Why is identification and classification of bacteria difficult

Moreover, the mass spectrometry approach presented allows the integration of data from different biological levels such as the genome and the proteome. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Competing interests: The authors T.

In general, new technologies for accurate and rapid identification of bacteria are essential to epidemiological surveillance, i. For classifying and identifying bacterial species, cumbersome physiological, serological, biochemical, chemotaxonomic, and more recently genomic methods have been routinely applied in microbiology [1].

For example, genetic approaches using digital genomic information for the detection of 16S rRNA genes provide specific tools for classification of bacteria. The analysis of DNA sequence similarities of housekeeping genes is also being used for multilocus sequence typing MLST approaches [2] , which can however be tedious in mass screening.

PCR-based methods to detect pathogens are available but cannot be used for classification, especially in the case of unknown bacterial samples. Moreover, the analysis of bacteria such as the highly conserved fire blight pathogen E. Mass spectrometric approaches that use molecular biological sample preparation have been shown recently in microbial typing [4] , [5]. These methods comprise highly sophisticated instrumentation, and the associated costs from sample preparation make the operation prohibitive for general use.

However, most of these procedures have so far not exceeded proof-of-principle level and were applied only to a limited number of bacterial species [7] , [8]. Moreover, all these procedures do not have proven maturity for easy and systematic application in microbiology. Consequently, biologists have not consistently utilized these approaches despite their great potential.

In the exemplary study of this article we focused on the mass spectrometry analysis of bacteria of the genus Erwinia and related phytopathogenic bacteria.

The genus Erwinia comprises several bacterial species, many of them connected to plant diseases [9]. The Erwinia species belong to the family of Enterobacteriaceae , which also include Escherichia coli , Yersinia spp.

Erwinia amylovora causes the devastating fire blight disease of rosaceous plants, such as apple and pear trees and some ornamentals. Since the last century, outbreaks of this disease have caused economical crisis in agriculture [10].

We describe a standardized sample preparation and analytical procedure for easy bacterial classification and identification by MALDI mass spectrometry detection of protein mass patterns Figure 1A. This method includes the use of advanced bioinformatics analysis and a database resource containing a comprehensive number of bacterial reference mass spectra. We enlarged the potential of this approach by genotyping an informative single nucleotide polymorphism SNP by mass spectrometry.

B: Classification of bacteria. Based on the protein mass patterns, bacterial strains can be clustered hierarchically.

A dendrogram generated by this approach including a comprehensive set of Erwinia type strains was displayed. Species with distance levels over had completely different mass signal patterns. Comparison of spectra of these species for a distance measure was thus uninformative. Strains clustering with distance levels lower than could be classified up to the species and partially to subspecies level. The limit of resolution was set by the distances derived from measurement variability.

All Erwinia bacteria analyzed in this study were conventionally cultured in liquid medium. This treatment completely destroyed the viability of the bacteria after an hour of fixation. Once the samples were fixed, they were treated with formic acid for cell wall disruption and acetonitrile for protein extraction Materials and Methods. Finally, a fraction of the protein samples was prepared on a MALDI target plate and mass spectra accumulation was performed automatically.

Applying the standardized experimental procedure including culturing in liquid media Materials and Methods , we generated a reference mass spectra database containing main spectra libraries MSPs of bacteria of the genus Erwinia and some other related bacteria. Therefore, we accumulated twenty mass spectra to achieve above average quality spectra.

Protein mass patterns were detected in the mass range of 2, to 20, Da. The building of a general database of reference mass spectra, which are produced by the standardized protocol applied in this study, is currently underway.

To date, this database comprises more than bacterial strains, including the genera Pantoea , Shigella , Listeria , Salmonella , and Klebsiella Materials and Methods.

The database has been implemented in our analysis software Materials and Methods and was used for the identification experiments shown below. To reproduce the results of this study and to test the software for additional applications, the software package and the reference mass spectra are freely available as a CD that can be requested from the authors.

For phylogenetic analysis, we clustered hierarchically mass spectra of type strains and others in dendrograms according to their mass signals and intensities Figure 1B. Each reference spectrum of a dataset was compared with the other reference spectra, thus resulting in a matrix of cross-wise identification values.

