U.S. patent application number 11/869449 was filed with the patent office on 2008-12-18 for methods for rapid identification of pathogens in humans and animals.
This patent application is currently assigned to Isis Pharmaceuticals, Inc.. Invention is credited to Stanley T. Crooke, David J. Ecker, Richard H. Griffey, Steven A. Hofstadler, John McNeil, Rangarajan Sampath.
Application Number | 20080311558 11/869449 |
Document ID | / |
Family ID | 32512832 |
Filed Date | 2008-12-18 |
United States Patent
Application |
20080311558 |
Kind Code |
A1 |
Ecker; David J. ; et
al. |
December 18, 2008 |
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) |
Correspondence
Address: |
Casimir Jones, S.C.
440 Science Drive, SUITE 203
Madison
WI
53711
US
|
Assignee: |
Isis Pharmaceuticals, Inc.
Carlsbad
CA
|
Family ID: |
32512832 |
Appl. No.: |
11/869449 |
Filed: |
October 9, 2007 |
Related U.S. Patent Documents
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Application
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Filing Date |
Patent Number |
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10660122 |
Sep 11, 2003 |
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11869449 |
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10323233 |
Dec 18, 2002 |
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10660122 |
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10326051 |
Dec 18, 2002 |
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10323233 |
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10325526 |
Dec 18, 2002 |
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10326051 |
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10325527 |
Dec 18, 2002 |
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10325526 |
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09798007 |
Mar 2, 2001 |
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10325527 |
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60431319 |
Dec 6, 2002 |
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60443443 |
Jan 29, 2003 |
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60443788 |
Jan 30, 2003 |
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60447529 |
Feb 14, 2003 |
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Current U.S.
Class: |
435/5 ; 435/6.11;
435/6.12 |
Current CPC
Class: |
C12Q 1/689 20130101;
Y10T 436/24 20150115; C12Q 1/686 20130101; C12Q 1/686 20130101;
C12Q 2565/627 20130101 |
Class at
Publication: |
435/5 ;
435/6 |
International
Class: |
C12Q 1/70 20060101
C12Q001/70; C12Q 1/68 20060101 C12Q001/68 |
Goverment Interests
STATEMENT OF GOVERNMENT SUPPORT
[0002] This invention was made with United States Government
support under DARPA contract MDA972-00-C-0053. The United States
Government has certain rights in the invention.
Claims
1-22. (canceled)
23. A method of identifying a bioagent comprising the steps of:
selecting at least one pair of oligonucleotide primers, wherein one
member of said pair of primers hybridizes to a first conserved
region of nucleic acid from said bioagent and the other member of
said pair of primers hybridizes to a second conserved region of
nucleic acid from said bioagent, wherein said first and second
conserved regions flank a variable nucleic acid region that when
amplified creates a unique base composition signature among at
least five bioagents; amplifying nucleic acid from at least one
said bioagent with said pair of oligonucleotide primers to produce
an amplification product; determining the molecular mass of said
amplification product by mass spectrometry; calculating the base
composition of said amplification product from said molecular mass;
comparing said base composition of at least one said bioagent to
five or more calculated or measured base compositions of
amplification products of known bioagents produced by using said at
least one pair of oligonucleotide primers, thereby identifying said
bioagent.
24. The method of claim 23, wherein said comparing comprises
comparing said base composition of at least one said bioagent to
nineteen or more calculated or measured base compositions of
amplification products of known bioagents produced by using said at
least one pair of oligonucleotide primers.
25. The method of claim 23, wherein said bioagent is a bacterium,
virus, mold, fungus or parasite.
26. A method of identifying a bioagent comprising the steps of:
selecting at least one pair of oligonucleotide primers, wherein one
member of said pair of primers hybridizes to a first conserved
region of a nucleic acid encoding a protein that participates in
translation, replication, recombination, repair, transcription,
nucleotide metabolism, amino acid metabolism, lipid metabolism,
uptake, secretion, antibiotic resistance, virulence, or
pathogenicity, and the other member of said pair of primers
hybridizes to a second conserved region of said nucleic acid
encoding a protein that participates in translation, replication,
recombination, repair, transcription, nucleotide metabolism, amino
acid metabolism, lipid metabolism, uptake, secretion, antibiotic
resistance, virulence, or pathogenicity, wherein said first and
second conserved regions flank a variable nucleic acid region that
when amplified creates unique base composition signatures among at
least five bioagents; amplifying nucleic acid from at least one
said bioagent with said pair of oligonucleotide primers to produce
an amplification product; determining the molecular mass of said
amplification product by mass spectrometry; calculating the base
composition of said amplification product from said molecular mass;
comparing said base composition of at least one said bioagent to
five or more calculated or measured base compositions of
amplification products of known bioagents produced by using said at
least one pair of oligonucleotide primers, thereby identifying said
bioagent.
27. The method of claim 26, wherein said molecular masses of said
amplification products are determined by ES1-TOF mass
spectrometry.
28. The method of claim 26, wherein said bioagent is a bacterium,
virus, mold, fungus or parasite.
29. The method of claim 26, wherein said comparing comprises
comparing said base composition of at least one said bioagent to
nineteen or more calculated or measured base compositions of
amplification products of known bioagents produced by using said at
least one pair of oligonucleotide primers.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] The present application is a continuation of U.S.
application Ser. No. 10/660,122, filed Sep. 11, 2003, which in
turn: 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. 10/325,526 filed
Dec. 18, 2002; 4) is a continuation in part of U.S. application
Ser. No. 10/325,527 filed Dec. 18, 2002; 5) is a
continuation-in-part of U.S. application Ser. No. 09/798,007 filed
Mar. 2, 2001; 6) claims the benefit of U.S. provisional application
Ser. No. 60/431,319 filed Dec. 6, 2002; 7) claims the benefit of
U.S. provisional application Ser. No. 60/443,443 filed Jan. 29,
2003; 8) claims the benefit of U.S. provisional application Ser.
No. 60/443,788 filed Jan. 30, 2003; and 9) claims the benefit of
U.S. provisional application Ser. No. 60/447,529 filed Feb. 14,
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 described 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). The 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 describes 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 discloses 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 describes methods, PCR primers and kits for use
in analyzing preselected DNA tandem nucleotide repeat alleles by
mass spectrometry.
[0025] WO 98/12355 discloses 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 disclosed 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 discloses 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 describes 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 describes 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 disclosed,
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 described.
[0029] WO 97/37041, WO 99/31278 and U.S. Pat. No. 5,547,835
describe methods of sequencing nucleic acids using mass
spectrometry. U.S. Pat. Nos. 5,622,824, 5,872,003 and 5,691,141
describe 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 (FIG. 1A-1, 1A-2, 1A-3,
1A-4, and 1A-5), 23S rRNA (3'-half, FIG. 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.sub.--1337F)
and Bacillus anthracis (B. anthr. 16S.sub.--1337F), amplified using
the same primers. The two strands differ by only two (AT-->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 56 mer 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.sub.--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.sub.--1337 46 base pair duplex.
[0047] FIG. 11 is an ESI-TOF-MS of a 56 mer 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 the 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
A. Introduction
[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.
