U.S. patent application number 15/237261 was filed with the patent office on 2017-02-16 for methods for rapid identification of pathogens in humans and animals.
The applicant listed for this patent is Ibis Biosciences, Inc.. Invention is credited to Lawrence B. Blyn, Stanley T. Crooke, David J. Ecker, Richard H. Griffey, Thomas A. Hall, Steven A. Hofstadler, John McNeil, Raymond Ranken, Rangarajan Sampath.
Application Number | 20170044630 15/237261 |
Document ID | / |
Family ID | 32719811 |
Filed Date | 2017-02-16 |
United States Patent
Application |
20170044630 |
Kind Code |
A1 |
Ecker; David J. ; et
al. |
February 16, 2017 |
Methods For Rapid Identification Of Pathogens In Humans And
Animals
Abstract
The present invention provides methods of: identifying pathogens
in biological samples from humans and animals, resolving a
plurality of etiologic agents present in samples obtained from
humans and animals, determining detailed genetic information about
such pathogens or etiologic agents, and rapid detection and
identification of bioagents from environmental, clinical or other
samples.
Inventors: |
Ecker; David J.; (Encinitas,
CA) ; Griffey; Richard H.; (Vista, CA) ;
Sampath; Rangarajan; (San Diego, CA) ; Hofstadler;
Steven A.; (Oceanside, CA) ; McNeil; John; (La
Jolla, CA) ; Crooke; Stanley T.; (Carlsbad, CA)
; Blyn; Lawrence B.; (Mission Viejo, CA) ; Ranken;
Raymond; (Encinitas, CA) ; Hall; Thomas A.;
(Oceanside, CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Ibis Biosciences, Inc. |
Carlsbad |
CA |
US |
|
|
Family ID: |
32719811 |
Appl. No.: |
15/237261 |
Filed: |
August 15, 2016 |
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13174254 |
Jun 30, 2011 |
9416424 |
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15237261 |
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11930002 |
Oct 30, 2007 |
8071309 |
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13174254 |
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10728486 |
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7718354 |
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11930002 |
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10323233 |
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10728486 |
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Feb 14, 2003 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
C12Q 1/6872 20130101;
G01N 21/64 20130101; G16B 25/00 20190201; C12Q 1/689 20130101; G01N
1/30 20130101; C12Q 1/701 20130101; C12Q 1/70 20130101; G01N 23/00
20130101; C12Q 2600/16 20130101 |
International
Class: |
C12Q 1/70 20060101
C12Q001/70; G06F 19/20 20060101 G06F019/20; C12Q 1/68 20060101
C12Q001/68 |
Goverment Interests
STATEMENT OF GOVERNMENT SUPPORT
[0002] This invention was made with United States government
support under DARPA/SPO contract BAA00-09. The United States
government may have certain rights in the invention.
Claims
1.-49. (canceled)
50. A method for identifying a bioagent, comprising: a) contacting
a sample suspected of containing a bioagent with a plurality of
primer pairs to generate one or more amplicons no more than about
30-250 nucleotides in length, wherein each of said primer pairs
hybridize to conserved nucleic acid sequences flanking a variable
sequence; wherein a first primer pair of said plurality of primer
pairs generates an amplicon from a first bioagent but not a second
bioagent, and a second primer pair of said plurality of primer
pairs generates an amplicon from said second bioagent but not said
first bioagent, wherein said first and second bioagents differ in
genus; b) determining the mass, base composition, or sequence of
said one or more amplicons; and c) identifying one or more
bioagents in said sample by comparing the determined mass, base
composition, or sequence to a database comprising predetermined
masses, base compositions, or sequences from a plurality of known
organisms.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] The present application: 1) is a continuation-in-part of
U.S. application Ser. No. 10/323,233 filed Dec. 18, 2002; 2) is a
continuation-in-part of U.S. application Ser. No. 10/326,051 filed
Dec. 18, 2002; 3) is a continuation-in-part of U.S. application
Ser. No. 10/325,527 filed Dec. 18, 2002; 4) is a
continuation-in-part of U.S. application Ser. No. 10/325,526 filed
Dec. 18, 2002; 5) claims the benefit of U.S. provisional
application Serial No. 60/431,319 filed Dec. 6, 2002; 6) claims the
benefit of U.S. provisional application Ser. No. 60/443,443 filed
Jan. 29, 2003; 7) claims the benefit of U.S. provisional
application Ser. No. 60/443,788 filed Jan. 30, 2003; 8) claims the
benefit of U.S. provisional application Ser. No. 60/447,529 filed
Feb. 14, 2003; and 9) claims the benefit of U.S. provisional
application Ser. No. 60/501,926 filed Sep. 11, 2003, each of which
is incorporated herein by reference in its entirety.
FIELD OF THE INVENTION
[0003] The present invention relates generally to clinical
applications of directed to the identification of pathogens in
biological samples from humans and animals. The present invention
is also directed to the resolution of a plurality of etiologic
agents present in samples obtained from humans and animals. The
invention is further directed to the determination of detailed
genetic information about such pathogens or etiologic agents.
[0004] The identification of the bioagent is important for
determining a proper course of treatment and/or eradication of the
bioagent in such cases as biological warfare and natural
infections. Furthermore, the determination of the geographic origin
of a selected bioagent will facilitate the identification of
potential criminal identity. The present invention also relates to
methods for rapid detection and identification of bioagents from
environmental, clinical or other samples. The methods provide for
detection and characterization of a unique base composition
signature (BCS) from any bioagent, including bacteria and viruses.
The unique BCS is used to rapidly identify the bioagent.
BACKGROUND OF THE INVENTION
[0005] In the United States, hospitals report well over 5 million
cases of recognized infectious disease-related illnesses annually.
Significantly greater numbers remain undetected, both in the
inpatient and community setting, resulting in substantial morbidity
and mortality. Critical intervention for infectious disease relies
on rapid, sensitive and specific detection of the offending
pathogen, and is central to the mission of microbiology
laboratories at medical centers. Unfortunately, despite the
recognition that outcomes from infectious illnesses are directly
associated with time to pathogen recognition, as well as accurate
identification of the class and species of microbe, and ability to
identify the presence of drug resistance isolates, conventional
hospital laboratories often remain encumbered by traditional slow
multi-step culture based assays. Other limitations of the
conventional laboratory which have become increasingly apparent
include: extremely prolonged wait-times for pathogens with long
generation time (up to several weeks); requirements for additional
testing and wait times for speciation and identification of
antimicrobial resistance; diminished test sensitivity for patients
who have received antibiotics; and absolute inability to culture
certain pathogens in disease states associated with microbial
infection.
[0006] For more than a decade, molecular testing has been heralded
as the diagnostic tool for the new millennium, whose ultimate
potential could include forced obsolescence of traditional hospital
laboratories. However, despite the fact that significant advances
in clinical application of PCR techniques have occurred, the
practicing physician still relies principally on standard
techniques. A brief discussion of several existing applications of
PCR in the hospital-based setting follows.
[0007] Generally speaking molecular diagnostics have been
championed for identifying organisms that cannot be grown in vitro,
or in instances where existing culture techniques are insensitive
and/or require prolonged incubation times. PCR-based diagnostics
have been successfully developed for a wide variety of microbes.
Application to the clinical arena has met with variable success,
with only a few assays achieving acceptance and utility.
[0008] One of the earliest, and perhaps most widely recognized
applications of PCR for clinical practice is in detection of
Mycobacterium tuberculosis. Clinical characteristics favoring
development of a nonculture-based test for tuberculosis include
week to month long delays associated with standard testing,
occurrence of drug-resistant isolates and public health imperatives
associated with recognition, isolation and treatment. Although
frequently used as a diagnostic adjunctive, practical and routine
clinical application of PCR remains problematic due to significant
inter-laboratory variation in sensitivity, and inadequate
specificity for use in low prevalence populations, requiring
further development at the technical level. Recent advances in the
laboratory suggest that identification of drug resistant isolates
by amplification of mutations associated with specific antibiotic
resistance (e.g., rpoB gene in rifampin resistant strains) may be
forthcoming for clinical use, although widespread application will
require extensive clinical validation.
[0009] One diagnostic assay, which has gained widespread
acceptance, is for C. trachomatis. Conventional detection systems
are limiting due to inadequate sensitivity and specificity (direct
immunofluorescence or enzyme immunoassay) or the requirement for
specialized culture facilities, due to the fastidious
characteristics of this microbe. Laboratory development, followed
by widespread clinical validation testing in a variety of acute and
nonacute care settings have demonstrated excellent sensitivity
(90-100%) and specificity (97%) of the PCR assay leading to its
commercial development. Proven efficacy of the PCR assay from both
genital and urine sampling, have resulted in its application to a
variety of clinical setting, most recently including routine
screening of patients considered at risk.
[0010] While the full potential for PCR diagnostics to provide
rapid and critical information to physicians faced with difficult
clinical-decisions has yet to be realized, one recently developed
assay provides an example of the promise of this evolving
technology. Distinguishing life-threatening causes of fever from
more benign causes in children is a fundamental clinical dilemma
faced by clinicians, particularly when infections of the central
nervous system are being considered. Bacterial causes of meningitis
can be highly aggressive, but generally cannot be differentiated on
a clinical basis from aseptic meningitis, which is a relatively
benign condition that can be managed on an outpatient basis.
Existing blood culture methods often take several days to turn
positive, and are often confounded by poor sensitivity or
false-negative findings in patients receiving empiric
antimicrobials. Testing and application of a PCR assay for
enteroviral meningitis has been found to be highly sensitive. With
reporting of results within 1 day, preliminary clinical trials have
shown significant reductions in hospital costs, due to decreased
duration of hospital stays and reduction in antibiotic therapy.
Other viral PCR assays, now routinely available include those for
herpes simplex virus, cytomegalovirus, hepatitis and HIV. Each has
a demonstrated cost savings role in clinical practice, including
detection of otherwise difficult to diagnose infections and newly
realized capacity to monitor progression of disease and response to
therapy, vital in the management of chronic infectious
diseases.
[0011] The concept of a universal detection system has been
forwarded for identification of bacterial pathogens, and speaks
most directly to the possible clinical implications of a
broad-based screening tool for clinical use. Exploiting the
existence of highly conserved regions of DNA common to all
bacterial species in a PCR assay would empower physicians to
rapidly identify the presence of bacteremia, which would profoundly
impact patient care. Previous empiric decision making could be
abandoned in favor of educated practice, allowing appropriate and
expeditious decision-making regarding need for antibiotic therapy
and hospitalization.
[0012] Experimental work using the conserved features of the 16S
rRNA common to almost all bacterial species, is an area of active
investigation. Hospital test sites have focused on "high yield"
clinical settings where expeditious identification of the presence
of systemic bacterial infection has immediate high morbidity and
mortality consequences. Notable clinical infections have included
evaluation of febrile infants at risk for sepsis, detection of
bacteremia in febrile neutropenic cancer patients, and examination
of critically ill patients in the intensive care unit. While
several of these studies have reported promising results (with
sensitivity and specificity well over 90%), significant technical
difficulties (described below) remain, and have prevented general
acceptance of this assay in clinics and hospitals (which remain
dependent on standard blood culture methodologies). Even the
revolutionary advances of real-time PCR technique, which offers a
quantitative more reproducible and technically simpler system,
remains encumbered by inherent technical limitations of the PCR
assay.
[0013] The principle shortcomings of applying PCR assays to the
clinical setting include: inability to eliminate background DNA
contamination; interference with the PCR amplification by
substrates present in the reaction; and limited capacity to provide
rapid reliable speciation, antibiotic resistance and subtype
identification. Some laboratories have recently made progress in
identifying and removing inhibitors; however background
contamination remains problematic, and methods directed towards
eliminating exogenous sources of DNA report significant diminution
in assay sensitivity. Finally, while product identification and
detailed characterization has been achieved using sequencing
techniques, these approaches are laborious and time-intensive thus
detracting from its clinical applicability.
[0014] Rapid and definitive microbial identification is desirable
for a variety of industrial, medical, environmental, quality, and
research reasons. Traditionally, the microbiology laboratory has
functioned to identify the etiologic agents of infectious diseases
through direct examination and culture of specimens. Since the
mid-1980s, researchers have repeatedly demonstrated the practical
utility of molecular biology techniques, many of which form the
basis of clinical diagnostic assays. Some of these techniques
include nucleic acid hybridization analysis, restriction enzyme
analysis, genetic sequence analysis, and separation and
purification of nucleic acids (See, e.g., J. Sambrook, E. F.
Fritsch, and T. Maniatis, Molecular Cloning: A Laboratory Manual,
2nd Ed., Cold Spring Harbor Laboratory Press, Cold Spring Harbor,
N.Y., 1989). These procedures, in general, are time-consuming and
tedious. Another option is the polymerase chain reaction (PCR) or
other amplification procedure that amplifies a specific target DNA
sequence based on the flanking primers used. Finally, detection and
data analysis convert the hybridization event into an analytical
result.
[0015] Other not yet fully realized applications of PCR for
clinical medicine is the identification of infectious causes of
disease previously described as idiopathic (e.g. Bartonella
henselae in bacillary angiomatosis, and Tropheryma whippellii as
the uncultured bacillus associated with Whipple's disease).
Further, recent epidemiological studies which suggest a strong
association between Chlamydia pneumonia and coronary artery
disease, serve as example of the possible widespread, yet
undiscovered links between pathogen and host which may ultimately
allow for new insights into pathogenesis and novel life sustaining
or saving therapeutics.
[0016] For the practicing clinician, PCR technology offers a yet
unrealized potential for diagnostic omnipotence in the arena of
infectious disease. A universal reliable infectious disease
detection system would certainly become a fundamental tool in the
evolving diagnostic armamentarium of the 21.sup.st century
clinician. For front line emergency physicians, or physicians
working in disaster settings, a quick universal detection system,
would allow for molecular triage and early aggressive targeted
therapy. Preliminary clinical studies using species specific probes
suggest that implementing rapid testing in acute care setting is
feasible. Resources could thus be appropriately applied, and
patients with suspected infections could rapidly be risk stratified
to the different treatment settings, depending on the pathogen and
virulence. Furthermore, links with data management systems, locally
regionally and nationally, would allow for effective
epidemiological surveillance, with obvious benefits for antibiotic
selection and control of disease outbreaks.
[0017] For the hospitalists, the ability to speciate and subtype
would allow for more precise decision-making regarding
antimicrobial agents. Patients who are colonized with highly
contagious pathogens could be appropriately isolated on entry into
the medical setting without delay. Targeted therapy will diminish
development of antibiotic resistance. Furthermore, identification
of the genetic basis of antibiotic resistant strains would permit
precise pharmacologic intervention. Both physician and patient
would benefit with less need for repetitive testing and elimination
of wait times for test results.
[0018] It is certain that the individual patient will benefit
directly from this approach. Patients with unrecognized or
difficult to diagnose infections would be identified and treated
promptly. There will be reduced need for prolonged inpatient stays,
with resultant decreases in iatrogenic events.
[0019] Mass spectrometry provides detailed information about the
molecules being analyzed, including high mass accuracy. It is also
a process that can be easily automated. Low-resolution MS may be
unreliable when used to detect some known agents, if their spectral
lines are sufficiently weak or sufficiently close to those from
other living organisms in the sample. DNA chips with specific
probes can only determine the presence or absence of specifically
anticipated organisms. Because there are hundreds of thousands of
species of benign bacteria, some very similar in sequence to threat
organisms, even arrays with 10,000 probes lack the breadth needed
to detect a particular organism.
[0020] Antibodies face more severe diversity limitations than
arrays. If antibodies are designed against highly conserved targets
to increase diversity, the false alarm problem will dominate, again
because threat organisms are very similar to benign ones.
Antibodies are only capable of detecting known agents in relatively
uncluttered environments.
[0021] Several groups have reported detection of PCR products using
high resolution electrospray ionization-Fourier transform-ion
cyclotron resonance mass spectrometry (ESI-FT-ICR MS). Accurate
measurement of exact mass combined with knowledge of the number of
at least one nucleotide allowed calculation of the total base
composition for PCR duplex products of approximately 100 base
pairs. (Aaserud et al., J. Am. Soc. Mass Spec., 1996, 7, 1266-1269;
Muddiman et al., Anal. Chem., 1997, 69, 1543-1549; Wunschel et al.,
Anal. Chem., 1998, 70, 1203-1207; Muddiman et al., Rev. Anal.
Chem., 1998, 17, 1-68). Electrospray ionization-Fourier
transform-ion cyclotron resistance (ESI-FT-ICR) MS may be used to
determine the mass of double-stranded, 500 base-pair PCR products
via the average molecular mass (Hurst et al., Rapid Commun. Mass
Spec. 1996, 10, 377-382). Use of matrix-assisted laser desorption
ionization-time of flight (MALDI-TOF) mass spectrometry for
characterization of PCR products has been described. (Muddiman et
al., Rapid Commun. Mass Spec., 1999, 13, 1201-1204). However, the
degradation of DNAs over about 75 nucleotides observed with MALDI
limited the utility of this method.
[0022] U.S. Pat. No. 5,849,492 reports a method for retrieval of
phylogenetically informative DNA sequences which comprise searching
for a highly divergent segment of genomic DNA surrounded by two
highly conserved segments, designing the universal primers for PCR
amplification of the highly divergent region, amplifying the
genomic DNA by PCR technique using universal primers, and then
sequencing the gene to determine the identity of the organism.
[0023] U.S. Pat. No. 5,965,363 reports methods for screening
nucleic acids for polymorphisms by analyzing amplified target
nucleic acids using mass spectrometric techniques and to procedures
for improving mass resolution and mass accuracy of these
methods.
[0024] WO 99/14375 reports methods, PCR primers and kits for use in
analyzing preselected DNA tandem nucleotide repeat alleles by mass
spectrometry.
[0025] WO 98/12355 reports methods of determining the mass of a
target nucleic acid by mass spectrometric analysis, by cleaving the
target nucleic acid to reduce its length, making the target
single-stranded and using MS to determine the mass of the
single-stranded shortened target. Also reported are methods of
preparing a double-stranded target nucleic acid for MS analysis
comprising amplification of the target nucleic acid, binding one of
the strands to a solid support, releasing the second strand and
then releasing the first strand which is then analyzed by MS. Kits
for target nucleic acid preparation are also provided.
[0026] PCT WO97/33000 reports methods for detecting mutations in a
target nucleic acid by nonrandomly fragmenting the target into a
set of single-stranded nonrandom length fragments and determining
their masses by MS.
[0027] U.S. Pat. No. 5,605,798 reports a fast and highly accurate
mass spectrometer-based process for detecting the presence of a
particular nucleic acid in a biological sample for diagnostic
purposes.
[0028] WO 98/21066 reports processes for determining the sequence
of a particular target nucleic acid by mass spectrometry. Processes
for detecting a target nucleic acid present in a biological sample
by PCR amplification and mass spectrometry detection are reported,
as are methods for detecting a target nucleic acid in a sample by
amplifying the target with primers that contain restriction sites
and tags, extending and cleaving the amplified nucleic acid, and
detecting the presence of extended product, wherein the presence of
a DNA fragment of a mass different from wild-type is indicative of
a mutation. Methods of sequencing a nucleic acid via mass
spectrometry methods are also reported.
[0029] WO 97/37041, WO 99/31278 and U.S. Pat. No. 5,547,835 report
methods of sequencing nucleic acids using mass spectrometry. U.S.
Pat. Nos. 5,622,824, 5,872,003 and 5,691,141 report methods,
systems and kits for exonuclease-mediated mass spectrometric
sequencing.
[0030] Thus, there is a need for a method for bioagent detection
and identification which is both specific and rapid, and in which
no nucleic acid sequencing is required. The present invention
addresses this need.
SUMMARY OF THE INVENTION
[0031] The present invention is directed towards methods of
identifying a pathogen in a biological sample by obtaining nucleic
acid from a biological sample, selecting at least one pair of
intelligent primers with the capability of amplification of nucleic
acid of the pathogen, amplifying the nucleic acid with the primers
to obtain at least one amplification product, determining the
molecular mass of at least one amplification product from which the
pathogen is identified. Further, this invention is directed to
methods of epidemic surveillance. By identifying a pathogen from
samples acquired from a plurality of geographic locations, the
spread of the pathogen to a given geographic location can be
determined.
[0032] The present invention is also directed to methods of
diagnosis of a plurality of etiologic agents of disease in an
individual by obtaining a biological sample from an individual,
isolating nucleic acid from the biological sample, selecting a
plurality of amplification primers with the capability of
amplification of nucleic acid of a plurality of etiologic agents of
disease, amplifying the nucleic acid with a plurality of primers to
obtain a plurality of amplification products corresponding to a
plurality of etiologic agents, determining the molecular masses of
the plurality of unique amplification products which identify the
members of the plurality of etiologic agents.
[0033] The present invention is also directed to methods of in
silico screening of primer sets to be used in identification of a
plurality of bioagents by preparing a base composition probability
cloud plot from a plurality of base composition signatures of the
plurality of bioagents generated in silico, inspecting the base
composition probability cloud plot for overlap of clouds from
different bioagents, and choosing primer sets based on minimal
overlap of the clouds.
[0034] The present invention is also directed to methods of
predicting the identity of a bioagent with a heretofore unknown
base composition signature by preparing a base composition
probability cloud plot from a plurality of base composition
signatures of the plurality of bioagents which includes the
heretofore unknown base composition, inspecting the base
composition probability cloud for overlap of the heretofore unknown
base composition with the cloud of a known bioagent such that
overlap predicts that the identity of the bioagent with a
heretofore unknown base composition signature equals the identity
of the known bioagent.
[0035] The present invention is also directed to methods for
determining a subspecies characteristic for a given pathogen in a
biological sample by identifying the pathogen in a biological
sample using broad range survey primers or division-wide primers,
selecting at least one pair of drill-down primers to amplify
nucleic acid segments which provide a subspecies characteristic
about the pathogen, amplifying the nucleic acid segments to produce
at least one drill-down amplification product and determining the
base composition signature of the drill-down amplification product
wherein the base composition signature provides a subspecies
characteristic about the pathogen.
[0036] The present invention is also directed to methods of
pharmacogenetic analysis by obtaining a sample of genomic DNA from
an individual, selecting a segment of the genomic DNA which
provides pharmacogenetic information, using at least one pair of
intelligent primers to produce an amplification product which
comprises the segment of genomic DNA and determining the base
composition signature of the amplification product, wherein the
base composition signature provides pharmacogenetic information
about said individual.
BRIEF DESCRIPTION OF THE DRAWINGS
[0037] FIGS. 1A-1H and FIG. 2 are consensus diagrams that show
examples of conserved regions from 16S rRNA (FIGS. 1A-1, 1A-2,
1A-3, 1A-4, and 1A-5), 23S rRNA (3'-half, FIGS. 1B, 1C, and 1D;
5'-half, FIG. 1E-F), 23S rRNA Domain I (FIG. 1G), 23S rRNA Domain
IV (FIG. 1H) and 16S rRNA Domain III (FIG. 2) which are suitable
for use in the present invention. Lines with arrows are examples of
regions to which intelligent primer pairs for PCR are designed. The
label for each primer pair represents the starting and ending base
number of the amplified region on the consensus diagram. Bases in
capital letters are greater than 95% conserved; bases in lower case
letters are 90-95% conserved, filled circles are 80-90% conserved;
and open circles are less than 80% conserved. The label for each
primer pair represents the starting and ending base number of the
amplified region on the consensus diagram. The nucleotide sequence
of the 16S rRNA consensus sequence is SEQ ID NO:3 and the
nucleotide sequence of the 23S rRNA consensus sequence is SEQ ID
NO:4.
[0038] FIG. 2 shows a typical primer amplified region from the 16S
rRNA Domain III shown in FIG. 1A-1.
[0039] FIG. 3 is a schematic diagram showing conserved regions in
RNase P. Bases in capital letters are greater than 90% conserved;
bases in lower case letters are 80-90% conserved; filled circles
designate bases which are 70-80% conserved; and open circles
designate bases that are less than 70% conserved.
[0040] FIG. 4 is a schematic diagram of base composition signature
determination using nucleotide analog "tags" to determine base
composition signatures.
[0041] FIG. 5 shows the deconvoluted mass spectra of a Bacillus
anthracis region with and without the mass tag phosphorothioate A
(A*). The two spectra differ in that the measured molecular weight
of the mass tag-containing sequence is greater than the unmodified
sequence.
[0042] FIG. 6 shows base composition signature (BCS) spectra from
PCR products from Staphylococcus aureus (S. aureus 16S_1337F) and
Bacillus anthracis (B. anthr. 16S_1337F), amplified using the same
primers. The two strands differ by only two (AT.fwdarw.CG)
substitutions and are clearly distinguished on the basis of their
BCS.
[0043] FIG. 7 shows that a single difference between two sequences
(A14 in B. anthracis vs. A15 in B. cereus) can be easily detected
using ESI-TOF mass spectrometry.
[0044] FIG. 8 is an ESI-TOF of Bacillus anthracis spore coat
protein sspE 56mer plus calibrant. The signals unambiguously
identify B. anthracis versus other Bacillus species.
[0045] FIG. 9 is an ESI-TOF of a B. anthracis synthetic 16S_1228
duplex (reverse and forward strands). The technique easily
distinguishes between the forward and reverse strands.
[0046] FIG. 10 is an ESI-FTICR-MS of a synthetic B. anthracis
16S_1337 46 base pair duplex.
[0047] FIG. 11 is an ESI-TOF-MS of a 56mer oligonucleotide (3
scans) from the B. anthracis saspB gene with an internal mass
standard. The internal mass standards are designated by
asterisks.
[0048] FIG. 12 is an ESI-TOF-MS of an internal standard with 5 mM
TBA-TFA buffer showing that charge stripping with tributylammonium
trifluoroacetate reduces the most abundant charge state from
[M-8H+]8- to [M-3H+]3-.