This matrix is used to calculate the distance values for each pair. Based on these distance values the dendrogram was generated using the according function of the statistical toolbox of Matlab 7. The clustering approach applied was based on similarity scores implemented in the analysis software. Interestingly, the mass spectrometry and the sequence-based dendrograms showed similar clustering within related species. As for the other cell parameters, co-evolution of 16S rRNA sequences and ribosomal proteins, which are mainly detected by mass spectrometry from whole bacterial cells, could be assumed [11].

Thus, the mass spectrometry-based dendrogram made significant sense from a biological point of view. This observation was confirmed by data generated from housekeeping genes, as well as microbiological and biochemical studies [12]. The two exemplary E. The average reproducibility of the procedure is exemplarily documented in Figure 3. In general, we observed coefficient of variation CV values slightly above 0.

For comparison to mass spectrometry based clustering a conventional 16S rRNA sequence-based dendrogram generated with ClustalW and Mega 3. Phylogenetic distances were estimated by the method of Jukes and Cantor [15]. The tree topology was inferred by neighbor-joining method with a bootstrap value of For the reconstruction of phylogeny, the neighbor-joining and maximum-parsimony procedures produced similar results.

The experimental variation was presumably caused mainly by slight changes in matrix-protein co-crystallization such as room temperature, pressure, and humidity.

In this exemplary experiment, the coefficient of variation CV for intra run was 0. Similar CVs were observed for the identification experiments presented in the paper. As shown in Table 1 , the robust analysis algorithms applied can easily handle the experimental variation that is associated with our approach. With the entire reference spectra library, we could identify unambiguously bacteria of the genus Erwinia.

Therefore, we analyzed a number of isolates from different locations of the world and samples from necrotic wood of diseased pear trees from Carinthia.

In many cases, plant samples contained mixtures of bacteria of the genus Erwinia or other genera. The approach became robust against growth times once the bacteria have entered the stationary phase data not shown.

With our approach, an identification score of 2. As summarized in Table 1 , we identified unambiguously pathogenic bacteria such as E. Erwinia quercinia from a variety of plant samples. Figure 4 shows a typical result of an identification experiment performed in this study.

Although the bacteria were grown on different media, similar identification scores were obtained due to their almost identical mass spectra Table 1. We could even detect the fire blight pathogen in washes of plant tissue from in vitro propagated pears that were infected with about 10 7 cells of E. In a larger study, we additionally analyzed over several months a comprehensive number of isolates Table 1. We screened successfully the mass spectra of these isolates with the entire reference spectra database.

In all cases we correctly detected the respective samples with similar identification scores. All mass spectrometry-based identification results presented in this paper were consistent with 16S rRNA sequencing and microbiological and biochemical data derived from intensively studied isolates that were stored in our laboratory. A typical mass spectrum of a bacterial sample taken from necrotic wood compared with a matching spectrum from the reference library.

Top Original mass spectra, Middle respective pseudo gel-view showing a bar-code of masses and their intensities, Bottom identification by comparison of experimental and reference mass spectra using a pattern matching algorithm. In this example, a highly reliable identification score of 2. For accurate identification of closely related species such as the plant pathogens E.

This algorithm uses selected characteristic mass signals to which specific values can be assigned in the analysis Figure 5. As shown in Table 2 , weighted pattern-matching helped to neatly determine very closely related strains that could not be distinguished by the initial pattern-matching procedure.

The settings used in this study are summarized in Table 3. Borrelia burgdorferi. However, names of places should not be used as nouns in the genitive case. For the Prokaryotes Bacteria and Archaea the rank kingdom is not used although some authors refer to phyla as kingdoms. If a new or amended species is placed in new ranks, according to Rule 9 of the Bacteriological Code the name is formed by the addition of an appropriate suffix to the stem of the name of the type genus.

For subclass and class the reccomendation from is generally followed, resulting in a neutral plural, however a few names do not follow this and instead keep into account Graeco-Latin grammar e. Phyla are not covered by the Bacteriological Code, however, the scientific community generally follows the Ncbi and Lpsn taxonomy, where the name of the phylum is generally the plural of the type genus, with the exception of the Firmicutes, Cyanobacteria, and Proteobacteria, whose names do not stem from a genus name.

The higher taxa proposed by Cavalier-Smith are generally disregarded by the molecular phylogeny community vide supra. Privacy Policy. Skip to main content. Microbial Evolution, Phylogeny, and Diversity. Search for:. Classification of Microorganisms. The Taxonomic Scheme Bacterial taxonomy is the rank-based classification of bacteria.

