U.S. patent application number 10/439706 was filed with the patent office on 2005-02-03 for secondary structure defining database and methods for determining identity and geographic origin of an unknown bioagent thereby.
Invention is credited to Crooke, Stanley T., Ecker, David J., Griffey, Richard H., Hofstadler, Steven A., McNeil, John, Sampath, Rangarajan.
Application Number | 20050027459 10/439706 |
Document ID | / |
Family ID | 25398831 |
Filed Date | 2005-02-03 |
United States Patent
Application |
20050027459 |
Kind Code |
A1 |
Ecker, David J. ; et
al. |
February 3, 2005 |
Secondary structure defining database and methods for determining
identity and geographic origin of an unknown bioagent thereby
Abstract
The present invention relates generally to the field of
investigational bioinformatics and more particularly to secondary
structure defining databases. The present invention further relates
to methods for interrogating a database as a source of molecular
masses of known bioagents for comparing against the molecular mass
of an unknown or selected bioagent to determine either the identity
of the selected bioagent, and/or to determine the origin of the
selected bioagent. The identification of the bioagent is important
for determining a proper course of treatment and/or irradication of
the bioagent in such cases as biological warfare. Furthermore, the
determination of the geographic origin of a selected bioagent will
facilitate the identification of potential criminal identity.
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: |
COZEN O'CONNOR, P.C.
1900 MARKET STREET
PHILADELPHIA
PA
19103-3508
US
|
Family ID: |
25398831 |
Appl. No.: |
10/439706 |
Filed: |
May 16, 2003 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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10439706 |
May 16, 2003 |
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10340321 |
Jan 10, 2003 |
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10340321 |
Jan 10, 2003 |
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09891793 |
Jun 26, 2001 |
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Current U.S.
Class: |
702/20 ; 435/5;
435/6.11; 435/6.12 |
Current CPC
Class: |
G16B 20/00 20190201;
G16B 20/20 20190201; G16B 40/10 20190201; G16B 15/10 20190201; G16B
99/00 20190201; G16B 15/00 20190201; Y02A 90/10 20180101; G16H
50/80 20180101; G16B 50/00 20190201 |
Class at
Publication: |
702/020 ;
435/005; 435/006 |
International
Class: |
C12Q 001/70; C12Q
001/68; G06F 019/00; G01N 033/48; G01N 033/50 |
Goverment Interests
[0001] This invention was made with United States Government
support under DARPA/SPO contract BAA00-09. The United States
Government may have certain rights in the invention.
Claims
What is claimed is:
1. A method of identifying an unknown bioagent using a database of
molecular masses of known bioagents comprising: contacting nucleic
acid from said bioagent with at least one pair of oligonucleotide
primers that hybridize to sequences of said nucleic acid, wherein
said sequences flank a variable nucleic acid sequence of said
bioagent; producing an amplification product of said variable
nucleic acid sequence; determining a first molecular mass of said
amplification product; and comparing said first molecular mass to
the molecular masses of known bioagents, thereby identifying the
unknown bioagent.
2. The method of claim 1 wherein said sequences to which said at
least one pair of oligonucleotide primers hybridize are highly
conserved.
3. The method of claim 1 wherein said sequences to which said at
least one pair of oligonucleotide primers hybridize are highly
conserved across at least two species.
4. The method of claim 1 further comprising the step of isolating a
nucleic acid from said bioagent prior to contacting said nucleic
acid with said at least one pair of oligonucleotide primers,
wherein the comparing step further comprises comparing a base-pair
count resulting from a translation of the corresponding molecular
mass, and wherein a master database of molecular masses of known
bioagents further includes a translation of said molecular masses
of known bioagents to corresponding base-pair counts of each known
bioagent resulting from a specific primer pair set and comparing
the base-pair count of said unknown bioagent against the obtained
base-pair count of known bioagents for the selected primer pair set
for determining the identity of said unknown bioagent.
5. The method of claim 4 further comprising the step of reconciling
the database of molecular masses of known bioagents with the master
database of molecular masses of known bioagents.
6. The method of claim 1 wherein said bioagent is a bacterium,
virus, cell or spore.
7. The method of claim 1 wherein said amplification product is
ionized by electorospray ionization, matrix assisted laser
desorption or fast atom bombardment prior to molecular mass
determination.
8. The method of claim 1 wherein said molecular mass is determined
by mass spectrometry.
9. The method of claim 5 wherein said master database of molecular
masses of known bioagents and the database of molecular masses of
known bioagents are reconciled over a network.
10. The method of claim 4 wherein the identity is determined by
statistically correlating the molecular mass of the unknown
bioagent with at least one molecular mass of said master
database.
11. A database having cell-data positional significance comprising
at least a first table of a plurality of data-containing cells,
said first table organized into at least a first row and a second
row, each row having columns and data-containing cells; and wherein
said data-containing cells have an alignment with at least one
other row for differentiating aligned from non-aligned
data-containing cells, and wherein said differentiation in
alignment of said data-containing cells designates a structural
feature of a polymer.
12. The database according to claim 11 wherein said alignment is a
vertical alignment according to base pair homology along a linear
segment within each polymer.
13. The database according to claim 11 wherein said vertical
alignment further aligns cell-data according to inter-species
conserved regions.
14. The database according to claim 11 wherein the structural
feature is a bulge or a loop.
15. The database according to claim 11 wherein the polymer is an
RNA.
