Patent Grant
8073627
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.
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.
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.
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.
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.
One diagnostic assay, which has gained widespread acceptance, is for C. trachomatis. Conventional detection systems are limiting due to inadequate sensitivity and specificity (direct immunofluorescence or enzyme immunoassay) or the requirement for specialized culture facilities, due to the fastidious characteristics of this microbe. Laboratory development, followed by widespread clinical validation testing in a variety of acute and nonacute care settings have demonstrated excellent sensitivity (90-100%) and specificity (97%) of the PCR assay leading to its commercial development. Proven efficacy of the PCR assay from both genital and urine sampling, have resulted in its application to a variety of clinical setting, most recently including routine screening of patients considered at risk.
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.
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.
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.
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.
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.
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.
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 21st 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.
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.
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.
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.
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.
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.
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.
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.
WO 99/14375 describes methods, PCR primers and kits for use in analyzing preselected DNA tandem nucleotide repeat alleles by mass spectrometry.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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).
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.
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.
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
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—971-1062, 16S—1228-1310 and 16S—1100-1188 regions are 98-99% conserved in about 900 species of bacteria (16S=16S rRNA, numbers indicate nucleotide position). In one embodiment of the present invention, primers used in the present method bind to one or more of these regions or portions thereof.
As will be appreciated by those of skill in the art, each representative primer pair is preferably designed to yield amplification products, i.e. amplicons, having molecular masses which vary amongst different bioagents. Consequently, the amplification products have the capacity to yield identifying information to distinguish between a plurality of bioagents. In one embodiment, such amplification products have the capacity to yield identifying information sufficient to distinguish among three or more bioagents. In other embodiments, such amplification products have the capacity to yield identifying information sufficient to distinguish among as many as five, 10, 50, 100 or more bioagents. Such amplification products enable unbiased simultaneous parallel identification of bioagents in clinical or environmental samples in a manner not previously possible because existing bioagent detection methods such as probe-based methods and culture methods are biased toward detection of particular bioagents.
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.
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.
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, “rna.icmb.utexas.edu/.” There is also a publicly available rRNA database created and maintained by the University of Antwerp, Belgium on the world wide web of the Internet at, for example, “rrna.uia.ac.be.”
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.
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.
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 3rd 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-∃-D-ribofuranosyl)-imidazole-4-carboxamide (Sala et al., Nucl. Acids Res., 1996, 24, 3302-3306).
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.
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.
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 (
Biological warfare fungus biowarfare agents include, but are not limited to, coccidioides immitis (Coccidioidomycosis).
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.
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.
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).
Examples of (+)-strand RNA viruses include, but are not limited to, picornaviruses (e.g., coxsackievirus, echovirus, human coxsackievirus A, human echovirus, human enterovirus, human poliovirus, hepatitis A virus, human parechovirus, human rhinovirus), astroviruses (e.g., human astrovirus), calciviruses (e.g., chiba virus, chitta virus, human calcivirus, norwalk virus), nidovirales (e.g., human coronavirus, human torovirus), flaviviruses (e.g., dengue virus 1-4, Japanese encephalitis virus, Kyanasur forest disease virus, Murray Valley encephalitis virus, Rocio virus, St. Louis encephalitis virus, West Nile virus, yellow fever virus, hepatitis c virus) and togaviruses (e.g., Chikugunya virus, Eastern equine encephalitis virus, Mayaro virus, O′ nyong-nyong virus, Ross River virus, Venezuelan equine encephalitis virus, Rubella virus, hepatitis E virus). The hepatitis C virus has a 5′-untranslated region of 340 nucleotides, an open reading frame encoding 9 proteins having 3010 amino acids and a 3′-untranslated region of 240 nucleotides. The 5′-UTR and 3′-UTR are 99% conserved in hepatitis C viruses.
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.
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.
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.
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.).
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.
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.
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.
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 13C and 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+]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 [13C, 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).
While mass measurements of intact nucleic acid regions are believed to be adequate to determine most bioagents, tandem mass spectrometry (MSn) 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).
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.
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.
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
The mass tag phosphorothioate A (A*) was used to distinguish a Bacillus anthracis cluster. The B. anthracis (A14G9C14T9) had an average MW of 14072.26, and the B. anthracis (A1A*13G9C14T9) 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
In another example, assume the measured molecular masses of each strand are 30,000.115Da and 31,000.115 Da respectively, and the measured number of dT and dA residues are (30,28) and (28,30). If the molecular mass is accurate to 100 ppm, there are 7 possible combinations of dG+dC possible for each strand. However, if the measured molecular mass is accurate to 10 ppm, there are only 2 combinations of dG+dC, and at 1 ppm accuracy there is only one possible base composition for each strand.
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.
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.
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.
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.
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.
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/.”
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 O results (“-” indicates a deletion). These sequences can be distinguished by designing a single primer pair which flanks these regions, followed by amplification and mass determination.
The logic of the query is always available to the user and can be branched out from any point to create alternate search paths. Queries of user-defined complexity and depth can be annotated, saved as XML files and re-run or edited at a later time. The lists of results can be manually manipulated or combined with other lists using set logic (union, intersection, difference, etc.). Result lists can display any level of depth for a query from a single result to an entire history for the query that produced those results. Individual items in the list will display further detail when selected, and the display may be simple text output, more complex HTML or even graphical in nature. Our automatically generated interfaces use HTML as the default detail display, but the methods can be manually replaced with more complex visualization code. We use HTML not only for display formatting, but for the ability to embed URLs to other public data sources, allowing us to link from within the system to externally available information.
Referring now to
Certain aspects of the present invention include process steps and instructions described herein in the form of an algorithm. It should be noted that the process steps and instructions of the present invention could be embodied in software, firmware or hardware, and when embodied in software, could be downloaded to reside on and be operated from different platforms used by real time network operating systems.