B. Bioagents
[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, 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 which 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 (Proc. Natl. Acad. Sci. U.S.A., 1996, 93, 10268;
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.
C. Selection of "Bioagent Identifying Amplicons"
[0067] Since genetic data provide the underlying basis for
identification of bioagents by the methods of the present
invention, it is necessary 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 which 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.
[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 requires selection of a variable
region with appropriate variability to resolve the identity of a
given bioagent.
[0069] 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."
[0070] 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.
[0071] 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.
[0072] 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.
[0073] 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 which target 16S or 23S ribosomal RNA.
[0074] 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.sub.--971-1062, 16S.sub.--1228-1310 and
16S.sub.--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.
[0075] 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 which 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.
[0076] "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.
[0077] "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
[0078] 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.
[0079] 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).
[0080] 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.
D. Characterization of Bioagent Identifying Amplicons
[0081] 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.
[0082] 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.sub.--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.sub.--1100" intelligent primer pair
yields molecular masses of 55009 and 55005 Da for E. coli and
Salmonella typhimurium, respectively. Furthermore, the
"23S.sub.--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.
[0083] 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.
[0084] 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.
[0085] 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.
[0086] 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.
E. Mass Spectrometric Characterization of Bioagent Identifying
Amplicons
[0087] Mass spectrometry (MS)-based detection of PCR products
provides a means for determination of BCS which 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.).
[0088] 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.
[0089] 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.
[0090] 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.
[0091] 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.
[0092] 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 84 mer 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).
[0093] 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).
[0094] 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.
[0095] If there are two or more targets of similar molecular mass,
or if a single amplification reaction results in a product which
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-(trifluoromethyl)deoxythymidine 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.
[0096] 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 Total Base Base base base Double
Single mass info info comp. comp. strand 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
[0097] 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.
[0098] In another example, assume the measured molecular masses of
each strand are 30,000.115Da 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.
[0099] 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.
[0100] 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 which 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.
F. Base Composition Signatures as Indices of Bioagent Identifying
Amplicons
[0101] 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.
[0102] 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.
[0103] 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.
[0104] 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 which
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.
G. Fields of Application of the Present Invention
[0105] 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.
[0106] 1. Identification of Pathogens in Humans and Animals
[0107] 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.
[0108] 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.
[0109] 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.
[0110] 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.
[0111] 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.
[0112] 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.
[0113] 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.
[0114] 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, B., 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.
[0115] 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.
[0116] 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.
[0117] 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.
[0118] 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.
[0119] 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/."
[0120] 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 0 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.
[0121] The method of the present invention can also be used for
detection and identification of blood-borne pathogens such as
Staphylococcus aureus for example.
[0122] 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, New
York), 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, B., Facklam, R.
& Thompson, T. (1996) J. Clin. Micro. 34, 953-958; Beall, B.,
et al. (1997) J. Clin. Micro. 35, 1231-1235; Facklam, R., et al.
(1999) Emerging Infectious Diseases 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, R., et al. (2002) Clinical Infectious Diseases 34,
28-38). Recently, a strategy known as Multi Locus Sequence Typing
(MLST) was developed to follow the molecular Epidemiology of GAS
(13). In MLST, internal fragments of seven housekeeping genes are
amplified, sequenced, and compared to a database of previously
studied isolates (www.test.mlst.net/).
[0123] 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.
[0124] 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.
[0125] 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.
[0126] 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, A. S., 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, S, and R. Higuchi, Nature 1989, 339,
237-238).
[0127] 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, R. H. 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.
[0128] 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.sub.--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 capricolumn
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)
[0129] 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.
[0130] 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).
[0131] 2. Identification of Bioagents of Biological Warfare
[0132] 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, L. H. 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.
[0133] 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.
[0134] 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.
[0135] 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).
[0136] Fungal biowarfare agents include, but are not limited to,
Coccidioides immitis (Coccidioidomycosis), and Magnaporthe
grisea.
[0137] 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.
[0138] Parasites that could be used in biological warfare include,
but are not limited to: Ascaris suum, Giardia lamblia,
Cryptosporidium, and Schistosoma.
[0139] 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.
[0140] 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).
[0141] 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.
[0142] 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 which flank the variable regions.
[0143] 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.
[0144] 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
[0145] 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.
[0146] 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).
[0147] Swab Sample Protocol: 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
[0148] 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
[0149] FTICR Instrumentation: 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.
[0150] Modified ESI Source: In sample-limited analyses, analyte
solutions are delivered at 150 mL/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.
[0151] Apparatus for Infrared Multiphoton Dissociation: 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
[0152] 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.
[0153] 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.sub.--1337 or 23S.sub.--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.
[0154] FIG. 6 shows the use of ESI-FT-ICR MS for measurement of
exact mass. The spectra from 46 mer 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
[0155] 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
[0156] 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.9) 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
[0157] 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.sub.--971, 16S.sub.--1228 or 16S.sub.--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.
[0158] 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
[0159] Table 5 shows the expected molecular weight and base
composition of region 16S.sub.--1100-1188 in Mycobacterium avium
and Streptomyces sp.
TABLE-US-00006 TABLE 5 Organism Molecular Region name Length weight
Base comp. 16S_1100-1188 Mycobac- 82 25624.1728
A.sub.16G.sub.32C.sub.18T.sub.16 terium avium 16S_1100-1188
Strepto- 96 29904.871 A.sub.17G.sub.38C.sub.27T.sub.14 myces
sp.