[0049] FIG. 13 is a portion of a secondary structure defining
database according to one embodiment of the present invention,
where two examples of selected sequences are displayed graphically
thereunder.
[0050] FIG. 14 is a three dimensional graph demonstrating the
grouping of sample molecular weight according to species.
[0051] FIG. 15 is a three dimensional graph demonstrating the
grouping of sample molecular weights according to species of virus
and mammal infected.
[0052] FIG. 16 is a three dimensional graph demonstrating the
grouping of sample molecular weights according to species of virus,
and animal-origin of infectious agent.
[0053] FIG. 17 is a figure depicting how a typical triangulation
method of the present invention provides for the identification of
an unknown bioagent without prior knowledge of the unknown agent.
The use of different primer sets to distinguish and identify the
unknown is also depicted as primer sets I, II and III within this
figure. A three-dimensional graph depicts all of bioagent space
(170), including the unknown bioagent, which after use of primer
set I (171) according to a method according to the present
invention further differentiates and classifies bioagents according
to major classifications (176) which, upon further analysis using
primer set II (172) differentiates the unknown agent (177) from
other, known agents (173) and finally, the use of a third primer
set (175) further specifies subgroups within the family of the
unknown (174).
[0054] FIG. 18 shows a representative base composition probability
cloud for a region of the RNA polymerase B gene from a cluster of
enterobacteria. The dark spheres represent the actual base
composition of the organisms. The lighter spheres represent the
transitions among base compositions observed in different isolates
of the same species of organism.
[0055] FIG. 19 shows resolution of enterobacteriae members with
primers targeting RNA polymerase B (rpoB). A single pair of primers
targeting a hyper-variable region within rpoB was sufficient to
resolve most members of this group at the genus level (Salmonella
from Escherichia from Yersinia) as well as the species/strain level
(E. coli K12 from O157). All organisms with the exception of Y.
pestis were tested in the lab and the measured base counts (shown
with arrow) matched the predictions in every case.
[0056] FIG. 20 shows detection of S. aureus in blood. Spectra on
the right indicate signals corresponding to S. aureus detection in
spiked wells A1 and A4 with no detection in control wells A2 and
A3.
[0057] FIG. 21 shows a representative base composition distribution
of human adenovirus strain types for a single primer pair region on
the hexon gene. The circles represent different adenovirus
sequences in our database that were used for primer design.
Measurement of masses and base counts for each of the unknown
samples A, B, C and D matched one or more of the known groups of
adenoviruses.
[0058] FIG. 22 shows a representative broad range survey/drill-down
process as applied to emm-typing of streptococcus pyogenes (Group A
Streptococcus: GAS). Genetic material is extracted (201) and
amplified using broad range survey primers (202). The amplification
products are analyzed (203) to determine the presence and identity
of bioagents at the species level. If Streptococcus pyogenes is
detected (204), the emm-typing "drill-down" primers are used to
reexamine the extract to identify the emm-type of the sample (205).
Different sets of drill down primers can be employed to determine a
subspecies characteristic for various strains of various bioagents
(206).
[0059] FIG. 23 shows a representative base composition distribution
of bioagents detected in throat swabs from military personnel using
a broad range primer pair directed to 16S rRNA.
[0060] FIG. 24 shows a representative deconvoluted ESI-FTICR
spectra of the PCR products produced by the gtr primer for samples
12 (top) and 10 (bottom) corresponding to emm types 3 and 6,
respectively. Accurate mass measurements were obtained by using an
internal mass standard and post-calibrating each spectrum; the
experimental mass measurement uncertainty on each strand is +0.035
Daltons (1 ppm). Unambiguous base compositions of the amplicons
were determined by calculating all putative base compositions of
each stand within the measured mass (and measured mass uncertainty)
and selecting complementary pairs within the mass measurement
uncertainty. In all cases there was only one base composition
within 25 ppm. The measured mass difference of 15.985 Da between
the strands shown on the left is in excellent agreement with the
theoretical mass difference of 15.994 Da expected for an A to G
substitution.
[0061] FIG. 25 shows representative results of the base composition
analysis on throat swab samples using the six primer pairs, 5'-emm
gene sequencing and the MLST gene sequencing method of the present
invention for an outbreak of Streptococcus pyogenes (group A
streptococcus; GAS) at a military training camp.
[0062] FIG. 26 shows: a) a representative ESI-FTICR mass spectrum
of a restriction digest of a 986 bp region of the 16S ribosomal
gene from E. coli K12 digested with a mixture of BstNI, BsmFI,
BfaI, and NcoI; b) a deconvoluted representation (neutral mass) of
the above spectrum showing the base compositions derived from
accurate mass measurements of each fragment; and c) a
representative reconstructed restriction map showing complete base
composition coverage for nucleotides 1-856. The NcoI did not
cut.
[0063] FIG. 27 shows a representative base composition distribution
of poxviruses for a single primer pair region on the DNA-dependent
polymerase B gene (DdDpB). The spheres represent different poxvirus
sequences that were used for primer design.
DESCRIPTION OF EMBODIMENTS
[0064] The present invention provides, inter alia, methods for
detection and identification of bioagents in an unbiased manner
using "bioagent identifying amplicons." "Intelligent primers" are
selected to hybridize to conserved sequence regions of nucleic
acids derived from a bioagent and which bracket variable sequence
regions to yield a bioagent identifying amplicon which can be
amplified and which is amenable to molecular mass determination.
The molecular mass then provides a means to uniquely identify the
bioagent without a requirement for prior knowledge of the possible
identity of the bioagent. The molecular mass or corresponding "base
composition signature" (BCS) of the amplification product is then
matched against a database of molecular masses or base composition
signatures. Furthermore, the method can be applied to rapid
parallel "multiplex" analyses, the results of which can be employed
in a triangulation identification strategy. The present method
provides rapid throughput and does not require nucleic acid
sequencing of the amplified target sequence for bioagent detection
and identification.
[0065] In the context of this invention, a "bioagent" is any
organism, cell, or virus, living or dead, or a nucleic acid derived
from such an organism, cell or virus. Examples of bioagents
include, but are not limited, to cells (including, but not limited
to, human clinical samples, bacterial cells and other pathogens)
viruses, fungi, and protists, parasites, and pathogenicity markers
(including, but not limited to, pathogenicity islands, antibiotic
resistance genes, virulence factors, toxin genes and other
bioregulating compounds). Samples may be alive or dead or in a
vegetative state (for example, vegetative bacteria or spores) and
may be encapsulated or bioengineered. In the context of this
invention, a "pathogen" is a bioagent that causes a disease or
disorder.
[0066] Despite enormous biological diversity, all forms of life on
earth share sets of essential, common features in their genomes.
Bacteria, for example have highly conserved sequences in a variety
of locations on their genomes. Most notable is the universally
conserved region of the ribosome, but there are also conserved
elements in other non-coding RNAs, including RNAse P and the signal
recognition particle (SRP) among others. Bacteria have a common set
of absolutely required genes. About 250 genes are present in all
bacterial species (Mushegian et al., Proc. Natl. Acad. Sci. U.S.A.,
1996, 93, 10268; and Fraser et al., Science, 1995, 270, 397),
including tiny genomes like Mycoplasma, Ureaplasma and Rickettsia.
These genes encode proteins involved in translation, replication,
recombination and repair, transcription, nucleotide metabolism,
amino acid metabolism, lipid metabolism, energy generation, uptake,
secretion and the like. Examples of these proteins are DNA
polymerase III beta, elongation factor TU, heat shock protein
groEL, RNA polymerase beta, phosphoglycerate kinase, NADH
dehydrogenase, DNA ligase, DNA topoisomerase and elongation factor
G. Operons can also be targeted using the present method. One
example of an operon is the bfp operon from enteropathogenic E.
coli. Multiple core chromosomal genes can be used to classify
bacteria at a genus or genus species level to determine if an
organism has threat potential. The methods can also be used to
detect pathogenicity markers (plasmid or chromosomal) and
antibiotic resistance genes to confirm the threat potential of an
organism and to direct countermeasures.
[0067] Since genetic data provide the underlying basis for
identification of bioagents by the methods of the present
invention, it is prudent to select segments of nucleic acids which
ideally provide enough variability to distinguish each individual
bioagent and whose molecular mass is amenable to molecular mass
determination. In one embodiment of the present invention, at least
one polynucleotide segment is amplified to facilitate detection and
analysis in the process of identifying the bioagent. Thus, the
nucleic acid segments that provide enough variability to
distinguish each individual bioagent and whose molecular masses are
amenable to molecular mass determination are herein described as
"bioagent identifying amplicons." The term "amplicon" as used
herein, refers to a segment of a polynucleotide which is amplified
in an amplification reaction. In some embodiments of the present
invention, bioagent identifying amplicons comprise from about 45 to
about 150 nucleobases (i.e. from about 45 to about 150 linked
nucleosides). One of ordinary skill in the art will appreciate that
the invention embodies compounds of 45, 46, 47, 48, 49, 50, 51, 52,
53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69,
70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86,
87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102,
103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115,
116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128,
129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141,
142, 143, 144, 145, 146, 147, 148, 149, and 150 nucleobases in
length.
[0068] As used herein, "intelligent primers" are primers that are
designed to bind to highly conserved sequence regions that flank an
intervening variable region and yield amplification products which
ideally provide enough variability to distinguish each individual
bioagent, and which are amenable to molecular mass analysis. By the
term "highly conserved," it is meant that the sequence regions
exhibit between about 80-100%, or between about 90-100%, or between
about 95-100% identity. The molecular mass of a given amplification
product provides a means of identifying the bioagent from which it
was obtained, due to the variability of the variable region. Thus,
design of intelligent primers involves selection of a variable
region with appropriate variability to resolve the identity of a
particular bioagent. It is the combination of the portion of the
bioagent nucleic acid molecule sequence to which the intelligent
primers hybridize and the intervening variable region that makes up
the bioagent identifying amplicon. Alternately, it is the
intervening variable region by itself that makes up the bioagent
identifying amplicon.
[0069] It is understood in the art that the sequence of a primer
need not be 100% complementary to that of its target nucleic acid
to be specifically hybridizable. Moreover, a primer may hybridize
over one or more segments such that intervening or adjacent
segments are not involved in the hybridization event (e.g., a loop
structure or hairpin structure). The primers of the present
invention can comprise at least 70%, at least 75%, at least 80%, at
least 85%, at least 90%, at least 95%, or at least 99% sequence
complementarity to the target region within the highly conserved
region to which they are targeted. For example, an intelligent
primer wherein 18 of 20 nucleobases are complementary to a highly
conserved region would represent 90 percent complementarity to the
highly conserved region. In this example, the remaining
noncomplementary nucleobases may be clustered or interspersed with
complementary nucleobases and need not be contiguous to each other
or to complementary nucleobases. As such, a primer which is 18
nucleobases in length having 4 (four) noncomplementary nucleobases
which are flanked by two regions of complete complementarity with
the highly conserved region would have 77.8% overall
complementarity with the highly conserved region and would thus
fall within the scope of the present invention. Percent
complementarity of a primer with a region of a target nucleic acid
can be determined routinely using BLAST programs (basic local
alignment search tools) and PowerBLAST programs known in the art
(Altschul et al., J. Mol. Biol., 1990, 215, 403-410; Zhang and
Madden, Genome Res., 1997, 7, 649-656).
[0070] Percent homology, sequence identity or complementarity, can
be determined by, for example, the Gap program (Wisconsin Sequence
Analysis Package, Version 8 for Unix, Genetics Computer Group,
University Research Park, Madison Wis.), using default settings,
which uses the algorithm of Smith and Waterman (Adv. Appl. Math.,
1981, 2, 482-489). In some embodiments, complementarity of
intelligent primers, is between about 70% and about 80%. In other
embodiments, homology, sequence identity or complementarity, is
between about 80% and about 90%. In yet other embodiments,
homology, sequence identity or complementarity, is about 90%, about
92%, about 94%, about 95%, about 96%, about 97%, about 98%, about
99% or about 100%.
[0071] The intelligent primers of this invention comprise from
about 12 to about 35 nucleobases (i.e. from about 12 to about 35
linked nucleosides). One of ordinary skill in the art will
appreciate that the invention embodies compounds of 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 nucleobases in length.
[0072] One having skill in the art armed with the preferred
bioagent identifying amplicons defined by the primers illustrated
herein will be able, without undue experimentation, to identify
additional intelligent primers.
[0073] In one embodiment, the bioagent identifying amplicon is a
portion of a ribosomal RNA (rRNA) gene sequence. With the complete
sequences of many of the smallest microbial genomes now available,
it is possible to identify a set of genes that defines "minimal
life" and identify composition signatures that uniquely identify
each gene and organism. Genes that encode core life functions such
as DNA replication, transcription, ribosome structure, translation,
and transport are distributed broadly in the bacterial genome and
are suitable regions for selection of bioagent identifying
amplicons. Ribosomal RNA (rRNA) genes comprise regions that provide
useful base composition signatures. Like many genes involved in
core life functions, rRNA genes contain sequences that are
extraordinarily conserved across bacterial domains interspersed
with regions of high variability that are more specific to each
species. The variable regions can be utilized to build a database
of base composition signatures. The strategy involves creating a
structure-based alignment of sequences of the small (16S) and the
large (23S) subunits of the rRNA genes. For example, there are
currently over 13,000 sequences in the ribosomal RNA database that
has been created and maintained by Robin Gutell, University of
Texas at Austin, and is publicly available on the Institute for
Cellular and Molecular Biology web page on the world wide web of
the Internet at, for example, "rna.icmb.utexas.edu/." There is also
a publicly available rRNA database created and maintained by the
University of Antwerp, Belgium on the world wide web of the
Internet at, for example, "rrna.uia.ac.be."
[0074] These databases have been analyzed to determine regions that
are useful as bioagent identifying amplicons. The characteristics
of such regions include: a) between about 80 and 100%, or greater
than about 95% identity among species of the particular bioagent of
interest, of upstream and downstream nucleotide sequences which
serve as sequence amplification primer sites; b) an intervening
variable region which exhibits no greater than about 5% identity
among species; and c) a separation of between about 30 and 1000
nucleotides, or no more than about 50-250 nucleotides, or no more
than about 60-100 nucleotides, between the conserved regions.
[0075] As a non-limiting example, for identification of Bacillus
species, the conserved sequence regions of the chosen bioagent
identifying amplicon must be highly conserved among all Bacillus
species while the variable region of the bioagent identifying
amplicon is sufficiently variable such that the molecular masses of
the amplification products of all species of Bacillus are
distinguishable.
[0076] Bioagent identifying amplicons amenable to molecular mass
determination are either of a length, size or mass compatible with
the particular mode of molecular mass determination or compatible
with a means of providing a predictable fragmentation pattern in
order to obtain predictable fragments of a length compatible with
the particular mode of molecular mass determination. Such means of
providing a predictable fragmentation pattern of an amplification
product include, but are not limited to, cleavage with restriction
enzymes or cleavage primers, for example.
[0077] Identification of bioagents can be accomplished at different
levels using intelligent primers suited to resolution of each
individual level of identification. "Broad range survey"
intelligent primers are designed with the objective of identifying
a bioagent as a member of a particular division of bioagents. A
"bioagent division" is defined as group of bioagents above the
species level and includes but is not limited to: orders, families,
classes, clades, genera or other such groupings of bioagents above
the species level. As a non-limiting example, members of the
Bacillus/Clostridia group or gamma-proteobacteria group may be
identified as such by employing broad range survey intelligent
primers such as primers that target 16S or 23S ribosomal RNA.
[0078] In some embodiments, broad range survey intelligent primers
are capable of identification of bioagents at the species level.
One main advantage of the detection methods of the present
invention is that the broad range survey intelligent primers need
not be specific for a particular bacterial species, or even genus,
such as Bacillus or Streptomyces. Instead, the primers recognize
highly conserved regions across hundreds of bacterial species
including, but not limited to, the species described herein. Thus,
the same broad range survey intelligent primer pair can be used to
identify any desired bacterium because it will bind to the
conserved regions that flank a variable region specific to a single
species, or common to several bacterial species, allowing unbiased
nucleic acid amplification of the intervening sequence and
determination of its molecular weight and base composition. For
example, the 16S_971-1062, 16S_1228-1310 and 16S_1100-1188 regions
are 98-99% conserved in about 900 species of bacteria (16S=16S
rRNA, numbers indicate nucleotide position). In one embodiment of
the present invention, primers used in the present method bind to
one or more of these regions or portions thereof.
[0079] Due to their overall conservation, the flanking rRNA primer
sequences serve as good intelligent primer binding sites to amplify
the nucleic acid region of interest for most, if not all, bacterial
species. The intervening region between the sets of primers varies
in length and/or composition, and thus provides a unique base
composition signature. Examples of intelligent primers that amplify
regions of the 16S and 23S rRNA are shown in FIGS. 1A-1H. A typical
primer amplified region in 16S rRNA is shown in FIG. 2. The arrows
represent primers that bind to highly conserved regions that flank
a variable region in 16S rRNA domain III. The amplified region is
the stem-loop structure under "1100-1188." It is advantageous to
design the broad range survey intelligent primers to minimize the
number of primers required for the analysis, and to allow detection
of multiple members of a bioagent division using a single pair of
primers. The advantage of using broad range survey intelligent
primers is that once a bioagent is broadly identified, the process
of further identification at species and sub-species levels is
facilitated by directing the choice of additional intelligent
primers.
[0080] "Division-wide" intelligent primers are designed with an
objective of identifying a bioagent at the species level. As a
non-limiting example, a Bacillus anthracis, Bacillus cereus and
Bacillus thuringiensis can be distinguished from each other using
division-wide intelligent primers. Division-wide intelligent
primers are not always required for identification at the species
level because broad range survey intelligent primers may provide
sufficient identification resolution to accomplishing this
identification objective.
[0081] "Drill-down" intelligent primers are designed with an
objective of identifying a sub-species characteristic of a
bioagent. A "sub-species characteristic" is defined as a property
imparted to a bioagent at the sub-species level of identification
as a result of the presence or absence of a particular segment of
nucleic acid. Such sub-species characteristics include, but are not
limited to, strains, sub-types, pathogenicity markers such as
antibiotic resistance genes, pathogenicity islands, toxin genes and
virulence factors. Identification of such sub-species
characteristics is often critical for determining proper clinical
treatment of pathogen infections.
Chemical Modifications of Intelligent Primers
[0082] Ideally, intelligent primer hybridization sites are highly
conserved in order to facilitate the hybridization of the primer.
In cases where primer hybridization is less efficient due to lower
levels of conservation of sequence, intelligent primers can be
chemically modified to improve the efficiency of hybridization.
[0083] For example, because any variation (due to codon wobble in
the 3.sup.rd position) in these conserved regions among species is
likely to occur in the third position of a DNA triplet,
oligonucleotide primers can be designed such that the nucleotide
corresponding to this position is a base which can bind to more
than one nucleotide, referred to herein as a "universal base." For
example, under this "wobble" pairing, inosine (I) binds to U, C or
A; guanine (G) binds to U or C, and uridine (U) binds to U or C.
Other examples of universal bases include nitroindoles such as
5-nitroindole or 3-nitropyrrole (Loakes et al., Nucleosides and
Nucleotides, 1995, 14, 1001-1003), the degenerate nucleotides dP or
dK (Hill et al.), an acyclic nucleoside analog containing
5-nitroindazole (Van Aerschot et al., Nucleosides and Nucleotides,
1995, 14, 1053-1056) or the purine analog
1-(2-deoxy-.beta.-D-ribofuranosyl)-imidazole-4-carboxamide (Sala et
al., Nucl. Acids Res., 1996, 24, 3302-3306).
[0084] In another embodiment of the invention, to compensate for
the somewhat weaker binding by the "wobble" base, the
oligonucleotide primers are designed such that the first and second
positions of each triplet are occupied by nucleotide analogs which
bind with greater affinity than the unmodified nucleotide. Examples
of these analogs include, but are not limited to, 2,6-diaminopurine
which binds to thymine, propyne T which binds to adenine and
propyne C and phenoxazines, including G-clamp, which binds to G.
Propynylated pyrimidines are described in U.S. Pat. Nos. 5,645,985,
5,830,653 and 5,484,908, each of which is commonly owned and
incorporated herein by reference in its entirety. Propynylated
primers are claimed in U.S. Ser. No. 10/294,203 which is also
commonly owned and incorporated herein by reference in entirety.
Phenoxazines are described in U.S. Pat. Nos. 5,502,177, 5,763,588,
and 6,005,096, each of which is incorporated herein by reference in
its entirety. G-clamps are described in U.S. Pat. Nos. 6,007,992
and 6,028,183, each of which is incorporated herein by reference in
its entirety.
[0085] A theoretically ideal bioagent detector would identify,
quantify, and report the complete nucleic acid sequence of every
bioagent that reached the sensor. The complete sequence of the
nucleic acid component of a pathogen would provide all relevant
information about the threat, including its identity and the
presence of drug-resistance or pathogenicity markers. This ideal
has not yet been achieved. However, the present invention provides
a straightforward strategy for obtaining information with the same
practical value based on analysis of bioagent identifying amplicons
by molecular mass determination.
[0086] In some cases, a molecular mass of a given bioagent
identifying amplicon alone does not provide enough resolution to
unambiguously identify a given bioagent. For example, the molecular
mass of the bioagent identifying amplicon obtained using the
intelligent primer pair "16S_971" would be 55622 Da for both E.
coli and Salmonella typhimurium. However, if additional intelligent
primers are employed to analyze additional bioagent identifying
amplicons, a "triangulation identification" process is enabled. For
example, the "16S_1100" intelligent primer pair yields molecular
masses of 55009 and 55005 Da for E. coli and Salmonella
typhimurium, respectively. Furthermore, the "23S_855" intelligent
primer pair yields molecular masses of 42656 and 42698 Da for E.
coli and Salmonella typhimurium, respectively. In this basic
example, the second and third intelligent primer pairs provided the
additional "fingerprinting" capability or resolution to distinguish
between the two bioagents.
[0087] In another embodiment, the triangulation identification
process is pursued by measuring signals from a plurality of
bioagent identifying amplicons selected within multiple core genes.
This process is used to reduce false negative and false positive
signals, and enable reconstruction of the origin of hybrid or
otherwise engineered bioagents. In this process, after
identification of multiple core genes, alignments are created from
nucleic acid sequence databases. The alignments are then analyzed
for regions of conservation and variation, and bioagent identifying
amplicons are selected to distinguish bioagents based on specific
genomic differences. For example, identification of the three part
toxin genes typical of B. anthracis (Bowen et al., J. Appl.
Microbiol., 1999, 87, 270-278) in the absence of the expected
signatures from the B. anthracis genome would suggest a genetic
engineering event.
[0088] The triangulation identification process can be pursued by
characterization of bioagent identifying amplicons in a massively
parallel fashion using the polymerase chain reaction (PCR), such as
multiplex PCR, and mass spectrometric (MS) methods. Sufficient
quantities of nucleic acids should be present for detection of
bioagents by MS. A wide variety of techniques for preparing large
amounts of purified nucleic acids or fragments thereof are well
known to those of skill in the art. PCR requires one or more pairs
of oligonucleotide primers that bind to regions which flank the
target sequence(s) to be amplified. These primers prime synthesis
of a different strand of DNA with synthesis occurring in the
direction of one primer towards the other primer. The primers, DNA
to be amplified, a thermostable DNA polymerase (e.g. Taq
polymerase), the four deoxynucleotide triphosphates, and a buffer
are combined to initiate DNA synthesis. The solution is denatured
by heating, then cooled to allow annealing of newly added primer,
followed by another round of DNA synthesis. This process is
typically repeated for about 30 cycles, resulting in amplification
of the target sequence.
[0089] Although the use of PCR is suitable, other nucleic acid
amplification techniques may also be used, including ligase chain
reaction (LCR) and strand displacement amplification (SDA). The
high-resolution MS technique allows separation of bioagent spectral
lines from background spectral lines in highly cluttered
environments.
[0090] In another embodiment, the detection scheme for the PCR
products generated from the bioagent(s) incorporates at least three
features. First, the technique simultaneously detects and
differentiates multiple (generally about 6-10) PCR products.
Second, the technique provides a molecular mass that uniquely
identifies the bioagent from the possible primer sites. Finally,
the detection technique is rapid, allowing multiple PCR reactions
to be run in parallel.
[0091] Mass spectrometry (MS)-based detection of PCR products
provides a means for determination of BCS that has several
advantages. MS is intrinsically a parallel detection scheme without
the need for radioactive or fluorescent labels, since every
amplification product is identified by its molecular mass. The
current state of the art in mass spectrometry is such that less
than femtomole quantities of material can be readily analyzed to
afford information about the molecular contents of the sample. An
accurate assessment of the molecular mass of the material can be
quickly obtained, irrespective of whether the molecular weight of
the sample is several hundred, or in excess of one hundred thousand
atomic mass units (amu) or Daltons. Intact molecular ions can be
generated from amplification products using one of a variety of
ionization techniques to convert the sample to gas phase. These
ionization methods include, but are not limited to, electrospray
ionization (ES), matrix-assisted laser desorption ionization
(MALDI) and fast atom bombardment (FAB). For example, MALDI of
nucleic acids, along with examples of matrices for use in MALDI of
nucleic acids, are described in WO 98/54751 (Genetrace, Inc.).
[0092] In some embodiments, large DNAs and RNAs, or large
amplification products therefrom, can be digested with restriction
endonucleases prior to ionization. Thus, for example, an
amplification product that was 10 kDa could be digested with a
series of restriction endonucleases to produce a panel of, for
example, 100 Da fragments. Restriction endonucleases and their
sites of action are well known to the skilled artisan. In this
manner, mass spectrometry can be performed for the purposes of
restriction mapping.