Learning Objectives Outline the factors that play a role in the classification of bacterial taxonomy. Key Takeaways Key Points Bacterial species differ amongst each other based on several characteristics, allowing for their identification and classification.

Gram staining results are most commonly used as a classification tool. Key Terms bacteria : A type, species, or strain of bacterium. Each group is given a rank and groups of a given rank can be aggregated to form a super group of higher rank and thus create a hierarchical classification. It is based on the chemical and physical properties of their cell walls.

Primarily, it detects peptidoglycan, which is present in a thick layer in Gram positive bacteria. The Diagnostic Scheme Diagnosis of infectious disease sometimes involves identifying an infectious agent either directly or indirectly.

Learning Objectives Outline the various types of diagnostic methods used to diagnose a microbial infection. Key Takeaways Key Points Diagnosis of infectious disease is nearly always initiated by medical history and physical examination. Diagnostic methods include: Microbial culture, microscopy, biochemical tests and molecular diagnostics. Key Terms Diagnosis : Diagnosis of infectious disease sometimes involves identifying an infectious agent either directly or indirectly.

The host may be an animal including humans , a plant, or even another microorganism. The Species Concept in Microbiology The number of species of bacteria and archaea is surprisingly small, despite their early evolution, genetic, and ecological diversity. Learning Objectives Describe the concept of polyphasic species.

The most commonly accepted definition is the polyphasic species definition, which takes into account both phenotypic and genetic differences. Key Terms bacteria : Bacteria constitute a large domain of prokaryotic microorganisms. Typically a few micrometres in length, bacteria have a wide range of shapes, ranging from spheres to rods and spirals. Bacteria were among the first life forms to appear on Earth, and are present in most habitats on the planet. A species is often defined as a group of organisms capable of interbreeding and producing fertile offspring.

Can this be done for roughly the same cost as a standard battery of biochemical identification tests and a genotype analysis? Can it be done with the same or better speed and efficiency as current methods? These are the challenges faced by researchers who are developing and using genomics-based identification methods.

It is difficult to predict how soon, if ever, whole-genome sequencing will be used as a routine means of bacterial identification; however, it is certain that the multilocus sequencing approaches described above will expand and mature rapidly. While we appreciate the technological challenges of DNA sequencing per se, perhaps an even greater challenge will be the establishment of large, integrated databases that allow for the rapid assembly of sequence data to help researchers make robust comparisons among sequences and predict identifications between bacteria with a high degree of confidence.

The lack of standardization for MLSA analysis needs to be addressed so that standards can be developed for comparisons of multiple taxa. Once these are in place, it will become progressively easier to develop MLSA- or MLST-type sequence-based strategies that accurately target multiple genes and can be used to provide a full range of genotypic information for all bacteria and archaea.

Microarrays are another technology that shows promise as a means of simultaneously identifying specific microbes and providing ecological context for the population structure and functional structure of a given microbial community. Microarrays work on the general principle of spotting probes for hundreds or thousands of genes onto a substrate e. The sample DNA or RNA is labeled with a fluorescent reporter molecule so that samples that hybridize with probes on the microarray can be detected rapidly.

Another example is the geochip, which has been developed to identify microbes involved in essential biogeochemical processes such as metal transformations, contaminant degradation, and primary carbon cycling He et al. In the clinical realm, the use of microarrays is moving forward rapidly, both for diagnostic purposes and for understanding the fundamentals of disease pathology Frye et al. However, because of their inherent complexity and relative expense, microarrays have yet to be used as standard methods in microbial identification.

Although genotypic information is valuable in identifying an organism and determining how it is related to others, methods that probe an organism's phenotypic properties remain critical for understanding the physiological and functional activities of an organism at the protein level. Phenotypic methods that determine the activity of specific enzymes, such as catalase or oxidase, or metabolic functions, such as the ability to degrade lactose, have long been a mainstay of bacterial identification.

The advent of new proteomics tools that are based primarily on mass spectrometry and allow rapid interrogation of biomolecules produced by an organism offers an excellent complement to classical microbiological and genomics-based techniques for bacterial classification, identification, and phenotypic characterization. What is also interesting is that some of these techniques are integrating genotypic and proteomic data to provide more complete information.

See figure 2 for a general integrated proteomics flowchart. In addition to the above-mentioned classical proteomics approaches, Fourier-transform infrared spectroscopy FT-IR has been used to classify and identify bacterial samples see, e.

