits described above and elsewhere herein in methods
for treating, preventing or inhibiting the development of inSIRS,
ipSIRS or a particular stage of ipSIRS (e.g., mild sepsis, severe
sepsis and septic shock) in a subject. These methods generally
comprise (1) correlating a reference IRS biomarker profile with the
presence or absence of a condition selected from a healthy
condition, SIRS, inSIRS, ipSIRS, or a particular stage of ipSIRS,
wherein the reference IRS biomarker profile evaluates at least one
(e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 etc.) IRS biomarker; (2)
obtaining an IRS biomarker profile of a sample (i.e., "a sample IRS
biomarker profile") from a subject, wherein the sample IRS
biomarker profile evaluates for an individual IRS biomarker in the
reference IRS biomarker profile a corresponding IRS biomarker; (3)
determining a likelihood of the subject having or not having the
condition based on the sample IRS biomarker profile and the
reference IRS biomarker profile, and administering to the subject,
on the basis that the subject has an increased likelihood of having
inSIRS, an effective amount of an agent that treats or ameliorates
the symptoms or reverses or inhibits the development of inSIRS, or
administering to the subject, on the basis that the subject has an
increased likelihood of having ipSIRS or a particular stage of
ipSIRS, an effective amount of an agent that treats or ameliorates
the symptoms or reverses or inhibits the development of ipSIRS or
the particular stage of ipSIRS.
[0675] In some embodiments the methods and kits of the present
invention are used for monitoring, treatment and management of
conditions that can lead to inSIRS or ipSIRS, illustrative examples
of which include retained placenta, meningitis, endometriosis,
shock, toxic shock (i.e., sequelae to tampon use), gastroenteritis,
appendicitis, ulcerative colitis, Crohn's disease, inflammatory
bowel disease, acid gut syndrome, liver failure and cirrhosis,
failure of colostrum transfer in neonates, ischemia (in any organ),
bacteraemia, infections within body cavities such as the
peritoneal, pericardial, thecal, and pleural cavities, burns,
severe wounds, excessive exercise or stress, haemodialysis,
conditions involving intolerable pain (e.g., pancreatitis, kidney
stones), surgical operations, and non-healing lesions. In these
embodiments, the methods or kits of the present invention are
typically used at a frequency that is effective to monitor the
early development of inSIRS, ipSIRS or particular stages of ipSIRS,
to thereby enable early therapeutic intervention and treatment of
that condition. In illustrative examples, the diagnostic methods or
kits are used at least at 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,
13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23 or 24 hour intervals or
at least 1, 2, 3, 4, 5 or 6 day intervals, or at least weekly,
fortnightly or monthly. Accordingly, the present invention
encompasses the use of the methods and kits of the present
invention for early diagnosis of inSIRS, ipSIRS or particular
stages of ipSIRS.
[0676] The term "early diagnosis" as used herein with "early
detection" refers to specific screening/monitoring processes that
allow detection and evaluation of inSIRS, ipSIRS or particular
stages of ipSIRS at an early point in disease development and/or
progression. For example, since both inSIRS and ipSIRS patients
present with similar clinical signs, early detection of ipSIRS can
be achieved through a plurality of evaluations of patients with
inSIRS to detect a transition to ipSIRS.
[0677] The present invention can be practiced in the field of
predictive medicine for the purposes of diagnosis or monitoring the
presence or development of a condition selected from inSIRS, ipSIRS
or a particular stage of ipSIRS in a subject, and/or monitoring
response to therapy efficacy.
[0678] The IRS biomarker profile further enables determination of
endpoints in pharmacotranslational studies. For example, clinical
trials can take many months or even years to establish the
pharmacological parameters for a medicament to be used in treating
or preventing inSIRS, ipSIRS or a particular stage of ipSIRS (e.g.,
mild sepsis, severe sepsis and septic shock). However, these
parameters may be associated with an IRS biomarker profile
associated with a health state (e.g., a healthy condition). Hence,
the clinical trial can be expedited by selecting a treatment
regimen (e.g., medicament and pharmaceutical parameters), which
results in an IRS biomarker profile associated with the desired
health state (e.g., healthy condition). This may be determined for
example by (1) providing a correlation of a reference IRS biomarker
profile with the likelihood of having the healthy condition; (2)
obtaining a corresponding IRS biomarker profile of a subject having
inSIRS, ipSIRS or a particular stage of ipSIRS after treatment with
a treatment regimen, wherein a similarity of the subject's IRS
biomarker profile after treatment to the reference IRS biomarker
profile indicates the likelihood that the treatment regimen is
effective for changing the health status of the subject to the
desired health state (e.g., healthy condition). This aspect of the
present invention advantageously provides methods of monitoring the
efficacy of a particular treatment regimen in a subject (for
example, in the context of a clinical trial) already diagnosed with
a condition selected from inSIRS, ipSIRS or a particular stage of
ipSIRS. These methods take advantage of IRS biomarkers that
correlate with treatment efficacy, for example, to determine
whether the IRS biomarker profile of a subject undergoing treatment
partially or completely normalizes during the course of or
following therapy or otherwise shows changes associated with
responsiveness to the therapy.
[0679] The IRS biomarker profile further enables stratification of
patients prior to enrolment in pharmacotranslational studies. For
example, a clinical trial can be expedited by selecting a priori
patients with a particular IRS biomarker profile that would most
benefit from a particular treatment regimen (e.g., medicament and
pharmaceutical parameters). For instance, patient enrolment into a
clinical trial testing the efficacy of a new antibiotic would best
include patients with an IRS biomarker profile that indicated that
they had ipSIRS rather than inSIRS, and as such the selected
patients would most likely benefit from the new therapy. Further,
and by example, patient enrolment into a clinical trial testing the
efficacy of a new inotrope would best include patients with an IRS
biomarker profile that indicated that they had the shock stage of
ipSIRS rather than inSIRS or other stage of ipSIRS, and as such the
selected patients would most likely benefit from the new
therapy.
[0680] As used herein, the term "treatment regimen" refers to
prophylactic and/or therapeutic (i.e., after onset of a specified
condition) treatments, unless the context specifically indicates
otherwise. The term "treatment regimen" encompasses natural
substances and pharmaceutical agents (i.e., "drugs") as well as any
other treatment regimen including but not limited to dietary
treatments, physical therapy or exercise regimens, surgical
interventions, and combinations thereof.
[0681] Thus, the invention provides methods of correlating a
reference IRS biomarker profile with an effective treatment regimen
for a condition selected from inSIRS, ipSIRS or a particular stage
of ipSIRS (e.g., mild sepsis, severe sepsis and septic shock),
wherein the reference IRS biomarker profile evaluates at least one
(e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, etc.) IRS biomarker. These
methods generally comprise: (a) determining a sample IRS biomarker
profile from a subject with the condition prior to treatment (i.e.,
baseline), wherein the sample IRS biomarker profile evaluates for
an individual IRS biomarker in the reference IRS biomarker profile
a corresponding IRS biomarker; and correlating the sample IRS
biomarker profile with a treatment regimen that is effective for
treating that condition.
[0682] The invention further provides methods of determining
whether a treatment regimen is effective for treating a subject
with a condition selected from inSIRS, ipSIRS or a particular stage
of ipSIRS (e.g., mild sepsis, severe sepsis and septic shock).
These methods generally comprise: (a) correlating a reference
biomarker profile prior to treatment (i.e., baseline) with an
effective treatment regimen for the condition, wherein the
reference IRS biomarker profile evaluates at least one (e.g., 1, 2,
3, 4, 5, 6, 7, 8, 9, 10, etc.) IRS biomarker; and (b) obtaining a
sample IRS biomarker profile from the subject after treatment,
wherein the sample IRS biomarker profile evaluates for an
individual IRS biomarker in the reference IRS biomarker profile a
corresponding IRS biomarker, and wherein the sample IRS biomarker
profile after treatment indicates whether the treatment regimen is
effective for treating the condition in the subject.
[0683] The invention can also be practiced to evaluate whether a
subject is responding (i.e., a positive response) or not responding
(i.e., a negative response) to a treatment regimen. This aspect of
the invention provides methods of correlating an IRS biomarker
profile with a positive and/or negative response to a treatment
regimen. These methods generally comprise: (a) obtaining an IRS
biomarker profile from a subject with a condition selected from
inSIRS, ipSIRS or a particular stage of ipSIRS (e.g., mild sepsis,
severe sepsis and septic shock) following commencement of the
treatment regimen, wherein the IRS biomarker profile evaluates at
least one (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, etc.) IRS
biomarker; and (b) correlating the IRS biomarker profile from the
subject with a positive and/or negative response to the treatment
regimen.
[0684] The invention also provides methods of determining a
positive and/or negative response to a treatment regimen by a
subject with a condition selected from inSIRS, ipSIRS or a
particular stage of ipSIRS (e.g., mild sepsis, severe sepsis and
septic shock). These methods generally comprise: (a) correlating a
reference IRS biomarker profile with a positive and/or negative
response to the treatment regimen, wherein the reference IRS
biomarker profile evaluates at least one (e.g., 1, 2, 3, 4, 5, 6,
7, 8, 9, 10, etc.) IRS biomarker; and (b) determining a sample IRS
biomarker profile from the subject, wherein the subject's sample
IRS biomarker profile evaluates for an individual IRS biomarker in
the reference IRS biomarker profile a corresponding IRS biomarker
and indicates whether the subject is responding to the treatment
regimen.
[0685] In some embodiments, the methods further comprise
determining a first sample IRS biomarker profile from the subject
prior to commencing the treatment regimen (i.e., a baseline
profile), wherein the first sample IRS biomarker profile evaluates
at least one (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, etc.) IRS
biomarker; and comparing the first sample IRS biomarker profile
with a second sample IRS biomarker profile from the subject after
commencement of the treatment regimen, wherein the second sample
IRS biomarker profile evaluates for an individual IRS biomarker in
the first sample IRS biomarker profile a corresponding IRS
biomarker.
[0686] This aspect of the invention can be practiced to identify
responders or non-responders relatively early in the treatment
process, i.e., before clinical manifestations of efficacy. In this
way, the treatment regimen can optionally be discontinued, a
different treatment protocol can be implemented and/or supplemental
therapy can be administered. Thus, in some embodiments, a sample
IRS biomarker profile is obtained within about 2 hours, 4 hours, 6
hours, 12 hours, 1 day, 2 days, 3 days, 4 days, 5 days, 1 week, 2
weeks, 3 weeks, 4 weeks, 6 weeks, 8 weeks, 10 weeks, 12 weeks, 4
months, six months or longer of commencing therapy.
[0687] In order that the invention may be readily understood and
put into practical effect, particular preferred embodiments will
now be described by way of the following non-limiting examples.
EXAMPLES
Example 1
Monitoring Severity of IPSIRS in Patients in Intensive Care
[0688] Patients admitted to intensive care (ICU) often have ipSIRS,
or develop ipSIRS during their ICU stay. The ultimate aim of
intensive care is to ensure the patient survives and is discharged
to a general ward in the minimum time. Patients in intensive care
with diagnosed ipSIRS are usually administered a number of
therapeutic compounds many of which have opposing actions on the
immune system and many of which could be counterproductive
depending on the severity of ipSIRS (mild sepsis, severe sepsis,
septic shock). Monitoring intensive care patients on a regular
basis with biomarkers of the present invention will allow medical
practitioners to determine the stage of ipSIRS and hence choice of
therapies and patient management procedures, and ultimately
response to therapy. Information provided by these biomarkers
disclosed herein ("the IRS biomarkers") will therefore allow
medical practitioners to tailor and modify therapies to ensure
patients survive and spend less time in intensive care. Less time
in intensive care leads to considerable savings in medical expenses
including through less occupancy time and appropriate use and
timing of medications. Practical examples of the use of the IRS
biomarkers in Tables 1-6 are described.
[0689] Tables 1, 2 and 3 list those top 10 IRS biomarkers (by
example) in ascending order of p value (less than 0.05) when
comparing the clinical groups of mild sepsis, severe sepsis and
septic shock (severe versus mild, shock versus mild and shock
versus severe--the appropriate column is filled grey for each group
in respective tables). In this and the following examples
significance is defined when a p value is less than 0.05. P values
were determined by adjusted t-test (Benjamini & Hochberg, 1995)
in the case of healthy vs. other and inSIRS vs. ipSIRS, and by
Tukey's Honestly Significant Difference for analysis of variance
(ANOVA) for the mild/severe/shock comparisons. For the groups
severe versus mild, shock versus mild and shock versus severe there
were 72, 120 and 47 biomarkers respectively with a p value less
than 0.05.
[0690] Tables 4, 5 and 6 list those top 10 biomarkers (by example)
in descending order of Area Under Curve (AUC) value when comparing
the clinical groups of mild sepsis, severe sepsis and septic shock
(severe versus mild, shock versus mild and shock versus severe--the
appropriate column is filled grey for each group in respective
tables). For the groups severe versus mild, shock versus mild and
shock versus severe there were 34, 17 and 2 biomarkers respectively
with an AUC greater than 0.8 (a nominal cut-off above which would
be considered to be good).
[0691] In each of Tables 1-6 a SEQ ID NO. is provided for each IRS
biomarker (IRS biomarker polynucleotides range from SEQ ID NO.
1-319, IRS biomarker polypeptides range from SEQ ID No. 320-619),
along with a database identification tag (e.g. NM_), a gene name
(Gene Name) if there is one, and either; mean expression values for
healthy (HC), inSIRS, mild sepsis, severe sepsis and septic shock,
and p values for HC vs. all other groups, inSIRS vs. ipSIRS, mild
sepsis versus severe sepsis, mild sepsis versus septic shock and
septic shock versus severe sepsis, or AUC values for HC vs. Sick,
HC vs. inSIRS, HC vs. ipSIRS, inSIRS vs. ipSIRS, Mild Sepsis versus
Severe Sepsis, Mild Sepsis versus Septic Shock and Septic Shock
versus Severe Sepsis. Such biomarkers have clinical utility in
determining ipSIRS severity based on these groups. By example, in
Table 1, Severe versus Mild p Value, it can be seen that the gene
PLEKHA3 has a significant p value for both Severe versus Mild and
Shock versus Mild and therefore has utility in separating mild
sepsis from both severe sepsis and septic shock. In Table 2, Severe
versus Mild Area Under Curve, it can be seen that the gene PLEKHA3
has an AUC of 0.8748 and therefore has most utility in separating
mild sepsis from severe sepsis. It can be seen that the p value for
PLEKHA3 for separating septic shock from severe sepsis is not
significant (>0.05) and therefore this biomarker has no utility
in separating these two groups. From the columns in the table
containing mean expression data it can be seen that PLEKHA3 is
down-regulated in both severe sepsis (6.689) and septic shock
(6.825) compared to mild sepsis (7.281) (also see FIG. 1).
[0692] Further and by example in Table 3, Shock vs. Mild p Value,
it can be seen that the biomarker VAMP2 has utility in separating
mild sepsis from septic shock but also from severe sepsis. VAMP2
does not have utility in separating septic shock from severe sepsis
(p=0.708038) but does have further utility in separating healthy
from other groups. From the mean expression columns it can also be
seen that the expression level of VAMP2 is downregulated in both
severe sepsis (8.454) and septic shock (8.353) compared to mild
sepsis (9.016) (see also FIG. 2). In Table 4, Shock vs. Mild Area
Under Curve, it can be seen that VAMP2 has an AUC of 0.8342.
[0693] Further and by example in Table 5, Shock versus Severe p
Value, it can be seen that the biomarker ITK has utility in
separating Shock versus Severe Sepsis and Mild Sepsis, and healthy
from other groups but no utility in separating Severe Sepsis and
Mild Sepsis. From the mean expression values for ITK it can be seen
that it is comparatively downregulated in Septic Shock compared to
both Severe and Mild Sepsis (see also FIG. 3). In Table 6, Shock
versus Severe Area Under Curve, it can be seen that ITK has an AUC
of 0.8054.