[0093] Upon ionization, several peaks are observed from one sample
due to the formation of ions with different charges. Averaging the
multiple readings of molecular mass obtained from a single mass
spectrum affords an estimate of molecular mass of the bioagent.
Electrospray ionization mass spectrometry (ESI-MS) is particularly
useful for very high molecular weight polymers such as proteins and
nucleic acids having molecular weights greater than 10 kDa, since
it yields a distribution of multiply-charged molecules of the
sample without causing a significant amount of fragmentation.
[0094] The mass detectors used in the methods of the present
invention include, but are not limited to, Fourier transform ion
cyclotron resonance mass spectrometry (FT-ICR-MS), ion trap,
quadrupole, magnetic sector, time of flight (TOF), Q-TOF, and
triple quadrupole.
[0095] In general, the mass spectrometric techniques which can be
used in the present invention include, but are not limited to,
tandem mass spectrometry, infrared multiphoton dissociation and
pyrolytic gas chromatography mass spectrometry (PGC-MS). In one
embodiment of the invention, the bioagent detection system operates
continually in bioagent detection mode using pyrolytic GC-MS
without PCR for rapid detection of increases in biomass (for
example, increases in fecal contamination of drinking water or of
germ warfare agents). To achieve minimal latency, a continuous
sample stream flows directly into the PGC-MS combustion chamber.
When an increase in biomass is detected, a PCR process is
automatically initiated. Bioagent presence produces elevated levels
of large molecular fragments from, for example, about 100-7,000 Da
which are observed in the PGC-MS spectrum. The observed mass
spectrum is compared to a threshold level and when levels of
biomass are determined to exceed a predetermined threshold, the
bioagent classification process described hereinabove (combining
PCR and MS, such as FT-ICR MS) is initiated. Optionally, alarms or
other processes (halting ventilation flow, physical isolation) are
also initiated by this detected biomass level.
[0096] The accurate measurement of molecular mass for large DNAs is
limited by the adduction of cations from the PCR reaction to each
strand, resolution of the isotopic peaks from natural abundance
.sup.13C and .sup.15N isotopes, and assignment of the charge state
for any ion. The cations are removed by in-line dialysis using a
flow-through chip that brings the solution containing the PCR
products into contact with a solution containing ammonium acetate
in the presence of an electric field gradient orthogonal to the
flow. The latter two problems are addressed by operating with a
resolving power of >100,000 and by incorporating isotopically
depleted nucleotide triphosphates into the DNA. The resolving power
of the instrument is also a consideration. At a resolving power of
10,000, the modeled signal from the [M-14H+].sup.14- charge state
of an 84mer PCR product is poorly characterized and assignment of
the charge state or exact mass is impossible. At a resolving power
of 33,000, the peaks from the individual isotopic components are
visible. At a resolving power of 100,000, the isotopic peaks are
resolved to the baseline and assignment of the charge state for the
ion is straightforward. The [.sup.13C,.sup.15N]-depleted
triphosphates are obtained, for example, by growing microorganisms
on depleted media and harvesting the nucleotides (Batey et al.,
Nucl. Acids Res., 1992, 20, 4515-4523).
[0097] While mass measurements of intact nucleic acid regions are
believed to be adequate to determine most bioagents, tandem mass
spectrometry (MS.sup.n) techniques may provide more definitive
information pertaining to molecular identity or sequence. Tandem MS
involves the coupled use of two or more stages of mass analysis
where both the separation and detection steps are based on mass
spectrometry. The first stage is used to select an ion or component
of a sample from which further structural information is to be
obtained. The selected ion is then fragmented using, e.g.,
blackbody irradiation, infrared multiphoton dissociation, or
collisional activation. For example, ions generated by electrospray
ionization (ESI) can be fragmented using IR multiphoton
dissociation. This activation leads to dissociation of glycosidic
bonds and the phosphate backbone, producing two series of fragment
ions, called the w-series (having an intact 3' terminus and a 5'
phosphate following internal cleavage) and the .alpha.-Base series
(having an intact 5' terminus and a 3' furan).
[0098] The second stage of mass analysis is then used to detect and
measure the mass of these resulting fragments of product ions. Such
ion selection followed by fragmentation routines can be performed
multiple times so as to essentially completely dissect the
molecular sequence of a sample.
[0099] If there are two or more targets of similar molecular mass,
or if a single amplification reaction results in a product that has
the same mass as two or more bioagent reference standards, they can
be distinguished by using mass-modifying "tags." In this embodiment
of the invention, a nucleotide analog or "tag" is incorporated
during amplification (e.g., a 5-(trifluoromethyl) deoxythymidine
triphosphate) which has a different molecular weight than the
unmodified base so as to improve distinction of masses. Such tags
are described in, for example, PCT WO97/33000, which is
incorporated herein by reference in its entirety. This further
limits the number of possible base compositions consistent with any
mass. For example, 5-(trifluoromethyDdeoxythymidine triphosphate
can be used in place of dTTP in a separate nucleic acid
amplification reaction. Measurement of the mass shift between a
conventional amplification product and the tagged product is used
to quantitate the number of thymidine nucleotides in each of the
single strands. Because the strands are complementary, the number
of adenosine nucleotides in each strand is also determined.
[0100] In another amplification reaction, the number of G and C
residues in each strand is determined using, for example, the
cytidine analog 5-methylcytosine (5-meC) or propyne C. The
combination of the A/T reaction and G/C reaction, followed by
molecular weight determination, provides a unique base composition.
This method is summarized in FIG. 4 and Table 1.
TABLE-US-00001 TABLE 1 Total Total Base Base base mass info info
comp. Double strand Single strand this this other Top Bottom Mass
tag sequence Sequence strand strand strand strand strand T*mass
T*ACGT*ACGT* T*ACGT*ACGT* 3x 3T 3A 3T 3A (T*-T) = x AT*GCAT*GCA 2A
2T 2C 2G 2G 2C AT*GCAT*GCA 2x 2T 2A C*mass TAC*GTAC*GT TAC*GTAC*GT
2x 2C 2G (C*-C) = y ATGC*ATGC*A ATGC*ATGC*A 2x 2C 2G
[0101] The mass tag phosphorothioate A (A*) was used to distinguish
a Bacillus anthracis cluster. The B. anthracis
(A.sub.14G.sub.9C.sub.14T.sub.9) had an average MW of 14072.26, and
the B. anthracis (A.sub.1A*.sub.13G.sub.9C.sub.14T.sub.9) had an
average molecular weight of 14281.11 and the phosphorothioate A had
an average molecular weight of +16.06 as determined by ESI-TOF MS.
The deconvoluted spectra are shown in FIG. 5.
[0102] In another example, assume the measured molecular masses of
each strand are 30,000.115 Da and 31,000.115 Da respectively, and
the measured number of dT and dA residues are (30, 28) and (28,
30). If the molecular mass is accurate to 100 ppm, there are 7
possible combinations of dG+dC possible for each strand. However,
if the measured molecular mass is accurate to 10 ppm, there are
only 2 combinations of dG+dC, and at 1 ppm accuracy there is only
one possible base composition for each strand.
[0103] Signals from the mass spectrometer may be input to a
maximum-likelihood detection and classification algorithm such as
is widely used in radar signal processing. The detection processing
uses matched filtering of BCS observed in mass-basecount space and
allows for detection and subtraction of signatures from known,
harmless organisms, and for detection of unknown bioagent threats.
Comparison of newly observed bioagents to known bioagents is also
possible, for estimation of threat level, by comparing their BCS to
those of known organisms and to known forms of pathogenicity
enhancement, such as insertion of antibiotic resistance genes or
toxin genes.
[0104] Processing may end with a Bayesian classifier using log
likelihood ratios developed from the observed signals and average
background levels. The program emphasizes performance predictions
culminating in probability-of-detection versus
probability-of-false-alarm plots for conditions involving complex
backgrounds of naturally occurring organisms and environmental
contaminants. Matched filters consist of a priori expectations of
signal values given the set of primers used for each of the
bioagents. A genomic sequence database (e.g. GenBank) is used to
define the mass basecount matched filters. The database contains
known threat agents and benign background organisms. The latter is
used to estimate and subtract the signature produced by the
background organisms. A maximum likelihood detection of known
background organisms is implemented using matched filters and a
running-sum estimate of the noise covariance. Background signal
strengths are estimated and used along with the matched filters to
form signatures that are then subtracted. The maximum likelihood
process is applied to this "cleaned up" data in a similar manner
employing matched filters for the organisms and a running-sum
estimate of the noise-covariance for the cleaned up data.
[0105] Although the molecular mass of amplification products
obtained using intelligent primers provides a means for
identification of bioagents, conversion of molecular mass data to a
base composition signature is useful for certain analyses. As used
herein, a "base composition signature" (BCS) is the exact base
composition determined from the molecular mass of a bioagent
identifying amplicon. In one embodiment, a BCS provides an index of
a specific gene in a specific organism.
[0106] Base compositions, like sequences, vary slightly from
isolate to isolate within species. It is possible to manage this
diversity by building "base composition probability clouds" around
the composition constraints for each species. This permits
identification of organisms in a fashion similar to sequence
analysis. A "pseudo four-dimensional plot" can be used to visualize
the concept of base composition probability clouds (FIG. 18).
Optimal primer design requires optimal choice of bioagent
identifying amplicons and maximizes the separation between the base
composition signatures of individual bioagents. Areas where clouds
overlap indicate regions that may result in a misclassification, a
problem which is overcome by selecting primers that provide
information from different bioagent identifying amplicons, ideally
maximizing the separation of base compositions. Thus, one aspect of
the utility of an analysis of base composition probability clouds
is that it provides a means for screening primer sets in order to
avoid potential misclassifications of BCS and bioagent identity.
Another aspect of the utility of base composition probability
clouds is that they provide a means for predicting the identity of
a bioagent whose exact measured BCS was not previously observed
and/or indexed in a BCS database due to evolutionary transitions in
its nucleic acid sequence.
[0107] It is important to note that, in contrast to probe-based
techniques, mass spectrometry determination of base composition
does not require prior knowledge of the composition in order to
make the measurement, only to interpret the results. In this
regard, the present invention provides bioagent classifying
information similar to DNA sequencing and phylogenetic analysis at
a level sufficient to detect and identify a given bioagent.
Furthermore, the process of determination of a previously unknown
BCS for a given bioagent (for example, in a case where sequence
information is unavailable) has downstream utility by providing
additional bioagent indexing information with which to populate BCS
databases. The process of future bioagent identification is thus
greatly improved as more BCS indexes become available in the BCS
databases.
[0108] Another embodiment of the present invention is a method of
surveying bioagent samples that enables detection and
identification of all bacteria for which sequence information is
available using a set of twelve broad-range intelligent PCR
primers. Six of the twelve primers are "broad range survey primers"
herein defined as primers targeted to broad divisions of bacteria
(for example, the Bacillus/Clostridia group or
gamma-proteobacteria). The other six primers of the group of twelve
primers are "division-wide" primers herein defined as primers that
provide more focused coverage and higher resolution. This method
enables identification of nearly 100% of known bacteria at the
species level. A further example of this embodiment of the present
invention is a method herein designated "survey/drill-down" wherein
a subspecies characteristic for detected bioagents is obtained
using additional primers. Examples of such a subspecies
characteristic include but are not limited to: antibiotic
resistance, pathogenicity island, virulence factor, strain type,
sub-species type, and clade group. Using the survey/drill-down
method, bioagent detection, confirmation and a subspecies
characteristic can be provided within hours. Moreover, the
survey/drill-down method can be focused to identify bioengineering
events such as the insertion of a toxin gene into a bacterial
species that does not normally make the toxin.
[0109] The present methods allow extremely rapid and accurate
detection and identification of bioagents compared to existing
methods. Furthermore, this rapid detection and identification is
possible even when sample material is impure. The methods leverage
ongoing biomedical research in virulence, pathogenicity, drug
resistance and genome sequencing into a method which provides
greatly improved sensitivity, specificity and reliability compared
to existing methods, with lower rates of false positives. Thus, the
methods are useful in a wide variety of fields, including, but not
limited to, those fields discussed below.
[0110] In other embodiments of the invention, the methods disclosed
herein can identify infectious agents in biological samples. At
least a first biological sample containing at least a first
unidentified infectious agent is obtained. An identification
analysis is carried out on the sample, whereby the first infectious
agent in the first biological sample is identified. More
particularly, a method of identifying an infectious agent in a
biological entity is provided. An identification analysis is
carried out on a first biological sample obtained from the
biological entity, whereby at least one infectious agent in the
biological sample from the biological entity is identified. The
obtaining and the performing steps are, optionally, repeated on at
least one additional biological sample from the biological
entity.
[0111] The present invention also provides methods of identifying
an infectious agent that is potentially the cause of a health
condition in a biological entity. An identification analysis is
carried out on a first test sample from a first infectious agent
differentiating area of the biological entity, whereby at least one
infectious agent is identified. The obtaining and the performing
steps are, optionally, repeated on an additional infectious agent
differentiating area of the biological entity.
[0112] Biological samples include, but are not limited to, hair,
mucosa, skin, nail, blood, saliva, rectal, lung, stool, urine,
breath, nasal, ocular sample, or the like. In some embodiments, one
or more biological samples are analyzed by the methods described
herein. The biological sample(s) contain at least a first
unidentified infectious agent and may contain more than one
infectious agent. The biological sample(s) are obtained from a
biological entity. The biological sample can be obtained by a
variety of manners such as by biopsy, swabbing, and the like. The
biological samples may be obtained by a physician in a hospital or
other health care environment. The physician may then perform the
identification analysis or send the biological sample to a
laboratory to carry out the analysis.
[0113] Biological entities include, but are not limited to, a
mammal, a bird, or a reptile. The biological entity may be a cow,
horse, dog, cat, or a primate. The biological entity can also be a
human. The biological entity may be living or dead.
[0114] An infectious agent differentiating area is any area or
location within a biological entity that can distinguish between a
harmful versus normal health condition. An infectious agent
differentiating area can be a region or area of the biological
entity whereby an infectious agent is more likely to predominate
from another region or area of the biological entity. For example,
infectious agent differentiating areas may include the blood
vessels of the heart (heart disease, coronary artery disease,
etc.), particular portions of the digestive system (ulcers, Crohn's
disease, etc.), liver (hepatitis infections), and the like. In some
embodiments, one or more biological samples from a plurality of
infectious agent differentiating areas is analyzed the methods
described herein.
[0115] Infectious agents of the invention may potentially cause a
health condition in a biological entity. Health conditions include
any condition, syndrome, illness, disease, or the like, identified
currently or in the future by medical personnel. Infectious agents
include, but are not limited to, bacteria, viruses, parasites,
fungi, and the like.
[0116] In other embodiments of the invention, the methods disclosed
herein can be used to screen blood and other bodily fluids and
tissues for pathogenic and non-pathogenic bacteria, viruses,
parasites, fungi and the like. Animal samples, including but not
limited to, blood and other bodily fluid and tissue samples, can be
obtained from living animals, who are either known or not known to
or suspected of having a disease, infection, or condition.
Alternately, animal samples such as blood and other bodily fluid
and tissue samples can be obtained from deceased animals. Blood
samples can be further separated into plasma or cellular fractions
and further screened as desired. Bodily fluids and tissues can be
obtained from any part of the animal or human body. Animal samples
can be obtained from, for example, mammals and humans.
[0117] Clinical samples are analyzed for disease causing bioagents
and biowarfare pathogens simultaneously with detection of bioagents
at levels as low as 100-1000 genomic copies in complex backgrounds
with throughput of approximately 100-300 samples with simultaneous
detection of bacteria and viruses. Such analyses provide additional
value in probing bioagent genomes for unanticipated modifications.
These analyses are carried out in reference labs, hospitals and the
LRN laboratories of the public health system in a coordinated
fashion, with the ability to report the results via a computer
network to a common data-monitoring center in real time. Clonal
propagation of specific infectious agents, as occurs in the
epidemic outbreak of infectious disease, can be tracked with base
composition signatures, analogous to the pulse field gel
electrophoresis fingerprinting patterns used in tracking the spread
of specific food pathogens in the Pulse Net system of the CDC
(Swaminathan et al., Emerging Infectious Diseases, 2001, 7,
382-389). The present invention provides a digital barcode in the
form of a series of base composition signatures, the combination of
which is unique for each known organism. This capability enables
real-time infectious disease monitoring across broad geographic
locations, which may be essential in a simultaneous outbreak or
attack in different cities.
[0118] In other embodiments of the invention, the methods disclosed
herein can be used for detecting the presence of pathogenic and
non-pathogenic bacteria, viruses, parasites, fungi and the like in
organ donors and/or in organs from donors. Such examination can
result in the prevention of the transfer of, for example, viruses
such as West Nile virus, hepatitis viruses, human immunodeficiency
virus, and the like from a donor to a recipient via a transplanted
organ. The methods disclosed herein can also be used for detection
of host versus graft or graft versus host rejection issues related
to organ donors by detecting the presence of particular antigens in
either the graft or host known or suspected of causing such
rejection. In particular, the bioagents in this regard are the
antigens of the major histocompatibility complex, such as the HLA
antigens. The present methods can also be used to detect and track
emerging infectious diseases, such as West Nile virus infection,
HIV-related diseases.
[0119] In other embodiments of the invention, the methods disclosed
herein can be used for pharmacogenetic analysis and medical
diagnosis including, but not limited to, cancer diagnosis based on
mutations and polymorphisms, drug resistance and susceptibility
testing, screening for and/or diagnosis of genetic diseases and
conditions, and diagnosis of infectious diseases and conditions. In
context of the present invention, pharmacogenetics is defined as
the study of variability in drug response due to genetic factors.
Pharmacogenetic investigations are often based on correlating
patient outcome with variations in genes involved in the mode of
action of a given drug. For example, receptor genes, or genes
involved in metabolic pathways. The methods of the present
invention provide a means to analyze the DNA of a patient to
provide the basis for pharmacogenetic analysis.
[0120] The present method can also be used to detect single
nucleotide polymorphisms (SNPs), or multiple nucleotide
polymorphisms, rapidly and accurately. A SNP is defined as a single
base pair site in the genome that is different from one individual
to another. The difference can be expressed either as a deletion,
an insertion or a substitution, and is frequently linked to a
disease state. Because they occur every 100-1000 base pairs, SNPs
are the most frequently bound type of genetic marker in the human
genome.
[0121] For example, sickle cell anemia results from an A-T
transition, which encodes a valine rather than a glutamic acid
residue. Oligonucleotide primers may be designed such that they
bind to sequences that flank a SNP site, followed by nucleotide
amplification and mass determination of the amplified product.
Because the molecular masses of the resulting product from an
individual who does not have sickle cell anemia is different from
that of the product from an individual who has the disease, the
method can be used to distinguish the two individuals. Thus, the
method can be used to detect any known SNP in an individual and
thus diagnose or determine increased susceptibility to a disease or
condition.
[0122] In one embodiment, blood is drawn from an individual and
peripheral blood mononuclear cells (PBMC) are isolated and
simultaneously tested, such as in a high-throughput screening
method, for one or more SNPs using appropriate primers based on the
known sequences which flank the SNP region. The National Center for
Biotechnology Information maintains a publicly available database
of SNPs on the world wide web of the Internet at, for example,
"ncbi.nlm.nih.gov/SNP/."
[0123] The method of the present invention can also be used for
blood typing. The gene encoding A, B or O blood type can differ by
four single nucleotide polymorphisms. If the gene contains the
sequence CGTGGTGACCCTT (SEQ ID NO:5), antigen A results. If the
gene contains the sequence CGTCGTCACCGCTA (SEQ ID NO:6) antigen B
results. If the gene contains the sequence CGTGGT-ACCCCTT (SEQ ID
NO:7), blood group O results ("-" indicates a deletion). These
sequences can be distinguished by designing a single primer pair
which flanks these regions, followed by amplification and mass
determination.
[0124] The method of the present invention can also be used for
detection and identification of blood-borne pathogens such as
Staphylococcus aureus for example.
The method of the present invention can also be used for strain
typing of respiratory pathogens in epidemic surveillance. Group A
streptococci (GAS), or Streptococcus pyogenes, is one of the most
consequential causes of respiratory infections because of
prevalence and ability to cause disease with complications such as
acute rheumatic fever and acute glomerulonephritis. GAS also causes
infections of the skin (impetigo) and, in rare cases, invasive
disease such as necrotizing fasciitis and toxic shock syndrome.
Despite many decades of study, the underlying microbial ecology and
natural selection that favors enhanced virulence and explosive GAS
outbreaks is still poorly understood. The ability to detect GAS and
multiple other pathogenic and non-pathogenic bacteria and viruses
in patient samples would greatly facilitate our understanding of
GAS epidemics. It is also essential to be able to follow the spread
of virulent strains of GAS in populations and to distinguish
virulent strains from less virulent or avirulent streptococci that
colonize the nose and throat of asymptomatic individuals at a
frequency ranging from 5-20% of the population (Bisno, A. L. (1995)
in Principles and Practice of Infectious Diseases, eds. Mandell, G.
L., Bennett, J. E. & Dolin, R. (Churchill Livingston, N.Y.),
Vol. 2, pp. 1786-1799). Molecular methods have been developed to
type GAS based upon the sequence of the emm gene that encodes the
M-protein virulence factor (Beall et al., J. Clin. Micro., 1996,
34, 953-958; Beall et al., J. Clin. Micro., 1997, 35, 1231-1235;
and Facklam et al., Emerging Infectious Diseases, 1999, 5,
247-253). Using this molecular classification, over 150 different
emm-types are defined and correlated with phenotypic properties of
thousands of GAS isolates
(www.cdc.gov/ncidod/biotech/strep/strepindex.html) (Facklam et al.,
Clinical Infectious Diseases, 2002, 34, 28-38).
[0125] Recently, a strategy known as Multi Locus Sequence Typing
(MLST) was developed to follow the molecular Epidemiology of GAS.
In MLST, internal fragments of seven housekeeping genes are
amplified, sequenced, and compared to a database of previously
studied isolates (www.test.mlst.net/).
[0126] The present invention enables an emm-typing process to be
carried out directly from throat swabs for a large number of
samples within 12 hours, allowing strain tracking of an ongoing
epidemic, even if geographically dispersed, on a larger scale than
ever before achievable.
[0127] In another embodiment, the present invention can be employed
in the serotyping of viruses including, but not limited to,
adenoviruses. Adenoviruses are DNA viruses that cause over 50% of
febrile respiratory illnesses in military recruits. Human
adenoviruses are divided into six major serogroups (A through F),
each containing multiple strain types. Despite the prevalence of
adenoviruses, there are no rapid methods for detecting and
serotyping adenoviruses.
[0128] In another embodiment, the present invention can be employed
in distinguishing between members of the Orthopoxvirus genus.
Smallpox is caused by the Variola virus. Other members of the genus
include Vaccinia, Monkeypox, Camelpox, and Cowpox. All are capable
of infecting humans, thus, a method capable of identifying and
distinguishing among members of the Orthopox genus is a worthwhile
objective.
[0129] In another embodiment, the present invention can be employed
in distinguishing between viral agents of viral hemorrhagic fevers
(VHF). VHF agents include, but are not limited to, Filoviridae
(Marburg virus and Ebola virus), Arenaviridae (Lassa, Junin,
Machupo, Sabia, and Guanarito viruses), Bunyaviridae (Crimean-Congo
hemorrhagic fever virus (CCHFV), Rift Valley fever virus, and Hanta
viruses), and Flaviviridae (yellow fever virus and dengue virus).
Infections by VHF viruses are associated with a wide spectrum of
clinical manifestations such as diarrhea, myalgia, cough, headache,
pneumonia, encephalopathy, and hepatitis. Filoviruses,
arenaviruses, and CCHFV are of particular relevance because they
can be transmitted from human to human, thus causing epidemics with
high mortality rates (Khan et al., Am. J. Trop. Med. Hyg., 1997,
57, 519-525). In the absence of bleeding or organ manifestation,
VHF is clinically difficult to diagnose, and the various etiologic
agents can hardly be distinguished by clinical tests. Current
approaches to PCR detection of these agents are time-consuming, as
they include a separate cDNA synthesis step prior to PCR, agarose
gel analysis of PCR products, and in some instances a second round
of nested amplification or Southern hybridization. PCRs for
different pathogens have to be run assay by assay due to
differences in cycling conditions, which complicate broad-range
testing in a short period. Moreover, post-PCR processing or nested
PCR steps included in currently used assays increase the risk of
false positive results due to carryover contamination (Kwok et al.,
Nature, 1989, 339, 237-238).
[0130] In another embodiment, the present invention, can be
employed in the diagnosis of a plurality of etiologic agents of a
disease. An "etiologic agent" is herein defined as a pathogen
acting as the causative agent of a disease. Diseases may be caused
by a plurality of etiologic agents. For example, recent studies
have implicated both human herpesvirus 6 (HHV-6) and the obligate
intracellular bacterium Chlamydia pneumoniae in the etiology of
multiple sclerosis (Swanborg, Microbes and Infection, 2002, 4,
1327-1333). The present invention can be applied to the
identification of multiple etiologic agents of a disease by, for
example, the use of broad range bacterial intelligent primers and
division-wide primers (if necessary) for the identification of
bacteria such as Chlamydia pneumoniae followed by primers directed
to viral housekeeping genes for the identification of viruses such
as HHV-6, for example.
