U.S. patent application number 13/984257 was filed with the patent office on 2014-06-05 for microarray for detecting viable organisms.
This patent application is currently assigned to THE REGENTS OF THE UNIVERSITY OF CALIFORNIA. The applicant listed for this patent is Gary Aandersen, Todd DeSantis, Christina Dock, Kasthuri Venkateswaran. Invention is credited to Gary Aandersen, Todd DeSantis, Christina Dock, Kasthuri Venkateswaran.
Application Number | 20140155283 13/984257 |
Document ID | / |
Family ID | 46638982 |
Filed Date | 2014-06-05 |
United States Patent
Application |
20140155283 |
Kind Code |
A1 |
Venkateswaran; Kasthuri ; et
al. |
June 5, 2014 |
MICROARRAY FOR DETECTING VIABLE ORGANISMS
Abstract
A methodology of microarray using the fluorescent DNA
intercalating agent propidium monoazide (PMA) to selectively block
DNA of dead cells from amplification and its application in
detecting and enumerating viable microbes in complex microbial
communities is described. A phylogenetic array is used in the
preferred embodiment to enhance the sensitivity of the method. The
PMA-Microarray assay is particularly applicable for monitoring
samples from environments with extremely low microbial burden such
as spacecraft surfaces.
Inventors: |
Venkateswaran; Kasthuri;
(Arcadia, CA) ; Dock; Christina; (Pasadena,
CA) ; Aandersen; Gary; (Berkeley, CA) ;
DeSantis; Todd; (Livermore, CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Venkateswaran; Kasthuri
Dock; Christina
Aandersen; Gary
DeSantis; Todd |
Arcadia
Pasadena
Berkeley
Livermore |
CA
CA
CA
CA |
US
US
US
US |
|
|
Assignee: |
THE REGENTS OF THE UNIVERSITY OF
CALIFORNIA
Oakland
CA
|
Family ID: |
46638982 |
Appl. No.: |
13/984257 |
Filed: |
February 10, 2012 |
PCT Filed: |
February 10, 2012 |
PCT NO: |
PCT/US12/24724 |
371 Date: |
January 21, 2014 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
61441820 |
Feb 11, 2011 |
|
|
|
Current U.S.
Class: |
506/9 ;
506/16 |
Current CPC
Class: |
C12Q 1/6848 20130101;
C12Q 1/6848 20130101; C12Q 2563/173 20130101; C12Q 2525/186
20130101; C12Q 1/689 20130101 |
Class at
Publication: |
506/9 ;
506/16 |
International
Class: |
C12Q 1/68 20060101
C12Q001/68 |
Goverment Interests
STATEMENT OF GOVERNMENTAL SUPPORT
[0005] The invention claimed herein was made in the performance of
work under DOE and NASA contracts, and is subject to the provisions
of Public Law 96-517 (35 USC 202) in which the Contractor has
elected to retain title. This invention was made in part under
Contract DE-AC02-05CH11231 awarded by the Department of Energy and
under Contract Nos. NNH09ZDA001N and NAS7-1407 awarded by NASA. The
government has certain rights in this invention.
Claims
1. A method for detecting live cells in a sample comprising:
selectively amplifying a nucleic acid from live cells from a sample
comprising live and dead cells; and detecting the presence,
absence, relative abundance, and/or quantity of one or more
operational taxon units (OTUs) in the sample based on hybridization
of amplified nucleic acid to a plurality of probes complementary to
16s rRNA sequences, wherein said one or more OTUs consist
essentially of live cells from said sample.
2. The method of claim 1, wherein said nucleic acid is DNA.
3. The method of claim 1, wherein said selectively amplifying
comprises pre-treating the sample with an agent that selectively
modifies a nucleic acid of dead cells.
4. The method of claim 3, wherein the agent that selectively
modifies a nucleic acid of dead cells is a DNA intercalating
agent.
5. The method of claim 4, wherein said DNA intercalating agent is
propidium monoazide.
6. The method of claim 1, wherein said probes are used to detect
the presence, absence, relative abundance, and/or quantity of at
least 10,000 different OTUs in a single assay.
7. The method of claim 1, wherein said presence, absence, relative
abundance, and/or quantity is detected with a confidence level
greater than 95%.
8. The method of claim 1, further comprising quantifying the number
of live cells in the sample.
9. A method for detecting live cells in a sample comprising:
selectively amplifying a nucleic acid from live cells from a sample
comprising live and dead cells; and determining the presence,
absence, relative abundance, and/or quantity of at least 1,000
different OTUs in a single assay, wherein said OTUs consist
essentially of live cells from said sample.
10. A method for detecting live cells in a sample comprising: (a)
selectively amplifying a nucleic acid from live cells from a sample
comprising live and dead cells; (b) hybridizing amplified nucleic
acid to a plurality of probes; (c) determining hybridization signal
strength distributions for a plurality of different interrogation
probes, each of which is complementary to a section within one or
more highly conserved polynucleotides in one or more target OTUs;
(d) determining hybridization signal strength distributions for a
plurality of mismatch probes, wherein for each interrogation probe,
one or more different corresponding mismatch probes comprising one
or more nucleotide mismatches with said section within said one or
more highly conserved polynucleotides are included in the plurality
of mismatch probes; and (e) using the hybridization signal
strengths of the interrogation probes and mismatch probes to
determine the probability that the hybridization signal for the
different interrogation probes represents the presence, absence,
relative abundance, and/or quantity of said one or more OTUs,
wherein said one or more OTUs consist essentially of live cells
from said sample.
11. The method of claim 9 or 10, wherein said selectively
amplifying comprises pre-treating the sample with an agent that
selectively modifies a nucleic acid of dead cells.
12. The method of claim 11, wherein the agent that selectively
modifies a nucleic acid of dead cells is a DNA intercalating
agent.
13. The method of claim 12, wherein said DNA intercalating agent is
propidium monoazide.
14. The method of claim 9 or 10, wherein contacting said sample
with an agent that selectively modifies a nucleic acid of dead
cells in said sample is followed by exposure to visible light.
15. The method of claim 10, wherein said highly conserved
polynucleotides are selected from the group consisting of 16S rRNA
gene, 23S rRNA gene, 5S rRNA gene, 5.8S rRNA gene, 12S rRNA gene,
18S rRNA gene, 28S rRNA gene, gyrB gene, rpoB gene, fusA gene, recA
gene, coxl gene, nif13 gene, RNA molecules derived therefrom, and a
combination thereof.
16. The method of claim 10, wherein each interrogation probe has 4
or more corresponding mismatch probes in the plurality of mismatch
probes.
17. The method of claim 10, wherein said probes are used to detect
the presence, absence, relative abundance, and/or quantity of at
least 10,000 different OTUs in a single assay.
18. The method of claim 10, wherein said probes are attached to a
substrate.
19. The method of claim 18, wherein said substrate comprises a bead
or a microsphere.
20. The method of claim 18, wherein said substrate comprises glass,
plastic, or silicon
21. The method of claim 9 or 10, wherein said presence, absence,
relative abundance, and/or quantity is detected with a confidence
level greater than 95%.
22. The method of claim 9, further comprising quantifying the
number of live cells in the sample.
23. A kit for detecting live cells in a sample comprising: (a) an
agent that selectively modifies a nucleic acid of dead cells; (b) a
plurality of different interrogation probes, each of which is
complementary to a section within one or more highly conserved
polynucleotides in one or more target operational taxon units
(OTUs); and, (c) a plurality of mismatch probes, wherein for each
interrogation probe, one or more different corresponding mismatch
probes comprising one or more nucleotide mismatches with said
section within said one or more highly conserved polynucleotides
are included in the plurality of mismatch probes.
24. The kit of claim 23, wherein the agent that selectively
modifies a nucleic acid of dead cells is a DNA intercalating
agent.
25. The kit of claim 23, wherein said DNA intercalating agent is
propidium monoazide.
26. The kit of claim 23, wherein each interrogation probe has 4 or
more corresponding mismatch probes in the plurality of mismatch
probes.
27. The kit of claim 23, wherein said highly conserved
polynucleotides are selected from the group consisting of 16S rRNA
gene, 23S rRNA gene, 5S rRNA gene, 5.8S rRNA gene, 12S rRNA gene,
18S rRNA gene, 28S rRNA gene, gyrB gene, rpoB gene, fusA gene, recA
gene, coxl gene, nif13 gene, RNA molecules derived therefrom, and a
combination thereof.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application claims priority under 35 U.S.C.
.sctn.119(e) to U.S. Provisional Application No. 61/441,820 filed
Feb. 11, 2011, the content of which is herein expressly
incorporated by references in their entireties.
[0002] This application is related to U.S. Application Ser. No.
61/259,565 [Attorney Docket No. IB-2733P1], filed on Nov. 9, 2009;
U.S. Application Ser. No. 61/317,644 [Attorney Docket No.
IB-2733P2], filed on Mar. 25, 2010; U.S. Application Ser. No.
61/347,817 [Attorney Docket No. IB-2733P3], filed on May 24, 2010
and co-pending U.S. patent application Ser. No. 12/474,204, filed
on May 28, 2009, which are hereby incorporated by reference in
their entirety.
[0003] This application is related to the co-pending international
application having application number PCT/US2010/040106 [Attorney
Docket No. IB-2733PCT], filed on Jun. 25, 2010, which is
incorporated herein by reference.
[0004] This application is also related to the co-pending U.S.
patent application Ser. No. 13/152,213, filed on Jun. 2, 2011,
which is hereby incorporated by reference in its entirety.
BACKGROUND
[0006] There is a continuing need for the development of a
sensitive, rapid method that will detect and enumerate total viable
microorganisms. Culture-based plating methods, including the NASA
standard assay, have remained the "gold standard," even though such
methods are both laborious and time consuming. Nucleic-acid-based
amplification such as the polymerase chain reaction (PCR),
particularly real-time quantitative-PCR (qPCR), provides a
promising approach that allows rapid detection in a wide range of
applications. Nonetheless, PCR-based methods are rarely used for
process validation, because such methods cannot discriminate
between live and inactive microorganisms due to the high stability
of the DNA from dead bacterial cells. Detecting the number or kinds
of viable microorganisms is useful in a variety of
applications.
SUMMARY OF THE INVENTION
[0007] The present application provides a method of detecting and
enumerating low levels of microorganisms. The method combines
propidium monoazide (PMA) assay with PhyloChip microarray analysis,
allowing rapid, selective detection and enumeration of viable
microbes in microbial communities containing both live and dead
microorganisms.
[0008] In one aspect, the present application provides a method for
detecting live cells in a sample. In some embodiments, the method
comprises selectively amplifying a nucleic acid from live cells
from a sample comprising live and dead cells; and detecting the
presence, absence, relative abundance, and/or quantity of one or
more operational taxon units (OTUs) in the sample based on
hybridization of amplified nucleic acid to a plurality of probes
complementary to 16s rRNA sequences, wherein said one or more OTUs
consist essentially of viable organisms from said sample. In some
embodiments, the nucleic acid is DNA. In some embodiments,
selectively amplifying a nucleic acid comprises pre-treating the
sample with an agent that selectively modifies a nucleic acid of
dead cells. In some embodiments, the agent that selectively
modifies a nucleic acid of dead cells is a DNA intercalating agent,
such as propidium monoazide. In some embodiments, the probes are
used to detect the presence, absence, relative abundance, and/or
quantity of at least 10,000 different OTUs in a single assay. In
some embodiments, the presence, absence, relative abundance, and/or
quantity is detected with a confidence level greater than 95%. In
some embodiments, the method further comprises quantifying the
number of live cells in the sample.
[0009] In one aspect, the present application provides a method for
detecting viable organisms in a sample. In some embodiments, the
method comprises selectively amplifying a nucleic acid from live
cells from a sample comprising live and dead cells; and determining
the presence, absence, relative abundance, and/or quantity of at
least 1,000 different OTUs in a single assay, wherein said OTUs
consist essentially of viable organisms from said sample. In some
embodiments, selectively amplifying comprises pre-treating the
sample with an agent that selectively modifies a nucleic acid of
dead cells. In some embodiments, the agent that selectively
modifies a nucleic acid of dead cells is a DNA intercalating agent,
such as propidium monoazide. In some embodiments, contacting a
sample with an agent that selectively modifies a nucleic acid of
dead cells in the sample is followed by exposure to visible light.
In some embodiments, the presence, absence, relative abundance,
and/or quantity is detected with a confidence level greater than
95%. In some embodiments, the method further comprises quantifying
the number of live cells in the sample.
[0010] In one aspect, the present application provides a method for
detecting and optionally quantifying viable organisms in a sample.
In some embodiments, the method comprises (a) selectively
amplifying a nucleic acid from live cells from a sample comprising
live and dead cells; (b) hybridizing amplified nucleic acid to a
plurality of probes; (c) determining hybridization signal strength
distributions for a plurality of different interrogation probes,
each of which is complementary to a section within one or more
highly conserved polynucleotides in one or more target OTUs; (d)
determining hybridization signal strength distributions for a
plurality of mismatch probes, wherein for each interrogation probe,
one or more different corresponding mismatch probes comprising one
or more nucleotide mismatches with said section within said one or
more highly conserved polynucleotides are included in the plurality
of mismatch probes; and, (e) using the hybridization signal
strengths of the interrogation probes and mismatch probes to
determine the probability that the hybridization signal for the
different interrogation probes represents the presence, absence,
relative abundance, and/or quantity of said one or more OTUs,
wherein said one or more OTUs consist essentially of viable
organisms from said sample. In some embodiments, selectively
amplifying comprises pre-treating the sample with an agent that
selectively modifies a nucleic acid of dead cells. In some
embodiments, the agent that selectively modifies a nucleic acid of
dead cells is a DNA intercalating agent, such as propidium
monoazide. In some embodiments, contacting a sample with an agent
that selectively modifies a nucleic acid of dead cells in the
sample is followed by exposure to visible light. In some
embodiments, the highly conserved polynucleotides are selected from
the group consisting of 16S rRNA gene, 23S rRNA gene, 5S rRNA gene,
5.8S rRNA gene, 12S rRNA gene, 18S rRNA gene, 28S rRNA gene, gyrB
gene, rpoB gene, fusA gene, recA gene, coxl gene, nif13 gene, RNA
molecules derived therefrom, and a combination thereof. In some
embodiments, each interrogation probe has 4 or more corresponding
mismatch probes in the plurality of mismatch probes. In some
embodiments, the probes are used to detect the presence, absence,
relative abundance, and/or quantity of at least 10,000 different
OTUs in a single assay. In some embodiments, the probes are
attached to a substrate, such as a bead or a microsphere. In some
embodiments, the probes are attached to a substrate comprising
glass, plastic, or silicon. In some embodiments, the presence,
absence, relative abundance, and/or quantity is detected with a
confidence level greater than 95%.
[0011] In one aspect, the present application provides a kit for
detecting and optionally quantifying live cells in a sample. In
some embodiments, the kit comprises (a) an agent that selectively
modifies a nucleic acid of dead cells; (b) a plurality of different
interrogation probes, each of which is complementary to a section
within one or more highly conserved polynucleotides in one or more
target operational taxon units (OTUs); and, (c) a plurality of
mismatch probes, wherein for each interrogation probe, one or more
different corresponding mismatch probes comprising one or more
nucleotide mismatches with said section within said one or more
highly conserved polynucleotides are included in the plurality of
mismatch probes. In some embodiments, the agent that selectively
modifies a nucleic acid of dead cells is a DNA intercalating agent,
such as propidium monoazide. In some embodiments, each
interrogation probe has 4 or more corresponding mismatch probes in
the plurality of mismatch probes. In some embodiments, the highly
conserved polynucleotides are selected from the group consisting of
16S rRNA gene, 23S rRNA gene, 5S rRNA gene, 5.8S rRNA gene, 12S
rRNA gene, 18S rRNA gene, 28S rRNA gene, gyrB gene, rpoB gene, fusA
gene, recA gene, coxl gene, nif13 gene, RNA molecules derived
therefrom, and a combination thereof.
INCORPORATION BY REFERENCE
[0012] All publications, patents, and patent applications mentioned
in this specification are herein incorporated by reference to the
same extent as if each individual publication, patent, or patent
application was specifically and individually indicated to be
incorporated by reference.
BRIEF DESCRIPTION OF THE DRAWINGS
[0013] The novel features of the invention are set forth with
particularity in the appended claims. A better understanding of the
features and advantages of the present invention will be obtained
by reference to the following detailed description that sets forth
illustrative embodiments, in which the principles of the invention
are utilized, and the accompanying drawings of which:
[0014] FIG. 1 shows a schematic of the rapid measurement of
bacterial spores using PMA system by fluorescence method.
[0015] FIG. 2 shows a comprehensive flow chart depicting a
PMA-Microarray experimental process.
[0016] FIG. 3 shows a graph of real-time PCR using an E. coli DNA
control before and after treatment with PMA.
[0017] FIG. 4 shows a graph of B. pumilus SAFR-032 spores with
different treatments. Gene target for qPCR is gyrB.
[0018] FIG. 5 shows a graph of heat resistance of B. pumilus spores
at 90.degree. C. The D-values for plate and qPCR are 5 and 33 min,
respectively.
[0019] FIG. 6 shows a graph of UV inactivation curve of B. pumilus
spores. Curves are based on the averages of all results in
triplicate.
[0020] FIG. 7 illustrates an example of a suitable computer system
environment.
DETAILED DESCRIPTION
Definitions
[0021] As used herein, the term "oligonucleotide" refers to a
polynucleotide, usually single stranded, that is either a synthetic
polynucleotide or a naturally occurring polynucleotide. The length
of an oligonucleotide is generally governed by the particular role
thereof, such as, for example, probe, primer and the like. Various
techniques can be employed for preparing an oligonucleotide, for
instance, biological synthesis or chemical synthesis. A nucleic
acid disclosed herein will generally contain phosphodiester bonds,
although in some cases, as outlined below, nucleic acid analogs are
included that may have alternate backbones, comprising, for
example, phosphoramide (Beaucage, et al., Tetrahedron, 49(10):1925
(1993) and references therein; Letsinger, J. Org. Chem., 35:3800
(1970); Sprinzl, et al., Eur. J. Biochem., 81:579 (1977);
Letsinger, et al., Nucl. Acids Res., 14:3487 (1986); Sawai, et al.,
Chem. Lett., 805 (1984), Letsinger, et al., J. Am. Chem. Soc.,
110:4470 (1988); and Pauwels, et al., Chemica Scripta, 26:141
(1986)); phosphorothioate (Mag, et al, Nucleic Acids Res., 19:1437
(1991); and U.S. Pat. No. 5,644,048); phosphorodithioate (Briu, et
al., J. Am. Chem. Soc., 111:2321 (1989)); 0-methylphophoroamidite
linkages (see Eckstein, Oligonucleotides and Analogues: A Practical
Approach, Oxford University Press); and peptide nucleic acid
backbones and linkages (see Egholm, J. Am. Chem. Soc., 114:1895
(1992); Meier, et al., Chem. Int. Ed. Engl., 31:1008 (1992);
Nielsen, Nature, 365:566 (1993); Carlsson, et al., Nature, 380:207
(1996), all of which are incorporated by reference)). Other analog
nucleic acids include those with positive backbones (Denpcy, et
al., Proc. Natl. Acad. Sci. USA, 92:6097 (1995)); non-ionic
backbones (U.S. Pat. Nos. 5,386,023; 5,637,684; 5,602,240;
5,216,141; and 4,469,863; Kiedrowshi, et al., Angew. Chem. Intl.
Ed. English, 30:423 (1991); Letsinger, et al., J. Am. Chem. Soc.,
110:4470 (1988); Letsinger, et al., Nucleosides & Nucleotides,
13:1597 (1994); Chapters 2 and 3, ASC Symposium Series 580,
"Carbohydrate Modifications in Antisense Research", Ed. Y. S.
Sanghui and P. Dan Cook; Mesmaeker, et al., Bioorganic &
Medicinal Chem. Lett., 4:395 (1994); Jeffs, et al., J. Biomolecular
NMR, 34:17 (1994); Tetrahedron Lett., 37:743 (1996)); and
non-ribose backbones, including those described in U.S. Pat. Nos.
5,235,033 and 5,034,506, and Chapters 6 and 7, ASC Symposium Series
580, "Carbohydrate Modifications in Antisense Research", Ed. Y. S.
Sanghui and P. Dan Cook. Nucleic acids containing one or more
carbocyclic sugars are also included within the definition of
nucleic acids (see Jenkins, et al., Chem. Soc. Rev., (1995) pp.
169-176). Several nucleic acid analogs are described in Rawls, C
& E News, Jun. 2, 1997, page 35. All of these references are
hereby expressly incorporated by reference.
[0022] The nucleic acid may be DNA, RNA, or a hybrid and may
contain any combination of deoxyribo- and ribo-nucleotides, and any
combination of bases, including uracil, adenine, thymine, cytosine,
guanine, inosine, xanthanine, hypoxanthanine, isocytosine,
isoguanine, and base analogs such as nitropyrrole and nitroindole,
etc. Oligonucleotides can be synthesized by standard methods such
as those used in commercial automated nucleic acid synthesizers and
later attached to an array, bead or other suitable surface.
Alternatively, the oligonucleotides can be synthesized directly on
the assay surface using photolithographic or other techniques. In
some embodiments, linkers are used to attach the oligonucleotides
to an array surface or to beads.
[0023] As used herein, the terms "nucleic acid," "nucleic acid
molecule," and "polynucleotide" refer to a compound or composition
that is a polymeric nucleotide or nucleic acid polymer. The nucleic
acid molecule may be a natural compound or a synthetic compound.
The nucleic acid molecule can have from about 2 to 5,000,000 or
more nucleotides. The larger nucleic acid molecules are generally
found in the natural state. In an isolated state, the nucleic acid
molecule can have about 10 to 50,000 or more nucleotides, usually
about 100 to 20,000 nucleotides. It is thus obvious that isolation
of a nucleic acid molecule from the natural state often results in
fragmentation. It may be useful to fragment longer target nucleic
acid molecules, particularly RNA, prior to hybridization to reduce
competing intramolecular structures. Fragmentation can be achieved
chemically, enzymatically, or mechanically. Typically, when the
sample contains DNA, a nuclease such as deoxyribonuclease (DNase)
is employed to cleave the phosphodiester linkages. Nucleic acid
molecules, and fragments thereof, include, but are not limited to,
purified or unpurified forms of DNA (dsDNA and ssDNA) and RNA,
including tRNA, mRNA, rRNA, mitochondrial DNA and RNA, chloroplast
DNA and RNA, DNA/RNA hybrids, biological material or mixtures
thereof, genes, chromosomes, plasmids, cosmids, the genomes of
microorganisms, e.g., bacteria, yeasts, phage, chromosomes,
viruses, viroids, molds, fungi, or other higher organisms such as
plants, fish, birds, animals, humans, and the like. The
polynucleotide can be only a minor fraction of a complex mixture
such as a biological sample.
[0024] As used herein, the term "hybridize" refers to the process
by which single strands of polynucleotides form a double-stranded
structure through hydrogen bonding between the constituent bases.
The ability of two polynucleotides to hybridize with each other is
based on the degree of complementarity of the two polynucleotides,
which in turn is based on the fraction of matched complementary
nucleotide pairs. The more nucleotides in a given polynucleotide
that are complementary to another polynucleotide, the more
stringent the conditions can be for hybridization and the more
specific will be the binding between the two polynucleotides.
Increased stringency may be achieved by elevating the temperature,
increasing the ratio of co-solvents, lowering the salt
concentration, and combinations thereof.
[0025] As used herein, the terms "complementary," "complement," and
"complementary nucleic acid sequence" refer to the nucleic acid
strand that is related to the base sequence in another nucleic acid
strand by the Watson-Crick base-pairing rules. In general, two
polynucleotides are complementary when one polynucleotide can bind
another polynucleotide in an anti-parallel sense wherein the 3'-end
of each polynucleotide binds to the 5'-end of the other
polynucleotide and each A, T(U), G, and C of one polynucleotide is
then aligned with a T(U), A, C, and G, respectively, of the other
polynucleotide. Polynucleotides that comprise RNA bases can also
include complementary G/U or U/G basepairs. Two complementary
strands may comprise complementary regions comprising all or one or
more portions of one or both strands.
