back to our examples, samples collected from staff, visitors, patients, and locations in the hospital could be cultured to identify those that contain S. aureus, which is a common commensal bacterium that is easily identified on growth medium. The different isolates of S. aureus would be subjected to MLST analysis. Progeny or clonal isolates will have the same DNA sequences in most of the multiple loci, whereas strains from a different source may have loci with sequence variations. The resulting profile would indicate whether patients are infected with the same strain of S. aureus and where this strain may have arisen in the hospital. Similarly, MLST analysis can trace the sources of L. monocytogenes through the chain of food preparation of the outbreak mentioned earlier as well as previous outbreaks and thereby identify the root of the contamination problem.
MLST analysis does not distinguish whether multiple sequence differences observed between alleles is a result of multiple point mutations or a single recombination event. Consequently, MLST analysis does not assign a higher similarity value to sequences differing by a single nucleotide compared to sequences with multiple nucleotide differences. To determine phylogenetic relationships of closely related species with high clonal evolution, multilocus sequence analysis (MLSA) is used, in which the selected housekeeping and virulence gene sequences are first concatenated (i.e., virtually linked in tandem to each other) before performing comparative phylogenetic analysis. This process provides greater discriminatory power for determining phylogenetic relationships.
MLSA analysis has recently been greatly extended by using whole-genome sequence determinations, rather than sequences of a limited number of housekeeping or virulence genes. MLSA by whole-genome sequencing provides higher resolution for differentiating bacterial isolates and for tracking the evolution and spread of virulent and/or antibiotic-resistant bacteria. As an example of the impact that whole-genome sequencing has had on MLSA, let us consider carbapenem-resistant Klebsiella pneumoniae, which has emerged as a serious clinical problem in hospitals. Previously, the vast majority of resistant clinical isolates had been genetically characterized by MLST as a single multilocus type (designated as ST258), leading to the hypothesis that a single clone of ST258 was responsible for the global spread of these multidrug-resistant bacteria. However, whole-genome sequencing and phylogenetic analysis of a large number of ST258 clinical isolates from diverse geographic locations revealed unexpected diversity among the isolates, including divergence into two distinct genetic clades due to a 215-kB region that appears to be a hotspot for DNA recombination events. This region also contains genes involved in capsule polysaccharide biosynthesis that likely contributed to the observed serological variation. In another example, phylogenetic relationship analysis based on whole-genome sequencing of 132 clinical isolates from around the world was used to track the recent evolutionary history of the dysentery (bloody diarrhea) pathogen Shigella sonnei from Europe to other parts of the world (Figure 5-10).
Figure 5-10. Phylogenetic relationship analysis using comparative whole-genome sequence profiling to track the evolution and dissemination of the dysentery pathogen Shigella sonnei. The dysentery pathogen Shigella sonnei was once predominant in developed countries, but it is now emerging as a major problem in developing countries. Whole-genome sequencing of 132 globally distributed clinical isolates, followed by phylogenetic analysis, showed that the current S. sonnei strains descended from a common ancestor in Europe less than 500 years ago. The results also showed that by the late 19th century, S. sonnei had diverged into four distinct lineages with strong regional clustering. The heat map shows the distribution of genes associated with antibiotic resistance. Known antibiotic-resistance mutations in the gene encoding DNA gyrase, GyrA, are indicated by color. Probable multidrug-resistance (MDR) gene acquisition events are boxed in red. Geographically localized clonal expansions are highlighted with their median estimated divergences dates. Reprinted from Holt KE, Baker S, Weill FX, Holmes EC, Kitchen A, Yu J, Sangal V, Brown DJ, Coia JE, Kim DW, Choi SY, Kim SH, da Silveira WD, Pickard DJ, Farrar JJ, Parkhill J, Dougan G, Thomson NR. 2012. Nat Genet 44:1056–1059, with permission.
Characterizing Microbiomes by Using Metagenomic Analysis
The various rRNA gene sequencing approaches mentioned previously only give information regarding what types of microbes are present in a community. A limitation of this type of approach is its failure to generate functional genomic information for deciphering the metabolic contributions of the microbes present in an ecosystem. For example, it does not provide direct information about which microbes might have a symbiotic relationship by producing and secreting metabolic products that could cross-feed other microbial species or the host. The current state of mega-scale DNA sequencing technology has spurred interest in a more ambitious approach to analyzing the body’s microbiota: determining the sequences of all of the individual microbial genomes of the body (the microbiomes). The goal of this approach, called metagenomic analysis, is designed to go beyond the question of cataloging what organisms are present (i.e., the census question) to the question of what the metabolic and physiologic potential of the microbiota is (i.e., the metabolic genes and pathways that are present).
But what can be done about species whose genomes are incomplete or not in the database at all? To answer this question, the next stage of metagenomic analysis involves isolation and mega-scale sequencing of all genomic DNA from an entire mixed microbial population (called the metagenome) so as to harvest the remarkable and vast diversity present. In fact, the advances in robotics, ultra-high-throughput, massively parallel sequencing, and bioinformatics assembly technologies are already leading to determination of complete genomes of microbes directly (without cultivation or cloning of individual isolates) from the mixed genomic DNA samples isolated from complex microbiota communities. For instance, metagenomic analysis can be applied to profiling strain-level variation in microbial communities (Figure 5-11).
Figure 5-11. Metagenomic profiling of strain-level variation in microbial communities. (A) Mapping paired-end sequencing reads to microbial reference genomes reveals not only the genomes that are present in a community, but also differences between the isolates of particular species and the reference isolate. In this example, most positions have 4x coverage, represented by four sequencing reads mapped to each position in the reference genome sequences from bacterial species A and B. Gene deletion events can be detected with relatively low coverage of the reference genome; no reads from the sample map to deleted genes (in orange). Higher sequencing coverage of the genomes facilitates differentiating between sequencing error and true nucleotide-level strain variation. Such variation includes fixed differences (in which the sample is consistently different from the reference at some site) and single nucleotide polymorphisms (SNPs; in which a site occurs in two or more states in the sample). Sequence reads that do not map together (blue reads from individual 1 and red reads from individual 2) indicate additional community variation, including the insertion of genomic material not found in the reference genome by mechanisms such as horizontal gene transfer (HGT). (B) Mapping reads to reference genomes can reveal patterns of gene presence or absence, which is a form of strain variation. Here, two individuals sampled at two time points (t = 0 and t = 1 year) are distinguished by the presence or absence of genes in species A. The blue genes are stably present in individual 1 and stably absent in individual 2, whereas the red genes are stably present in individual 2 and stably absent in individual 1. Adapted from Franzosa EA, Hsu T, Sirota-Madi A, Shafquat A, Abu-Ali G, Morgan XC, Huttenhower C. 2015. Nat Rev Microbiol 13(6):360–372, with permission.
Assembling individual genome sequences from many thousands of sequences is still challenging for existing bioinformatics programs, but here again, rapid advances are being made in the analysis of the huge volume of new sequence data that is emerging, and these advances are beginning to make what seems unimaginable today feasible tomorrow (in biotechnology, as in most modern scientific endeavors, “impossible” just means it is “not possible yet”). Interpretation
