Biochemistry and Pharmacology - Research Publications
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ItemIn silico serotyping of E. coli from short read data identifies limited novel O-loci but extensive diversity of O:H serotype combinations within and between pathogenic lineages(MICROBIOLOGY SOC, 2016-07)The lipopolysaccharide (O) and flagellar (H) surface antigens of Escherichia coli are targets for serotyping that have traditionally been used to identify pathogenic lineages. These surface antigens are important for the survival of E. coli within mammalian hosts. However, traditional serotyping has several limitations, and public health reference laboratories are increasingly moving towards whole genome sequencing (WGS) to characterize bacterial isolates. Here we present a method to rapidly and accurately serotype E. coli isolates from raw, short read WGS data. Our approach bypasses the need for de novo genome assembly by directly screening WGS reads against a curated database of alleles linked to known and novel E. coli O-groups and H-types (the EcOH database) using the software package srst2. We validated the approach by comparing in silico results for 197 enteropathogenic E. coli isolates with those obtained by serological phenotyping in an independent laboratory. We then demonstrated the utility of our method to characterize isolates in public health and clinical settings, and to explore the genetic diversity of >1500 E. coli genomes from multiple sources. Importantly, we showed that transfer of O- and H-antigen loci between E. coli chromosomal backbones is common, with little evidence of constraints by host or pathotype, suggesting that E. coli 'strain space' may be virtually unlimited, even within specific pathotypes. Our findings show that serotyping is most useful when used in combination with strain genotyping to characterize microevolution events within an inferred population structure.
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ItemGeneMates: an R package for detecting horizontal gene co-transfer between bacteria using gene-gene associations controlled for population structure(BMC, 2020-09-24)BACKGROUND: Horizontal gene transfer contributes to bacterial evolution through mobilising genes across various taxonomical boundaries. It is frequently mediated by mobile genetic elements (MGEs), which may capture, maintain, and rearrange mobile genes and co-mobilise them between bacteria, causing horizontal gene co-transfer (HGcoT). This physical linkage between mobile genes poses a great threat to public health as it facilitates dissemination and co-selection of clinically important genes amongst bacteria. Although rapid accumulation of bacterial whole-genome sequencing data since the 2000s enables study of HGcoT at the population level, results based on genetic co-occurrence counts and simple association tests are usually confounded by bacterial population structure when sampled bacteria belong to the same species, leading to spurious conclusions. RESULTS: We have developed a network approach to explore WGS data for evidence of intraspecies HGcoT and have implemented it in R package GeneMates ( github.com/wanyuac/GeneMates ). The package takes as input an allelic presence-absence matrix of interested genes and a matrix of core-genome single-nucleotide polymorphisms, performs association tests with linear mixed models controlled for population structure, produces a network of significantly associated alleles, and identifies clusters within the network as plausible co-transferred alleles. GeneMates users may choose to score consistency of allelic physical distances measured in genome assemblies using a novel approach we have developed and overlay scores to the network for further evidence of HGcoT. Validation studies of GeneMates on known acquired antimicrobial resistance genes in Escherichia coli and Salmonella Typhimurium show advantages of our network approach over simple association analysis: (1) distinguishing between allelic co-occurrence driven by HGcoT and that driven by clonal reproduction, (2) evaluating effects of population structure on allelic co-occurrence, and (3) direct links between allele clusters in the network and MGEs when physical distances are incorporated. CONCLUSION: GeneMates offers an effective approach to detection of intraspecies HGcoT using WGS data.