[0160] Table 6 shows base composition (single strand) results for
16S.sub.--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.sub.--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 A.sub.20G.sub.33C.sub.21T.sub.16
A.sub.20G.sub.33C.sub.21T.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 A.sub.23G.sub.26C.sub.24T.sub.15
A.sub.23G.sub.26C.sub.24T.sub.15 Clostridium perfringens
A.sub.23G.sub.27C.sub.19T.sub.19 A.sub.23G.sub.27C.sub.20T.sub.18
A.sub.23G.sub.27C.sub.20T.sub.18 A.sub.23G.sub.27C.sub.20T.sub.18
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 A.sub.23G.sub.29C.sub.22T.sub.15
A.sub.23G.sub.29C.sub.22T.sub.15 Vibrio cholerae
A.sub.23G.sub.30C.sub.21T.sub.16 A.sub.23G.sub.31C.sub.21T.sub.15
A.sub.23G.sub.31C.sub.21T.sub.15 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 A.sub.24G.sub.26C.sub.20T.sub.18
A.sub.24G.sub.26C.sub.20T.sub.18 A.sub.24G.sub.26C.sub.20T.sub.18
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 A.sub.24G.sub.28C.sub.21T.sub.16
A.sub.24G.sub.28C.sub.21T.sub.16 A.sub.24G.sub.28C.sub.21T.sub.16
Pseudomonas putida A.sub.24G.sub.29C.sub.21T.sub.16
A.sub.24G.sub.30C.sub.21T.sub.15 A.sub.24G.sub.30C.sub.21T.sub.15
A.sub.24G.sub.30C.sub.21T.sub.15 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.18T.sub.20
[0161] 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.sub.--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.21G.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 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.22G.sub.26C.sub.20T.sub.23
A.sub.24G.sub.23C.sub.19T.sub.16 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
A.sub.30G.sub.18C.sub.15T.sub.19 A.sub.24G.sub.19C.sub.12T.sub.18
Mycoplasma pneumoniae A.sub.28G.sub.23C.sub.15T.sub.22
A.sub.27G.sub.19C.sub.16T.sub.20 A.sub.23G.sub.20C.sub.14T.sub.16
Escherichia coli A.sub.22G.sub.28C.sub.20T.sub.22
A.sub.24G.sub.25C.sub.21T.sub.13 A.sub.23G.sub.29C.sub.22T.sub.15
Shigella dysenteriae A.sub.22G.sub.28C.sub.21T.sub.21
A.sub.24G.sub.25C.sub.21T.sub.13 A.sub.23G.sub.29C.sub.22T.sub.15
Proteus vulgaris A.sub.23G.sub.26C.sub.22T.sub.21
A.sub.26G.sub.24C.sub.19T.sub.14 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 A.sub.20G.sub.25C.sub.21T.sub.23
A.sub.23G.sub.26C.sub.17T.sub.17 A.sub.24G.sub.26C.sub.19T.sub.19
Rickettsia prowazekii A.sub.21G.sub.26C.sub.24T.sub.25
A.sub.24G.sub.23C.sub.16T.sub.19 A.sub.26G.sub.28C.sub.18T.sub.18
Rickettsia rickettsii A.sub.21G.sub.26C.sub.25T.sub.24
A.sub.24G.sub.24C.sub.17T.sub.17
A.sub.26G.sub.28C.sub.20T.sub.16
[0162] The sequence of B. anthracis and B. cereus in region
16S.sub.--971 is shown below. Shown in bold is the single base
difference between the two species which 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)
GCGAAGAACCUUACCAGGUNUUGACAUCCUCUGACAACCCUAGAGAUAGG
GCUUCUCCUUCGGGAGCAGAGUGACAGGUGGUGCAUGGUU B.cereus_16S_971 (SEQ ID
NO: 2) GCGAAGAACCUUACCAGGUCUUGACAUCCUCUGAAAACCCUAGAGAUAGG
GCUUCUCCUUCGGGAGCAGAGUGACAGGUGGUGCAUGGUU
Example 6
ESI-TOF MS of sspE 56-mer Plus Calibrant
[0163] 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.sub.--1228 Duplex
[0164] 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.sub.--1337 46 Base Pair
Duplex
[0165] 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
[0166] 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
[0167] 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
[0168] 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
[0169] 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
[0170] 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
[0171] 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.
[0172] 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 cited
in the present application is incorporated herein by reference in
its entirety.
Example 15
Demonstration of Detection and Identification of Five Species of
Bacteria in a Mixture
[0173] 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 Forward Primer SEQ ID Reverse
Primer SEQ ID Pair Name Sequence NO: Sequence NO: 16S_EC_107
GTGAGATGTTGGGTTAAGTCCC 8 GACGTCATCCCCACCTTCCTC 9 7_1195 GTAACGAG
16S_EC_108 ATGTTGGGTTAAGTCCCGCAAC 10 TTGACGTCATCCCCACCTTCCT 11
2_1197 GAG C 16S_EC_109 TTAAGTCCCGCAACGATCGCAA 12
TGACGTCATCCCCACCTTCCTC 13 0_1196 16S_EC_122 GCTACACACGTGCTACAATG 14
CGAGTTGCAGACTGCGATCCG 15 2_1323 16S_EC_133 AAGTCGGAATCGCTAGTAATCG
16 GACGGGCGGTGTGTACAAG 17 2_1407 16S_EC_30.sub.--
TGAACGCTGGTGGCATGCTTAA 18 TACGCATTACTCACCCGTCCGC 19 126 CAC
16S_EC_38.sub.-- GTGGCATGCCTAATACATGCAA 20 TTACTCACCCGTCCGCCGCT 21
120 GTCG 16S_EC_49.sub.-- TAACACATGCAAGTCGAACG 22 TTACTCACCCGTCCGCC
23 120 16S_EC_683 GTGTAGCGGTGAAATGCG 24 GTATCTAATCCTGTTTGCTCCC 25