16. A method for reconciling a first file and a second file, said
second file corresponding at least in part to said first file, said
first file and said second file each containing records, said
records corresponding to rows in a table of a dimensional database
having rows and columns defined by data-cells having data-cell
positional significance, said method comprising: comparing said
first file and said corresponding second file with a backup file
containing records from a previous reconciliation of said first
file and said corresponding second file to identify new, updated or
deleted records; creating a reconcile file containing information
pertaining to said new, updated or deleted records identified in
said comparing step; and copying the contents of said reconcile
file to said first file, said corresponding second file and a new
backup file.
17. A service providing information related to a bioagent
comprising: providing a dimensional master database for storing a
molecular mass, an identity and a detail corresponding to a
plurality of known bioagents and, said master database storing the
molecular mass, the identity and the detail for a plurality of
known bioagents; interrogating the master database with an
identification request of an unknown bioagent to generate a
response; and delivering said response from the master database to
a requester.
18. The service according to claim 17 wherein the molecular mass is
of a selected portion of the known bioagent, the identity comprises
at least a geographic origin and a name for the known bioagent, and
the detail comprises at least a treatment.
19. The service according to claim 17 wherein the request comprises
a symptomatology and the identification comprises a recommended
pair of primers for hybridizing to sequences of nucleic acid
flanking a variable nucleic acid sequence of the unknown bioagent,
and said pair of primers are hybridized to the sequences of nucleic
acid flanking a variable nucleic acid sequence of the unknown
bioagent.
20. The service according to claim 19 wherein the nucleic acid
sequence of the unknown bioagent between said pair of primers
defines the selected portion of both the known bioagents and the
unknown bioagent.
21. The service according to claim 20 wherein the response is
delivered through a network.
22. The service according to claim 20 wherein the request comprises
a molecular mass of the unknown bioagent for the selected portion
and where the response generated thereto comprises a set of
molecular masses for analogous selected portions of know bioagents,
and said set comprising at least one molecular mass from the master
database.
23. The service according to claim 21 wherein the network is a
local area network.
24. The service according to claim 21 wherein the network is a wide
area network.
25. The service according to claim 22 wherein the network is the
internet.
26. A method of determining a geographical origin of a selected
bioagent using a database of molecular masses of known bioagents
comprising: contacting nucleic acid from said selected bioagent
with at least one pair of oligonucleotide primers which hybridize
to sequences of said nucleic acid, wherein said sequences flank a
variable nucleic acid sequence of the bioagent; producing an
amplification product of said variable nucleic acid sequence;
determining a first molecular mass of said amplification product;
and comparing said first molecular mass to the molecular masses of
known bioagents for determining a geographic origin of said
selected bioagent, said comparison determining an identity and a
geographic origin of said selected bioagent.
27. The method of claim 26 wherein said sequences to which said at
least one pair of oligonucleotide primers hybridize are highly
conserved.
28. The method of claim 26 wherein said sequences to which said at
least one pair of oligonucleotide primers hybridize are highly
conserved across species.
29. The method of claim 26 further comprising the step of isolating
a nucleic acid from said selected bioagent prior to contacting said
nucleic acid with said at least one pair of oligonucleotide
primers, wherein the comparing step further comprises interrogating
a master database of molecular masses of known bioagents for
obtaining molecular masses of known bioagents and comparing the
molecular mass of said selected bioagent against the obtained
molecular masses of known bioagents thereby determining an origin
of said selected bioagent.
30. The method of claim 29 further comprising the step of
reconciling the database of molecular masses of known bioagents
with the master database of molecular masses of known
bioagents.
31. The method of claim 26 wherein said bioagent is a bacterium,
virus, cell or spore.
32. The method of claim 26 wherein said amplification product is
ionized by electorospray ionization, matrix assisted laser
desorption or fast atom bombardment prior to molecular mass
determination.
33. The method of claim 26 wherein said molecular mass is
determined by mass spectrometry.
34. The method of claim 29 wherein said master database of
molecular masses of known bioagents and the database of molecular
masses of known bioagents are reconciled over a network.
35. The method of claim 29 wherein the origin comprises a
statistical group of matching molecular masses and the geographic
origin corresponding thereto.
Description
FIELD OF THE INVENTION
[0002] The present invention relates generally to the field of
investigational bioinformatics and more particularly to secondary
structure defining databases. The present invention further relates
to methods for interrogating a database as a source of molecular
masses of known bioagents for comparing against the molecular mass
of an unknown or selected bioagent to determine either the identity
of the selected bioagent, and/or to determine the origin of the
selected bioagent. The identification of the bioagent is important
for determining a proper course of treatment and/or irradication of
the bioagent in such cases as biological warfare. 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
[0003] 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.
[0004] 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.
[0005] 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.
[0006] 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.
[0007] One diagnostic assay, which has gained widespread
acceptance, is for C. trachomatis. Conventional detection systems
are limiting due to inadequate sensitivity and specificity (direct
immunofluoresence 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.
[0008] 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.
[0009] 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.
[0010] 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.
[0011] 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.
[0012] 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 which 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.
[0013] 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.
[0014] 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.
[0015] 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.
[0016] 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.
[0017] 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.
[0018] 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.
[0019] 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.
[0020] 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.
[0021] 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.
[0022] WO 99/14375 describes methods, PCR primers and kits for use
in analyzing preselected DNA tandem nucleotide repeat alleles by
mass spectrometry.
[0023] 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.
[0024] 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.
[0025] 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.
[0026] 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.
[0027] 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.