The present invention also relates to an apparatus for performing the operations herein. This apparatus may be specially constructed for the required purposes, or it may comprise a general-purpose computer selectively activated or reconfigured by a computer program stored in the computer. Such a computer program may be stored in a computer readable storage medium, such as, but is not limited to, any type of disk including floppy disks, optical disks, CD-ROMs, magnetic-optical disks, read-only memories (ROMs), random access memories (RAMs), EPROMs, EEPROMs, magnetic or optical cards, application specific integrated circuits (ASICs), or any type of media suitable for storing electronic instructions, and each coupled to a computer system bus. Furthermore, the computers referred to in the specification may include a single processor or may be architectures employing multiple processor designs for increased computing capability.
The algorithms and displays presented herein are not inherently related to any particular computer or other apparatus. Various general-purpose systems may also be used with programs in accordance with the teachings herein, or it may prove convenient to construct more specialized apparatus to perform the required method steps. The required structure for a variety of these systems will appear from the description above. In addition, the present invention is not described with reference to any particular programming language.
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.
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.
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.
Apparatus for Infrared Multiphoton Dissociation: A 25 watt CW CO2 laser operating at 10.6 μm 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.
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
Included in the short bacterial database cross-section in Table 2 are many well known pathogens/biowarfare agents (shown in bold/red typeface) such as Bacillus anthracis or Yersinia pestis as well as some of the bacterial organisms found commonly in the natural environment such as Streptomyces. Even closely related organisms can be distinguished from each other by the appropriate choice of primers. For instance, two low G+C organisms, Bacillus anthracis and Staph aureus, can be distinguished from each other by using the primer pair defined by 16S—1337 or 23S—855 (ΔM of 4 Da).
Acinetobacter calcoaceticus
55005
54388
28448
35238
51296
30295
42651
39560
56850
Bacillus cereus
Bordetella bronchiseptica
Borrelia burgdorferi
58098
55011
28448
35854
50683
Campylobacter jejuni
55000
55007
29063
35855
50676
30295
42036
38941
56230
55006
53767
28445
35855
51291
30300
42656
39562
54999
Clostridium difficile
Enterococcus faecalis
55622
55009
28445
35857
51301
30301
42656
39562
54999
53769
54385
28445
35856
51298
Haemophilus influenzae
Klebsiella pneumoniae
55618
55626
28446
35857
51303
Mycobacterium avium
Mycobacterium leprae
Mycobacterium tuberculosis
Mycoplasma genitalium
Mycoplasma pneumoniae
Neisseria gonorrhoeae
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
Streptomyces
Treponema pallidum
55625
55626
28443
35857
52536
29063
30303
35241
50675
Vibrio parahaemolyticus
55620
55626
28443
35857
51299
1Molecular mass distribution of PCR amplified regions for a selection of organisms (rows) across various primer pairs (columns).
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.
A conserved Bacillus region from B. anthracis (A14G9C14T9) and B. cereus (A15G9C13T9) having a C to A base change was synthesized and subjected to ESI-TOF MS. The results are shown in
In other examples of the present invention, the pathogen Vibrio cholera can be distinguished from Vibrio parahemolyticus with ΔM>600 Da using one of three 16S primer sets shown in Table 2 (16S—971, 16S—1228 or 16S—1294) as shown in Table 4. The two mycoplasma species in the list (M. genitalium and M. pneumoniae) can also be distinguished from each other, as can the three mycobacteriae. While the direct mass measurements of amplified products can identify and distinguish a large number of organisms, measurement of the base composition signature provides dramatically enhanced resolving power for closely related organisms. In cases such as Bacillus anthracis and Bacillus cereus that are virtually indistinguishable from each other based solely on mass differences, compositional analysis or fragmentation patterns are used to resolve the differences. The single base difference between the two organisms yields different fragmentation patterns, and despite the presence of the ambiguous/unidentified base N at position 20 in B. anthracis, the two organisms can be identified.
Tables 4a-b show examples of primer pairs from Table 1 which distinguish pathogens from background.
Bacillus anthracis
Staphylococcus aureus
Vibrio cholerae
Vibrio parahaemolyticus
Table 4 shows the expected molecular weight and base composition of region 16S—1100-1188 in Mycobacterium avium and Streptomyces sp.
Mycobacterium
avium
Streptomyces sp.
Table 5 shows base composition (single strand) results for 16S—1100-1188 primer amplification reactions different species of bacteria. Species which are repeated in the table (e.g., Clostridium botulinum) are different strains which have different base compositions in the 16S—1100-1188 region.
Mycobacterium avium
Streptomyces sp.
Ureaplasma urealyticum
Streptomyces sp.
Mycobacterium leprae
M. tuberculosis
Nocardia asteroides
Fusobacterium necroforum
Listeria monocytogenes
Clostridium botulinum
Neisseria gonorrhoeae
Bartonella quintana
Enterococcus faecalis
Bacillus megaterium
Bacillus subtilis
Pseudomonas aeruginosa
Legionella pneumophila
Mycoplasma pneumoniae
Clostridium botulinum
Enterococcus faecium
Acinetobacter calcoaceti
Leptospira borgpeterseni
Leptospira interrogans
Clostridium perfringens
Bacillus anthracis
Bacillus cereus
Bacillus thuringiensis
Aeromonas hydrophila
Escherichia coli
Pseudomonas putida
Escherichia coli
Shigella dysenteriae
Vibrio cholerae
Aeromonas hydrophila
Aeromonas salmonicida
Mycoplasma genitalium
Clostridium botulinum
Bordetella bronchiseptica
Francisella tularensis
Bacillus anthracis
Campylobacter jejuni
Staphylococcus aureus
Helicobacter pylori
Helicobacter pylori
Moraxella catarrhalis
Haemophilus influenzae Rd
Chlamydia trachomatis
Chlamydophila pneumoniae
C. pneumonia AR39
Pseudomonas putida
Proteus vulgaris
Yersinia pestis
Yersinia pseudotuberculos
Clostridium botulinum
Clostridium tetani
Francisella tularensis
Acinetobacter calcoacetic
Bacteriodes fragilis
Chlamydophila psittaci
Borrelia burgdorferi
Streptobacillus monilifor
Rickettsia prowazekii
Rickettsia rickettsii
Mycoplasma mycoides
The same organism having different base compositions are different strains. Groups of organisms which are highlighted or in italics have the same base compositions in the amplified region. Some of these organisms can be distinguished using multiple primers. For example, Bacillus anthracis can be distinguished from Bacillus cereus and Bacillus thuringiensis using the primer 16S—971-1062 (Table 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.
