Using complete genome comparisons to identify sequences whose presence accurately predicts clinically important phenotypes

The evolution of anaerobic growth in Saccharomycotina yeasts

Humans rely on the ability of budding yeasts to grow without oxygen in industrial scale fermentations that produce beverages, foods, and biofuels. Oxygen is deeply woven into the energy metabolism and biosynthetic capabilities of budding yeasts. While diverse ecological habitats may provide wide varieties of different carbon and nitrogen sources for yeasts to utilize, there is no direct substitute for molecular oxygen, only a range of availability. Understanding how a small subset of budding yeasts evolved the ability to grow without oxygen could expand the set of useful species in industrial scale fermentations as well as provide insight into the cryptic field of yeast ecology. However, we still do not yet appreciate the full breadth of species that can growth without oxygen, what genes underlie this adaptation, and how these genes have evolved.

Large-scale genomic sequencing of extraintestinal pathogenic Escherichia coli strains

Large-scale bacterial genome sequencing efforts to date have provided limited information on the most prevalent category of disease: sporadically acquired infections caused by common pathogenic bacteria. Here, we performed whole-genome sequencing and de novo assembly of 312 blood- or urine-derived isolates of extraintestinal pathogenic (ExPEC) Escherichia coli, a common agent of sepsis and community-acquired urinary tract infections, obtained during the course of routine clinical care at a single institution. We find that ExPEC E. coli are highly genomically heterogeneous, consistent with pan-genome analyses encompassing the larger species. Investigation of differential virulence factor content and antibiotic resistance phenotypes reveals markedly different profiles among lineages and among strains infecting different body sites. We use high-resolution molecular epidemiology to explore the dynamics of infections at the level of individual patients, including identification of possible person-to-person transmission. Notably, a limited number of discrete lineages caused the majority of bloodstream infections, including one subclone (ST131-H30) responsible for 28% of bacteremic E. coli infections over a 3-yr period. We additionally use a microbial genome-wide-association study (GWAS) approach to identify individual genes responsible for antibiotic resistance, successfully recovering known genes but notably not identifying any novel factors. We anticipate that in the near future, whole-genome sequencing of microorganisms associated with clinical disease will become routine. Our study reveals what kind of information can be obtained from sequencing clinical isolates on a large scale, even well-characterized organisms such as E. coli, and provides insight into how this information might be utilized in a healthcare setting.

When whole-genome alignments just won't work: kSNP v2 software for alignment-free SNP discovery and phylogenetics of hundreds of microbial genomes

Effective use of rapid and inexpensive whole genome sequencing for microbes requires fast, memory efficient bioinformatics tools for sequence comparison. The kSNP v2 software finds single nucleotide polymorphisms (SNPs) in whole genome data. kSNP v2 has numerous improvements over kSNP v1 including SNP gene annotation; better scaling for draft genomes available as assembled contigs or raw, unassembled reads; a tool to identify the optimal value of k; distribution of packages of executables for Linux and Mac OS X for ease of installation and user-friendly use; and a detailed User Guide. SNP discovery is based on k-mer analysis, and requires no multiple sequence alignment or the selection of a single reference genome. Most target sets with hundreds of genomes complete in minutes to hours. SNP phylogenies are built by maximum likelihood, parsimony, and distance, based on all SNPs, only core SNPs, or SNPs present in some intermediate user-specified fraction of targets. The SNP-based trees that result are consistent with known taxonomy. kSNP v2 can handle many gigabases of sequence in a single run, and if one or more annotated genomes are included in the target set, SNPs are annotated with protein coding and other information (UTRs, etc.) from Genbank file(s). We demonstrate application of kSNP v2 on sets of viral and bacterial genomes, and discuss in detail analysis of a set of 68 finished E. coli and Shigella genomes and a set of the same genomes to which have been added 47 assemblies and four “raw read” genomes of H104:H4 strains from the recent European E. coli outbreak that resulted in both bloody diarrhea and hemolytic uremic syndrome (HUS), and caused at least 50 deaths.