795 16S_EC_713 AGAACACCGATGGCGAAGGC 26 CGTGGACTACCAGGGTATCTA 27 809
16S_EC_785 GGATTAGAGACCCTGGTAGTCC 28 GGCCGTACTCCCCAGGCG 29 897
16S_EC_785 GGATTAGATACCCTGGTAGTCC 30 GGCCGTACTCCCCAGGCG 31 _897_2
ACGC 16S_EC_789 TAGATACCCTGGTAGTCCACGC 32 CGTACTCCCCAGGCG 33 894
16S_EC_960 TTCGATGCAACGCGAAGAACCT 34 ACGAGCTGACGACAGCCATG 35 1073
16S_EC_969 ACGCGAAGAACCTTACC 36 ACGACACGAGCTGACGAC 37 1078
23S_EC_182 CTGACACCTGCCCGGTGC 38 GACCGTTATAGTTACGGCC 39 6_1924
23S_EC_264 TCTGTCCCTAGTACGAGAGGAC 40 TGCTTAGATGCTTTCAGC 41 5_2761
CGG 23S_EC_264 CTGTCCCTAGTACGAGACGACC 42 GTTTCATGCTTAGATGCTTTCA 43
5_2767 GG GC 23S_EC_493 GGGGAGTGAAAGAGATCCTGAA 44
ACAAAACGTACGCCGTCACCC 45 _571 ACCG 23S_EC_493
GGGGAGTGAAAGAGATCCTGAA 46 ACAAAAGGCACGCCATCACCC 47 _571_2 ACCG
23S_EC_971 CGAGAGGGAAACAACCCAGACC 48 TGGCTGCTTCTAAGCCAAC 49 1077
INFB_EC_13 TGCTCGTGGTGCACAAGTAACG 50 TGCTCCTTTCGCATGGTTAATT 51
65_1467 GATATTA GCTTCAA RPOC_EC_10 CAAAACTTATTAGGTAAGCGTG 52
TCAAGCGCCATTTCTTTTGGTA 53 18_1124 TTGACT AACCACAT RPOC_EC_10
CAAAACTTATTAGGTAAGCGTG 54 TCAAGCGCCATCTCTTTCGGTA 55 18_1124_2
TTGACT ATCCACAT RPOC_EC_11 TAAGAAGCCGGAAACCATCAAC 56
GGCGCTTGTACTTACCGCAC 57 4_232 TACCG RPOC_EC_21 TGATTCTCGTGCCCGTGGT
58 TTGGCCATCAGGCCACGCATAC 59 78_2246 RPOC_EC_21 TGATTCCGGTGCCCGTGGT
60 TTGGCCATCAGACCACGCATAC 61 78_2246_2 RPOC_EC_22
CTGGCAGGTATGCGTGGTCTGA 62 CGCACCGTGGGTTGAGATGAAG 63 18_2337 TG TAC
RPOC_EC_22 CTTGCTGGTATGCGTGGTCTGA 64 CGCACCATGCGTAGAGATGAAG 65
18_2337_2 TG TAC RPOC_EC_80 CGTCGGCTGATTAACCGTAACA 66
GTTTTTCGTTGCGTACGATGAT 67 8_889 ACCG GTC RPOC_EC_80
CGTCGTGTAATTAACCGTAACA 68 ACGTTTTTCGTTTTGAACGATA 69 8_891 ACCG
ATGCT RPOC_EC_99 CAAAGGTAAGCAAGGTCGTTTC 70 CGAACGGCCTGAGTAGTCAACA
71 3_1059 CGTCA CG RPOC_EC_99 CAAAGGTAAGCAAGGACGTTTC 72
CGAACGGCCAGAGTAGTCAACA 73 3_1059_2 CGTCA CG TUFB_EC_23
TAGACTGCCCAGGACACGCTG 74 GCCGTCCATCTGAGCAGCACC 75 9_303 TUFB_EC_23
TTGACTGCCCAGGTCACGCTG 76 GCCGTCCATTTGAGCAGCACC 77 9_303_2
TUFB_EC_97 AACTACCGTCCGCAGTTCTACT 78 GTTGTCGCCACGCATAACCATT 79
6_1068 TCC TC TUFB_EC_97 AACTACCGTCCTCACTTCTACT 80
GTTGTCACCAGGCATTACCATT 81 6_1068_2 TCC TC TUFB_EC_98
CCACAGTTCTACTTCCGTACTA 82 TCCAGGCATTACCATTTCTACT 83 5_1062 CTGACG
CCTTCTCG RPLB_EC_65 GACCTACAGTAAGAGGTTCTGT 84
TCCAAGTGCTGGTTTACCCCAT 85 0_762 AATGAACC GG RPLB_EC_68
CATCCACACGGTGGTGGTGAAG 86 GTGCTGCTTTACCCCATGGAGT 87 8_757 G
RPOC_EC_10 CGTGTTGACTATTCGGGGCGTT 88 ATTCAAGAGCCATTTCTTTTGG 89
36_1126 CAG TAAACCAC RPOB_EC_37 TCAACAACCTCTTGGAGGTAAA 90
TTTCTTGAAGAGTATGAGCTGC 91 62_3865 GCTCAGT TCCGTAAG RBLB_EC_68
CATCCACACGGTGGTGGTGAAG 92 TGTTTTGTATCCAAGTGCTGGT 93 8_771 G TTACCCC
VALS_EC_11 CGTGGCGGCGTGGTTATCGA 94 CGGTACGAACTGGATGTCGCCG 95
05_1218 TT RPOB_EC_18 TATCGCTCAGGCGAACTCCAAC 96
GCTGGATTCGCCTTTGCTACG 97 45_1929 RPLB_EC_66 TGTAATGAACCCTAATGACCAT
98 CCAAGTGCTGGTTTACCCCATG 99 9_761 CCACACGG GAGTA RPLB_EC_67
TAATGAACCCTAATGACCATCC 100 TCCAAGTGCTGGTTTACCCCAT 101 1_762
ACACGGTG GGAG RPOB_EC_37 CTTGGAGGTAAGTCTCATTTTG 102
CGTATAAGCTGCACCATAAGCT 103 75_3858 GTGGGCA TGTAATGC VALS_EC_18
CGACGCGCTGCGCTTCAC 104 GCGTTCCACAGCTTGTTGCAGA 105 33_1943 AG
RPOB_EC_13 GACCACCTCGGCAACCGT 106 TTCGCTCTCGGCCTGGCC 107 36_1455
TUFB_EC_22 GCACTATGCACACGTAGATTGT 108 TATAGCACCATCCATCTGAGCG 109
5_309 CCTGG GCAC DNAK_EC_42 CGGCGTACTTCAACGACAGCCA 110
CGCGGTCGGCTCGTTGATGA 111 8_522 VALS_EC_19 CTTCTGCAACAAGCTGTGGAAC
112 TCGCAGTTCATCAGCACGAAGC 113 20_1970 GC G TUFB_EC_75
AAGACGACCTGCACGGGC 114 GCGCTCCACGTCTTCACGC 115 7_867 23S_EC_264
CTGTTCTTAGTACGAGAGGACC 116 TTCGTGCTTAGATGCTTTCAG 117 6_2765
16S_EC_969 ACGCGAAGAACCTTACpC 118 ACGACACGAGCpTpGACGAC 119 _1078_3P
16S_EC_972 CGAAGAACpCpTTACC 120 ACACGAGCpTpGAC 121 _1075_4P
16S_EC_972 CGAAGAACCTTACC 122 ACACGAGCTGAC 123 _1075 23S_EC_-
CCTGATAAGGGTGAGGTCG 124 ACGTCCTTCATCGCCTCTGA 125 347_59 23S_EC_-
GTTGTGAGGTTAAGCGACTAAG 126 CTATCGGTCAGTCAGGAGTAT 127 7_450 23S_EC_-
GTTGTGAGGTTAAGCGACTAAG 128 TTGCATCGGGTTGGTAAGTC 129 7_910
23S_EC_430 ATACTCCTGACTGACCGATAG 130 AACATAGCCTTCTCCGTCC 131 _1442
23S_EC_891 GACTTACCAACCCGATGCAA 132 TACCTTAGGACCGTTATAGTTA 133
_1931 CG 23S_EC_142 GGACGGAGAAGGCTATGTT 134 CCAAACACCGCCGTCGATAT
135 4_2494 23S_EC_190 CGTAACTATAACGGTCCTAAGG 136
GCTTACACACCCGGCCTATC 137 8_2852 TA 23S_EC_247 ATATCGACGGCGGTGTTTGG
138 GCGTGACAGGCAGGTATTC 139 5_3209 16S_EC_- AGTCTCAAGAGTGAACACGTAA
140 GCTGCTGGCACGGAGTTA 141 60_525 16S_EC_326 GACACGGTCCAGACTCCTAC
142 CCATGCAGCACCTGTCTC 143 _1058 16S_EC_705 GATCTGGAGGAATACCGGTG
144 ACGGTTACCTTGTTACGACT 145 _1512 16S_EC_126 GAGAGCAAGCGGACCTCATA
146 CCTCCTGCGTGCAAAGC 147 8_1775 GROL_EC_94 TGGAAGATCTGGGTCAGGC 148
CAATCTGCTGACGGATCTGAGC 149 1_1060 INFB_EC_11 GTCGTGAAAACGAGCTGGAAGA
150 CATGATGGTCACAACCGG 151 03_1191 HFLB_EC_10
TGGCGAACCTGGTGAACGAAGC 152 CTTTCGCTTTCTCGAACTCAAC 153 82_1168 CAT
INFB_EC_19 CGTCAGGGTAAATTCCGTGAAG 154 AACTTCGCCTTCGGTCATGTT 155