Aeromonas hydrophila
Aeromonas salmonicida
Bacillus anthracis
Bacillus cereus
Bacillus thuringiensis
Chlamydia trachomatis
Chlamydia pneumoniae
AR39
Leptospira borgpetersenii
Leptospira interrogans
Mycoplasma genitalium
Mycoplasma pneumoniae
Escherichia coli
Shigella dysenteriae
Proteus vulgaris
Yersinia pestis
Yersinia
pseudotuberculosis
Francisella tularensis
Rickettsia prowazekii
Rickettsia rickettsii
The sequence of B. anthracis and B. cereus in region 16S—971 is shown below. Shown in bold is the single base difference between the two species which can be detected using the methods of the present invention. B. anthracis has an ambiguous base at position 20.
B. anthracis
—16S—971 GCGAAGAACCUUACCAGGUNUUGACAUCCUCUGACAACCCUAGAGAUA GGGCUUCUCCUUCGGGAGCAGAGUGACAGGUGGUGCAUGGUU (SEQ ID NO:4)
B. cereus
—16S—971 GCGAAGAACCUUACCAGGUCUUGACAUCCUCUGAAAACCCUAGAGAUA GGGCUUCUCCUUCGGGAGCAGAGUGACAGGUGGUGCAUGGUU (SEQ ID NO:5)
The mass measurement accuracy that can be obtained using an internal mass standard in the ESI-MS study of PCR products is shown in
An ESI-TOF MS spectrum was obtained from an aqueous solution containing 5 μM 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 (
An ESI-FTICR-MS spectrum was obtained from an aqueous solution containing 5 μM 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 (
ESI-TOF MS spectra were obtained on a synthetic 56-mer oligonucleotide (5 μM) from the saspB gene of B. anthracis containing an internal mass standard at an ESI of 1.7 μL/min as a function of sample consumption. The results (
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]8− to [M-3H+]3− (
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.
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 interne connection, searching for a molecular mass match.
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.
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.
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.
This application is a continuation-in-part of U.S. application Ser. No. 09/891,793, filed on Jun. 26, 2001. This application is also a continuation-in-part of U.S. application Ser. No. 10/728,486 titled “Methods For Rapid Identification of Pathogens In Humans and Animals,” filed on Dec. 5, 2003. This application also claims the benefit of U.S. Provisional Application 60/509,911, titled “Database For Microbial Investigations,” filed on Oct. 9, 2003. In addition, this application is related to U.S. application Ser. No. 10/418,514, (U.S. Publication No. US2004-0209260), filed on Apr. 18, 2003. Each of the above applications is incorporated by reference in its entirety into this application.
This invention was made with United States Government support under DARPA contract MDA972-00-C-0053. The United States Government has certain rights in the invention.
| Number | Name | Date | Kind |
|---|---|---|---|
| 4075475 | Risby et al. | Feb 1978 | A |
| 4683195 | Mullis et al. | Jul 1987 | A |
| 4683202 | Mullis | Jul 1987 | A |
| 4965188 | Mullis et al. | Oct 1990 | A |
| 5015845 | Allen et al. | May 1991 | A |
| 5072115 | Zhou | Dec 1991 | A |
| 5143905 | Sivasubramanian et al. | Sep 1992 | A |
| 5213961 | Bunn et al. | May 1993 | A |
| 5219727 | Wang et al. | Jun 1993 | A |
| 5288611 | Kohne | Feb 1994 | A |
| 5436129 | Stapleton | Jul 1995 | A |
| 5451500 | Stapleton | Sep 1995 | A |
| 5472843 | Milliman | Dec 1995 | A |
| 5476774 | Wang et al. | Dec 1995 | A |
| 5484908 | Froehler et al. | Jan 1996 | A |
| 5502177 | Matteucci et al. | Mar 1996 | A |
| 5503980 | Cantor | Apr 1996 | A |
| 5504327 | Sproch et al. | Apr 1996 | A |
| 5504329 | Mann et al. | Apr 1996 | A |
| 5523217 | Lupski et al. | Jun 1996 | A |
| 5527669 | Resnick et al. | Jun 1996 | A |
| 5527675 | Coull et al. | Jun 1996 | A |
| 5547835 | Koster | Aug 1996 | A |
| 5567587 | Kohne | Oct 1996 | A |
| 5576204 | Blanco et al. | Nov 1996 | A |
| 5580733 | Levis et al. | Dec 1996 | A |
| 5605798 | Koster | Feb 1997 | A |