Overview of proteomics approaches in bacterial identification and characterization. The bacterial sample can be analyzed using either a gel-based or a mass spectrometry MS —based approach. In the gel-based approach, bacterial lysate is prepared and run on one- or two-dimensional sodium dodecyl sulfate—polyacrylamide gel electrophoresis SDS-PAGE. The SDS-PAGE can be analyzed by comparing it directly with available gel images in the database, or by excising the protein spots and using trypsin digestion and mass analysis for identification.

On the basis of the protein pattern analysis or the identified proteins, or both, the bacterium from which the lysate was prepared will be identified using bioinformatics analysis database search and computer algorithm analysis.

The unknown bacterium will be identified either by comparing the resulting mass spectra with a collective proteomics database containing mass spectra of known bacteria, or by searching and matching the sequence of a panel of proteins with proteins of known bacteria in the protein database. Mass spectrometry is a powerful analytical technique that has been used to identify unknown compounds, quantify known compounds, and elucidate the structure and chemical properties of molecules.

The development of mass spectrometry can be traced back to the late 19th century, when it was first used by J. Thomson to measure the mass-to-charge ratio of electrons. With the refine ment of this technology throughout the 20th century, mass-spectrometry applications have been expanded to include physical measurement, chemical characterization, and biological identification. One of the major breakthroughs in mass spectrometry for the analysis of biological molecules was the soft ionization method i.

Until the development of the soft ionization method, the application of mass spectrometry to biological materials was limited by the requirement that the sample be in vapor phase before ionization.

Soft ionization has made it possible to study larger biological molecules and perform analyte sampling and ionization directly from native samples, including whole cells, using mass spectrometry Fenn et al. Since its initial implementation for bacterial identification in , mass spectrometry has helped to resolve time-constraint dilemmas imposed by traditional bacterial identification and characterization methods, and has permitted the generation of protein profiles specific enough for the identification of antibiotic-resistant bacteria and their molecular components.

Schematic representation of soft ionization techniques used in mass spectrometry. The sample to be analyzed the analyte is mixed with organic matrices and deposited on the sample plate in the form of a small spot. The mixture is ionized by the laser beam. The resulting ions move toward the mass analyzer, and the mass is detected to obtain the mass spectrum. The analyte is mixed with a solvent and sprayed from a narrow tube. Positively charged droplets in the spray move toward the mass-spectrometer sampling orifice under the influence of electrostatic forces and pressure differentials.

As the droplets move to the orifice, the solvent evaporates, causing the analyte ions to move toward the analyzer for mass analysis. MALDI-TOF-MS is the most commonly used mass spectral method for bacterial analysis because a it can be used to analyze whole bacterial cells directly; b it can produce relatively simple, reproducible spectra patterns over a broad mass range under well-controlled experimental conditions; c the spectra patterns contain characteristic information that can be used to identify and characterize bacterial species by comparing the spectra fingerprints of the unknown species with known library fingerprints; and d a number of known, taxonomically important protein markers can be used directly for identifying bacterial species.

In , Holland and colleagues published an article on the first use of MALDI-TOF-MS for the rapid identification of whole bacteria, either by comparison with archived reference spectra or by coanalysis with cultures of known bacteria. Staphylococcus aureus , a bacterium commonly found on human skin, causes infection during times of uncontrolled growth. Improper use of antibiotics has rendered S. The first outbreak of methicillin-resistant S. Since then, the threat of MRSA has spread from hospitals and clinical settings to schools and public communities, thus necessitating the use of techniques that can rapidly identify and discriminate MRSA from methicillin-sensitive S.

In this method, a sample is taken from a single bacterial colony and smeared onto a sample slide. The system is equipped with bioinformatics tools clustering and phylogenetic dendrogram construction that allow for the rapid identification and characterization of a known or unknown bacterial culture on the basis of proteomics signatures. ESI-MS also has the potential to play an important role in bacterial characterization, especially for the analysis of cellular components. This technique allows for the analysis of both intracellular and extracellular proteins, carbohydrates, and lipids.

A major advantage of ESI-MS is its ability to perform tandem mass spectrometry, in which the protein of interest can be fragmented for a second mass analysis that provides protein fragment sequence information, or a peptide fragmentation fingerprint, that can then be applied to a database search to identify that specific protein.