[0026] As used herein, the term "clustering tree" refers to a
hierarchical tree structure in which observations, such as
organisms, genes, and polynucleotides, are separated into one or
more clusters. The root node of a clustering tree consists of a
single cluster containing all observations, and the leaf nodes
correspond to individual observations. A clustering tree can be
constructed on the basis of a variety of characteristics of the
observations, such as sequences of the genes and morphological
traits of the organisms. Many techniques known in the art, e.g.
hierarchical clustering analysis, can be used to construct a
clustering tree. A non-limiting example of the clustering tree is a
phylogenetic, taxonomic or evolutionary tree.
[0027] As used herein, the terms "operational taxon unit," "OTU,"
"taxon," "hierarchical cluster," and "cluster" are used
interchangeably. An operational taxon unit (OTU) refers to a group
of one or more organisms that comprises a node in a clustering
tree. The level of a cluster is determined by its hierarchical
order. In some embodiments, an OTU is a group tentatively assumed
to be a valid taxon for purposes of phylogenetic analysis. In some
embodiments, an OTU is any of the extant taxonomic units under
study. In yet other embodiments, an OTU is given a name and a rank.
For example, an OTU can represent a domain, a sub-domain, a
kingdom, a sub-kingdom, a phylum, a sub-phylum, a class, a
sub-lass, an order, a sub-order, a family, a subfamily, a genus, a
subgenus, or a species. In some embodiments, OTUs can represent one
or more organisms from the kingdoms eubacteria, protista, or fungi
at any level of a hierarchal order. In some embodiments, an OTU
represents a prokaryotic or fungal order.
[0028] As used herein, the term "kmer" refers to a polynucleotide
of length k. In some embodiments, k is an integer from 1 to 1000.
In some embodiments, k is 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,
13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29,
30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46,
47, 48, 49, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 125, 150,
175, 200, 250, 300, 400, 500, 600, 700, 800, 900, or 1000.
[0029] As used herein, the term "perfect match probe" (PM probe)
refers to a kmer which is 100% complementary to at least a portion
of a highly conserved target gene or polynucleotide. The perfect
complementarity usually exists throughout the length of the probe.
Perfect probes, however, may have a segment or segments of perfect
complementarity that is/are flanked by leading or trailing
sequences lacking complementarity to the target gene or
polynucleotide.
[0030] As used herein, the term "mismatch probe" (MM probe) refers
a control probe that is identical to a corresponding PM probe at
all positions except for one, 2, 3, 4, 5, 6, 7, 8, 9 or 10
nucleotides of the PM probe. Typically, the non-identical position
or positions are located at or near the center of the PM probe. In
some embodiments, the mismatch probes are universal mismatch
probes, e.g., a collection of mismatch probes that have no more
than a set number of nucleotide variations or substitutions
compared to positive probes. For example, the universal mismatch
probes may differ in nucleotide sequence by no more than five
nucleotides compared to any one PM probe in the PM probe set. In
some embodiments, a MM probe is used adjacent to each test probe,
e.g., a PM probe targeting a bacterial 16S rRNA sequence, in the
array.
[0031] As used herein, the term "probe pair" refers to a PM probe
and a corresponding MM probe. In some embodiments, the PM probes
and the MM probes are scored in relation to each other during data
processing and statistic analysis. As used herein, the term "a
probe pair associated with an OTU" is defined as a pair of probes
consisting of an OTU-specific PM probe and its corresponding MM
probe.
[0032] As used herein, a "sample" is from any source, including,
but not limited to a biological sample, a gas sample, a fluid
sample, a solid sample, or any mixture thereof.
[0033] As used herein, a "microorganism" or "organism" includes,
but is not limited to, a virus, viroids, bacteria, archaea, fungi,
protozoa and the like.
[0034] The term "sensitivity" refers to a measure of the proportion
of actual positives which are correctly identified as such.
[0035] The term "specificity" refers to a measure of the proportion
of actual negatives which are correctly identified as such.
[0036] The term "confidence level" refers to the likelihood,
expressed as a percentage, that the results of a test are real and
repeatable, and not random. Confidence levels are used to indicate
the reliability of an estimate and can be calculated by a variety
of methods.
Live Cell Detection
[0037] In some embodiments, the methods comprise selectively
amplifying a nucleic acid from live cells from a sample comprising
live and dead cells. In some embodiments, the nucleic acid is DNA
and/or RNA. In some embodiments, selectively amplifying comprises
pre-treating the sample with an agent that selectively modifies a
nucleic acid of dead cells. Selective modification can be the
result of differences between the accessibility of DNA in live
cells and the DNA in and/or derived from dead cells to the
selective agent, such as an agent that is a cell membrane- and/or
cell wall-impermeant agent. In some embodiments, the DNA of dead
cells is more accessible the selective agent than the DNA of live
cells. Selective modification can comprise intercalation in the
nucleic acid. Exposure to an agent that selectively modifies a
nucleic acid of dead cells may be followed by exposure to a second
agent that further modifies the nucleic acid modified by the
selective agent. In some embodiments, the second agent is visible
light. In some embodiments, modified nucleic acid is selectively
removed prior to amplification. In some embodiments, modified
nucleic acid is present during amplification but is not efficiently
amplified. For example, unmodified DNA can be amplified at least
about 5-fold, 10-fold, 25-fold, 50-fold, 100-fold, 200-fold,
300-fold, 400-fold, 500-fold, 600-fold, 700-fold, 800-fold,
900-fold, 1000-fold, 2000-fold, 5000-fold, 10000-fold, 20000-fold,
50000-fold, 100000-fold, or more than modified DNA. In some
embodiments, the agent that selectively modifies a nucleic acid of
dead cells is a DNA intercalating agent. Examples of DNA
intercalating agents include, but are not limited to ethidium
compounds, such as ethidium monoazide, ethidium bromide, and
ethidium diazide; propidium compounds, such as propidium monozaide
and propidium iodide; and 7-Amino-actinomycin D.
[0038] DNA intercalating agents such as ethidium monoazide (EMA)
and propidium monoazide (PMA) can be used to selectively
distinguish between viable and dead bacterial cells, because they
selectively penetrate the membranes of dead cells. Once penetrated,
they intercalate the DNA and, upon photolysis using visible light,
produce stable DNA monoadducts, which are not efficiently amplified
by PCR, either through removal during or subsequent to the DNA
extraction process, or by serving as poor templates in the
amplification process. Therefore, once PMA is used to treat
bacterial cells prior to DNA extraction, only the DNA of viable
cells will be available for PCR amplification. In some embodiments,
excess amounts of the selective agent are removed before nucleic
acid extraction. Following selective amplification of nucleic acid
from live cells, amplified nucleic acid can be subjected to a
detection process, such as quantitative PCR or a probe-based
analysis. In some embodiments, amplified nucleic acids are
hybridized with a plurality of probes.
[0039] In some embodiments, detection of amplified nucleic acid
comprises detection of rRNA or DNA encoding rRNA, such as 16S rRNA.
Molecular methods based on detecting and analyzing 16S rRNA have
been developed to enable the identification of specific
microorganism in a mixed bacterial population. Among such methods,
DNA microarray analysis is significantly more sensitive and robust
than traditional rRNA gene sequencing methods. A 16S rRNA-based
phylogenetic microarray ("PhyloChip") allows detailed measurements
of microbial community composition in a high-throughput and
reproducible manner. Phylogenetic microarrays and methods of making
and using the same are described in U.S. Application Ser. No.
61/259,565 [Attorney Docket No. IB-2733P1], filed on Nov. 9, 2009;
U.S. Application Ser. No. 61/317,644 [Attorney Docket No.
IB-2733P2], filed on Mar. 25, 2010; U.S. Application Ser. No.
61/347,817 [Attorney Docket No. IB-2733P3], filed on May 24, 2010;
and co-pending U.S. patent application Ser. No. 12/474,204, filed
on May 28, 2009, and co-pending international application having
application number PCT/US2010/040106 [Attorney Docket No.
IB-2733PCT], filed on Jun. 25, 2010, all of which are hereby
incorporated by reference in their entirety for all purposes.
[0040] Features of phylogenetic microarrays disclosed herein
include the use of multiple oligonucleotide probes for every known
category of prokaryotic organisms for high-confidence detection,
and the pairing of at least one mismatch probe for every perfectly
matched probe to minimize the effect of nonspecific hybridization.
In some embodiments, each perfect match probe corresponds to 1, 2,
3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, or
more mismatch probes. These and other features, alone or in
combination as described herein, make arrays of the methods and
systems disclosed herein extremely sensitive, allowing
identification of very low levels of microorganisms.
Biosignatures
[0041] In one aspect, the present application utilizes a
biosignature of OTUs. As used herein, the term "biosignature"
refers to an association of the level of one or more members of one
or more OTUs with a particular condition or state, such as a
classification, diagnosis, prognosis, and/or predicted outcome of a
disease condition in a subject. In some embodiments, the
biosignature comprises a determination of the presence, absence,
relative abundance, and/or quantity of at least 1, 2, 3, 4, 5, 10,
20, 50, 100, 250, 500, 1000, 5000, 10,000, 20,000, 30,000, 40,000,
50,000, 60,000, 70,000, 80,000, 90,000, 100,000, 250,000, 500,000
or 1,000,000 OTUs in a sample using a single assay. In some
embodiments, the biosignature comprises the presence of or changes
in the level of at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 30,
40, 50, 75, 100, 125, 150, 175, 200, 250, 300, or more OTUs. In
some embodiments, the OTUs consist essentially of viable organisms
from a sample.
[0042] In some embodiments, the biosignature is associated with a
single condition. In some embodiments, the biosignature is
associated with a combination of conditions. A biosignature can be
obtained for any sample, including but not limited to: tissue
samples; cell culture samples; bacterial culture samples; samples
obtained from a subject, including biopsies, body fluids and other
excreted material; pulmonary samples; environmental samples; other
samples as described herein; materials derived therefrom; and
combinations thereof. In some embodiments, a biosignature of a test
sample is compared to a known biosignature, and a determination is
made as to likelihood that the biosignatures are the same. The
biosignature to which the biosignature of the test sample is
compared can be determined before, after, or at substantially the
same time as that of the test sample. Biosignatures can be the
result of one or more analyses of one or more samples from a
particular source. In some embodiments, a biosignature is
indicative of a response to treatment. In some embodiments, a
biosignature is used as a basis for the selection of a mode of
treatment.
[0043] In some embodiments, the biosignature of a test sample is a
combination of two or more independent biosignatures, such as 2, 3,
4, 5, 6, 7, 8, 9, 10 or more independent biosignatures. In some
embodiments, each of the two or more biosignatures contained in a
sample are assayed simultaneously. In a further embodiment, a
subset of biosignatures can be evaluated through the use of
low-density detection systems, comprising the determination of the
presence, absence, relative abundance, and/or quantity of no more
than 10, 25, 50, 100, 250, 500, 1000, 2000, or 5000 OTUs.
[0044] In some embodiments, a biosignature comprises a measure of
the number of members, such as the number of live members, in one
or more bacterial families or OTUs. The number of members can range
from 0 to 10000 or more, such as 0 to 5000, 0 to 2500, 0 to 1000, 0
to 2000, 0 to 1000, 0 to 900, 0 to 800, 0 to 700, 0 to 600, 0 to
500, 0 to 400, 0 to 300, 0 to 200, 0 to 100, 0 to 50, 0 to 25, 0 to
20, 0 to 10, or 0 to 5. In some embodiments, a biosignature
comprises the presence of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,
13, 14, 15, 16, 17, 18, 19, 20, 30, 40, 50, 60, 70, 80, 90, 100,
200, 300, 400, 500, 600, 700, 800, 900, 1000, 2000, 2500, 5000,
10000, or more members of one or more bacterial families or OTUs,
or the presence of a range that includes any two of these values as
end points. In some embodiments, a biosignature comprises a ratio
between numbers of members in two or more bacterial families or
OTUs. The numerator and denominator of such ratios can include
overlapping sets of bacterial families or OTUs. Ratios of the
numbers of members in two or more bacterial families can compare a
first set of one or more bacterial families or OTUs to a second set
of one or more bacterial families or OTUs, where there is at least
one bacterial family or OTU difference between the first and second
set. A set of bacterial families or OTUs can include 1, 2, 3, 4, 5,
6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 40,
50, 60, 70, 80, 90, 100, or more OTUs.
[0045] In one aspect, the present application provides methods,
systems, and compositions for detecting and identifying a plurality
of biomolecules and/or organisms in a sample. The present
application utilizes the ability to differentiate between
individual organisms or OTUs, as well as, in some embodiments,
between live and dead members of OTUs. In one aspect, the
individual organisms or OTUs are identified using organism-specific
and/or OTU-specific probes, e.g., oligonucleotide probes. More
specifically, some embodiments relate to selecting
organism-specific and/or OTU-specific oligonucleotide probes useful
in detecting and identifying biomolecules and organisms in a
sample. In some embodiments, an oligonucleotide probe is selected
on the basis of the cross-hybridization pattern of the
oligonucleotide probe to regions within a target oligonucleotide
and its homologs in a plurality of organisms. The homologs can have
nucleotide sequences that are at least 80%, 85%, 90%, 91%, 92%,
93%, 94%, 95%, 96%, 97%, 98%, 99% or 99.5% identical. Such
oligonucleotides can be gene, or intergenetic sequences, in whole
or a portion thereof. The oligonucleotides can range from 10 to
over 10,000 nucleotides in length. In some other embodiments, a
method is provided for detecting the presence of an OTU in a sample
based at least partly on the cross-hybridization of the
OTU-specific oligonucleotide probes to probes specific for other
organisms or OTUs. In some embodiments, the biosignature to which a
sample biosignature is compared comprises a positive result for the
presence of the targets for one or more probes.
[0046] In one aspect, the present application provides a diagnostic
system for the determination or evaluation of a biosignature of a
sample. In some embodiments, the diagnostic system comprises at
least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 30, 40, 50, 75, 100,
125, 150, 175, 200, 250, 300, or more probes. In some embodiments,
the diagnostic system comprises up to 1, 2, 3, 4, 5, 6, 7, 8, 9,
10, 15, 20, 30, 40, 50, 75, 100, 125, 150, 175, 200, 250, 300, or
more probes.
High Capacity Systems
[0047] In one aspect of the present application, a high capacity
system is provided for determining a biosignature of a sample by
assessing the total microorganism population of a sample in terms
of the microorganisms present and optionally their percent
composition of the total population. In some embodiments, the
microorganisms so determined consist essentially of viable
organisms from a sample. In some embodiments, the system comprises
a plurality of probes that are capable of determining the presence
or quantity of at least 1000, 5000, 10000, 20000, 30000, 40000,
50000, 60000, or more different OTUs in a single assay. Typically,
the probes selectively hybridize to a highly conserved
polynucleotide. Usually, the probes hybridize to the same highly
conserved polynucleotide or within a portion thereof. Generally,
the highly conserved polynucleotide or fragment thereof comprises a
gene or fragment thereof. Non-limiting examples of highly conserved
polynucleotides comprise nucleotide sequences found in the 16S rRNA
gene, 23S rRNA gene, 5S rRNA gene, 5.8S rRNA gene, 12S rRNA gene,
18S rRNA gene, 28S rRNA gene, gyrB gene, rpoB gene, fusA gene, recA
gene, coxl gene and/or nifD gene. In other embodiments, 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, 15 or more, 20
or more, 25 or more, or 50 or more collections of probes are
employed, each of which specifically hybridizes to a different
highly conserved polynucleotide or portion thereof. For example, a
first collection of probes binds to the same region of the 16S rRNA
gene, a second collection of probes binds to the same region of the
16S rRNA gene that is different from the region bound by probes in
the first collection, and a third collection of probes binds to the
same region of the 23S rRNA gene. The use of two or more
collections of probes where each collection recognizes distinct and
separate highly conserved polynucleotides or portions thereof
allows for the generation and testing of more probes the use of
which can provide greater discrimination between species or
OTUs.
[0048] Highly conserved polynucleotides usually show at least 80%,
85%, 90%, 92%, 94%, 95%, or 97% homology across a domain, kingdom,
phylum, class, order, family or genus, respectively. The sequences
of these polynucleotides can be used for determining evolutionary
lineage or making a phylogenetic determination and are also known
as phylogenetic markers. In some embodiments, a biosignature
comprises the presence, absence, and/or abundance of a combination
of phylogenetice markers. The OTUs detected by the probes disclosed
herein can be bacterial, archeal, fungal, or eukaryotic in origin.
Additionally, the methodologies disclosed herein can be used to
quantify OTUs that are bacterial, archaeal, fungal, or eukaryotic.
By combining the various probe sets, a system for the detection of
bacteria, archaea, fungi, eukaryotes, or combinations thereof can
be designed. Such a universal microorganism test that is conducted
as a single assay can provide great benefit for assessing and
understanding the composition and ecology of numerous environments,
including characterization of biosignatures for various samples,
environments, conditions, and contaminants.
[0049] In another aspect of the present application, a system is
provided that is capable of determining the probability of presence
and optionally quantity of at least 10000, 20000, 30000, 40000,
50000, 60000, or more different OTUs of a single domain in a single
assay. In some embodiments, such a system makes a probability
determination with a confidence level greater than 90%, 91%, 92%,
93%, 94%, 95%, 99% or 99.5%. In some embodiments, a biosignature
can comprise the combined result of each probability
determination.
[0050] Some embodiments provide a method of selecting an
oligonucleotide probe that is specific for a node in a clustering
tree. In some embodiments, the method comprises selecting a highly
conserved target polynucleotide and its homologs for a plurality of
organisms; clustering the polynucleotides and homologs of the
plurality of organisms into a clustering tree; and determining a
cross-hybridization pattern of a candidate oligonucleotide probe
that hybridizes to a first polynucleotide to each node on the
clustering tree. This determination is performed (e.g., in silico)
to determine the likelihood that the probe would cross hybridize
with homologs of its target complementary sequence. The candidate
oligonucleotide probe can be complementary to a highly conserved
target polynucleotide, a fragment of the highly conserved target or
one of its homologs in one of the plurality of organisms. In some
embodiments, a method is provided for the determination of the
cross-hybridization pattern of a variant of the candidate
oligonucleotide probe to each node on the clustering tree, wherein
the variant corresponds to the candidate oligonucleotide probe but
comprises at least 1 nucleotide mismatch; and selecting or
rejecting the candidate oligonucleotide probe on the basis of the
cross-hybridization pattern of the candidate oligonucleotide probe
and the cross-hybridization pattern of the variant. In some
embodiments, the node is an operational taxon unit (OTU). In some
embodiments, the node is a single organism.
[0051] Some embodiments provide a method of selecting an
OTU-specific oligonucleotide probe for use in detecting a plurality
of organisms in a sample. In some embodiments, the method
comprises: selecting a highly conserved target polynucleotide and
its homologs from the plurality of organisms; clustering the
polynucleotides of the target gene and its homologs from the
plurality of organisms into one or more operational taxonomic units
(OTUs), wherein each OTU comprises one or more groups of similar
nucleotide sequence; determining the cross-hybridization pattern of
a candidate OTU-specific oligonucleotide probe to the OTUs, wherein
the candidate OTU-specific oligonucleotide probe corresponds to a
fragment of the target gene or its homolog from one of the
plurality of organisms; determining the cross-hybridization pattern
of a variant of the candidate OTU-specific oligonucleotide probe to
the OTUs, wherein the variant comprises at least 1 nucleotide
mismatch from the candidate OTU-specific oligonucleotide probe; and
selecting or rejecting the candidate OTU-specific oligonucleotide
probe on the basis of the cross-hybridization pattern of the
candidate OTU-specific oligonucleotide probe and the
cross-hybridization pattern of the variant. In some embodiments,
the candidate OTU-specific oligonucleotide probe is selected if the
candidate OTU-specific oligonucleotide probe does not
cross-hybridize with any polynucleotide that is complementary to
probes from other OTUs. In further embodiments, the candidate
OTU-specific oligonucleotide probe is selected if the candidate
OTU-specific oligonucleotide probe cross-hybridizes with the
polynucleotide in no more than 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,
12, 13, 14, 15, 20, 30, 40, 50, 100, 200, 500, or 1000 other OTU
groups.
[0052] Some embodiments provide a method of selecting a set of
organism-specific oligonucleotide probes for use in detecting a
plurality of organisms in a sample. In some embodiments, the method
comprises: identifying a highly conserved target polynucleotide and
its homologs in the plurality of organisms; determining the
cross-hybridization pattern of a candidate organism-specific
oligonucleotide probe to the sequences of the highly conserved
target polynucleotide and its homologs in the plurality of
organisms, wherein the candidate oligonucleotide probe corresponds
to a fragment of the target sequence or its homolog from one of the
plurality of organisms; determining the cross-hybridization pattern
of a variant of the candidate organism-specific oligonucleotide
probe to the sequences of the highly conserved target sequence and
its homologs in the plurality of organisms, wherein the variant
comprises at least 1 nucleotide mismatch from the candidate
organism-specific oligonucleotide probe; and selecting or rejecting
the candidate organism-specific oligonucleotide probe on the basis
of the cross-hybridization pattern of the candidate
organism-specific oligonucleotide probe and the cross-hybridization
pattern of the variant of the candidate organism-specific
oligonucleotide probe.
[0053] In some embodiments, an OTU-specific oligonucleotide probe
does not cross-hybridize with any polynucleotide that is
complementary to probes from other OTUs. In other embodiments, an
OTU-specific oligonucleotide probe cross-hybridizes with the
polynucleotide in no more than 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,
12, 13, 14, 15, 20, 30, 40, 50, 100, 200, 500, or 1000 other OTU
groups. Some embodiments utilize a set of organism-specific
oligonucleotide probes for use in detecting a plurality of
organisms in a sample. In further embodiments, the candidate
organism-specific oligonucleotide probe is selected if the
candidate organism-specific oligonucleotide probe only hybridizes
with the target nucleic acid molecule of no more than 1, 2, 3, 4,
5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 20, 30, 40, 50 unique
organisms in the plurality of organisms. In other embodiments, the
process is iterative with multiple candidate specific-specific
oligonucleotide probes selected. Frequently, the selected
organism-specific oligonucleotide probes are clustered and aligned
into groups of similar sequences that allow for the detection of an
organism with high confidence based on no more than 1, 2, 3, 4, 5,
6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23,
24, 25, 26, 27, 28, 29, 30, 35, 40, 50, or 60 organism-specific
oligonucleotide probe matches per OTU. Generally, the candidate
organism that the organism-specific oligonucleotide probes detect
corresponds to a leaf or node of at least one phylogenetic,
genealogic, evolutionary, or taxonomic tree. Knowledge of the
position that a candidate organism detected by the
organism-specific oligonucleotide probe occupies on a tree provides
relational information of the organism to other members of its
domain, phylum, class, subclass, order, family, subfamily, or
genus.
[0054] In some embodiments, the method disclosed herein selects
and/or utilizes a set of organism-specific oligonucleotide probes
that are a hierarchical set of oligonucleotide probes that can be
used to detect and differentiate a plurality of organisms. In some
embodiments, the method selects and/or utilizes organism-specific
or OTU-specific oligonucleotide probes that allow a comprehensive
screen for at least 80%, 85%, 90%, 95%, 99% or 100% of all known
bacterial or archaeal taxa in a single analysis, and thus provides
an enhanced detection of different desired taxonomic groups. In
some embodiments, the identity of all known bacterial or archaeal
taxa comprises taxa that were previously identified by the use of
oligonucleotide specific probes, PCR cloning, and sequencing
methods. Some embodiments provide methods of selecting and/or
utilizing a set of oligonucleotide probes capable of correctly
categorizing mixed target nucleic acid molecules into their proper
operational taxonomic unit (OTU) designations. Such methods can
provide comprehensive prokaryotic or eukaryotic identification, and
thus comprehensive biosignature characterization.