| 5608217 | Franzen et al. | Mar 1997 | A |
| 5612179 | Simons | Mar 1997 | A |
| 5622824 | Koster | Apr 1997 | A |
| 5625184 | Vestal et al. | Apr 1997 | A |
| 5639606 | Willey | Jun 1997 | A |
| 5641632 | Kohne | Jun 1997 | A |
| 5645985 | Froehler et al. | Jul 1997 | A |
| 5683869 | Ramsay Shaw et al. | Nov 1997 | A |
| 5686242 | Bruice et al. | Nov 1997 | A |
| 5691141 | Koster | Nov 1997 | A |
| 5700642 | Monforte et al. | Dec 1997 | A |
| 5702895 | Matsunaga et al. | Dec 1997 | A |
| 5707802 | Sandhu et al. | Jan 1998 | A |
| 5712125 | Uhlen | Jan 1998 | A |
| 5716825 | Hancock et al. | Feb 1998 | A |
| 5727202 | Kucala | Mar 1998 | A |
| 5745751 | Nelson et al. | Apr 1998 | A |
| 5747246 | Pannetier et al. | May 1998 | A |
| 5747251 | Carson et al. | May 1998 | A |
| 5753467 | Jensen et al. | May 1998 | A |
| 5753489 | Kistner et al. | May 1998 | A |
| 5759771 | Tilanus | Jun 1998 | A |
| 5763169 | Sandhu et al. | Jun 1998 | A |
| 5763588 | Matteucci et al. | Jun 1998 | A |
| 5770367 | Southern et al. | Jun 1998 | A |
| 5777324 | Hillenkamp | Jul 1998 | A |
| 5814442 | Natarajan et al. | Sep 1998 | A |
| 5822824 | Dion | Oct 1998 | A |
| 5828062 | Jarrell et al. | Oct 1998 | A |
| 5830653 | Froehler et al. | Nov 1998 | A |
| 5830655 | Monforte et al. | Nov 1998 | A |
| 5830853 | Backstrom et al. | Nov 1998 | A |
| 5832489 | Kucala | Nov 1998 | A |
| 5834255 | Van Gemen et al. | Nov 1998 | A |
| 5845174 | Yasui et al. | Dec 1998 | A |
| 5849492 | Rogan | Dec 1998 | A |
| 5849497 | Steinman | Dec 1998 | A |
| 5849901 | Mabilat et al. | Dec 1998 | A |
| 5851765 | Koster | Dec 1998 | A |
| 5856174 | Lipshutz et al. | Jan 1999 | A |
| 5864137 | Becker et al. | Jan 1999 | A |
| 5866429 | Bloch | Feb 1999 | A |
| 5869242 | Kamb | Feb 1999 | A |
| 5871697 | Rothberg et al. | Feb 1999 | A |
| 5872003 | Koster | Feb 1999 | A |
| 5876936 | Ju | Mar 1999 | A |
| 5876938 | Stolowitz et al. | Mar 1999 | A |
| 5885775 | Haff et al. | Mar 1999 | A |
| 5900481 | Lough et al. | May 1999 | A |
| 5928905 | Stemmer et al. | Jul 1999 | A |
| 5928906 | Koster | Jul 1999 | A |
| 5965363 | Monforte et al. | Oct 1999 | A |
| 5965383 | Vogel et al. | Oct 1999 | A |
| 5972693 | Rothberg et al. | Oct 1999 | A |
| 5976798 | Parker et al. | Nov 1999 | A |
| 5981176 | Wallace | Nov 1999 | A |
| 5981190 | Israel | Nov 1999 | A |
| 5994066 | Bergeron et al. | Nov 1999 | A |
| 6001564 | Bergeron et al. | Dec 1999 | A |
| 6005096 | Matteucci et al. | Dec 1999 | A |
| 6007690 | Nelson et al. | Dec 1999 | A |
| 6007992 | Lin et al. | Dec 1999 | A |
| 6015666 | Springer et al. | Jan 2000 | A |
| 6018713 | Coli et al. | Jan 2000 | A |
| 6024925 | Little et al. | Feb 2000 | A |
| 6028183 | Lin et al. | Feb 2000 | A |
| 6043031 | Koster et al. | Mar 2000 | A |
| 6046005 | Ju | Apr 2000 | A |
| 6051378 | Monforte et al. | Apr 2000 | A |
| 6054278 | Dodge et al. | Apr 2000 | A |
| 6055487 | Margery et al. | Apr 2000 | A |
| 6060246 | Summerton et al. | May 2000 | A |
| 6061686 | Gauvin et al. | May 2000 | A |
| 6063031 | Cundari et al. | May 2000 | A |
| 6074823 | Koster | Jun 2000 | A |
| 6074831 | Yakhini et al. | Jun 2000 | A |
| 6090558 | Butler et al. | Jul 2000 | A |
| 6104028 | Hunter et al. | Aug 2000 | A |
| 6110710 | Smith et al. | Aug 2000 | A |
| 6111251 | Hillenkamp | Aug 2000 | A |
| 6133436 | Koster et al. | Oct 2000 | A |
| 6140053 | Koster | Oct 2000 | A |
| 6146144 | Fowler et al. | Nov 2000 | A |
| 6146854 | Koster et al. | Nov 2000 | A |
| 6153389 | Haarer et al. | Nov 2000 | A |
| 6159681 | Zebala | Dec 2000 | A |
| 6180339 | Sandhu et al. | Jan 2001 | B1 |
| 6180372 | Franzen | Jan 2001 | B1 |
| 6187842 | Kobayashi et al. | Feb 2001 | B1 |
| 6194144 | Koster | Feb 2001 | B1 |
| 6197498 | Koster | Mar 2001 | B1 |
| 6214555 | Leushner et al. | Apr 2001 | B1 |
| 6218118 | Sampson et al. | Apr 2001 | B1 |
| 6221587 | Ecker et al. | Apr 2001 | B1 |
| 6221598 | Schumm et al. | Apr 2001 | B1 |