TABLE-US-00001 TABLE 1 Severe versus Mild p Value SEQ ID Gene HC
inSIRS Mild Severe Shock Number Database ID Name Mean Mean Mean
Mean Mean pval.HC.vs.Other 285 NM_019091 PLEKHA3 7.058 6.910 7.281
6.689 6.825 1.000000 587 NM_019091 PLEKHA3 7.058 6.910 7.281 6.689
6.825 1.000000 232 gi|14250459 NA 6.389 5.970 6.875 5.976 6.059
1.000000 gi|14250459 NA 6.389 5.970 6.875 5.976 6.059 1.000000 195
NM_004897 MINPP1 8.212 7.697 8.228 7.151 7.317 0.006705 504
NM_004897 MINPP1 8.212 7.697 8.228 7.151 7.317 0.006705 288
NM_024613 PLEKHF2 7.432 7.660 8.044 7.255 7.671 0.789194 590
NM_024613 PLEKHF2 7.432 7.660 8.044 7.255 7.671 0.789194 190
NM_176818 SFRS9 9.739 9.666 9.715 10.226 10.128 1.000000 499
NM_176818 SFRS9 9.739 9.666 9.715 10.226 10.128 1.000000 207
NR_002612 DLEU2 6.549 6.894 7.347 6.699 6.850 0.000171 NR_002612
DLEU2 6.549 6.894 7.347 6.699 6.850 0.000171 197 NM_006969 ZNF28
4.936 4.511 5.008 4.554 4.612 0.021118 506 NM_006969 ZNF28 4.936
4.511 5.008 4.554 4.612 0.021118 71 NM_002269 KPNA5 6.822 5.908
6.451 5.758 5.712 0.000000 387 NM_002269 KPNA5 6.822 5.908 6.451
5.758 5.712 0.000000 278 NM_001130059 ATF7 5.212 5.212 5.253 5.605
5.372 1.000000 580 NM_001130059 ATF7 5.212 5.212 5.253 5.605 5.372
1.000000 81 NR_046099 LOC284757 6.913 8.028 6.894 7.534 7.376
0.000001 NR_046099 LOC284757 6.913 8.028 6.894 7.534 7.376 0.000001
SEQ ID pval pval pval Number pval.inSIRS.vs.ipSIRS Severe.vs.Mild
Shock.vs.Mild Shock.vs.Severe 285 1.000000 0.000001 0.000011
0.409707 587 1.000000 0.000001 0.000011 0.409707 232 0.638753
0.000001 0.000000 0.869569 0.638753 0.000001 0.000000 0.869569 195
1.000000 0.000005 0.000008 0.707929 504 1.000000 0.000005 0.000008
0.707929 288 1.000000 0.000012 0.023331 0.029137 590 1.000000
0.000012 0.023331 0.029137 190 0.000618 0.000018 0.000064 0.627141
499 0.000618 0.000018 0.000064 0.627141 207 1.000000 0.000019
0.000152 0.504529 1.000000 0.000019 0.000152 0.504529 197 0.027741
0.000020 0.000018 0.814925 506 0.027741 0.000020 0.000018 0.814925
71 1.000000 0.000021 0.000000 0.945115 387 1.000000 0.000021
0.000000 0.945115 278 0.438013 0.000033 0.171523 0.007397 580
0.438013 0.000033 0.171523 0.007397 81 0.000004 0.000039 0.000364
0.493398 0.000004 0.000039 0.000364 0.493398
TABLE-US-00002 TABLE 2 Severe versus Mild Area Under Curve (AUC)
SEQ ID Database Gene HC vs. HC vs. HC vs. inSIRS vs. Mild vs. Mild
vs. Severe vs. Number ID Name Sick AUC SIRS AUC ipSIRS AUC ipSIRS
AUC Severe AUC Shock AUC Shock AUC 195 NM_004897 MINPP1 0.689217443
0.699775533 0.685388685 0.532134532 0.88707483 0.779591837
0.488435374 504 NM_004897 MINPP1 0.689217443 0.699775533
0.685388685 0.532134532 0.88707483 0.779591837 0.488435374 285
NM_019091 PLEKHA3 0.584378734 0.644781145 0.562474562 0.541791542
0.874829932 0.794285714 0.619047619 587 NM_019091 PLEKHA3
0.584378734 0.644781145 0.562474562 0.541791542 0.874829932
0.794285714 0.619047619 282 NM_002692 POLE2 0.675029869 0.693602694
0.668294668 0.501165501 0.854421769 0.707755102 0.563265306 584
NM_002692 POLE2 0.675029869 0.693602694 0.668294668 0.501165501
0.854421769 0.707755102 0.563265306 288 NM_024613 PLEKHF2
0.654868578 0.667227834 0.65038665 0.538794539 0.850340136
0.692244898 0.718367347 590 NM_024613 PLEKHF2 0.654868578
0.667227834 0.65038665 0.538794539 0.850340136 0.692244898
0.718367347 190 NM_176818 SFRS9 0.597520908 0.557800224 0.653846154
0.748917749 0.840816327 0.79755102 0.587755102 499 NM_176818 SFRS9
0.597520908 0.557800224 0.653846154 0.748917749 0.840816327
0.79755102 0.587755102 170 NM_003608 GPR65 0.765681004 0.735690236
0.776556777 0.6003996 0.839455782 0.615510204 0.740136054 480
NM_003608 GPR65 0.765681004 0.735690236 0.776556777 0.6003996
0.839455782 0.615510204 0.740136054 232 gi|14250459 NA 0.571983274
0.7003367 0.525437525 0.661338661 0.838095238 0.83755102
0.559183673 gi|14250459 NA 0.571983274 0.7003367 0.525437525
0.661338661 0.838095238 0.83755102 0.559183673 197 NM_006969 ZNF28
0.689964158 0.837261504 0.636548637 0.670662671 0.836734694
0.796734694 0.536054422 506 NM_006969 ZNF28 0.689964158 0.837261504
0.636548637 0.670662671 0.836734694 0.796734694 0.536054422 81
NR_046099 LOC284757 0.738948626 0.939393939 0.666259666 0.835497835
0.834013605 0.749387755 0.613605442 NR_046099 LOC284757 0.738948626
0.939393939 0.666259666 0.835497835 0.834013605 0.749387755
0.613605442 71 NM_002269 KPNA5 0.85483871 0.895061728 0.84025234
0.564768565 0.831292517 0.844897959 0.530612245 387 NM_002269 KPNA5
0.85483871 0.895061728 0.84025234 0.564768565 0.831292517
0.844897959 0.530612245
TABLE-US-00003 TABLE 3 Shock vs. Mild p Value SEQ ID Database Gene
HC inSIRS Mild Severe Shock Number ID Name Mean Mean Mean Mean Mean
pval.HC.vs.Other 59 NM_014232 VAMP2 9.213 8.896 9.016 8.454 8.353
0.000000 376 NM_014232 VAMP2 9.213 8.896 9.016 8.454 8.353 0.000000
71 NM_002269 KPNA5 6.822 5.908 6.451 5.758 5.712 0.000000 387
NM_002269 KPNA5 6.822 5.908 6.451 5.758 5.712 0.000000 232
gi|14250459 NA 6.389 5.970 6.875 5.976 6.059 1.000000 gi|14250459
NA 6.389 5.970 6.875 5.976 6.059 1.000000 246 NM_032828 ZNF587
8.514 8.816 8.783 8.381 8.101 1.000000 549 NM_032828 ZNF587 8.514
8.816 8.783 8.381 8.101 1.000000 195 NM_004897 MINPP1 8.212 7.697
8.228 7.151 7.317 0.006705 504 NM_004897 MINPP1 8.212 7.697 8.228
7.151 7.317 0.006705 107 NM_153236 GIMAP7 9.533 8.865 8.974 8.682
8.112 0.000000 420 NM_153236 GIMAP7 9.533 8.865 8.974 8.682 8.112
0.000000 285 NM_019091 PLEKHA3 7.058 6.910 7.281 6.689 6.825
1.000000 587 NM_019091 PLEKHA3 7.058 6.910 7.281 6.689 6.825
1.000000 58 NM_002121 HLA-DPB1 11.414 10.665 10.623 9.971 9.578
0.000000 375 NM_002121 HLA-DPB1 11.414 10.665 10.623 9.971 9.578
0.000000 304 NR_033759 ATP5L 7.242 7.337 7.379 6.824 6.772 1.000000
NR_033759 ATP5L 7.242 7.337 7.379 6.824 6.772 1.000000 197
NM_006969 ZNF28 4.936 4.511 5.008 4.554 4.612 0.021118 506
NM_006969 ZNF28 4.936 4.511 5.008 4.554 4.612 0.021118 SEQ ID pval
pval pval Number pval.inSIRS.vs.IpSIRS Severe.vs.Mild Shock.vs.Mild
Shock.vs.Severe 59 0.297018 0.000084 0.000000 0.708038 376 0.297018
0.000084 0.000000 0.708038 71 1.000000 0.000021 0.000000 0.945115
387 1.000000 0.000021 0.000000 0.945115 232 0.638753 0.000001
0.000000 0.869569 0.638753 0.000001 0.000000 0.869569 246 0.003242
0.018153 0.000001 0.136854 549 0.003242 0.018153 0.000001 0.136854
195 1.000000 0.000005 0.000008 0.707929 504 1.000000 0.000005
0.000008 0.707929 107 1.000000 0.310769 0.000008 0.014285 420
1.000000 0.310769 0.000008 0.014285 285 1.000000 0.000001 0.000011
0.409707 587 1.000000 0.000001 0.000011 0.409707 58 0.067333
0.026428 0.000013 0.253098 375 0.067333 0.026428 0.000013 0.253098
304 0.748516 0.000612 0.000014 0.929559 0.748516 0.000612 0.000014
0.929559 197 0.027741 0.000020 0.000018 0.814925 506 0.027741
0.000020 0.000018 0.814925
TABLE-US-00004 TABLE 4 Shock vs. Mild Area Under Curve (AUC) SEQ ID
Database Gene HC vs. HC vs. HC vs. inSIRS vs. Mild vs. Mild vs.
Severe vs. Number ID Name Sick AUC SIRS AUC ipSIRS AUC ipSIRS AUC
Severe AUC Shock AUC Shock AUC 71 NM_002269 KPNA5 0.85483871
0.895061728 0.84025234 0.564768565 0.831292517 0.844897959
0.530612245 387 NM_002269 KPNA5 0.85483871 0.895061728 0.84025234
0.564768565 0.831292517 0.844897959 0.530612245 246 NM_032828
ZNF587 0.506571087 0.632435466 0.539072039 0.68964369 0.723809524
0.84244898 0.644897959 549 NM_032828 ZNF587 0.506571087 0.632435466
0.539072039 0.68964369 0.723809524 0.84244898 0.644897959 232
gi|14250459 NA 0.571983274 0.7003367 0.525437525 0.661338661
0.838095238 0.83755102 0.559183673 gi|14250459 NA 0.571983274
0.7003367 0.525437525 0.661338661 0.838095238 0.83755102
0.559183673 59 NM_014232 VAMP2 0.814366786 0.78338945 0.825600326
0.636363636 0.810884354 0.834285714 0.54829932 376 NM_014232 VAMP2
0.814366786 0.78338945 0.825600326 0.636363636 0.810884354
0.834285714 0.54829932 107 NM_153236 GIMAP7 0.829898447 0.80359147
0.839438339 0.603063603 0.617687075 0.826938776 0.710204082 420
NM_153236 GIMAP7 0.829898447 0.80359147 0.839438339 0.603063603
0.617687075 0.826938776 0.710204082 58 NM_002121 HLA-DPB1
0.857228196 0.814814815 0.872608873 0.682317682 0.693877551
0.809795918 0.628571429 375 NM_002121 HLA-DPB1 0.857228196
0.814814815 0.872608873 0.682317682 0.693877551 0.809795918
0.628571429 110 gi|13182974 NA 0.796893668 0.92704826 0.74969475
0.743589744 0.681632653 0.808163265 0.617687075 423 gi|13182974 NA
0.796893668 0.92704826 0.74969475 0.743589744 0.681632653
0.808163265 0.617687075 64 NM_004172 SLC1A3 0.954599761 0.985409652
0.943426943 0.636030636 0.557823129 0.806530612 0.823129252 381
NM_004172 SLC1A3 0.954599761 0.985409652 0.943426943 0.636030636
0.557823129 0.806530612 0.823129252 190 NM_176818 SFRS9 0.597520908
0.557800224 0.653846154 0.748917749 0.840816327 0.79755102
0.587755102 499 NM_176818 SFRS9 0.597520908 0.557800224 0.653846154
0.748917749 0.840816327 0.79755102 0.587755102 197 NM_006969 ZNF28
0.689964158 0.837261504 0.636548637 0.670662671 0.836734694
0.796734694 0.536054422 506 NM_006969 ZNF28 0.689964158 0.837261504
0.636548637 0.670662671 0.836734694 0.796734694 0.536054422
TABLE-US-00005 TABLE 5 Shock versus Severe p Value SEQ ID Database
Gene HC inSIRS Mild Severe Shock Number ID Name Mean Mean Mean Mean
Mean pval.HC.vs.Other 79 NM_005546 ITK 9.271 8.099 8.227 8.536
7.635 0.000000 395 NM_005546 ITK 9.271 8.099 8.227 8.536 7.635
0.000000 34 NM_001744 CAMK4 8.155 6.723 6.902 7.152 6.470 0.000000
352 NM_001744 CAMK4 8.155 6.723 6.902 7.152 6.470 0.000000 64
NM_004172 SLC1A3 5.849 6.892 6.472 6.447 7.373 0.000000 381
NM_004172 SLC1A3 5.849 6.892 6.472 6.447 7.373 0.000000 171
NR_046000 IRF4 8.200 7.491 7.843 8.381 7.762 0.000105 NR_046000
IRF4 8.200 7.491 7.843 8.381 7.762 0.000105 271 NM_173485 TSHZ2
7.382 6.972 6.846 7.429 6.929 0.004682 574 NM_173485 TSHZ2 7.382
6.972 6.846 7.429 6.929 0.004682 22 NM_005449 FAIM3 10.259 9.101
9.036 9.174 8.464 0.000000 340 NM_005449 FAIM3 10.259 9.101 9.036
9.174 8.464 0.000000 44 NM_032047 B3GNT5 6.871 8.033 8.009 7.744
8.548 0.000000 362 NM_032047 B3GNT5 6.871 8.033 8.009 7.744 8.548
0.000000 229 NM_207647 FSD1L 4.605 4.402 4.801 4.565 5.099 1.000000
534 NM_207647 FSD1L 4.605 4.402 4.801 4.565 5.099 1.000000 198
gi|21538810 NPCDR1 5.404 5.022 4.784 5.166 4.817 0.000001 507
gi|21538810 NPCDR1 5.404 5.022 4.784 5.166 4.817 0.000001 220
NM_207627 ABCG1 8.318 7.923 7.960 8.214 7.791 0.000000 526
NM_207627 ABCG1 8.318 7.923 7.960 8.214 7.791 0.000000 SEQ ID pval
pval pval Number pval.inSIRS.vs.Sepsis Severe.vs.Mild Shock.vs.Mild
Shock.vs.Severe 79 1.000000 0.276607 0.002832 0.000063 395 1.000000
0.276607 0.002832 0.000063 34 1.000000 0.305982 0.011176 0.000339
352 1.000000 0.305982 0.011176 0.000339 64 1.000000 0.993673
0.000056 0.000352 381 1.000000 0.993673 0.000056 0.000352 171
0.000070 0.002770 0.825556 0.000501 0.000070 0.002770 0.825556
0.000501 271 1.000000 0.000051 0.739871 0.000534 574 1.000000
0.000051 0.739871 0.000534 22 1.000000 0.732360 0.001453 0.000582
340 1.000000 0.732360 0.001453 0.000582 44 1.000000 0.438680
0.013149 0.000955 362 1.000000 0.438680 0.013149 0.000955 229
0.000000 0.235142 0.050177 0.001077 534 0.000000 0.235142 0.050177
0.001077 198 1.000000 0.000589 0.919120 0.001847 507 1.000000
0.000589 0.919120 0.001847 220 1.000000 0.112606 0.270961 0.003184
526 1.000000 0.112606 0.270961 0.003184
TABLE-US-00006 TABLE 6 Shock versus Severe Area Under Curve (AUC)
SEQ ID Database Gene HC vs. HC vs. HC vs. inSIRS vs. Mild vs. Mild
vs. Severe vs. Number ID Name Sick AUC SIRS AUC ipSIRS AUC ipSIRS
AUC Severe AUC Shock AUC Shock AUC 64 NM_004172 SLC1A3 0.954599761
0.985409652 0.943426943 0.636030636 0.557823129 0.806530612
0.823129252 381 NM_004172 SLC1A3 0.954599761 0.985409652
0.943426943 0.636030636 0.557823129 0.806530612 0.823129252 79
NM_005546 ITK 0.888888889 0.873737374 0.894383394 0.520812521
0.646258503 0.735510204 0.805442177 395 NM_005546 ITK 0.888888889
0.873737374 0.894383394 0.520812521 0.646258503 0.735510204
0.805442177 22 NM_005449 FAIM3 0.951612903 0.930415264 0.959299959
0.596070596 0.57414966 0.749387755 0.793197279 340 NM_005449 FAIM3
0.951612903 0.930415264 0.959299959 0.596070596 0.57414966
0.749387755 0.793197279 171 NR_046000 IRF4 0.776433692 0.946127946
0.714896215 0.741258741 0.759183673 0.604897959 0.782312925
NR_046000 IRF4 0.776433692 0.946127946 0.714896215 0.741258741
0.759183673 0.604897959 0.782312925 34 NM_001744 CAMK4 0.939217443
0.921436588 0.945665446 0.55977356 0.594557823 0.725714286
0.776870748 352 NM_001744 CAMK4 0.939217443 0.921436588 0.945665446
0.55977356 0.594557823 0.725714286 0.776870748 271 NM_173485 TSHZ2
0.705197133 0.733445567 0.694953195 0.543456543 0.828571429
0.528979592 0.776870748 574 NM_173485 TSHZ2 0.705197133 0.733445567
0.694953195 0.543456543 0.828571429 0.528979592 0.776870748 88
NM_181506 LRRC70 0.795997611 0.49382716 0.901098901 0.857808858
0.736054422 0.515102041 0.76462585 402 NM_181506 LRRC70 0.795997611
0.49382716 0.901098901 0.857808858 0.736054422 0.515102041
0.76462585 176 NM_002230 JUP 0.830346476 0.75308642 0.858363858
0.573093573 0.500680272 0.765714286 0.760544218 485 NM_002230 JUP
0.830346476 0.75308642 0.858363858 0.573093573 0.500680272
0.765714286 0.760544218 44 NM_032047 B3GNT5 0.9369773 0.948933782
0.932641433 0.548784549 0.565986395 0.692244898 0.759183673 362
NM_032047 B3GNT5 0.9369773 0.948933782 0.932641433 0.548784549
0.565986395 0.692244898 0.759183673 235 NM_003531 HIST1H3C
0.781212664 0.881593715 0.744810745 0.672327672 0.643537415
0.651428571 0.752380952 539 NM_003531 HIST1H3C 0.781212664
0.881593715 0.744810745 0.672327672 0.643537415 0.651428571
0.752380952
Example 2
Differentiating INSIRS and IPSIRS in Post-Surgical and Medical
Patients
[0694] Surgical and medical patients often develop inSIRS
post-surgery, post-procedural or as part of a co-morbidity or
co-morbidities. Such inpatients have a higher incidence of inSIRS
and a higher risk of developing ipSIRS. Medical care in such
patients therefore involves monitoring for signs of inSIRS and
ipSIRS, differentiating between these two conditions, and
determining at the earliest possible time when a patient
transitions from inSIRS to ipSIRS. The treatment and management of
inSIRS and ipSIRS patients is different, since inSIRS patients can
be put on mild anti-inflammatory drugs or anti-pyretics and ipSIRS
patients must be started on antibiotics as soon as possible for
best outcomes. Monitoring post-surgical and medical patients on a
regular basis with biomarkers of the present invention will allow
nursing and medical practitioners to differentiate inSIRS and
ipSIRS at an early stage and hence make informed decisions on
choice of therapies and patient management procedures, and
ultimately response to therapy. Information provided by these
biomarkers will therefore allow medical practitioners to tailor and
modify therapies to ensure patients recover quickly from surgery
and do not develop ipSIRS. Less time in hospital and less
complications leads to considerable savings in medical expenses
including through less occupancy time and appropriate use and
timing of medications. Practical examples of the use of the
biomarkers in Tables 7 and 8 are described.