[0055] In some embodiments, the selected OTU-specific
oligonucleotide probe is used to calculate the relative abundance
of one or more organisms that belong to a specific OTU at differing
levels of taxonomic identification. In some embodiments, an array
or collection of microparticles comprising at least one
organism-specific or OTU-specific oligonucleotide probe selected by
the method disclosed herein is provided to infer specific microbial
community activities. For example, the identity of individual taxa
in a microbial consortium from an anaerobic environment for
instance, a marsh, can be determined along with their relative
abundance. If the consortium is suspected of harboring
microorganisms capable of butanol fermentation, then after
providing a suitable feedstock in an anaerobic environment if the
production of butanol is noted, then those taxa responsible for
butanol fermentation can be inferred by the microorganisms that
have abundant quantities of 16S rRNA. The present application
provides methods to measure taxa abundance based on the detection
of directly labeled 16S rRNA.
[0056] In some embodiments, multiple probes are selected for
increasing the confidence level and/or sensitivity level of
identification of a particular organism or OTU. The use of multiple
probes can greatly increase the confidence level of a match to a
particular organism. In some embodiments, the selected
organism-specific oligonucleotide probes are clustered and aligned
into groups of similar sequence such that detection of an organism
is based on 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,
17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 35 or more
oligonucleotide probe matches. In some embodiments, the
oligonucleotide probes are specific for a species. In other
embodiments, the oligonucleotide probe recognizes related organisms
such as organisms in the same subgenus, genus, subfamily, family,
sub-order, order, sub-class, class, sub-phylum, phylum,
sub-kingdom, or kingdom.
[0057] Perfect match (PM) probes are perfectly complementary to the
target polynucleotide, e.g., a sequence that identifies a
particular organism or OTU. In some embodiments, a system comprises
mismatch (MM) control probes. Usually, MM probes are otherwise
identical to PM probes, but differ by one or more nucleotides.
Probes with one or more mismatch can be used to indicate
non-specific binding and a possible non-match to the target
sequence. In some embodiments, the MM probes have one mismatch
located in the center of the probe, e.g., in position 13 for a
25mer probe. The MM probe is scored in relation to its
corresponding PM probe as a "probe pair." MM probes can be used to
estimate the background hybridization, thereby reducing the
occurrence of false positive results due to non-specific
hybridization, a significant problem with many current detection
systems. If an array is used, such as an Affymetrix high density
probe array or Illumina bead array, ideally, the MM probe is
positioned adjacent or close to its corresponding PM probe on the
array. Sample PM and MM probes are provided as SEQ ID NOs:
1-50.
[0058] In general, each MM probe differs from its corresponding PM
probe by at least one nucleotide. In some embodiments, the MM probe
differs from its corresponding PM probe by 1, 2, 3, 4, 5, 6, 7, 8,
9 or 10 nucleotides. Within a MM probe, the mismatched nucleotide
or nucleotides can include any of the 3 central bases that are not
found in the same position or positions in the PM probe. For
example, with a 25mer PM probe that has a guanine at the 13.sup.th
position, i.e., the central nucleotide, the MM probes comprise
probes with adenine, thymine, uracil or cytosine at the 13.sup.th
position. Similarly, with a 25mer PM probe with an adenine at the
12.sup.th nucleotide position and a guanine at the 13.sup.th
nucleotide position when read from the 3' direction, the possible
MM probes comprise probes with guanine at the 12.sup.th nucleotide
and adenine, thymine or cytosine at the 13.sup.th nucleotide
position; cytosine at the 12.sup.th nucleotide position and
adenine, thymine or cytosine at the 13.sup.th nucleotide position;
and thymine at the 12.sup.th nucleotide position and adenine,
thymine or cytosine at the 13.sup.th nucleotide position. In some
embodiments, the mismatched nucleotide or nucleotides include any
one or more of the nucleotides in a corresponding PM probe.
Increasing the number of MM probes and/or the mis-match positions
represented can be used to enhance quantification, accuracy, and
confidence.
[0059] Some embodiments relate to a method of selecting and/or
utilizing a set of oligonucleotide probes that enable simultaneous
identification of multiple prokaryotic taxa with a relatively high
confidence level. Typically, the confidence level of identification
is at least 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or
99.5%. In general, an OTU refers to an individual species or group
of highly related species that share an average of at least 80%,
85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% 99.5%, or
more sequence homology in a highly conserved region. Multiple MM
probes can be utilized to enhance the quantification and confidence
of the measure. In some embodiments, each interrogation probe (e.g.
PM probe) of a plurality of interrogation probes has from about 1
to about 20 or more corresponding mismatch control probes, such as
from 2 to 20, 3 to 20, 4 to 20, 5 to 20, 6 to 20, 7 to 20, 8 to 20,
9 to 20, 10 to 20, 11 to 20, 12 to 20, 13 to 20, 14 to 20, 15 to
20, 16 to 20, 17 to 20, 18 to 20, or 19 to 20 corresponding
mismatch control probes. In further embodiments, each interrogation
probe has from about 1 to about 10, about 1 to about 5, about 1 to
4, 1 to 3, 2 or 1 corresponding mismatch probes. In some
embodiments, each interrogation probe has about 1, 2, 3, 4, 5, 6,
7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, or more
corresponding mismatch probes. These interrogation probes target
unique regions within a target nucleic acid sequence, e.g., a 16S
rRNA gene, and provide the means for identifying at least about 10,
20, 50, 100, 500, 1000, 2000, 5000, 10000, 20000, 30000, 40000,
50000, 60000, 70000, 80000, 90000, 100000, 250000, 500000, or
1000000 taxa. In some embodiments, multiple targets can be
simultaneously assayed or detected in a single assay through a
high-density oligonucleotide probe system. The sum of all target
hybridizations is used to identify specific prokaryotic taxa. The
result is a more efficient and less time consuming method of
identifying unculturable or unknown organisms. The present
application can also provide results that could not previously be
achieved, e.g., providing results in hours where other methods
would require days. In some embodiments, a microbiome (i.e.,
sample) can be assayed to determine the identity and optionally the
abundance of its constituent microorganisms in less than 20, 19,
18, 17, 16, 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, or 1
hour.
[0060] In some embodiments, the set of OTU-specific oligonucleotide
probes comprises from about 1 to about 500 probes for each
taxonomic group. In some embodiments, the probes are proteins
including antibodies, or nucleic acid molecules including
oligonucleotides or fragments thereof. In some embodiments, an
oligonucleotide probe corresponds to a nucleotide fragment of the
target nucleic acid molecule. In some embodiments, from about 1 to
about 500, about 2 to about 200, about 5 to about 150, about 8 to
about 100, about 10 to about 35, or about 12 to about 30
oligonucleotide probes can be designed for each taxonomic grouping.
In other embodiments, a taxonomic group can have at least 1, 2, 3,
4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21,
22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 40, or more
probes. In some embodiments, various taxonomic groups can have
different numbers of probes, while in other embodiments, all
taxonomic groups have a fixed number of probes per group. Multiple
probes in a taxonomic group can provide additional data that can be
used to make a determination, also known as "making a call" as to
whether an OTU is present or not. Multiple probes also allow for
the removal of one or more probes from the analysis based on
insufficient signal strength, cross hybridization or other
anomalies. Removing probes can increase the confidence level of
results and further allow for the detection of low abundant
microorganisms. The oligonucleotide probes can each be from about 5
to about 100 nucleotides, from about 10 to about 50 nucleotides,
from about 15 to about 35 nucleotides, or from about 20 to about 30
nucleotides. In some embodiments, the probes are at least 5-mers,
6-mers, 7-mers, 8-mers, 9-mers, 10-mers, 11-mers, 12-mers, 13-mers,
14-mers, 15-mers, 16-mers, 17-mers, 18-mers, 19-mers, 20-mers,
21-mers, 22-mers, 23-mers, 24-mers, 25-mers, 26-mers, 27-mers,
28-mers, 29-mers, 30-mers, 31-mers, 32-mers, 33-mers, 34-mers,
35-mers, 36-mers, 37-mers, 38-mers, 39-mers, 40-mers, 41-mers,
42-mers, 43-mers, 44-mers, 45-mers, 46-mers, 47-mers, 48-mers,
49-mers, 50-mers, 51-mers, 52-mers, 53-mers, 54-mers, 55-mers,
56-mers, 57-mers, 58-mers, 59-mers, 60-mers, 61-mers, 62-mers,
63-mers, 64-mers, 65-mers, 66-mers, 67-mers, 68-mers, 69-mers,
70-mers, 71-mers, 72-mers, 73-mers, 74-mers, 75-mers, 76-mers,
77-mers, 78-mers, 79-mers, 80-mers, 81-mers, 82-mers, 83-mers,
84-mers, 85-mers, 86-mers, 87-mers, 88-mers, 89-mers, 90-mers,
91-mers, 92-mers, 93-mers, 94-mers, 95-mers, 96-mers, 97-mers,
98-mers, 99-mers, 100-mers or combinations thereof.
[0061] Some embodiments provide methods of selecting multiple,
confirmatory, organism-specific or OTU-specific probes to increase
the confidence of detection. In some embodiments, the methods also
select one or more mismatch (MM) probes for every perfect match
(PM) probe to minimize the effect of cross-hybridization by
non-target regions. The organism-specific and OTU-specific
oligonucleotide probes selected by the methods disclosed herein can
simultaneously identify thousands of taxa present in an
environmental sample and allow accurate identification of
microorganisms and their phylogenetic relationships in a community
of interest. Systems that use the organism-specific and
OTU-specific oligonucleotide probes selected by the methods
disclosed herein and the computational analysis disclosed herein
have numerous advantages over rRNA gene sequencing techniques. Such
advantages include reduced cost per microbiome analysis, and
increased processing speed per sample or microbiome from both the
physical analysis and the computational analysis point of view. In
general, the analysis procedures are not adversely affected by
chimeras, are not subject to creating artificial phylotypes, and
are not subject to barcode PCR bias. Additionally, quantitative
standards can be run with a microbiome sample disclosed herein.
[0062] Some embodiments provide a method for selecting and/or
utilizing a set of OTU- or organism-specific oligonucleotide probes
for use in an analysis system or bead multiplex system for
simultaneously detecting a plurality of organisms in a sample. The
method targets known diversity within target nucleic acid molecules
to determine microbial community composition and establish a
biosignature. The target nucleic acid molecule is typically a
highly conserved polynucleotide. In some embodiments, the highly
conserved polynucleotide is from a highly conserved gene, whereas
in other embodiments the polynucleotide is from a highly conserved
region of a gene with moderate or large sequence variation. In
further embodiments, the highly conserved region can be an intron,
exon, or a linking section of nucleic acid that separates two
genes. In some embodiments, the highly conserved polynucleotide is
from a "phylogenetic" gene. Phylogenetic genes include, but are not
limited to, the 5.8S rRNA gene, 12S rRNA gene, 16S rRNA
gene-prokaryotic, 16S rRNA gene-mitochondrial, 18S rRNA gene, 23S
rRNA gene, 28S rRNA gene, gyrB gene, rpoB gene, fusA gene, recA
gene, coxl gene, and the nifD gene. With eukaryotes, the rRNA gene
can be nuclear, mitochondrial, or both. In some embodiments, the
16S-23S rRNA gene internal transcribed spacer (ITS) can be used for
differentiation of closely related taxa with or without the use of
other rRNA genes. For example, rRNA, e.g., 16S or 23S rRNA, acts
directly in the protein assembly machinery as a functional molecule
rather than having its genetic code translated into protein. Due to
structural constraints of 16S rRNA, specific regions throughout the
gene have a highly conserved polynucleotide sequence; although,
non-structural segments can have a high degree of variability.
Probing the regions of high variability can be used to identify
OTUs that represent a single species level, while regions of less
variability can be used to identify OTUs that represent a subgenus,
a genus, a subfamily, a family, a sub-order, an order, a sub-class,
a class, a sub-phylum, a phylum, a sub-kingdom, or a kingdom. The
methods disclosed herein can be used to select organism-specific
and OTU-specific oligonucleotide probes that offer a high level of
specificity for the identification of specific organisms, OTUs
representing specific organisms, or OTUs representing specific
taxonomic group of organisms. The systems and methods disclosed
herein are particularly useful in identifying closely related
microorganisms and OTUs from a background or pool of closely
related organisms.
[0063] The probes selected and/or utilized by the methodologies
disclosed herein can be organized into OTUs that provide an assay
with a sensitivity and/or specificity of more than 80%, 85%, 90%,
91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99%. In some embodiments,
sensitivity and specificity depends on the hybridization signal
strength, number of probes in the OTU, the number of potential
cross hybridization reactions, the signal strength of the mismatch
probes, if present, background noise, or combinations thereof. In
some embodiments, an OTU containing one probe can provide an assay
with a sensitivity and specificity of at least 90%, while another
OTU can require at least 20 probes to provide an assay with
sensitivity and specificity of at least 90%.
[0064] Some embodiments relate to methods for phylogenetic analysis
system design and signal processing and interpretation for use in
detecting and identifying a plurality of biomolecules and organisms
in a sample. More specifically, some embodiments relate to a method
of selecting a set of organism-specific oligonucleotide probes for
use in detecting a plurality of organisms in a sample with a high
confidence level. Some embodiments relate to a method of selecting
a set of OTU-specific oligonucleotide probes for use in detecting a
plurality of organisms in a sample with a high confidence
level.
[0065] In the case of highly conserved polynucleotides like 16S
rRNA that can have only one to a few nucleotides of sequence
variability over any 15- to 30-bp region targeted by probes for
discrimination between related microbial species, it is
advantageous to maximize the probe-target sequence specificity in
an assay system. Some embodiments provide methods of selecting
organism-specific oligonucleotide probes that effectively minimize
the influence of cross-hybridization. In some embodiments, the
method comprises: (a) identifying sequences of a target nucleic
acid molecule corresponding to the plurality of organisms; (b)
determining the cross-hybridization pattern of a candidate
organism-specific oligonucleotide probe to the target nucleic acid
molecule from the plurality of organisms, wherein the candidate
oligonucleotide probe corresponds to a sequence fragment of the
target nucleic acid molecule from the plurality of organisms; (c)
determining the cross-hybridization pattern of a variant of the
candidate organism-specific oligonucleotide probe to the target
nucleic acid molecule from the plurality of organisms, wherein the
variant of the candidate organism-specific oligonucleotide probe
comprises at least 1 nucleotide mismatch compared to the candidate
organism-specific oligonucleotide probe; and (d) selecting or
rejecting the candidate organism-specific oligonucleotide probe on
the basis of the cross-hybridization pattern of the candidate
organism-specific oligonucleotide probe and the cross-hybridization
pattern of the variant of the candidate organism-specific
oligonucleotide probe. In some embodiments, a method of selecting a
set of OTU-specific oligonucleotide probes for use in detecting a
plurality of organisms in a sample is provided. In some
embodiments, the method comprises: (a) identifying sequences of a
target nucleic acid molecule corresponding to the plurality of
organisms; (b) clustering the sequences of the target nucleic acid
molecule from the plurality of organisms into one or more
Operational Taxonomic Units (OTUs), wherein each OTU comprises one
or more groups of similar sequences; (c) determining the
cross-hybridization pattern of a candidate OTU-specific
oligonucleotide probe to the OTUs, wherein the candidate
OTU-specific oligonucleotide probe corresponds to a sequence
fragment of the target nucleic acid molecule from one of the
plurality of organisms; (d) determining the cross-hybridization
pattern of a variant of the candidate OTU-specific oligonucleotide
probe to the OTUs, wherein the variant of the candidate
OTU-specific oligonucleotide probe comprises at least 1 nucleotide
mismatch compared to the candidate OTU-specific oligonucleotide
probe; and (e) selecting or rejecting the candidate OTU-specific
oligonucleotide probe on the basis of the cross-hybridization
pattern of the candidate OTU-specific oligonucleotide probe to the
OTUs and the cross-hybridization pattern of the variant of the
candidate OTU-specific oligonucleotide probe to the OTUs. In some
embodiments, candidate OTU-specific oligonucleotide probe are
rejected when the candidate OTU-specific oligonucleotide probe or
its variant are predicted to cross-hybridize with other target
sequences. In some embodiments, a predetermined amount of predicted
cross-hybridization is allowed.
[0066] In some embodiments, selected oligonucleotide probes are
synthesized by any relevant method known in the art. Some examples
of suitable methods include printing with fine-pointed pins onto
glass slides, photolithography using pre-made masks,
photolithography using dynamic micromirror devices, ink jet
printing, or electrochemistry. In one example, a photolithographic
method can be used to directly synthesize the chosen
oligonucleotide probes onto a surface. Suitable examples for the
surface include glass, plastic, silicon and any other surface
available in the art. In certain examples, the oligonucleotide
probes can be synthesized on a glass surface at an approximate
density from about 1,000 probes per .mu.m.sup.2 to about 100,000
probes per .mu.m.sup.2, preferably from about 2000 probes per
.mu.m.sup.2 to about 50,000 probes per .mu.m.sup.2, more preferably
from about 5000 probes per .mu.m.sup.2 to about 20,000 probes per
.mu.m.sup.2. In one example, the density of the probes is about
10,000 probes per .mu.m.sup.2. The number of probes on the array
can be quite large e.g., at least 10.sup.5, 10.sup.6, 10.sup.7,
10.sup.8 or 10.sup.9 probes per array. Usually, for large arrays
only a relatively small proportion (i.e., less than about 1%, 0.1%
0.01%, 0.001%, 0.00001%, 0.000001% or 0.0000001%) of the total
number of probes of a given length target an individual OTU.
Frequently, lower limit arrays have no more than 10, 25, 50, 100,
500, 1000, 5000, or 10000, 25000, 50000, 100000 or 250000
probes.
[0067] Typically, the arrays or microparticles have probes to one
or more highly conserved polynucleotides. The arrays or
microparticles can have further probes (e.g. confirmatory probes)
that hybridize to functionally expressed genes, thereby providing
an alternate or confirmatory signal upon which to base the
identification of a taxon. For example, an array can contain probes
to 16S rRNA gene sequences from Yersinia pestis and Vibrio cholerae
and also confirmatory probes to Y. pestis cafl virulence gene or V.
cholerae zonula occludens toxin (zot) gene. The detection of
hybridization signals based on probes binding to 16S rRNA
polynucleotides associated with a particular OTU coupled with the
detection of a hybridization signal based on a confirmatory probe
can provide a higher level of confidence that the OTU is present.
For instance, if hybridization signals are detected for the probes
associated Y. pestis OTU and the confirmatory probe also displays a
hybridization signal for the expression of Y. pestis cafl then the
confidence level subscribed to the presence or quantity of Y.
pestis will be higher than the confidence level obtained from the
use of OTU probes alone.
[0068] A range of lengths of probes can be employed on the arrays
or microparticles. As noted above, a probe can consist exclusively
of a complementary segments, or can have one or more complementary
segments juxtaposed by flanking, trailing and/or intervening
segments. In the latter situation, the total length of
complementary segment(s) can be more important that the length of
the probe. In functional terms, the complementary segment(s) of the
PM probes should be sufficiently long to allow the PM probes to
hybridize more strongly to a target polynucleotide e.g., 16S rRNA,
compared with a MM probe. A PM probe usually has a single
complementary segment having a length of at least 15 nucleotides,
and more usually at least 16, 17, 18, 19, 20, 21, 22, 23, 24, 25 or
30 bases exhibiting perfect complementarity.
[0069] In some arrays of microparticles, all probes are the same
length. In other arrays of microparticles, probe length varies
between quantification standard (QS) probes, negative control (NC)
probes, probe pairs, probe sets (OTUs) and combinations thereof.
For example, some arrays can have groups of OTUs that comprise
probe pairs that are all 23 mers, together with other groups of
OTUs or probe sets that comprise probe pairs that are all 25 mers.
Additional groups of probes pairs of other lengths can be added.
Thus, some arrays can contain probe pairs having sizes of 15 mers,
16mers, 17mers, 18mers, 19mers, 20mers, 21mers, 22mers, 23mers,
24mers, 25 mers, 26mers, 27 mers, 28mers, 29 mers, 30mers, 31mers,
32mers, 33mers, 34mers, 35mers, 36mers, 37mers, 38mers, 39mers,
40mers or combinations thereof. Other arrays can have different
size probes within the same group, OTU, or probe set. In these
arrays, the probes in a given OTU or probe set can vary in length
independently of each other. Having different length probes can be
used to equalize hybridization signals from probes depending on the
hybridization stability of the oligonucleotide probe at the pH,
temperature, and ionic conditions of the reaction.
[0070] In another aspect of the present application, a system is
provided for determining the presence or quantity of a plurality of
different OTUs in a single assay where the system comprises a
plurality of polynucleotide interrogation probes, a plurality of
polynucleotide positive control probes, and a plurality of
polynucleotide negative control probes. In some embodiments, the
system is capable of detecting the presence, absence, relative
abundance, and/or quantity of at least about 5, 10, 20, 50, 100,
250, 500, 1000, 5000, 10000, 20000, 30000, 40000, 50000, 60000,
70000, 80000, 90000, 100000, 250000, 500000 or 1000000 OTUs in a
sample using a single assay. In some embodiments, the
polynucleotide positive control probes include 1) probes that
target sequences of prokaryotic or eukaryotic metabolic genes
spiked into the target nucleic acid sequences in defined quantities
prior to fragmentation, or 2) probes complimentary to a pre-labeled
oligonucleotide added into the hybridization mix after
fragmentation and labeling. The control added prior to
fragmentation collectively tests the fragmentation, biotinylation,
hybridization, staining and scanning efficiency of the system. It
also allows the overall fluorescent intensity to be normalized
across multiple analysis components used in a single or combined
experiment, such as when two or more arrays are used in a single
experiment or when data from two separate experiments is combined.
The second control directly assays the hybridization, staining and
scanning of the system. Both types of control can be used in a
single experiment.
[0071] In some embodiments, the QS standards (positive controls)
are PM probes. In other embodiments, the QS standards are PM and MM
probe pairs. In further embodiments, the QS standards comprise a
combination of PM and MM probe pairs and PM probes without
corresponding MM probes. In some embodiments, the QS standards
comprise at least one, two, three, four, five, six, seven, eight,
nine, ten or more MM probes for each corresponding PM probe. In a
further embodiment, the QS standards comprise at least one, two,
three, four, five, six, seven, eight, nine, ten or more PM probes
for each corresponding MM probe. A system can comprise at least 1
positive control probe for each 1, 10, 100, or 1000 different
interrogation probes.
[0072] In some cases, the spiked-in oligonucleotides that are
complementary to the positive control probes vary in G+C content,
uracil content, concentration, or combinations thereof. In some
embodiments, the G+C % ranges from about 30% to about 70%, about
35% to about 65% or about 40% to about 60%. QS standards can also
be chosen based on the uracil incorporation frequency. The QS
standards can incorporate uracil in a range from about 1 in 100 to
about 60 in 100, about 4 in 100 to about 50 in 100, or about 10 in
100 to about 50 in 100. In some cases, the concentration of these
added oligonucleotides will range over 1, 2, 3, 4, 5, 6, or 7
orders of magnitude. Concentration ranges of about 10.sup.5 to
10.sup.14, 10.sup.6 to 10.sup.13, 10.sup.7 to 10.sup.12, 10.sup.7
to 10.sup.11, 10.sup.8 to 10.sup.11, and 10.sup.8 to 10.sup.10 can
be employed and generally feature a linear hybridization signal
response across the range. In some embodiments, positive control
probes for the conduction of the methods disclosed herein comprise
polynucleotides that are complementary to the positive control
sequences shown in Table 1. Other genes that can be used as targets
for positive controls include genes encoding structural proteins,
proteins that control growth, cell cycle or reproductive
regulation, and housekeeping genes. Additionally, synthetic genes
based on highly conserved genes or other highly conserved
polynucleotides can be added to the sample. Useful highly conserved
genes from which synthetic genes can be designed include 16S rRNA
genes, 18S rRNA genes, 23SrRNA genes. Exemplary control probes are
provided as SEQ ID NOs:51-100.