| 6221601 | Koster et al. | Apr 2001 | B1 |
| 6221605 | Koster | Apr 2001 | B1 |
| 6225450 | Koster | May 2001 | B1 |
| 6235476 | Bergmann et al. | May 2001 | B1 |
| 6235478 | Koster | May 2001 | B1 |
| 6235480 | Shultz et al. | May 2001 | B1 |
| 6238871 | Koster | May 2001 | B1 |
| 6238927 | Abrams et al. | May 2001 | B1 |
| 6239159 | Brown et al. | May 2001 | B1 |
| 6258538 | Koter et al. | Jul 2001 | B1 |
| 6261769 | Everett et al. | Jul 2001 | B1 |
| 6265716 | Hunter et al. | Jul 2001 | B1 |
| 6265718 | Park et al. | Jul 2001 | B1 |
| 6266131 | Hamada et al. | Jul 2001 | B1 |
| 6266144 | Li | Jul 2001 | B1 |
| 6268129 | Gut et al. | Jul 2001 | B1 |
| 6268131 | Kang et al. | Jul 2001 | B1 |
| 6268144 | Koster | Jul 2001 | B1 |
| 6268146 | Shultz | Jul 2001 | B1 |
| 6270973 | Lewis et al. | Aug 2001 | B1 |
| 6270974 | Shultz et al. | Aug 2001 | B1 |
| 6274726 | Laugharn, Jr. et al. | Aug 2001 | B1 |
| 6277573 | Koster et al. | Aug 2001 | B1 |
| 6277578 | Shultz et al. | Aug 2001 | B1 |
| 6277634 | McCall et al. | Aug 2001 | B1 |
| 6300076 | Koster | Oct 2001 | B1 |
| 6303297 | Lincoln et al. | Oct 2001 | B1 |
| 6312893 | Van Ness et al. | Nov 2001 | B1 |
| 6312902 | Shultz et al. | Nov 2001 | B1 |
| 6322970 | Little et al. | Nov 2001 | B1 |
| 6361940 | Van Ness et al. | Mar 2002 | B1 |
| 6372424 | Brow et al. | Apr 2002 | B1 |
| 6389428 | Rigault et al. | May 2002 | B1 |
| 6391551 | Shultz et al. | May 2002 | B1 |
| 6393367 | Tang et al. | May 2002 | B1 |
| 6419932 | Dale | Jul 2002 | B1 |
| 6423966 | Hillenkamp et al. | Jul 2002 | B2 |
| 6428955 | Koster et al. | Aug 2002 | B1 |
| 6428956 | Crooke et al. | Aug 2002 | B1 |
| 6432651 | Hughes et al. | Aug 2002 | B1 |
| 6436635 | Fu et al. | Aug 2002 | B1 |
| 6436640 | Simmons et al. | Aug 2002 | B1 |
| 6453244 | Oefner et al. | Sep 2002 | B1 |
| 6458533 | Felder et al. | Oct 2002 | B1 |
| 6468743 | Romick et al. | Oct 2002 | B1 |
| 6468748 | Monforte et al. | Oct 2002 | B1 |
| 6475143 | Iliff | Nov 2002 | B2 |
| 6475736 | Stanton, Jr. | Nov 2002 | B1 |
| 6475738 | Shuber et al. | Nov 2002 | B2 |
| 6479239 | Anderson et al. | Nov 2002 | B1 |
| 6500621 | Koster | Dec 2002 | B2 |
| 6553317 | Lincoln et al. | Apr 2003 | B1 |
| 6558902 | Hillenkamp | May 2003 | B1 |
| 6563025 | Song et al. | May 2003 | B1 |
| 6566055 | Monforte et al. | May 2003 | B1 |
| 6568055 | Tang et al. | May 2003 | B1 |
| 6582916 | Schmidt et al. | Jun 2003 | B1 |
| 6586584 | McMillian et al. | Jul 2003 | B2 |
| 6589485 | Koster | Jul 2003 | B2 |
| 6602662 | Koster et al. | Aug 2003 | B1 |
| 6605433 | Fliss et al. | Aug 2003 | B1 |
| 6610492 | Stanton, Jr. et al. | Aug 2003 | B1 |
| 6613509 | Chen | Sep 2003 | B1 |
| 6613520 | Ashby | Sep 2003 | B2 |
| 6623928 | Van Ness et al. | Sep 2003 | B2 |
| 6638714 | Linnen et al. | Oct 2003 | B1 |
| 6680476 | Hidalgo et al. | Jan 2004 | B1 |
| 6682889 | Wang et al. | Jan 2004 | B1 |
| 6705530 | Kiekhaefer | Mar 2004 | B2 |
| 6706530 | Hillenkamp | Mar 2004 | B2 |
| 6716634 | Myerson | Apr 2004 | B1 |
| 6783939 | Olmsted et al. | Aug 2004 | B2 |
| 6800289 | Nagata et al. | Oct 2004 | B2 |
| 6813615 | Colasanti et al. | Nov 2004 | B1 |
| 6836742 | Brekenfeld | Dec 2004 | B2 |
| 6852487 | Barany et al. | Feb 2005 | B1 |
| 6856914 | Pelech | Feb 2005 | B1 |
| 6875593 | Froehler et al. | Apr 2005 | B2 |
| 6906316 | Sugiyama et al. | Jun 2005 | B2 |
| 6906319 | Hoyes | Jun 2005 | B2 |
| 6914137 | Baker | Jul 2005 | B2 |
| 6977148 | Dean et al. | Dec 2005 | B2 |
| 6994962 | Thilly | Feb 2006 | B1 |
| 7022835 | Rauth et al. | Apr 2006 | B1 |
| 7024370 | Epler et al. | Apr 2006 | B2 |
| 7108974 | Ecker et al. | Sep 2006 | B2 |
| 7198893 | Köster et al. | Apr 2007 | B1 |
| 7217510 | Ecker et al. | May 2007 | B2 |
| 7226739 | Ecker et al. | Jun 2007 | B2 |
| 7255992 | Ecker et al. | Aug 2007 | B2 |
| 7285422 | Little et al. | Oct 2007 | B1 |
| 7312036 | Sampath et al. | Dec 2007 | B2 |
| 7321828 | Cowsert et al. | Jan 2008 | B2 |