[0695] Table 7 lists the top 10 biomarkers (of 216) in order of
ascending p value when comparing the two clinical groups of inSIRS
and ipSIRS. A SEQ ID NO. is provided for each IRS biomarker (IRS
biomarker polynucleotides range from SEQ ID NO. 1-319, IRS
biomarker polypeptides range from SEQ ID No. 320-619), along with a
database identification tag (e.g. NM_), a gene name (Gene Name) if
there is one, mean expression values for healthy (HC), inSIRS, mild
sepsis, severe sepsis and septic shock, and p values for HC vs. all
other groups, inSIRS vs. ipSIRS, mild sepsis versus severe sepsis,
mild sepsis versus septic shock and septic shock versus severe
sepsis. All biomarkers have clinical utility in distinguishing
inSIRS and ipSIRS and for distinguishing inSIRS and ipSIRS as early
as possible. Seven (7) of these biomarkers are also useful in
distinguishing healthy control from sick although this has no
clinical utility for post-surgical or medical patients. Some of
these biomarkers also have limited utility in determining ipSIRS
severity as indicated by respective p values less than 0.05. By
example, in Table 7, inSIRS vs. ipSIRS p Value, it can be seen that
the gene C11orf82 has a significant p value for both inSIRS versus
ipSIRS and Healthy versus other groups and therefore has utility in
separating healthy and inSIRS patients from septic patients. From
the columns in the table containing mean expression data it can be
seen that C11orf82 is down-regulated in both inSIRS (5.888) and
healthy controls (5.776) compared to septic patients of all classes
(mild (6.889), severe (7.153) and shock (7.293)) (7.281) (also see
FIG. 4).
[0696] Table 8 lists the top 10 biomarkers (of 104 with an AUC
>0.8) in order of descending AUC when comparing the two clinical
groups of inSIRS and ipSIRS and it can be seen that C11orf82, PLAC8
and INSIG1 have AUCs of 0.9477, 0.9210 and 0.9120, respectively
(see also FIGS. 4, 5 and 6).
TABLE-US-00007 TABLE 7 inSIRS versus ipSIRS p Value SEQ ID Database
Gene HC inSIRS Mild Severe Shock Number ID Name Mean Mean Mean Mean
Mean 12 NM_145018 C11orf82 5.776 5.888 6.889 7.153 7.293 330
NM_145018 C11orf82 5.776 5.888 6.889 7.153 7.293 83 NR_036641 PDGFC
6.098 6.117 6.987 7.044 7.466 NR_036641 PDGFC 6.098 6.117 6.987
7.044 7.466 106 NM_018375 SLC39A9 8.038 7.719 8.121 8.368 8.428 419
NM_018375 SLC39A9 8.038 7.719 8.121 8.368 8.428 150 NM_030796 VOPP1
9.302 8.771 9.510 9.318 9.517 461 NM_030796 VOPP1 9.302 8.771 9.510
9.318 9.517 73 NM_001257400 CD63 9.235 9.126 9.718 9.990 10.159 389
NM_001257400 CD63 9.235 9.126 9.718 9.990 10.159 55 NM_014143 CD274
5.508 5.656 7.557 7.211 7.237 372 NM_014143 CD274 5.508 5.656 7.557
7.211 7.237 111 NM_198336 INSIG1 8.081 7.370 8.062 7.867 7.913 424
NM_198336 INSIG1 8.081 7.370 8.062 7.867 7.913 76 ENST00000443533
DDAH2 8.067 8.170 8.630 8.707 9.015 392 ENST00000443533 DDAH2 8.067
8.170 8.630 8.707 9.015 115 NM_003546 HIST1H4L 9.807 7.908 9.466
9.602 9.065 428 NM_003546 HIST1H4L 9.807 7.908 9.466 9.602 9.065
226 NM_003537 HIST1H3B 8.783 7.684 8.739 9.501 8.852 532 NM_003537
HIST1H3B 8.783 7.684 8.739 9.501 8.852 pval pval pval pval pval SEQ
ID HC vs. inSIRS vs. Severe vs. Shock vs. Shock vs. Number Other
ipSIRS Mild Mild Severe 12 0.000000 0.000000 0.322762 0.032568
0.722429 330 0.000000 0.000000 0.322762 0.032568 0.722429 83
0.000000 0.000000 0.970637 0.064634 0.196970 0.000000 0.000000
0.970637 0.064634 0.196970 106 1.000000 0.000000 0.034062 0.001276
0.808136 419 1.000000 0.000000 0.034062 0.001276 0.808136 150
1.000000 0.000000 0.298375 0.997162 0.269787 461 1.000000 0.000000
0.298375 0.997162 0.269787 73 0.000000 0.000000 0.156468 0.002260
0.485665 389 0.000000 0.000000 0.156468 0.002260 0.485665 55
0.000000 0.000000 0.536662 0.490374 0.996684 372 0.000000 0.000000
0.536662 0.490374 0.996684 111 0.001237 0.000000 0.123875 0.197540
0.883915 424 0.001237 0.000000 0.123875 0.197540 0.883915 76
0.000000 0.000000 0.868573 0.011535 0.108528 392 0.000000 0.000000
0.868573 0.011535 0.108528 115 0.000032 0.000000 0.878290 0.231084
0.140998 428 0.000032 0.000000 0.878290 0.231084 0.140998 226
1.000000 0.000000 0.042709 0.908040 0.098544 532 1.000000 0.000000
0.042709 0.908040 0.098544
TABLE-US-00008 TABLE 8 inSIRS versus ipSIRS Area Under Curve (AUC)
SEQ ID Database Gene HC vs. HC vs. HC vs. inSIRS vs. Mild vs. Mild
vs. Severe vs. Number ID Name Sick AUC SIRS AUC ipSIRS AUC ipSIRS
AUC Severe AUC Shock AUC Shock AUC 12 NM_145018 C11orf82
0.873058542 0.580246914 0.979242979 0.947718948 0.619047619
0.650612245 0.555102041 330 NM_145018 C11orf82 0.873058542
0.580246914 0.979242979 0.947718948 0.619047619 0.650612245
0.555102041 72 NM_001130715 PLAC8 0.635902031 0.828282828
0.804232804 0.921078921 0.506122449 0.653061224 0.642176871 388
NM_001130715 PLAC8 0.635902031 0.828282828 0.804232804 0.921078921
0.506122449 0.653061224 0.642176871 132 gi|21757933 NA 0.533004779
0.867564534 0.588319088 0.912753913 0.708843537 0.631020408
0.540136054 445 gi|21757933 NA 0.533004779 0.867564534 0.588319088
0.912753913 0.708843537 0.631020408 0.540136054 111 NM_198336
INSIG1 0.695191159 0.957351291 0.6001221 0.912087912 0.666666667
0.631836735 0.518367347 424 NM_198336 INSIG1 0.695191159
0.957351291 0.6001221 0.912087912 0.666666667 0.631836735
0.518367347 90 gi|21749325 CDS2 0.669354839 0.730078563 0.814204314
0.907092907 0.586394558 0.56244898 0.504761905 gi|21749325 CDS2
0.669354839 0.730078563 0.814204314 0.907092907 0.586394558
0.56244898 0.504761905 150 NM_030796 VOPP1 0.53875448 0.937710438
0.605921856 0.906759907 0.63537415 0.544489796 0.66122449 461
NM_030796 VOPP1 0.53875448 0.937710438 0.605921856 0.906759907
0.63537415 0.544489796 0.66122449 106 NM_018375 SLC39A9 0.559587814
0.775533109 0.681115181 0.901098901 0.730612245 0.735510204
0.557823129 419 NM_018375 SLC39A9 0.559587814 0.775533109
0.681115181 0.901098901 0.730612245 0.735510204 0.557823129 37
NM_199135 FOXD4L3 0.815860215 0.49382716 0.928164428 0.900765901
0.597278912 0.608163265 0.48707483 355 NM_199135 FOXD4L3
0.815860215 0.49382716 0.928164428 0.900765901 0.597278912
0.608163265 0.48707483 68 NM_018639 WSB2 0.782108722 0.581369248
0.913919414 0.9004329 0.555102041 0.533877551 0.530612245 384
NM_018639 WSB2 0.782108722 0.581369248 0.913919414 0.9004329
0.555102041 0.533877551 0.530612245 73 NM_001257400 CD63 0.73655914
0.612233446 0.863044363 0.897768898 0.644897959 0.72244898
0.613605442 389 NM_001257400 CD63 0.73655914 0.612233446
0.863044363 0.897768898 0.644897959 0.72244898 0.613605442
Example 3
Differentiating Both INSIRS and IPSIRS in Emergency Department
Patients and Determining Degree of Illness
[0697] Patients presenting to emergency departments often have a
fever, which is one (of four) of the clinical signs of inSIRS. Such
patients need to be assessed to determine if they have either
inSIRS or ipSIRS. Further it is important to determine how sick
they are to be able to make a judgement call on whether to admit
the patient or not. As mentioned above, the treatment and
management of pyretic, inSIRS and septic patients are different. By
way of example, a patient with a fever without other inSIRS
clinical signs and no obvious source of infection may be sent home,
or provided with other non-hospital services, without further
hospital treatment. However, a patient with a fever may have early
ipSIRS and not admitting such a patient may put their life at risk.
Because these biomarkers can differentiate inSIRS and ipSIRS and
determine how sick a patient is they will allow medical
practitioners to triage emergency department patients quickly and
effectively. Accurate triage decision-making insures that patients
requiring hospital treatment are given it, and those that don't are
provided with other appropriate services. Practical examples of the
use of the biomarkers in Tables 9 and 10 are described.
[0698] Table 9 lists 30 significant biomarkers when comparing the
groups of healthy and sick (sick consisting of those patients with
either inSIRS or ipSIRS) and inSIRS versus ipSIRS. A SEQ ID NO. is
provided for each IRS biomarker (IRS biomarker polynucleotides
range from SEQ ID NO. 1-319, IRS biomarker polypeptides range from
SEQ ID No. 320-619), along with a database identification tag (e.g.
NM_), a gene name (Gene Name) if there is one, mean expression
values for healthy (HC), inSIRS, mild sepsis, severe sepsis and
septic shock, and p values for HC vs. all other groups, inSIRS vs.
ipSIRS, mild sepsis versus severe sepsis, mild sepsis versus septic
shock and septic shock versus severe sepsis. Such biomarkers have
clinical utility in distinguishing healthy from sick patients and
inSIRS from ipSIRS patients. By example, in Table 9, Healthy versus
inSIRS versus ipSIRS, it can be seen that the gene FCGR1A has a
significant p value for both inSIRS versus ipSIRS and Healthy
versus other groups and therefore has utility in separating healthy
and inSIRS and ipSIRS patients. From the columns in the table
containing mean expression data it can be seen that FCGR1A is
up-regulated in inSIRS (9.281) compared to healthy controls (7.871)
but more so in ipSIRS patients (9.985-10.308). Such a upward
gradient in gene expression can be used to determine the degree of
illness in patients presenting to an emergency department allowing
clinicians to risk stratify and triage with greater certainty (see
also FIG. 7).
[0699] Table 10 lists 10 significant biomarkers when comparing the
groups of healthy and sick (sick consisting of those patients with
either inSIRS or ipSIRS) and inSIRS versus ipSIRS. A SEQ ID NO. is
provided for each IRS biomarker (IRS biomarker polynucleotides
range from SEQ ID NO. 1-319, IRS biomarker polypeptides range from
SEQ ID No. 320-619), along with a database identification tag (e.g.