69_2058 TTAA GROL_EC_21 GGTGAAAGAAGTTGCCTCTAAA 156
TTCAGGTCCATCGGGTTCATGC 157 9_350 GC C VALS_EC_11
CGTGGCGGCGTGGTTATCGA 158 ACGAACTGGATGTCGCCGTT 159 05_1214
16S_EC_556 CGGAATTACTGGGCGTAAAG 160 CGCATTTCACCGCTACAC 161 _700
RPOC_EC_12 ACCCAGTGCTGCTGAACCGTGC 162 GTTCAAATGCCTGGATACCCA 163
56_1315 16S_EC_774 GGGAGCAAACAGGATTAGATAC 164 CGTACTCCCCAGGCG 165
_894 RPOC_EC_15 TGGCCCGAAAGAAGCTGAGCG 166 ACGCGGGCATGCAGAGATGCC 167
84_1643 16S_EC_108 ATGTTGGGTTAAGTCCCGC 168 TGACGTCATCCCCACCTTCC 169
2_1196
16S_EC_138 CTTGTACACACCGCCCGTC 170 AAGGAGGTGATCCAGCC 171 9_1541
16S_EC_130 CGGATTGGAGTCTGCAACTCG 172 GACGGGCGGTGTGTACAAG 173 3_1407
23S_EC_23.sub.-- GGTGGATGCCTTGGC 174 GGGTTTCCCCATTCGG 175 130
23S_EC_187 GGGAACTGAAACATCTAAGTA 176 TTCGCTCGCCGCTAC 177 _256
23S_EC_160 TACCCCAAACCGACACAGG 178 CCTTCTCCCGAAGTTACG 179 2_1703
23S_EC_168 CCGTAACTTCGGGAGAAGG 180 CACCGGGCAGGCGTC 181 5_1842
23S_EC_182 GACGCCTGCCCGGTGC 182 CCGACAAGGAATTTCGCTACC 183 7_1949
23S_EC_243 AAGGTACTCCGGGGATAACAGG 184 AGCCGACATCGAGGTGCCAAAC 185
4_2511 C 23S_EC_259 GACAGTTCGGTCCCTATC 186 CCGGTCCTCTCGTACTA 187
9_2669 23S_EC_265 TAGTACGAGAGGACCGG 188 TTAGATGCTTTCAGCACTTATC 189
3_2758 23S_BS_- AAACTAGATAACAGTAGACATC 190 GTGCGCCCTTTCTAACTT 191
68_21 AC 16S_EC_8_3 AGAGTTTGATCATGGCTCAG 192 ACTGCTGCCTCCCGTAG 193
58 16S_EC_314 CACTGGAACTGAGACACGG 194 CTTTACGCCCAGTAATTCCG 195 _575
16S_EC_518 CCAGCAGCCGCGGTAATAC 196 GTATCTAATCCTGTTTGCTCCC 197 _795
16S_EC_683 GTGTAGCGGTGAAATGCG 198 GGTAAGGTTCTTCGCGTTG 199 _985
16S_EC_937 AAGCGGTGGAGCATGTGG 200 ATTGTAGCACGTGTGTAGCCC 201 _1240
16S_EC_119 CAAGTCATCATGGCCCTTA 202 AAGGAGGTGATCCAGCC 203 5_1541
16S_EC_8_1 ACAGTTTGATCATGGCTCAG 204 AAGGAGGTGATCCAGCC 205 541
23S_EC_183 ACCTGCCCAGTGCTGGAAG 206 TCGCTACCTTAGGACCGT 207 1_1936
16S_EC_138 GCCTTGTACACACCTCCCGTC 208 CACGGCTACCTTGTTACGAC 209
7_1513 16S_EC_139 TTGTACACACCGCCCGTCATAC 210 CCTTGTTACGACTTCACCCC
211 0_1505 16S_EC_136 TACGGTGAATACGTTCCCGGG 212
ACCTTGTTACGACTTCACCCCA 213 7_1506 16S_EC_804 ACCACGCCGTAAACGATGA
214 CCCCCGTCAATTCCTTTGAGT 215 _929 16S_EC_791
GATACCCTGGTAGTCCACACCG 216 GCCTTGCGACCGTACTCCC 217 _904 16S_EC_789
TAGATACCCTGGTAGTCCACGC 218 GCGACCGTACTCCCCAGG 219 _899 16S_EC_109
TAGTCCCGCAACGAGCGC 220 GACGTCATCCCCACCTTCCTCC 221 2_1195 23S_EC_258
TAGAACGTCGCGAGACAGTTCG 222 AGTCCATCCCGGTCCTCTCG 223 6_2677
HEXAMER_EC GAGGAAAGTCCGGGCTC 224 ATAAGCCGGGTTCTGTCG 225 61_362
RNASEP_BS.sub.-- GAGGAAAGTCCATGCTCGC 226 GTAAGCCATGTTTTGTTCCATC 227
43_384 RNASEP_EC.sub.-- GAGGAAAGTCCGGGCTC 228 ATAAGCCGGGTTCTGTCG
229 61_362 YAED_TRNA.sub.-- GCGGGATCCTCTAGAGGTGTTA 230
GCGGGATCCTCTAGAAGACCTC 231 ALA- AATAGCCTGGCAG CTGCGTGCAAAGC
RRNH_EC_51 3_49 RNASEP_SA.sub.-- GAGGAAAGTCCATGCTCAC 232
ATAAGCCATGTTCTGTTCCATC 233 31_379 16S_EC_108 ATGTTGGGTTAAGTCCCGC
234 AAGGAGGTGATCCAGCC 235 2_1541 16S_EC_556 CGGAATTACTGGGCGTAAAG
236 GTATCTAATCCTGTTTGCTCCC 237 _795 16S_EC_108 ATGTTGGGTTAAGTCCCGC
238 TGACGTCATGCCCACCTTCC 239 2_1196_10G 16S_EC_108
ATGTTGGGTTAAGTCCCGC 240 TGACGTCATGGCCACCTTCC 241 2_1196_10G _11G
TRNA_ILERR GCGGGATCCTCTAGACCTGATA 242 GCGGGATCCTCTAGAGCGTGAC 243
NH_ASPRRNH AGGGTGAGGTCG AGGCAGGTATTC _EC_32_41 16S_EC_969
ACGCGAAGAACCTTACC 244 GACGGGCGGTGTGTACAAG 245 _1407 16S_EC_683
GTGTAGCGGTGAAATGCG 246 CGAGTTGCAGACTGCGATCCG 247 _1323
16S_EC_49.sub.-- TAACACATGCAAGTCGAACG 248 CGTACTCCCCAGGCG 249 894
16S_EC_49.sub.-- TAACACATGCAAGTCGAACG 250 ACGACACGAGCTGACGAC 251
1078 CYA_BA_134 ACAACGAAGTACAATACAAGAC 252 CTTCTACATTTTTAGCCATCAC
253 9_1447 16S_EC_109 TTAAGTCCCGCAACGAGCGCAA 254
TGACGTCATCCCCACCTTCCTC 255 0_1196_2 16S_EC_405
TGAGTGATGAAGGCCTTACGGT 256 CGGCTGCTGGCACGAAGTTAG 257 _527 TGTAAA
GROL_EC_49 ATGGACAAGGTTGGCAAGGAAG 258 TAGCCGCGGTCGAATTGCAT 259
6_596 G GROL_EC_51 AAGGAAGGCGTGATCACCGTTG 260
CCGCGGTCGAATTGCATGCCTT 261 1_593 AAGA C VALS_EC_18 ACGCGCTGCGCTTCAC
262 TTGCAGAAGTTGCGGTAGCC 263 35_1928 RPOB_EC_13 TCGACCACCTGGGCAACC
264 ATCAGGTCGTGCGGCATCA 265 34_1478 DNAK_EC_42 CACGGTGCCGGCGTACT
266 GCGGTCGGCTCGTTGATGAT 267 0_521 RPOB_EC_37
TTGGAGGTAAGTCTCATTTTGG 268 AAGCTGCACCATAAGCTTGTAA 269 76_3853 TGG
TGC RPOB_EC_38 CAGCGTTTCGGCGAAATGGA 270 CGACTTGACGGTTAACATTTCC 271
02_3885 TG RPOB_EC_37 GGGCAGCGTTTCGGCGAAATGG 272
GTCCGACTTGACGGTCAACATT 273 99_3888 A TCCTG RPOC_EC_21
CAGGAGTCGTTCAACTCGATCT 274 ACGCCATCAGGCCACGCAT 275 46_2245 ACATGAT
ASPS_EC_40 GCACAACCTGCGGCTGCG 276 ACGGCACGAGGTAGTCGC 277 5_538
RPOC_EC_13 CGCCGACTTCGACGGTGACC 278 GAGCATCAGCGTGCGTGCT 279 74_1455
TUFB_EC_95 CCACACGCCGTTCTTCAACAAC 280 GGCATCACCATTTCCTTGTCCT 281
7_1058 T TCG 16S_EC_7_1 GAGAGTTTGATCCTGGCTCAGA 282
TGTTACTCACCCGTCTGCCACT 283 22 ACGAA VALS_EC_61 ACCGAGCAAGGAGACCAGC
284 TATAACGCACATCGTCAGGGTG 285 0_727 A
[0174] 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.