[0028] 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
[0029] The present invention is directed to method of identifying
an unknown bioagent using a database, such as a database stored on,
for example, a local computer or perhaps a database accessible over
a network or on the internet. This database of molecular masses of
known bioagents provides a standard of comparison for determining
both identity and geographic origin of the unknown bioagent. The
nucleic acid from said bioagent is first contacted with at least
one pair of oligonucleotide primers which hybridize to sequences of
said nucleic acid that flank a variable nucleic acid sequence of
the bioagent. Using PCR technology an amplification product of this
variable nucleic acid sequence is made. After standard isolation,
the molecular mass of this amplification product is determined
using known mass-spec techniques. This molecular mass is compared
to the molecular mass of known bioagents within the database, for
identifying the unknown bioagent.
[0030] This invention is also directed to databases having
cell-data positional significance comprising at least a first table
that includes a plurality of data-containing cells. The table is
organized into at least a first row and a second row, each row
having columns which are aligned relative to each other so that
inter-row conserved regions are aligned. This alignment facilitates
the analysis of regions, which are highly conserved between
species. This alignment further provides insight into secondary
polymer structure by this alignment. Although this invention is
directed to a database where each row describes any polymer, in a
preferred embodiment, the polymer is an RNA. Other alignments that
operate in the same manner are also contemplated.
[0031] Another embodiment of this invention is a method for
reconciling the content of two databases such that the content of
each is a mirror of the other.
[0032] Another embodiment is directed to determining the geographic
origin of a bioagent using a database of molecular masses of known
bioagents comprising contacting a nucleic acid from the selected
bioagent with at least one pair of oligonucleotide primers which
hybridize to sequences of the nucleic acid, where the sequences
flank a variable nucleic acid sequence of the bioagent. This
hybridized region is isolated and amplified through standard PCR
techniques known in the art. The molecular mass is determined of
this amplified product through any technique known in the art such
as, Mass-spectrometry for example. This molecular mass is compared
to the molecular masses stored in the database of known bioagents
thereby determining a group of probabilistically reasonable
geographic origins for the selected bioagent.
BRIEF DESCRIPTION OF THE DRAWINGS
[0033] FIGS. 1A-1I are representative consensus diagrams that show
examples of conserved regions from 16S rRNA (FIG. 1A-1B), 23S rRNA
(3'-half, FIG. 1C-1D; 5'-half, FIG. 1E-F), 23S rRNA Domain I (FIG.
1G), 23S rRNA Domain IV (FIG. 1H) and 16S rRNA Domain III (FIG. 1I)
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.
[0034] FIG. 2 shows a typical primer amplified region from the 16S
rRNA Domain III shown in FIG. 1C.
[0035] 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.
[0036] FIG. 4 is a schematic diagram of base composition signature
determination using nucleotide analog "tags" to determine base
composition signatures.
[0037] 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.
[0038] FIG. 6 shows base composition signature (BCS) spectra from
PCR products from Staphylococcus aureus (S. aureus 16S.sub.--1337F)
and Bacillus anthracus (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.
[0039] 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.
[0040] FIG. 8 is an ESI-TOF of Bacillus anthracis spore coat
protein sspE 56mer plus calibrant. The signals unambiguously
identify B. anthracis versus other Bacillus species.
[0041] 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.
[0042] FIG. 10 is an ESI-FTICR-MS of a synthetic B. anthracis
16S.sub.--1337 46 base pair duplex.
[0043] FIG. 11 is an ESI-TOF-MS of a 56mer oligonucleotide (3
scans) from the B. anthracis saspB gene with an internal mass
standard. The internal mass standards are designated by
asterisks.
[0044] 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-.
[0045] 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.
[0046] FIG. 14 is a three dimensional graph demonstrating the
grouping of sample molecular weight according to species.
[0047] FIG. 15 is a three dimensional graph demonstrating the
grouping of sample molecular weights according to species of virus
and mammal infected.
[0048] 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.
[0049] 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).
DESCRIPTION OF PREFERRED EMBODIMENTS
[0050] The present invention provides a combination of a non-PCR
biomass detection mode, preferably high-resolution MS, with
PCR-based BCS technology using "intelligent primers" which
hybridize to conserved sequence regions of nucleic acids derived
from a bioagent and which bracket variable sequence regions that
uniquely identify the bioagent. The high-resolution MS technique is
used to determine the molecular mass and base composition signature
(BCS) of the amplified sequence region. This unique "base
composition signature" (BCS) is then input to a maximum-likelihood
detection algorithm for matching against a database of base
composition signatures in the same amplified region. The present
method combines PCR-based amplification technology (which provides
specificity) and a molecular mass detection mode (which provides
speed and does not require nucleic acid sequencing of the amplified
target sequence) for bioagent detection and identification.
[0051] 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. Thus, the method is
useful in a wide variety of fields, including, but not limited to,
environmental testing (e.g., detection and discrimination of
pathogenic vs. non-pathogenic bacteria in water or other samples),
germ warfare (allowing immediate identification of the bioagent and
appropriate treatment), pharmacogenetic analysis and medical
diagnosis (including 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). 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.
[0052] The present methods can be used, for example, to detect and
classify any biological agent, including bacteria, viruses, fungi
and toxins. As one example, where the agent is a biological threat,
the information obtained is used to determine practical information
needed for countermeasures, including toxin genes, pathogenicity
islands and antibiotic resistance genes. 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.
[0053] 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.