A study by Krishnamurthy and Ross reported that the total analysis time leading to unambiguous bacterial identification in samples is less than 10 minutes, with reproducible results. These efforts exemplify how the integrated genotypic and proteomics technologies provide an even more powerful tool for bacterial identification.

Additional descriptions of the ESI-MS technique and its applications for bacterial characterization may be found in a review article by Bons and colleagues The resolution of ESI-MS is such that specific intracellular biomarkers for individual micro organisms can be distinguished with enough confidence that they can be identified in unknown samples.

For the protein analysis, comparing the experimentally obtained protein profile of an unknown bacterial species with the profile information found in a proteomics database will allow for the identification and characterization of the unknown species. Once the putative virulence factors are identified, their functions and mechanism can be further characterized by phenotypic analyses such as mutagenesis, conventional biochemical methods, and structural biology.

SELDI is a relatively new technology, designed to perform mass spectrometric analysis of protein mixtures retained on chemically e. These varied chemical and biochemical surfaces allow differential capture of proteins based on the intrinsic properties of the proteins themselves.

The SELDI mass spectrometer produces spectra of complex protein mixtures based on the mass-to-charge ratio of the proteins in the mixture and their binding affinity to the chip surface. Differentially expressed proteins may then be determined from these protein profiles by comparing peak intensity.

Figure 4 illustrates the general procedure. This technique utilizes aluminum-based chips, engineered with chemically or biologically modified surfaces.

These varied surfaces allow the differential capture of proteins based on the intrinsic properties of the proteins themselves.

Bacterial lysates are applied directly to the surfaces, where proteins with affinities to the surface will bind. Following a series of washes to remove nonspecifically bound proteins, the bound proteins are profiled using the integrated mass analyzer to generate a mass spectrum for further analysis.

SELDI technology has been applied extensively to biomarker and protein profiling studies in the field of oncology Yip and Lomas By contrast, only a limited number of reports have investigated the applicability of SELDI for detecting and identifying bacterial pathogens Seo et al.

However, these limited study results demonstrate that SELDI technology offers an alternative approach to the other techniques for exploring bacterial proteomes, ultimately permitting bacterial identification based on a comparison of protein profiles and patterns. An example of how SELDI technology has been applied is its use in distinguishing between four subspecies of Francisella tularensis, the causative agent of tularemia in humans.

Of the four subspecies of F. Lundquist and colleagues showed that SELDI time-of-flight mass spectrometry is capable of generating unique and reproducible protein profiles for each subspecies, allowing the subspecies to be distinguished from one another. Although the use of mass spectrometry has great potential for identifying bacteria by their spectral profile, many factors affect the reproducibility of bacterial spectra. Sample preparation, matrix selection, and differences in instrument quality and performance can all have an impact on the reproducibility of protein profiles Wunschel et al.

Just as important, the physiological state of the cell may also influence the results of mass spectral analysis, and thus both the growth medium and the growth stage of the cells must be taken into account. Using MALDI-TOF-MS technology to analyze and discriminate foodborne microorganisms, Mazzeo and colleagues concluded that the growth time did not affect the bacterial protein profile, but that different growth media did affect the mass spectra of Escherichia coli. Similarly, Walker and associates showed that culture medium—especially with the addition of blood, as in Columbia blood agar—will cause variation in mass spectra profiles.

These results emphasize the need to develop standardized techniques for preparing samples to use in the creation of mass spectra databases for bacterial identification. Bacteria may also be differentiated on the basis of their cellular protein contents. The most established technique for examining cellular protein content is to lyse cells and separate their entire protein complement using SDS-PAGE.

This results in a migration pattern of the protein bands that is characteristic for a given bacterial strain Vandamme et al. Researchers can identify bacteria by comparing their migration patterns with reference gel patterns in an established database. However, because SDS-PAGE analysis is slow and labor-intensive, and because the application necessitates precise culture conditions that yield fairly large amounts of sample material, it is not particularly useful for rapid identification of bacteria, particularly for field and point-of-care applications.

In , O'Farrell introduced 2DE as a method for separating complex mixtures of cellular proteins. In 2DE, proteins are separated by IEF electrophoresis in a pH gradient according to each protein's isoelectric point in the first dimension, followed by the second-dimension SDS-PAGE separation according to the relative molecular weight of each protein.

After the second-dimension separation, the gel can be stained with standard or sensitive staining solutions so that protein spots can be visualized and analyzed. Protein gel patterns or 2DE maps from known bacteria can be further scanned, analyzed, and stored in a reference database.