NM_), a gene name (Gene Name) if there is one, mean expression
values for healthy (HC), inSIRS, mild sepsis, severe sepsis and
septic shock, and p values for HC vs. all other groups, inSIRS vs.
ipSIRS, mild sepsis versus severe sepsis, mild sepsis versus septic
shock and septic shock versus severe sepsis. Such biomarkers have
clinical utility in distinguishing healthy from sick patients and
inSIRS from ipSIRS patients. By example, in Table 10, Healthy
versus inSIRS versus ipSIRS, it can be seen that the gene CHI3L1
has a significant p value for both inSIRS versus ipSIRS and Healthy
versus other groups and therefore has utility in separating healthy
and inSIRS and septic patients. From the columns in the table
containing mean expression data it can be seen that CHI3L1 is
down-regulated in inSIRS (9.876) compared to healthy controls
(10.47) but more so in ipSIRS patients (8.64-9.035). Such a
downward gradient in gene expression can be used to determine the
degree of illness in patients presenting to an emergency department
allowing clinicians to risk stratify and triage with greater
certainty (see also FIG. 8).
TABLE-US-00009 TABLE 9 Healthy versus inSIRS versus ipSIRS p Value
SEQ ID Database Gene HC inSIRS Mild Severe Shock Number ID Name
Mean Mean Mean Mean Mean 11 NR_045213 FCGR1A 7.871 9.281 10.308
9.985 10.273 Non- NR_045213 FCGR1A 7.871 9.281 10.308 9.985 10.273
coding 20 NM_153046 TDRD9 4.986 5.567 6.483 6.937 7.385 338
NM_153046 TDRD9 4.986 5.567 6.483 6.937 7.385 29 NM_020370 GPR84
6.712 8.157 9.030 8.980 9.583 347 NM_020370 GPR84 6.712 8.157 9.030
8.980 9.583 25 NM_018367 ACER3 7.317 7.845 8.701 8.417 9.050 343
NM_018367 ACER3 7.317 7.845 8.701 8.417 9.050 86 NM_000860 HPGD
5.621 6.238 7.085 6.908 7.946 400 NM_000860 HPGD 5.621 6.238 7.085
6.908 7.946 65 NM_006418 OLFM4 6.365 7.209 8.023 9.641 9.322 382
NM_006418 OLFM4 6.365 7.209 8.023 9.641 9.322 8 NM_004054 C3AR1
8.429 9.449 10.261 10.439 10.593 327 NM_004054 C3AR1 8.429 9.449
10.261 10.439 10.593 6 NM_002934 RNASE2 9.164 10.500 11.243 11.670
11.388 325 NM_002934 RNASE2 9.164 10.500 11.243 11.670 11.388 21
NM_032045 KREMEN1 8.626 9.409 10.143 10.189 10.055 339 NM_032045
KREMEN1 8.626 9.409 10.143 10.189 10.055 1 NM_003268 TLR5 7.747
9.010 9.726 9.979 10.311 320 NM_003268 TLR5 7.747 9.010 9.726 9.979
10.311 280 NM_153021 PLB1 8.205 8.887 9.574 9.463 10.019 582
NM_153021 PLB1 8.205 8.887 9.574 9.463 10.019 15 NM_004482 GALNT3
5.685 6.251 6.916 6.728 7.075 333 NM_004482 GALNT3 5.685 6.251
6.916 6.728 7.075 161 NM_001816 CEACAM8 7.336 7.874 8.503 9.775
9.287 472 NM_001816 CEACAM8 7.336 7.874 8.503 9.775 9.287 36
NM_007115 TNFAIP6 7.738 9.246 9.829 9.631 9.738 354 NM_007115
TNFAIP6 7.738 9.246 9.829 9.631 9.738 4 NM_016021 UBE2J1 8.792
9.555 10.118 10.044 10.335 323 NM_016021 UBE2J1 8.792 9.555 10.118
10.044 10.335 35 NM_015268 DNAJC13 7.507 8.083 8.596 8.693 8.878
353 NM_015268 DNAJC13 7.507 8.083 8.596 8.693 8.878 pval pval pval
pval pval SEQ ID HC vs. inSIRS vs. Severe vs. Shock vs. Shock vs.
Number Other ipSIRS Mild Mild Severe 11 0.000000 0.001046 0.284201
0.980298 0.366022 Non- 0.000000 0.001046 0.284201 0.980298 0.366022
coding 20 0.000000 0.000000 0.248195 0.001153 0.259068 338 0.000000
0.000000 0.248195 0.001153 0.259068 29 0.000000 0.001894 0.989573
0.184680 0.221197 347 0.000000 0.001894 0.989573 0.184680 0.221197
25 0.000000 0.000000 0.362961 0.132905 0.008450 343 0.000000
0.000000 0.362961 0.132905 0.008450 86 0.000000 0.000025 0.895298
0.035905 0.027021 400 0.000000 0.000025 0.895298 0.035905 0.027021
65 0.000000 0.000069 0.003150 0.006691 0.784808 382 0.000000
0.000069 0.003150 0.006691 0.784808 8 0.000000 0.000016 0.650271
0.142678 0.725241 327 0.000000 0.000016 0.650271 0.142678 0.725241
6 0.000000 0.002979 0.095954 0.690976 0.351809 325 0.000000
0.002979 0.095954 0.690976 0.351809 21 0.000000 0.000079 0.962640
0.837239 0.731337 339 0.000000 0.000079 0.962640 0.837239 0.731337
1 0.000000 0.000225 0.275728 0.000244 0.110996 320 0.000000
0.000225 0.275728 0.000244 0.110996 280 0.000000 0.000133 0.872838
0.059398 0.037699 582 0.000000 0.000133 0.872838 0.059398 0.037699
15 0.000000 0.000000 0.407446 0.422801 0.051201 333 0.000000
0.000000 0.407446 0.422801 0.051201 161 0.000000 0.001298 0.011921
0.098854 0.501991 472 0.000000 0.001298 0.011921 0.098854 0.501991
36 0.000000 1.000000 0.712067 0.908467 0.905260 354 0.000000
1.000000 0.712067 0.908467 0.905260 4 0.000000 0.000015 0.817659
0.104460 0.049488 323 0.000000 0.000015 0.817659 0.104460 0.049488
35 0.000000 0.000000 0.833718 0.133216 0.512817 353 0.000000
0.000000 0.833718 0.133216 0.512817
TABLE-US-00010 TABLE 10 Healthy versus inSIRS versus ipSIRS p Value
SEQ ID Database Gene HC inSIRS Mild Severe Shock Number ID Name
Mean Mean Mean Mean Mean 104 NM_001276 CHI3L1 10.470 9.876 8.640
9.035 8.726 417 NM_001276 CHI3L1 10.470 9.876 8.640 9.035 8.726 122
NM_001143804 PHOSPHO1 11.398 10.826 10.374 9.837 10.185 435
NM_001143804 PHOSPHO1 11.398 10.826 10.374 9.837 10.185 40
NM_016523 KLRF1 6.343 5.438 5.022 4.504 4.543 358 NM_016523 KLRF1
6.343 5.438 5.022 4.504 4.543 33 NM_000953 PTGDR 9.310 8.373 8.028
7.790 7.577 351 NM_000953 PTGDR 9.310 8.373 8.028 7.790 7.577 103
ENST00000381907 KLRD1 8.651 8.097 7.766 7.201 7.123 416
ENST00000381907 KLRD1 8.651 8.097 7.766 7.201 7.123 pval pval pval
pval pval SEQ ID HC vs. inSIRS vs. Severe vs. Shock vs. Shock vs.
Number Other ipSIRS Mild Mild Severe 104 0.000000 0.000056 0.485576
0.954853 0.641602 417 0.000000 0.000056 0.485576 0.954853 0.641602
122 0.000000 0.048380 0.088690 0.661703 0.354179 435 0.000000
0.048380 0.088690 0.661703 0.354179 40 0.000000 0.007278 0.033428
0.021421 0.979558 358 0.000000 0.007278 0.033428 0.021421 0.979558
33 0.000000 0.007043 0.500548 0.040553 0.570947 351 0.000000
0.007043 0.500548 0.040553 0.570947 103 0.000000 0.011838 0.056985
0.008125 0.944270 416 0.000000 0.011838 0.056985 0.008125
0.944270
Example 4
Differentiating Healthy from Sick Patients and Determining Degree
of Illness
[0700] Patients presenting to medical clinics often have any one of
the four clinical signs of inSIRS (increased heart rate, increased
respiratory rate, abnormal white blood cell count, fever or
hypothermia). Many different clinical conditions can present with
one of the four clinical signs of inSIRS and such patients need to
be assessed to determine if they have either inSIRS or ipSIRS and
to exclude other differential diagnoses. By way of example, a
patient with colic might also present with clinical signs of
increased heart rate. Differential diagnoses could be (but not
limited to) appendicitis, urolithiasis, cholecystitis,
pancreatitis, enterocolitis. In each of these conditions it would
be important to determine if there was a systemic inflammatory
response (inSIRS) or whether an infection was contributing to the
condition. The treatment and management of patients with and
without systemic inflammation and/or infection are different.
Because these biomarkers can differentiate healthy from sick
(inSIRS and ipSIRS), and determine the degree of systemic
involvement, the use of them will allow medical practitioners to
determine the next medical procedure(s) to perform to
satisfactorily resolve the patient issue. Practical examples of the
use of the biomarkers in Tables 11, 12, 13 and 14 are
described.
[0701] Table 11 lists 20 significant biomarkers (of 150) when
comparing the groups of healthy and sick (sick consisting of those
patients with either inSIRS or ipSIRS). A SEQ ID NO. is provided
for each IRS biomarker (IRS biomarker polynucleotides range from
SEQ ID NO. 1-319, IRS biomarker polypeptides range from SEQ ID No.
320-619), along with a database identification tag (e.g. NM_), a
gene name (Gene Name) if there is one, mean expression values for
healthy (HC), inSIRS, mild sepsis, severe sepsis and septic shock,
and p values for HC vs. all other groups, inSIRS vs. ipSIRS, mild
sepsis versus severe sepsis, mild sepsis versus septic shock and
septic shock versus severe sepsis. Such biomarkers have clinical
utility in distinguishing healthy from sick patients and
determining the level of systemic inflammation and/or infection.
For example, in Table 11, Healthy versus Sick, it can be seen that
the gene CD177 has a significant p value for healthy control versus
other groups and therefore has utility in separating healthy and
sick patients. From the columns in the table containing mean
expression data it can be seen that CD177 is up-regulated in inSIRS
(10.809) compared to healthy controls (8.091) but more so in ipSIRS
patients (11.267-12.088). Such up-regulated differences in gene
expression can be used to determine the degree of systemic
inflammation and infection in patients presenting to clinics
allowing clinicians to more easily determine the next medical
procedure(s) to perform to satisfactorily resolve the patient issue
(see also FIG. 9).
[0702] Further, and by example, in Table 11, Healthy versus Sick,
it can be seen that the gene GNLY has a significant p value for
healthy control versus other groups and therefore has utility in
separating healthy and sick patients. From the columns in the table
containing mean expression data it can be seen that GNLY is
down-regulated in inSIRS (9.428) compared to healthy controls
(10.653) but more so in septic patients (9.305-8.408). GNLY has an
AUC of 0.9445 (not shown) for separating healthy and sick patients.
Such down-regulated differences in gene expression can be used to
determine the degree of systemic inflammation and infection in
patients presenting to clinics allowing clinicians to more easily
determine the next medical procedure(s) to perform to
satisfactorily resolve the patient issue (see also FIG. 10).
[0703] Table 12 lists the top 10 biomarkers (of 118 with an AUC of
at least 0.8) for separating healthy from sick patients (sick being
those patients with either inSIRS or ipSIRS) by decreasing value of
Area Under Curve (AUC). It can be seen that the highest AUC is for
CD177 for separating healthy from sick (0.9929) (see also FIG.
9).
[0704] Table 13 lists the top 10 biomarkers (of 152 with an AUC of
at least 0.8) for separating healthy from inSIRS patients by
decreasing value of Area Under Curve (AUC). It can be seen that the
highest AUC is for BMX for separating healthy from inSIRS (1). That
is, this biomarker alone can perfectly separate these two groups
(see also FIG. 11).
[0705] Table 14 lists the top 10 biomarkers (of 140 with an AUC of
at least 0.8) for separating healthy from ipSIRS patients by
decreasing value of Area Under Curve (AUC). It can be seen that the
highest AUC is for TLR5 for separating healthy from ipSIRS (0.9945)
(see also FIG. 12).
TABLE-US-00011 TABLE 11 Healthy versus Sick p Value pval pval pval
pval pval HC SIRS Severe Shock Shock SEQ ID Database Gene HC SIRS
Mild Severe Shock vs. vs. vs. vs. vs. Number ID Name Mean Mean Mean
Mean Mean Other ipSIRS Mild Mild Severe 2 NM_020406 CD177 8.091
10.809 11.267 12.088 12.044 0.000000 0.087061 0.048910 0.027926
0.991139 321 NM_020406 CD177 8.091 10.809 11.267 12.088 12.044
0.000000 0.087061 0.048910 0.027926 0.991139 10 NM_001244438 ARG1
5.410 9.054 7.895 8.254 8.919 0.000000 1.000000 0.628534 0.008931
0.209877 329 NM_001244438 ARG1 5.410 9.054 7.895 8.254 8.919
0.000000 1.000000 0.628534 0.008931 0.209877 3 NM_004666 VNN1 7.736
10.013 10.007 10.629 10.876 0.000000 1.000000 0.087388 0.002402
0.671136 322 NM_004666 VNN1 7.736 10.013 10.007 10.629 10.876
0.000000 1.000000 0.087388 0.002402 0.671136 7 NM_080387 CLEC4D
7.187 9.915 9.238 9.152 9.828 0.000000 0.383427 0.945300 0.034026
0.035853 326 NM_080387 CLEC4D 7.187 9.915 9.238 9.152 9.828
0.000000 0.383427 0.945300 0.034026 0.035853 29 NM_020370 GPR84
6.712 8.157 9.030 8.980 9.583 0.000000 0.001894 0.989573 0.184680
0.221197 347 NM_020370 GPR84 6.712 8.157 9.030 8.980 9.583 0.000000
0.001894 0.989573 0.184680 0.221197 24 NM_003855 IL18R1 5.516 8.101
7.098 7.538 8.097 0.000000 1.000000 0.373616 0.001873 0.205385 342
NM_003855 IL18R1 5.516 8.101 7.098 7.538 8.097 0.000000 1.000000
0.373616 0.001873 0.205385 65 NM_006418 OLFM4 6.365 7.209 8.023
9.641 9.322 0.000000 0.000068 0.003150 0.006691 0.784808 382
NM_006418 OLFM4 6.365 7.209 8.023 9.641 9.322 0.000000 0.000068
0.003150 0.006691 0.784808 11 NR_045213 FCGR1A 7.871 9.281 10.308
9.985 10.273 0.000000 0.001046 0.284201 0.980298 0.366022 NR_045213
FCGR1A 7.871 9.281 10.308 9.985 10.273 0.000000 0.001046 0.284201
0.980298 0.366022 6 NM_002934 RNASE2 9.164 10.500 11.243 11.670
11.388 0.000000 0.002979 0.095954 0.690976 0.351809 325 NM_002934
RNASE2 9.164 10.500 11.243 11.670 11.388 0.000000 0.002979 0.095954
0.690976 0.351809 14 NM_006433 GNLY 10.653 9.428 9.305 8.659 8.408
0.000000 0.020098 0.014566 0.000045 0.511511 332 NM_006433 GNLY
10.653 9.428 9.305 8.659 8.408 0.000000 0.020098 0.014566 0.000045
0.511511
TABLE-US-00012 TABLE 12 Healthy versus Sick Area Under Curve (AUC)
SEQ ID Database Gene HC vs. HC vs. HC vs. SIRS vs. Mild vs. Mild
vs. Severe vs. Number ID Name Sick AUC inSIRS AUC ipSIRS AUC ipSIRS
AUC Severe AUC Shock AUC Shock AUC 2 NM_020406 CD177 0.992980884
0.991582492 0.993487993 0.718281718 0.668027211 0.675102041
0.540136054 321 NM_020406 CD177 0.992980884 0.991582492 0.993487993
0.718281718 0.668027211 0.675102041 0.540136054 7 NM_080387 CLEC4D
0.981780167 0.998877666 0.975579976 0.64968365 0.52244898
0.671020408 0.691156463 326 NM_080387 CLEC4D 0.981780167
0.998877666 0.975579976 0.64968365 0.52244898 0.671020408
0.691156463 18 NM_203281 BMX 0.979988053 1 0.972730973 0.56043956
0.639455782 0.749387755 0.644897959 336 NM_203281 BMX 0.979988053 1
0.972730973 0.56043956 0.639455782 0.749387755 0.644897959 3
NM_004666 VNN1 0.979241338 0.996632997 0.972934473 0.663003663
0.648979592 0.710204082 0.575510204 322 NM_004666 VNN1 0.979241338
0.996632997 0.972934473 0.663003663 0.648979592 0.710204082
0.575510204 29 NM_020370 GPR84 0.974313023 0.92704826 0.991452991
0.738927739 0.496598639 0.608163265 0.623129252 347 NM_020370 GPR84
0.974313023 0.92704826 0.991452991 0.738927739 0.496598639
0.608163265 0.623129252 10 NM_001244438 ARG1 0.970878136
0.999438833 0.960520961 0.644355644 0.561904762 0.683265306
0.662585034 329 NM_001244438 ARG1 0.970878136 0.999438833
0.960520961 0.644355644 0.561904762 0.683265306 0.662585034 24
NM_003855 IL18R1 0.966845878 0.989337823 0.958689459 0.62970363
0.62585034 0.715102041 0.639455782 342 NM_003855 IL18R1 0.966845878
0.989337823 0.958689459 0.62970363 0.62585034 0.715102041
0.639455782 26 NM_006459 ERLIN1 0.964755078 0.994949495 0.953805454
0.694971695 0.561904762 0.639183673 0.594557823 344 NM_006459
ERLIN1 0.964755078 0.994949495 0.953805454 0.694971695 0.561904762
0.639183673 0.594557823 5 NM_018285 IMP3 0.96385902 0.997755331
0.951566952 0.817515818 0.610884354 0.742040816 0.614965986 324
NM_018285 IMP3 0.96385902 0.997755331 0.951566952 0.817515818
0.610884354 0.742040816 0.614965986 1 NM_003268 TLR5 0.962365591
0.873737374 0.994505495 0.808524809 0.606802721 0.768979592
0.672108844 320 NM_003268 TLR5 0.962365591 0.873737374 0.994505495
0.808524809 0.606802721 0.768979592 0.672108844
TABLE-US-00013 TABLE 13 Healthy versus inSIRS Area Under Curve
(AUC) SEQ ID Database Gene HC vs. HC vs. HC vs. SIRS vs. Mild vs.