TABLE-US-00001 TABLE 1 Positive Control Sequences Positive Control
ID Description AFFX-BioB-5_at E. coli biotin synthetase
AFFX-BioB-M_at E. coli biotin synthetase AFFX-BioC-5_at E. coli
bioC protein AFFX-BioC-3_at E. coli bioC protein AFFX-BioDn-3_at E.
coli dethiobiotin synthetase AFFX-CreX-5_at Bacteriophage P1 cre
recombinase protein AFFX-DapX-5_at B. subtilis dapB,
dihydrodipicolinate reductase AFFX-DapX-M_at B. subtilis dapB,
dihydrodipicolinate reductase YFL039C Saccharomyces, Gene for actin
(Act 1p) protein YER022W Saccharomyces, RNA polymerase II mediator
complex subunit (SRB4p) YER 148 W Saccharomyces, TATA-binding
protein, general transcription factor (SPT15) YEL002C
Saccharomyces, Beta subunit of the oligosaccharyl transferase (OST)
glycoprotein complex (WBP1) YEL024W Saccharomyces,
Ubiquinol-cytochrome-c reductase (RIP1) Synthetic 16S rRNA controls
SYNM neurolyt_st Synthetic derivative of Mycoplasma neurolyticum
16S rRNA gene SYNLc.oenos_st Synthetic derivative of Leuconostoc
oenos 16S rRNA gene SYNCau.cres8_st Synthetic derivative of
Caulobacter crescenius 16S rRNA gene SYNFer.nodosm_st Synthetic
derivative of Fervidobacterium nodosum 16S rRNA gene
SYNSap.grandi_st Synthetic derivative of Saprospira grandis 16S
rRNA gene
[0073] In some embodiments, the negative controls comprise PM and
MM probe pairs. In further embodiments, the negative controls
comprise a combination of PM and MM probe pairs and PM probes
without corresponding MM probes. In other embodiments, the negative
control probes comprise at least one, two, three, four, five, six,
seven, eight, nine, ten or more MM probes for each corresponding
negative control PM probe. A system can comprise at least 1
negative control probe for each 1, 10, 100, or 1000 different
interrogation probes (PMs).
[0074] In some embodiments, the negative control probes hybridize
weakly, if at all, to 16S rRNA gene or other highly conserved gene
targets. The negative control probes can be complementary to
metabolic genes of prokaryotic or eukaryotic origin. Generally,
with negative control probes, no target material is spiked into the
sample. In some embodiments, negative control probes are from the
same collection of probes that are also used for positive controls,
but no material complementary to the negative control probes are
spiked into the sample, in contrast to the positive control probe
methodology. In essence, the control probes can be universal
control probes and play the role of positive or negative control
probes depending on the system's design. One of skill in the art
will appreciate that the universal control probes are not limited
to highly conserved sequence analysis systems and have applications
beyond the present embodiments disclosed herein.
[0075] In a further embodiment, probes to non-highly conserved
polynucleotides are added to a system to provide species-specific
identification or confirmation of results achieved with the probes
to the highly conserved polynucleotides. Usually, these
"confirmatory" probes cross hybridize very weakly, if at all, to
highly conserved polynucleotides recognized by the perfect match
probes. Useful species-specific genes include metabolic genes,
genes encoding structural proteins, proteins that control growth,
cell cycle or reproductive regulation, housekeeping genes or genes
that encode virulence, toxins, or other pathogenic factors. In some
embodiments, the system comprises at least 1, 5, 10, 20, 30, 40, 50
60, 70, 80, 90 100, 150, 200, 250, 300, 400, 500, 600, 700, 800,
900, 1000, 5000 or 10000 species-specific probes.
[0076] In some embodiments, a system comprises an array.
Non-limiting examples of arrays include microarrays, bead arrays,
through-hole arrays, well arrays, and other arrays known in the art
suitable for use in hybridizing probes to targets. Arrays can be
arranged in any appropriate configuration, such as, for example, a
grid of rows and columns. Some areas of an array comprise the OTU
detection probes whereas other areas can be used for image
orientation, normalization controls, signal scaling, noise
reduction processing, or other analyses. Control probes can be
placed in any location in the array, including along the perimeter
of the array, diagonally across the array, in alternating sections
or randomly. In some embodiments, the control probes on the array
comprise probe pairs of PM and MM probes. The number of control
probes can vary, but typically the number of control probes on the
array range from 1 to about 500,000. In some embodiments, at least
10, 100, 500, 1000, 5000, 10000, 25000, 50000, 100000, 250000 or
500000 control probes are present. When control probe pairs are
used, the probe pairs will range from 1 to about 250000 pairs. In
some embodiments, at least 5, 50, 250, 500, 2,500, 5,000, 12500,
25000, 50000, 125000 or 250000 control probe pairs are present. The
arrays can have other components besides the probes, such as
linkers attaching the probes to a support. In some embodiments,
materials for fabricating the array can be obtained from Affymetrix
(Santa Clara, Calif.), GE Healthcare (Little Chalfont,
Buckinghamshire, United Kingdom) or Agilent Technologies (Palo
Alto, Calif.)
[0077] Besides arrays where probes are attached to the array
substrate, numerous other technologies can be employed in the
disclosed system. In some embodiments, the probes are attached to
beads that are then placed on an array as disclosed by Ng et al.
(Ng et al. A spatially addressable bead-based biosensor for simple
and rapid DNA detection. Biosensors & Bioelectronics,
23:803-810, 2008).
[0078] In some embodiments, probes are attached to beads or
microspheres, the hybridization reactions are performed in
solution, and then the beads are analyzed by flow cytometry, as
exemplified by the Luminex multiplexed assay system. In this
analysis system, homogeneous bead subsets, each with beads that are
tagged or labeled with a plurality of identical probes, are
combined to produce a pooled bead set that is hybridized with a
sample and then analyzed in real time with flow cytometry, as
disclosed in U.S. Pat. No. 6,524,793. Bead subsets can be
distinguished from each other by variations in the tags or labels,
e.g., using variability in laser excitable dye content.
[0079] In some further embodiments, probes are attached to
cylindrical glass microbeads as exemplified by the Illumina
Veracode multiplexed assay system. Here, subsets of microbeads
embedded with identical digital holographic elements are used to
create unique subsets of probe-labeled microbeads. After
hybridization, the microbeads are excited by laser light and the
microbead code and probe label are read in real time multiplex
assay.
[0080] In some embodiments, a solution based assay system is
employed as exemplified by the NanoString nCounter Analysis System
(Geiss G et al. Direct multiplexed measurement of gene expression
with color-coded probe pairs. Nature Biotech. 26:317-325, 2008).
With this methodology, a sample is mixed with a solution of
reporter probes that recognize unique sequences and capture probes
that allow the complexes formed between the nucleic acids in the
sample and the reporter probes to be immobilized on a solid surface
for data collection. Each reporter probe is color-coded and is
detected through fluorescence.
[0081] In a further embodiment, branched DNA technology, as
exemplified by Panomics QuantiGene Plex 2.0 assay system, is used.
Branched DNA technology comprises a sandwich nucleic acid
hybridization assay for RNA detection and quantification that
amplifies the reporter signal rather than the sequence. By
measuring the RNA at the sample source, the assay avoids variations
or errors inherent to extraction and amplification of target
polynucleotides. The QuantiGene Plex technology can be combined
with multiplex bead based assay system such as the Luminex system
described above to enable simultaneous quantification of multiple
RNA targets directly from whole cells or purified RNA
preparations.
Sample Sources and Nucleic Acid Preparation
[0082] In some embodiments, the sample used can be an ecosystems
sample. Ecosystems include microbiomes associated with plants,
animals, and humans. Animal and human associated microbiomes
include those found in the gastrointestinal tract, respiratory
system, nares, urogenital tract, mammary glands, oral cavity,
auditory canal, feces, urine, and skin. In some embodiments, the
sample can be any kind of clinical or medical sample. For example,
samples from blood, urine, feces, nares, the lungs, the gut, other
bodily fluids or excretions, materials derived therefrom, or
combinations thereof of mammals can be assayed using the array
system. Also, the probes selected by the methods disclosed herein
and the array system of the present embodiments can be used to
identify an infection in the blood of an animal. The probes
selected by the methods disclosed herein and the array system of
the present embodiments can also be used to assay medical samples
that are directly or indirectly exposed to the outside of the body,
such as the lungs, ear, nose, throat, the entirety of the digestive
system or the skin of an animal. In some embodiments, a sample
includes cell culture samples and/or bacterial culture samples. In
some embodiments, a sample comprises a pulmonary sample from a
subject, including but not limited to sputum, endotracheal
aspirate, bronchoalveolar lavage sample, a swab of the endotrachea,
materials derived therefrom, or combinations thereof.
[0083] Techniques and systems to obtain nucleic acids from multiple
organisms in a sample, such as an ecosystem, medical, or clinical
sample, are well known by persons skilled in the art. In some
embodiments, a sample is treated with an agent that selectively
modifies a nucleic acid of dead cells. In some embodiments, samples
treated with an agent that selectively modifies a nucleic acid of
dead cells are treated with a second agent, such as light (e.g.
visible light), and optionally washed to remove excess selective
agent before extracting nucleic acid. Many commercially available
DNA extraction and purification kits can be used. Samples with
lower than 2 pg purified DNA can require amplification, which can
be performed using conventional techniques known in the art, such
as a whole community genome amplification (WCGA) method (Wu et al.,
Appl. Environ. Microbiol. (2006) 72, 4931-4941). In some
embodiments, highly conserved sequences such as those found in the
16S RNA gene, 23S RNA gene, 5S RNA gene, 5.8S rRNA gene, 12S rRNA
gene, 18S rRNA gene, 28S rRNA gene, gyrB gene, rpoB gene, fusA
gene, recA gene, coxl gene and nifD gene are amplified. Usually,
amplification is performed using PCR, but other types of nucleic
acid amplification can be employed. Generally, amplification is
performed using a single pair of universal primers specific to a
highly conserved sequence. For redundancy or for increased amount
of total amplicon concentration, two or more universal probe pairs
each specific to a different highly conserved sequence can be used.
Representative PCR primers include: bacterial primers 27F and
1492R. In some embodiments, a nucleic acid sample is amplified
using a collection of primers each comprising one or more
nucleotide positions selected at random from two or more different
nucleotides. In some embodiments, primers, nucleotides, or other
reagents used in an amplification reaction are labeled to produced
labeled amplification products.
[0084] A gel electrophoresis method can also be used to isolate
community RNA (McGrath et al., J. Microbiol. Methods (2008)
75:172-176). Samples with lower than 5 pg purified RNA may require
amplification, which can be performed using conventional techniques
known in the art, such as a whole community RNA amplification
approach (WCRA) (Gao et al., Appl. Environ. Microbiol. (2007)
73:563-571) to obtain cDNA. In some embodiments, sampling and DNA
extraction are conducted as previously described (DeSantis et al.,
Microbial Ecology, 53(3):371-383, 2007).
[0085] In some embodiments, DNA; total RNA, or a fraction thereof,
including rRNA, 16S rRNA, and 23S rRNA; or combinations thereof are
directly labeled and used without any amplification.
Probe Preparations
[0086] Techniques and means for generating oligonucleotide probes
to be used on analysis systems, beads or in other systems are
well-known by persons skilled in the art. For example, the
oligonucleotide probes can be generated by synthesis of synthetic
polynucleotides or oligonucleotides, e.g., using N-phosphonate or
phosphoramidite chemistries (Froehler et al., Nucleic Acid Res.
14:5399-5407 (1986); McBride et al., Tetrahedron Lett. 24:246-248
(1983)). Synthetic sequences are typically between about 10 and
about 500 bases in length, more typically between about 15 and
about 100 bases, and most preferably between about 20 and about 40
bases in length. In some embodiments, synthetic nucleic acids
include non-natural bases, such as, but by no means limited to,
inosine. An example of a suitable nucleic acid analogue is peptide
nucleic acid (see, e.g., Egholm et al., Nature 363:566-568 (1993);
U.S. Pat. No. 5,539,083). In some embodiments, at least 10, 25, 50,
100, 500, 1,000, 5,000, 10,000, 20,000, 40,000, 50,000, 60,000,
70,000, 80,000, 90,000 100,000, 200,000, 500,000, 1,000,000 or
2,000,000 probes are included on the array. In further embodiments,
each PM probe has one or more corresponding MM probe present on the
array. Typically, each PM-MM probe pair is associated with an OTU.
In some embodiments, at least 10, 25, 50, 100, 500, 1,000, 5,000,
10,000, 20,000, 40,000, 50,000, 60,000, 70,000, 80,000, 90,000
100,000, 200,000 or 500,000 probe pairs are placed on the array.
Generally, sets of probe pairs have at least 1, 2, 3, 4, 5, 6, 7,
8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24,
25, 26, 27, 28, 29, 30, 31, 32, 33, 34 or 35 probe pairs
present.
[0087] In some embodiments, positive control probes that are
complementary to particular sequences in the target sequences
(e.g., 16S rRNA gene) are used as internal quantification standards
(QS) and included in the system. In other embodiments, positive
control probes, also known as internal DNA quantification standards
(QS) probes are probes that hybridize to spiked-in nucleic acid
sequence targets. Usually, the sequences are from metabolic genes.
In some embodiments, negative control (NC) probes, e.g., probes
that are not complementary or do not appreciably hybridize to
sequences in the target sequences (e.g., 16S rRNA gene) are
included on the array. Unlike the QS probes, no target material is
spiked into the sample mix for the NC probes, prior to sample
processing.
[0088] In some embodiments, the probes are synthesized separately
and then attached to a solid support or surface, which can be made,
e.g., from glass, latex, plastic (e.g., polypropylene, nylon,
polystyrene), polyacrylamide, nitrocellulose, gel, silicon, or
other porous or nonporous material. In some embodiments, the
surface is spherical or cylindrical as in the case of microbeads or
rods. In other embodiments, the surface is planar, as in an array
or microarray.
[0089] Oligonucleotides produced using techniques known in the art
can be built on and/or coupled to microspheres, beads, microbeads,
rods, or other microscopic particles for use in arrays, flow
cytometry, and other multiplex assay systems. Numerous
microparticles are commercially available from about 0.01 to 100
micrometers in diameter. Generally, microparticles from about
0.1-50 .mu.m, about 1-20 .mu.m, or about 3-10 .mu.m are preferred.
The size and shapes of microparticles can be uniform or they can
vary. In some embodiments, sublots of different sizes, shapes or
both are conjugated to probes before combining the sublots to make
a final mixed lot of labeled microparticles. The individual sublots
can therefore be distinguished and classified based on their size
and shape. The size of the microparticles can be measured in
practically any flow cytometry apparatus by so-called forward or
small-angle scatter light. The shape of the particle can be also
discriminated by flow cytometry, e.g., by high-resolution
slit-scanning method.
[0090] Microparticles can be made out of any solid or semisolid
material including glass, glass composites, metals, ceramics, or
polymers. Frequently, the microparticles are polystyrene or latex
material, but any type of polymeric material is acceptable
including but not limited to brominated polystyrene, polyacrylic
acid, polyacrylonitrile, polyacrylamide, polyacrolein,
polybutadiene, polydimethylsiloxane, polyisoprene, polyurethane,
polyvinylacetate, polyvinylchloride, polyvinylpyridine,
polyvinylbenzylchloride, polyvinyltoluene, polyvinylidene chloride,
polydivinylbenzene, polymethylmethacrylate, or combinations
thereof. Microparticles can be magnetic or non-magnetic and can
also have a fluorescent dye, quantum dot, or other indicator
material incorporated into the microparticle structure or attached
to the surface of the microparticles. Frequently, microparticles
can also contain 1 to 30% of a cross-linking agent, such as divinyl
benzene, ethylene glycol dimethacrylate, trimethylol propane
trimethacrylate, or N,N'methylene-bis-acrylamide or other
functionally equivalent agents known in the art.
[0091] Embodiments disclosed herein are applicable for use in any
analysis system, including but not limited to bead or solution
multiplex reaction platforms, or across multiple platforms, for
example, Affymetrix GeneChip.RTM. Arrays, Illumina BeadChip.RTM.
Arrays, Luminex xMAP.RTM. Technology, Agilent Two-Channel Arrays,
MAGIChips (Analysis systems of Gel-immobilized Compounds) or the
NanoString nCounter Analysis System. The Affymetrix (Santa Clara,
Calif., USA) platform DNA arrays can have the oligonucleotide
probes (approximately 25mer) synthesized directly on the glass
surface by a photolithography method at an approximate density of
10,000 molecules per .mu.m.sup.2 (Chee et al., Science (1996)
274:610-614). Spotted DNA arrays use oligonucleotides that are
synthesized individually at a predefined concentration and are
applied to a chemically activated glass surface. In general,
oligonucleotide lengths can range from a few nucleotides to
hundreds of bases in length, but are typically from about 10mer to
50mer, about 15mer to 40mer, or about 20mer to about 30mer in
length.
Target Labeling
[0092] In some embodiments, the nucleic acid targets are labeled so
that a laser scanner tuned to a specific wavelength of light can
measure the number of fluorescent molecules that hybridized to a
specific DNA probe. For arrays, the nucleic acid targets are
typically fragmented to between 15 and 100 nucleotides in length
and a biotinylated nucleotide is added to the end of the fragment
by terminal DNA transferase. At a later stage, the biotinylated
fragments that hybridize to the oligonucleotide probes are used as
a substrate for the addition of multiple phycoerythrin fluorophores
by a sandwich (Streptavidin) method. For some arrays, such as those
made by AGILENT or NIMBLEGEN, the purified community DNA can be
fluorescently labeled by random priming using the Klenow fragment
of DNA polymerase and more than one fluorescent moiety can be used
(e.g. controls could be labeled with Cy3, and experimental samples
labeled with Cy5 for direct comparison by hybridization to a single
analysis system). Some labeling methods incorporate the molecular
label into the target during an amplification or enzymatic step to
produce multiple labeled copies of the target.
[0093] In some embodiments, the detection system is able to measure
the microbial diversity of complex communities without PCR
amplification, and consequently, without the inherent biases
associated with PCR amplification. In some embodiments, nucleic
acid from dead cells is selectively removed before detection, such
that detection is directed to determining the presence, absence,
relative abundance, and/or quantity of live organisms in a sample,
such as live members of one or more OTUs. Actively metabolizing
cells typically contain about 20,000 or more ribosomes for protein
assembly compared to quiescent or dead cells that have few. In some
embodiments, rRNA can be purified directly from a sample and
processed with no amplification step, thereby reducing or avoiding
bias caused by preferential amplification of some sequences over
others. Thus, in some embodiments, the signal from the analysis
system can reflect the true number of rRNA molecules that are
present in the samples. This can be expressed as the number of
cells multiplied by the number of rRNA copies within each cell. The
number of cells in a sample can then be inferred by several
different methods, such as, for example, quantitative real-time
PCR, or FISH (fluorescence in situ hybridization.). Then the
average number of ribosomes within each cell can be calculated.
Hybridization
[0094] Hybridizations can be carried out under conditions
well-known by persons skilled in the art. See Rhee et al. (Appl.
Environ. Microbiol. (2004) 70:4303-4317) and Wu et al. (Appl.
Environ. Microbiol. (2006) 72:4931-4941). The temperature can be
varied to reduce or increase stringency and allow the detection of
more or less divergent sequences. Robotic hybridization and
stringency wash stations can be used to give more consistent
results and reduce processing time. In some embodiments, the
hybridization and washing process can be accomplished in less than
about half an hour, 1 hour, 2 hours, 3 hours, 4 hours, 5 hours, 6
hours, 7 hours, 8 hours, 9 hours, 10 hours, 11 hours, 12 hours, 14
hours, 16 hours, 18 hours, 20 hours or 24 hours. Generally,
hybridization and washing times are reduced for microparticle based
detection systems owing to the greater accessibility of the probes
to the target molecules. Generally, hybridization times can be
reduced for low complexity assays and/or assays for which there is
an excess of target analytes.
Signal Quantification
[0095] After hybridization, arrays can be scanned using any
suitable scanning device. Non-limiting examples of conventional
microarray scanners include GeneChip Scanner 3000 or GeneArray
Scanner, (Affymetrix, Santa Clara, Calif.); and ProScan Array
(Perkin Elmer, Boston, Mass.); and can be equipped with lasers
having resolutions of 10 pm or finer. The scanned image displays
can be captured as a pixel image, saved, and analyzed by
quantifying the pixel density (intensity) of each spot on the array
using image quantification software (e.g., GeneChip Analysis system
Analysis Suite, version 5.1 Affymetrix, Santa Clara, Calif.; and
ImaGene 6.0, Biodiscovery Inc. Los Angeles, Calif., USA). For each
probe, an individual signal value can be obtained through imaging
parsing and conversion to xy-coordinates. Intensity summaries for
each feature can be created and variance estimations among the
pixels comprising a feature can be calculated.
[0096] With flow cytometry based detection systems, a
representative fraction of microparticles in each sublot of
microparticles can be examined. The individual sublots, also known
as subsets, can be prepared so that microparticles within a sublot
are relatively homogeneous, but differ in at least one
distinguishing characteristic from microparticles in any other
sublot. Therefore, the sublot to which a microparticle belongs can
readily be determined from different sublots using conventional
flow cytometry techniques as described in U.S. Pat. No. 6,449,562.
Typically, a laser is shined on individual microparticles and at
least three known classification parameter values measured: forward
light scatter (C.sub.1) which generally correlates with size and
refractive index; side light scatter (C.sub.2) which generally
correlates with size; and fluorescent emission in at least one
wavelength (C.sub.3) which generally results from the presence of
fluorochrome incorporated into the labeled target sequence. Because
microparticles from different subsets differ in at least one of the
above listed classification parameters, and the classification
parameters for each subset are known, a microparticle's sublot
identity can be verified during flow cytometric analysis of the
pool of microparticles in a single assay step and in real-time. For
each sublot of microparticles representing a particular probe, the
intensity of the hybridization signal can be calculated along with
signal variance estimations after performing background
subtraction.
Data Processing and Statistical Analysis
[0097] Simultaneous detection of at least 500, 1000, 5000, 10000,
20000, 30000, 40000, 50000, 60000, or more taxa with a high level
of confidence can incorporate techniques to de-convolute the signal
intensity of numerous probe sets into probability estimates. In
some embodiments, the methods, compositions and systems enable
detection in one assay the presence or absence of a microorganism
in a community of microorganisms, such as an environmental or
clinical sample when the microorganism comprises less than 0.05% of
the total population of microorganisms. In some embodiments,
detection includes determining the quantity of the microorganism,
e.g., the percentage of the microorganism in the total
microorganism population. De-convolution techniques can include the
incorporation of NC probe pairs into the analysis system and the
use of the data to fit the hybridization signals from the QS probe
pairs to the hybridization distribution of the NC probe pairs.
[0098] De-convolution techniques can allow the detection and
quantification of nucleic acids in a sample and by inference, the
detection and quantification of microorganisms in a sample. In one
aspect of the present application, a system is provided for
determining the presence or quantity of a microorganism in a sample
comprising contacting a sample with a plurality of probes,
detecting the hybridization signals of the sample nucleic acids
with the probes and de-convoluting the signals to determine the
presence, absence and/or quantity of a particular nucleic acid
present in a population of nucleic acids where the particular
nucleic acid is present at less than 0.01% of the total nucleic
acid population. In some embodiments, the particular nucleic acid
is at least 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96% or 97%
homologous to other nucleic acids in the population.
[0099] In some embodiments, the data output from an imaged or
scanned sample is de-convoluted and analyzed using the following
methods. Using an array as an illustrative example, the
hybridization signals are converted to xy-coordinates with
intensity summaries and variance estimates generated for the pixels
using commercial software. The data is outputted using a standard
data format like a CEL file (Affymetrix), or a Feature Report file
(NimbleGen).