[0054] 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 using base composition signatures (BCS). While the
base composition of a gene fragment is not as information-rich as
the sequence itself, there is no need to analyze the complete
sequence of the gene if the short analyte sequence fragment is
properly chosen. A database of reference sequences can be prepared
in which each sequence is indexed to a unique base composition
signature, so that the presence of the sequence can be inferred
with accuracy from the presence of the signature. The advantage of
base composition signatures is that they can be quantitatively
measured in a massively parallel fashion using multiplex PCR (PCR
in which two or more primer pairs amplify target sequences
simultaneously) and mass spectrometry. These multiple primer
amplified regions uniquely identify most threat and ubiquitous
background bacteria and viruses. In addition, cluster-specific
primer pairs distinguish important local clusters (e.g., anthracis
group).
[0055] In the context of this invention, a "bioagent" is any
organism, living or dead, or a nucleic acid derived from such an
organism. Examples of bioagents include, but are not limited to,
cells (including, but not limited to, human clinical samples,
bacterial cells and other pathogens), viruses, toxin genes and
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.
[0056] As used herein, a "base composition signature" (BCS) is the
exact base composition from selected fragments of nucleic acid
sequences that uniquely identifies the target gene and source
organism. BCS can be thought of as unique indexes of specific
genes.
[0057] As used herein, "intelligent primers" are primers which bind
to sequence regions which flank an intervening variable region. In
a preferred embodiment, these sequence regions which flank the
variable region are highly conserved among different species of
bioagent. For example, the sequence regions may be highly conserved
among all Bacillus species. By the term "highly conserved," it is
meant that the sequence regions exhibit between about 80-100%, more
preferably between about 90-100% and most preferably between about
95-100% identity. Examples of intelligent primers which amplify
regions of the 16S and 23S rRNA are shown in FIGS. 1A-1I. A typical
primer amplified region in 16S rRNA is shown in FIG. 2. The arrows
represent primers which 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."
[0058] One main advantage of the detection methods of the present
invention is that the 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 primer pair can be used to
identify any desired bacterium because it will bind to the
conserved regions which flank a variable region specific to a
single species, or common to several bacterial species, allowing
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.
[0059] The present invention provides a combination of a non-PCR
biomass detection mode, preferably high-resolution MS, with nucleic
acid amplification-based BCS technology using "intelligent primers"
which hybridize to conserved regions and which bracket variable
regions that uniquely identify the bioagent(s). Although the use of
PCR is preferred, 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. The resolved
spectral lines are then translated to BCS which are input to a
maximum-likelihood detection algorithm matched against spectra for
one or more known BCS. Preferably, the bioagent BCS spectrum is
matched against one or more databases of BCS from vast numbers of
bioagents. Preferably, the matching is done using a
maximum-likelihood detection algorithm.
[0060] In one embodiment, base composition signatures are
quantitatively measured in a massively parallel fashion using the
polymerase chain reaction (PCR), preferably 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 which
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.
[0061] The "intelligent primers" define the target sequence region
to be amplified and analyzed. In one embodiment, the target
sequence is 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 preferred regions for BCS analysis. 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, "ma.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."
[0062] These databases have been analyzed to determine regions that
are useful as base composition signatures. The characteristics of
such regions include: a) between about 80 and 100%, preferably
>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, preferably no more than about 50-250 nucleotides, and
more preferably no more than about 60-100 nucleotides, between the
conserved regions.
[0063] Due to their overall conservation, the flanking rRNA primer
sequences serve as good "universal" primer binding sites to amplify
the 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.
[0064] It is advantageous to design the "intelligent primers" to be
as universal as possible to minimize the number of primers which
need to be synthesized, and to allow detection of multiple species
using a single pair of primers. These primer pairs can be used to
amplify variable regions in these species. 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-carbo- xamide (Sala
et al., Nucl. Acids Res., 1996, 24, 3302-3306).
[0065] 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.
Propynes are described in U.S. Pat. Nos. 5,645,985, 5,830,653 and
5,484,908, each of which is incorporated herein by reference in its
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.
[0066] 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.
[0067] 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).
[0068] Biological warfare fungus biowarfare agents include, but are
not limited to, coccidioides immitis (Coccidioidomycosis).
[0069] Biological warfare toxin genes capable of being detected by
the methods of the present invention include, but are not limited
to, botulism, T-2 mycotoxins, ricin, staph enterotoxin B,
shigatoxin, abrin, aflatoxin, Clostridium perfringens epsilon
toxin, conotoxins, diacetoxyscirpenol, tetrodotoxin and
saxitoxin.
[0070] 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.
[0071] 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).
[0072] Examples of (+)-strand RNA viruses include, but are not
limited to, picomaviruses (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.
[0073] 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.
[0074] 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 BCS 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.
[0075] 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.
[0076] Mass spectrometry (MS)-based detection of PCR products
provides all of these features with additional advantages. MS is
intrinsically a parallel detection scheme without the need for
radioactive or fluorescent labels, since every amplification
product with a unique base composition 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.).
[0077] 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.
[0078] 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.
[0079] 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, preferably FT-ICR MS) is initiated. Optionally, alarms
or other processes (halting ventilation flow, physical isolation)
are also initiated by this detected biomass level.
[0080] The accurate measurement of molecular mass for large DNAs is
limited by the adduction of cations from the PCR reaction to each
strand, resolution of the isotopic peaks from natural abundance
.sup.13C and .sup.15N isotopes, and assignment of the charge state
for any ion. The cations are removed by in-line dialysis using a
flow-through chip that brings the solution containing the PCR
products into contact with a solution containing ammonium acetate
in the presence of an electric field gradient orthogonal to the
flow. The latter two problems are addressed by operating with a
resolving power of >100,000 and by incorporating isotopically
depleted nucleotide triphosphates into the DNA. The resolving power
of the instrument is also a consideration. At a resolving power of
10,000, the modeled signal from the [M-14H+].sup.14- charge state
of an 84mer PCR product is poorly characterized and assignment of
the charge state or exact mass is impossible. At a resolving power
of 33,000, the peaks from the individual isotopic components are
visible. At a resolving power of 100,000, the isotopic peaks are
resolved to the baseline and assignment of the charge state for the
ion is straightforward. The [.sup.13C, .sup.15N]-depleted
triphosphates are obtained, for example, by growing microorganisms
on depleted media and harvesting the nucleotides (Batey et al.,
Nucl. Acids Res., 1992, 20, 4515-4523).
