Jonathan A. Eisen | Search bioRxiv

bioRxiv2016

Recent Outbreaks of Shigellosis in California Caused by Two Distinct Populations of Shigella sonnei With Increased Virulence or Fluoroquinolone Resistance

Shigella sonnei has caused unusually large outbreaks of shigellosis in California in 2014 - 2015. Preliminary data indicated the involvement of two distinct yet related bacterial populations, one from San Diego and San Joaquin (SD/SJ) and one from the San Francisco (SF) Bay area. Whole genome sequencing of sixty-eight outbreak and archival isolates of S. sonnei was performed to investigate the microbiological factors related to these outbreaks. Both SD/SJ and SF populations, as well as almost all of the archival S. sonnei isolates belonged to sequence type 152 (ST152). Genome-wide SNP analysis clustered the majority of California (CA) isolates to an earlier described global Lineage III, which has persisted in CA since 1986. Isolates in the SD/SJ population had a novel Shiga-toxin (STX)-encoding lambdoid bacteriophage, most closely related to that found in an Escherichia coli O104:H4 strain responsible for a large outbreak. However, the STX genes (stxla and stxlb) from this novel phage had sequences most similar to the phages from S. flexneri and S. dysenteriae. The isolates in the SF population yielded evidence of fluoroquinolone resistance acquired via the accumulation of point mutations in gyrA and parC genes. Thus, the CA S. sonnei lineage continues to evolve by the acquisition of increased virulence and antibiotic resistance, and enhanced monitoring is advocated for its early detection in future outbreaks.

Microbiology↗

bioRxiv2015

Automated and accurate estimation of gene family abundance from shotgun metagenomes

Shotgun metagenomic DNA sequencing is a widely applicable tool for characterizing the functions that are encoded by microbial communities. Several bioinformatic tools can be used to functionally annotate metagenomes, allowing researchers to draw inferences about the functional potential of the community and to identify putative functional biomarkers. However, little is known about how decisions made during annotation affect the reliability of the results. Here, we use statistical simulations to rigorously assess how to optimize annotation accuracy and speed, given parameters of the input data like read length and library size. We identify best practices in metagenome annotation and use them to guide the development of the Shotgun Metagenome Annotation Pipeline (ShotMAP). ShotMAP is an analytically flexible, end-to-end annotation pipeline that can be implemented either on a local computer or a cloud compute cluster. We use ShotMAP to assess how different annotation databases impact the interpretation of how marine metagenome and metatranscriptome functional capacity changes across seasons. We also apply ShotMAP to data obtained from a clinical microbiome investigation of inflammatory bowel disease. This analysis finds that gut microbiota collected from Crohns disease patients are functionally distinct from gut microbiota collected from either ulcerative colitis patients or healthy controls, with differential abundance of metabolic pathways related to host-microbiome interactions that may serve as putative biomarkers of disease.\n\nAuthor SummaryMicrobial communities perform a wide variety of functions, from marine photosynthesis to aiding digestion in the human gut. Shotgun \"metagenomic\" sequencing can be used to sample millions of short DNA sequences from such communities directly, without needing to first culture its constituents in the laboratory. Using these data, researchers can survey which functions are encoded by mapping these short sequences to known protein families and pathways. Several tools for this annotation already exist. But, annotation is a multi-step process that includes identification of genes in a metagenome and determination of the type of protein each gene encodes. We currently know little about how different choices of parameters during annotation influences the final results. In this work, we systematically test how several key decisions affect the accuracy and speed of annotation, and based on these results, develop new software for annotation, which we named ShotMAP. We then use ShotMAP to functionally characterize marine communities and gut communities in a clinical cohort of inflammatory bowel disease. We find several functions are differentially represented in the gut microbiome of Crohns disease patients, which could be candidates for biomarkers and could also offer insight into the pathophysiology of Crohns. ShotMAP is freely available (https://github.com/sharpton/shotmap).

Bioinformatics↗