[0100] The hybridization signals undergo background subtraction.
Typically, the background intensity is computed independently for
each quadrant as the average signal intensity of the least intense
2% of the probes in the quadrant. Other threshold values can also
be used, e.g., 0.5%, 1%, 3%, 4%, 5% or 10%. Background intensity is
then subtracted from all probes in a quadrant before further
computation is performed. This noise removal procedure can be done
on a quadrant-by-quadrant basis or across a whole array.
[0101] In some embodiment, array signals are normalized to allow
for the comparison of results achieved in different experiments or
for the comparison of replicate experiments. Normalization can be
achieved by a number of methods. In some embodiments,
reproducibility between different probes for the same target are
evaluated using a Position Dependent Nearest Neighbor (PDNN) model
as described in Zhang L. et al., A model of molecular interactions
on short oligonucleotide analysis systems, Nat. Biotechnol. 2003,
21(7):818-821. The PDNN model allows estimation of the sequence
specific noise signal and a non-specific background signal, and
thus enables estimation of the true intensity for the probes.
[0102] In other embodiments, per-array models of signal and
background distributions using responses observed from comparison
of the PM and MM probe pairs and the internal DNA quantification
standards (QS) probe pairs are created. In some embodiments, the
probability that each probe pair is "positive" is determined by
calculating a difference score, d, for each probe pair. d can be
defined as:
d = 1 - ( PM - MM PM + MM ) Eqn . 1 ##EQU00001##
[0103] wherein:
[0104] PM=scaled intensity of the perfect match probe;
[0105] MM=scaled intensity of the mismatch probe; and,
[0106] d=pair difference score.
The value of d can range from 0 to 2. When PM>>MM, the value
of d approaches 0; when PM=MM, d=1; and when PM<<MM, the
value of d approaches 2.
[0107] In some embodiments, the internal DNA quantification
standards (QS) and negative control (NC) probe pairs are binned and
sorted by attributes of the probes. Examples of the attributes of
the probes that can be used in the embodiments disclosed herein
include, but are not limited to binding energy; base composition,
including A+T count, G+C count, and T count; sequence complexity;
cross-hybridization binding energy; secondary structure; hair-pin
forming potential; melting temperature; and length of the probe.
These attributes of the probes can affect hybridization properties
of the probes, for example, A+T count may affect hydrogen bonding
of the probe, and T count may affect the length and base
composition of the fragments produced by the use of DNase.
Fragmentation with other enzyme systems may be influenced by the
composition of other bases.
[0108] In some embodiments, QS and NC probe pairs are binned and
sorted based on the individual probe's A+T count and T count. For
each bin (A+T count by T count), the d values from the negative
control probes are fit to a normal distribution to derive the scale
(mean) and shape (standard deviation). Then, the d values from QS
are fit to a gamma distribution to derive scale and shape. For each
array, multiple density plots are produced by this process. In
general, even one extra T can result in appreciable difference in
the probe gamma scale parameter.
[0109] The parameters derived from gamma and normal distributions
are used to derive a pair response score, r, for each probe pair. r
is an indicator of the probability that a probe pair is positive,
i.e., the probability for a probe pair to be responsive to the
target sequence. r may be defined as:
r = ( pdf .gamma. ( X = d ) pdf .gamma. ( X = d ) + pdf norm ( X =
d ) ) Eqn . 2 ##EQU00002##
[0110] where:
r=response score to measure the potential that a specific probe
pair is binding a target sequence and not a background signal, i.e.
the probability of the probe pair being positive for the specific
target sequence; pdf.sub..gamma. (X=d)=probability that d could be
drawn from the gamma distribution estimated for the target class
ATx Ty; pdf.sub.norm (X=d)=probability that d could be drawn from
the normal distribution estimated for the target class ATx Ty. r
can range from 0 to 1. r approaches 1 when PM>>MM, and r
approaches 0 when PM<<MM.
[0111] Each set of interrogation probe pairs, e.g., an OTU, can be
scored based on pair response scores, cross-hybridization
relationships or both. In some embodiments, the system removes data
from at least a subset of probe pair sets before making a final
call on the presence or quantity of said microorganisms. In some
embodiments, the data is removed based on interrogation probe cross
hybridization potential. In some embodiments, the scoring of probe
pairs is performed by a two-stage process as discussed below.
[0112] For example, a two stage analysis can be performed wherein
only probe pairs that pass a first stage are analyzed in the next
stage. In the first stage, the distribution of r across each set of
probe pairs, R, is determined. For each set of probe pairs that is
associated with an OTU, the r values of all probe pairs are ranked
within the set, and percentage of probe pairs that meet one or more
threshold r values are determined. Frequently, three threshold
determinations are made at 25% increments across the total range of
ranked probe pairs (interquartile Q1, Q2, and Q3); however, any
number of threshold determinations or percentage increments can be
used. For example, a determination can use one increment at 70% in
which probe pairs must pass a threshold value of 80%.
[0113] Typically, to differentiate signal from noise, an OTU is
considered to pass Stage 1 if Q1, Q2, and Q3 of the set of probe
pairs that is associated with this OTU surpass the threshold of
Q1.sub.min, Q2.sub.min, and Q3.sub.min, respectively. That is, for
an OTU to pass Stage 1, the r value of 75% of the probe pairs in
the set of probe pairs that is associated with that OTU has to be
at least Q1.sub.min, the r value of 50% of the probe pairs in that
set of probe pairs have to be at least Q2.sub.min, and the r value
of 25% of the probe pairs in that set of probe pairs have to be at
least Q3.sub.min. Q1.sub.min is at least about 0.5, about 0.55,
about 0.6, about 0.65, about 0.7, about 0.75, about 0.8, about
0.82, about 0.84, about 0.86, about 0.88, about 0.90, about 0.91,
about 0.92, about 0.93, about 0.94, about 0.95, about 0.96, about
0.97, about 0.98, or about 0.99. Q2.sub.min is at least about 0.5,
about 0.55, about 0.6, about 0.65, about 0.7, about 0.75, about
0.8, about 0.82, about 0.84, about 0.86, about 0.88, about 0.90,
about 0.91, about 0.92, about 0.93, about 0.94, about 0.95, about
0.96, about 0.97, about 0.98, or about 0.99. Q3.sub.min is at least
about 0.5, about 0.55, about 0.6, about 0.65, about 0.7, about
0.75, about 0.8, about 0.82, about 0.84, about 0.86, about 0.88,
about 0.90, about 0.91, about 0.92, about 0.93, about 0.94, about
0.95, about 0.96, about 0.97, about 0.98, about 0.99, about 0.992,
about 0.994, about 0.996, about 0.998, or about 0.999. In some
embodiments, Q1.sub.min, Q2.sub.min, and Q3.sub.min are determined
empirically from spike-in experiments. For example, Q1.sub.min,
Q2.sub.min, and Q3.sub.min are chosen to allow 2 pM amplicon
concentration to pass. In some embodiments, Q1.sub.min, Q2.sub.min,
and Q3.sub.min are 0.98, 0.97, and 0.82, respectively. These
threshold numbers were empirically derived using DNase to fragment
the sample sequences. Since DNase has a T-bias, the use of other
enzymes can require a shift in the threshold numbers and can be
empirically derived.
[0114] In the second stage only the OTUs passing the first are
considered as potential sources of cross-hybridization. In some
embodiments, for each OTU, only probe-pairs with r>0.5 (these
are the probe pairs considered as to be likely responsive to the
target sequence) are further analyzed. In other instances, only
probe pairs with r>0.6, 0.7, 0.8, or 0.9 are considered
responsive and are further analyzed. Probe pairs that are unlikely
to be responsive (i.e., r<0.5) are not analyzed further even if
their set R, was responsive overall. R.sub.0.5 represents the
subset of probe pairs in which all probe pairs have r>0.5.
Typically, based on the interquartile Q1, Q2 and Q3 values chosen
at Stage 1, most of the probe pairs in the OTUs passing Stage 1 are
analyzed. In other embodiments, only the probe-pairs with
r>0.55, 0.60, 0.65, 0.70, 0.75, 0.80, 0.85, or 0.90 are further
analyzed.
[0115] For each probe pair in the R.sub.0.5 subset, the count of
putatively cross-hybridizing OTUs (i.e., the number of OTUs with
which the probe pair can cross-hybridize) is determined. In this
process, only the OTUs that have passed Stage 1 are considered as
potential sources of cross-hybridization. Each probe pair in the
R.sub.0.5 subset is penalized by dividing its r value by the count
of putatively cross-hybridizing OTUs to determine its modified
possibility of being positive. The modified possibility of being
positive for a probe pair may be represented by a r.sub.x value.
r.sub.x may be defined as:
r x = r scalarS 1 x Eqn . 3 ##EQU00003##
[0116] where
[0117] S.sub.1=Set of OTUs passing Stage1; and,
[0118] S.sub.1x=Set of OTUs passing Stage 1 with cross
hybridization potential to the given probe pair.
[0119] r.sub.x is proportional to the response of the probe pair
and the specificity of the probe pair given the community observed
during the first stage. r.sub.x value can range from 0 to 1. For
each set of probe pairs associated with an OTU, r.sub.x are
calculated for each probe pair and ranked within the set.
Interquartile Q1, Q2, Q3 values for the distribution of r.sub.x
value in each set of probe pairs are determined. The taxon
represented by the OTU is considered to be present if Q1 is greater
than Q.sub.x1, Q2 is greater than Q.sub.x2, or Q3 is greater than
Q.sub.x3. Q.sub.x1 is at least about 0.5, at least about 0.55, at
least about 0.6, at least about 0.65, at least about 0.7 at least
about 0.75, at least at least about 0.8, at least about 0.85, at
least about 0.90, at least about 0.95, or at least about 0.97.
Q.sub.x2 is at least about 0.5, at least about 0.55, at least about
0.6, at least about 0.65, at least about 0.7 at least about 0.75,
at least at least about 0.8, at least about 0.85, at least about
0.90, at least about 0.95, or at least about 0.97. Q.sub.x3 is at
least about 0.5, at least about 0.55, at least about 0.6, at least
about 0.65, at least about 0.7 at least about 0.75, at least at
least about 0.8, at least about 0.85, at least about 0.90, at least
about 0.95, or at least about 0.97. In some embodiments, Q.sub.x1
is at least 0.66, that is, 75% of the probe pairs in the set of the
probe pairs have a r.sub.x value that is at least 0.66.
[0120] A two stage hybridization signal analysis procedure can be
performed on hybridization signals from any array or microparticle
generated data set, including data generated from the use of any
combination of probes selected using the disclosed methodologies.
In some embodiments, the second stage of the procedure penalizes
probes based on the number of cross-hybridizations, the intensity
of the cross-hybridization signals or a combination of the two.
[0121] The method disclosed herein is useful for hierarchical probe
set scoring. An OTU may be present at a node at any hierarchical
level on a clustering tree. As used herein, an OTU is a group of
one or more organisms, such as a domain, a sub-domain, a kingdom, a
subkingdom, a phylum, a sub-phylum, a class, a sub-class, an order,
a sub-order, a family, a subfamily, a genus, a subgenus, a species,
or any cluster. In some embodiments, a R.sub.0.5 set is collected
for each node on the phylogenetic tree and consists of all unique
probes from subordinate R.sub.0.5 sets. For example, for
calculating r.sub.x values for probe pairs in a R.sub.0.5 set for
an OTU representing an "order," the count of putatively
cross-hybridizing equally-ranked taxa (i.e., "order" node)
containing at least one sequence with cross-hybridization potential
is used as the denominator in Eqn. 3.
[0122] In some embodiments, the OTUs at the leaf level (e.g.,
species, subgenus or genus) are first analyzed. Then each
successive level of nodes in the clustering tree is analyzed. In
some embodiments, the analysis is performed up to the domain level.
In some embodiments, the analysis is performed up to the phylum
level. In some other embodiments, the analysis is performed up to
the kingdom level. Penalization for cross-hybridization in Eqn. 3
is only performed for probes on the same taxonomy level. All
present taxa are quantified using the mean scaled PM probe
intensity after discarding the highest and lowest value of the set
R (HybScore). In some embodiments, only taxa present at a first
level are analyzed further.
[0123] In some embodiments, a summary abundance score is
determined. Corrected abundance scores are created based on G+C
content and uracil incorporation. Generally, probes with higher G+C
content produce a higher hybridization signal that is typically
compensated for correcting the abundance scores.
[0124] The probability of detection for each taxonomic node is
determined by summarizing terminal node detection and the breadth
of cross-hybridization relationships. Hierarchical probes are
scored for evidence of novel organisms based on cluster
analysis.
[0125] In some embodiments, the system is capable of analyzing
other data in conjunction with that obtained from the analysis of
probe hybridization signal strength. In some embodiments, the
system can analyze sequencing reaction data including that obtained
with high-through put sequencing techniques. In some embodiments,
the sequencing data is from same regions of the same highly
conserved sequence analyzed by the method disclosed herein using
probes.
High Capacity Analysis System Applications
[0126] Numerous subject-derived samples can be assayed to determine
the sample's microbiome composition. By having an assay system
capable of detecting in a single assay the presence and optionally
quantity of at least 10,000, 20,000, 30,000, 40,000, 50,000,
60,000, 70,000, 80,000, 90,000, 100,000, 200,000, 500,000 or
1,000,000 bacterial or archeal taxa, a complete picture of a
microbiotic ecosystem, such as the living microorganisms in the
ecosystem, can be achieved quickly and at relatively low cost
providing the ability to examine numerous subjects.
[0127] The elucidation of a specific microbiome associated with an
ecosystem, animal, human, organ system, condition, and the like
allows for the generation of a "signature," "biosignature," or
"fingerprint" of the particular environment sampled, terms used
interchangeably herein. If the biosignature is from a normal or
healthy system or subject, or is from a subject free from a
condition under examination, then the associated biosignature can
be used as a reference for the comparison of later samples from the
same or other subjects to monitor for changes that are associated
with an abnormal or unhealthy state or condition. For example, if a
later biosignature of a subject shows that the microbiome has
shifted away from that associated with a healthy pulmonary status,
then preemptive measures could be taken to prevent a continued
shift.
[0128] Similarly, a biosignature of an environment can be compared
to a biosignature generated from a pool of samples that represent
an average or normal biosignature for a population or collection of
environments. For example, a sample from an unhealthy individual or
environment could be assayed and the microbial biosignature
compared to the biosignature seen in a healthy population at large
or unaffected environment. If one or more microorganisms are
detected in the unhealthy individual that are either not seen in
the general population or not seen at the same prevalence then
therapeutic measures can be taken to selectively eliminate or
reduce in number the microorganisms associated with the unhealthy
state. Once a relationship is known between the prevalence of a
particular microorganism or group of microorganisms (e.g. one or
more OTUs that consist essentially of viable organisms from a
sample) and a disease state, then disease progression or treatment
response can also be monitored, diagnosed, and/or predicted using
the present systems and methods.
[0129] Numerous microbiomes of animals or humans can be analyzed
with the present systems and methods including the gut, respiratory
system, urogenital tract, mammary glands, skin, oral cavity,
auditory canal, and skin. Clinical samples such as blood, sputum,
nares, feces, and urine can be used with the method. From the
analysis of normal individuals and those suffering from a disease
or condition, a large database of fingerprints or biosignatures can
be assembled. By comparing the biosignatures between healthy and
disease related states, associations can be made as to the
influence and importance of individual components of the
microbiome.
[0130] Once these associations are made, treatments can be designed
and tested to alter the composition of the microbiota seen in the
disease state. Additionally, by regularly monitoring the microbial
composition of an affected organ system in a diseased individual,
disease progress or response to therapy can be observed and if
need, additional therapeutic measures taken to alter the microbiome
composition to one that is more representative of that seen in a
healthy population.
[0131] An interesting property of bacteria that has great
importance in healthcare, water quality and food safety is quorum
sensing. Many bacteria are able to sense the presence of other
members of their species or related species and upon reaching a
specific density the bacteria start producing various virulence or
pathogenicity factors. In other words, the bacteria's gene
expression is coordinated as a group. For example, some bacteria
produce exopolysaccharides that are known as "slime layers." The
secretion of exopolysaccharidse can decrease the ability of white
blood cells to phagocytize the microorganisms and make the
microorganisms more resistant to therapeutics or cleaning agents.
Traditional methodologies require the detection of specific gene
expression in order to detect or study quorum sensing and other
population induced effects. The present systems and methods can be
used to understand the changes that occur in a microbiome that are
associated with a given effect such as biofilm formation or
toxicity production. One can develop protocols with the present
systems and methods to look for and determine conditions that lead
to quorum sensing. For example, testing samples at various
timepoints and under varying conditions can lead to determining how
and when to intervene or reverse population induced expression of
virulence or pathogenicity factors.
[0132] In some embodiments, a method is provided to identify a new
indicator species for an environmental or health condition with the
present systems and methods. The condition can be that of a normal
or healthy state. Alternatively, the indicator species can be for
an unhealthy or abnormal condition. To identify a new indicator
species, a normal sample is simultaneously assayed to determine the
presence or quantity of each OTU associated with all known
bacteria, archae, or fungi; this test result is compared to the
results achieved in the simultaneous assay of sample from the
environment of the condition where the presence or quantity of each
OTU associated with all known bacteria, archae, or fungi was
determined. Microorganisms that change in abundance at least
2-fold, 3-fold, 4-fold, 5-fold, 10-fold, 20-fold, 50-fold or
100-fold, either increasing in abundance or decreasing in abundance
represent putative indicator species for a condition.
[0133] In other embodiments, methods are provided for identifying
indicators species associated with a disease state, disease
progression, treatment regimen, probiotic administration, including
progression of disease. In some embodiments, methods are provided
for monitoring a change in the environment or health status
associated with introducing one or more new microorganisms into a
community. For example, measures to increase a particular
microorganism's percentage of the gut microbiome in an individual,
such as feeding a person yogurt or a food supplement containing L.
casei, can be monitored using the present methods and systems. In
some embodiments, the presence, absence, relative abundance, and/or
quantity of desired and/or undesired live microorganisms in food
and food supplements can be evaluated by the methods disclosed
herein. For example, the viability over time of microorganisms in a
probiotic supplement can be determined and used as a measure of
shelf-life and/or potency of the probiotic supplement.
Combined Analysis
[0134] The ability to identify and quantitate the microorganisms in
a sample (e.g. the live microorganisms in a sample comprising live
and dead microorganisms) can be combined with a gene expression
technology such as a functional gene array to correlate populations
with observed gene expression. Similarly, microbiome composition
analysis can be correlated with the presence of chemicals, proteins
including enzymes, toxins, drugs, antibiotics or other sample
constituents. For instance, nucleic acids isolated from a soil
sample can be analyzed to elucidate the microbiome composition
(e.g. biosignature) and also to identify expressed genes. In the
bare, nutrient-poor soils on the Antarctic, this analysis
associated chitinase and mannanase expression with Bacteroidetes
and CH.sub.4-related genes with Alphaproteobacteria. (Yergeau et
al., Environmental microarray analyses of Antarctic soil microbial
communities. ISME J. 3:340-351, 2009). Significant correlations
were also found between taxon abundances and C- and N-cycle gene
abundance. From this data, one can predict that certain organisms
or groups of organisms are required or account for the majority of
an expected or observed enzymatic or degradative process. For
example, members of the Bacteroidetes phylum probably degrade the
majority of environmental chitin, a major constituent of
exoskeletons of insect and arthropods and also of fungi cell walls,
at the sample locale.
[0135] This methodology can be used to identify new antibiotic
producing organisms, even ones that are unculturable. For instance,
soil extracts can be tested for antibiotic activity. If a positive
extract is found, a sample of the soil from which a portion was
extracted for antibiotic can be analyzed for microbial composition
and perhaps gene expression. Major constituents of the microbiome
could be correlated with antibiotic activity with the correlation
strengthened through gene expression data allowing one to predict
that a particular organism or group of organisms is responsible for
the observed antibiotic activity.
[0136] In some embodiments, a method is provided for making a
prediction about a sample comprising a) determining microorganism
population data as the probability of the presence or absence of at
least 100 OTUs of microorganisms in said sample; b) determining
gene expression data of one or more genes by said microorganisms in
said sample and c) using said expression data and population data
to make a prediction about said sample. In some embodiments, the
prediction entails the identity of a microorganism responsible for
a characteristic or condition observed in an environment.
[0137] Other combined analysis methods include the use of a
diffusion chamber to retain microorganisms in a sample while one or
more constituents or parameters of the sample are changed. For
instance, the salinity or pH of the sample can be changed abruptly
or gradually over time. Following specific time intervals, the
microbiome of the sample in the diffusion chamber can be
determined. Microorganisms that cannot tolerate the new environment
conditions will die, become reduced in number due to unfavorable
conditions or predation, or remain static in their numbers. In
contrast, microorganisms that can tolerate the new conditions will
at least maintain their number or thrive, perhaps becoming a
dominant population. Use of a diffusion chamber coupled with a
system capable of detecting the presence or quantity of at least
10,000 OTUs can allow the identification of microorganisms that
perish or fail to thrive when placed in a new environment. Such
microorganisms are termed "transient", meaning that their percent
composition of the microbiome changes quickly. The identification
of transient microorganisms can be used to ascertain the time
and/or place they were introduced into an environment. Different
transient microorganisms can have different half-lives for a
particular condition.
[0138] Diffusion chambers can also take the form of a
semi-permeable capsule, tube, rod, or sphere or other solid or
semi-solid object. A microbiome or a select group of bacteria can
be placed inside the capsule, that is then sealed and introduced
into an environment for a specified period of time. Upon removal,
the capsule is opened and the microbiome or select group of
bacteria sampled to ascertain changes in the presence or quantity
of the individual constituents. The capsule can be removed once or
periodically to sample the microbiome. Alternatively, multiple
single use capsules with identical quantities of the microbiome can
be used, each one removed and sampled at a different time point.
Microbiomes placed in capsules or other semi-permeable containers
can be introduced into a living organism, usually through an
orifice, to measure changes to the microbiome composition
associated with a particular organ or system environment. For
example, a semi-permeable capsule or tube containing a microbiome
can be introduced into the gastrointestinal system through the
mouth or anus. A microbiome from a healthy individual can be
introduced in this manner into an unhealthy individual, such as a
patient suffering from Crohn's disease or irritable bowel syndrome
to ascertain the effect of the unhealthy condition on the normal,
healthy individual associated microbiome. In this manner, the
efficacy of drug effectiveness and treatment protocols could also
be evaluated based on the effects of the gut ecology on a known
microbiome.
Low Density-Special Purpose Detection Systems
[0139] In some embodiments, probes are selected for constructing
special purpose systems including those with arrays or
microparticles. Typically, special purpose "low density" systems,
are designed for use in a specific environment or for a particular
application and usually feature a reduced number of probes,
"down-selected" probes, that are specific to organisms that are
known or expected to be present in the particular environment, such
as associated with a particular biosignature. In some cases the
biosignature is fecal contamination. Typically, a low density
system comprises no more than 10, 20, 50, 100, 200, 500, 1,000,
2,000, 5,000 or 10,000 down selected probes or 5, 10, 25, 50, 100,
250, 500, 1,000, 2,500 or 5,000 down selected probes probe pairs
(PM and MM probes). In some embodiments, only 1, 2, 3, 4, 5, 6, 7,
8, 9, or 10 probes are used per OTU. In further embodiments, only
PM probes are used. Generally, these down-selected probes have
robust hybridization signals and few or no cross hybridizations. In
some embodiments, the collection of down selected probes have a
median cross hybridization potential number of less than 20, 15,
10, 8, 7, 6, 5, 4, 3, 2, or 1 per probe. Frequently the down
selected probes belong to OTUs that have reduced numbers of probes.