[0081] 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 a-Base series(having
an intact 5' terminus and a 3' furan).
[0082] 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.
[0083] If there are two or more targets of similar base composition
or 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.
[0084] 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.
1TABLE 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
[0085] The mass tag phosphorothioate A (A*) was used to distinguish
a Bacillus anthracis cluster. The B. anthracis
(A.sub.14G.sub.9C.sub.14T.su- b.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.
[0086] In another example, assume the measured molecular masses of
each strand are 30,000.115 Da and 31,000.115 Da respectively, and
the measured number of dT and dA residues are (30,28) and (28,30).
If the molecular mass is accurate to 100 ppm, there are 7 possible
combinations of dG+dC possible for each strand. However, if the
measured molecular mass is accurate to 10 ppm, there are only 2
combinations of dG+dC, and at 1 ppm accuracy there is only one
possible base composition for each strand.
[0087] 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.
[0088] 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.
[0089] In one embodiment, a strategy to "triangulate" each organism
by measuring signals from multiple core genes is used to reduce
false negative and false positive signals, and enable
reconstruction of the origin or hybrid or otherwise engineered
bioagents. 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
potential primer binding sites flanking variable regions are
identified. Next, amplification target regions for signature
analysis are selected which distinguishes organisms based on
specific genomic differences (i.e., base composition). For example,
detection of signatures for 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.
[0090] 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.
[0091] 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 which 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.
[0092] In one embodiment, blood is drawn from an individual and
peripheral blood mononuclear cells (PBMC) are isolated and
simultaneously tested, preferably 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/."
[0093] 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:1), antigen A results. If the
gene contains the sequence CGTCGTCACCGCTA (SEQ ID NO:2) antigen B
results. If the gene contains the sequence CGTGGT-ACCCCTT (SEQ ID
NO:3), 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.
[0094] While the present invention has been described with
specificity in accordance with certain of its preferred
embodiments, the following examples serve only to illustrate the
invention and are not intended to limit the same.
EXAMPLES
Example 1
[0095] Nucleic Acid Isolation and PCR
[0096] 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.
Example 2
[0097] Mass Spectrometry
[0098] 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.
[0099] Modified ESI Source: In sample-limited analyses, analyte
solutions are delivered at 150 nL/minute to a 30 mm i.d.
fused-silica ESI emitter mounted on a 3-D micromanipulator. The ESI
ion optics consist 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 which
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.
[0100] 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
[0101] Identification of Bioagents
[0102] 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.
[0103] 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).
2TABLE 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
Kiebsiella 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.
[0104] FIG. 6 shows the use of ESI-FT-ICR MS for measurement of
exact mass. The spectra from 46mer PCR products originating at
position 1337 of the 16S rRNA from S. aureus (upper) and B.
anthracis (lower) are shown. These data are from the region of the
spectrum containing signals from the [M-8H+].sup.8- charge states
of the respective 5'-3' strands. The two strands differ by two
(AT.fwdarw.CG) substitutions, and have measured masses of 14206.396
and 14208.373.+-.0.010 Da, respectively. The possible base
compositions derived from the masses of the forward and reverse
strands for the B. anthracis products are listed in Table 3.
3TABLE 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
[0105] 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
[0106] BCS of Region from Bacillus anthracis and Bacillus
cereus
[0107] 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
[0108] Identification of Additional Bioagents
[0109] 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.
[0110] Tables 4a-b show examples of primer pairs from Table 1 which
distinguish pathogens from background.
4 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
[0111]
5 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
[0112] Table 4 shows the expected molecular weight and base
composition of region 16S.sub.13 1100-1188 in Mycobacterium avium
and Streptomyces sp.
6TABLE 5 Organism Molecular Region name Length weight Base comp.
16S.sub.-- Mycobacterium 82 25624.1728
A.sub.16G.sub.32C.sub.18T.sub.16 1100-1188 avium 16S.sub.--
Streptomyces 96 29904.871 A.sub.17G.sub.38C.sub.27T.sub.14
1100-1188 sp.
[0113] Table 5 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.
7TABLE 6 Organism name Base comp. Organism name Base comp.
Mycobacterium avium A.sub.16G.sub.32C.sub.18T.s- ub.16 Vibrio
cholerae A.sub.23G.sub.30C.sub.21T.sub.16 Streptomyces sp.
A.sub.17G.sub.38C.sub.27T.sub.14 A.sub.23G.sub.31C.sub.21T.sub.15
Ureaplasma urealyticum A.sub.18G.sub.30C.sub.17T.sub.17
A.sub.23G.sub.31C.sub.21T.sub.15 Streptomyces sp.