| 7349808 | Kreiswirth et al. | Mar 2008 | B1 |
| 7390458 | Burow et al. | Jun 2008 | B2 |
| 7419787 | Köster | Sep 2008 | B2 |
| 7501251 | Köster et al. | Mar 2009 | B2 |
| 7666588 | Ecker et al. | Feb 2010 | B2 |
| 7718354 | Ecker et al. | May 2010 | B2 |
| 7741036 | Ecker et al. | Jun 2010 | B2 |
| 7781162 | Ecker et al. | Aug 2010 | B2 |
| 20010039263 | Matthes et al. | Nov 2001 | A1 |
| 20020006611 | Portugal et al. | Jan 2002 | A1 |
| 20020028923 | Cowsert et al. | Mar 2002 | A1 |
| 20020042112 | Koster et al. | Apr 2002 | A1 |
| 20020042506 | Kristyanne et al. | Apr 2002 | A1 |
| 20020045178 | Cantor et al. | Apr 2002 | A1 |
| 20020055101 | Bergeron et al. | May 2002 | A1 |
| 20020090320 | Burow et al. | Jul 2002 | A1 |
| 20020120408 | Kreiswirth et al. | Aug 2002 | A1 |
| 20020137057 | Wold et al. | Sep 2002 | A1 |
| 20020138210 | Wilkes et al. | Sep 2002 | A1 |
| 20020150903 | Koster et al. | Oct 2002 | A1 |
| 20020150927 | Matray et al. | Oct 2002 | A1 |
| 20020168630 | Fleming et al. | Nov 2002 | A1 |
| 20020187490 | Tiedje et al. | Dec 2002 | A1 |
| 20030017487 | Xue et al. | Jan 2003 | A1 |
| 20030027135 | Ecker et al. | Feb 2003 | A1 |
| 20030039976 | Haff et al. | Feb 2003 | A1 |
| 20030050470 | An et al. | Mar 2003 | A1 |
| 20030064483 | Shaw et al. | Apr 2003 | A1 |
| 20030073112 | Zhang et al. | Apr 2003 | A1 |
| 20030082539 | Ecker et al. | May 2003 | A1 |
| 20030084483 | Simpson et al. | May 2003 | A1 |
| 20030101172 | De La Huerga | May 2003 | A1 |
| 20030104410 | Mittmann | Jun 2003 | A1 |
| 20030104699 | Minamihaba et al. | Jun 2003 | A1 |
| 20030113738 | Liu et al. | Jun 2003 | A1 |
| 20030113745 | Monforte et al. | Jun 2003 | A1 |
| 20030119018 | Omura et al. | Jun 2003 | A1 |
| 20030129589 | Koster et al. | Jul 2003 | A1 |
| 20030134312 | Burgoyne et al. | Jul 2003 | A1 |
| 20030148281 | Glucksmann | Aug 2003 | A1 |
| 20030148284 | Vision et al. | Aug 2003 | A1 |
| 20030167133 | Ecker et al. | Sep 2003 | A1 |
| 20030167134 | Ecker et al. | Sep 2003 | A1 |
| 20030175695 | Ecker et al. | Sep 2003 | A1 |
| 20030175696 | Ecker et al. | Sep 2003 | A1 |
| 20030175697 | Ecker et al. | Sep 2003 | A1 |
| 20030175729 | Van Eijk et al. | Sep 2003 | A1 |
| 20030186247 | Smarason et al. | Oct 2003 | A1 |
| 20030187588 | Ecker et al. | Oct 2003 | A1 |
| 20030187593 | Ecker et al. | Oct 2003 | A1 |
| 20030187615 | Epler et al. | Oct 2003 | A1 |
| 20030190605 | Ecker et al. | Oct 2003 | A1 |
| 20030190635 | McSwiggen | Oct 2003 | A1 |
| 20030194699 | Lewis et al. | Oct 2003 | A1 |
| 20030203398 | Bramucci et al. | Oct 2003 | A1 |
| 20030220844 | Marmellos et al. | Nov 2003 | A1 |
| 20030224377 | Wengel et al. | Dec 2003 | A1 |
| 20030225529 | Ecker et al. | Dec 2003 | A1 |
| 20030228571 | Ecker et al. | Dec 2003 | A1 |
| 20030228597 | Cowsert et al. | Dec 2003 | A1 |
| 20030228613 | Bornarth et al. | Dec 2003 | A1 |
| 20040005555 | Rothman et al. | Jan 2004 | A1 |
| 20040013703 | Ralph et al. | Jan 2004 | A1 |
| 20040014957 | Eldrup et al. | Jan 2004 | A1 |
| 20040023207 | Polansky | Feb 2004 | A1 |
| 20040023209 | Jonasson | Feb 2004 | A1 |
| 20040029129 | Wang et al. | Feb 2004 | A1 |
| 20040038206 | Zhang et al. | Feb 2004 | A1 |
| 20040038208 | Fisher et al. | Feb 2004 | A1 |
| 20040038234 | Gut et al. | Feb 2004 | A1 |
| 20040038385 | Langlois et al. | Feb 2004 | A1 |
| 20040081993 | Cantor et al. | Apr 2004 | A1 |
| 20040101809 | Weiss et al. | May 2004 | A1 |
| 20040110169 | Ecker et al. | Jun 2004 | A1 |
| 20040111221 | Beattie et al. | Jun 2004 | A1 |
| 20040117129 | Ecker et al. | Jun 2004 | A1 |
| 20040117354 | Azzaro et al. | Jun 2004 | A1 |
| 20040121309 | Ecker et al. | Jun 2004 | A1 |
| 20040121310 | Ecker et al. | Jun 2004 | A1 |
| 20040121311 | Ecker et al. | Jun 2004 | A1 |
| 20040121312 | Ecker et al. | Jun 2004 | A1 |
| 20040121313 | Ecker et al. | Jun 2004 | A1 |
| 20040121314 | Ecker et al. | Jun 2004 | A1 |
| 20040121315 | Ecker et al. | Jun 2004 | A1 |