Mild vs. Severe vs. Number ID Name Sick AUC inSIRS AUC ipSIRS AUC
ipSIRS AUC Severe AUC Shock AUC Shock AUC 18 NM_203281 BMX
0.979988053 1 0.972730973 0.56043956 0.639455782 0.749387755
0.644897959 336 NM_203281 BMX 0.979988053 1 0.972730973 0.56043956
0.639455782 0.749387755 0.644897959 10 NM_001244438 ARG1
0.970878136 0.999438833 0.960520961 0.644355644 0.561904762
0.683265306 0.662585034 329 NM_001244438 ARG1 0.970878136
0.999438833 0.960520961 0.644355644 0.561904762 0.683265306
0.662585034 7 NM_080387 CLEC4D 0.981780167 0.998877666 0.975579976
0.64968365 0.52244898 0.671020408 0.691156463 326 NM_080387 CLEC4D
0.981780167 0.998877666 0.975579976 0.64968365 0.52244898
0.671020408 0.691156463 5 NM_018285 IMP3 0.96385902 0.997755331
0.951566952 0.817515818 0.610884354 0.742040816 0.614965986 324
NM_018285 IMP3 0.96385902 0.997755331 0.951566952 0.817515818
0.610884354 0.742040816 0.614965986 3 NM_004666 VNN1 0.979241338
0.996632997 0.972934473 0.663003663 0.648979592 0.710204082
0.575510204 322 NM_004666 VNN1 0.979241338 0.996632997 0.972934473
0.663003663 0.648979592 0.710204082 0.575510204 26 NM_006459 ERLIN1
0.964755078 0.994949495 0.953805454 0.694971695 0.561904762
0.639183673 0.594557823 344 NM_006459 ERLIN1 0.964755078
0.994949495 0.953805454 0.694971695 0.561904762 0.639183673
0.594557823 17 NM_207113 SLC37A3 0.954301075 0.99382716 0.93996744
0.582417582 0.619047619 0.653061224 0.551020408 335 NM_207113
SLC37A3 0.954301075 0.99382716 0.93996744 0.582417582 0.619047619
0.653061224 0.551020408 38 NM_004994 MMP9 0.935782557 0.993265993
0.914936915 0.625374625 0.653061224 0.653877551 0.48707483 356
NM_004994 MMP9 0.935782557 0.993265993 0.914936915 0.625374625
0.653061224 0.653877551 0.48707483 120 NM_004244 CD163 0.842293907
0.993265993 0.787545788 0.716949717 0.481632653 0.663673469
0.648979592 433 NM_004244 CD163 0.842293907 0.993265993 0.787545788
0.716949717 0.481632653 0.663673469 0.648979592 46 NM_006212 PFKFB2
0.922341697 0.992704826 0.896825397 0.678654679 0.51292517
0.679183673 0.68707483 363 NM_006212 PFKFB2 0.922341697 0.992704826
0.896825397 0.678654679 0.51292517 0.679183673 0.68707483
TABLE-US-00014 TABLE 14 Healthy versus ipSIRS Area Under Curve
(AUC) SEQ ID Database Gene HC vs. HC vs. HC vs. SIRS vs. Mild vs.
Mild vs. Severe vs. Number ID Name Sick AUC inSIRS AUC ipSIRS AUC
ipSIRS AUC Severe AUC Shock AUC Shock AUC 1 NM_003268 TLR5
0.962365591 0.873737374 0.994505495 0.808524809 0.606802721
0.768979592 0.672108844 320 NM_003268 TLR5 0.962365591 0.873737374
0.994505495 0.808524809 0.606802721 0.768979592 0.672108844 2
NM_020406 CD177 0.992980884 0.991582492 0.993487993 0.718281718
0.668027211 0.675102041 0.540136054 321 NM_020406 CD177 0.992980884
0.991582492 0.993487993 0.718281718 0.668027211 0.675102041
0.540136054 29 NM_020370 GPR84 0.974313023 0.92704826 0.991452991
0.738927739 0.496598639 0.608163265 0.623129252 347 NM_020370 GPR84
0.974313023 0.92704826 0.991452991 0.738927739 0.496598639
0.608163265 0.623129252 20 NM_153046 TDRD9 0.929062127 0.758136925
0.991045991 0.844488844 0.640816327 0.734693878 0.636734694 338
NM_153046 TDRD9 0.929062127 0.758136925 0.991045991 0.844488844
0.640816327 0.734693878 0.636734694 4 NM_016021 UBE2J1 0.959677419
0.888327722 0.985551486 0.826173826 0.468027211 0.644897959
0.682993197 323 NM_016021 UBE2J1 0.959677419 0.888327722
0.985551486 0.826173826 0.468027211 0.644897959 0.682993197 11
NR_045213 FCGR1A 0.954749104 0.87037037 0.985347985 0.77988678
0.612244898 0.492244898 0.621768707 NR_045213 FCGR1A 0.954749104
0.87037037 0.985347985 0.77988678 0.612244898 0.492244898
0.621768707 6 NM_002934 RNASE2 0.94937276 0.854657688 0.983719984
0.780552781 0.67755102 0.564081633 0.643537415 325 NM_002934 RNASE2
0.94937276 0.854657688 0.983719984 0.780552781 0.67755102
0.564081633 0.643537415 8 NM_004054 C3AR1 0.950119474 0.868125701
0.97985348 0.832833833 0.529251701 0.609795918 0.582312925 327
NM_004054 C3AR1 0.950119474 0.868125701 0.97985348 0.832833833
0.529251701 0.609795918 0.582312925 12 NM_145018 C11orf82
0.873058542 0.580246914 0.979242979 0.947718948 0.619047619
0.650612245 0.555102041 330 NM_145018 C11orf82 0.873058542
0.580246914 0.979242979 0.947718948 0.619047619 0.650612245
0.555102041 13 NM_018099 FAR2 0.942502987 0.843434343 0.978428978
0.794205794 0.561904762 0.746122449 0.693877551 331 NM_018099 FAR2
0.942502987 0.843434343 0.978428978 0.794205794 0.561904762
0.746122449 0.693877551
Example 5
Differential Expression of IRS Biomarkersmarkers Between Healthy,
INSIRS, Mild Sepsis, Severe Sepsis and Septic Shock
[0706] Presented below in FIGS. 13 to 331 are "Box and Whisker"
plots for each of the 319 biomarkers where the bottom and top of
the box are the first and third quartiles, and the band inside the
box is the second quartile (the median) (of gene expression).
Biomarkers are presented in order of ascending adjusted p value
when comparing "All Classes" (i.e., healthy control, referred to as
"Healthy" in FIGS. 13-331; inSIRS, referred to as "SIRS" in FIGS.
13-331; mild sepsis referred to as "Mild" in FIGS. 13-331; severe
sepsis, referred to as "Severe" in FIGS. 13-331; and septic shock,
referred to as "Shock" in FIGS. 13-331)--varying from 6.49E-48 to
1.00, and according to the following table (Table 15). Appropriate
choice and use of such markers can be used to select patients for
inclusion in, or exclusion from, clinical trials. Further, such
markers can be used to determine the efficacy of treatment,
therapies or management regimens in patients by determining whether
a patient has transitioned from one condition to another and by
determining the stage or degree of a particular condition. For
example, an exemplary clinical trial design testing for the
efficacy of an inotrope may include only those patients with shock
ipSIRS that are most likely to best respond to such a drug. In
addition, and following inclusion of such patients and treatment
with the inotrope, such patients could be monitored to determine
if, when, how quickly and to what degree they respond to the
inotrope by their transition from shock ipSIRS to other degrees of
ipSIRS, inSIRS or health. Similarly, a model clinical trial design
testing for the efficacy of an antibiotic, or combination of
antibiotics, may include only those patients with ipSIRS, and not
inSIRS, that are most likely to best respond to such a drug. In
addition, and following inclusion of such patients and treatment
with the antibiotic(s), such patients could be monitored to
determine if, when, how quickly and to what degree they respond to
the antibiotic(s) by their transition from ipSIRS to inSIRS or
health. Similarly, an exemplary clinical trial design testing for
the efficacy of an immune modulating drug (e.g. a steroid) may
include only those patients with known stages of ipSIRS, for
example those recovering from ipSIRS or those in the early stages
of ipSIRS. Following inclusion of such patients and treatment with
the immune modulating drug, such patients could be monitored to
determine if, when, how quickly and to what degree they respond to
the immune modulating drug by their transition from ipSIRS to
inSIRS or health. The biomarker response and outcome (e.g. reduced
length of hospital stay, reduced mortality) of patients in various
stages of ipSIRS (early, late) treated with an immune modulating
drug may also indicate when such a drug is best administered for
maximum benefit.