A.sub.19G.sub.36C.sub.24T.sub.18 Mycoplasma genitalium
A.sub.24G.sub.19C.sub.12T.sub.18 Mycobacterium leprae
A.sub.20G.sub.32C.sub.22T.sub.16 Clostridium botulinum
A.sub.24G.sub.25C.sub.18T.sub.20 A.sub.20G.sub.33C.sub.21T.sub.16
Bordetella bronchiseptica A.sub.24G.sub.26C.sub.19T.sub.14
A.sub.20G.sub.33C.sub.21T.sub.16 Francisella tularensis
A.sub.24G.sub.26C.sub.19T.sub.19 Fusobacterium necroforum
A.sub.21G.sub.26C.sub.22T.sub.16 A.sub.24G.sub.26C.sub.20T.sub.18
Listeria monocytogenes A.sub.21G.sub.27C.sub.19T.sub.19
A.sub.24G.sub.26C.sub.20T.sub.18 Clostridium botulinum
A.sub.21G.sub.27C.sub.19T.sub.21 A.sub.24G.sub.26C.sub.20T.sub.18
Neisseria gonorrhoeae A.sub.21G.sub.28C.sub.21T.sub.18 Helicobacter
pylori A.sub.24G.sub.26C.sub.20T.sub.19 Bartonella quintana
A.sub.21G.sub.30C.sub.22T.sub.16 Helicobacter pylori
A.sub.24G.sub.26C.sub.21T.sub.18 Enterococcus faecalis
A.sub.22G.sub.27C.sub.20T.sub.19 Moraxella catarrhalis
A.sub.24G.sub.26C.sub.23T.sub.16 Bacillus megaterium
A.sub.22G.sub.28C.sub.20T.sub.18 Haemophilus influenzae Rd
A.sub.24G.sub.28C.sub.20T.sub.17 Bacillus subtilis
A.sub.22G.sub.28C.sub.21T.sub.17 A.sub.24G.sub.28C.sub.21T.sub.16
Pseudomonas aeruginosa A.sub.22G.sub.29C.sub.23T.sub.15
A.sub.24G.sub.28C.sub.21T.sub.16 Legionella pneumophila
A.sub.22G.sub.32C.sub.20T.sub.16 A.sub.24G.sub.28C.sub.21T.sub.16
Mycoplasma pneumoniae A.sub.23G.sub.20C.sub.14T.sub.16 Pseudomonas
putida A.sub.24G.sub.29C.sub.21T.sub.16 Clostridium botulinum
A.sub.23G.sub.26C.sub.20T.sub.19 A.sub.24G.sub.30C.sub.21T.sub.15
Enterococcus faecium A.sub.23G.sub.26C.sub.21T.sub.18
A.sub.24G.sub.30C.sub.21T.sub.15 Acinetobacter calcoaceti
A.sub.23G.sub.26C.sub.21T.sub.19 A.sub.24G.sub.30C.sub.21T.sub.15
A.sub.23G.sub.26C.sub.24T.sub.15 Clostridium botulinum
A.sub.25G.sub.24C.sub.18T.sub.21 A.sub.23G.sub.26C.sub.24T.sub.15
Clostridium tetani A.sub.25G.sub.25C.sub.18T.sub.20 Clostridium
perfringens A.sub.23G.sub.27C.sub.19T.sub.19 Francisella tularensis
A.sub.25G.sub.25C.sub.19T.sub.19 A.sub.23G.sub.27C.sub.20T.sub.18
Acinetobacter calcoacetic A.sub.25G.sub.26C.sub.20T.sub.19
A.sub.23G.sub.27C.sub.20T.sub.18 Bacteriodes fragilis
A.sub.25G.sub.27C.sub.16T.sub.22 A.sub.23G.sub.27C.sub.20T.sub.18
Chlamydophila psittaci A.sub.25G.sub.27C.sub.21T.sub.16 Aeromonas
hydrophila A.sub.23G.sub.29C.sub.21T.sub.16 Borrelia burgdorferi
A.sub.25G.sub.29C.sub.17T.sub.19 Escherichia coli
A.sub.23G.sub.29C.sub.21T.sub.16 Streptobacillus monilifor
A.sub.26G.sub.26C.sub.20T.sub.16 Pseudomonas putida
A.sub.23G.sub.29C.sub.21T.sub.17 Rickettsia prowazekii
A.sub.26G.sub.28C.sub.18T.sub.18 A.sub.23G.sub.29C.sub.22T.sub.15
Rickettsia rickettsii A.sub.26G.sub.28C.sub.20T.sub.16
A.sub.23G.sub.29C.sub.22T.sub.15 Mycoplasma mycoides
A.sub.28G.sub.23C.sub.16T.sub.20
[0114] 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 6). 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.
8TABLE 7 Organism 16S_971-1062 16S_1228-1310 16S_1100-1188
Aeromonas hydrophila A.sub.21G.sub.29C.sub.2- 2T.sub.20
A.sub.22G.sub.27C.sub.21T.sub.13 A.sub.23G.sub.31C.sub.21T.sub.1- 5
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.21T.sub.22
A.sub.24G.sub.22C.sub.19- T.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
[0115] 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.