[0131] In other embodiments of the invention, the methods disclosed
herein can be used for detection and identification of pathogens in
livestock. Livestock includes, but is not limited to, cows, pigs,
sheep, chickens, turkeys, goats, horses and other farm animals. For
example, conditions classified by the California Department of Food
and Agriculture as emergency conditions in livestock
(www.cdfa.ca.gov/ahfss/ah/pdfs/CA_reportable_disease_list_05292002.pdf)
include, but are not limited to: Anthrax (Bacillus anthracis),
Screwworm myiasis (Cochliomyia hominivorax or Chrysomya bezziana),
African trypanosomiasis (Tsetse fly diseases), Bovine babesiosis
(piroplasmosis), Bovine spongiform encephalopathy (Mad Cow),
Contagious bovine pleuropneumonia (Mycoplasma mycoides mycoides
small colony), Foot-and-mouth disease (Hoof-and-mouth), Heartwater
(Cowdria ruminantium), Hemorrhagic septicemia (Pasteurella
multocida serotypes B:2 or E:2), Lumpy skin disease, Malignant
catarrhal fever (African type), Rift Valley fever, Rinderpest
(Cattle plague), Theileriosis (Corridor disease, East Coast fever),
Vesicular stomatitis, Contagious agalactia (Mycoplasma species),
Contagious caprine pleuropneumonia (Mycoplasma capricolum
capripneumoniae), Nairobi sheep disease, Peste des petits ruminants
(Goat plague), Pulmonary adenomatosis (Viral neoplastic pneumonia),
Salmonella abortus ovis, Sheep and goat pox, African swine fever,
Classical swine fever (Hog cholera), Japanese encephalitis, Nipah
virus, Swine vesicular disease, Teschen disease (Enterovirus
encephalomyelitis), Vesicular exanthema, Exotic Newcastle disease
(Viscerotropic velogenic Newcastle disease), Highly pathogenic
avian influenza (Fowl plague), African horse sickness, Dourine
(Trypanosoma equiperdum), Epizootic lymphangitis (equine
blastomycosis, equine histoplasmosis), Equine piroplasmosis
(Babesia equi, B. caballi), Glanders (Farcy) (Pseudomonas mallei),
Hendra virus (Equine morbillivirus), Horse pox, Surra (Trypanosoma
evansi), Venezuelan equine encephalomyelitis, West Nile Virus,
Chronic wasting disease in cervids, and Viral hemorrhagic disease
of rabbits (calicivirus)
[0132] Conditions classified by the California Department of Food
and Agriculture as regulated conditions in livestock include, but
are not limited to: rabies, Bovine brucellosis (Brucella abortus),
Bovine tuberculosis (Mycobacterium bovis), Cattle scabies (multiple
types), Trichomonosis (Tritrichomonas fetus), Caprine and ovine
brucellosis (excluding Brucella ovis), Scrapie, Sheep scabies (Body
mange) (Psoroptes ovis), Porcine brucellosis (Brucella suis),
Pseudorabies (Aujeszky's disease), Ornithosis (Psittacosis or avian
chlamydiosis) (Chlamydia psittaci), Pullorum disease (Fowl typhoid)
(Salmonella gallinarum and pullorum), Contagious equine metritis
(Taylorella equigenitalis), Equine encephalomyelitis (Eastern and
Western equine encephalitis), Equine infectious anemia (Swamp
fever), Duck viral enteritis (Duck plague), and Tuberculosis in
cervids.
[0133] Additional conditions monitored by the California Department
of Food and Agriculture include, but are not limited to: Avian
tuberculosis (Mycobacterium avium), Echinococcosis/Hydatidosis
(Echinococcus species), Leptospirosis, Anaplasmosis (Anaplasma
marginale or A. centrale), Bluetongue, Bovine cysticercosis (Taenia
saginata in humans), Bovine genital campylobacteriosis
(Campylobacter fetus venerealis), Dermatophilosis
(Streptothricosis, mycotic dermatitis) (Dermatophilus congolensis),
Enzootic bovine leukosis (Bovine leukemia virus), Infectious bovine
rhinotracheitis (Bovine herpesvirus-1), Johne's disease
(Paratuberculosis) (Mycobacterium avium paratuberculosis),
Malignant catarrhal fever (North American), Q Fever (Coxiella
burnetii), Caprine (contagious) arthritis/encephalitis, Enzootic
abortion of ewes (Ovine chlamydiosis) (Chlamydia psittaci),
Maedi-Visna (Ovine progressive pneumonia), Atrophic rhinitis
(Bordetella bronchiseptica, Pasteurella multocida), Porcine
cysticercosis (Taenia solium in humans), Porcine reproductive and
respiratory syndrome, Transmissible gastroenteritis (coronavirus),
Trichinellosis (Trichinella spiralis), Avian infectious bronchitis,
Avian infectious laryngotracheitis, Duck viral hepatitis, Fowl
cholera (Pasteurella multocida), Fowl pox, Infectious bursal
disease (Gumboro disease), Low pathogenic avian influenza, Marek's
disease, Mycoplasmosis (Mycoplasma gallisepticum), Equine influenza
Equine rhinopneumonitis (Equine herpesvirus-1), Equine viral
arteritis, and Horse mange (multiple types).
[0134] A key problem in determining that an infectious outbreak is
the result of a bioterrorist attack is the sheer variety of
organisms that might be used by terrorists. According to a recent
review (Taylor et al., Philos. Trans. R. Soc. Lond. B. Biol. Sci.,
2001, 356, 983-989), there are over 1400 organisms infectious to
humans; most of these have the potential to be used in a
deliberate, malicious attack. These numbers do not include numerous
strain variants of each organism, bioengineered versions, or
pathogens that infect plants or animals. Paradoxically, most of the
new technology being developed for detection of biological weapons
incorporates a version of quantitative PCR, which is based upon the
use of highly specific primers and probes designed to selectively
identify specific pathogenic organisms. This approach requires
assumptions about the type and strain of bacteria or virus which is
expected to be detected. Although this approach will work for the
most obvious organisms, like smallpox and anthrax, experience has
shown that it is very difficult to anticipate what a terrorist will
do.
[0135] The present invention can be used to detect and identify any
biological agent, including bacteria, viruses, fungi and toxins
without prior knowledge of the organism being detected and
identified. As one example, where the agent is a biological threat,
the information obtained such as the presence of toxin genes,
pathogenicity islands and antibiotic resistance genes for example,
is used to determine practical information needed for
countermeasures. In addition, the methods can be used to identify
natural or deliberate engineering events including chromosome
fragment swapping, molecular breeding (gene shuffling) and emerging
infectious diseases. The present invention provides broad-function
technology that may be the only practical means for rapid diagnosis
of disease caused by a biowarfare or bioterrorist attack,
especially an attack that might otherwise be missed or mistaken for
a more common infection.
[0136] Bacterial biological warfare agents capable of being
detected by the present methods include, but are not limited to,
Bacillus anthracis (anthrax), Yersinia pestis (pneumonic plague),
Franciscella tularensis (tularemia), Brucella suis, Brucella
abortus, Brucella melitensis (undulant fever), Burkholderia mallei
(glanders), Burkholderia pseudomalleii (melioidosis), Salmonella
typhi (typhoid fever), Rickettsia typhii (epidemic typhus),
Rickettsia prowasekii (endemic typhus) and Coxiella burnetii (Q
fever), Rhodobacter capsulatus, Chlamydia pneumoniae, Escherichia
coli, Shigella dysenteriae, Shigella flexneri, Bacillus cereus,
Clostridium botulinum, Coxiella burnetti, Pseudomonas aeruginosa,
Legionella pneumophila, and Vibrio cholerae.
[0137] Besides 16S and 23S rRNA, other target regions suitable for
use in the present invention for detection of bacteria include, but
are not limited to, 5S rRNA and RNase P (FIG. 3).
[0138] Fungal biowarfare agents include, but are not limited to,
Coccidioides immitis (Coccidioidomycosis), and Magnaporthe
grisea.
[0139] Biological warfare toxin genes capable of being detected by
the methods of the present invention include, but are not limited
to, botulinum toxin, T-2 mycotoxins, ricin, staph enterotoxin B,
shigatoxin, abrin, aflatoxin, Clostridium perfringens epsilon
toxin, conotoxins, diacetoxyscirpenol, tetrodotoxin and
saxitoxin.
[0140] Parasites that could be used in biological warfare include,
but are not limited to: Ascaris suum, Giardia lamblia,
Cryptosporidium, and Schistosoma.
[0141] Biological warfare viral threat agents are mostly RNA
viruses (positive-strand and negative-strand), with the exception
of smallpox. Every RNA virus is a family of related viruses
(quasispecies). These viruses mutate rapidly and the potential for
engineered strains (natural or deliberate) is very high. RNA
viruses cluster into families that have conserved RNA structural
domains on the viral genome (e.g., virion components, accessory
proteins) and conserved housekeeping genes that encode core viral
proteins including, for single strand positive strand RNA viruses,
RNA-dependent RNA polymerase, double stranded RNA helicase,
chymotrypsin-like and papain-like proteases and methyltransferases.
"Housekeeping genes" refers to genes that are generally always
expressed and thought to be involved in routine cellular
metabolism.
[0142] Examples of (-)-strand RNA viruses include, but are not
limited to, arenaviruses (e.g., sabia virus, lassa fever, Machupo,
Argentine hemorrhagic fever, flexal virus), bunyaviruses (e.g.,
hantavirus, nairovirus, phlebovirus, hantaan virus, Congo-crimean
hemorrhagic fever, rift valley fever), and mononegavirales (e.g.,
filovirus, paramyxovirus, ebola virus, Marburg, equine
morbillivirus).
[0143] Examples of (+)-strand RNA viruses include, but are not
limited to, picornaviruses (e.g., coxsackievirus, echovirus, human
coxsackievirus A, human echovirus, human enterovirus, human
poliovirus, hepatitis A virus, human parechovirus, human
rhinovirus), astroviruses (e.g., human astrovirus), calciviruses
(e.g., chiba virus, chitta virus, human calcivirus, norwalk virus),
nidovirales (e.g., human coronavirus, human torovirus),
flaviviruses (e.g., dengue virus 1-4, Japanese encephalitis virus,
Kyanasur forest disease virus, Murray Valley encephalitis virus,
Rocio virus, St. Louis encephalitis virus, West Nile virus, yellow
fever virus, hepatitis c virus) and togaviruses (e.g., Chikugunya
virus, Eastern equine encephalitis virus, Mayaro virus,
O'nyong-nyong virus, Ross River virus, Venezuelan equine
encephalitis virus, Rubella virus, hepatitis E virus). The
hepatitis C virus has a 5'-untranslated region of 340 nucleotides,
an open reading frame encoding 9 proteins having 3010 amino acids
and a 3'-untranslated region of 240 nucleotides. The 5'-UTR and
3'-UTR are 99% conserved in hepatitis C viruses.
[0144] In one embodiment, the target gene is an RNA-dependent RNA
polymerase or a helicase encoded by (+)-strand RNA viruses, or RNA
polymerase from a (-)-strand RNA virus. (+)-strand RNA viruses are
double stranded RNA and replicate by RNA-directed RNA synthesis
using RNA-dependent RNA polymerase and the positive strand as a
template. Helicase unwinds the RNA duplex to allow replication of
the single stranded RNA. These viruses include viruses from the
family picornaviridae (e.g., poliovirus, coxsackievirus,
echovirus), togaviridae (e.g., alphavirus, flavivirus, rubivirus),
arenaviridae (e.g., lymphocytic choriomeningitis virus, lassa fever
virus), cononaviridae (e.g., human respiratory virus) and Hepatitis
A virus. The genes encoding these proteins comprise variable and
highly conserved regions that flank the variable regions.
[0145] In one embodiment, the method can be used to detect the
presence of antibiotic resistance and/or toxin genes in a bacterial
species. For example, Bacillus anthracis comprising a tetracycline
resistance plasmid and plasmids encoding one or both anthracis
toxins (px01 and/or px02) can be detected by using antibiotic
resistance primer sets and toxin gene primer sets. If the B.
anthracis is positive for tetracycline resistance, then a different
antibiotic, for example quinalone, is used.
[0146] While the present invention has been described with
specificity in accordance with certain of its embodiments, the
following examples serve only to illustrate the invention and are
not intended to limit the same.
EXAMPLES
Example 1
Nucleic Acid Isolation and PCR
[0147] In one embodiment, nucleic acid is isolated from the
organisms and amplified by PCR using standard methods prior to BCS
determination by mass spectrometry. Nucleic acid is isolated, for
example, by detergent lysis of bacterial cells, centrifugation and
ethanol precipitation. Nucleic acid isolation methods are described
in, for example, Current Protocols in Molecular Biology (Ausubel et
al.) and Molecular Cloning; A Laboratory Manual (Sambrook et al.).
The nucleic acid is then amplified using standard methodology, such
as PCR, with primers which bind to conserved regions of the nucleic
acid which contain an intervening variable sequence as described
below.
[0148] General Genomic DNA Sample Prep Protocol: Raw samples are
filtered using Supor-200 0.2 .mu.m membrane syringe filters (VWR
International). Samples are transferred to 1.5 ml eppendorf tubes
pre-filled with 0.45 g of 0.7 mm Zirconia beads followed by the
addition of 350 .mu.l of ATL buffer (Qiagen, Valencia, Calif.). The
samples are subjected to bead beating for 10 minutes at a frequency
of 19 l/s in a Retsch Vibration Mill (Retsch). After
centrifugation, samples are transferred to an S-block plate
(Qiagen) and DNA isolation is completed with a BioRobot 8000
nucleic acid isolation robot (Qiagen).
[0149] Swab Sample Protocol:
[0150] Allegiance S/P brand culture swabs and collection/transport
system are used to collect samples. After drying, swabs are placed
in 17.times.100 mm culture tubes (VWR International) and the
genomic nucleic acid isolation is carried out automatically with a
Qiagen Mdx robot and the Qiagen QIAamp DNA Blood BioRobot Mdx
genomic preparation kit (Qiagen, Valencia, Calif.).
Example 2
Mass Spectrometry
[0151] FTICR Instrumentation:
[0152] The FTICR instrument is based on a 7 tesla actively shielded
superconducting magnet and modified Bruker Daltonics Apex II 70e
ion optics and vacuum chamber. The spectrometer is interfaced to a
LEAP PAL autosampler and a custom fluidics control system for high
throughput screening applications. Samples are analyzed directly
from 96-well or 384-well microtiter plates at a rate of about 1
sample/minute. The Bruker data-acquisition platform is supplemented
with a lab-built ancillary NT datastation which controls the
autosampler and contains an arbitrary waveform generator capable of
generating complex rf-excite waveforms (frequency sweeps, filtered
noise, stored waveform inverse Fourier transform (SWIFT), etc.) for
sophisticated tandem MS experiments. For oligonucleotides in the
20-30-mer regime typical performance characteristics include mass
resolving power in excess of 100,000 (FWHM), low ppm mass
measurement errors, and an operable m/z range between 50 and 5000
m/z.
[0153] Modified ESI Source:
[0154] In sample-limited analyses, analyte solutions are delivered
at 150 nL/minute to a 30 mm i.d. fused-silica ESI emitter mounted
on a 3-D micromanipulator. The ESI ion optics consists of a heated
metal capillary, an rf-only hexapole, a skimmer cone, and an
auxiliary gate electrode. The 6.2 cm rf-only hexapole is comprised
of 1 mm diameter rods and is operated at a voltage of 380 Vpp at a
frequency of 5 MHz. A lab-built electro-mechanical shutter can be
employed to prevent the electrospray plume from entering the inlet
capillary unless triggered to the "open" position via a TTL pulse
from the data station. When in the "closed" position, a stable
electrospray plume is maintained between the ESI emitter and the
face of the shutter. The back face of the shutter arm contains an
elastomeric seal that can be positioned to form a vacuum seal with
the inlet capillary. When the seal is removed, a 1 mm gap between
the shutter blade and the capillary inlet allows constant pressure
in the external ion reservoir regardless of whether the shutter is
in the open or closed position. When the shutter is triggered, a
"time slice" of ions is allowed to enter the inlet capillary and is
subsequently accumulated in the external ion reservoir. The rapid
response time of the ion shutter (<25 ms) provides reproducible,
user defined intervals during which ions can be injected into and
accumulated in the external ion reservoir.
[0155] Apparatus for Infrared Multiphoton Dissociation:
[0156] A 25 watt CW CO.sub.2 laser operating at 10.6 .mu.m has been
interfaced to the spectrometer to enable infrared multiphoton
dissociation (IRMPD) for oligonucleotide sequencing and other
tandem MS applications. An aluminum optical bench is positioned
approximately 1.5 m from the actively shielded superconducting
magnet such that the laser beam is aligned with the central axis of
the magnet. Using standard IR-compatible mirrors and kinematic
mirror mounts, the unfocused 3 mm laser beam is aligned to traverse
directly through the 3.5 mm holes in the trapping electrodes of the
FTICR trapped ion cell and longitudinally traverse the hexapole
region of the external ion guide finally impinging on the skimmer
cone. This scheme allows IRMPD to be conducted in an m/z selective
manner in the trapped ion cell (e.g. following a SWIFT isolation of
the species of interest), or in a broadband mode in the high
pressure region of the external ion reservoir where collisions with
neutral molecules stabilize IRMPD-generated metastable fragment
ions resulting in increased fragment ion yield and sequence
coverage.
Example 3
Identification of Bioagents
[0157] Table 2 shows a small cross section of a database of
calculated molecular masses for over 9 primer sets and
approximately 30 organisms. The primer sets were derived from rRNA
alignment. Examples of regions from rRNA consensus alignments are
shown in FIGS. 1A-1C. Lines with arrows are examples of regions to
which intelligent primer pairs for PCR are designed. The primer
pairs are >95% conserved in the bacterial sequence database
(currently over 10,000 organisms). The intervening regions are
variable in length and/or composition, thus providing the base
composition "signature" (BCS) for each organism. Primer pairs were
chosen so the total length of the amplified region is less than
about 80-90 nucleotides. The label for each primer pair represents
the starting and ending base number of the amplified region on the
consensus diagram.
[0158] Included in the short bacterial database cross-section in
Table 2 are many well known pathogens/biowarfare agents (shown in
bold/red typeface) such as Bacillus anthracis or Yersinia pestis as
well as some of the bacterial organisms found commonly in the
natural environment such as Streptomyces. Even closely related
organisms can be distinguished from each other by the appropriate
choice of primers. For instance, two low G+C organisms, Bacillus
anthracis and Staph aureus, can be distinguished from each other by
using the primer pair defined by 16S_1337 or 23S_855 (.DELTA.M of 4
Da).
TABLE-US-00002 TABLE 2 Cross Section Of A Database Of Calculated
Molecular Masses.sup.1 Primer Regions Bug Name 16S_971 16S_1100
16S_1337 16S_1294 16S_1228 23S_1021 23S_855 23S_193 23S_115
Acinetobacter calcoaceticus 55619.1 55004 28446.7 35854.9 51295.4
30299 42654 39557.5 54999 55005 54388 28448 35238 51296 30295 42651
39560 56850 Bacillus cereus 55622.1 54387.9 28447.6 35854.9 51296.4
30295 42651 39560.5 56850.3 Bordetella bronchiseptica 56857.3
51300.4 28446.7 35857.9 51307.4 30299 42653 39559.5 51920.5
Borrelia burgdorferi 56231.2 55621.1 28440.7 35852.9 51295.4 30297
42029.9 38941.4 52524.6 58098 55011 28448 35854 50683 Campylobacter
jejuni 58088.5 54386.9 29061.8 35856.9 50674.3 30294 42032.9
39558.5 45732.5 55000 55007 29063 35855 50676 30295 42036 38941
56230 55006 53767 28445 35855 51291 30300 42656 39562 54999
Clostridium difficile 56855.3 54386.9 28444.7 35853.9 51296.4 30294
41417.8 39556.5 55612.2 Enterococcus faecalis 55620.1 54387.9
28447.6 35858.9 51296.4 30297 42652 39559.5 56849.3 55622 55009
28445 35857 51301 30301 42656 39562 54999 53769 54385 28445 35856
51298 Haemophilus influenzae 55620.1 55006 28444.7 35855.9 51298.4
30298 42656 39560.5 55613.1 Klebsiella pneumoniae 55622.1 55008
28442.7 35856.9 51297.4 30300 42655 39562.5 55000 55618 55626 28446
35857 51303 Mycobacterium avium 54390.9 55631.1 29064.8 35858.9
51915.5 30298 42656 38942.4 56241.2 Mycobacterium leprae 54389.9
55629.1 29064.8 35860.9 51917.5 30298 42656 39559.5 56240.2
Mycobacterium tuberculosis 54390.9 55629.1 29064.8 35860.9 51301.4
30299 42656 39560.5 56243.2 Mycoplasma genitalium 53143.7 45115.4
29061.8 35854.9 50671.3 30294 43264.1 39558.5 56842.4 Mycoplasma
pneumoniae 53143.7 45118.4 29061.8 35854.9 50673.3 30294 43264.1
39559.5 56843.4 Neisseria gonorrhoeae 55627.1 54389.9 28445.7
35855.9 51302.4 30300 42649 39561.5 55000 55623 55010 28443 35858
51301 30298 43272 39558 55619 58093 55621 28448 35853 50677 30293
42650 39559 53139 58094 55623 28448 35853 50679 30293 42648 39559
53755 55622 55005 28445 35857 51301 30301 42658 55623 55009 28444
35857 51301 Staphylococcus aureus 56854.3 54386.9 28443.7 35852.9
51294.4 30298 42655 39559.5 57466.4 Streptomyces 54389.9 59341.6
29063.8 35858.9 51300.4 39563.5 56864.3 Treponema pallidum 56245.2
55631.1 28445.7 35851.9 51297.4 30299 42034.9 38939.4 57473.4 55625
55626 28443 35857 52536 29063 30303 35241 50675 Vibrio
parahaemolyticus 54384.9 55626.1 28444.7 34620.7 50064.2 55620
55626 28443 35857 51299 .sup.1Molecular mass distribution of PCR
amplified regions for a selection of organisms (rows) across
various primer pairs (columns). Pathogens are shown in bold. Empty
cells indicate presently incomplete or missing data.
[0159] FIG. 6 shows the use of ESI-FT-ICR MS for measurement of
exact mass. The spectra from 46mer PCR products originating at
position 1337 of the 16S rRNA from S. aureus (upper) and B.
anthracis (lower) are shown. These data are from the region of the
spectrum containing signals from the [M-8H+].sup.8- charge states
of the respective 5'-3' strands. The two strands differ by two
(AT.fwdarw.CG) substitutions, and have measured masses of 14206.396
and 14208.373+0.010 Da, respectively. The possible base
compositions derived from the masses of the forward and reverse
strands for the B. anthracis products are listed in Table 3.
TABLE-US-00003 TABLE 3 Possible base composition for B. anthracis
products Calc. Mass Error Base Comp. 14208.2935 0.079520 A1 G17 C10
T18 14208.3160 0.056980 A1 G20 C15 T10 14208.3386 0.034440 A1 G23
C20 T2 14208.3074 0.065560 A6 G11 C3 T26 14208.3300 0.043020 A6 G14
C8 T18 14208.3525 0.020480 A6 G17 C13 T10 14208.3751 0.002060 A6
G20 C18 T2 14208.3439 0.029060 A11 G8 C1 T26 14208.3665 0.006520
A11 G11 C6 T18 14208.3890 0.016020 A11 G14 C11 T10 14208.4116
0.038560 A11 G17 C16 T2 14208.4030 0.029980 A16 G8 C4 T18
14208.4255 0.052520 A16 G11 C9 T10 14208.4481 0.075060 A16 G14 C14
T2 14208.4395 0.066480 A21 G5 C2 T18 14208.4620 0.089020 A21 G8 C7
T10 14079.2624 0.080600 A0 G14 C13 T19 14079.2849 0.058060 A0 G17
C18 T11 14079.3075 0.035520 A0 G20 C23 T3 14079.2538 0.089180 A5 G5
C1 T35 14079.2764 0.066640 A5 G8 C6 T27 14079.2989 0.044100 A5 G11
C11 T19 14079.3214 0.021560 A5 G14 C16 T11 14079.3440 0.000980 A5
G17 C21 T3 14079.3129 0.030140 A10 G5 C4 T27 14079.3354 0.007600
A10 G8 C9 T19 14079.3579 0.014940 A10 G11 C14 T11 14079.3805
0.037480 A10 G14 C19 T3 14079.3494 0.006360 A15 G2 C2 T27
14079.3719 0.028900 A15 G5 C7 T19 14079.3944 0.051440 A15 G8 C12
T11 14079.4170 0.073980 A15 G11 C17 T3 14079.4084 0.065400 A20 G2
C5 T19 14079.4309 0.087940 A20 G5 C10 T13
Among the 16 compositions for the forward strand and the 18
compositions for the reverse strand that were calculated, only one
pair (shown in bold) are complementary, corresponding to the actual
base compositions of the B. anthracis PCR products.
Example 4
BCS of Region from Bacillus anthracis and Bacillus cereus
[0160] A conserved Bacillus region from B. anthracis
(A.sub.14G.sub.9C.sub.14T.sub.9) and B. cereus
(A.sub.15G.sub.9C.sub.13T.sub.19) having a C to A base change was
synthesized and subjected to ESI-TOF MS. The results are shown in
FIG. 7 in which the two regions are clearly distinguished using the
method of the present invention (MW=14072.26 vs. 14096.29).
Example 5
Identification of Additional Bioagents
[0161] In other examples of the present invention, the pathogen
Vibrio cholera can be distinguished from Vibrio parahemolyticus
with .DELTA.M>600 Da using one of three 16S primer sets shown in
Table 2 (16S_971, 16S_1228 or 16S_1294) as shown in Table 4. The
two mycoplasma species in the list (M. genitalium and M.
pneumoniae) can also be distinguished from each other, as can the
three mycobacteriae. While the direct mass measurements of
amplified products can identify and distinguish a large number of
organisms, measurement of the base composition signature provides
dramatically enhanced resolving power for closely related
organisms. In cases such as Bacillus anthracis and Bacillus cereus
that are virtually indistinguishable from each other based solely
on mass differences, compositional analysis or fragmentation
patterns are used to resolve the differences. The single base
difference between the two organisms yields different fragmentation
patterns, and despite the presence of the ambiguous/unidentified
base N at position 20 in B. anthracis, the two organisms can be
identified.