TABLE-US-00015 TABLE 15 Healthy versus inSIRS versus ipSIRS versus
Mild versus Severe versus Shock p Value and Area Under Curve (AUC)
SEQ ID Database Gene HC inSIRS Mild Severe Number ID Name Mean Mean
Mean Mean 1 NM_003268 TLR5 7.747 9.010 9.726 9.979 2 NM_020406
CD177 8.091 10.809 11.267 12.088 3 NM_004666 VNN1 7.736 10.013
10.007 10.629 4 NM_016021 UBE2J1 8.792 9.555 10.118 10.044 5
NM_018285 IMP3 7.951 6.465 7.032 6.934 6 NM_002934 RNASE2 9.164
10.500 11.243 11.670 7 NM_080387 CLEC4D 7.187 9.915 9.238 9.152 8
NM_004054 C3AR1 8.429 9.449 10.261 10.439 9 NM_001145772 GPR56
9.741 8.456 8.297 7.926 10 NM_001244438 ARG1 5.410 9.054 7.895
8.254 11 NR_045213 FCGR1A 7.871 9.281 10.308 9.985 12 NM_145018
C11orf82 5.776 5.888 6.889 7.153 13 NM_018099 FAR2 8.164 8.881
9.322 9.439 14 NM_006433 GNLY 10.653 9.428 9.305 8.659 15 NM_004482
GALNT3 5.685 6.251 6.916 6.728 16 NM_002544 OMG 4.756 5.187 5.644
5.799 17 NM_207113 SLC37A3 8.600 10.048 9.633 9.916 18 NM_203281
BMX 4.804 6.547 6.012 6.397 19 NM_004099 STOM 9.914 10.377 10.824
10.894 20 NM_153046 TDRD9 4.986 5.567 6.483 6.937 21 NM_032045
KREMEN1 8.626 9.409 10.143 10.189 22 NM_005449 FAIM3 10.259 9.101
9.036 9.174 23 NM_014358 CLEC4E 8.446 10.547 9.491 9.618 24
NM_003855 IL18R1 5.516 8.101 7.098 7.538 25 NM_018367 ACER3 7.317
7.845 8.701 8.417 26 NM_006459 ERLIN1 7.136 9.162 8.418 8.526 27
NM_004612 TGFBR1 9.328 9.614 10.165 9.999 28 NM_001145775 FKBP5
9.006 11.106 10.185 10.457 29 NM_020370 GPR84 6.712 8.157 9.030
8.980 30 NM_182597 C7orf53 6.397 6.878 7.266 7.760 31 NM_153021
PLB1 8.205 8.887 9.574 9.463 32 NM_013352 DSE 7.272 7.680 8.183
8.109 33 NM_000953 PTGDR 9.310 8.373 8.028 7.790 34 NM_001744 CAMK4
8.155 6.723 6.902 7.152 35 NM_015268 DNAJC13 7.507 8.083 8.596
8.693 36 NM_007115 TNFAIP6 7.738 9.246 9.829 9.631 37 NM_199135
FOXD4L3 6.441 6.501 7.402 7.610 38 NM_004994 MMP9 10.179 12.012
11.383 11.801 39 NM_000637 GSR 8.866 9.322 9.492 10.037 40
NM_016523 KLRF1 6.343 5.438 5.022 4.504 41 NM_053282 SH2D1B 8.067
6.992 6.896 6.451 42 NM_001004441 ANKRD34B 4.809 5.415 5.855 6.471
43 NM_001136258 SGMS2 6.693 7.708 7.553 7.712 44 NM_032047 B3GNT5
6.871 8.033 8.009 7.744 45 NR_026575 GK3P 4.227 5.729 5.316 5.552
46 NM_006212 PFKFB2 7.955 10.444 9.336 9.326 47 NM_007166 PICALM
9.079 9.433 9.822 9.993 48 NM_152637 METTL7B 6.693 7.153 7.628
7.927 49 NM_003542 HIST1H4C 11.803 9.795 10.815 10.617 50 NM_145005
C9orf72 8.262 8.742 9.312 9.073 51 NM_003533 HIST1H3I 10.878 9.003
10.144 10.141 52 NM_021082 SLC15A2 7.246 7.309 7.861 8.034 53
NM_030956 TLR10 6.794 6.842 7.713 7.823 54 NM_001124 ADM 8.676
8.896 9.739 9.441 55 NM_014143 CD274 5.508 5.656 7.557 7.211 56
NM_001311 CRIP1 8.880 6.932 7.984 7.844 57 NM_001099660 LRRN3 7.163
5.997 5.841 6.367 58 NM_002121 HLA-DPB1 11.414 10.665 10.623 9.971
59 NM_014232 VAMP2 9.213 8.896 9.016 8.454 60 NM_006714 SMPDL3A
6.243 6.701 7.288 7.563 61 NM_005531 IFI16 8.973 10.323 9.950 9.942
62 NM_016475 JKAMP 8.440 8.442 9.231 8.827 63 ENST00000371443
MRPL41 8.037 6.496 7.288 7.313 64 NM_004172 SLC1A3 5.849 6.892
6.472 6.447 65 NM_006418 OLFM4 6.365 7.209 8.023 9.641 66
NM_001164116 CASS4 8.124 7.848 7.410 7.217 67 ENST00000533734 TCN1
6.015 6.936 7.241 8.481 68 NM_018639 WSB2 8.870 8.808 9.618 9.714
69 ENST00000405140 CLU 9.016 9.264 9.889 10.137 70 NM_001163278
ODZ1 6.088 7.677 6.668 7.303 71 NM_002269 KPNA5 6.822 5.908 6.451
5.758 72 NM_001130715 PLAC8 10.873 10.024 11.434 11.500 73
NM_001257400 CD63 9.235 9.126 9.718 9.990 74 NM_006665 HPSE 8.103
8.173 9.127 9.174 75 NM_152367 C1orf161 5.851 6.354 6.479 6.738 76
ENST00000443533 DDAH2 8.067 8.170 8.630 8.707 77 NM_001199805 KLRK1
8.677 7.641 7.492 7.142 78 NM_024524 ATP13A3 7.668 7.763 8.429
8.547 79 NM_005546 ITK 9.271 8.099 8.227 8.536 80 NM_021127 PMAIP1
6.940 5.860 6.770 6.251 81 NR_046099 LOC284757 6.913 8.028 6.894
7.534 82 NM_002080 GOT2 6.854 5.823 6.017 6.378 83 NR_036641 PDGFC
6.098 6.117 6.987 7.044 84 NM_012200 B3GAT3 7.939 7.030 7.516 7.658
85 NM_003545 HIST1H4E 10.534 9.717 9.907 9.362 86 NM_000860 HPGD
5.621 6.238 7.085 6.908 87 NM_031950 FGFBP2 8.090 7.266 7.130 6.722
88 NM_181506 LRRC70 3.455 3.495 4.144 3.763 89 NM_018342 TMEM144
5.697 6.377 6.781 7.043 90 gi|21749325 CDS2 10.235 9.988 10.651
10.561 91 NM_001725 BPI 7.724 8.603 8.894 10.119 92 ENST00000379215
ECHDC3 7.486 8.705 7.801 7.715 93 NM_001837 CCR3 7.078 6.226 6.015
6.079 94 NM_014181 HSPC159 9.120 9.779 9.933 10.248 95 NM_018324
OLAH 4.483 6.220 5.483 6.162 96 NM_006243 PPP2R5A 8.141 9.000 8.646
8.931 97 NM_001193451 TMTC1 6.316 7.153 7.497 7.783 98 NM_001023570
EAF2 8.389 8.607 9.323 9.016 99 NM_001268 RCBTB2 8.646 8.621 9.321
9.357 100 NM_021982 SEC24A 7.847 7.914 8.266 8.571 101 NM_001017995
SH3PXD2B 5.800 6.949 6.262 6.384 102 NM_001130688 HMGB2 7.782 9.225
8.769 8.869 103 ENST00000381907 KLRD1 8.651 8.097 7.766 7.201 104
NM_001276 CHI3L1 10.470 9.876 8.640 9.035 105 NM_174938 FRMD3 6.218
6.408 6.959 6.889 106 NM_018375 SLC39A9 8.038 7.719 8.121 8.368 107
NM_153236 GIMAP7 9.533 8.865 8.974 8.682 108 NM_016476 ANAPC11
6.716 5.940 6.219 6.214 109 NM_019037 EXOSC4 8.216 8.199 8.716
8.845 110 gi|13182974 NA 7.793 8.895 8.712 8.321 111 NM_198336
INSIG1 8.081 7.370 8.062 7.867 112 ENST00000542161 FOLR3 7.767
8.283 8.505 9.059 113 NM_001024630 RUNX2 9.359 9.363 8.874 8.973
114 NM_018457 PRR13 8.919 9.881 9.380 9.204 115 NM_003546 HIST1H4L
9.807 7.908 9.466 9.602 116 NM_002305 LGALS1 10.393 10.059 10.730
10.741 117 NM_001295 CCR1 9.036 9.458 10.071 10.000 118 NM_003596
TPST1 8.526 10.334 9.295 9.600 119 NM_019111 HLA-DRA 11.467 10.758
10.870 10.441 120 NM_004244 CD163 6.823 8.730 7.652 7.643 121
NM_005306 FFAR2 9.559 9.849 10.479 10.309 122 NM_001143804 PHOSPHO1
11.398 10.826 10.374 9.837 123 NM_005729 PPIF 9.131 8.392 8.994
8.669 124 NM_001199760 MTHFS 8.177 9.060 8.644 8.530 125 NM_015190
DNAJC9 7.382 5.889 6.594 7.251 126 NM_005564 LCN2 7.729 8.168 8.921
10.113 127 ENST00000233057 EIF2AK2 7.237 8.670 8.347 8.159 128
NM_006498 LGALS2 6.920 6.085 6.594 6.235 129 NM_001199922 SIAE
6.721 6.530 7.174 7.284 130 NM_004644 AP3B2 5.979 6.181 6.485 6.665
131 NM_152701 ABCA13 5.814 6.131 6.688 7.350 132 gi|21757933 NA
7.336 7.963 7.065 7.340 133 NR_026586 EFCAB2 4.462 4.942 5.648
4.939 134 NM_170745 HIST1H2AA 6.310 6.872 6.483 6.652 135 NR_024610
HINT1 7.948 6.705 7.591 7.235 136 NM_003535 HIST1H3J 8.323 6.703
7.484 7.314 137 NM_001785 CDA 10.407 11.415 11.045 11.032 138
NM_003864 SAP30 9.022 9.883 9.258 9.251 139 NM_001040196 AGTRAP
10.055 9.933 10.594 10.356 140 NM_033050 SUCNR1 3.660 3.734 4.329
4.517 141 NM_002454 MTRR 8.163 7.862 8.523 8.796 142 NM_001168357
PLA2G7 6.797 6.685 5.928 5.753 143 NM_016108 AIG1 6.298 6.237 6.932
6.735 144 NM_013363 PCOLCE2 5.863 5.909 6.292 6.366 145 NM_080491
GAB2 9.471 10.280 9.787 9.810 146 NM_012262 HS2ST1 7.018 6.874
7.363 7.378 147 NM_003529 HIST1H3A 7.822 6.477 7.240 7.007 148
gi|21757754 C22orf37 8.047 7.525 8.170 7.898 149 ENST00000443117
HLA-DPA1 11.917 11.327 11.426 10.930 150 NM_030796 VOPP1 9.302
8.771 9.510 9.318 151 NM_001135147 SLC39A8 7.820 7.364 8.061 7.951
152 NM_002417 MKI67 5.979 5.822 6.140 6.894 153 NM_000578 SLC11A1
10.812 11.719 11.333 11.110 154 NM_001657 AREG 6.075 6.806 6.139
6.126 155 NM_005502 ABCA1 7.734 7.909 8.601 8.816 156 NM_001201427
DAAM2 6.591 8.217 7.135 7.167 157 NM_002343 LTF 8.098 8.330 8.923
9.937 158 NM_178174 TREML1 8.869 9.169 9.868 10.187 159 NM_004832
GSTO1 7.113 7.036 7.593 7.598 160 NM_000956 PTGER2 8.918 8.348
9.019 8.983 161 NM_001816 CEACAM8 7.336 7.874 8.503 9.775 162
NM_016184 CLEC4A 8.210 8.289 9.043 8.708 163 NR_002217 PMS2CL 7.598
6.664 7.289 7.057 164 NM_001193374 RETN 7.747 7.886 8.097 8.248 165
NM_000922 PDE3B 8.142 8.242 7.627 7.734 166 NM_018837 SULF2 9.831
9.704 9.064 9.248 167 NM_001145001 NEK6 9.503 9.287 9.835 9.710 168
NM_022145 CENPK 7.073 6.142 6.664 6.040 169 NM_145725 TRAF3 8.046
7.482 8.145 8.152 170 NM_003608 GPR65 9.085 9.452 9.834 9.216 171
NR_046000 IRF4 8.200 7.491 7.843 8.381 172 gi|42521648 MACF1 6.473
6.545 7.013 7.187 173 NM_001144 AMFR 9.420 8.994 9.671 9.823 174
NM_000985 RPL17 6.122 5.182 5.758 5.556 175 NM_003749 IRS2 8.965
9.531 8.841 8.998 176 NM_002230 JUP 8.165 7.803 7.812 7.804 177
NM_013230 CD24 5.563 5.793 6.173 7.205 178 NM_004481 GALNT2 8.541
8.534 8.896 8.980 179 NM_007355 HSP90AB1 9.881 8.635 9.085 9.720
180 NM_024656 GLT25D1 9.757 9.336 9.948 9.861 181 NM_001001658
OR9A2 4.207 4.662 4.825 4.991 182 NM_001178135 HDHD1A 8.039 7.927
8.296 8.300 183 NM_001141945 ACTA2 6.977 6.906 7.430 7.434 184
NM_152282 ACPL2 6.821 7.762 7.139 7.253 185 NM_001137550 LRRFIP1
6.512 6.814 6.827 7.037 186 NM_001161352 KCNMA1 5.664 6.042 6.492
6.317 187 gi|12584148 OCR1 8.817 9.952 8.387 8.844 188 NM_000885
ITGA4 8.779 7.932 8.394 7.981 189 NM_001412 EIF1AX 7.439 6.463
7.251 6.667 190 NM_176818 SFRS9 9.739 9.666 9.715 10.226 191
NM_206831 DPH3 6.211 6.602 6.923 6.606 192 NM_001031711 ERGIC1
9.539 10.203 9.742 9.565 193 NM_007261 CD300A 9.890 9.479 10.058
10.054 194 NM_001085386 NF-E4 7.348 8.202 7.715 8.545 195 NM_004897
MINPP1 8.212 7.697 8.228 7.151 196 NM_003141 TRIM21 8.072 8.151
8.840 8.371 197 NM_006969 ZNF28 4.936 4.511 5.008 4.554 198
gi|21538810 NPCDR1 5.404 5.022 4.784 5.166 199 gi|15530286 NA 9.276
9.224 8.804 8.909 200 gi|7021995 NA 7.607 8.002 6.917 7.075 201
NM_000201 ICAM1 8.842 8.625 9.470 9.156 202 NM_005645 TAF13 5.332
5.173 5.949 5.474 203 NM_000917 P4HA1 6.365 6.096 6.773 6.664 204
NM_207445 C15orf54 4.953 4.588 4.394 4.290 205 NM_002108 HAL 7.142
6.909 7.654 7.331 206 NM_015998 KLHL5 9.003 9.971 9.113 9.407 207
NR_002612 DLEU2 6.549 6.894 7.347 6.699 208 NM_015199 ANKRD28 7.286
7.393 7.898 7.722 209 ENST00000375864 LY6G5B 9.037 8.992 8.780
8.654 210 ENST00000344062 KIAA1257 6.868 7.365 6.882 6.874 211
NM_004528 MGST3 9.104 8.519 9.273 9.098 212 NM_015187 KIAA0746
8.174 7.591 8.170 8.747 213 NM_001540 HSPB1 9.140 8.923 9.664 9.580
214 NM_005508 CCR4 7.105 6.356 6.598 6.829 215 NM_001071 TYMS 6.084
5.695 6.186 6.854 216 ENST00000536831 RRP12 8.946 8.381 8.821 8.752
217 NM_176816 CCDC125 7.600 8.401 7.883 8.048 218 NM_003521
HIST1H2BM 10.104 9.242 10.213 10.837 219 NM_002612 PDK4 7.445 8.411
8.080 8.035 220 NM_207627 ABCG1 8.318 7.923 7.960 8.214 221
NM_000576 IL1B 9.070 9.172 10.021 9.550 222 NM_003246 THBS1 8.599
9.860 8.993 9.423 223 NM_000419 ITGA2B 8.899 8.768 9.482 9.747 224
NM_005780 LHFP 6.216 6.391 6.523 6.665 225 NM_002287 LAIR1 9.265
9.118 9.815 9.654 226 NM_003537 HIST1H3B 8.783 7.684 8.739 9.501
227 gi|29387167 ZRANB1 8.205 9.041 8.641 8.455 228 ENST00000525158
TIMM10 7.454 6.704 7.473 7.175 229 NM_207647 FSD1L 4.605 4.402
4.801 4.565 230 NM_021066 HIST1H2AJ 5.699 4.152 4.990 3.871 231
ENST00000362012 PTGS1 8.883 8.605 9.293 9.429 232 gi|14250459 NA
6.389 5.970 6.875 5.976 233 NM_080678 UBE2F 7.698 7.618 8.235 8.225
234 NM_001104595 FAM118A 8.366 7.706 7.946 8.222 235 NM_003531
HIST1H3C 9.254 7.630 9.025 9.427 236 NM_003965 CCRL2 6.401 6.488
6.982 6.754 237 NR_003094 E2F6 4.235 3.673 4.143 3.579 238
NM_198275 MPZL3 10.241 10.754 10.209 10.039 239 NM_080725 SRXN1
9.497 9.560 9.732 9.815 240 NM_004357 CD151 9.083 8.712 9.309 9.522
241 NM_003536 HIST1H3H 9.933 8.623 9.688 9.538
242 NM_031919 FSD1L 2.752 2.614 2.977 2.804 243 NM_001131065 RFESD
6.745 6.242 6.502 5.562 244 NM_012112 TPX2 5.722 5.535 5.917 6.431
245 NM_006272 S100B 5.289 4.661 4.917 4.533 246 NM_032828 ZNF587
8.514 8.816 8.783 8.381 247 NM_152501 PYHIN1 8.390 8.040 7.744
7.890 248 NM_020775 KIAA1324 9.383 9.433 8.708 9.010 249 NM_002483
CEACAM6 5.249 5.373 5.959 6.957 250 NM_001130415 APOLD1 7.318 6.858
7.205 7.363 251 NM_000134 FABP2 4.244 4.518 4.396 4.549 252
NM_001080424 KDM6B 9.854 10.383 10.147 9.749 253 ENST00000390265
IGK@ 10.393 9.201 10.307 11.107 254 NM_006097 MYL9 9.235 9.445
10.021 9.931 255 NM_021058 HIST1H2BJ 5.949 5.710 6.330 6.446 256
NM_138327 TAAR1 5.009 5.081 5.282 5.490 257 NM_001828 CLC 10.866
9.692 9.618 9.860 258 NM_001199208 CYP4F3 9.187 10.093 9.402 9.895
259 NM_024548 CEP97 6.566 6.717 6.887 7.228 260 NM_138927 SON 8.449
7.921 8.330 7.930 261 NM_002198 IRF1 10.490 10.067 10.764 10.511
262 NM_182914 SYNE2 7.764 8.208 7.453 7.752 263 NM_000902 MME 9.625
10.744 9.401 10.054 264 NM_024552 LASS4 8.222 7.808 7.961 8.050 265
NM_001925 DEFA4 7.421 7.383 8.289 9.454 266 NM_024913 C7orf58 7.727