To identify an unknown species, a 2DE map from the unknown sample is generated by running a 2DE gel and then comparing it with 2DE maps in the reference database for identification. When used as a stand-alone technique, 2DE is most often used for analyzing protein mixtures, isolating proteins of interest for identification, and comparing differential expression patterns of different types of samples.

For more complex proteomics analysis, 2DE is greatly enhanced when combined with mass spectrometry. The team identified several proteins associated with the exosporium of B. Using these same methods, the whole proteome or subproteome or both has also been made available for many other bacteria, including E.

A collective proteomics database with complete 2DE maps and mass spectra of known bacteria will allow investigators to compare and identify unknown bacteria with great efficiency.

However, building such a database will not be an easy undertaking. One thing modern genomic and proteomic approaches share is the generation of large data sets for any individual sample that is analyzed. These present a real challenge in terms of archiving the data, processing and integrating data from many samples so that it can be used for comparative purposes in the broadest way possible, and developing robust quality control and quality assurance practices.

All this should be done in an environment that is accessible and easy to use for a broad group of scientists, many of whom may have little experience with a particular method of analysis. At present there are no comprehensive genomic or proteomic databases for bacterial identification.

There are, however, a great number of databases and tools available for both genomic and proteomic analysis that are essential for providing integrated data for specific types of analysis. Two examples of databases that provide excellent analysis for identification based on 16S rRNA gene sequence analysis are the Ribosomal Database Project Cole et al.

Protein identification and characterization has been carried out with more depth since the development of predictive tools such as GlycoMod and databases such as PhosphoSite, which can reveal possible posttranslational modifications not otherwise accounted for in databases consisting simply of theoretical spectra Barrett et al.

More important, algorithms are evolving to adapt to actual experimental occurrences and parameters. One example of such adaptation is the development of a recent algorithm that predicts missed cleavage sites as they typically occur during protein digests, in order to ease the return of, and render greater confidence to, mass spectra probability matches Siepen et al.

The goal of the ProDB platform proposed by Wilke and colleagues is not only to integrate data derived from various databases to enrich a protein profile but also to archive experimental conditions and parameters, such as growth and culture conditions as they might apply to bacteria, to account for the subsequent effects on mass-spectra profile generation.

Archiving and integrating specific experimental conditions as part of the proteomic bioinformatics may alleviate the need for stringent culturing standards when attempting to identify proteins that aid the characterization and identification of clinically important bacteria. For a more comprehensive review on proteomic data analysis, see Lisacek and colleagues Advanced genomics and proteomics technologies will continue to play a critical role in bacterial identification and characterization in the 21st century.

Bacterial characterization has a number of practical applications, aside from being fundamental to questions of bacterial systematics, taxonomy, and evolution. Rapid identification and discrimination of pathogenic microbes has a major impact on public health in terms of correct diagnosis and timely disease treatment.

The ability to identify specific indicator organisms is also important for determining water quality, and an enhanced understanding of the population structure of these organisms can allow researchers to identify the source of a particular contaminant. For example, methods are being developed to determine whether fecal bacteria found in public water supplies are from humans, mammals, or birds.

This kind of information has a significant impact for treatment options. As researchers learn more about the community fabric of microbial ecosystems, it is likely that we will come to recognize sentinel microbes that will tell us, by their presence or absence and abundance, important information about the state of that ecosystem.

For example, the identification of microbes that carry out specific transformations of nitrogen or phosphorus might indicate the status of these important nutrients in aquatic or soil ecosystems. Likewise, the presence of microorganisms with certain biodegradative capacities could be an indicator of specific pollutants in an environment. The ability to rapidly identify these individual organisms within populations of thousands of different species is essential for understanding how they will affect our ecosystems.

Bacterial characterization will also assist in elucidating the mechanisms that govern microbial pathogenesis, and allow for the discovery of important protein targets essential to the development of vaccines, diagnostic kits, and therapeutics for infectious diseases.

It is these kinds of applications that make the continued development of techniques for bacterial identification important both for basic science and for the maintenance of human and environmental health. We thank Scott Jenkins for his help in editing the manuscript and David Cleland for his help in preparing figure 1. We also acknowledge that many excellent papers, particularly those providing examples of the use of the technologies described herein, could not be cited because of space limitations.

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