[0175] 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.
[0176] 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
[0177] 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
[0178] The virus detection capability of the present invention was
demonstrated in collaboration with Naval health officers using
adenoviruses as an example.
[0179] 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 Primer Pair Forward Primer SEQ ID
Reverse Primer SEQ ID Name Sequence NO: Sequence NO: HEX_HAD7 + 4 +
2 AGACCCAATTACATTGGCTT 286 CCACTGCTGTTGTAGTACAT 287 1_934_995
HEX_HAD7 + 4 + 2 ATGTACTACAACAGTACTGG 288 CAAGTCAACCACAGCATTCA 289
1_976_1050 HEX_HAD7 + 4 + 2 GGGCTTATGTACTACAACAG 290
TCTGTCTTGCAAGTCAACCAC 291 1_970_1059 HEX_HAD7 + 3_7
GGAATTTTTTGATGGTAGAGA 292 TAAAGCACAATTTCAGGCG 293 71_827 HEX_HAD4 +
16.sub.-- TAGATCTGGCTTTCTTTGAC 294 ATATGAGTATCTGGAGTCTGC 295
746_848 HEX_HAD7_509 GGAAAGACATTACTGCAGACA 296 CCAACTTGAGGCTCTGGCTG
297 _578 HEX_HAD4_121 ACAGACACTTACCAGGGTG 298 ACTGTGGTGTCATCTTTGTC
299 6_1289 HEX_HAD21_51 TCACTAAAGACAAAGGTCTTCC 300
GGCTTCGCCGTCTGTAATTTC 301 5_567 HEX_HAD_1342 CGGATCCAAGCTAATCTTTGG
302 GGTATGTACTCATAGGTGTTG 303 _1469 GTG HEX_HAD7 + 4 + 2
AGACpCpCAATTpACpATpTGG 304 CpCpAGTGCTGTpTpGTAGTA 305 1_934_995P CTT
CAT HEX_HAD7 + 4 + 2 ATpGTpACTpACAACAGTACpT 306
CAAGTpCpAACCACAGCATpT 307 1_976_1050P pGG pCA HEX_HAD7 + 4 + 2
GGGCpTpTATpGTpACTACAAC 308 TCTGTpCpTTGCAAGTpCpAA 309 1_970_1059P
pAG CCAC HEX_HAD7 + 3_7 GGAATTpTpTpTpTGATGGTAG 310
TAAAGCACAATpTpTpCpAGG 311 71_827P AGA CG HEX_HAD4 + 16.sub.--
TAGATCTGGCTpTpTpCpTTTG 312 ATATGAGTATpCpTpGGAGTp 313 746_848P AC
CpTGC HEX_HAD_1342 CGGATpCCAAGCpTAATCpTpT 314 GGTATGTACTCATAGGTCTpT
315 _1469P TGG pGGTG HEX_HAD7 + 21 + AACAGACCCAATTACATTGGCT 316
GAGGCACTTGTATGTGGAAAG 317 3_931_1645 T G HEX_HAD4 + 2_9
ATGCCTAACAGACCCAATTACA 318 TTCATGTAGTCGTAGGTGTTG 319 25_1469 T G
HEX_HAD7 + 21 + CGCGCCTAATACATCTCAGTGG 320 AAGCCAATGTAATTGCGTCTG
321 3_384_953 AT TT HEX_HAD4 + 2_3 CTACTCTGGCACTGCCTACAAC 322
ATGTAATTGGGTCTGTTAGGC 323 45_947 AT HEX_HAD2_772
CAATCCGTTCTGGTTCCGGATG 324 CTTGCCGGTCGTTCAAAGAGG 325 _865 AA TAG
HEX_HAD7 + 4 + 2 AGTCCGGGTCTGGTGCAG 326 CGGTCGGTGGTCACATC 327
1_73_179 HEX_HAD7 + 4 + 2 ATGGCCACCCCATCGATG 328
CTGTCCGGCGATGTGCATG 329 1_1_54 HEX_HAD7 + 4 + 2
GGTCGTTATGTGCCTTTCCACA 330 TCCTTTCTGAAGTTCCACTCA 331 1_1612_1718 T
TAGG HEX_HAD7 + 4 + 2 ACAACATTGGCTACCAGGGCTT 332
CCTGCCTGCTCATAGGCTGGA 333 1_2276_2368 AGTT
[0180] 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.
[0181] 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
[0182] Genome preparation: 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.
[0183] 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 at
95.degree. C., 15 s at 58.degree. C., and 15 s at 72.degree. C.
[0184] Broad-range primers: 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 23 S) and the gene encoding ribosomal protein L3 (rpoC) are
shown in Table 10.
TABLE-US-00012 TABLE 10 Broad Range Primer Pairs SEQ Length Target
Direc- ID of Gene tion Primer 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
[0185] Emm-typing primers: 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, M. C., 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 Length SEQ of Target Direc- ID Ampli- Gene tion Primer NO
con gki F GGGGATTCAGCCATCAAAGCAGCTATTGA 342 116 C gki R
CCAACCTTTTCCACAACAGAATCAGC 343 116 gtr F
CCTTACTTCGAACTATGAATCTTTTGGAA 344 115 G 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
[0186] Microbiology: 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.
[0187] Sequencing: 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, B., et al. J. Clin.
Micro., 1996, 34, 953-958; Facklam, R., 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, M. C., et al.,
Infection and Immunity 2001, 69, 2416-2427). The emm-type was
determined from comparison to the MLST database.