6.800 7.706 7.617 267 ENST00000549649 DYNLL1 7.250 7.328 8.200
7.946 268 gi|38532374 NA 5.269 5.274 4.828 4.979 269 NM_000250 MPO
7.605 7.565 8.053 8.694 270 NM_001874 CPM 5.874 6.569 5.926 5.965
271 NM_173485 TSHZ2 7.382 6.972 6.846 7.429 272 NR_038064 PLIN2
8.210 8.534 8.470 8.589 273 NM_024556 FAM118B 7.287 7.256 7.846
7.892 274 NM_001199873 B4GALT3 9.539 8.790 9.278 9.265 275
NM_006989 RASA4 8.298 8.139 7.796 8.031 276 NM_001257971 CTSL1
6.074 5.925 6.577 6.106 277 NM_000270 NP 9.487 9.103 9.780 10.001
278 NM_001130059 ATF7 5.212 5.212 5.253 5.605 279 NM_003118 SPARC
9.083 9.207 9.737 9.964 280 NM_153021 PLB1 6.867 7.077 7.318 7.615
281 NM_001170330 C4orf3 7.478 7.240 7.324 7.402 282 NM_002692 POLE2
7.205 6.613 7.184 6.047 283 NM_001192 TNFRSF17 4.474 4.005 4.587
5.008 284 NM_145032 FBXL13 6.474 6.987 6.791 7.387 285 NM_019091
PLEKHA3 7.058 6.910 7.281 6.689 286 NM_024956 TMEM62 7.599 7.189
7.875 7.664 287 NM_052960 RBP7 7.270 7.808 7.218 7.267 288
NM_024613 PLEKHF2 7.432 7.660 8.044 7.255 289 NM_002923 RGS2 11.584
12.239 11.737 11.649 290 NM_004691 ATP6V0D1 11.562 11.584 11.951
11.656 291 NM_144563 RPIA 9.444 9.221 8.913 8.388 292 NM_020397
CAMK1D 9.001 9.118 8.603 8.572 293 NM_016232 IL1RL1 5.573 6.273
5.757 6.219 294 NM_138460 CMTM5 7.473 7.266 7.853 7.851 295
NM_004847 AIF1 8.167 8.310 8.735 8.045 296 NM_001928 CFD 10.389
9.779 9.631 9.496 297 NM_144765 MPZL2 7.015 7.320 6.845 6.660 298
gi|27884043 LOC100128751 8.552 8.877 8.333 8.552 299 NM_144646 IGJ
8.646 7.900 8.962 10.005 300 NM_139286 CDC26 7.968 7.634 8.113
7.886 301 NM_006241 PPP1R2 7.446 7.738 7.546 7.286 302 NM_000564
IL5RA 6.871 6.298 6.092 6.455 303 NM_001113738 ARL17P1 8.846 8.829
8.802 8.293 304 NR_033759 ATP5L 7.242 7.337 7.379 6.824 305
NM_176885 TAS2R31 6.228 5.589 5.847 6.020 306 NM_001024599
HIST2H2BF 8.854 9.615 9.386 9.211 307 NM_001743 CALM2 8.475 9.041
9.003 8.357 308 NM_019073 SPATA6 6.797 7.301 7.095 6.806 309
ENST00000390285 IGLV6-57 5.779 5.379 6.029 6.575 310 NM_020362
C1orf128 9.258 9.026 8.605 8.315 311 NM_181623 KRTAP15-1 6.250
6.690 6.548 6.617 312 NM_006417 IFI44 6.559 6.924 8.107 6.674 313
NM_001178126 IGL@ 7.052 6.606 7.027 7.343 314 gi|21707823 NA 4.903
5.476 4.955 4.639 315 NM_003001 SDHC 7.530 6.874 7.879 7.593 316
NM_152995 NFXL1 7.326 6.876 7.694 7.644 317 NM_000170 GLDC 5.599
5.395 5.679 5.770 318 NM_001199743 DCTN5 8.646 8.336 8.737 8.794
319 NM_014736 KIAA0101 4.443 3.951 4.537 4.749 pval pval pval pval
pval HC inSIRS Severe Shock Shock SEQ ID Shock vs vs vs vs vs
Number Mean Other Sepsis Mild Mild Severe 1 10.311 0.000000
0.000225 0.275728 0.000244 0.110996 2 12.044 0.000000 0.087061
0.048910 0.027926 0.991139 3 10.876 0.000000 1.000000 0.087388
0.002402 0.671136 4 10.335 0.000000 0.000015 0.817659 0.104460
0.049488 5 6.723 0.000000 0.000000 0.645365 0.004479 0.136619 6
11.388 0.000000 0.002979 0.095954 0.690976 0.351809 7 9.828
0.000000 0.383427 0.945300 0.034026 0.035853 8 10.593 0.000000
0.000016 0.650271 0.142678 0.725241 9 7.611 0.000000 0.009068
0.118387 0.000147 0.212773 10 8.919 0.000000 1.000000 0.628534
0.008931 0.209877 11 10.273 0.000000 0.001046 0.284201 0.980298
0.366022 12 7.293 0.000000 0.000000 0.322762 0.032568 0.722429 13
9.813 0.000000 0.000434 0.708180 0.000658 0.034215 14 8.408
0.000000 0.020098 0.014566 0.000045 0.511511 15 7.075 0.000000
0.000000 0.407446 0.422801 0.051201 16 6.063 0.000000 0.000000
0.486295 0.001484 0.125031 17 9.990 0.000000 1.000000 0.200617
0.035680 0.892137 18 6.839 0.000000 1.000000 0.251812 0.000431
0.163472 19 11.148 0.000000 0.000000 0.861884 0.017391 0.144729 20
7.385 0.000000 0.000000 0.248195 0.001153 0.259068 21 10.055
0.000000 0.000079 0.962640 0.837239 0.731337 22 8.464 0.000000
1.000000 0.732360 0.001453 0.000582 23 9.945 0.000000 0.000082
0.796454 0.024690 0.225126 24 8.097 0.000000 1.000000 0.373616
0.001873 0.205385 25 9.050 0.000000 0.000000 0.362961 0.132905
0.008450 26 8.855 0.000000 0.431436 0.880592 0.070482 0.316561 27
10.368 0.000000 0.000000 0.333550 0.115281 0.005971 28 10.750
0.000000 0.035769 0.441852 0.011707 0.389198 29 9.583 0.000000
0.001894 0.989573 0.184680 0.221197 30 7.532 0.000000 0.000080
0.007994 0.141534 0.338948 31 10.019 0.000000 0.000133 0.872838
0.059398 0.037699 32 8.384 0.000000 0.000062 0.830987 0.170622
0.085371 33 7.577 0.000000 0.007043 0.500548 0.040553 0.570947 34
6.470 0.000000 1.000000 0.305982 0.011176 0.000339 35 8.878
0.000000 0.000000 0.833718 0.133216 0.512817 36 9.738 0.000000
1.000000 0.712067 0.908467 0.905260 37 7.649 0.000000 0.000000
0.417434 0.196375 0.968937 38 11.830 0.000000 1.000000 0.196819
0.086637 0.992129 39 9.928 0.000000 0.000039 0.000432 0.001207
0.709854 40 4.543 0.000000 0.007278 0.033428 0.021421 0.979558 41
6.450 0.000000 0.652812 0.064249 0.026676 0.999993 42 6.738
0.000000 0.000000 0.074815 0.001252 0.606835 43 8.128 0.000000
1.000000 0.670176 0.001769 0.073073 44 8.548 0.000000 1.000000
0.438680 0.013149 0.000955 45 5.937 0.000000 1.000000 0.493837
0.002433 0.158402 46 10.196 0.000000 0.069041 0.999591 0.014557
0.037351 47 10.284 0.000000 0.000000 0.500534 0.001958 0.138243 48
8.037 0.000000 0.000000 0.315445 0.060594 0.854279 49 10.323
0.000000 0.000004 0.695152 0.055254 0.448113 50 9.243 0.000000
0.000039 0.180477 0.821921 0.417016 51 9.669 0.000000 0.000000
0.999875 0.029147 0.070614 52 8.227 0.000000 0.000000 0.350572
0.002949 0.273300 53 7.900 0.000000 0.000000 0.798408 0.423055
0.895083 54 9.674 0.000000 0.000001 0.091052 0.855508 0.225820 55
7.237 0.000000 0.000000 0.536662 0.490374 0.996684 56 7.526
0.000000 0.002476 0.799183 0.046887 0.320589 57 5.906 0.000000
1.000000 0.002710 0.877917 0.009767 58 9.578 0.000000 0.067333
0.026428 0.000013 0.253098 59 8.353 0.000000 0.297018 0.000084
0.000000 0.708038 60 8.227 0.000000 0.000000 0.609142 0.000879
0.060210 61 9.860 0.000000 0.020087 0.998663 0.799026 0.869508 62
9.150 0.000000 0.000000 0.000984 0.661905 0.010409 63 7.279
0.000000 0.000000 0.986016 0.997776 0.974877 64 7.373 0.000000
1.000000 0.993673 0.000056 0.000352 65 9.322 0.000000 0.000068
0.003150 0.006691 0.784808 66 7.047 0.000000 0.000000 0.428924
0.022397 0.521806 67 8.420 0.000000 0.000290 0.005182 0.001932
0.986095 68 9.657 0.000000 0.000000 0.761189 0.939475 0.910197 69
9.813 0.000000 0.000063 0.216377 0.819582 0.075588 70 7.416
0.000000 0.245652 0.024855 0.001421 0.884318 71 5.712 0.000000
1.000000 0.000021 0.000000 0.945115 72 11.772 1.000000 0.000000
0.950268 0.172935 0.420732 73 10.159 0.000000 0.000000 0.156468
0.002260 0.485665 74 8.937 0.000000 0.000000 0.958822 0.401504
0.346256 75 6.845 0.000000 0.417891 0.196267 0.015651 0.756446 76
9.015 0.000000 0.000000 0.868573 0.011535 0.108528 77 6.815
0.000000 1.000000 0.344640 0.006675 0.394595 78 8.804 0.000000
0.000000 0.777976 0.039725 0.310273 79 7.635 0.000000 1.000000
0.276607 0.002832 0.000063 80 6.214 0.000000 0.000000 0.002936
0.000187 0.968287 81 7.376 0.000001 0.000004 0.000039 0.000364
0.493398 82 6.047 0.000000 0.490985 0.040847 0.969911 0.066504 83
7.466 0.000000 0.000000 0.970637 0.064634 0.196970 84 7.632
0.000000 0.000000 0.387020 0.434149 0.966275 85 9.307 0.000000
1.000000 0.026325 0.003375 0.962707 86 7.946 0.000000 0.000024
0.895298 0.035905 0.027021 87 6.520 0.000000 0.976589 0.222869
0.013639 0.687542 88 4.135 0.000000 0.000000 0.020091 0.996732
0.024012 89 6.824 0.000000 0.914921 0.404820 0.968067 0.529328 90
10.570 0.041844 0.000000 0.584395 0.561379 0.994393 91 9.589
0.000000 0.026705 0.001145 0.046592 0.253799 92 8.165 0.000000
0.001975 0.857762 0.030361 0.018763 93 5.783 0.000000 1.000000
0.937302 0.322103 0.253166 94 10.249 0.000000 1.000000 0.218914
0.132537 0.999989 95 6.492 0.000000 1.000000 0.125688 0.003063
0.604976 96 8.952 0.000000 1.000000 0.109345 0.035683 0.988342 97
7.961 0.000000 0.543009 0.580949 0.154696 0.809916 98 9.301
0.000000 0.000012 0.105531 0.985106 0.141523 99 9.340 0.000000
0.000001 0.961397 0.985058 0.991536 100 8.641 0.000000 0.000000
0.094884 0.010201 0.880791 101 6.870 0.000000 1.000000 0.786615
0.000756 0.026622 102 8.910 0.000000 1.000000 0.894388 0.745151
0.981575 103 7.123 0.000000 0.011838 0.056985 0.008125 0.944270 104
8.726 0.000000 0.000056 0.485576 0.954853 0.641602 105 6.703
0.000000 0.000000 0.872556 0.096505 0.389383 106 8.428 1.000000
0.000000 0.034062 0.001276 0.808136 107 8.112 0.000000 1.000000
0.310769 0.000008 0.014285 108 6.368 0.000000 0.016248 0.998808
0.245788 0.323554 109 9.307 0.000000 0.000000 0.796042 0.002754
0.059157 110 8.114 0.000000 0.003518 0.039103 0.000094 0.391472 111
7.913 0.001237 0.000000 0.123875 0.197540 0.883915 112 9.163
0.000000 0.039807 0.074391 0.008772 0.909407 113 8.703 0.000000
0.000000 0.683914 0.223731 0.064525 114 9.375 0.000000 0.000085
0.390820 0.998964 0.411767 115 9.065 0.000032 0.000000 0.878290
0.231084 0.140998 116 10.938 0.246894 0.000000 0.995433 0.137821
0.261564 117 10.253 0.000000 0.009681 0.937695 0.574455 0.448234
118 9.760 0.000000 0.055822 0.518907 0.136190 0.833502 119 10.188
0.000000 1.000000 0.091925 0.000575 0.429491 120 8.413 0.000000
0.092636 0.999632 0.037288 0.077812 121 10.573 0.000000 0.002329
0.564897 0.791331 0.256398 122 10.185 0.000000 0.048380 0.088690
0.661703 0.354179 123 8.682 0.000000 0.001262 0.016029 0.006615
0.993122 124 8.897 0.000000 0.406481 0.678016 0.086391 0.022355 125
6.665 0.000000 0.000027 0.007512 0.921502 0.019348 126 10.092
0.000000 0.000002 0.028722 0.011016 0.998889 127 8.071 0.000000
0.263528 0.709245 0.379350 0.929139 128 6.142 0.000000 0.375827
0.045528 0.001960 0.806041 129 7.068 0.000300 0.000000 0.666202
0.603123 0.213378 130 7.116 0.000000 0.000003 0.646507 0.001477
0.071475 131 7.182 0.000000 0.000000 0.066387 0.130673 0.834277 132
7.275 1.000000 0.000000 0.034322 0.071492 0.818353 133 5.021
0.000000 1.000000 0.000756 0.000575 0.898330 134 6.820 0.000002
0.020011 0.118625 0.000044 0.124349 135 7.052 0.000000 0.041290
0.142709 0.003549 0.590766 136 7.135 0.000000 0.000274 0.715862
0.160141 0.691744 137 11.020 0.000000 0.043371 0.996584 0.982475
0.996794 138 9.295 0.000000 0.001328 0.998061 0.924872 0.921435 139
10.502 0.000787 0.000000 0.083006 0.602023 0.382046 140 4.334
0.000000 0.000000 0.487465 0.999396 0.504866 141 8.849 0.122045
0.000000 0.216837 0.057755 0.942570 142 5.816 0.000000 0.011194
0.534519 0.708535 0.921793 143 7.005 0.000001 0.000000 0.470358
0.871019 0.246707 144 6.785 0.000000 0.000000 0.925694 0.013235
0.089534 145 9.689 0.000000 0.000000 0.982341 0.635805 0.599323 146
7.465 0.001571 0.000000 0.990909 0.556564 0.723490 147 6.871
0.000000 0.000513 0.477800 0.090217 0.776827 148 7.869 0.152851
0.000000 0.021435 0.002302 0.954990 149 10.582 0.000000 1.000000
0.125876 0.000597 0.352649 150 9.517 1.000000 0.000000 0.298375
0.997162 0.269787 151 8.434 1.000000 0.000000 0.788039 0.031226
0.013434 152 6.463 0.105108 0.000000 0.000163 0.097385 0.045251 153
11.214 0.000000 0.000048 0.338648 0.659426 0.788341 154 6.310
0.001304 0.023771 0.980680 0.018616 0.030209 155 8.449 0.000000
0.000000 0.574359 0.692624 0.204976 156 7.607 0.000000 0.072069
0.992564 0.124949 0.254325 157 9.639 0.000000 0.000019 0.012909
0.052398 0.670808 158 9.876 0.000000 0.000638 0.449927 0.999311
0.468015 159 7.768 0.000017 0.000000 0.999247 0.345252 0.470840 160
9.315 1.000000 0.000000 0.970366 0.070654 0.081972 161 9.287
0.000000 0.001298 0.011921 0.098854 0.501991 162 9.051 0.000001
0.000030 0.153416 0.998827 0.141809 163 7.040 0.000000 0.000440
0.281314 0.146553 0.993528 164 8.835 0.000000 0.000109 0.767125
0.000526 0.022922 165 7.730 0.000586 0.000010 0.567468 0.494918
0.999337 166 8.696 0.000000 0.000000 0.721156 0.183689 0.059640 167
9.943 0.153083 0.000000 0.541334 0.546065 0.125355
168 6.012 0.000000 1.000000 0.003871 0.000401 0.988210 169 7.882
1.000000 0.000003 0.997488 0.011707 0.028412 170 9.665 0.000000
1.000000 0.000115 0.360571 0.006354 171 7.762 0.000105 0.000070
0.002770 0.825556 0.000501 172 7.377 0.000009 0.000481 0.609788
0.061186 0.553735 173 9.635 1.000000 0.000000 0.543847 0.954865
0.395265 174 5.513 0.000000 0.000708 0.298432 0.096378 0.944801 175
9.024 1.000000 0.000068 0.322209 0.133169 0.969809 176 7.467
0.000000 1.000000 0.997835 0.006010 0.023849 177 7.139 0.000000
0.044339 0.008714 0.004161 0.979397 178 9.333 0.000001 0.000000
0.887956 0.017521 0.130219 179 9.186 0.000000 0.052850 0.015572