[0162] Tables 4a-b show examples of primer pairs from Table 1 which
distinguish pathogens from background.
TABLE-US-00004 TABLE 4a Organism name 23S_855 16S_1337 23S_1021
Bacillus anthracis 42650.98 28447.65 30294.98 Staphylococcus aureus
42654.97 28443.67 30297.96
TABLE-US-00005 TABLE 4b Organism name 16S_971 16S_1294 16S_1228
Vibrio cholerae 55625.09 35856.87 52535.59 Vibrio parahaemolyticus
54384.91 34620.67 50064.19
[0163] Table 5 shows the expected molecular weight and base
composition of region 16S_1100-1188 in Mycobacterium avium and
Streptomyces sp.
TABLE-US-00006 TABLE 5 Molecular Region Organism name Length weight
Base comp. 16S_1100- Mycobacterium 82 25624.1728
A.sub.16G.sub.32C.sub.18T.sub.16 1188 avium 16S_1100- Streptomyces
sp. 96 29904.871 A.sub.17G.sub.38C.sub.27T.sub.14 1188
[0164] Table 6 shows base composition (single strand) results for
16S_1100-1188 primer amplification reactions different species of
bacteria. Species which are repeated in the table (e.g.,
Clostridium botulinum) are different strains which have different
base compositions in the 16S_1100-1188 region.
TABLE-US-00007 TABLE 6 Organism name Base comp. Mycobacterium avium
A.sub.16G.sub.32C.sub.18T.sub.16 Streptomyces sp.
A.sub.17G.sub.38C.sub.27T.sub.14 Ureaplasma urealyticum
A.sub.18G.sub.30C.sub.17T.sub.17 Streptomyces sp.
A.sub.19G.sub.36C.sub.24T.sub.18 Mycobacterium leprae
A.sub.20G.sub.32C.sub.22T.sub.16 Fusobacterium necroforum
A.sub.21G.sub.26C.sub.22T.sub.18 Listeria monocytogenes
A.sub.21G.sub.27C.sub.19T.sub.19 Clostridium botulinum
A.sub.21G.sub.27C.sub.19T.sub.21 Neisseria gonorrhoeae
A.sub.21G.sub.28C.sub.21T.sub.18 Bartonella quintana
A.sub.21G.sub.30C.sub.22T.sub.16 Enterococcus faecalis
A.sub.22G.sub.27C.sub.20T.sub.19 Bacillus megaterium
A.sub.22G.sub.28C.sub.20T.sub.18 Bacillus subtilis
A.sub.22G.sub.28C.sub.21T.sub.17 Pseudomonas aeruginosa
A.sub.22G.sub.29C.sub.23T.sub.15 Legionella pneumophila
A.sub.22G.sub.32C.sub.20T.sub.16 Mycoplasma pneumoniae
A.sub.23G.sub.20C.sub.14T.sub.16 Clostridium botulinum
A.sub.23G.sub.26C.sub.20T.sub.19 Enterococcus faecium
A.sub.23G.sub.26C.sub.21T.sub.18 Acinetobacter calcoaceti
A.sub.23G.sub.26C.sub.21T.sub.19 Clostridium perfringens
A.sub.23G.sub.27C.sub.19T.sub.19 Aeromonas hydrophila
A.sub.23G.sub.29C.sub.21T.sub.16 Escherichia coli
A.sub.23G.sub.29C.sub.21T.sub.16 Pseudomonas putida
A.sub.23G.sub.29C.sub.21T.sub.17 Vibrio cholerae
A.sub.23G.sub.30C.sub.21T.sub.16 Mycoplasma genitalium
A.sub.24G.sub.19C.sub.12T.sub.18 Clostridium botulinum
A.sub.24G.sub.25C.sub.18T.sub.20 Bordetella bronchiseptica
A.sub.24G.sub.26C.sub.19T.sub.14 Francisella tularensis
A.sub.24G.sub.26C.sub.19T.sub.19 Helicobacter pylori
A.sub.24G.sub.26C.sub.20T.sub.19 Helicobacter pylori
A.sub.24G.sub.26C.sub.21T.sub.18 Moraxella catarrhalis
A.sub.24G.sub.26C.sub.23T.sub.16 Haemophilus influenzae Rd
A.sub.24G.sub.28C.sub.20T.sub.17 Pseudomonas putida
A.sub.24G.sub.29C.sub.21T.sub.16 Clostridium botulinum
A.sub.25G.sub.24C.sub.18T.sub.21 Clostridium tetani
A.sub.25G.sub.25C.sub.18T.sub.20 Francisella tularensis
A.sub.25G.sub.25C.sub.19T.sub.19 Acinetobacter calcoacetic
A.sub.25G.sub.26C.sub.20T.sub.19 Bacteriodes fragilis
A.sub.25G.sub.27C.sub.16T.sub.22 Chlamydophila psittaci
A.sub.25G.sub.27C.sub.21T.sub.16 Borrelia burgdorferi
A.sub.25G.sub.29C.sub.17T.sub.19 Streptobacillus monilifor
A.sub.26G.sub.26C.sub.20T.sub.16 Rickettsia prowazekii
A.sub.26G.sub.28C.sub.18T.sub.18 Rickettsia rickettsii
A.sub.26G.sub.28C.sub.20T.sub.16 Mycoplasma mycoides
A.sub.28G.sub.23C.sub.16T.sub.20
[0165] The same organism having different base compositions are
different strains. Groups of organisms which are highlighted or in
italics have the same base compositions in the amplified region.
Some of these organisms can be distinguished using multiple
primers. For example, Bacillus anthracis can be distinguished from
Bacillus cereus and Bacillus thuringiensis using the primer
16S_971-1062 (Table 7). Other primer pairs which produce unique
base composition signatures are shown in Table 6 (bold). Clusters
containing very similar threat and ubiquitous non-threat organisms
(e.g. anthracis cluster) are distinguished at high resolution with
focused sets of primer pairs. The known biowarfare agents in Table
6 are Bacillus anthracis, Yersinia pestis, Francisella tularensis
and Rickettsia prowazekii.
TABLE-US-00008 TABLE 7 Organism 16S_971-1062 16S_1228-1310
16S_1100-1188 Aeromonas hydrophila A.sub.21G.sub.29C.sub.22T.sub.20
A.sub.22G.sub.27C.sub.21T.sub.13 A.sub.23G.sub.31C.sub.21T.sub.15
Aeromonas salmonicida A.sub.21G.sub.29C.sub.22T.sub.20
A.sub.22G.sub.27C.sub.21T.sub.13 A.sub.23G.sub.31C.sub.21T.sub.15
Bacillus anthracis A.sub.24G.sub.22C.sub.19T.sub.18
A.sub.23G.sub.27C.sub.20T.sub.18 Bacillus cereus
A.sub.22G.sub.27C.sub.22T.sub.22 A.sub.24G.sub.22C.sub.19T.sub.18
A.sub.23G.sub.27C.sub.20T.sub.18 Bacillus thuringiensis
A.sub.22G.sub.27C.sub.21T.sub.22 A.sub.24G.sub.22C.sub.19T.sub.18
A.sub.23G.sub.27C.sub.20T.sub.18 Chlamydia trachomatis
A.sub.24G.sub.28C.sub.21T.sub.16 Chlamydia pneumoniae AR39
A.sub.26G.sub.23C.sub.20T.sub.22 A.sub.26G.sub.22C.sub.16T.sub.18
A.sub.24G.sub.28C.sub.21T.sub.16 Leptospira borgpetersenii
A.sub.22G.sub.26C.sub.20T.sub.21 A.sub.22G.sub.25C.sub.21T.sub.15
A.sub.23G.sub.26C.sub.24T.sub.15 Leptospira interrogans
A.sub.22G.sub.26C.sub.20T.sub.21 A.sub.22G.sub.25C.sub.21T.sub.15
A.sub.23G.sub.26C.sub.24T.sub.15 Mycoplasma genitalium
A.sub.28G.sub.23C.sub.15T.sub.22 Mycoplasma pneumoniae
A.sub.28G.sub.23C.sub.15T.sub.22 Escherichia coli
A.sub.24G.sub.25C.sub.21T.sub.13 A.sub.23G.sub.29C.sub.22T.sub.15
Shigella dysenteriae A.sub.24G.sub.25C.sub.21T.sub.13
A.sub.23G.sub.29C.sub.22T.sub.15 Proteus vulgaris
A.sub.24G.sub.30C.sub.21T.sub.15 Yersinia pestis
A.sub.24G.sub.25C.sub.21T.sub.22 A.sub.25G.sub.24C.sub.20T.sub.14
A.sub.24G.sub.30C.sub.21T.sub.15 Yersinia pseudotuberculosis
A.sub.24G.sub.25C.sub.21T.sub.22 A.sub.25G.sub.24C.sub.20T.sub.14
A.sub.24G.sub.30C.sub.21T.sub.15 Francisella tularensis Rickettsia
prowazekii Rickettsia rickettsii
[0166] The sequence of B. anthracis and B. cereus in region 16S_971
is shown below. Shown in bold is the single base difference between
the two species that can be detected using the methods of the
present invention. B. anthracis has an ambiguous base at position
20.
TABLE-US-00009 B. anthracis_16S_971 (SEQ ID NO: 1)
GCGAAGAACCUUACCAGGUNUUGACAUCCUCUGACAACCCUAGAGAUAG
GGCUUCUCCUUCGGGAGCAGAGUGACAGGUGGUGCAUGGUU B. cereus_16S_971 (SEQ ID
NO: 2) GCGAAGAACCUUACCAGGUCUUGACAUCCUCUGAAAACCCUAGAGAUAG
GGCUUCUCCUUCGGGAGCAGAGUGACAGGUGGUGCAUGGUU
Example 6
ESI-TOF MS of sspE 56-mer Plus Calibrant
[0167] The mass measurement accuracy that can be obtained using an
internal mass standard in the ESI-MS study of PCR products is shown
in FIG. 8. The mass standard was a 20-mer phosphorothioate
oligonucleotide added to a solution containing a 56-mer PCR product
from the B. anthracis spore coat protein sspE. The mass of the
expected PCR product distinguishes B. anthracis from other species
of Bacillus such as B. thuringiensis and B. cereus.
Example 7
B. anthracis ESI-TOF Synthetic 16S_1228 Duplex
[0168] An ESI-TOF MS spectrum was obtained from an aqueous solution
containing 5 .mu.M each of synthetic analogs of the expected
forward and reverse PCR products from the nucleotide 1228 region of
the B. anthracis 16S rRNA gene. The results (FIG. 9) show that the
molecular weights of the forward and reverse strands can be
accurately determined and easily distinguish the two strands. The
[M-21H.sup.+].sup.21- and [M-20H.sup.+].sup.20-. charge states are
shown.
Example 8
ESI-FTICR-MS of Synthetic B. anthracis 16S_1337 46 Base Pair
Duplex
[0169] An ESI-FTICR-MS spectrum was obtained from an aqueous
solution containing 5 .mu.M each of synthetic analogs of the
expected forward and reverse PCR products from the nucleotide 1337
region of the B. anthracis 16S rRNA gene. The results (FIG. 10)
show that the molecular weights of the strands can be distinguished
by this method. The [M-16H.sup.+].sup.16- through
[M-10H.sup.+].sup.10- charge states are shown. The insert
highlights the resolution that can be realized on the FTICR-MS
instrument, which allows the charge state of the ion to be
determined from the mass difference between peaks differing by a
single 13C substitution.
Example 9
ESI-TOF MS of 56-mer Oligonucleotide from saspB Gene of B.
anthracis with Internal Mass Standard
[0170] ESI-TOF MS spectra were obtained on a synthetic 56-mer
oligonucleotide (5 .mu.M) from the saspB gene of B. anthracis
containing an internal mass standard at an ESI of 1.7 .mu.L/min as
a function of sample consumption. The results (FIG. 11) show that
the signal to noise is improved as more scans are summed, and that
the standard and the product are visible after only 100 scans.
Example 10
ESI-TOF MS of an Internal Standard with Tributylammonium
(TBA)-Trifluoroacetate (TFA) Buffer
[0171] An ESI-TOF-MS spectrum of a 20-mer phosphorothioate mass
standard was obtained following addition of 5 mM TBA-TFA buffer to
the solution. This buffer strips charge from the oligonucleotide
and shifts the most abundant charge state from [M-8H.sup.+].sup.8-
to [M-3H.sup.+].sup.3- (FIG. 12).
Example 11
Master Database Comparison
[0172] The molecular masses obtained through Examples 1-10 are
compared to molecular masses of known bioagents stored in a master
database to obtain a high probability matching molecular mass.
Example 12
Master Data Base Interrogation over the Internet
[0173] The same procedure as in Example 11 is followed except that
the local computer did not store the Master database. The Master
database is interrogated over an internet connection, searching for
a molecular mass match.
Example 13
Master Database Updating
[0174] The same procedure as in example 11 is followed except the
local computer is connected to the internet and has the ability to
store a master database locally. The local computer system
periodically, or at the user's discretion, interrogates the Master
database, synchronizing the local master database with the global
Master database. This provides the current molecular mass
information to both the local database as well as to the global
Master database. This further provides more of a globalized
knowledge base.
Example 14
Global Database Updating
[0175] The same procedure as in example 13 is followed except there
are numerous such local stations throughout the world. The
synchronization of each database adds to the diversity of
information and diversity of the molecular masses of known
bioagents.
Example 15
Demonstration of Detection and Identification of Five Species of
Bacteria in a Mixture
[0176] Broad range intelligent primers were chosen following
analysis of a large collection of curated bacterial 16S rRNA
sequences representing greater than 4000 species of bacteria.
Examples of primers capable of priming from greater than 90% of the
organisms in the collection include, but are not limited to, those
exhibited in Table 8 wherein Tp=5'propynylated uridine and
Cp=5'propynylated cytidine.
TABLE-US-00010 TABLE 8 Intelligent Primer Pairs for Identification
of Bacteria Forward Reverse Primer SEQ SEQ Pair Name Forward Primer
Sequence ID NO: Reverse Primer Sequence ID NO: 16S_EC_1077_1195
GTGAGATGTTGGGTTAAGTCCCG 8 GACGTCATCCCCACCTTCCTC 9 TAACGAG
16S_EC_1082_1197 ATGTTGGGTTAAGTCCCGCAACG 10 TTGACGTCATCCCCACCTTCCTC
11 AG 16S_EC_1090_1196 TTAAGTCCCGCAACGATCGCAA 12
TGACGTCATCCCCACCTTCCTC 13 16S_EC_1222_1323 GCTACACACGTGCTACAATG 14
CGAGTTGCAGACTGCGATCCG 15 16S_EC_1332_1407 AAGTCGGAATCGCTAGTAATCG 16
GACGGGCGGTGTGTACAAG 17 16S_EC_30_126 TGAACGCTGGTGGCATGCTTAAC 18
TACGCATTACTCACCCGTCCGC 19 AC 168_EC_38_120 GTGGCATGCCTAATACATGCAAG
20 TTACTCACCCGTCCGCCGCT 21 TCG 16S_EC_49_120 TAACACATGCAAGTCGAACG
22 TTACTCACCCGTCCGCC 23 16S_EC_683_795 GTGTAGCGGTGAAATGCG 24
GTATCTAATCCTGTTTGCTCCC 25 16S_EC_713_809 AGAACACCGATGGCGAAGGC 26
CGTGGACTACCAGGGTATCTA 27 16S_EC_785_897 GGATTAGAGACCCTGGTAGTCC 28
GGCCGTACTCCCCAGGCG 29 16S_EC_785_897_2 GGATTAGATACCCTGGTAGTCCA 30
GGCCGTACTCCCCAGGCG 31 CGC 16S_EC_789_894 TAGATACCCTGGTAGTCCACGC 32
CGTACTCCCCAGGCG 33 16S_EC_960_1073 TTCGATGCAACGCGAAGAACCT 34
ACGAGCTGACGACAGCCATG 35 16S_EC_969_1078 ACGCGAAGAACCTTACC 36
ACGACACGAGCTGACGAC 37 23S_EC_1826_1924 CTGACACCTGCCCGGTGC 38
GACCGTTATAGTTACGGCC 39 23S_EC_2645_2761 TCTGTCCCTAGTACGAGAGGAC 40
TGCTTAGATGCTTTCAGC 41 CGG 23S_EC_2645_2767 CTGTCCCTAGTACGAGAGGACC
42 GTTTCATGCTTAGATGCTTTCA 43 23S_EC_493_571 GGGGAGTGAAAGAGATCCTGAAA
44 ACAAAAGGTACGCCGTCACCC 45 CCG 23S_EC_493_571_2
GGGGAGTGAAAGAGATCCTGAAA 46 ACAAAAGGCACGCCATCACCC 47 CCG
23S_EC_971_1077 CGAGAGGGAAACAACCCAGACC 48 TGGCTGCTTCTAAGCCAAC 49
INFB_EC_1365_1467 TGCTCGTGGTGCACAAGTAACGG 50
TGCTGCTTTCGCATGGTTAATTG 51 ATATTA CTTCAA RPOC_EC_1018_1124
CAAAACTTATTAGGTAAGCGTGT 52 TCAAGCGCCATTTCTTTTGGTAA 53 TGACT ACCACAT
RPOC_EC_1018_1124_2 CAAAACTTATTAGGTAAGCGTGT 54
TCAAGCGCCATCTCTTTCGGTAA 55 TGACT TCCACAT RPOC_EC_114_232
TAAGAAGCCGGAAACCATCAACT 56 GGCGCTTGTACTTACCGCAC 57 ACCG
RPOC_EC_2178_2246 TGATTCTGGTGCCCGTGGT 58 TTGGCCATCAGGCCACGCATAC 59
RPOC_EC_2178_2246_2 TGATTCCGGTGCCCGTGGT 60 TTGGCCATCAGACCACGCATAC
61 RPOC_EC_2218_2337 CTGGCAGGTATGCGTGGTCTGAT 62
CGCACCGTGGGTTGAGATGAAGT 63 G AC RPOC_EC_2218_2337_2
CTTGCTGGTATGCGTGGTCTGAT 64 CGCACCATGCGTAGAGATGAAGT 65 G AC
RPOC_EC_808_889 CGTCGGGTGATTAACCGTAACAA 66 GTTTTTCGTTGCGTACGATGATG
67 CCG TC RPOC_EC_808_891 CGTCGTGTAATTAACCGTAACAA 68
ACGTTTTTCGTTTTGAACGATAA 69 CCG TGCT RPOC_EC_993_1059
CAAAGGTAAGCAAGGTCGTTTCC 70 CGAACGGCCTGAGTAGTCAACAC 71 GTCA G
RPOC_EC_993_1059_2 CAAAGGTAAGCAAGGACGTTTCC 72
CGAACGGCCAGAGTAGTCAACAC 73 GTCA G TUFB_EC_239_303
TAGACTGCCCAGGACACGCTG 74 GCCGTCCATCTGAGCAGCACC 75 TUFB_EC_239_303_2
TTGACTGCCCAGGTCACGCTG 76 GCCGTCCATTTGAGCAGCACC 77 TUFB_EC_976_1068
AACTACCGTCCGCAGTTCTACTT 78 GTTGTCGCCAGGCATAACCATTT 79 CC C
TUFB_EC_976_1068_2 AACTACCGTCCTCAGTTCTACTT 80
GTTGTCACCAGGCATTACCATTT 81 CC C TUGB_EC_985_1062
CCACAGTTCTACTTCCGTACTAC 82 TCCAGGCATTACCATTTCTACTC 83 TGACG CTTCTGG
RPLB_EC_650_762 GACCTACAGTAAGAGGTTCTGTA 84 TCCAAGTGCTGGTTTACCCCATG
85 ATGAACC G RPLB_EC_688_757 CATCCACACGGTGGTGGTGAAGG 86
GTGCTGGTTTACCCCATGGAGT 87 RPOC_EC_1036_1126 CGTGTTGACTATTCGGGGCGTTC
88 ATTCAAGAGCCATTTCTTTTGGT 89 AG AAACCAC RPOB_EC_3762_3865
TCAACAACCTCTTGGAGGTAAAG 90 TTTCTTGAAGAGTATGAGCTGCT 91 CTCAGT
CCGTAAG RPLB_EC_688_771 CATCCACACGGTGGTGGTGAAGG 92
TGTTTTGTATCCAAGTGCTGGTT 93 TACCCC VALS_EC_1105_1218
CGTGGCGGCGTGGTTATCGA 94 CGGTACGAACTGGATGTCGCCGT 95 T
RPOB_EC_1845_1929 TATCGCTCAGGCGAACTCCAAC 96 GCTGGATTCGCCTTTGCTACG
97 RPLB_EC_669_761 TGTAATGAACCCTAATGACCATC 98
CCAAGTGCTGGTTTACCCCATGG 99 CACACGG AGTA RPLB_EC_671_762
TAATGAACCCTAATGACCATCCA 100 TCCAAGTGCTGGTTTACCCCATG 101 CACGGTG GAG
RPOB_EC_3775_3858 CTTGGAGGTAAGTCTCATTTTGG 102
CGTATAAGCTGCACCATAAGCTT 103 TGGGCA GTAATGC VALS_EC_1833_1943
CGACGCGCTGCGCTTCAC 104 GCGTTCCACAGCTTGTTGCAGAA 105 G
RPOB_EC_1336_1455 GACCACCTCGGCAACCGT 106 TTCGCTCTCGGCCTGGCC 107
TUFB_EC_225_309 GCACTATGCACACGTAGATTGTC 108 TATAGCACCATCCATCTGAGCGG
108 CTGG CAC DNAK_EC_428_522 CGGCGTACTTCAACGACAGCCA 110
CGCGGTCGGCTCGTTGATGA 111 VALS_EC_1920_1970 CTTCTGCAACAAGCTGTGGAACG
112 TCGCAGTTCATCAGCACGAAGCG 113 C TUFB_EC_757_867
AAGACGACCTGCACGGGC 114 GCGCTCCACGTCTTCACGC 115 23S_EC_2646_2765
CTGTTCTTAGTACGAGAGGACC 116 TTCGTGCTTAGATGCTTTCAG 117
16S_EC_969_1078_3P ACGCGAAGAACCTTACpC 118 ACGACACGAGCpTpGACGAC 119
16S_EC_972_1075_4P CGAAGAACpCpTTACC 120 ACACGAGCpTpGAC 121
16S_EC_972_1075 CGAAGAACCTTACC 122 ACACGAGCTGAC 123 23S_EC_-347_59
CCTGATAAGGGTGAGGTCG 124 ACGTCCTTCATCGCCTCTGA 125 23S_EC_-7_450
GTTGTGAGGTTAAGCGACTAAG 126 CTATCGGTCAGTCAGGAGTAT 127 23S_EC_-7_910
GTTGTGAGGTTAAGCGACTAAG 128 TTGCATCGGGTTGGTAAGTC 129 23S_EC_430_1442
ATACTCCTGACTGACCGATAG 130 AACATAGCCTTCTCCGTCC 131 23S_EC_891_1931
GACTTACCAACCCGATGCAA 132 TACCTTAGGACCGTTATAGTTAC 133 G
23S_EC_1424_2494 GGACGGAGAAGGCTATGTT 134 CCAAACACCGCCGTCGATAT 135
23S_EC_1908_2852 CGTAACTATAACGGTCCTAAGGT 136 GCTTACACACCCGGCCTATC
137 A 23S_EC_2475_3209 ATATCGACGGCGGTGTTTGG 138 GCGTGACAGGCAGGTATTC
139 16S_EC_-60_525 AGTCTCAAGAGTGAACACGTAA 140 GCTGCTGGCACGGAGTTA
141 16S_EC_326_1058 GACACGGTCCAGACTCCTAC 142 CCATGCAGCACCTGTCTC 143
16S_EC_705_1512 GATCTGGAGGAATACCGGTG 144 ACGGTTACCTTGTTACGACT 145
16S_EC_1268_1775 GAGAGCAAGCGGACCTCATA 146 CCTCCTGCGTGCAAAGC 147
GROL_EC_941_1060 TGGAAGATCTGGGTCAGGC 148 CAATCTGCTGACGGATCTGAGC 149
INFB_EC_1103_1191 GTCGTGAAAACGAGCTGGAAGA 150 CATGATGGTCACAACCGG 151
HFLB_EC_1082_1168 TGGCGAACCTGGTGAACGAAGC 152
CTTTCGCTTTCTCGAACTCAACC 153 AT INFB_EC_1969_2058
CGTCAGGGTAAATTCCGTGAAGT 154 AACTTCGCCTTCGGTCATGTT 155 TAA
GROL_EC_219_350 GGTGAAAGAAGTTGCCTCTAAAG 156 TTCAGGTCCATCGGGTTCATGCC
157 C VALS_EC_2105_1214 CGTGGCGGCGTGGTTATCGA 158
ACGAACTGGATGTCGCCGTT 159 16S_EC_556_700 CGGAATTACTGGGCGTAAAG 160
CGCATTTCACCGCTACAC 161 RPOC_EC_1256_1315 ACCCAGTGCTGCTGAACCGTGC 162
GTTCAAATGCCTGGATACCCA 163 16S_EC_774_894 GGGAGCAAACAGGATTAGATAC 164
CGTACTCCCCAGGCG 165 RPOC_EC_1584_1643 TGGCCCGAAAGAAGCTGAGCG 166
ACGCGGGCATGCAGAGATGCC 167 16S_EC_1082_1196 ATGTTGGGTTAAGTCCCGC 168
TGACGTCATCCCCACCTTCC 169 16S_EC_1389_1541 CTTGTACACACCGCCCGTC 170
AAGGAGGTGATCCAGCC 171 16S_EC_1303_1407 CGGATTGGAGTCTGCAACTCG 172
GACGGGCGGTGTGTACAAG 173 23S_EC_23_130 GGTGGATGCCTTGGC 174
GGGTTTCCCCATTCGG 175 23S_EC_187_256 GGGAACTGAAACATCTAAGTA 176
TTCGCTCGCCGCTAC 177 23S_EC_1602_1703 TACCCCAAACCGACACAGG 178
CCTTCTCCCGAAGTTACG 179 23S_EC_1685_1842 CCGTAACTTCGGGAGAAGG 180
CACCGGGCAGGCGTC 181 23S_EC_1827_1949 GACGCCTGCCCGGTGC 182
CCGACAAGGAATTTCGCTACC 183 23S_EC_2434_2511 AAGGTACTCCGGGGATAACAGGC
184 AGCCGACATCGAGGTGCCAAAC 185 23S_EC_2599_2669 GACAGTTCGGTCCCTATC
186 CCGGTCCTCTCGTACTA 187 23S_EC_2653_2758 TAGTACGAGAGGACCGG 188
TTAGATGCTTTCAGCACTTATC 189 23S_BS_-68_21 AAACTAGATAACAGTAGACATCA
190 GTGCGCCCTTTCTAACTT 191 C 16S_EC_8_358 AGAGTTTGATCATGGCTCAG 192
ACTGCTGCCTCCCGTAG 193 16S_EC_314_575 CACTGGAACTGAGACACGG 194
CTTTACGCCCAGTAATTCCG 195 16S_EC_518_795 CCAGCAGCCGCGGTAATAC 196
GTATCTAATCCTGTTTGCTCCC 197 16S_EC_683_985 GTGTAGCGGTGAAATGCG 198
GGTAAGGTTCTTCGCGTTG 199 16S_EC_937_1240 AAGCGGTGGAGCATGTGCC 200
ATTGTAGCACGTGTGTAGCCC 201 16S_EC_1195_1541 CAAGTCATCATGGCCCTTA 202
AAGGAGGTGATCCAGCC 203 16S_EC_8_1541 AGAGTTTGATCATGGCTCAG 204
AAGGAGGTGATCCAGCC 205 23S_EC_1831_1936 ACCTGCCCAGTGCTGGAAG 206
TCGCTACCTTAGGACCGT 207 16S_EC_1387_1513 GCCTTGTACACACCTCCCGTC 208
CACGGCTACCTTGTTACGAC 209
16S_EC_1390_1505 TTGTACACACCGCCCGTCATAC 210 CCTTGTTACGACTTCACCCC
211 16S_EC_1367_1506 TACGGTGAATACGTTCCCGGG 212
ACCTTGTTACGACTTCACCCCA 213 16S_EC_804_929 ACCACGCCGTAAACGATGA 214
CCCCCGTCAATTCCTTTGAGT 215 16S_EC_791_904 GATACCCTGGTAGTCCACACCG 216
GCCTTGCGACCGTACTCCC 217 16S_EC_789_899 TAGATACCCTGGTAGTCCACGC 218
GCGACCGTACTCCCCAGG 219 16S_EC_1092_1195 TAGTCCCGCAACGAGCGC 220
GACGTCATCCCCACCTTCCTCC 221 23S_EC_2586_2677 TAGAACGTCGCGAGACAGTTCG
222 AGTCCATCCCGGTCCTCTCG 223 HEXAMER_EC61_362 GAGGAAAGTCCGGGCTC 224
ATAAGCCGGGTTCTGTCG 225 RNASEP_BS_43_384 GAGGAAAGTCCATGCTCGC 226
GTAAGCCATGTTTTGTTCCATC 227 RNASEP_EC_61_362 GAGGAAAGTCCGGGCTC 228
ATAAGCCGGGTTCTGTCG 229 YAED_TRNA_ALA-RRNH_EC_513_49
GCGGGATCCTCTAGAGGTGTTAA 230 GCGGGATCCTCTAGAAGACCTCC 231
ATAGCCTGGCAG TGCGTGCAAAGC RNASEP_SA_31_379 GAGGAAAGTCCATGCTCAC 232
ATAAGCCATGTTCTGTTCCATC 233 16S_EC_1082_1541 ATGTTGGGTTAAGTCCCGC 234
AAGGAGGTGATCCAGCC 235 16S_EC_556_795 CGGAATTACTGGGCGTAAAG 236
GTATCTAATCCTGTTTGCTCCC 237 16S_EC_1082_1196_10G ATGTTGGGTTAAGTCCCGC
238 TGACGTCATGCCCACCTTCC 239 16S_EC_1082_1196_10G_11G
ATGTTGGGTTAAGTCCCGC 240 TGACGTCATGGCCACCTTCC 241
TRNA_ILERRNH_ASPRRNHEC_32_41 GCGGGATCCTCTAGACCTGATAA 242
GCGGGATCCTCTAGAGCGTGACA 243 GGTGAGGTCG GGCAGGTATTC 16S_EC_969_1407
ACGCGAAGAACCTTACC 244 GACGGGCGGTGTGTACAAG 245 16S_EC_683_1323
GTGTAGCGGTGAAATGCG 246 CGAGTTGCAGACTGCGATCCG 247 16S_EC_49_894
TAACACATGCAAGTCGAACG 248 CGTACTCCCCAGGCG 249 16S_EC_49_1078
TAACACATGCAAGTCGAACG 250 ACGACACGAGCTGACGAC 251 CYA_BA_1349_1447
ACAACGAAGTACAATACAAGAC 252 CTTCTACATTTTTAGCCATCAC 253
16S_EC_1090_1196_2 TTAAGTCCCGCAACGAGCGCAA 254
TGACGTCATCCCCACCTTCCTC 255 16S_EC_405_527 TGAGTGATGAAGGCCTTAGGGTT
256 CGGCTGCTGGCACGAAGTTAG 257 GTAAA GROL_EC_496_596
ATGGACAAGGTTGGCAAGGAAGG 258 TAGCCGCGGTCGAATTGCAT 259
GROL_EC_511_593 AAGGAAGGCGTGATCACCGTTGA 260 CCGCGGTCGAATTGCATGCCTTC
261 AGA VALS_EC_1835_1928 ACGCGCTGCGCTTCAC 262 TTGCAGAAGTTGCGGTAGCC
263 RPOB_EC_1334_1478 TCGACCACCTGGGCAACC 264 ATCAGGTCGTGCGGCATCA
265 DNAK_EC_420_521 CACGGTGCCGGCGTACT 266 GCGGTCGGCTCGTTGATGAT 267
RPOB_EC_3776_3853 TTGGAGGTAAGTCTCATTTTGGT 268
AAGCTGCACCATAAGCTTGTAAT 269 GG GC RPOB_EC_3802_3885
CAGCGTTTCGGCGAAATGGA 270 CGACTTGACGGTTAACATTTCCT 271 G
RPOB_EC_3799_3888 GGGCAGCGTTTCGGCGAAATGGA 272
GTCCGACTTGACGGTCAACATTT 273 CCTG RPOC_EC_2146_2245
CAGGAGTCGTTCAACTCGATCTA 274 ACGCCATCAGGCCACGCAT 275 CAGGAT
ASPS_EC_405_538 GCACAACCTGCGGCTGCG 276 ACGGCACGAGGTAGTCGC 277
RPOC_EC_1374_1455 CGCCGACTTCGACGGTGACC 278 GAGCATCAGCGTGCGTGCT 279
TUFB_EC_957_1058 CCACACGCCGTTCTTCAACAACT 280
GGCATCACCATTTCCTTGTCCTT 281 CG 16S_EC_7_122 GAGAGTTTGATCCTGGCTCAGAA
282 TGTTACTCACCCGTCTGCCACT 283 CGAA VALS_EC_610_727
ACCGAGCAAGGAGACCAGC 284 TATAACGCACATCGTCAGGGTGA 285
[0177] For evaluation in the laboratory, five species of bacteria
were selected including three .gamma.-proteobacteria (E. coli, K.