[0188] Broad Range Survey/Drill-Down Process (100): 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.
[0189] 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).
[0190] Broad Range Survey Priming: 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).
[0191] 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).
[0192] Drill Down Priming (Emm-Typing): 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, muri,
mutS, recP, xpt, yqiL) are amplified, sequenced and compared to a
database of previously studied isolates whose emm-types have been
determined (Horner, M. J., 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, M. C., 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.
[0193] 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.
[0194] 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
[0195] 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 FIG. 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 which 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.
[0196] 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.
[0197] 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
[0198] 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: BstNI,
BsmFI, Bfal, and NcoI. FIG. 26(a) illustrates the complexity of the
resulting ESI-FTICR mass spectrum which 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 NcoI 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.
[0199] 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.
[0200] 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
[0201] 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 Primer
Pair Forward Primer SEQ ID Reverse Primer SEQ ID Name Sequence NO:
Sequence NO: A25L_NC00161 GTACTGAATCCGCCTAAG 354
GTGAATAAAGTATCGCCCTAA 355 1_28_127 TA A18R_NC00161
GAAGTTGAACCGGGATCA 356 ATTATCGGTCGTTGTTAATGT 357 1_100_207
A18R_NC00161 CTGTCTGTAGATAAACTAGGAT 358 CGTTCTTCTCTGGAGGAT 359
1_1348_1445 T E9L_NC001611 CGATACTACGGACGC 360
CTTTATGAATTACTTTACATA 361 _1119_1222 T K8R_NC001611
CTCCTCCATCACTAGGAA 362 CTATAACATTGAAAGCTTATT 363 _221_311 G
A24R_NC00161 CGCGATAATAGATAGTGCTAAA 364 GCTTCCACCAGGTCATTAA 365
1_795_878 C A25L_NC00161 GTACpTpGAATpCpCpGCpCpT 366
GTGAATAAAGTATpCpGCpCp 367 1_28_127P AAG CpTpAATA A18R_NC00161
GAAGTpTpGAACpCpGGGATCA 368 ATTATCGGTpCpGTpTpGTpT 369 1_100_207P
pAATGT A18R_NC00161 CTGTpCpTpGTAGATAAACpTp 370
CGTTCpTpTpCpTpCpTpGGA 371 1_1348_1445P AGGATT GGAT E9L_NC001611
CGATACpTpACpGGACGC 372 CTTTATGAATpTpACpTpTpT 373 _1119_1222P
pACATAT K8R_NC001611 CTpCpCpTCpCpATCACpTpAG 374
CTATAACATpTpCpAAAGCpT 375 _221_311P GAA pTpATTG A24R_NC00161
CGCGATpAATpAGATAGTpGCp 376 GCTTCpCpACpCAGGTpCATp 377 1_795_8782
TpAAAC TAA
[0202] 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.
[0203] 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
[0204] 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.
[0205] 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.
[0206] 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 cited
in the present application is incorporated herein by reference in
its entirety
Sequence CWU 1
1
382190RNABacillus anthracismisc_feature(20)..(20)N = A, U, G or C
1gcgaagaacc uuaccaggun uugacauccu cugacaaccc uagagauagg gcuucuccuu
60cgggagcaga gugacaggug gugcaugguu 90290RNABacillus cereus
2gcgaagaacc uuaccagguc uugacauccu cugaaaaccc uagagauagg gcuucuccuu
60cgggagcaga gugacaggug gugcaugguu 9031542RNAArtificial
Sequencemisc_feature16S rRNA consensus sequence 3nnnnnnnaga
guuugaucnu ggcucagnnn gaacgcuggc ggnnngcnun anacaugcaa 60gucgancgnn
nnnnnnnnnn nnnnnnnnnn nnnnnnnnnn agnggcnnac gggugaguaa
120nncnunnnna nnunccnnnn nnnnnggnan annnnnnnga aannnnnnnu
aauaccnnau 180nnnnnnnnnn nnnnaaagnn nnnnnnnnnn nnnnnnnnnn
nnnnnngann nnnnnnngnn 240nnaunagnun guuggunngg uaanggcnna
ccaagncnnn gannnnuagc ngnncugaga 300ggnngnncng ccacanuggn
acugaganac ggnccanacu ccuacgggag gcagcagunn 360ggaaunuunn
ncaauggnng naanncugan nnagcnannc cgcgugnnng anganggnnu
420nnngnungua aannncunun nnnnnngang annnnnnnnn nnnnnnnnnn
nnnnnnnnnu 480gacnnuannn nnnnannaag nnncggcnaa cuncgugcca
gcagccgcgg uaauacgnag 540gnngcnagcg uunnncggan unanugggcg
uaaagngnnn gnaggnggnn nnnnnngunn 600nnngunaaan nnnnnngcun
aacnnnnnnn nnncnnnnnn nacnnnnnnn cungagnnnn 660nnagnggnnn
nnngaauunn nnguguagng gugnaauncg naganaunng nangaanacc
720nnungcgaag gcnnnnnncu ggnnnnnnac ugacncunan nnncgaaagc
nugggnagcn 780aacaggauua gauacccugg uaguccangc nnuaaacgnu
gnnnnnunnn ngnnngnnnn 840nnnnnnnnnn nnnnnnnnna nnnaacgnnn
uaannnnncc gccuggggag uacgnncgca 900agnnunaaac ucaaangaau
ugacggggnc cngcacaagc ngnggagnau guggnuuaau 960ucgangnnac
gcgnanaacc uuaccnnnnn uugacaunnn nnnnnnnnnn nnganannnn
1020nnnnnnnnnn nnnnnnnnnn nnnacaggug nugcauggnu gucgucagcu
cgugnnguga 1080gnuguugggu uaagucccgn aacgagcgca acccnnnnnn
nnnguuncna ncnnnnnnnn 1140ngngnacucn nnnnnnacug ccnnngnnaa
nnnggaggaa ggnggggang acgucaanuc 1200nucaugnccc uuangnnnng
ggcuncacac nuncuacaau ggnnnnnaca nngngnngcn 1260annnngnnan
nnnnagcnaa ncnnnnaaan nnnnucnnag uncggaungn nnncugcaac