In some embodiments, the OTUs of a down select probe collection
have a median number of less than 25, 20, 15, 14, 13, 12, 11, 10,
9, 8, 7, 6, 5, 4, 3 or 2 probes per OTU. Generally, low density
systems feature probes that recognize no more than 10, 25, 50, 100,
250, 500, 1,000, 2,000, or 5,000 taxa. For a set number of probes,
a number of design strategies can be employed for low density
systems. One approach is to maximize the number of OTUs identified,
e.g., use one probe per OTU with no mismatch probes. Another
approach is to select probes based on the desired confidence level.
Here, multiple probes for each OTU along with corresponding
mismatch probes can be required to achieve at least 95% confidence
level for the presence and quantity of each OTU. The probes for a
particular low density application can be selected by applying a
sample from an appropriate environment to a high density analysis
system, e.g., a detection system that can in a single assay
determine the probability of the presence or quantity of at least
10,000, 20,000, 30,000, 40,000, 50,000, 60,000, 70,000, 80,000,
90,000, 100,000, 250,000, 500,00 or 1,000,000 OTUs of a single
domain, such as bacteria, archea, or fungi, or alternatively, for
each known OTU of a single domain. Probes associated with prevalent
OTUs can be selected for a low density system. Alternately, the
OTUs seen in a sample of interest can be compared with a control
sample and shared OTUs subtracted out with the probes associated
with the remaining OTUs selected for the low density system.
Additionally, probes can be selected based on a change in
prevalence of OTUs between the environment of interest and a
control environment. For example, OTUs that are at least 2-fold
5-fold, 10-fold, 100-fold or 1,000-fold more abundant in the sample
of interest compared to the control sample are included in the down
selected probe set. Using this information, a down selected array,
bead multiplex system or other low density assay system is
designed.
[0140] "Low density" assays systems can be used to identify select
microorganisms and determine the percentage composition of various
select microorganisms in relation to each other. Low density assay
systems can be constructed using probes selected through the
disclosed methodologies. These low density systems can identify at
least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 30, 40, 50, 60, 70,
80, 90, 100, 200, 500, 1000 or more microorganisms. Representative
microorganisms to be identified and optionally quantified are
listed in Table 2.
TABLE-US-00002 TABLE 2 Representative Microorganisms Recognized by
Low Density Assay Systems Species Application Listeria
monocytogenes Food safety, environmental surveillance of food
processing plants Salmonella enterica Food safety, environmental
surveillance of food subsp. enterica serovar processing plants
Enteritidis Pseudomonas aeruginosa Pulmonary health
[0141] Low density assays systems are useful for numerous
environmental and clinical applications. Exemplary applications are
listed in Table 2. Medical conditions that can be identified,
diagnosed, prognosed, tracked, or treated based on data obtained
with a low density system include but are not limited to, cystic
fibrosis, chronic obstructive pulmonary disease, Crohn's Disease,
irritable bowel syndrome, cancer, rhinitis, stomach ulcers,
colitis, atopy, asthma, neonatal necrotizing enterocolitis,
obesity, periodontal disease and any disease or disorder caused by,
aggravated by or related to the presence, absence or population
change of a microorganism. Through the judicious selection of OTUs
to be included in a system, the system becomes a diagnostic device
capable of diagnosing one or more conditions or diseases with a
high level of confidence producing very low rates of false positive
or false negative readings.
[0142] In some embodiments, the low density systems also feature
confirmatory probes that are specific (complimentary) for genes or
sequences expressed in specific organisms. For example, the cafl
virulence gene of Yersinia pestis and the zonula occludens toxin
(zot) gene of Vibrio cholerae and also confirmatory probes to Y.
pestis or V. cholerae.
Kits
[0143] As used herein a "kit" refers to any delivery system for
delivering materials or reagents for carrying out a method
disclosed herein. In the context of assays, such delivery systems
include systems that allow for the storage, transport, or delivery
of arrays or beads with probes, reaction reagents (e.g., probes,
enzymes, etc. in the appropriate containers) and/or supporting
materials (e.g., buffers, written instructions for performing the
assay etc.) from one location to another. For example, kits include
one or more enclosures (e.g., boxes) containing the relevant
reaction reagents and/or supporting materials for assays disclosed
herein.
[0144] In one aspect of the present application, kits for analysis
of nucleic acid targets are provided. According to some
embodiments, a kit includes a plurality of probes capable of
determining the presence or quantity over 10, 20, 50, 100, 200,
500, 1000, 2000, 5000, 10000, 20000, 30000, 40000 50000 or 60000
different OTUs in a single assay. Such probes can be coupled to,
for example, an array or plurality of microbeads. In some aspects a
kit comprises at least 5, 10, 15, 20, 50, 100, 200, 500, 1000,
2000, 5000, 10000, 20000, 50000, 100000, 200000, 500000, 1000000 or
2000000 interrogation probes selected using the disclosed
methodologies and/or for use in the identification and/or
comparison of a biosignature of one or more samples.
[0145] The kit can also include reagents for sample processing,
including one or more agents described herein, alone or in
combination. In some embodiments, the reagents comprise an agent
that selectively modifies nucleic acid of dead cells. In some
embodiments, the reagents comprise reagents for the PCR
amplification of sample nucleic acids including primers to amplify
regions of a highly conserved sequence, such as regions of the 16S
rRNA gene. In some embodiments, the reagents comprise reagents for
the direct labeling of RNA, such as rRNA. In further embodiments,
the kit includes instructions for using the kit. In other
embodiments, the kit includes a password or other permission for
the electronic access to a remote data analysis and manipulation
software program. Such kits will have a variety of uses, including
environmental monitoring, diagnosing disease, monitoring disease
progress or response to treatment, and identifying a contamination
source and/or the presence, absence, or amount of one or more
contaminants.
Computer Implemented Methods
[0146] FIG. 7 illustrates an example of a suitable computing system
environment or architecture in which computing subsystems may
provide processing functionality to execute software embodiments of
the present application, including probe selection, analysis of
samples, and remote networking. The method or system disclosed
herein may also operational with numerous other general purpose or
special purpose computing system including personal computers,
server computers, hand-held or laptop devices, multiprocessor
systems, and the like.
[0147] The method or system may be described in the general context
of computer-executable instructions, such as program modules, being
executed by a computer. The method or system may also be practiced
in distributed computing environments where tasks are performed by
remote processing devices that are linked through a communications
network.
[0148] With reference to FIG. 7, an exemplary system for
implementing the method or system includes a general purpose
computing device in the form of a computer 102.
[0149] Components of computer 102 may include, but are not limited
to, a processing unit 104, a system memory 106, and a system bus
108 that couples various system components including the system
memory to the processing unit 104.
[0150] Computer 102 typically includes a variety of computer
readable media. Computer readable media includes both volatile and
nonvolatile media, removable and non-removable media and a may
comprise computer storage media. Computer storage media includes,
but is not limited to, RAM, ROM, EEPROM, flash memory or other
memory technology, CD-ROM, digital versatile disks (DVD) or other
optical disk storage, magnetic cassettes, magnetic tape, magnetic
disk storage or other magnetic storage devices.
[0151] The system memory 106 includes computer storage media in the
form of volatile and/or nonvolatile memory such as read only memory
(ROM) 110 and random access memory (RAM) 112. A basic input/output
system 114 (BIOS), containing the basic routines that help to
transfer information between elements within computer 102, such as
during start-up, is typically stored in ROM 110. RAM 112 typically
contains data and/or program modules that are immediately
accessible to and/or presently being operated on by processing unit
104. FIG. 7 illustrates operating system 132, application programs
134 such as sequence analysis, probe selection, signal analysis and
cross-hybridization analysis programs, other program modules 136,
and program data 138.
[0152] The computer 102 can also include other
removable/non-removable, volatile/nonvolatile computer storage
media. By way of example only, FIG. 7 illustrates a hard disk drive
116 that reads from or writes to non-removable, nonvolatile
magnetic media, a magnetic disk drive 118 that reads from or writes
to a removable, nonvolatile magnetic disk 120, and an optical disk
drive 122 that reads from or writes to a removable, nonvolatile
optical disk 124 such as a CD ROM or other optical media. Other
removable/non-removable, volatile/nonvolatile computer storage
media that can be used in the exemplary operating environment
include magnetic tape cassettes, flash memory cards, digital
versatile disks, digital video tape, solid state RAM, solid state
ROM, and the like. The hard disk drive 116 is typically connected
to the system bus 108 through a non-removable memory interface such
as interface 126, and magnetic disk drive 118 and optical disk
drive 122 are typically connected to the system bus 108 by a
removable memory interface, such as interface 128 or 130.
[0153] The drives and their associated computer storage media
discussed above and illustrated in FIG. 7, provide storage of
computer readable instructions, data structures, program modules
and other data for the computer 102. In FIG. 7, for example, hard
disk drive 116 is illustrated as storing operating system 132,
application programs 134, other program modules 136, and program
data 138. A user may enter commands and information into the
computer 102 through input devices such as a keyboard 140 and a
mouse, trackball or touch pad 142. These and other input devices
are often connected to the processing unit 104 through a user input
interface 144 that is coupled to the system bus, but may be
connected by other interface and bus structures, such as a parallel
port or a universal serial bus (USB). A monitor 158 or other type
of display device is also connected to the system bus 108 via an
interface, such as a video interface or graphics display interface
156. In addition to the monitor 158, computers can also include
other peripheral output devices such as speakers (not shown) and
printer (not shown), which can be connected through an output
peripheral interface (not shown).
[0154] The computer 102 can be integrated into an analysis system,
such as a microarray or other probe system described herein.
Alternatively, the data generated by an analysis system can be
imported into the computer system using various means known in the
art.
[0155] The computer 102 can operate in a networked environment
using logical connections to one or more remote computers or
analysis systems. The remote computer can be a personal computer, a
server, a router, a network PC, a peer device or other common
network node, and typically includes many or all of the elements
described above relative to the computer 102. The logical
connections depicted in FIG. 7 include a local area network (LAN)
148 and a wide area network (WAN) 150, but can also include other
networks. Such networking environments are commonplace in offices,
enterprise-wide computer networks, intranets and the Internet. When
used in a LAN networking environment, the computer 102 is connected
to the LAN 148 through a network interface or adapter 152. When
used in a WAN networking environment, the computer 102 typically
includes a modem 154 or other means for establishing communications
over the WAN 150, such as the Internet. The modem 154, which can be
internal or external, can be connected to the system bus 108 via
the user input interface 144, or other appropriate mechanism. In a
networked environment, program modules depicted relative to the
computer 102, or portions thereof, can be stored in the remote
memory storage device.
[0156] In further aspects of the present application,
computer-implemented methods are provided for analyzing the
presence or quantity of over 20, 50, 100, 200, 500, 1,000, 2,000,
5,000, 10,000, 20,000, 30,000, 40,000 50,000 or 60,000 different
OTUs in a single assay. In some embodiments, computer executable
logic is provided for determining the presence or quantity of one
or more microorganisms in a sample comprising: logic for analyzing
intensities from a set of probes that selectively binds each of at
least 20, 50, 100, 200, 500, 1,000, 2,000, 5,000, 10,000, 20,000,
30,000, 40,000 50,000 or 60,000 unique and highly conserved
polynucleotides and determining the presence of at least 97% of all
species present in said sample with at least 90%, 95%, 96%, 97%,
98%, 99% or 99.5% confidence level.
[0157] In some embodiments, computer executable logic is provided
for determining probability that one or more organisms, from a set
of different organisms, are present in a sample. The computer logic
comprises processes or instructions for determining the likelihood
that individual interrogation probe intensities are accurate based
on comparison with intensities of negative control probes and
positive control probes; a process or instructions for determining
likelihood that an individual OTU is present based on intensities
of interrogation probes from OTUs that pass a first quantile
threshold; and a process or instructions for penalizing one or more
OTUs that have passed the first quantile threshold based on their
potential for cross-hybridizing with other probes that have also
passed the first quantile threshold.
[0158] In some embodiments, computer executable logic is provided
for determining the presence of one or more microorganisms in a
sample. The logic allows for the analysis of a set of at least 1000
different interrogation perfect probes. The logic further provides
for the discarding of information from at least 10% of the
interrogation perfect match probes in the process of making the
determination. In some embodiments, the computer executable logic
is stored on computer readable media and represents a computer
software product.
[0159] In some embodiments, computer software products are provided
wherein computer executable logic embodying aspects of the
invention is stored on computer media like hard drives or optical
drives. In some embodiments, the computer software products
comprise instructions that when executed perform the methods
described herein for determining candidate probes.
[0160] In some further embodiments, computer systems are provided
that can perform the methods of the inventions. In some
embodiments, the computer system is integrated into and is part of
an analysis system, like a flow cytometer or a microarray imaging
device. In other embodiments, the computer system is connected to
or ported to an analysis system. In some embodiments, the computer
system is connected to an analysis system by a network connection.
In one example of a system that employs a network connection, a
sample is imaged using a commercially available imaging system and
software. The data is outputted using a standard data format like a
CEL file (AFFYMETRIX.RTM.), or a Feature Report file
(NIMBLEGEN.RTM.). Then the data is sent to a remote or central
location for analysis using a method disclosed herein. In some
embodiments, a standardized analysis is performed providing signal
normalization, OTU quantification, and visual analytics. In other
embodiments, a customized analysis is performed using a fixed
protocol designed for the user's particular needs. In still other
embodiments, a user configurable analysis is used, include a
protocol that allows for the user to adjust at least one variable
before each analysis run.
[0161] After processing, the results are stored in an exchangeable
binary format for later use or sharing. Additionally, hybridization
scores and OTU probability values can be exported to a tab
delimited file or in a format compatible with UniFrac (Lozupone, et
al., UniFrac--an online tool for comparing microbial community
diversity in a phylogenetic context, BMC Bioinformatics, 7, 371;
2006) for further statistical analysis of the detected sample
communities.
[0162] In some embodiments, multiple, interactive views of the data
are available, including taxonomic trees, heatmaps, hierarchical
clustering, parallel coordinates (time series), bar plots, and
multidimensional scaling scatterplots. In some embodiments, the
taxonomy tree displays the mean intensities for each detected OTU
and displays the leaves of the tree as a heatmap of samples. The
tree can be dynamically pruned by filtering OTUs below a certain
intensity or probability threshold. Additionally, the tree can be
summarized at any level from phylum to subfamily. In other
embodiments, the user can hierarchically cluster both OTUs and
samples using any of the standard distance and linkage methods from
the integrated C Clustering Library (de Hoon, et al., Open source
clustering software, Bioinformatics, 20, 1453-1454; 2004), and the
resulting dendrograms displayed in a secondary heatmap window. In
some embodiments, a third window is provided that displays
interactive bar plots of differential OTU intensities to facilitate
pairwise comparison of samples. For any two samples, the height of
the difference bars displays either the absolute or relative
difference in mean intensity between OTUs. The bars can be grouped
and sorted along the horizontal axis by any taxonomic rank for easy
identification and comparison. Synchronized selection and filtering
affords users the unique ability to seamlessly navigate between
multiple views of the data. For example, users can select a cluster
in the hierarchical clustering window and simultaneously view the
selected organisms in the taxonomy tree, immediately revealing both
their phylogenetic and environmental relationship. In further
embodiments, the data from the analysis system, i.e., analysis
system or flow cytometer, can be co-analyzed and displayed with
high-throughput sequencing data. In some embodiments, for each
organism identified as present in the sample, the user is able to
view a list of other environments where the particular organism is
found.
[0163] In some embodiments, the screen displays are dynamic and
synchronized to allow the selection or filtration of OTUs with
changes to any view simultaneously reflected in all other views.
Additionally, OTUs confirmed by 16S rRNA gene, 18S rRNA gene, or
23S rRNA gene sequencing can be co-displayed in all views.
Business Methods
[0164] In some aspects of the present application, a business
method is provided wherein a client images an array or scans a lot
of microparticles and sends a file containing the data to a service
provider for analysis. The service provider analyzes the data and
provides a report to the user in return for financial compensation.
In some embodiments, the user has access to the service provider's
analysis system and can manipulate and adjust the analysis
parameters or the display of the results.
[0165] In another aspect of the present application, a business
method is provided wherein a client sends a sample to be processed,
imaged or scanned and the data analyzed for the presence or
quantity of organisms. The service provider sends a report to the
client in return for financial compensation. In some embodiments,
the client has access to a suite of data analysis and display
programs for the further analysis and viewing of the data. In
further embodiments, the service provider first provides a system
or kit to the client. The kit can include a system to assay a
majority, or the entirety of the microbiome present or the system
can contain "down-selected" probes designed for particular
applications. After sample processing and imaging, the client sends
the data for analysis by the service provider. In some embodiments,
the client report is electronic. In other embodiments, the client
is provided access to a suite of data analysis and display programs
for the further viewing, manipulation, comparison and analysis of
the data. In some embodiments, the client is provided access to a
proprietary database in which to compare results. In other
embodiments, the client is provided access to one or more public
databases, or a combination of private and public database for the
comparison of results. In some embodiments, the proprietary
database includes the pooled results (fingerprints, biosignatures)
for normal samples or the pooled results from particular abnormal
situations such as a disease state. In some embodiments, the
biosignatures are continuously and automatically updated upon
receipt of a new sample analysis.
[0166] In some embodiments, the database further comprises highly
conserved sequence listings. In some embodiments, the database is
updated automatically as new sequence information becomes
available, for instance, from the National Institutes of Health's
Human Microbiome Project. In further embodiments, probe sets are
automatically updated based on the new sequence information.
Continuous upgrading of the sequence information and refinement of
the probe sets allow for increasing accuracy and resolution in
determining the composition of microbiomes and the quantity of
their individual constituents. In some embodiments, the system
compares earlier microbiome biosignatures with later microbiome
biosignatures from the same or substantially similar environments
and analyzes the changes in probe set composition and hybridization
signal analysis parameters for information that is useful in
improving or refining the discrimination between related OTUs,
identification and quantification of microbiome constituents, or
increasing accuracy of the determinations.
[0167] In some embodiments, the database compiles information about
specific microbiomes, for example, the microbiota associated with
healthy and unhealthy human intestinal microflora including, age,
gender and general health status of host, geographical location of
host, host's diet (i.e., Western, Asian or vegetarian), water
source, host's occupation or social status, host's housing
status.
[0168] In some embodiments, the reference healthy/normal signatures
for adults, male and female, and children can be used as benchmarks
to identify presymptomatic and symptomatic disease states, response
to treatments/therapies, infection, and/or secondary infection
associated with disease. In some embodiments, the client is
provided with a diagnosis or treatment recommendation based on the
comparison between the client's sample microbiome and one or more
reference microbiome.
[0169] In some embodiments, the present application contemplates
using 454 pyrosequencing for detection of OTUs in a sample. The
OTUs are detected using barcoded amplicons. The amplification can
occur on beads. 454 sequencing protocols are described in, for
example, (Poinar H N, et. al Science. 2006 Jan. 20; 311(5759) pages
392-4) which is incorporated herein by reference.
EXAMPLES
[0170] The following examples are given for the purpose of
illustrating various embodiments of the invention and are not meant
to limit the present invention in any fashion. The present
examples, along with the methods described herein are presently
representative of preferred embodiments, are exemplary, and are not
intended as limitations on the scope of the invention. Changes
therein and other uses which are encompassed within the spirit of
the invention as defined by the scope of the claims will occur to
those skilled in the art.
Example 1
Exclusion of Dead Cell DNA from Detection
[0171] FIG. 1 illustrates one non-limiting embodiment of the
present application. To briefly summarize, a test sample suspected
of containing a mixture of vegetative cells, live, and dead
bacterial endospores is first heat shocked at 80.degree. C. for 15
minutes (Step 1). This step will eliminate any vegetative cells
present in the sample, whereas spores will remain unaffected. The
sample is then treated with PMA followed by incubation in the dark.
Thereafter, the sample is exposed to visible light for 5 minutes,
so that the DNA from dead cells only will be cross-linked. In
general, excess PMA is removed from a sample after the treatment
step and prior to or as part of subsequent nucleic acid extractions
steps, such as by centrifugation and washing. Following the initial
PMA treatment, the sample is concentrated by centrifugation and the
fluorescence of the resulting reaction mixture, which contains dead
cells and spores, is measured. The sample is then divided into two.
The first aliquot is heated to 100.degree. C. for 18 minutes (Step
2a) to kill all microbes, including endospores, and the
fluorescence of the reaction mixture is measured as mentioned
before. The Step 2a fluorescence measured is subtracted from Step 1
fluorescence to determine total viable spores in the test sample.
In Step 2b, qPCR is used to provide confirmatory quantitation of
the total viable spores by detecting DNA concentration, since the
PMA intercalated DNA from dead cells/spores would be unavailable
for PCR amplification. In some embodiments, the total detection
time, including PCR reaction for low-biomass samples (1 to 10.sup.3
spores per reaction), will be within 60 minutes.
[0172] A simplified work flow of the PMA-Microarray approach is
shown in FIG. 2. To summarize, appropriate samples are taken
according to established sampling protocols. Each sample is then
divided into three portions and processed separately. The first
sample is treated with PMA, light inactivated, and DNA is extracted
using the Maxwell 16 automated system (Promega Corp.). In the
second sample, DNA is extracted using an automated DNA extracting
system.
[0173] In some embodiments, DNA from both the first and second
samples is then used for 16S rRNA gradient PCR, the product of
which is further used for PhyloChip analysis; the 16S rRNA qPCR is
used to further measure the total microbial concentration with and
without PMA treatment; and the third and last sample is used for
microscopy analysis. The BacLight stain (Invitrogen Crop.) is used
to distinguish between viable and nonviable cells present in a
sample; samples that are stained green are viable and samples that
are stained red are nonviable. In some embodiments, these results
will then be used to correlate with the PhyloChip, and qPCR
results.
[0174] In some embodiments, the methods are used for the detection
and identification of only viable microbes in clean rooms or
satellites, such as for the purposes of planetary protection.
Example 2
PMA Method
[0175] An objective was to evaluate a rapid and sensitive spore
detection concept that will estimate viable microbial spores.
[0176] Real-time quantitative-PCR methods can be used to estimate
microbial populations. This unique method is based on the use of a
fluorescent DNA intercalating agent propidium monoazide (PMA),
which can only penetrate the membranes of dead cells. The
combination approach described in this example is referred to as
the "PMA-method." The present example describes the development of
a reliable and efficient method for extracting DNA from spores. The
spore structure is complex and made up of several layers that
impart resistance to extreme environmental conditions. These
several layers which include the cortex and coat structures
together are probably responsible for difficulties in releasing DNA
from spores. Using a urea-based extraction buffer, it was possible
to degrade the outer spore coats. With the spore coats removed the
spores were more susceptible to lytic degradation. After lytic
degradation, DNA extraction of both viable and dead spores was
consistent in yield and amplification efficiency. Several
alternative DNA extraction methods were tried including the method
by Rawsthorne et al (Rawsthorne and Phister 2009, Letters in
Applied Microbiology, 49: 652-654), which utilized DTT, and were
found to be less efficient.
[0177] DNA intercalating agents have been used to selectively
distinguish between viable and dead bacterial cells, as they can
only penetrate the membrane of dead cells (Rudi et al. 2005,
Applied and Environmental Microbiology, 71(2): 1018-1024; Nogva et
al. 2003, BioTechniques, 34: 804-813). Once the DNA of dead cells
is crosslinked, it is unavailable for PCR amplification (Nocker et
al. 2007, Applied and Environmental Microbiology, 73(16):
5111-5117). Recently, Rawsthorne et al (2009) demonstrated that PMA
can be used to selectively distinguish between viable and
non-viable B. subtilis spores.
[0178] DNA isolated from E. coli was treated with PMA to show that
PMA can be used to intercalate DNA and make it unavailable for PCR
amplification (FIG. 3).
[0179] Using the method of Rawsthorne et al (2009), B. pumilus
SAFR-032 spores were heat inactivated at 121.degree. C. for 15 min
in two consecutive cycles. DNA was extracted using both the urea
and DTT based methods. When compared to viable spores, DNA after
one and two consecutive autoclave cycles was significantly
degraded, and remaining DNA was barely detectable by qPCR. This
means that PMA pre-treatment of thermally inactivated B. pumilus
spores may not be necessary when using rigorous time/temperature
combinations.