| 20040121329 | Ecker et al. | Jun 2004 | A1 |
| 20040121335 | Ecker et al. | Jun 2004 | A1 |
| 20040121340 | Ecker et al. | Jun 2004 | A1 |
| 20040122598 | Ecker et al. | Jun 2004 | A1 |
| 20040122857 | Ecker et al. | Jun 2004 | A1 |
| 20040126764 | Lasken et al. | Jul 2004 | A1 |
| 20040137013 | Katinger et al. | Jul 2004 | A1 |
| 20040161770 | Ecker et al. | Aug 2004 | A1 |
| 20040180328 | Ecker et al. | Sep 2004 | A1 |
| 20040185438 | Ecker | Sep 2004 | A1 |
| 20040191769 | Marino et al. | Sep 2004 | A1 |
| 20040202997 | Ecker et al. | Oct 2004 | A1 |
| 20040209260 | Ecker et al. | Oct 2004 | A1 |
| 20040253583 | Ecker et al. | Dec 2004 | A1 |
| 20040253619 | Ecker et al. | Dec 2004 | A1 |
| 20050026147 | Walker et al. | Feb 2005 | A1 |
| 20050026641 | Hokao | Feb 2005 | A1 |
| 20050027459 | Ecker et al. | Feb 2005 | A1 |
| 20050065813 | Mishelevich et al. | Mar 2005 | A1 |
| 20050130196 | Hofstadler et al. | Jun 2005 | A1 |
| 20050130216 | Becker et al. | Jun 2005 | A1 |
| 20050142584 | Willson et al. | Jun 2005 | A1 |
| 20050250125 | Novakoff | Nov 2005 | A1 |
| 20050266397 | Ecker et al. | Dec 2005 | A1 |
| 20050266411 | Hofstadler et al. | Dec 2005 | A1 |
| 20060020391 | Kreiswirth et al. | Jan 2006 | A1 |
| 20060057605 | Sampath et al. | Mar 2006 | A1 |
| 20060121520 | Ecker et al. | Jun 2006 | A1 |
| 20060172330 | Osborn et al. | Aug 2006 | A1 |
| 20060205040 | Sampath | Sep 2006 | A1 |
| 20060240412 | Hall et al. | Oct 2006 | A1 |
| 20060259249 | Sampath et al. | Nov 2006 | A1 |
| 20070218467 | Ecker et al. | Sep 2007 | A1 |
| 20080160512 | Ecker et al. | Jul 2008 | A1 |
| 20080311558 | Ecker et al. | Dec 2008 | A1 |
| 20090004643 | Ecker et al. | Jan 2009 | A1 |
| 20090023150 | Koster et al. | Jan 2009 | A1 |
| 20090042203 | Koster | Feb 2009 | A1 |
| 20090092977 | Koster | Apr 2009 | A1 |
| 20090125245 | Hofstadler et al. | May 2009 | A1 |
| 20090148829 | Ecker et al. | Jun 2009 | A1 |
| 20090148836 | Ecker et al. | Jun 2009 | A1 |
| 20090148837 | Ecker et al. | Jun 2009 | A1 |
| 20090182511 | Ecker et al. | Jul 2009 | A1 |
| 20090239224 | Ecker et al. | Sep 2009 | A1 |
| 20090280471 | Ecker et al. | Nov 2009 | A1 |
| 20100070194 | Ecker et al. | Mar 2010 | A1 |
| 20100145626 | Ecker et al. | Jun 2010 | A1 |
| 20100184035 | Hall et al. | Jul 2010 | A1 |
| Number | Date | Country |
|---|---|---|
| 19732086 | Jan 1999 | DE |
| 19802905 | Jul 1999 | DE |
| 19824280 | Dec 1999 | DE |
| 19852167 | May 2000 | DE |
| 19943374 | Mar 2001 | DE |
| 10132147 | Feb 2003 | DE |
| 281390 | Sep 1988 | EP |
| 633321 | Jan 1995 | EP |
| 620862 | Apr 1998 | EP |
| 1035219 | Sep 2000 | EP |
| 1138782 | Oct 2001 | EP |
| 1234888 | Aug 2002 | EP |
| 1308506 | May 2003 | EP |
| 1310571 | May 2003 | EP |
| 1333101 | Aug 2003 | EP |
| 1365031 | Nov 2003 | EP |
| 1748072 | Jan 2007 | EP |
| 2811321 | Jan 2002 | FR |
| 2325002 | Nov 1998 | GB |
| 2339905 | Feb 2000 | GB |
| 5276999 | Oct 1993 | JP |
| 11137259 | May 1999 | JP |
| 24024206 | Jan 2004 | JP |
| 2004000200 | Jan 2004 | JP |
| 24201641 | Jul 2004 | JP |
| 24201679 | Jul 2004 | JP |
| WO8803957 | Jun 1988 | WO |
| WO9015157 | Dec 1990 | WO |
| WO9205182 | Apr 1992 | WO |
| WO9208117 | May 1992 | WO |
| WO9209703 | Jun 1992 | WO |
| WO9219774 | Nov 1992 | WO |
| WO 9303186 | Feb 1993 | WO |
| WO9305182 | Mar 1993 | WO |
| WO 9308297 | Apr 1993 | WO |
| WO 9416101 | Jul 1994 | WO |
| WO 9421822 | Sep 1994 | WO |
| WO9419490 | Sep 1994 | WO |
| WO 9504161 | Feb 1995 | WO |
| WO 9513396 | May 1995 | WO |
| WO9511996 | May 1995 | WO |
| WO9513395 | May 1995 | WO |
| WO9531997 | Nov 1995 | WO |
| WO9606187 | Feb 1996 | WO |
| WO9616186 | May 1996 | WO |
| WO 9629431 | Sep 1996 | WO |
| WO 9632504 | Oct 1996 | WO |
| WO 9637630 | Nov 1996 | WO |
| WO9635450 | Nov 1996 | WO |
| WO 9733000 | Sep 1997 | WO |
| WO9734909 | Sep 1997 | WO |
| WO 9737041 | Oct 1997 | WO |
| WO9747766 | Dec 1997 | WO |
| WO 9803684 | Jan 1998 | WO |
| WO 9812355 | Mar 1998 | WO |