pneumoniae and P. auergiosa) and two low G+C gram positive bacteria
(B. subtilitis and S. aureus). The identities of the organisms were
not revealed to the laboratory technicians.
[0178] Bacteria were grown in culture, DNA was isolated and
processed, and PCR performed using standard protocols. Following
PCR, all samples were desalted, concentrated, and analyzed by
Fourier Transform Ion Cyclotron Resonance (FTICR) mass
spectrometry. Due to the extremely high precision of the FTICR,
masses could be measured to within 1 Da and unambiguously
deconvoluted to a single base composition. The measured base
compositions were compared with the known base composition
signatures in our database. As expected when using broad range
survey 16S primers, several phylogenetic near-neighbor organisms
were difficult to distinguish from our test organisms. Additional
non-ribosomal primers were used to triangulate and further resolve
these clusters.
[0179] An example of the use of primers directed to regions of RNA
polymerase B (rpoB) is shown in FIG. 19. This gene has the
potential to provide broad priming and resolving capabilities. A
pair of primers directed against a conserved region of rpoB
provided distinct base composition signatures that helped resolve
the tight enterobacteriae cluster. Joint probability estimates of
the signatures from each of the primers resulted in the
identification of a single organism that matched the identity of
the test sample. Therefore a combination of a small number of
primers that amplify selected regions of the 16S ribosomal RNA gene
and a few additional primers that amplify selected regions of
protein encoding genes provide sufficient information to detect and
identify all bacterial pathogens.
Example 16
Detection of Staphylococcus aureus in Blood Samples
[0180] Blood samples in an analysis plate were spiked with genomic
DNA equivalent of 10.sup.3 organisms/ml of Staphylococcus aureus. A
single set of 16S rRNA primers was used for amplification.
Following PCR, all samples were desalted, concentrated, and
analyzed by Fourier Transform Ion Cyclotron Resonance (FTICR) mass
spectrometry. In each of the spiked wells, strong signals were
detected which are consistent with the expected BCS of the S.
aureus amplicon (FIG. 20). Furthermore, there was no robotic
carryover or contamination in any of the blood only or water blank
wells. Methods similar to this one will be applied for other
clinically relevant samples including, but not limited to: urine
and throat or nasal swabs.
Example 17
Detection and Serotyping of Viruses
[0181] The virus detection capability of the present invention was
demonstrated in collaboration with Naval health officers using
adenoviruses as an example.
[0182] All available genomic sequences for human adenoviruses
available in public databases were surveyed. The hexon gene was
identified as a candidate likely to have broad specificity across
all serotypes. Four primer pairs were selected from a group of
primers designed to yield broad coverage across the majority of the
adenoviral strain types (Table 9) wherein Tp=5'propynylated uridine
and Cp=5'propynylated cytidine.
TABLE-US-00011 TABLE 9 Intelligent Primer Pairs for Serotyping of
Adenoviruses Forward Reverse SEQ SEQ Primer Pair Name Forward
Primer Sequence ID NO: Reverse Primer Sequence ID NO: HEX_HAD7 + 4
+ AGACCCAATTACATTGGCTT 286 CCAGTGCTGTTGTAGTACAT 287 21_934_995
HEX_HAD7 + 4 + ATGTACTACAACAGTACTGG 288 CAAGTCAACCACAGCATTCA 289
21_976_1050 HEX_HAD7 + 4 + GGGCTTATGTACTACAACAG 290
TCTGTCTTGCAAGTCAACCAC 291 21_970_1059 HEX_HAD7 +
GGAATTTTTTGATGGTAGAGA 292 TAAAGCACAATTTCAGGCG 293 3_771_827
HEX_HAD4 + AGATCTGGCTTTCTTTGAC 294 ATATGAGTATCTGGAGTCTGC 295
16_746_848 HEX_HAD7_509_578 GGAAAGACATTACTGCAGACA 296
CCAACTTGAGGCTCTGGCTG 297 HEX_HAD4_1216_1289 ACAGACACTTACCAGGGTG 298
ACTGTGGTGTCATCTTTGTC 299 HEX_HAD21_515_567 TCACTAAAGACAAAGGTCTTCC
300 GGCTTCGCCGTCTGTAATTTC 301 HEX_HAD_1342_1469
CGGATCCAAGCTAATCTTTGG 302 GGTATGTACTCATAGGTGTTGGT 303 G HEX_HAD7 +
4 + AGACpCpCAATTpACpATpTGGC 304 CpCpAGTGCTGTpTpGTAGTACA 305
21_934_995p TT T HEX_HAD7 + 4 + ATpGTpACTpACAACAGTACpTp 306
CAAGTpCpAACCACAGCATpTpC 307 21_976_1050P GG A HEX_HAD7 + 4 +
GGGCpTpTATpGTpACTACAACp 308 TCTGTpCpTTGCAAGTpCpAACC 309
21_970_1059P AG AC HEX_HAD7 + GGAATTpTpTpTpTGATGGTAGA 310
TAAAGCACAATpTpTpCpAGGCG 311 3_771_827P GA HEX_HAD4 +
TAGATCTGGCTpTpTpCpTTTGA 312 ATATGAGTATpCpTpGGAGTpCp 313 16_746_848P
C TGC HEX_HAD_1342_1469P CGGATpCCAAGCpTAATCpTpTT 314
GGTATGTACTCATAGGTGTpTpG 315 GG GTG HEX_HAD7 + 21 +
AACAGACCCAATTACATTGGCTT 316 GAGGCACTTGTATGTGGAAAGG 317 3_931_1645
HEX_HAD4 + ATGCCTAACAGACCCAATTACAT 318 TTCATGTAGTCGTAGGTGTTGG 319
2_925_1469 HEX HAD7 + 21 + CGCGCCTAATACATCTCAGTGGA 320
AAGCCAATGTAATTGGGTCTGTT 321 3_384_953 T HEX_HAD4 +
CTACTCTGGCACTGCCTACAAC 322 ATGTAATTGGGTCTGTTAGGCAT 323 2_345_947
HEX_HAD2_772_865 CAATCCGTTCTGGTTCCGGATGA 324
CTTGCCGGTCGTTCAAAGAGGTA 325 A G HEX_HAD7 + 4 + AGTCCGGGTCTGGTGCAG
326 CGGTCGGTGGTCACATC 327 21_73_179 HEX_HAD7 + 4 +
ATGGCCACCCCATCGATG 328 CTGTCCGGCGATGTGCATG 329 21_1_54 HEX_HAD7 + 4
+ GGTCGTTATGTGCCTTTCCACAT 330 TCCTTTCTGAAGTTCCACTCATA 331
21_1612_1718 GG HEX_HAD7 + 4 + ACAACATTGGCTACCAGGGCTT 332
CCTGCCTGCTCATAGGCTGGAAG 333 21_2276_2368 TT
[0183] These primers also served to clearly distinguish those
strains responsible for most disease (types 3, 4, 7 and 21) from
all others. DNA isolated from field samples known to contain
adenoviruses were tested using the hexon gene PCR primers, which
provided unambiguous strain identification for all samples. A
single sample was found to contain a mixture of two viral DNAs
belonging to strains 7 and 21.
[0184] Test results (FIG. 21) showed perfect concordance between
predicted and observed base composition signatures for each of
these samples. Classical serotyping results confirmed each of these
observations. Processing of viral samples directly from collection
material such as throat swabs rather than from isolated DNA, will
result in a significant increase in throughput, eliminating the
need for virus culture.
Example 18
Broad Rapid Detection and Strain Typing of Respiratory Pathogens
for Epidemic Surveillance
[0185] Genome Preparation:
[0186] Genomic materials from culture samples or swabs were
prepared using a modified robotic protocol using DNeasy.TM. 96
Tissue Kit, Qiagen). Cultures of Streptococcus pyogenes were
pelleted and transferred to a 1.5 mL tube containing 0.45 g of 0.7
mm Zirconia beads (Biospec Products, Inc.). Cells were lysed by
shaking for 10 minutes at a speed of 19 l/s using a MM300 Vibration
Mill (Retsch, Germany). The samples were centrifuged for 5 min and
the supernatants transferred to deep well blocks and processed
using the manufacture's protocol and a Qiagen 8000 BioRobot.
[0187] PCR: PCR reactions were assembled using a Packard MPII
liquid handling platform and were performed in 50 .mu.L volume
using 1.8 units each of Platinum Taq (Invitrogen) and Hotstart PFU
Turbo (Stratagene) polymerases. Cycling was performed on a DNA
Engine Dyad (MJ Research) with cycling conditions consisting of an
initial 2 min at 95.degree. C. followed by 45 cycles of 20 sat
95.degree. C., 15 sat 58.degree. C., and 15 sat 72.degree. C.
[0188] Broad-Range Primers:
[0189] PCR primer design for base composition analysis from precise
mass measurements is constrained by an upper limit where ionization
and accurate deconvolution can be achieved. Currently, this limit
is approximately 140 base pairs. Primers designed to broadly
conserved regions of bacterial ribosomal RNAs (16 and 23S) and the
gene encoding ribosomal protein L3 (rpoC) are shown in Table
10.
TABLE-US-00012 TABLE 10 Broad Range Primer Pairs Length of Target
Gene Direction Primer SEQ ID NO Amplicon 16S_1 F
GGATTAGAGACCCTGGTAGTCC 334 116 16S_1 R GGCCGTACTCCCCAGGCG 335 116
16S_2 F TTCGATGCAACGCGAAGAACCT 336 115 16S_2 R ACGAGCTGACGACAGCCATG
337 115 23S F TCTGTCCCTAGTACGAGAGGACCGG 338 118 23S R
TGCTTAGATGCTTTCAGC 339 118 rpoC F CTGGCAGGTATGCGTGGTCTGATG 340 121
rpoC R CGCACCGTGGGTTGAGATGAAGTAC 341 121
[0190] Emm-Typing Primers:
[0191] The allelic profile of a GAS strain by Multilocus Sequencing
Technique (MLST) can be obtained by sequencing the internal
fragments of seven housekeeping genes. The nucleotide sequences for
each of these housekeeping genes, for 212 isolates of GAS (78
distinct emm types), are available (www.mlst.net). This corresponds
to one hundred different allelic profiles or unique sequence types,
referred to by Enright et al. as ST1-ST100 (Enright et al.,
Infection and Immunity, 2001, 69, 2416-2427). For each sequence
type, we created a virtual transcript by concatenating sequences
appropriate to their allelic profile from each of the seven genes.
MLST primers were designed using these sequences and were
constrained to be within each gene loci. Twenty-four primer pairs
were initially designed and tested against the sequenced GAS strain
700294. A final subset of six primer pairs Table 11 was chosen
based on a theoretical calculation of minimal number of primer
pairs that maximized resolution of between emm types.
TABLE-US-00013 TABLE 11 Drill-Down Primer Pairs Used in Determining
emm-type Target Length of Gene Direction Primer SEQ ID NO Amplicon
gki F GGGGATTCAGCCATCAAAGCAGCTATTGAC 342 116 gki R
CCAACCTTTTCCACAACAGAATCAGC 343 116 gtr F
CCTTACTTCGAACTATGAATCTTTTGGAAG 344 115 gtr R
CCCATTTTTTCACGCATGCTGAAAATATC 345 115 murI F
CGCAAAAAAATCCAGCTATTAGC 346 118 murI R
AAACTATTTTTTTAGCTATACTCGAACAC 347 118 mutS F
ATGATTACAATTCAAGAAGGTCGTCACGC 348 121 mutS R
TTGGACCTGTAATCAGCTGAATACTGG 349 121 xpt F
GATGACTTTTTAGCTAATGGTCAGGCAGC 350 122 xpt R
AATCGACGACCATCTTGGAAAGATTTCTC 351 122 yqiL F
GCTTCAGGAATCAATGATGGAGCAG 352 119 yqiL R GGGTCTACACCTGCACTTGCATAAC
353 119
[0192] Microbiology:
[0193] GAS isolates were identified from swabs on the basis of
colony morphology and beta-hemolysis on blood agar plates, gram
stain characteristics, susceptibility to bacitracin, and positive
latex agglutination reactivity with group A-specific antiserum.
[0194] Sequencing:
[0195] Bacterial genomic DNA samples of all isolates were extracted
from freshly grown GAS strains by using QIAamp DNA Blood Mini Kit
(Qiagen, Valencia, Calif.) according to the procedures described by
the manufacture. Group A streptococcal cells were subjected to PCR
and sequence analysis using emm-gene specific PCR as previously
described (Beall et al., J. Clin. Micro., 1996, 34, 953-958; and
Facklam et al., Emerg. Infect. Dis., 1999, 5, 247-253). Homology
searches on DNA sequences were conducted against known emm
sequences present in (www.cdc.gov/ncidod/biotech/infotech_hp.html).
For MLST analysis, internal fragments of seven housekeeping genes,
were amplified by PCR and analyzed as previously described (Enright
et al., Infection and Immunity 2001, 69, 2416-2427). The emm-type
was determined from comparison to the MLST database.
[0196] Broad Range Survey/Drill-Down Process (100):
[0197] For Streptococcus pyogenes, the objective was the
identification of a signature of the virulent epidemic strain and
determination of its emm-type. Emm-type information is useful both
for treatment considerations and epidemic surveillance. A total of
51 throat swabs were taken both from healthy recruits and from
hospitalized patients in December 2002, during the peak of a GAS
outbreak at a military training camp. Twenty-seven additional
isolates from previous infections ascribed to GAS were also
examined. Initially, isolated colonies were examined both from
throat culture samples and throat swabs directly without the
culture step. The latter path can be completed within 6-12 hours
providing information on a significant number of samples rapidly
enough to be useful in managing an ongoing epidemic.
[0198] The process of broad range survey/drill-down (200) is shown
in FIG. 22. A clinical sample such as a throat swab is first
obtained from an individual (201). Broad range survey primers are
used to obtain amplification products from the clinical sample
(202) which are analyzed to determine a BCS (203) from which a
species is identified (204). Drill-down primers are then employed
to obtain PCR products (205) from which specific information is
obtained about the species (such as Emm-type) (206).
[0199] Broad Range Survey Priming:
[0200] Genomic regions targeted by the broad range survey primers
were selected for their ability to allow amplification of virtually
all known species of bacteria and for their capability to
distinguish bacterial species from each other by base composition
analysis. Initially, four broad-range PCR target sites were
selected and the primers were synthesized and tested. The targets
included universally conserved regions of 16S and 23S rRNA, and the
gene encoding ribosomal protein L3 (rpoC).
[0201] While there was no special consideration of Streptococcus
pyogenes in the selection of the broad range survey primers (which
were optimized for distinguishing all important pathogens from each
other), analysis of genomic sequences showed that the base
compositions of these regions distinguished Streptococcus pyogenes
from other respiratory pathogens and normal flora, including
closely related species of streptococci, staphylococci, and bacilli
(FIG. 23).
[0202] Drill Down Priming (Emm-Typing):
[0203] In order to obtain strain-specific information about the
epidemic, a strategy was designed to measure the base compositions
of a set of fast clock target genes to generate strain-specific
signatures and simultaneously correlate with emm-types. In classic
MLST analysis, internal fragments of seven housekeeping genes (gki,
gtr, murl, mutS, recP, xpt, yqiL) are amplified, sequenced and
compared to a database of previously studied isolates whose
emm-types have been determined (Horner et al. Fundamental and
Applied Toxicology, 1997, 36, 147). Since the analysis enabled by
the present embodiment of the present invention provides base
composition data rather than sequence data, the challenge was to
identify the target regions that provide the highest resolution of
species and least ambiguous emm-classification. The data set from
Table 2 of Enright et al. (Enright et al. Infection and Immunity,
2001, 69, 2416-2427) to bioinformatically construct an alignment of
concatenated alleles of the seven housekeeping genes from each of
212 previously emm-typed strains, of which 101 were unique
sequences that represented 75 distinct emm-types. This alignment
was then analyzed to determine the number and location of the
optimal primer pairs that would maximize strain discrimination
strictly on base composition data.
[0204] An example of assignment of BCSs of PCR products is shown in
FIG. 24 where PCR products obtained using the gtr primer (a
drill-down emm-typing primer) from two different swab samples were
analyzed (sample 12--top and sample 10--bottom). The deconvoluted
ESI-FCTIR spectra provide accurate mass measurements of both
strands of the PCR products, from which a series of candidate BCSs
were calculated from the measured mass (and within the measured
mass uncertainty). The identification of complementary candidate
BCSs from each strand provides a means for unambiguous assignment
of the BCS of the PCR product. BCSs and molecular masses for each
strand of the PCR product from the two different samples are also
shown in FIG. 24. In this case, the determination of BCSs for the
two samples resulted in the identification of the emm-type of
Streptococcus pyogenes--sample 12 was identified as emm-type 3 and
sample 10 was identified as emm-type 6.
[0205] The results of the composition analysis using the six primer
pairs, 5'-emm gene sequencing and MLST gene sequencing method for
the GAS epidemic at a military training facility are compared in
FIG. 25. The base composition results for the six primer pairs
showed a perfect concordance with 5'-emm gene sequencing and MLST
sequencing methods. Of the 51 samples taken during the peak of the
epidemic, all but three had identical compositions and corresponded
to emm-type 3. The three outliers, all from healthy individuals,
probably represent non-epidemic strains harbored by asymptomatic
carriers. Samples 52-80, which were archived from previous
infections from Marines at other naval training facilities, showed
a much greater heterogeneity of composition signatures and
emm-types.
Example 19
Base Composition Probability Clouds
[0206] FIG. 18 illustrates the concept of base composition
probability clouds via a pseudo-four dimensional plot of base
compositions of enterobacteria including Y. pestis, Y.
psuedotuberculosis, S. typhimurium, S. typhi, Y. enterocolitica, E.
coli K12, and E. coli O157:H7. In the plot of FIGS. 18, A, C and G
compositions correspond to the x, y and z axes respectively whereas
T compositions are represented by the size of the sphere at the
junction of the x, y and z coordinates. There is no absolute
requirement for having a particular nucleobase composition
associated with a particular axis. For example, a plot could be
designed wherein G, T and C compositions correspond to the x, y and
z axes respectively whereas the A composition corresponds to the
size of the sphere at the junction of the x, y and z coordinates.