[0180] Alternative killing treatments including a lower heating
temperature (90.degree. C.) and the use of UV were utilized to see
how the DNA from B. pumilus is affected and if PMA treatment is
necessary. FIGS. 5 and 6 show plate count compared to qPCR results.
Based on the qPCR results, if the alternative treatments are used,
spores will have to be treated with PMA as the DNA is not
degraded.
[0181] A comprehensive census of the viable microbes on the
surfaces of spacecraft can be used to maximize detection of
resident microbiota. For example, PMA-based quantitation can be
used to elucidate the viable microbial community that could survive
to reach other planets.
Example 3
Evaluation of PMA-Treated Sample by Phylogenetic Array
[0182] A phylogenetic array is fabricated with some of the
organism-specific and OTU-specific 16s rRNA probes selected by the
methods described herein and in co-pending international
application PCT/US2010/040106. The phylogenetic array in this
example consists of 1,016,064 probe features, arranged as a grid of
1,008 rows and columns. Of these features, -90% are oligonucleotide
perfect match (PM) or mis-match (MM) probes with exact or inexact
complementarity, respectively, to 16s rRNA genes. In general, MM
probes have one or more sequence differences with respect to PM
probes, resulting in one or more mismatches with a target sequence.
Each PM probe is paired with a mismatch control probe to
distinguish target-specific hybridization from background and
non-target cross-hybridization. The remaining probes are used for
image orientation, normalization controls, or for pathogen-specific
signature amplicon detection using additional targeted regions of a
chromosome. Each high-density 16s rRNA gene microarray is designed
with additional probes that (1) target amplicons of prokaryotic
metabolic genes spiked into the 16s rRNA gene amplicon mix in
defined quantities just prior to fragmentation and (2) are
complementary to pre-labelled oligonucleotides added into the
hybridization mix. The first control collectively tests the target
fragmentation, labeling by biotinylation, array hybridization, and
staining/scanning efficiency. It also allows the overall
fluorescent intensity to be normalized across all the arrays in an
experiment. The second control directly assays the hybridization,
staining and scanning.
[0183] A microbial sample is treated with PMA followed by
incubation in the dark. Thereafter, the sample is exposed to
visible light for 5 minutes, so that the DNA from dead cells only
will be cross-linked. Following the initial PMA treatment, the
sample is concentrated by centrifugation, and DNA is extracted
using the Maxwell 16 automated system (Promega Corp.). Extracted
DNA is PCR amplified, and DNA is hybridized to the phylogenetic
array to detect and optionally quantify OTUs represented by viable
cells and/or spores from the sample.
[0184] Complementary targets to the probe sequences hybridize to
the array and fluorescent signals are captured as pixel images
using standard AFFYMETRIX.RTM. software (GeneChip Microarray
Analysis Suite, version 5.1) that reduce the data to an individual
signal value for each probe and is typically exported as a human
readable CEL' file. Background probes are identified from the CEL
file as those producing intensities in the lowest 2% of all
intensities. The average intensity of the background probes is
subtracted from the fluorescence intensity of all probes. The noise
value (N) is the variation in pixel intensity signals observed by
the scanner as it reads the array surface. The standard deviation
of the pixel intensities within each of the identified background
probe intensities is divided by the square root of the number of
pixels comprising that feature. The average of the resulting
quotients was used for N in the calculations described below.
[0185] Using previous methods, probe pairs scored as positive are
those that meet two criteria: (i) the fluorescence intensity from
the perfectly matched probe is at least 1.3 times greater than the
intensity from the mismatched control probe, and (ii) the
difference in intensity, PM minus MM, is at least 130 times greater
than the squared noise value (>130 N2). The positive fraction
(PosFrac) is calculated for each probe set as the number of
positive probe pairs divided by the total number of probe pairs in
a probe set. An OTU is considered `present` when its PosFrac for
the corresponding probe set was >0.92 (based on empirical data
from clone library analyses). Replicate arrays can be used
collectively in determining the presence of each OTU by requiring
each to exceed a PosFrac threshold. Present calls are propagated
upwards through the taxonomic hierarchy by considering any node
(subfamily, family, order, etc.) as `present` if at least one of
its subordinate OTUs is present.
[0186] Hybridization intensity us the measure of OTU abundance and
is calculated in arbitrary units for each probe set as the trimmed
average (maximum and minimum values removed before averaging) of
the PM minus MM intensity differences across the probe pairs in a
given probe set. All intensities<1 are shifted to 1 to avoid
errors in subsequent logarithmic transformations.
Example 4
Phylogenetic Array Analysis
[0187] Following sample preparation, application, incubation and
washing, using standard techniques, phylogenetic arrays were
scanned using a GeneArray Scanner from Affymetrix. The scan was
captured as a pixel image using standard AFFYMETRIX.RTM. software
(GCOS v1.6 using parameter: Percentile v6.0) that reduces the data
to an individual row in a text-encoded table for each probe. See
Table 3.
TABLE-US-00003 TABLE 3 Exemplary Display of Array Data [INTENSITY]
NumberCells = 506944 CellHeader = XY NPIXELS MEAN STDV 047.9 025
167.0 11060.2 025 4293.0 243.7 036 179.3 3681.5 025 4437.0
[0188] Each analysis system had approximately 1,016,000 cells, with
1 probe sequence per cell. The analysis system scanner recorded the
signal intensity across the array, which ranges from 0 to 65,000
arbitrary units (a.u) in a regular grid with -30-45 pixels per
cell. A 2 pixel margin was used between adjacent cells, leaving
approximately 25-40 pixels per probe of usable signal. From these
pixels, the AFFYMETRIX.RTM. software computed the 75th percentile
average pixel intensity (denoted as the "MEAN"), the standard
deviation of signal intensity among the about 25-40 pixels (denoted
as the "STDV"), and the number of pixels used per cell (denoted as
"NPIXELS"). Any cells that had pixels that were three standard
deviations apart in signal intensity were classified as
outliers.
[0189] The analysis systems were divided into a user-defined number
of horizontal and vertical divisions. By default, four horizontal
and four vertical divisions were created resulting in 16 regularly
spaced sectors for independent background subtraction. The
background intensity was computed independently for each quadrant,
as the average signal intensity of the least intense 2% (by
default) of probes in that quadrant. The background intensity was
then subtracted from all probes before further computation.
[0190] The noise value was estimated according to recommendations
in the AFFYMETRIX.RTM. GeneChip User Guide v3.3. Noise (N) was due
to variations in pixel intensity signals observed by the scanner as
it read the array surface and was calculated as the standard
deviation of the pixel intensities within each of the identified
background cells divided by the square root of the number of pixels
comprising that cell. The average of the resulting quotients was
used for N in the calculations described below:
N = i .di-elect cons. B Si pix i scalarB ##EQU00004##
[0191] where
[0192] B is a background cell
[0193] S.sub.i is the standard deviation among the pixels in B
[0194] pix.sub.i is the count of pixels in B
[0195] scalarB is the count of all background cells, cumulative
[0196] The intensities of all probes were then scaled so that the
average observed signal intensity of the spiked in probes had a
pre-determined signal strength. This was accomplished by finding a
scaling factor (Sf) in order to force the mean response of the
corresponding PM probes to a target mean using the equation
below:
Sf = e _ t / i .di-elect cons. Kpm e i scalarK pm ##EQU00005##
[0197] where
[0198] .sub.t=targeted mean intensity (default: 2500)
[0199] scalarK.sub.pm=count of probes complementing any
spike-in
[0200] S.sub.f=scaling factor
[0201] Typically, the pre-determined signal strengths ranged from
about 0 to about 65,000. Once the scaling factor was derived, all
cell intensities were multiplied by the scaling factor.
[0202] The noise (N) was scaled by the same factor:
N.sub.s=N.times.S.sub.f; where N.sub.s=scaled noise, N=unscaled
noise, and S.sub.f=scaling factor.
[0203] As an alternative or optional step, MM probes with high
hybridization signal responses were identified and the probe pairs
were eliminated where:
[ ( MM PM > srt .gamma. ) ( MM - PM > N s .times. sdtm
.gamma. ) ] [ PM .di-elect cons. O ] [ MM .di-elect cons. O ]
##EQU00006##
[0204] where:
[0205] N.sub.s=scaled noise
[0206] O=outlier set
The remaining probe pairs were scored by:
( PM MM > srt ) ( PM - MM > N s 2 .times. sdtm )
##EQU00007##
[0207] where
[0208] PM=sealed intensity of the perfect match probe
[0209] MM=scaled intensity of the perfect match probe
[0210] srt=standard ratio threshold (default: 1.3)
[0211] sdtm=standard difference threshold multiplier (default:
130)
[0212] N.sub.s=scaled noise
[0213] After classifying an OTU as "present", the present call was
propagated upwards through the taxonomic hierarchy by considering
any node (subfamily, family, order, etc.) as `present` if at least
one of its subordinate OTUs was present.
[0214] Hybridization intensity was the measure of OTU abundance and
was calculated in arbitrary units for each probe set as the trimmed
average (maximum and minimum values removed before averaging) of
the PM minus MM intensity differences across the probe pairs in a
given probe set.
[0215] While preferred embodiments of the present application have
been shown and described herein, it will be obvious to those
skilled in the art that such embodiments are provided by way of
example only. Numerous variations, changes, and substitutions will
now occur to those skilled in the art without departing from the
invention. It should be understood that various alternatives to the
embodiments of the invention described herein may be employed in
practicing the invention. It is intended that the following claims
define the scope of the invention and that methods and structures
within the scope of these claims and their equivalents be covered
thereby.
Example 5
Clean Room Sampling, PMA Processing and Phylogenetic Array
Analysis
[0216] The two Class 100K certified as per Fed-Std-209E (equivalent
ISO 14644-1 Class 8) cleanroom facilities were examined. Mission
critical hardware and components were assembled in the first
cleanroom site, Jet Propulsion Laboratory's Spacecraft Assembly
Facility [JPL-SAF], whereas the second cleanroom site, JPL
cleanroom 144, did not support mission critical activities but
maintained its Class 100K certification. Both of these cleanroom
facilities were operated at a positive pressure, with temperatures
in the range of 20+4.degree. C., and relative humidity ranging from
30 to 50%. In both cases, the total hydrocarbon content of the
facility air (gases and vapors) was below 15 ppm (calculated as
methane). The Class 100K environment typically did not exceed 10K
particulate obstruction levels during 30 days of
assembly/integration activities. Measured non-volatile residue
levels did not exceed 0.3 .mu.g/cm.sup.2.
[0217] For convenience and differentiation, 454
pyrosequencing-based sequence discrimination results in the
clustering in what are referred to as "molecular operational
taxonomic unit (MOTU)." Variation in DNA sequence among microbes
can arise via naturally occurring evolutionary events and/or
methodological errors (e.g., homopolymer repetition in
pyrosequencing). It is the goal of the MOTU-based classification
and clustering system presented here, and in detail elsewhere
(Blaxter & Floyd, TRENDS in Ecology and Evolution 18: 268-269,
2003), to separate these two sources of sequence variation based on
known error rates in sequencing and measured levels of difference
across various taxonomical schemes. The accuracy and specificity of
a MOTU-based system can be derived from measured levels of
between-taxa and within-group variation from well-defined
populations, and of observational error obtained by re-sequencing
(Blaxter & Floyd, 2003). In a similar vein, PhyloChip DNA
microarrays employ multiple .about.25 nt probes which collectively
represent the full length .about.1.5-kb 16S rRNA gene of each
taxon. In this study, PhyloChip-derived taxonomic units (OTU) are
delineated in accordance with the hybridization scores of a given
set of 37 or more specific 25-mer probes, which have been
previously designed based on the prevalence of members of a given
OTU, and dissimilarity in DNA sequences outside of the given OTU.
Ultimately, a microorganism can be assigned to only one given MOTU
via either similarity within a homologous sequenced DNA fragment or
hybridization-based OTU, but neither MOTU nor OTU need be congruent
with other taxonomic schemes.
[0218] PhyloChip G3 (DNA Microarray, Generation 3) Analysis:
[0219] Bacterial 16S rRNA genes were amplified from DNA
preparations from each sample using the primers 27F (5'-AGA GTT TGA
TCC TGG CTC AG) and 1492R (GGT TAC CTT GTT ACG ACT T). PCR
conditions were as follows: 1 cycle of initial melting for 3 min at
95.degree. C., followed by 35 cycles of 30 sec melting at
95.degree. C., 30 sec annealing over a 48-57.5.degree. C. gradient,
and 2 min extension at 72.degree. C., with a final 10 min
incubation at 72.degree. C. To maximize observed diversity, four
separate PCR reactions were performed for each sample using a
gradient of annealing temperatures (48.degree. C., 50.1.degree. C.,
54.4.degree. C., and 57.5.degree. C.). Archaeal amplification of
16S rRNA genes was performed with primers 4aF and 1492uR, and
resulting amplicons were purified via gel-excision if/when
quantifiable amounts of product had been generated. A maximum of
450 ng of PCR product from each sample was used for phylogenetic
microarray analysis. A detailed explanation of the processing of
the PhyloChip assay has been described elsewhere (Hazen, et al.,
Science 330: 204-208, 2010). Briefly, pooled PCR products from each
sampling event were spiked with known amounts of synthetic control
16S rRNA gene fragments and non-16S rRNA gene fragments.
Fluorescence intensities arising from these controls were used as
standards for normalization among samples. Target fragmentation,
biotin labeling, PhyloChip hybridization, scanning and staining, as
well as background subtraction, noise calculation and detection,
and quantification criteria, were performed as previously reported
(DeSantis, et al., Microb. Ecol. 53: 371-383, 2007; Hazen, et al.,
2010). OTUs were deemed present if quartiles of the ranked r scores
(response score to determine the potential of a probe pair
responding to a target and not to the background) met the following
criteria: rQ1.gtoreq.0.70, rQ2.gtoreq.0.95, rQ3.gtoreq.0.98 (stage
1 analysis). Subfamilies that had an rxQ3 value
(cross-hybridization adjusted response score) of .gtoreq.0.6 were
deemed present following an additional stage 2 analysis requirement
for OTU calling within this subfamily.
[0220] Phylochip G3 Data Processing:
[0221] Following stage 2 analysis, hybridization intensities were
log 2*1000 transformed for linearization. Subfamily filtering was
performed manually by choosing representative OTUs of each
subfamily depending on how often a OTU was called present
throughout the samples. OTUs and subfamilies were subsequently
clustered and classified in accordance with the recently released
Greengenes taxonomy (McDonald, et al., ISME J, 2011). In order to
better understand the phylogenetic relationships of detected
OTUs/subfamilies, multiple sequence alignments of representative
sequences were retrieved from the SILVA database (Pruesse, et al.,
Nucleic Acids Res. 35: 7188-7196, 2007). Neighbor joining
phylogenetic trees were compiled based on the 70,000 character
alignments of each OTU via MEGA4 software (Tamura, et al., Mol.
Biol. Evol. 24: 1596-1599, 2007). Subsequently, trees were mated
with corresponding heatmaps (either presence/absence or relative
abundance) and rendered in iTOL (Letunic & Bork, Bioinformatics
23: 127-128, 2007). Environmental clustering (Non-metric
Multi-Dimensional Scaling, N-MDS) based on abundance scores and
Bray Curtis distance was performed using the R programming
environment (www.r-project.org, Vegan and MASS package). Weighted
principal component analysis was performed using the FAST Unifrac
interface, where OTUs were grouped into subfamilies and the number
of distinct OTUs per subfamily served as weighting (Hamady, et al.,
ISME J 4: 17-27, 2010). Pearson correlation of abundance values
between observed OTUs and various environmental factors (pH,
temperature) were generated with Microsoft Excel (2007).
Correlations with r>|.877| for comparison among three samples
and r>|.811| for comparison among four samples were considered
statistically significant (null hypothesis: OTUs do not correlate
with the environmental factor tested). Graphs were constructed with
the SigmaPlot 10.0 software suite.
[0222] PhyloChip G3 DNA microarray analyses and associated
biostatistical processing were carried out as described recently
(Hazen et al., "Deep-sea oil plume enriches indigenous
oil-degrading bacteria," Science 330(6001):204-208 (2010); Cooper
et al., "Comparison of innovative molecular approaches and standard
spore assays for assessment of surface cleanliness." Appl. Environ.
Microbiol., 77(15):5438-5444 (2011), the contents of which are
hereby expressly incorporated by reference). OTUs that were called
present in negative control samples (e.g., sampling blanks,
handling controls) were removed from the entire data set.
Non-metric multi dimensional scaling was performed on abundance
values of resulting OTUs using the R programming environment (Vegan
package, http://www.r-project.org/). Phylogenetic classification of
OTUs was performed using GreenGenes (DeSantis, et al., Appl Environ
Microbiol 72: 5069-5072, 2006) in combination with SILVA (Pruesse,
et al., 2007) and RDP II (Cole, et al., 2005). OTUs that represents
majority of the living bacterial population were identified based
on two criteria: A) the OTU should be present based on
presence/absence call by the phyloChip analysis B) OUT should have
a higher abundance value in PMA treated sample as compared to
non-PMA treated sample. The total OTUs (1277) detected were grouped
into 126 OTUs at genus level by selecting only those which exhibit
higher abundance in PMA treated samples. Phylogenetic trees were
constructed in MEGA 4 (Tamura, et al., 2007) based on an 70,000
character alignment (SILVA, (Pruesse, et al., 2007) and the
neighbour joining method. Heat maps of abundance ratios and trees
were rendered in iTOL (Letunic & Bork, 2007).
[0223] 454 Pyrosequencing and Data Analysis:
[0224] The 16S universal bacterial primers 28F-5' GAG TTT GAT CNT
GGC TCA G 3' (SEQ ID NO: 101) and 519R 5'-GTN TTA CNG CGG CKG
CTG-3' (SEQ ID NO:102) were used to PCR amplify the .about.500 bp
V1 to V3 hypervariable region of the 16S rRNA gene. Bacterial
tag-encoded FLX amplicon pyrosequencing (bTEFAP) was performed as
described previously (Dowd, et al., 2008). In preparation for
FLX-Titanium sequencing (Roche, Nutley, N.J.), template DNA
fragment size and concentration were accurately measured with a
TBS-380 Fluorometer using picogreen fluorescent dyes (Turner
Biosystems, CA, USA). Total volume of PCR amplicon used for
emulsion PCR was 2 uL for strong positive (>15 pg/uL); 5 uL for
weak positive (5 and 15 pg/uL), and 20 uL for negative (<5
pg/uL) PCR products from individual samples. This normalization
step helped to minimize bias in resulting sequence and/or tag
frequency in favor of strong PCR products. A sample of
9.6.times.106 double-stranded DNA molecules/ml (mean=625 bp) were
combined with 9.6.times.106 DNA capture beads, and then amplified
by emulsion PCR. Following bead recovery and enrichment, the bead
attached DNAs were denatured with NaOH, and sequencing primers were
annealed. A four-region 454 sequencing run was performed on a GS
PicoTiterPlate (PTP) using the Genome Sequencer FLX System (Roche,
Nutley, N.J.). A maximum of 40 distinctly barcoded samples were
analyzed on each quarter region of the PTP. All FLX procedures were
performed according to the Genome Sequencer FLX System's
manufacturer's instructions (Roche, Nutley, N.J.). All bTEFAP
procedures were performed at the Research and Testing Laboratory
(RTL; Lubbock, Tex.) in accordance with well established protocols
(www.researchandtesting.com).
[0225] bTEFAP-Derived Bacterial Diversity and Data Analysis:
[0226] All resulting 454 pyrosequences were processed and analyzed
using the MOTHUR software package (Schloss, et al., Appl Environ
Microbiol 75: 7537-7541, 2009). The AmpliconNoise algorithm was
implemented to reduce sequencing error. Sequences were removed from
the analysis if they i) did not contain the primer and/or barcode
sequence, ii) had a total sequence length<200 bp, or iii) had a
quality score<25. The filtered sequences were assigned to
individual samples according to their previously engineered 12-nt
barcodes. Unique sequences were aligned using the SILVA reference
alignment (Schloss, 2009), and only the overlapping region was
considered for analyses. After subtracting all chimeras from
consideration, remaining high-quality sequences were clustered and
classified according to the recently released Greengenes training
set and taxonomy (McDonald, et al., 2011, Werner, et al., 2012).
Sequences were clustered into molecular operational taxonomic units
(MOTU) at the 0.03 level of resolution.
[0227] Controls:
[0228] Very low amounts of PCR products were obtained for BisKit
blank, negative control (<2 pg/uL). This indicates that DNA
associated with sampling devices and reagents used in this study is
in extremely low concentration and extraneous nature rather than
from intact bacterial cells. This extraneous DNA is not available
for PCR reaction after PMA treatment hence yields further reduced
PCR products.
[0229] Lab controls and field blanks were also treated with PMA to
assess relative percent viability of contaminant microbiota. For
all of these cleanroom control and blank samples rrn copy numbers
remained at 10.sup.2 irrespective of whether they had been
subjected to PMA treatment. The signals detected in these control
samples, implying a presence of <100 viable contaminant
organisms, were present at insignificant levels in relation to
those arising from the actual samples of interest, and were thus
ignored
[0230] Results:
[0231] Total bacterial burden as assessed by universal qPCR, and
the various characteristics of the samples collected during this
study were measured. When PMA was omitted from the molecular
processing, the qPCR-derived total bacterial burden
(viable+non-viable) of the floors and GSE samples collected in the
mission-critical JPL-SAF was .about.10.sup.6 rrn copies/m.sup.2.
After treatment with PMA, the viable bacterial population of
JPL-SAF samples as measured via PMA-qPCR was .about.7% and 41% for
floors and GSE, respectively. Samples collected from the floor and
GSE of the non-mission critical JPL-144 facility yielded much
higher rrn copy numbers (more than one-log) both with and without
PMA-treatment. However, the percent viable portion of the total
bacterial population present in the JPL-144 samples was .about.21%
and 7% for floors and GSE samples, respectively. Upon comparative
analysis, JPL-144 floors housed a .about.66.7-fold greater total
bacterial burden than their mission-critical JPL-SAF counterparts.
Similarly, JPL-144 GSE samples were .about.15% more laden with
total bacteria (viable+non-viable) than the JPL-SAF GSE
materials.
[0232] 454 Pyrosequencing:
[0233] The concentration of 500-bp amplicons resulting from pre-454
pyrosequencing PCR was <3.75 pg/uL for all samples treated with
PMA, and >5 pg/uL in all samples when PMA treatment was omitted.
A very strong 17 pg/uL PCR product resulted from one untreated
JPL-144 floor sample (GI-42-1), which was 2.3-fold higher than that
arising from its corresponding JPL-SAF floor sample (GI-36-4). The
untreated JPL-144 GSE material (GI-42-2) exhibited 1.6-fold greater
PCR amplicon concentrations than their untreated mission-critical
JPL-SAF GSE counterparts (GI-36-3). After having been pretreated
with PMA, even the mission critical SAC floor sample yielded fewer
PCR products (5.4 pg/uL) than the GSE material housed in the
JPL-144 cleanroom (8.9 pg/uL). In general, irrespective of the type
of sample analyzed, a considerable decrease in PCR yield was
observed for all samples pretreated with PMA.
[0234] A breakdown of the number of MOTUs observed in the various
samples was examined over the course of this study. Overall, the
JPL-144 GSE samples housed 4.7-fold more bacterial MOTUs than the
mission-critical GSE, whereas the cleanroom floor surfaces from
each of these cleanrooms yielded a roughly equivalent number of
MOTUs. The mission critical JPL-SAF floor was rich in MOTUs
affiliated with physiologically recalcitrant bacteria
(Actinobacteria, Deinococci, Acidobacteria, and Firmicutes),
whereas the JPL-144 cleanroom floor harbored predominantly
Proteobacterial MOTUs. It was particularly apparent that the few
acidobacterial MOTUs (4 MOTUs) in JPL-SAF floor samples were
present in great abundance (112 sequences). However, the GSE
samples exhibited no such corollaries between MOTU numbers and
sequence occurrence.