9 B. anthracis_16S_971 GCGAAGAACCUUACCAGGUNUUGACAUCCUCUGAC- AA (SEQ
ID NO:4) CCCUAGAGAUAGGGCUUCUCCUUCGGGAGCAGAGUGA CAGGUGGUGCAUGGUU B.
cereus_16S_971 GCGAAGAACCUUACCAGGUCUUGACAUCCUCUGAAAA (SEQ ID NO:5)
CCCUAGAGAUAGGGCUUCUCCUUCGGGAGCAGAGUGA CAGGUGGUGCAUGGUU
Example 6
[0116] ESI-TOF MS of sspE 56-mer Plus Calibrant
[0117] 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
[0118] B. anthracis ESI-TOF Synthetic 16S.sub.--1228 Duplex
[0119] 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
[0120] ESI-FTICR-MS of Synthetic B. anthracis 16S.sub.--1337 46
Base Pair Duplex
[0121] 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
[0122] ESI-TOF MS of 56-mer Oligonucleotide from saspB Gene of B.
anthracis with Internal Mass Standard
[0123] 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
[0124] ESI-TOF MS of an Internal Standard with Tributylammonium
(TBA)-trifluoroacetate (TFA) Buffer
[0125] 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
[0126] Master Database Comparison
[0127] 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
[0128] Master Data Base Interrogation over the Internet
[0129] 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
[0130] Master Database Updating
[0131] 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
[0132] Global Database Updating
[0133] 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.
[0134] 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.
Sequence CWU 1
1
7 1 90 RNA Bacillus anthracis misc_feature (20)..(20) N = A, U, G
or C 1 gcgaagaacc uuaccaggun uugacauccu cugacaaccc uagagauagg
gcuucuccuu 60 cgggagcaga gugacaggug gugcaugguu 90 2 90 RNA Bacillus
cereus 2 gcgaagaacc uuaccagguc uugacauccu cugaaaaccc uagagauagg
gcuucuccuu 60 cgggagcaga gugacaggug gugcaugguu 90 3 1542 RNA
Artificial Sequence misc_feature 16S rRNA consensus sequence 3
nnnnnnnaga guuugaucnu ggcucagnnn gaacgcuggc ggnnngcnun anacaugcaa
60 gucgancgnn nnnnnnnnnn nnnnnnnnnn nnnnnnnnnn agnggcnnac
gggugaguaa 120 nncnunnnna nnunccnnnn nnnnnggnan annnnnnnga
aannnnnnnu aauaccnnau 180 nnnnnnnnnn nnnnaaagnn nnnnnnnnnn
nnnnnnnnnn nnnnnngann nnnnnnngnn 240 nnaunagnun guuggunngg
uaanggcnna ccaagncnnn gannnnuagc ngnncugaga 300 ggnngnncng
ccacanuggn acugaganac ggnccanacu ccuacgggag gcagcagunn 360
ggaaunuunn ncaauggnng naanncugan nnagcnannc cgcgugnnng anganggnnu
420 nnngnungua aannncunun nnnnnngang annnnnnnnn nnnnnnnnnn
nnnnnnnnnu 480 gacnnuannn nnnnannaag nnncggcnaa cuncgugcca
gcagccgcgg uaauacgnag 540 gnngcnagcg uunnncggan unanugggcg
uaaagngnnn gnaggnggnn nnnnnngunn 600 nnngunaaan nnnnnngcun
aacnnnnnnn nnncnnnnnn nacnnnnnnn cungagnnnn 660 nnagnggnnn
nnngaauunn nnguguagng gugnaauncg naganaunng nangaanacc 720
nnungcgaag gcnnnnnncu ggnnnnnnac ugacncunan nnncgaaagc nugggnagcn
780 aacaggauua gauacccugg uaguccangc nnuaaacgnu gnnnnnunnn
ngnnngnnnn 840 nnnnnnnnnn nnnnnnnnna nnnaacgnnn uaannnnncc
gccuggggag uacgnncgca 900 agnnunaaac ucaaangaau ugacggggnc
cngcacaagc ngnggagnau guggnuuaau 960 ucgangnnac gcgnanaacc
uuaccnnnnn uugacaunnn nnnnnnnnnn nnganannnn 1020 nnnnnnnnnn
nnnnnnnnnn nnnacaggug nugcauggnu gucgucagcu cgugnnguga 1080
gnuguugggu uaagucccgn aacgagcgca acccnnnnnn nnnguuncna ncnnnnnnnn
1140 ngngnacucn nnnnnnacug ccnnngnnaa nnnggaggaa ggnggggang
acgucaanuc 1200 nucaugnccc uuangnnnng ggcuncacac nuncuacaau
ggnnnnnaca nngngnngcn 1260 annnngnnan nnnnagcnaa ncnnnnaaan
nnnnucnnag uncggaungn nnncugcaac 1320 ucgnnnncnu gaagnnggan
ucgcuaguaa ucgnnnauca gnangnnncg gugaauacgu 1380 ucncgggncu
uguacacacc gcccgucann ncangnnagn nnnnnnnncc nnaagnnnnn 1440
nnnnnnncnn nnnngnnnnn nnnnncnang gnnnnnnnnn nganugggnn naagucguaa
1500 caagguancc nuannngaan nugnggnugg aucaccuccu un 1542 4 2904 RNA