Furthermore, a different representation can be made of the "pseudo
fourth" dimension i.e.: other than the size of the sphere at
junction of the x, y and z coordinates. For example, a symbol
having vector information such as an arrow or a cone can be rotated
at an angle that varies proportionally with the composition of the
nucleobase corresponding to the pseudo fourth dimension. The choice
of axes and pseudo fourth dimensional representation is typically
made with the aim of optimal visualization of the data being
presented.
[0207] A similar base composition probability cloud analysis has
been presented for a series of viruses in U.S. provisional patent
application Ser. No. 60/431,319, which is commonly owned and
incorporated herein by reference in its entirety. In this base
composition probability cloud analysis, the closely related Dengue
virus types 1-4 are clearly distinguishable from each other. This
example is indicative of a challenging scenario for species
identification based on BCS analysis because RNA viruses have a
high mutation rate, it would be expected to be difficult to resolve
closely related species. However, as this example illustrates, BCS
analysis, aided by base composition probability cloud analysis is
capable of resolution of closely related viral species.
[0208] A base composition probability cloud can also be represented
as a three dimensional plot instead of a pseudo-four dimensional
plot. An example of such a three dimensional plot is a plot of G, A
and C compositions correspond to the x, y and z axes respectively,
while the composition of T is left out of the plot. Another such
example is a plot where the compositions of all four nucleobases is
included: G, A and C+T compositions correspond to the x, y and z
axes respectively. As for the pseudo-four dimensional plots, the
choice of axes for a three dimensional plot is typically made with
the aim of optimal visualization of the data being presented.
Example 20
Biochemical Processing of Large Amplification Products for Analysis
by Mass Spectrometry
[0209] In the example illustrated in FIG. 26, a primer pair which
amplifies a 986 bp region of the 16S ribosomal gene in E. coli
(K12) was digested with a mixture of 4 restriction enzymes: BstN1,
BsmF1, Bfa1, and Nco1. FIG. 26(a) illustrates the complexity of the
resulting ESI-FTICR mass spectrum that contains multiple charge
states of multiple restriction fragments. Upon mass deconvolution
to neutral mass, the spectrum is significantly simplified and
discrete oligonucleotide pairs are evident (FIG. 26b). When base
compositions are derived from the masses of the restriction
fragments, perfect agreement is observed for the known sequence of
nucleotides 1-856 (FIG. 26c); the batch of Nco1 enzyme used in this
experiment was inactive and resulted in a missed cleavage site and
a 197-mer fragment went undetected as it is outside the mass range
of the mass spectrometer under the conditions employed.
Interestingly however, both a forward and reverse strand were
detected for each fragment measured (solid and dotted lines in,
respectively) within 2 ppm of the predicted molecular weights
resulting in unambiguous determination of the base composition of
788 nucleotides of the 985 nucleotides in the amplicon. The
coverage map offers redundant coverage as both 5' to 3' and 3' to
5' fragments are detected for fragments covering the first 856
nucleotides of the amplicon.
[0210] This approach is in many ways analogous to those widely used
in MS-based proteomics studies in which large intact proteins are
digested with trypsin, or other proteolytic enzyme(s), and the
identity of the protein is derived by comparing the measured masses
of the tryptic peptides with theoretical digests. A unique feature
of this approach is that the precise mass measurements of the
complementary strands of each digest product allow one to derive a
de novo base composition for each fragment, which can in turn be
"stitched together" to derive a complete base composition for the
larger amplicon. An important distinction between this approach and
a gel-based restriction mapping strategy is that, in addition to
determination of the length of each fragment, an unambiguous base
composition of each restriction fragment is derived. Thus, a single
base substitution within a fragment (which would not be resolved on
a gel) is readily observed using this approach. Because this study
was performed on a 7 Tesla ESI-FTICR mass spectrometer, better than
2 ppm mass measurement accuracy was obtained for all fragments.
Interestingly, calculation of the mass measurement accuracy
required to derive unambiguous base compositions from the
complementary fragments indicates that the highest mass measurement
accuracy actually required is only 15 ppm for the 139 bp fragment
(nucleotides 525-663). Most of the fragments were in the 50-70 bp
size-range which would require mass accuracy of only .about.50 ppm
for unambiguous base composition determination. This level of
performance is achievable on other more compact, less expensive MS
platforms such as the ESI-TOF suggesting that the methods developed
here could be widely deployed in a variety of diagnostic and human
forensic arenas.
[0211] This example illustrates an alternative approach to derive
base compositions from larger PCR products. Because the amplicons
of interest cover many strain variants, for some of which complete
sequences are not known, each amplicon can be digested under
several different enzymatic conditions to ensure that a
diagnostically informative region of the amplicon is not obscured
by a "blind spot" which arises from a mutation in a restriction
site. The extent of redundancy required to confidently map the base
composition of amplicons from different markers, and determine
which set of restriction enzymes should be employed and how they
are most effectively used as mixtures can be determined. These
parameters will be dictated by the extent to which the area of
interest is conserved across the amplified region, the
compatibility of the various restriction enzymes with respect to
digestion protocol (buffer, temperature, time) and the degree of
coverage required to discriminate one amplicon from another.
Example 21
Identification of members of the Viral Genus Orthopoxvirus
[0212] Primer sites were identified on three essential viral
genes--the DNA-dependent polymerase (DdDp), and two sub-units of
DNA-dependent RNA polymerases A and B (DdRpA and DdRpB). These
intelligent primers designed to identify members of the viral genus
Orthopoxvirus are shown in Table 12 wherein Tp=5'propynylated
uridine and Cp=5'propynylated cytidine.
TABLE-US-00014 TABLE 12 Intelligent Primer Pairs for Identification
of members of the Viral Genus Orthopoxvirus Forward Reverse SEQ SEQ
Primer Pair Name Forward Primer Sequence ID NO: Reverse Primer
Sequence ID NO: A25L_NC001611_28_127 GTACTGAATCCGCCTAAG 354
GTGAATAAAGTATCGCCCTAATA 355 A18R_NC001611_100_207
GAAGTTGAACCGGGATCA 356 ATTATCGGTCGTTGTTAATGT 357
A18R_NC001611_1348_1445 CTGTCTGTAGATAAACTAGGATT 358
CGTTCTTCTCTGGAGGAT 359 E9L_NC001611_1119_1222 CGATACTACGGACGC 360
CTTTATGAATTACTTTACATAT 361 K8R_NC001611_221_311 CTCCTCCATCACTAGGAA
362 CTATAACATTCAAAGCTTATTG 363 A24R_NC001611_795_878
CGCGATAATAGATAGTGCTAAAC 364 GCTTCCACCAGGTCATTAA 365
A25L_NC001611_28_127P GTACpTpGAATpCpCpGCpCpTA 366
GTGAATAAAGTATpCpGCpCpCp 367 AG TpAATA A18R_NC001611_100_207P
GAAGTpTpGAACpCpGGGATCA 368 ATTATCGGTpCpGTpTpGTpTpA 369 ATGT
A18R_NC001611_1348_1445P CTGTpCpTpGTAGATAAACpTpA 370
CGTTCpTpTpCpTpCpTpGGAGG 371 GGATT AT E9L_NC001611_1119_1222P
CGATACpTpACpGGACGC 372 CTTTATGAATpTpACpTpTpTpA 373 CATAT
K8R_NC001611_221_311P CTpCpCpTCpCpATCACpTpAGG 374
CTATAACATpTpCpAAAGCpTpT 375 AA pATTG A24R_NC001611_795_878P
CGCGATpAATpAGATAGTpGCpT 376 GCTTCpCpACpCAGGTpCATpTA 377 pAAAC A
[0213] As illustrated in FIG. 27, members of the Orthopoxvirus
genus group can be identified, distinguished from one another, and
distinguished from other members of the Poxvirus family using a
single pair of primers designed against the DdRpB gene.
[0214] Since the primers were designed across regions of high
conservation within this genus, the likelihood of missed detection
due to sequence variations at these sites is minimized. Further,
none of the primers is expected to amplify other viruses or any
other DNA, based on the data available in GenBank. This method can
be used for all families of viral threat agents and is not limited
to members of the Orthopoxvirus genus.
Example 22
Identification of Viruses that Cause Viral Hemorrhagic Fevers
[0215] In accordance with the present invention an approach of
broad PCR priming across several different viral species is
employed using conserved regions in the various viral genomes,
amplifying a small, yet highly informative region in these
organisms, and then analyzing the resultant amplicons with mass
spectrometry and data analysis. These regions will be tested with
live agents, or with genomic constructs thereof.
[0216] Detection of RNA viruses will necessitate a reverse
transcription (RT) step prior to the PCR amplification of the TIGER
reporter amplicon. To maximize throughput and yield while
minimizing the handling of the samples, commercial one-step reverse
transcription polymerase chain reaction (RT-PCR) kits will be
evaluated for use. If necessary, a one-step RT-PCR mix using our
selected DNA polymerase for the PCR portion of the reaction will be
developed. To assure there is no variation in our reagent
performance all new lots of enzymes, nucleotides and buffers will
be individually tested prior to use.
[0217] Various modifications of the invention, in addition to those
described herein, will be apparent to those skilled in the art from
the foregoing description. Such modifications are also intended to
fall within the scope of the appended claims. Each reference, web
site, Genebank accession number, etc. cited in the present
application is incorporated herein by reference in its entirety.
Sequence CWU 1
1
37711388RNAArtificial Sequence16S rRNA Sequence 1nnnnnnnaga
ggacnggcca gnnngaacgc ggcggnnngc nnanacagca agcgancgnn 60nnnnnnnnnn
nnnnnnnnnn nnnnnnnnnn agnggcnnac ggggagaann cnnnnnannn
120ccnnnnnnnn nggnanannn nnnngaaann nnnnnaaacc nnannnnnnn
nnnnnnnaaa 180gnnnnnnnnn nnnnnnnnnn nnnnnnnnng annnnnnnnn
gnnnnanagn ngggnnggaa 240nggcnnacca agncnnngan nnnagcngnn
cgagaggnng nncngccaca nggnacgaga 300nacggnccan acccacggga
ggcagcagnn ggaannnnca aggnngnaan ncgannnagc 360nannccgcgg
nnngangang gnnnnngnng aaannncnnn nnnnnganga nnnnnnnnnn
420nnnnnnnnnn nnnnnnnnga cnnannnnnn nannaagnnn cggcnaacnc
ggccagcagc 480cgcggaaacg naggnngcna gcgnnncgga nnangggcga
aagngnnngn aggnggnnnn 540nnnngnnnnn gnaaannnnn nngcnaacnn
nnnnnnnncn nnnnnnacnn nnnnncngag 600nnnnnnagng gnnnnnngaa
nnnnggagng ggnaancgna gananngnan gaanaccnnn 660gcgaaggcnn
nnnncggnnn nnnacgacnc nannnncgaa agcngggnag cnaacaggaa
720gaacccggag ccangcnnaa acgngnnnnn nnnngnnngn nnnnnnnnnn
nnnnnnnnnn 780nnannnaacg nnnaannnnn ccgccgggga gacgnncgca
agnnnaaacc aaangaagac 840ggggnccngc acaagcngng gagnagggna
acgangnnac gcgnanaacc accnnnnnga 900cannnnnnnn nnnnnnngan
annnnnnnnn nnnnnnnnnn nnnnnnnnac agggngcagg 960ngcgcagccg
gnnggagngg ggaagcccgn aacgagcgca acccnnnnnn nnngncnanc
1020nnnnnnnnng ngnaccnnnn nnnacgccnn ngnnaannng gaggaaggng
gggangacgc 1080aancncagnc ccangnnnng ggcncacacn ncacaaggnn
nnnacanngn gnngcnannn 1140ngnnannnnn agcnaancnn nnaaannnnn
cnnagncgga ngnnnncgca accgnnnncn 1200gaagnnggan cgcagaacgn
nnacagnang nnncgggaaa cgcncgggnc gacacaccgc 1260ccgcannnca
ngnnagnnnn nnnnnccnna agnnnnnnnn nnnncnnnnn ngnnnnnnnn
1320nncnanggnn nnnnnnnnga ngggnnnaag cgaacaagga nccnannnga
anngnggngg 1380acaccccn 138822654RNAArtificial Sequence16S rRNA
Consensus Sequence 2nnnnaagnnn nnaagngnnn nnggggagcc nggcnnnnnn
agncgangaa ggangnnnnn 60nncnncnnna nncnnnggnn agnngnnnnn nnncnnnnna
nccnnngnnc cgaaggggna 120acccnnnnnn nnnnnnnnnn nnannnnnnn
nnnnnnnnnn nnnnnnnnnn ngnnnacnnn 180nngaangaaa cacnagannn
nnaggaanag aaannaannn ngancnnnng agnggcgagc 240gaannngnan
nagncnnnnn nnnnnnnnnn nnnnnnnnnn annngaannn nnggnaagnn
300nnnnnnnann nggnanannc cngannnnaa annnnnnnnn nnnnnnnnnn
nnnnagannn 360cnnnncncgn gnnannnngn ngaannngnn nngaccannn
nnnaagncaa aacnnnnnnn 420gaccnaagng nannagacng ganggaaagg
ngaaaagnac ccnnnnnang ggaggaaana 480gnnccgaaac cnnnnncnan
aannngnnna gnnnnnnnnn nnnnnganng cgnccgnann 540agnnncngng
annnnnnnnn ngcnagnaan nnnnnnnngn agncgnagng aaancgagnn
600naanngngcg nnnagnnnnn gnnnnagacn cgaancnnng gancannnag
nncaggngaa 660gnnnnngaan annnnnggag gnccgaacnn nnnnnnggaa
aannnnnngg agannggnnn 720gnggngaaan ncnaancnaa cnnngnnaag
cggccnncga aannnnaggn nnngcnnnnn 780nnnnnnnnnn nggnggagag
cacgnnnnnn nnnnggnnnn nnnnnnnnna cnnannnnnn 840nnaaacncga
anccnnnnnn nnnnnnnnnn gnagnnannc nnngngngna annncnnngn
900nanagggnaa cancccagan cnncnnnaag gncccnaann nnnnnnaagg
gnaaangang 960gnnnnnncnn anacannnag gangggcaga agcagccanc
nnaaaganng cgaanagcca 1020cnncnagnnn nnnngcgcng annanancgg
gncaannnnn nnnccgaann nnnngnnnnn 1080nnnnnnnnnn nnnnnnngga
gnngagcgnn nnnnnnnnnn ngaagnnnnn nngnnannnn 1140nnnggannnn
nnnnnaggng nagnngnnan agancgannn nnnnnggana nncnnnnncn
1200ccgnannncn aaggnccnnn nnnangnnnc nnnnnngggn agcgnnncca
agnngagncn 1260ganangnnag nngaggnnan nnggnnaacc nnnacnnnnn
nnnnnnnnnn nnnnnngacg 1320nnnnnngnnn nnnnnnnnnn nnnnnggnnn
nnnnnnnnnn nnnnnnnnnn nnnnnnnnnn 1380nnnnnnnnnn nnnnnnnnnn
nnnnnnnnnn nnnnnnnnnn nnnnnnnnnn nnnnnnnnnn 1440nnnnnnnnnn
nnnnncnnga aaannnnnnn nnnnnnnnnn nnnnnnnnnn cgaccnnaaa
1500ccgacacagg ngnnnngnng agnanncnna ggngnnngnn nnaannnnnn
nnaaggaacn 1560ngcaaannnn nccganccgg nanaaggnnn ncnnnnnnnn
nnnnnnnnnn nnnnnnnnnn 1620nnnnnnnnnn nnngnnnnan nnannngnnn
nnnncnacga nnaaaaacac agnncnngcn 1680aanncgnaag nngangaang
gnngacnccg cccnggcnng aaggaanngn nnnnnnnngn 1740nnnngnnnnn
nnnnnnannn aagcccnngn aacggcggnn gaacaaacnn ccaaggagcg
1800aaaccgcggg aagccgaccn gcacgaangg ngnaangann nnnnnncgcc
nnnnnnnnnc 1860ncngngaann nannnnngna agagcnnnnn cncgcnnnnn
gacggaaaga ccccnngnan 1920cacnnannnn nnangnnnnn nnnnnnnnnn
gnnagnaagg nggagncnnn gannnnnnnn 1980cgnnagnnnn nnnggagncn
nnnngnnaac nacncnnnnn nnnnnnnnnc aacnnnnnnn 2040nnnnancnnn
nnnnnngaca ngnnngnngg gnagnacggg gcggnncccc naaanngaac
2100ggaggngnnc naaggnnncn annnnggnng gnnacnnnnn nnnagnnaan
ngnanaagnn 2160ngcnnacgnn agnnnnacnn nncgagcagn nncgaaagnn
ggnnnaggac cggnggnnnn 2220nnggaagngc cncgccaacg gaaaaagnac
ncnggggaaa caggcnannn ncccaagagn 2280canacgacgg nnnngggcac
ccgagcggcc ncncaccggg gcgnagnngg cccaagggnn 2340ggcgcgccnn
aaagnggnac gngagcgggn anaacgcgga gacagnggcc cacngnngng
2400ngngnnngan nngannngnn ngnncnagac gagaggaccg gnnngnacnn
ancncgggnn 2460ncnggnnngc cannngcann gcngnnagca nnnnggnnnn
gaaanngcga angcacaagn 2520nngaancnnn cnnnnagann agnnncncnn
nnnnnnnnnn nnnnnnnnag nnncnnnnna 2580gannannnng ngaaggnnng
nnngnaagnn nngnnannnn nnagnnnacn nnacaannnn 2640cnnnnnncnn nnnn
2654313DNAArtificial SequencePrimer 3cgtggtgacc ctt
13414DNAArtificial SequencePrimer 4cgtcgtcacc gcta
14513DNAArtificial SequencePrimer 5cgtggtaccc ctt 13690RNABacillus
anthracisPCR Primer 6gcgaagaacc uuaccaggun uugacauccu cugacaaccc
uagagauagg gcuucuccuu 60cgggagcaga gugacaggug gugcaugguu
90790RNABacillus cereus 7gcgaagaacc uuaccagguc uugacauccu
cugaaaaccc uagagauagg gcuucuccuu 60cgggagcaga gugacaggug gugcaugguu
90830DNAArtificial SequencePCR Primer 8gtgagatgtt gggttaagtc
ccgtaacgag 30921DNAArtificial SequencePCR Primer 9gacgtcatcc
ccaccttcct c 211025DNAArtificial SequencePCR Primer 10atgttgggtt
aagtcccgca acgag 251123DNAArtificial SequencePCR Primer
11ttgacgtcat ccccaccttc ctc 231222DNAArtificial SequencePCR Primer
12ttaagtcccg caacgatcgc aa 221322DNAArtificial SequencePCR Primer
13tgacgtcatc cccaccttcc tc 221420DNAArtificial SequencePCR Primer
14gctacacacg tgctacaatg 201521DNAArtificial SequencePCR Primer
15cgagttgcag actgcgatcc g 211622DNAArtificial SequencePCR Primer
16aagtcggaat cgctagtaat cg 221719DNAArtificial SequencePCR Primer
17gacgggcggt gtgtacaag 191825DNAArtificial SequencePCR Primer
18tgaacgctgg tggcatgctt aacac 251922DNAArtificial SequencePCR
Primer 19tacgcattac tcacccgtcc gc 222026DNAArtificial SequencePCR
Primer 20gtggcatgcc taatacatgc aagtcg 262120DNAArtificial
SequencePCR Primer 21ttactcaccc gtccgccgct 202220DNAArtificial
SequencePCR Primer 22taacacatgc aagtcgaacg 202317DNAArtificial
SequencePCR Primer 23ttactcaccc gtccgcc 172418DNAArtificial
SequencePCR Primer 24gtgtagcggt gaaatgcg 182522DNAArtificial
SequencePCR Primer 25gtatctaatc ctgtttgctc cc 222620DNAArtificial
SequencePCR Primer 26agaacaccga tggcgaaggc 202721DNAArtificial
SequencePCR Primer 27cgtggactac cagggtatct a 212822DNAArtificial
SequencePCR Primer 28ggattagaga ccctggtagt cc 222918DNAArtificial
SequencePCR Primer 29ggccgtactc cccaggcg 183026DNAArtificial
SequencePCR Primer 30ggattagata ccctggtagt ccacgc
263118DNAArtificial SequencePCR Primer 31ggccgtactc cccaggcg
183222DNAArtificial SequencePCR Primer 32tagataccct ggtagtccac gc
223315DNAArtificial SequencePCR Primer 33cgtactcccc aggcg
153422DNAArtificial SequencePCR Primer 34ttcgatgcaa cgcgaagaac ct
223520DNAArtificial SequencePCR Primer 35acgagctgac gacagccatg
203617DNAArtificial SequencePCR Primer 36acgcgaagaa ccttacc
173718DNAArtificial SequencePCR Primer 37acgacacgag ctgacgac
183818DNAArtificial SequencePCR Primer 38ctgacacctg cccggtgc
183919DNAArtificial SequencePCR Primer 39gaccgttata gttacggcc
194025DNAArtificial SequencePCR Primer 40tctgtcccta gtacgagagg
accgg 254118DNAArtificial SequencePCR Primer 41tgcttagatg ctttcagc
184224DNAArtificial SequencePCR Primer 42ctgtccctag tacgagagga ccgg
244324DNAArtificial SequencePCR Primer 43gtttcatgct tagatgcttt cagc
244426DNAArtificial SequencePCR Primer 44ggggagtgaa agagatcctg
aaaccg 264521DNAArtificial SequencePCR Primer 45acaaaaggta
cgccgtcacc c 214626DNAArtificial SequencePCR Primer 46ggggagtgaa
agagatcctg aaaccg 264721DNAArtificial SequencePCR Primer
47acaaaaggca cgccatcacc c 214822DNAArtificial SequencePCR Primer
48cgagagggaa acaacccaga cc 224919DNAArtificial SequencePCR Primer
49tggctgcttc taagccaac 195029DNAArtificial SequencePCR Primer
50tgctcgtggt gcacaagtaa cggatatta 295129DNAArtificial SequencePCR
Primer 51tgctgctttc gcatggttaa ttgcttcaa 295228DNAArtificial
SequencePCR Primer 52caaaacttat taggtaagcg tgttgact
285330DNAArtificial SequencePCR Primer 53tcaagcgcca tttcttttgg
taaaccacat 305428DNAArtificial SequencePCR Primer 54caaaacttat
taggtaagcg tgttgact 285530DNAArtificial SequencePCR Primer
55tcaagcgcca tctctttcgg taatccacat 305627DNAArtificial SequencePCR
Primer 56taagaagccg gaaaccatca actaccg 275720DNAArtificial
SequencePCR Primer 57ggcgcttgta cttaccgcac 205819DNAArtificial
SequencePCR Primer 58tgattctggt gcccgtggt 195922DNAArtificial
SequencePCR Primer 59ttggccatca ggccacgcat ac 226019DNAArtificial
SequencePCR Primer 60tgattccggt gcccgtggt 196122DNAArtificial
SequencePCR Primer 61ttggccatca gaccacgcat ac 226224DNAArtificial
SequencePCR Primer 62ctggcaggta tgcgtggtct gatg 246325DNAArtificial
SequencePCR Primer 63cgcaccgtgg gttgagatga agtac
256424DNAArtificial SequencePCR Primer 64cttgctggta tgcgtggtct gatg
246525DNAArtificial SequencePCR Primer 65cgcaccatgc gtagagatga
agtac 256626DNAArtificial SequencePCR Primer 66cgtcgggtga
ttaaccgtaa caaccg 266725DNAArtificial SequencePCR Primer
67gtttttcgtt gcgtacgatg atgtc 256826DNAArtificial SequencePCR
Primer 68cgtcgtgtaa ttaaccgtaa caaccg 266927DNAArtificial
SequencePCR Primer 69acgtttttcg ttttgaacga taatgct
277027DNAArtificial SequencePCR Primer 70caaaggtaag caaggtcgtt
tccgtca 277124DNAArtificial SequencePCR Primer 71cgaacggcct
gagtagtcaa cacg 247227DNAArtificial SequencePCR Primer 72caaaggtaag
caaggacgtt tccgtca 277324DNAArtificial SequencePCR Primer
73cgaacggcca gagtagtcaa cacg 247421DNAArtificial SequencePCR Primer
74tagactgccc aggacacgct g 217521DNAArtificial SequencePCR Primer
75gccgtccatc tgagcagcac c 217621DNAArtificial SequencePCR Primer
76ttgactgccc aggtcacgct g 217721DNAArtificial SequencePCR Primer
77gccgtccatt tgagcagcac c 217825DNAArtificial SequencePCR Primer
78aactaccgtc cgcagttcta cttcc 257924DNAArtificial SequencePCR