1320ucgnnnncnu gaagnnggan ucgcuaguaa ucgnnnauca gnangnnncg
gugaauacgu 1380ucncgggncu uguacacacc gcccgucann ncangnnagn
nnnnnnnncc nnaagnnnnn 1440nnnnnnncnn nnnngnnnnn nnnnncnang
gnnnnnnnnn nganugggnn naagucguaa 1500caagguancc nuannngaan
nugnggnugg aucaccuccu un 154242904RNAArtificial
Sequencemisc_feature23S rRNA consensus sequence 4nnnnaagnnn
nnaagngnnn nngguggaug ccunggcnnn nnnagncgan gaaggangnn 60nnnnncnncn
nnanncnnng gnnagnngnn nnnnnncnnn nnanccnnng nunuccgaau
120ggggnaaccc nnnnnnnnnn nnnnnnnnan nnnnnnnnnn nnnnnnnnnn
nnnnnnngnn 180nacnnnnnga anugaaacau cunaguannn nnaggaanag
aaannaannn ngauuncnnn 240nguagnggcg agcgaannng nannagncnn
nnnnnnnnnn nnnnnnnnnn nnnannngaa 300nnnnnuggna agnnnnnnnn
nannngguna nannccngua nnnnaaannn nnnnnnnnnn 360nnnnnnnnnn
aguannncnn nncncgngnn annnngunng aannngnnnn gaccannnnn
420naagncuaaa uacunnnnnn ngaccnauag ngnannagua cngugangga
aaggngaaaa 480gnacccnnnn nangggagug aaanagnncc ugaaaccnnn
nncnuanaan nngunnnagn 540nnnnnnnnnn nnnuganngc gunccuuuug
nannaugnnn cngnganuun nnnunnnnng 600cnagnuuaan nnnnnnnngn
agncgnagng aaancgagun nnaanngngc gnnnagunnn 660nngnnnnaga
cncgaancnn ngugancuan nnaugnncag gnugaagnnn nnguaanann
720nnnuggaggn ccgaacnnnn nnnnguugaa aannnnnngg augannugug
nnungnggng 780aaanncnaan cnaacnnngn nauagcuggu ucucnncgaa
annnnuuuag gnnnngcnun 840nnnnnnnnnn nnnnggnggu agagcacugn
nnnnnnnnng gnnnnnnnnn nnnnuacnna 900nnnnnnnnaa acuncgaaun
ccnnnnnnnn nnnnnnnngn agnnanncnn ngngngnuaa 960nnuncnnngu
nnanagggna acancccaga ncnncnnnua aggncccnaa nnnnnnnnua
1020aguggnaaan gangugnnnn nncnnanaca nnnaggangu uggcuuagaa
gcagccancn 1080uunaaagann gcguaanagc ucacunnucn agnnnnnnng
cgcngannau nuancgggnc 1140uaannnnnnn nccgaannnn nngnnnnnnn
nnnnnnnnnn nnnnngguag nngagcgunn 1200nnnnnnnnnn ngaagnnnnn
nngnnannnn nnnuggannn nnnnnnagug ngnaugnngn 1260naunaguanc
gannnnnnnn gugananncn nnnncnccgn annncnaagg nuuccnnnnn
1320nangnunnuc nnnnnngggu nagucgnnnc cuaagnngag ncnganangn
nuagnngaug 1380gnnannnggu nnauauuccn nnacnnnnnn nnnnnnnnnn
nnnnngacgn nnnnngnnnn 1440nnnnnnnnnn nnnnggnnnn nnnnnnnnnn
nnnnnnnnnn nnnnnnnnnn nnnnnnnnnn 1500nnnnnnnnnn nnnnnnnnnn
nnnnnnnnnn nnnnnnnnnn nnnnnnnnnn nnnnnnnnnn 1560nnnncnngaa
aannnnnnnn nnnnnnnnnn nnnnnnnnnc guaccnnaaa ccgacacagg
1620ungnnnngnn gagnanncnn aggngnnngn nnnaannnnn nnnaaggaac
unngcaaanu 1680nnnnccguan cuucggnana aggnnnncnn nnnnnnnnnn
nnnnnnnnnn nnnnnnnnnn 1740nnnnnnnnng nnnnannnan nngnnnnnnn
cnacuguuua nnaaaaacac agnncnnugc 1800naanncgnaa gnnganguau
anggnnugac nccugcccng ugcnngaagg uuaanngnnn 1860nnnnnngnnn
nngnnnnnnn nnnnannnaa gcccnnguna acggcggnng uaacuauaac
1920nnuccuaagg uagcgaaauu ccuugucggg uaaguuccga ccngcacgaa
nggngnaang 1980annnnnnnnc ugucucnnnn nnnnncncng ngaanuunna
nunnnnguna agaugcnnnn 2040uncncgcnnn nngacggaaa gaccccnngn
ancuuuacun nannnunnna nugnnnnnnn 2100nnnnnnnnug unnagnauag
gunggagncn nngannnnnn nncgnnagnn nnnnnggagn 2160cnnnnnugnn
auacnacncu nnnnnnnnnn nnnnucuaac nnnnnnnnnn nancnnnnnn
2220nnngacanug nnngnngggn aguuunacug gggcggunnc cuccnaaann
guaacggagg 2280ngnncnaagg unnncunann nnggnnggnn aucnnnnnnn
nagunnaann gnanaagnnn 2340gcnunacugn nagnnnnacn nnncgagcag
nnncgaaagn nggnnnuagu gauccggngg 2400unnnnnnugg aagngccnuc
gcucaacgga uaaaagnuac ncnggggaua acaggcunau 2460nnnncccaag
aguncanauc gacggnnnng uuuggcaccu cgaugucggc ucnucncauc
2520cuggggcugn agnngguccc aagggunngg cuguucgccn nuuaaagngg
nacgngagcu 2580ggguunanaa cgucgugaga caguungguc ccuaucngnn
gngngngnnn gannnuugan 2640nngnnnugnn cnuaguacga gaggaccggn
nngnacnnan cncuggugnn ncnguugunn 2700ngccannngc anngcngnnu
agcuannunn ggnnnngaua anngcugaan gcaucuaagn 2760nngaancnnn
cnnnnagann agnnnucncn nnnnnnnnnn nnnnnnnnna gnnncnnnnn
2820agannannnn gungauaggn nngnnnugna agnnnngnna nnnnunnagn
nnacnnnuac 2880uaaunnnncn nnnnncuunn nnnn 2904513DNAArtificial
Sequencemisc_featurePrimer 5cgtggtgacc ctt 13614DNAArtificial
Sequencemisc_featurePrimer 6cgtcgtcacc gcta 14713DNAArtificial
Sequencemisc_featurePrimer 7cgtggtaccc ctt 13830DNAArtificial
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 2225422DNAArtificial
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
19378375RNAArtificial SequenceBacterial Ribosomal RNA 378nnnnnnnnnn
nnnnnnnnnn nnnnnnnnnn nnnnnnnnnn nnnnnnnnnn nnnnnnnnng 60aggaaagucc
ggncucnnna nncannnugn nngnuaannn cnnnnnnngn nannnnngac
120naguncnaca gagngnnnnc cgccnnnnnn nnnnnnnnnn nnnnnnnggn
aagggugnaa 180nggnnnngua agagnncacc gnnnnnnnng nnannnnnnn
nnncnnggna aacuccnnnn 240gnagcaagnn nnnnnnngnn nnnnnnnnnn
nnngnncnnn nnnnnnnnnn nnannnngcu 300ngagnnnnnn ngnnannnnn
nnccnagann naugnnnnnn cnnnacagaa nccggcnuan 360nnnnnnnnnn nnnnn
3753799DNAArtificial SequenceDouble stranded nucleic acid
379tacgtacgt 93809DNAArtificial SequenceDouble stranded nucleic
acid 380atgcatgca 938165RNAArtificial SequenceSynthetic
oligonucleotide 381gacccauggu cgcucgcucc ucuccuacuu ggauaacugu
gguaauucua gagcuaauac 60augcc 6538248RNAArtificial
SequenceSynthetic oligonucleotide 382gccauaugga gggggauaac
uacuggaaac gguagcuaau accgcaua 48
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