[0235] Anywhere from one to 528 high-quality pyrosequences
(>250-bp) were obtained from samples that had been pretreated
with PMA, whereas 1,783 to 15,914 high-quality pyrosequences were
recovered from untreated samples. Regardless of sample type, PMA
treated samples consistently yielded significantly fewer
pyrosequences that their untreated counterparts. Even when PMA
treatment was omitted, the mission critical JPL-SAF cleanroom floor
sample (GI-36-4) yielded far fewer pyrosequences (4,318
pyrosequences) than the non-mission JPL-144 cleanroom floor sample
(GI-42-1; 15,914 pyrosequences). Approximately 59% of all of the
pyrosequences retrieved from these two distinct facility floor
samples were present in both, though this shared fraction
represented only 7.9% of the total observed MOTUs (69 out of 872).
The relative abundance of pyrosequences retrieved from the
untreated JPL-SAF samples was plotted as a Venn diagram. Even
though equivalent surface areas were sampled from the floor and GSE
of the JPL-SAF (18 m2 each), only 6,101 pyrosequences were
retrieved from the GSE samples whereas the floor gave rise to
20,232 sequences. Between the JPL-SAF floor and GSE samples,
.about.38% of the total pyrosequences observed were shared, but
constituted a mere .about.7.5% of the total MOTUs (43 out of
574).
[0236] The MOTUs shared between two distinct sample sets, for
example floors of JPL-SAF vs. JPL-144 or JPL-SAF floors vs. JPL-SAF
GSE, were comparatively analyzed. Whenever the difference in the
number of representative sequences per MOTU was less than 10, the
comparison between MOTU sets was deemed moot and thus omitted from
consideration. Following this subtraction, the cleanroom floor
comparative sample set retained .about.92% of the total
pyrosequences observed, however, 87.5% of the total MOTUs (711 out
of 872) were eliminated from this calculation. Similarly,
.about.40% of the total pyrosequences compared in the JPL-SAF floor
vs. GSE sample set were eliminated, corresponding to a 77.5% loss
of total MOTUs (445 out of 574).
[0237] Closer observation of sequences from JPL-SAF cleanroom floor
and GSE samples indicates that they predominantly belong to hardier
organisms such as Acidobacteria sp., Actinobacteria sp.,
Arsenicicoccus sp., Arthrobacter sp., Corynebacterium sp.,
Kineococcus sp., Propionibacterium sp., Nocardioides sp.,
Streptomyces sp., Bacillus sp., Clostridium sp., Lactobacillus sp.,
Deinococcus sp., Staphylococcus sp.
[0238] When treated with PMA, marked reduction in total number of
sequences (89 and 1) and OTUs (17 and 1) was observed for JPL-SAF
and 144 cleanroom floor samples respectively. The PMA effect was
significantly higher in non-mission critical floor samples (447 to
1 MOTU) indicating large number of dead cells or extraneous DNA
present on these floors. This viable population belongs to taxa
Firmicutes, Actinobacteria and Proteobacteria. Similar trend was
observed in PMA treated GSE samples (4 and 42 MOTUs) and (14 and
528 sequences) from JPL-SAF and 144 respectively. The hardy
organisms observed in cleanroom floor samples without PMA treatment
were absent or in very low number in PMA treated samples.
[0239] As described above, the system (e.g., PMA-microarrays) and
the methods disclosed herein can be used to accurately and
sensitively detect and quantify viable organisms in a sample.
Sequence CWU 1
1
102125DNAArtificial SequenceDescription of Artificial Sequence
Synthetic oligonucleotide Pair ID 265003; MM probe is SEQ ID NO2
1aatggaattt gatagaacct ctgga 25225DNAArtificial SequenceDescription
of Artificial Sequence Synthetic oligonucleotide Pair ID 265003; PM
probe is SEQ ID NO1 2aatggaattt gaaagaacct ctgga 25325DNAArtificial
SequenceDescription of Artificial Sequence Synthetic
oligonucleotide Pair ID 231625; MM probe is SEQ ID NO4 3cagtaaggca
tgagtaactt accgt 25425DNAArtificial SequenceDescription of
Artificial Sequence Synthetic oligonucleotide Pair ID 231625; PM
probe is SEQ ID NO3 4cagtaaggca tgggtaactt accgt 25525DNAArtificial
SequenceDescription of Artificial Sequence Synthetic
oligonucleotide Pair ID 186992; MM probe is SEQ ID NO6 5aaacgatgac
cactagacgt tgggg 25625DNAArtificial SequenceDescription of
Artificial Sequence Synthetic oligonucleotide Pair ID 186992; PM
probe is SEQ ID NO5 6aaacgatgac cattagacgt tgggg 25725DNAArtificial
SequenceDescription of Artificial Sequence Synthetic
oligonucleotide Pair ID 327194; MM probe is SEQ ID NO8 7ataaagttac
ttgcctgcat ttggt 25825DNAArtificial SequenceDescription of
Artificial Sequence Synthetic oligonucleotide Pair ID 327194; PM
probe is SEQ ID NO7 8ataaagttac ttccctgcat ttggt 25925DNAArtificial
SequenceDescription of Artificial Sequence Synthetic
oligonucleotide Pair ID 151981; MM probe is SEQ ID NO10 9ccgtagcgta
cgtgatctac gatta 251025DNAArtificial SequenceDescription of
Artificial Sequence Synthetic oligonucleotide Pair ID 151981; PM
probe is SEQ ID NO9 10ccgtagcgta cgggatctac gatta
251125DNAArtificial SequenceDescription of Artificial Sequence
Synthetic oligonucleotide Pair ID 191212; MM probe is SEQ ID NO12
11taaaatttcg atggatacgg attgg 251225DNAArtificial
SequenceDescription of Artificial Sequence Synthetic
oligonucleotide Pair ID 191212; PM probe is SEQ ID NO11
12taaaatttcg atcgatacgg attgg 251325DNAArtificial
SequenceDescription of Artificial Sequence Synthetic
oligonucleotide Pair ID 276480; MM probe is SEQ ID NO14
13caggcacgaa aacgtgggga tccaa 251425DNAArtificial
SequenceDescription of Artificial Sequence Synthetic
oligonucleotide Pair ID 276480; PM probe is SEQ ID NO13
14caggcacgaa aaggtgggga tccaa 251525DNAArtificial
SequenceDescription of Artificial Sequence Synthetic
oligonucleotide Pair ID 300668; MM probe is SEQ ID NO16
15cggttctttt tgatggcgac cggcg 251625DNAArtificial
SequenceDescription of Artificial Sequence Synthetic
oligonucleotide Pair ID 300668; PM probe is SEQ ID NO15
16cggttctttt tgttggcgac cggcg 251725DNAArtificial
SequenceDescription of Artificial Sequence Synthetic
oligonucleotide Pair ID 25579; MM probe is SEQ ID NO18 17ggccttagcc
cgtgctgccg tacga 251825DNAArtificial SequenceDescription of
Artificial Sequence Synthetic oligonucleotide Pair ID 25579; PM
probe is SEQ ID NO17 18ggccttagcc cgagctgccg tacga
251925DNAArtificial SequenceDescription of Artificial Sequence
Synthetic oligonucleotide Pair ID 176744; MM probe is SEQ ID NO20
19cgcatgtcga gtatgttaaa tatcc 252025DNAArtificial
SequenceDescription of Artificial Sequence Synthetic
oligonucleotide Pair ID 176744; PM probe is SEQ ID NO19
20cgcatgtcga gtttgttaaa tatcc 252125DNAArtificial
SequenceDescription of Artificial Sequence Synthetic
oligonucleotide Pair ID 263709; MM probe is SEQ ID NO22
21cccaggttcg atagtaccaa ttagg 252225DNAArtificial
SequenceDescription of Artificial Sequence Synthetic
oligonucleotide Pair ID 263709; PM probe is SEQ ID NO21
22cccaggttcg atggtaccaa ttagg 252325DNAArtificial
SequenceDescription of Artificial Sequence Synthetic
oligonucleotide Pair ID 289612; MM probe is SEQ ID NO24
23ccaaggacgt tcgtgagccg ttcga 252425DNAArtificial
SequenceDescription of Artificial Sequence Synthetic
oligonucleotide Pair ID 289612; PM probe is SEQ ID NO23
24ccaaggacgt tcttgagccg ttcga 252525DNAArtificial
SequenceDescription of Artificial Sequence Synthetic
oligonucleotide Pair ID 4284; MM probe is SEQ ID NO26 25aaacgaccca
ctgggactgt cctga 252625DNAArtificial SequenceDescription of
Artificial Sequence Synthetic oligonucleotide Pair ID 4284; PM
probe is SEQ ID NO25 26aaacgaccca ctaggactgt cctga
252725DNAArtificial SequenceDescription of Artificial Sequence
Synthetic oligonucleotide Pair ID 111799; MM probe is SEQ ID NO28
27aaaattgccc gaaggaggat ctatt 252825DNAArtificial
SequenceDescription of Artificial Sequence Synthetic
oligonucleotide Pair ID 111799; PM probe is SEQ ID NO27
28aaaattgccc gatggaggat ctatt 252925DNAArtificial
SequenceDescription of Artificial Sequence Synthetic
oligonucleotide Pair ID 57769; MM probe is SEQ ID NO30 29atattactcc
ccataacatt cgtcc 253025DNAArtificial SequenceDescription of
Artificial Sequence Synthetic oligonucleotide Pair ID 57769; PM
probe is SEQ ID NO29 30atattactcc ccctaacatt cgtcc
253125DNAArtificial SequenceDescription of Artificial Sequence
Synthetic oligonucleotide Pair ID 277222; MM probe is SEQ ID NO32
31aacttcctta gctccaaata cattc 253225DNAArtificial
SequenceDescription of Artificial Sequence Synthetic
oligonucleotide Pair ID 277222; PM probe is SEQ ID NO31
32aacttcctta gcaccaaata cattc 253325DNAArtificial
SequenceDescription of Artificial Sequence Synthetic
oligonucleotide Pair ID 39458; MM probe is SEQ ID NO34 33aagtcccgta
cgagcgcaac ccttg 253425DNAArtificial SequenceDescription of
Artificial Sequence Synthetic oligonucleotide Pair ID 39458; PM
probe is SEQ ID NO33 34aagtcccgta cgcgcgcaac ccttg
253525DNAArtificial SequenceDescription of Artificial Sequence
Synthetic oligonucleotide Pair ID 298577; MM probe is SEQ ID NO36
35aaggcccggc actaactatg accgg 253625DNAArtificial
SequenceDescription of Artificial Sequence Synthetic
oligonucleotide Pair ID 298577; PM probe is SEQ ID NO35
36aaggcccggc acgaactatg accgg 253725DNAArtificial
SequenceDescription of Artificial Sequence Synthetic
oligonucleotide Pair ID 126538; MM probe is SEQ ID NO38
37acagtactta aagaataagc accgg 253825DNAArtificial
SequenceDescription of Artificial Sequence Synthetic
oligonucleotide Pair ID 126538; PM probe is SEQ ID NO37
38acagtactta aaaaataagc accgg 253925DNAArtificial
SequenceDescription of Artificial Sequence Synthetic
oligonucleotide Pair ID 252982; MM probe is SEQ ID NO40
39aaatattggg gctcaacctt aagca 254025DNAArtificial
SequenceDescription of Artificial Sequence Synthetic
oligonucleotide Pair ID 252982; PM probe is SEQ ID NO39
40aaatattggg gcgcaacctt aagca 254125DNAArtificial
SequenceDescription of Artificial Sequence Synthetic
oligonucleotide Pair ID 197225; MM probe is SEQ ID NO42
41cgtatagtac ggtcagtttt ctaca 254225DNAArtificial
SequenceDescription of Artificial Sequence Synthetic
oligonucleotide Pair ID 197225; PM probe is SEQ ID NO41
42cgtatagtac gggcagtttt ctaca 254325DNAArtificial
SequenceDescription of Artificial Sequence Synthetic
oligonucleotide Pair ID 162084; MM probe is SEQ ID NO44
43ccgaaggacc ggttctcgct cgact 254425DNAArtificial
SequenceDescription of Artificial Sequence Synthetic
oligonucleotide Pair ID 162084; PM probe is SEQ ID NO43
44ccgaaggacc ggctctcgct cgact 254525DNAArtificial
SequenceDescription of Artificial Sequence Synthetic
oligonucleotide Pair ID 228980; MM probe is SEQ ID NO46
45cctagttacc gatcttgccc taggc 254625DNAArtificial
SequenceDescription of Artificial Sequence Synthetic
oligonucleotide Pair ID 228980; PM probe is SEQ ID NO45
46cctagttacc gagcttgccc taggc 254725DNAArtificial
SequenceDescription of Artificial Sequence Synthetic
oligonucleotide Pair ID 123763; MM probe is SEQ ID NO48
47cacagatttc cctgtgttat tcccc 254825DNAArtificial
SequenceDescription of Artificial Sequence Synthetic
oligonucleotide Pair ID 123763; PM probe is SEQ ID NO47
48cacagatttc ccggtgttat tcccc 254925DNAArtificial
SequenceDescription of Artificial Sequence Synthetic
oligonucleotide Pair ID 240310; MM probe is SEQ ID NO50
49tagcaataca tcaggtagtt agtgg 255025DNAArtificial
SequenceDescription of Artificial Sequence Synthetic
oligonucleotide Pair ID 240310; PM probe is SEQ ID NO49
50tagcaataca tccggtagtt agtgg 255125DNAArtificial
SequenceDescription of Artificial Sequence Synthetic
oligonucleotide Pair ID 16702; MM probe is SEQ ID NO52 51gactcaacag
gcatcacatc agata 255225DNAArtificial SequenceDescription of
Artificial Sequence Synthetic oligonucleotide Pair ID 16702; PM
probe is SEQ ID NO51 52gactcaacag gcttcacatc agata
255325DNAArtificial SequenceDescription of Artificial Sequence
Synthetic oligonucleotide Pair ID 16703; MM probe is SEQ ID NO54
53tatctgatgt gatgcctgtt gagtc 255425DNAArtificial
SequenceDescription of Artificial Sequence Synthetic
oligonucleotide Pair ID 16703; PM probe is SEQ ID NO53 54tatctgatgt
gaagcctgtt gagtc 255525DNAArtificial SequenceDescription of
Artificial Sequence Synthetic oligonucleotide Pair ID 24979; MM
probe is SEQ ID NO56 55tcgggcgttg ttaaatcaat caaga
255625DNAArtificial SequenceDescription of Artificial Sequence
Synthetic oligonucleotide Pair ID 24979; PM probe is SEQ ID NO55
56tcgggcgttg tttaatcaat caaga 255725DNAArtificial
SequenceDescription of Artificial Sequence Synthetic
oligonucleotide Pair ID 24980; MM probe is SEQ ID NO58 57tcttgattga
tttaacaacg cccga 255825DNAArtificial SequenceDescription of
Artificial Sequence Synthetic oligonucleotide Pair ID 24980; PM
probe is SEQ ID NO57 58tcttgattga ttaaacaacg cccga
255925DNAArtificial SequenceDescription of Artificial Sequence
Synthetic oligonucleotide Pair ID 41927; MM probe is SEQ ID NO60
59aagcatctga ctcaacaggc atcac 256025DNAArtificial
SequenceDescription of Artificial Sequence Synthetic
oligonucleotide Pair ID 41927; PM probe is SEQ ID NO59 60aagcatctga
ctgaacaggc atcac 256125DNAArtificial SequenceDescription of
Artificial Sequence Synthetic oligonucleotide Pair ID 41928; MM
probe is SEQ ID NO62 61gtgatgcctg ttgagtcaga tgctt
256225DNAArtificial SequenceDescription of Artificial Sequence
Synthetic oligonucleotide Pair ID 41928; PM probe is SEQ ID NO61
62gtgatgcctg ttcagtcaga tgctt 256325DNAArtificial
SequenceDescription of Artificial Sequence Synthetic
oligonucleotide Pair ID 44977; MM probe is SEQ ID NO64 63ctgatcgtat
gtatggtcta tggcc 256425DNAArtificial SequenceDescription of
Artificial Sequence Synthetic oligonucleotide Pair ID 44977; PM
probe is SEQ ID NO63 64ctgatcgtat gtttggtcta tggcc
256525DNAArtificial SequenceDescription of Artificial Sequence
Synthetic oligonucleotide Pair ID 44978; MM probe is SEQ ID NO66
65ggccatagac catacatacg atcag 256625DNAArtificial
SequenceDescription of Artificial Sequence Synthetic
oligonucleotide Pair ID 44978; PM probe is SEQ ID NO65 66ggccatagac
caaacatacg atcag 256725DNAArtificial SequenceDescription of
Artificial Sequence Synthetic oligonucleotide Pair ID 46837; MM
probe is SEQ ID NO68 67taaatcaatc aagacatccg gttgt
256825DNAArtificial SequenceDescription of Artificial Sequence
Synthetic oligonucleotide Pair ID 46837; PM probe is SEQ ID NO67
68taaatcaatc aacacatccg gttgt 256925DNAArtificial
SequenceDescription of Artificial Sequence Synthetic
oligonucleotide Pair ID 46838; MM probe is SEQ ID NO70 69acaaccggat
gtcttgattg attta 257025DNAArtificial SequenceDescription of
Artificial Sequence Synthetic oligonucleotide Pair ID 46838; PM
probe is SEQ ID NO69 70acaaccggat gtgttgattg attta
257125DNAArtificial SequenceDescription of Artificial Sequence
Synthetic oligonucleotide Pair ID 48919; MM probe is SEQ ID NO72
71cgatgatggc tccgatccct ttttc 257225DNAArtificial
SequenceDescription of Artificial Sequence Synthetic
oligonucleotide Pair ID 48919; PM probe is SEQ ID NO71 72cgatgatggc
tcggatccct ttttc 257325DNAArtificial SequenceDescription of
Artificial Sequence Synthetic oligonucleotide Pair ID 48920; MM
probe is SEQ ID NO74 73gaaaaaggga tcggagccat catcg
257425DNAArtificial SequenceDescription of Artificial Sequence
Synthetic oligonucleotide Pair ID 48920; PM probe is SEQ ID NO73
74gaaaaaggga tccgagccat catcg 257525DNAArtificial
SequenceDescription of Artificial Sequence Synthetic
oligonucleotide Pair ID 74275; MM probe is SEQ ID NO76 75cagtatcgca
ccgagcgcaa aattt 257625DNAArtificial SequenceDescription of
Artificial Sequence Synthetic oligonucleotide Pair ID 74275; PM
probe is SEQ ID NO75 76cagtatcgca cccagcgcaa aattt
257725DNAArtificial SequenceDescription of Artificial Sequence
Synthetic oligonucleotide Pair ID 74276; MM probe is SEQ ID NO78
77aaattttgcg ctcggtgcga tactg 257825DNAArtificial
SequenceDescription of Artificial Sequence Synthetic
oligonucleotide Pair ID 74276; PM probe is SEQ ID NO77 78aaattttgcg
ctgggtgcga tactg 257925DNAArtificial SequenceDescription of
Artificial Sequence Synthetic oligonucleotide Pair ID 80839; MM
probe is SEQ ID NO80 79aatcagcttc tgagaaaccg gttgt
258025DNAArtificial SequenceDescription of Artificial Sequence
Synthetic oligonucleotide Pair ID 80839; PM probe is SEQ ID NO79
80aatcagcttc tgtgaaaccg gttgt 258125DNAArtificial
SequenceDescription of Artificial Sequence Synthetic
oligonucleotide Pair ID 80840; MM probe is SEQ ID NO82 81acaaccggtt
tctcagaagc tgatt 258225DNAArtificial SequenceDescription of
Artificial Sequence Synthetic oligonucleotide Pair ID 80840; PM
probe is SEQ ID NO81 82acaaccggtt tcacagaagc tgatt
258325DNAArtificial SequenceDescription of Artificial Sequence
Synthetic oligonucleotide Pair ID 83957; MM probe is SEQ ID NO84
83ctcaacaggc atcacatcag ataat 258425DNAArtificial
SequenceDescription of Artificial Sequence Synthetic
oligonucleotide Pair ID 83957; PM probe is SEQ ID NO83
84ctcaacaggc atgacatcag ataat 258525DNAArtificial
SequenceDescription of Artificial Sequence Synthetic
oligonucleotide Pair ID 83958; MM probe is SEQ ID NO86 85attatctgat
gtgatgcctg ttgag 258625DNAArtificial SequenceDescription of
Artificial Sequence Synthetic oligonucleotide Pair ID 83958; PM
probe is SEQ ID NO85 86attatctgat gtcatgcctg ttgag
258725DNAArtificial SequenceDescription of Artificial Sequence
Synthetic oligonucleotide Pair ID 93455; MM probe is SEQ ID NO88
87tttctgtaaa acaggcatgg atatc 258825DNAArtificial
SequenceDescription of Artificial Sequence Synthetic
oligonucleotide Pair ID 93455; PM probe is SEQ ID NO87 88tttctgtaaa
actggcatgg atatc 258925DNAArtificial SequenceDescription of
Artificial Sequence Synthetic oligonucleotide Pair ID 93456; MM
probe is SEQ ID NO90 89gatatccatg cctgttttac agaaa
259025DNAArtificial SequenceDescription of Artificial Sequence
Synthetic oligonucleotide Pair ID 93456; PM probe is SEQ ID NO89
90gatatccatg ccagttttac agaaa 259125DNAArtificial
SequenceDescription of Artificial Sequence Synthetic
oligonucleotide Pair ID 140612; MM probe is SEQ ID NO92
91ctcaacatcc tcaaaatagt tggca 259225DNAArtificial
SequenceDescription of Artificial Sequence Synthetic
oligonucleotide Pair ID 140612; PM probe is SEQ ID NO91
92ctcaacatcc tctaaatagt tggca 259325DNAArtificial
SequenceDescription of Artificial Sequence Synthetic
oligonucleotide Pair ID 140613; MM probe is SEQ ID NO94
93tgccaactat tttgaggatg ttgag 259425DNAArtificial
SequenceDescription of Artificial Sequence Synthetic
oligonucleotide Pair ID 140613; PM probe is SEQ ID NO93
94tgccaactat ttagaggatg ttgag 259525DNAArtificial
SequenceDescription of Artificial Sequence Synthetic
oligonucleotide Pair ID 193149; MM probe is SEQ ID NO96
95atttcatcat cagtatcgca ccgag 259625DNAArtificial
SequenceDescription of Artificial Sequence Synthetic
oligonucleotide Pair ID 193149; PM probe is SEQ ID NO95
96atttcatcat cactatcgca ccgag 259725DNAArtificial
SequenceDescription of Artificial Sequence Synthetic
oligonucleotide Pair ID 193150; MM probe is SEQ ID NO98
97ctcggtgcga tactgatgat gaaat 259825DNAArtificial
SequenceDescription of Artificial Sequence Synthetic
oligonucleotide Pair ID 193150; PM probe is SEQ ID NO97
98ctcggtgcga tagtgatgat gaaat 259925DNAArtificial
SequenceDescription of Artificial Sequence Synthetic
oligonucleotide Pair ID 195043; MM probe is SEQ ID NO100
99taaaacaggc atggatatct gtgta 2510025DNAArtificial
SequenceDescription of Artificial Sequence Synthetic
oligonucleotide Pair ID 195043; PM probe is SEQ ID NO99
100taaaacaggc atcgatatct gtgta 2510119DNAArtificial Sequence16S
universal bacterial primer 28F 101gagtttgatc ntggctcag
1910218DNAArtificial Sequence16S universal bacterial primer 519R
102gtnttacngc ggckgctg 18
* * * * *
References