Primer 79gttgtcgcca ggcataacca tttc 248025DNAArtificial SequencePCR
Primer 80aactaccgtc ctcagttcta cttcc 258124DNAArtificial
SequencePCR Primer 81gttgtcacca ggcattacca tttc 248228DNAArtificial
SequencePCR Primer 82ccacagttct acttccgtac tactgacg
288330DNAArtificial SequencePCR Primer 83tccaggcatt accatttcta
ctccttctgg 308430DNAArtificial SequencePCR Primer 84gacctacagt
aagaggttct gtaatgaacc 308524DNAArtificial SequencePCR Primer
85tccaagtgct ggtttacccc atgg 248623DNAArtificial SequencePCR Primer
86catccacacg gtggtggtga agg 238722DNAArtificial SequencePCR Primer
87gtgctggttt accccatgga gt 228825DNAArtificial SequencePCR Primer
88cgtgttgact attcggggcg ttcag 258930DNAArtificial SequencePCR
Primer 89attcaagagc catttctttt ggtaaaccac 309029DNAArtificial
SequencePCR Primer 90tcaacaacct cttggaggta aagctcagt
299130DNAArtificial SequencePCR Primer 91tttcttgaag agtatgagct
gctccgtaag 309223DNAArtificial SequencePCR Primer 92catccacacg
gtggtggtga agg 239329DNAArtificial SequencePCR Primer 93tgttttgtat
ccaagtgctg gtttacccc 299420DNAArtificial SequencePCR Primer
94cgtggcggcg tggttatcga 209524DNAArtificial SequencePCR Primer
95cggtacgaac tggatgtcgc cgtt 249622DNAArtificial SequencePCR Primer
96tatcgctcag gcgaactcca ac 229721DNAArtificial SequencePCR Primer
97gctggattcg cctttgctac g 219830DNAArtificial SequencePCR Primer
98tgtaatgaac cctaatgacc atccacacgg 309927DNAArtificial SequencePCR
Primer 99ccaagtgctg gtttacccca tggagta 2710030DNAArtificial
SequencePCR Primer 100taatgaaccc taatgaccat ccacacggtg
3010126DNAArtificial SequencePCR Primer 101tccaagtgct ggtttacccc
atggag 2610229DNAArtificial SequencePCR Primer 102cttggaggta
agtctcattt tggtgggca 2910330DNAArtificial SequencePCR Primer
103cgtataagct gcaccataag cttgtaatgc 3010418DNAArtificial
SequencePCR Primer 104cgacgcgctg cgcttcac 1810524DNAArtificial
SequencePCR Primer 105gcgttccaca gcttgttgca gaag
2410618DNAArtificial SequencePCR Primer 106gaccacctcg gcaaccgt
1810718DNAArtificial SequencePCR Primer 107ttcgctctcg gcctggcc
1810827DNAArtificial SequencePCR Primer 108gcactatgca cacgtagatt
gtcctgg 2710926DNAArtificial SequencePCR Primer 109tatagcacca
tccatctgag cggcac 2611022DNAArtificial SequencePCR Primer
110cggcgtactt caacgacagc ca 2211120DNAArtificial SequencePCR Primer
111cgcggtcggc tcgttgatga 2011224DNAArtificial SequencePCR Primer
112cttctgcaac aagctgtgga acgc 2411323DNAArtificial SequencePCR
Primer 113tcgcagttca tcagcacgaa gcg 2311418DNAArtificial
SequencePCR Primer 114aagacgacct gcacgggc 1811519DNAArtificial
SequencePCR Primer 115gcgctccacg tcttcacgc 1911622DNAArtificial
SequencePCR Primer 116ctgttcttag tacgagagga cc 2211721DNAArtificial
SequencePCR Primer 117ttcgtgctta gatgctttca g 2111817DNAArtificial
SequencePCR Primer 118acgcgaagaa ccttacc
1711918DNAArtificial SequencePCR Primer 119acgacacgag ctgacgac
1812014DNAArtificial SequencePCR Primer 120cgaagaacct tacc
1412112DNAArtificial SequencePCR Primer 121acacgagctg ac
1212214DNAArtificial SequencePCR Primer 122cgaagaacct tacc
1412312DNAArtificial SequencePCR Primer 123acacgagctg ac
1212419DNAArtificial SequencePCR Primer 124cctgataagg gtgaggtcg
1912520DNAArtificial SequencePCR Primer 125acgtccttca tcgcctctga
2012622DNAArtificial SequencePCR Primer 126gttgtgaggt taagcgacta ag
2212721DNAArtificial SequencePCR Primer 127ctatcggtca gtcaggagta t
2112822DNAArtificial SequencePCR Primer 128gttgtgaggt taagcgacta ag
2212920DNAArtificial SequencePCR Primer 129ttgcatcggg ttggtaagtc
2013021DNAArtificial SequencePCR Primer 130atactcctga ctgaccgata g
2113119DNAArtificial SequencePCR Primer 131aacatagcct tctccgtcc
1913220DNAArtificial SequencePCR Primer 132gacttaccaa cccgatgcaa
2013324DNAArtificial SequencePCR Primer 133taccttagga ccgttatagt
tacg 2413419DNAArtificial SequencePCR Primer 134ggacggagaa
ggctatgtt 1913520DNAArtificial SequencePCR Primer 135ccaaacaccg
ccgtcgatat 2013624DNAArtificial SequencePCR Primer 136cgtaactata
acggtcctaa ggta 2413720DNAArtificial SequencePCR Primer
137gcttacacac ccggcctatc 2013820DNAArtificial SequencePCR Primer
138atatcgacgg cggtgtttgg 2013919DNAArtificial SequencePCR Primer
139gcgtgacagg caggtattc 1914022DNAArtificial SequencePCR Primer
140agtctcaaga gtgaacacgt aa 2214118DNAArtificial SequencePCR Primer
141gctgctggca cggagtta 1814220DNAArtificial SequencePCR Primer
142gacacggtcc agactcctac 2014318DNAArtificial SequencePCR Primer
143ccatgcagca cctgtctc 1814420DNAArtificial SequencePCR Primer
144gatctggagg aataccggtg 2014520DNAArtificial SequencePCR Primer
145acggttacct tgttacgact 2014620DNAArtificial SequencePCR Primer
146gagagcaagc ggacctcata 2014717DNAArtificial SequencePCR Primer
147cctcctgcgt gcaaagc 1714819DNAArtificial SequencePCR Primer
148tggaagatct gggtcaggc 1914922DNAArtificial SequencePCR Primer
149caatctgctg acggatctga gc 2215022DNAArtificial SequencePCR Primer
150gtcgtgaaaa cgagctggaa ga 2215118DNAArtificial SequencePCR Primer
151catgatggtc acaaccgg 1815222DNAArtificial SequencePCR Primer
152tggcgaacct ggtgaacgaa gc 2215325DNAArtificial SequencePCR Primer
153ctttcgcttt ctcgaactca accat 2515426DNAArtificial SequencePCR
Primer 154cgtcagggta aattccgtga agttaa 2615521DNAArtificial
SequencePCR Primer 155aacttcgcct tcggtcatgt t 2115624DNAArtificial
SequencePCR Primer 156ggtgaaagaa gttgcctcta aagc
2415723DNAArtificial SequencePCR Primer 157ttcaggtcca tcgggttcat
gcc 2315820DNAArtificial SequencePCR Primer 158cgtggcggcg
tggttatcga 2015920DNAArtificial SequencePCR Primer 159acgaactgga
tgtcgccgtt 2016020DNAArtificial SequencePCR Primer 160cggaattact
gggcgtaaag 2016118DNAArtificial SequencePCR Primer 161cgcatttcac
cgctacac 1816222DNAArtificial SequencePCR Primer 162acccagtgct
gctgaaccgt gc 2216321DNAArtificial SequencePCR Primer 163gttcaaatgc
ctggataccc a 2116422DNAArtificial SequencePCR Primer 164gggagcaaac
aggattagat ac 2216515DNAArtificial SequencePCR Primer 165cgtactcccc
aggcg 1516621DNAArtificial SequencePCR Primer 166tggcccgaaa
gaagctgagc g 2116721DNAArtificial SequencePCR Primer 167acgcgggcat
gcagagatgc c 2116819DNAArtificial SequencePCR Primer 168atgttgggtt
aagtcccgc 1916920DNAArtificial SequencePCR Primer 169tgacgtcatc
cccaccttcc 2017019DNAArtificial SequencePCR Primer 170cttgtacaca
ccgcccgtc 1917117DNAArtificial SequencePCR Primer 171aaggaggtga
tccagcc 1717221DNAArtificial SequencePCR Primer 172cggattggag
tctgcaactc g 2117319DNAArtificial SequencePCR Primer 173gacgggcggt
gtgtacaag 1917415DNAArtificial SequencePCR Primer 174ggtggatgcc
ttggc 1517516DNAArtificial SequencePCR Primer 175gggtttcccc attcgg
1617621DNAArtificial SequencePCR Primer 176gggaactgaa acatctaagt a
2117715DNAArtificial SequencePCR Primer 177ttcgctcgcc gctac
1517819DNAArtificial SequencePCR Primer 178taccccaaac cgacacagg
1917918DNAArtificial SequencePCR Primer 179ccttctcccg aagttacg
1818019DNAArtificial SequencePCR Primer 180ccgtaacttc gggagaagg
1918115DNAArtificial SequencePCR Primer 181caccgggcag gcgtc
1518216DNAArtificial SequencePCR Primer 182gacgcctgcc cggtgc
1618321DNAArtificial SequencePCR Primer 183ccgacaagga atttcgctac c
2118423DNAArtificial SequencePCR Primer 184aaggtactcc ggggataaca
ggc 2318522DNAArtificial SequencePCR Primer 185agccgacatc
gaggtgccaa ac 2218618DNAArtificial SequencePCR Primer 186gacagttcgg
tccctatc 1818717DNAArtificial SequencePCR Primer 187ccggtcctct
cgtacta 1718817DNAArtificial SequencePCR Primer 188tagtacgaga
ggaccgg 1718922DNAArtificial SequencePCR Primer 189ttagatgctt
tcagcactta tc 2219024DNAArtificial SequencePCR Primer 190aaactagata
acagtagaca tcac 2419118DNAArtificial SequencePCR Primer
191gtgcgccctt tctaactt 1819220DNAArtificial SequencePCR Primer
192agagtttgat catggctcag 2019317DNAArtificial SequencePCR Primer
193actgctgcct cccgtag 1719419DNAArtificial SequencePCR Primer
194cactggaact gagacacgg 1919520DNAArtificial SequencePCR Primer
195ctttacgccc agtaattccg 2019619DNAArtificial SequencePCR Primer
196ccagcagccg cggtaatac 1919722DNAArtificial SequencePCR Primer
197gtatctaatc ctgtttgctc cc 2219818DNAArtificial SequencePCR Primer
198gtgtagcggt gaaatgcg 1819919DNAArtificial SequencePCR Primer
199ggtaaggttc ttcgcgttg 1920018DNAArtificial SequencePCR Primer
200aagcggtgga gcatgtgg 1820121DNAArtificial SequencePCR Primer
201attgtagcac gtgtgtagcc c 2120219DNAArtificial SequencePCR Primer
202caagtcatca tggccctta 1920317DNAArtificial SequencePCR Primer
203aaggaggtga tccagcc 1720420DNAArtificial SequencePCR Primer
204agagtttgat catggctcag 2020517DNAArtificial SequencePCR Primer
205aaggaggtga tccagcc 1720619DNAArtificial SequencePCR Primer
206acctgcccag tgctggaag 1920718DNAArtificial SequencePCR Primer
207tcgctacctt aggaccgt 1820821DNAArtificial SequencePCR Primer
208gccttgtaca cacctcccgt c 2120920DNAArtificial SequencePCR Primer
209cacggctacc ttgttacgac 2021022DNAArtificial SequencePCR Primer
210ttgtacacac cgcccgtcat ac 2221120DNAArtificial SequencePCR Primer
211ccttgttacg acttcacccc 2021221DNAArtificial SequencePCR Primer
212tacggtgaat acgttcccgg g 2121322DNAArtificial SequencePCR Primer
213accttgttac gacttcaccc ca 2221419DNAArtificial SequencePCR Primer
214accacgccgt aaacgatga 1921521DNAArtificial SequencePCR Primer
215cccccgtcaa ttcctttgag t 2121622DNAArtificial SequencePCR Primer
216gataccctgg tagtccacac cg 2221719DNAArtificial SequencePCR Primer
217gccttgcgac cgtactccc 1921822DNAArtificial SequencePCR Primer
218tagataccct ggtagtccac gc 2221918DNAArtificial SequencePCR Primer
219gcgaccgtac tccccagg 1822018DNAArtificial SequencePCR Primer
220tagtcccgca acgagcgc 1822122DNAArtificial SequencePCR Primer
221gacgtcatcc ccaccttcct cc 2222222DNAArtificial SequencePCR Primer
222tagaacgtcg cgagacagtt cg 2222320DNAArtificial SequencePCR Primer
223agtccatccc ggtcctctcg 2022417DNAArtificial SequencePCR Primer
224gaggaaagtc cgggctc 1722518DNAArtificial SequencePCR Primer
225ataagccggg ttctgtcg 1822619DNAArtificial SequencePCR Primer
226gaggaaagtc catgctcgc 1922722DNAArtificial SequencePCR Primer
227gtaagccatg ttttgttcca tc 2222817DNAArtificial SequencePCR Primer
228gaggaaagtc cgggctc 1722918DNAArtificial SequencePCR Primer
229ataagccggg ttctgtcg 1823035DNAArtificial SequencePCR Primer
230gcgggatcct ctagaggtgt taaatagcct ggcag 3523135DNAArtificial
SequencePCR Primer 231gcgggatcct ctagaagacc tcctgcgtgc aaagc
3523219DNAArtificial SequencePCR Primer 232gaggaaagtc catgctcac
1923322DNAArtificial SequencePCR Primer 233ataagccatg ttctgttcca tc
2223419DNAArtificial SequencePCR Primer 234atgttgggtt aagtcccgc
1923517DNAArtificial SequencePCR Primer 235aaggaggtga tccagcc
1723620DNAArtificial SequencePCR Primer 236cggaattact gggcgtaaag
2023722DNAArtificial SequencePCR Primer 237gtatctaatc ctgtttgctc cc
2223819DNAArtificial SequencePCR Primer 238atgttgggtt aagtcccgc
1923920DNAArtificial SequencePCR Primer 239tgacgtcatg cccaccttcc
2024019DNAArtificial SequencePCR Primer 240atgttgggtt aagtcccgc
1924120DNAArtificial SequencePCR Primer 241tgacgtcatg gccaccttcc
2024234DNAArtificial SequencePCR Primer 242gcgggatcct ctagacctga
taagggtgag gtcg 3424334DNAArtificial SequencePCR Primer
243gcgggatcct ctagagcgtg acaggcaggt attc 3424417DNAArtificial
SequencePCR Primer 244acgcgaagaa ccttacc 1724519DNAArtificial
SequencePCR Primer 245gacgggcggt gtgtacaag 1924618DNAArtificial
SequencePCR Primer 246gtgtagcggt gaaatgcg 1824721DNAArtificial
SequencePCR Primer 247cgagttgcag actgcgatcc g 2124820DNAArtificial
SequencePCR Primer 248taacacatgc aagtcgaacg 2024915DNAArtificial
SequencePCR Primer 249cgtactcccc aggcg 1525020DNAArtificial
SequencePCR Primer 250taacacatgc aagtcgaacg 2025118DNAArtificial
SequencePCR Primer 251acgacacgag ctgacgac 1825222DNAArtificial
SequencePCR Primer 252acaacgaagt acaatacaag ac 2225322DNAArtificial
SequencePCR Primer 253cttctacatt tttagccatc ac 22254
22DNAArtificial SequencePCR Primer 254ttaagtcccg caacgagcgc aa
2225522DNAArtificial SequencePCR Primer 255tgacgtcatc cccaccttcc tc
2225628DNAArtificial SequencePCR Primer 256tgagtgatga aggccttagg
gttgtaaa 2825721DNAArtificial SequencePCR Primer 257cggctgctgg
cacgaagtta g 2125823DNAArtificial SequencePCR Primer 258atggacaagg
ttggcaagga agg 2325920DNAArtificial SequencePCR Primer
259tagccgcggt cgaattgcat 2026026DNAArtificial SequencePCR Primer
260aaggaaggcg tgatcaccgt tgaaga 2626123DNAArtificial SequencePCR
Primer 261ccgcggtcga attgcatgcc ttc 2326216DNAArtificial
SequencePCR Primer 262acgcgctgcg cttcac 1626320DNAArtificial
SequencePCR Primer 263ttgcagaagt tgcggtagcc 2026418DNAArtificial
SequencePCR Primer 264tcgaccacct gggcaacc 1826519DNAArtificial
SequencePCR Primer 265atcaggtcgt gcggcatca 1926617DNAArtificial
SequencePCR Primer 266cacggtgccg gcgtact 1726720DNAArtificial
SequencePCR Primer 267gcggtcggct cgttgatgat 2026825DNAArtificial
SequencePCR Primer 268ttggaggtaa gtctcatttt ggtgg
2526925DNAArtificial SequencePCR Primer 269aagctgcacc ataagcttgt
aatgc 2527020DNAArtificial SequencePCR Primer 270cagcgtttcg
gcgaaatgga 2027124DNAArtificial SequencePCR Primer 271cgacttgacg
gttaacattt cctg 2427223DNAArtificial SequencePCR Primer
272gggcagcgtt tcggcgaaat gga 2327327DNAArtificial SequencePCR
Primer 273gtccgacttg acggtcaaca tttcctg 2727429DNAArtificial
SequencePCR Primer 274caggagtcgt tcaactcgat ctacatgat
2927519DNAArtificial SequencePCR Primer 275acgccatcag
gccacgcat 1927618DNAArtificial SequencePCR Primer 276gcacaacctg
cggctgcg 1827718DNAArtificial SequencePCR Primer 277acggcacgag
gtagtcgc 1827820DNAArtificial SequencePCR Primer 278cgccgacttc
gacggtgacc 2027919DNAArtificial SequencePCR Primer 279gagcatcagc
gtgcgtgct 1928023DNAArtificial SequencePCR Primer 280ccacacgccg
ttcttcaaca act 2328125DNAArtificial SequencePCR Primer
281ggcatcacca tttccttgtc cttcg 2528227DNAArtificial SequencePCR
Primer 282gagagtttga tcctggctca gaacgaa 2728322DNAArtificial
SequencePCR Primer 283tgttactcac ccgtctgcca ct 2228419DNAArtificial
SequencePCR Primer 284accgagcaag gagaccagc 1928523DNAArtificial
SequencePCR Primer 285tataacgcac atcgtcaggg tga
2328620DNAArtificial SequencePCR Primer 286agacccaatt acattggctt
2028720DNAArtificial SequencePCR Primer 287ccagtgctgt tgtagtacat
2028820DNAArtificial SequencePCR Primer 288atgtactaca acagtactgg
2028920DNAArtificial SequencePCR Primer 289caagtcaacc acagcattca
2029020DNAArtificial SequencePCR Primer 290gggcttatgt actacaacag
2029121DNAArtificial SequencePCR Primer 291tctgtcttgc aagtcaacca c
2129221DNAArtificial SequencePCR Primer 292ggaatttttt gatggtagag a
2129319DNAArtificial SequencePCR Primer 293taaagcacaa tttcaggcg
1929420DNAArtificial SequencePCR Primer 294tagatctggc tttctttgac
2029521DNAArtificial SequencePCR Primer 295atatgagtat ctggagtctg c
2129621DNAArtificial SequencePCR Primer 296ggaaagacat tactgcagac a
2129720DNAArtificial SequencePCR Primer 297ccaacttgag gctctggctg
2029819DNAArtificial SequencePCR Primer 298acagacactt accagggtg
1929920DNAArtificial SequencePCR Primer 299actgtggtgt catctttgtc
2030022DNAArtificial SequencePCR Primer 300tcactaaaga caaaggtctt cc
2230121DNAArtificial SequencePCR Primer 301ggcttcgccg tctgtaattt c
2130221DNAArtificial SequencePCR Primer 302cggatccaag ctaatctttg g
2130324DNAArtificial SequencePCR Primer 303ggtatgtact cataggtgtt
ggtg 2430420DNAArtificial SequencePCR Primer 304agacccaatt
acattggctt 2030520DNAArtificial SequencePCR Primer 305ccagtgctgt
tgtagtacat 2030620DNAArtificial SequencePCR Primer 306atgtactaca
acagtactgg 2030720DNAArtificial SequencePCR Primer 307caagtcaacc
acagcattca 2030820DNAArtificial SequencePCR Primer 308gggcttatgt
actacaacag 2030921DNAArtificial SequencePCR Primer 309tctgtcttgc
aagtcaacca c 2131021DNAArtificial SequencePCR Primer 310ggaatttttt
gatggtagag a 2131119DNAArtificial SequencePCR Primer 311taaagcacaa
tttcaggcg 1931220DNAArtificial SequencePCR Primer 312tagatctggc
tttctttgac 2031321DNAArtificial SequencePCR Primer 313atatgagtat
ctggagtctg c 2131421DNAArtificial SequencePCR Primer 314cggatccaag
ctaatctttg g 2131524DNAArtificial SequencePCR Primer 315ggtatgtact
cataggtgtt ggtg 2431623DNAArtificial SequencePCR Primer
316aacagaccca attacattgg ctt 2331722DNAArtificial SequencePCR
Primer 317gaggcacttg tatgtggaaa gg 2231823DNAArtificial SequencePCR
Primer 318atgcctaaca gacccaatta cat 2331922DNAArtificial
SequencePCR Primer 319ttcatgtagt cgtaggtgtt gg 2232024DNAArtificial
SequencePCR Primer 320cgcgcctaat acatctcagt ggat
2432123DNAArtificial SequencePCR Primer 321aagccaatgt aattgggtct
gtt 2332222DNAArtificial SequencePCR Primer 322ctactctggc
actgcctaca ac 2232323DNAArtificial SequencePCR Primer 323atgtaattgg
gtctgttagg cat 2332424DNAArtificial SequencePCR Primer
324caatccgttc tggttccgga tgaa 2432524DNAArtificial SequencePCR
Primer 325cttgccggtc gttcaaagag gtag 2432618DNAArtificial
SequencePCR Primer 326agtccgggtc tggtgcag 1832717DNAArtificial
SequencePCR Primer 327cggtcggtgg tcacatc 1732818DNAArtificial
SequencePCR Primer 328atggccaccc catcgatg 1832919DNAArtificial
SequencePCR Primer 329ctgtccggcg atgtgcatg 1933023DNAArtificial
SequencePCR Primer 330ggtcgttatg tgcctttcca cat
2333125DNAArtificial SequencePCR Primer 331tcctttctga agttccactc
atagg 2533222DNAArtificial SequencePCR Primer 332acaacattgg
ctaccagggc tt 2233325DNAArtificial SequencePCR Primer 333cctgcctgct
cataggctgg aagtt 2533422DNAArtificial SequencePCR Primer
334ggattagaga ccctggtagt cc 2233518DNAArtificial SequencePCR Primer
335ggccgtactc cccaggcg 1833622DNAArtificial SequencePCR Primer
336ttcgatgcaa cgcgaagaac ct 2233720DNAArtificial SequencePCR Primer
337acgagctgac gacagccatg 2033825DNAArtificial SequencePCR Primer
338tctgtcccta gtacgagagg accgg 2533918DNAArtificial SequencePCR
Primer 339tgcttagatg ctttcagc 1834024DNAArtificial SequencePCR
Primer 340ctggcaggta tgcgtggtct gatg 2434125DNAArtificial
SequencePCR Primer 341cgcaccgtgg gttgagatga agtac
2534230DNAArtificial SequencePCR Primer 342ggggattcag ccatcaaagc
agctattgac 3034326DNAArtificial SequencePCR Primer 343ccaacctttt
ccacaacaga atcagc 2634430DNAArtificial SequencePCR Primer
344ccttacttcg aactatgaat cttttggaag 3034529DNAArtificial
SequencePCR Primer 345cccatttttt cacgcatgct gaaaatatc
2934623DNAArtificial SequencePCR Primer 346cgcaaaaaaa tccagctatt
agc 2334729DNAArtificial SequencePCR Primer 347aaactatttt
tttagctata ctcgaacac 2934829DNAArtificial SequencePCR Primer
348atgattacaa ttcaagaagg tcgtcacgc 2934927DNAArtificial SequencePCR
Primer 349ttggacctgt aatcagctga atactgg 2735029DNAArtificial
SequencePCR Primer 350gatgactttt tagctaatgg tcaggcagc
2935129DNAArtificial SequencePCR Primer 351aatcgacgac catcttggaa
agatttctc 2935225DNAArtificial SequencePCR Primer 352gcttcaggaa
tcaatgatgg agcag 2535325DNAArtificial SequencePCR Primer
353gggtctacac ctgcacttgc ataac 2535418DNAArtificial SequencePCR
Primer 354gtactgaatc cgcctaag 1835523DNAArtificial SequencePCR
Primer 355gtgaataaag tatcgcccta ata 2335618DNAArtificial
SequencePCR Primer 356gaagttgaac cgggatca 1835721DNAArtificial
SequencePCR Primer 357attatcggtc gttgttaatg t 2135823DNAArtificial
SequencePCR Primer 358ctgtctgtag ataaactagg att
2335918DNAArtificial SequencePCR Primer 359cgttcttctc tggaggat
1836015DNAArtificial SequencePCR Primer 360cgatactacg gacgc
1536122DNAArtificial SequencePCR Primer 361ctttatgaat tactttacat at
2236218DNAArtificial SequencePCR Primer 362ctcctccatc actaggaa
1836322DNAArtificial SequencePCR Primer 363ctataacatt caaagcttat tg
2236423DNAArtificial SequencePCR Primer 364cgcgataata gatagtgcta
aac 2336519DNAArtificial SequencePCR Primer 365gcttccacca ggtcattaa
1936618DNAArtificial SequencePCR Primer 366gtactgaatc cgcctaag
1836723DNAArtificial SequencePCR Primer 367gtgaataaag tatcgcccta
ata 2336818DNAArtificial SequencePCR Primer 368gaagttgaac cgggatca
1836921DNAArtificial SequencePCR Primer 369attatcggtc gttgttaatg t
2137023DNAArtificial SequencePCR Primer 370ctgtctgtag ataaactagg
att 2337118DNAArtificial SequencePCR Primer 371cgttcttctc tggaggat
1837215DNAArtificial SequencePCR Primer 372cgatactacg gacgc
1537322DNAArtificial SequencePCR Primer 373ctttatgaat tactttacat at
2237418DNAArtificial SequencePCR Primer 374ctcctccatc actaggaa
1837522DNAArtificial SequencePCR Primer 375ctataacatt caaagcttat tg
2237623DNAArtificial SequencePCR Primer 376cgcgataata gatagtgcta
aac 2337719DNAArtificial SequencePCR Primer 377gcttccacca ggtcattaa
19
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