0.861439 0.050094 180 10.201 1.000000 0.000001 0.826970 0.124352
0.061226 181 5.108 0.000000 1.000000 0.671976 0.216358 0.816042 182
8.508 0.156481 0.000026 0.999441 0.101404 0.187976 183 7.417
0.000137 0.000000 0.999465 0.993242 0.991119 184 7.209 0.000000
0.038264 0.736975 0.857652 0.955277 185 7.241 0.000015 1.000000
0.216163 0.000656 0.232230 186 7.025 0.000000 0.031350 0.804476
0.075606 0.033982 187 8.270 1.000000 0.000000 0.273663 0.890199
0.131897 188 7.906 0.000000 1.000000 0.064191 0.007047 0.909442 189
6.907 0.000022 0.033776 0.002586 0.056446 0.338019 190 10.128
1.000000 0.000618 0.000018 0.000064 0.627141 191 6.842 0.000000
1.000000 0.052319 0.764333 0.189405 192 9.579 0.014810 0.000006
0.364984 0.320097 0.993728 193 10.025 1.000000 0.000000 0.999363
0.929522 0.957280 194 7.934 0.000000 1.000000 0.002063 0.535657
0.030775 195 7.317 0.006705 1.000000 0.000005 0.000008 0.707929 196
8.271 0.002474 0.019577 0.006536 0.000092 0.782377 197 4.612
0.021118 0.027741 0.000020 0.000018 0.814925 198 4.817 0.000001
1.000000 0.000589 0.919120 0.001847 199 8.390 0.001392 0.000629
0.821276 0.021614 0.011283 200 6.999 0.480277 0.000000 0.721694
0.889525 0.926763 201 9.088 0.069361 0.000002 0.113961 0.015602
0.900519 202 5.798 0.054434 0.000001 0.004467 0.457202 0.072895 203
6.873 1.000000 0.000000 0.752172 0.731181 0.358637 204 4.370
0.000000 0.891418 0.669439 0.972296 0.787051 205 7.571 0.894890
0.000000 0.049449 0.758572 0.184215 206 9.083 0.009758 0.000000
0.315312 0.984513 0.248776 207 6.850 0.000171 1.000000 0.000019
0.000152 0.504529 208 7.941 0.000001 0.046283 0.544926 0.951892
0.391621 209 8.383 0.014059 0.001656 0.642377 0.004351 0.135693 210
6.689 1.000000 0.000000 0.997049 0.129430 0.242441 211 9.003
1.000000 0.000001 0.377410 0.050613 0.751799 212 8.502 1.000000
0.000002 0.015387 0.148674 0.453449 213 9.371 0.267148 0.000000
0.845749 0.072845 0.359260 214 6.321 0.000000 1.000000 0.321399
0.120202 0.005850 215 6.291 1.000000 0.000000 0.003309 0.816061
0.015734 216 8.721 0.000127 0.000063 0.794423 0.522969 0.952728 217
7.988 0.000029 0.000070 0.517640 0.700053 0.915690 218 10.261
1.000000 0.000000 0.055178 0.976895 0.083087 219 8.318 0.000000
1.000000 0.982556 0.521742 0.501879 220 7.791 0.000000 1.000000
0.112606 0.270961 0.003184 221 9.930 0.000002 0.124840 0.077098
0.874756 0.185371 222 9.387 0.000000 1.000000 0.179953 0.147689
0.987631 223 9.378 0.011285 0.000003 0.403390 0.825144 0.174155 224
6.778 0.000000 0.003992 0.475817 0.046096 0.625105 225 10.179
0.001517 0.000113 0.773401 0.179212 0.071167 226 8.852 1.000000
0.000000 0.042709 0.908040 0.098544 227 8.619 0.000016 0.001723
0.476723 0.985358 0.563742 228 6.921 0.000333 0.000431 0.308861
0.006345 0.426642 229 5.099 1.000000 0.000000 0.235142 0.050177
0.001077 230 4.070 0.000128 1.000000 0.008735 0.013543 0.852098 231
9.247 1.000000 0.000001 0.723148 0.952752 0.562936 232 6.059
1.000000 0.638753 0.000001 0.000000 0.869569 233 8.425 0.009739
0.003879 0.998629 0.495878 0.558822 234 7.802 0.000086 0.879292
0.116376 0.451548 0.008324 235 8.749 0.908521 0.000000 0.439119
0.594489 0.101186 236 7.040 0.000011 0.001007 0.386796 0.919567
0.226387 237 3.647 0.002951 1.000000 0.000262 0.000208 0.874090 238
10.276 1.000000 0.000000 0.418422 0.832036 0.186944 239 10.069
0.084230 0.126068 0.777086 0.005812 0.101705 240 9.473 1.000000
0.000001 0.408420 0.488688 0.954477 241 9.310 0.018867 0.000017
0.811822 0.177187 0.617435 242 3.257 0.713444 0.000000 0.455516
0.068219 0.006227 243 5.837 0.011176 1.000000 0.000486 0.005152
0.486482 244 6.063 1.000000 0.000000 0.009609 0.587740 0.085624 245
4.692 0.000031 1.000000 0.034824 0.206071 0.549376 246 8.101
1.000000 0.003242 0.018153 0.000001 0.136854 247 7.473 0.000001
1.000000 0.732308 0.247163 0.086327 248 8.061 0.053345 0.004214
0.630998 0.065841 0.013683 249 6.219 0.000014 0.000574 0.023214
0.699643 0.121152 250 7.253 0.906687 0.000002 0.263662 0.846643
0.520142 251 4.826 0.000004 1.000000 0.578201 0.004767 0.174039 252
9.701 1.000000 0.000332 0.072707 0.013999 0.962410 253 10.228
1.000000 0.000007 0.087003 0.967489 0.053514 254 9.763 0.000001
0.017414 0.905037 0.337602 0.704183 255 6.237 1.000000 0.000001
0.744162 0.779976 0.389990 256 5.498 0.000172 0.002470 0.224230
0.119996 0.998003 257 9.296 0.000000 1.000000 0.790640 0.575586
0.284504 258 9.361 0.001998 0.007361 0.085034 0.976056 0.056129 259
6.933 0.000121 1.000000 0.035314 0.917808 0.079681 260 8.146
0.000817 1.000000 0.003077 0.175728 0.168438 261 10.387 1.000000
0.000000 0.154204 0.005202 0.633454 262 7.322 1.000000 0.000000
0.184100 0.642671 0.032921 263 8.937 1.000000 0.000000 0.270540
0.411394 0.024808 264 8.099 0.000006 0.005941 0.629532 0.232135
0.868727 265 8.658 0.001307 0.000125 0.042189 0.642870 0.220174 266
7.307 0.136406 0.003979 0.899378 0.062794 0.276354 267 7.948
0.089409 0.021345 0.534724 0.441022 0.999958 268 4.814 0.188367
0.002418 0.307760 0.984538 0.242328 269 8.262 0.003309 0.000013
0.050366 0.644219 0.248933 270 6.153 0.263893 0.011698 0.965100
0.195596 0.424691 271 6.929 0.004682 1.000000 0.000051 0.739871
0.000534 272 8.747 0.000009 1.000000 0.661459 0.056585 0.485502 273
7.670 0.002863 0.002878 0.963051 0.489433 0.425727 274 9.060
0.000004 0.006105 0.996920 0.309691 0.456795 275 7.741 0.000237
0.794307 0.149393 0.868630 0.057952 276 6.472 0.556174 0.000261
0.038526 0.798163 0.134791 277 9.866 1.000000 0.000006 0.557046
0.887942 0.802758 278 5.372 1.000000 0.438013 0.000033 0.171523
0.007397 279 9.620 0.000424 0.113905 0.627252 0.847118 0.345974 280
7.539 0.001516 1.000000 0.250568 0.355397 0.913465 281 7.760
1.000000 0.245460 0.750146 0.000037 0.004098 282 6.359 0.156285
1.000000 0.000226 0.002251 0.494438 283 4.554 1.000000 0.000000
0.122703 0.982429 0.088234 284 6.914 0.000011 1.000000 0.027018
0.806772 0.098170 285 6.825 1.000000 1.000000 0.000001 0.000011
0.409707 286 7.625 1.000000 0.002245 0.254101 0.080350 0.953292 287
7.088 1.000000 0.000005 0.953122 0.639431 0.530295 288 7.671
0.789194 1.000000 0.000012 0.023331 0.029137 289 11.703 0.162049
0.000001 0.862749 0.969968 0.947024 290 11.692 0.064067 1.000000
0.011327 0.010003 0.932084 291 8.784 0.001733 0.264252 0.097918
0.824211 0.261542 292 8.527 0.163494 0.002054 0.980872 0.858346
0.960862 293 6.253 0.000028 1.000000 0.065034 0.016357 0.984841 294
7.716 1.000000 0.000000 0.999922 0.538365 0.635314 295 8.239
1.000000 1.000000 0.000120 0.001571 0.448676 296 9.702 0.000005
1.000000 0.830277 0.934225 0.650508 297 6.644 1.000000 0.000023
0.471520 0.309485 0.994504 298 8.283 1.000000 0.001039 0.155603
0.877005 0.063053 299 9.230 1.000000 0.000100 0.078833 0.793766
0.239952 300 8.065 1.000000 0.000005 0.095936 0.867921 0.226701 301
7.113 1.000000 0.001735 0.115620 0.000618 0.379268 302 5.954
0.000000 1.000000 0.186587 0.721925 0.044203 303 7.810 1.000000
1.000000 0.160174 0.000219 0.191966 304 6.772 1.000000 0.748516
0.000612 0.000014 0.929559 305 6.056 0.010741 0.032666 0.488873
0.253434 0.969967 306 8.840 0.014281 0.064555 0.679691 0.009289
0.184703 307 8.540 1.000000 1.000000 0.000906 0.007024 0.542344 308
7.221 0.000572 1.000000 0.238268 0.690136 0.055163 309 6.026
1.000000 0.000002 0.138073 0.999915 0.135136 310 8.523 0.000049
0.377958 0.469588 0.921733 0.676477 311 6.488 0.001154 1.000000
0.779960 0.773039 0.418346 312 6.408 1.000000 1.000000 0.007884
0.000183 0.836520 313 7.023 1.000000 0.000000 0.162844 0.999656
0.156176 314 4.576 1.000000 0.188292 0.144998 0.026471 0.921677 315
7.764 1.000000 0.000026 0.453417 0.841420 0.752519 316 7.620
1.000000 0.000002 0.977232 0.934372 0.994545 317 5.666 1.000000
0.000010 0.537664 0.982363 0.443725 318 8.672 1.000000 0.000532
0.865968 0.779044 0.519467 319 4.419 1.000000 0.000000 0.416052
0.694594 0.124264
Example 6
Ratios of IRS Biomarkersmarkers Between Healthy, INSIRS, Mild
Sepsis, Severe Sepsis and Septic Shock
[0707] Examples of the use of 2-gene ratios as a more informative
predictor of clinical condition than either of the two component
genes are presented in Tables 16, 17, 18, 19, 20 and 21. These
tables show instances of the prediction of Healthy and inSIRS
(Table 16), Healthy vs. ipSIRS (Table 17), inSIRS and ipSIRS (Table
18), Mild Sepsis vs. Vs Severe Sepsis (Table 19), Mild Sepsis Vs
Septic Shock (Table 20), and Severe Sepsis vs. Vs Septic Shock
(Table 21) using 2 genes and their ratios. Columns from left to
right are: name of the first component gene (Gene 1 Name), the
corresponding Area Under Curve for this gene (Gene 1 AUC), the
second component gene (Gene 2 Name), the corresponding AUC for this
gene (Gene 2 AUC), the AUC for this ratio (Ratio AUC), the
statistical significance using Delong's method (DeLong E R, DeLong
D M, Clarke-Pearson DL: Comparing the Areas under Two or More
Correlated Receiver Operating Characteristic Curves: A Non
parametric Approach. Biometrics 1988, 44:837-845) that the ratio is
a better predictor than Gene 1 (Ratio Signif to Gene 1), the
statistical significance using Delong's method that the ratio is a
better predictor than Genet (Ratio Signif to Gene 2). These tables
show results for which the ratio AUC is shown to be superior to
both of the component genes, and the improvement statistically
significant over both genes. Examples of less significant ratios,
or cases where the ratio is statistically superior to only one of
the component genes are not listed in these tables. Such ratios can
also be used in clinical trials in a similar fashion to that
described in Example 5.
TABLE-US-00016 Lengthy table referenced here
US20220243272A1-20220804-T00001 Please refer to the end of the
specification for access instructions.
TABLE-US-00017 Lengthy table referenced here
US20220243272A1-20220804-T00002 Please refer to the end of the
specification for access instructions.
TABLE-US-00018 Lengthy table referenced here
US20220243272A1-20220804-T00003 Please refer to the end of the
specification for access instructions.
TABLE-US-00019 Lengthy table referenced here
US20220243272A1-20220804-T00004 Please refer to the end of the
specification for access instructions.
TABLE-US-00020 Lengthy table referenced here
US20220243272A1-20220804-T00005 Please refer to the end of the
specification for access instructions.
TABLE-US-00021 Lengthy table referenced here
US20220243272A1-20220804-T00006 Please refer to the end of the
specification for access instructions.
[0708] Throughout this specification and claims which follow,
unless the context requires otherwise, the word "comprise", and
variations such as "comprises" or "comprising", will be understood
to imply the inclusion of a stated integer or group of integers or
steps but not the exclusion of any other integer or group of
integers.
[0709] Persons skilled in the art will appreciate that numerous
variations and modifications will become apparent. All such
variations and modifications, which become apparent to persons
skilled in the art, should be considered to fall within the spirit
and scope that the invention broadly appearing before
described.
TABLE-US-LTS-00001 LENGTHY TABLES The patent application contains a
lengthy table section. A copy of the table is available in
electronic form from the USPTO web site
(https://seqdata.uspto.gov/?pageRequest=docDetail&DocID=US20220243272A1).
An electronic copy of the table will also be available from the
USPTO upon request and payment of the fee set forth in 37 CFR
1.19(b)(3).
Sequence CWU 0 SQTB SEQUENCE LISTING The patent application
contains a lengthy "Sequence Listing" section. A copy of the
"Sequence Listing" is available in electronic form from the USPTO
web site
(https://seqdata.uspto.gov/?pageRequest=docDetail&DocID=US20220243272A1).
An electronic copy of the "Sequence Listing" will also be available
from the USPTO upon request and payment of the fee set forth in 37
CFR 1.19(b)(3).
0 SQTB SEQUENCE LISTING The patent application contains a lengthy
"Sequence Listing" section. A copy of the "Sequence Listing" is
available in electronic form from the USPTO web site
(https://seqdata.uspto.gov/?pageRequest=docDetail&DocID=US20220243272A1).
An electronic copy of the "Sequence Listing" will also be available
from the USPTO upon request and payment of the fee set forth in 37
CFR 1.19(b)(3).
* * * * *
References