| WO 9814616 | Apr 1998 | WO |
| WO 9815652 | Apr 1998 | WO |
| WO 9820020 | May 1998 | WO |
| WO 9820157 | May 1998 | WO |
| WO 9820166 | May 1998 | WO |
| WO 9826095 | Jun 1998 | WO |
| WO 9831830 | Jul 1998 | WO |
| WO9835057 | Aug 1998 | WO |
| WO9840520 | Sep 1998 | WO |
| WO 9854751 | Dec 1998 | WO |
| WO9854571 | Dec 1998 | WO |
| WO9905319 | Feb 1999 | WO |
| WO 9912040 | Mar 1999 | WO |
| WO 9914375 | Mar 1999 | WO |
| WO9913104 | Mar 1999 | WO |
| WO 9931278 | Jun 1999 | WO |
| WO9929898 | Jun 1999 | WO |
| WO9957318 | Nov 1999 | WO |
| WO9958713 | Nov 1999 | WO |
| WO9960183 | Nov 1999 | WO |
| WO0032750 | Jun 2000 | WO |
| WO0038636 | Jul 2000 | WO |
| WO0063362 | Oct 2000 | WO |
| WO0066762 | Nov 2000 | WO |
| WO0066789 | Nov 2000 | WO |
| WO0077260 | Dec 2000 | WO |
| WO0100828 | Jan 2001 | WO |
| WO0107648 | Feb 2001 | WO |
| WO0120018 | Mar 2001 | WO |
| WO0123604 | Apr 2001 | WO |
| WO0123608 | Apr 2001 | WO |
| WO0132930 | May 2001 | WO |
| WO0140497 | Jun 2001 | WO |
| WO0146404 | Jun 2001 | WO |
| WO0151661 | Jul 2001 | WO |
| WO0151662 | Jul 2001 | WO |
| WO0157263 | Aug 2001 | WO |
| WO0173119 | Oct 2001 | WO |
| WO0173199 | Oct 2001 | WO |
| WO0177392 | Oct 2001 | WO |
| WO0196388 | Dec 2001 | WO |
| WO0202811 | Jan 2002 | WO |
| WO0210186 | Feb 2002 | WO |
| WO0210444 | Feb 2002 | WO |
| WO 0222873 | Mar 2002 | WO |
| WO0218641 | Mar 2002 | WO |
| WO0221108 | Mar 2002 | WO |
| WO0224876 | Mar 2002 | WO |
| WO 0250307 | Jun 2002 | WO |
| WO 02057491 | Jul 2002 | WO |
| WO 02070664 | Sep 2002 | WO |
| WO02070728 | Sep 2002 | WO |
| WO02070737 | Sep 2002 | WO |
| WO 02077278 | Oct 2002 | WO |
| WO 02099034 | Dec 2002 | WO |
| WO02099095 | Dec 2002 | WO |
| WO02099129 | Dec 2002 | WO |
| WO02099130 | Dec 2002 | WO |
| WO 03002750 | Jan 2003 | WO |
| WO 03008636 | Jan 2003 | WO |
| WO03001976 | Jan 2003 | WO |
| WO 03016546 | Feb 2003 | WO |
| WO03012058 | Feb 2003 | WO |
| WO03012074 | Feb 2003 | WO |
| WO03014382 | Feb 2003 | WO |
| WO03018636 | Mar 2003 | WO |
| WO03020890 | Mar 2003 | WO |
| WO03033732 | Apr 2003 | WO |
| WO03054162 | Jul 2003 | WO |
| WO03054755 | Jul 2003 | WO |
| WO003060163 | Jul 2003 | WO |
| WO03075955 | Sep 2003 | WO |
| WO 03088979 | Oct 2003 | WO |
| WO 03093506 | Nov 2003 | WO |
| WO 03097869 | Nov 2003 | WO |
| WO0310219 | Dec 2003 | WO |
| WO03100035 | Dec 2003 | WO |
| WO03100068 | Dec 2003 | WO |
| WO03104410 | Dec 2003 | WO |
| WO03106635 | Dec 2003 | WO |
| WO2004003511 | Jan 2004 | WO |
| WO2004009849 | Jan 2004 | WO |
| WO2004011651 | Feb 2004 | WO |
| WO2004013357 | Feb 2004 | WO |
| WO2004040013 | May 2004 | WO |
| WO2004044123 | May 2004 | WO |
| WO20004044247 | May 2004 | WO |
| WO 2004052175 | Jun 2004 | WO |
| WO2004053076 | Jun 2004 | WO |
| WO2004053141 | Jun 2004 | WO |
| WO2004053164 | Jun 2004 | WO |
| WO2004060278 | Jul 2004 | WO |
| WO2004070001 | Aug 2004 | WO |
| WO2004072230 | Aug 2004 | WO |
| WO2004072231 | Aug 2004 | WO |
| WO2004101809 | Nov 2004 | WO |
| WO2005003384 | Jan 2005 | WO |
| WO2005009202 | Feb 2005 | WO |
| WO2005012572 | Feb 2005 | WO |
| WO2005024046 | Mar 2005 | WO |
| WO2005036369 | Apr 2005 | WO |
| WO 2005053141 | Jun 2005 | WO |
| WO2005054454 | Jun 2005 | WO |
| WO2005075686 | Aug 2005 | WO |
| WO2005086634 | Sep 2005 | WO |
| WO2005009197 | Oct 2005 | WO |
| WO2005098047 | Oct 2005 | WO |
| WO2005116263 | Dec 2005 | WO |
| WO2006089762 | Aug 2006 | WO |
| WO2006094238 | Sep 2006 | WO |
| WO2006135400 | Dec 2006 | WO |
| WO2007014045 | Feb 2007 | WO |
| WO2007086904 | Aug 2007 | WO |
| WO2008104002 | Aug 2008 | WO |
| WO2008118809 | Oct 2008 | WO |
| Number | Date | Country | |
|---|---|---|---|
| 20110238316 A1 | Sep 2011 | US |
| Number | Date | Country | |
|---|---|---|---|
| 60509911 | Oct 2003 | US |
| Number | Date | Country | |
|---|---|---|---|
| Parent | 09891793 | Jun 2001 | US |
| Child | 10754415 | US | |
| Parent | 10728486 | Dec 2003 | US |
| Child | 09891793 | US |