Artificial Sequence misc_feature 23S rRNA consensus sequence 4
nnnnaagnnn nnaagngnnn nngguggaug ccunggcnnn nnnagncgan gaaggangnn
60 nnnnncnncn nnanncnnng gnnagnngnn nnnnnncnnn nnanccnnng
nunuccgaau 120 ggggnaaccc nnnnnnnnnn nnnnnnnnan nnnnnnnnnn
nnnnnnnnnn nnnnnnngnn 180 nacnnnnnga anugaaacau cunaguannn
nnaggaanag aaannaannn ngauuncnnn 240 nguagnggcg agcgaannng
nannagncnn nnnnnnnnnn nnnnnnnnnn nnnannngaa 300 nnnnnuggna
agnnnnnnnn nannngguna nannccngua nnnnaaannn nnnnnnnnnn 360
nnnnnnnnnn aguannncnn nncncgngnn annnngunng aannngnnnn gaccannnnn
420 naagncuaaa uacunnnnnn ngaccnauag ngnannagua cngugangga
aaggngaaaa 480 gnacccnnnn nangggagug aaanagnncc ugaaaccnnn
nncnuanaan nngunnnagn 540 nnnnnnnnnn nnnuganngc gunccuuuug
nannaugnnn cngnganuun nnnunnnnng 600 cnagnuuaan nnnnnnnngn
agncgnagng aaancgagun nnaanngngc gnnnagunnn 660 nngnnnnaga
cncgaancnn ngugancuan nnaugnncag gnugaagnnn nnguaanann 720
nnnuggaggn ccgaacnnnn nnnnguugaa aannnnnngg augannugug nnungnggng
780 aaanncnaan cnaacnnngn nauagcuggu ucucnncgaa annnnuuuag
gnnnngcnun 840 nnnnnnnnnn nnnnggnggu agagcacugn nnnnnnnnng
gnnnnnnnnn nnnnuacnna 900 nnnnnnnnaa acuncgaaun ccnnnnnnnn
nnnnnnnngn agnnanncnn ngngngnuaa 960 nnuncnnngu nnanagggna
acancccaga ncnncnnnua aggncccnaa nnnnnnnnua 1020 aguggnaaan
gangugnnnn nncnnanaca nnnaggangu uggcuuagaa gcagccancn 1080
uunaaagann gcguaanagc ucacunnucn agnnnnnnng cgcngannau nuancgggnc
1140 uaannnnnnn nccgaannnn nngnnnnnnn nnnnnnnnnn nnnnngguag
nngagcgunn 1200 nnnnnnnnnn ngaagnnnnn nngnnannnn nnnuggannn
nnnnnnagug ngnaugnngn 1260 naunaguanc gannnnnnnn gugananncn
nnnncnccgn annncnaagg nuuccnnnnn 1320 nangnunnuc nnnnnngggu
nagucgnnnc cuaagnngag ncnganangn nuagnngaug 1380 gnnannnggu
nnauauuccn nnacnnnnnn nnnnnnnnnn nnnnngacgn nnnnngnnnn 1440
nnnnnnnnnn nnnnggnnnn nnnnnnnnnn nnnnnnnnnn nnnnnnnnnn nnnnnnnnnn
1500 nnnnnnnnnn nnnnnnnnnn nnnnnnnnnn nnnnnnnnnn nnnnnnnnnn
nnnnnnnnnn 1560 nnnncnngaa aannnnnnnn nnnnnnnnnn nnnnnnnnnc
guaccnnaaa ccgacacagg 1620 ungnnnngnn gagnanncnn aggngnnngn
nnnaannnnn nnnaaggaac unngcaaanu 1680 nnnnccguan cuucggnana
aggnnnncnn nnnnnnnnnn nnnnnnnnnn nnnnnnnnnn 1740 nnnnnnnnng
nnnnannnan nngnnnnnnn cnacuguuua nnaaaaacac agnncnnugc 1800
naanncgnaa gnnganguau anggnnugac nccugcccng ugcnngaagg uuaanngnnn
1860 nnnnnngnnn nngnnnnnnn nnnnannnaa gcccnnguna acggcggnng
uaacuauaac 1920 nnuccuaagg uagcgaaauu ccuugucggg uaaguuccga
ccngcacgaa nggngnaang 1980 annnnnnnnc ugucucnnnn nnnnncncng
ngaanuunna nunnnnguna agaugcnnnn 2040 uncncgcnnn nngacggaaa
gaccccnngn ancuuuacun nannnunnna nugnnnnnnn 2100 nnnnnnnnug
unnagnauag gunggagncn nngannnnnn nncgnnagnn nnnnnggagn 2160
cnnnnnugnn auacnacncu nnnnnnnnnn nnnnucuaac nnnnnnnnnn nancnnnnnn
2220 nnngacanug nnngnngggn aguuunacug gggcggunnc cuccnaaann
guaacggagg 2280 ngnncnaagg unnncunann nnggnnggnn aucnnnnnnn
nagunnaann gnanaagnnn 2340 gcnunacugn nagnnnnacn nnncgagcag
nnncgaaagn nggnnnuagu gauccggngg 2400 unnnnnnugg aagngccnuc
gcucaacgga uaaaagnuac ncnggggaua acaggcunau 2460 nnnncccaag
aguncanauc gacggnnnng uuuggcaccu cgaugucggc ucnucncauc 2520
cuggggcugn agnngguccc aagggunngg cuguucgccn nuuaaagngg nacgngagcu
2580 ggguunanaa cgucgugaga caguungguc ccuaucngnn gngngngnnn
gannnuugan 2640 nngnnnugnn cnuaguacga gaggaccggn nngnacnnan
cncuggugnn ncnguugunn 2700 ngccannngc anngcngnnu agcuannunn
ggnnnngaua anngcugaan gcaucuaagn 2760 nngaancnnn cnnnnagann
agnnnucncn nnnnnnnnnn nnnnnnnnna gnnncnnnnn 2820 agannannnn
gungauaggn nngnnnugna agnnnngnna nnnnunnagn nnacnnnuac 2880
uaaunnnncn nnnnncuunn nnnn 2904 5 13 DNA Artificial Sequence
misc_feature Primer 5 cgtggtgacc ctt 13 6 14 DNA Artificial
Sequence misc_feature Primer 6 cgtcgtcacc gcta 14 7 13 DNA
Artificial Sequence misc_feature Primer 7 cgtggtaccc ctt 13
* * * * *