Biology subjects

Coll, F.

Publications and source records attributed to Coll, F..

3 recordsLinked to original sources

bioRxiv2018

One Health genomic surveillance of Escherichia coli demonstrates distinct lineages and mobile genetic elements in isolates from humans versus livestock

Livestock have been proposed as a reservoir for drug-resistant Escherichia coli that infect humans. We isolated and sequenced 431 E. coli (including 155 ESBL-producing isolates) from cross-sectional surveys of livestock farms and retail meat in the East of England. These were compared with the genomes of 1517 E. coli associated with bloodstream infection in the United Kingdom. Phylogenetic core genome comparisons demonstrated that livestock and patient isolates were genetically distinct, indicating that E. coli causing serious human infection do not directly originate from livestock. By contrast, we observed highly related isolates from the same animal species on different farms. Analysis of accessory (variable) genomes identified a virulence cassette associated previously with cystitis and neonatal meningitis that was only present in isolates from humans. Screening all 1948 isolates for accessory genes encoding antibiotic resistance revealed 41 different genes present in variable proportions of humans and livestock isolates. We identified a low prevalence of shared antimicrobial resistance genes between livestock and humans based on analysis of mobile genetic elements and long-read sequencing. We conclude that in this setting, there was limited evidence to support the suggestion that antimicrobial resistant pathogens that cause serious infection in humans originate from livestock.\n\nImportanceThe increasing prevalence of E. coli bloodstream infections is a serious public health problem. We used genomic epidemiology in a One Health study conducted in the East of England to examine putative sources of E. coli associated with serious human disease. E. coli from 1517 patients with bloodstream infection were compared with 431 isolates from livestock farms and meat. Livestock-associated and bloodstream isolates were genetically distinct populations based on core genome and accessory genome analyses. Identical antimicrobial resistance genes were found in livestock and human isolates, but there was little overlap in the mobile elements carrying these genes. In addition, a virulence cassette found in humans isolates was not identified in any livestock-associated isolate. Our findings do not support the idea that E. coli causing invasive disease or their resistance genes are commonly acquired from livestock.

genomics↗

bioRxiv2018

rPinecone: Define sub-lineages of a clonal expansion via a phylogenetic tree

The ability to distinguish between pathogens is a fundamental requirement to understand the epidemiology of infectious diseases. Phylogenetic analysis of genomic data can provide a powerful platform to identify lineages within bacterial populations, and thus inform outbreak investigation and transmission dynamics. However, resolving differences between pathogens associated with low variant (LV) populations carrying low median pairwise single nucleotide variant (SNV) distances, remains a major challenge. Here we present rPinecone, an R package designed to define sub-lineages within closely related LV populations. rPinecone uses a root-to-tip directional approach to define sub-lineages within a phylogenetic tree according to SNV distance from the ancestral node. The utility of this program was demonstrated using genomic data of two LV populations: a hospital outbreak of methicillin-resistant Staphylococcus aureus and endemic Salmonella Typhi from rural Cambodia. rPinecone identified the transmission branches of the hospital outbreak and geographically-confined lineages in Cambodia. Sub-lineages identified by rPinecone in both analyses were phylogenetically robust. It is anticipated that rPinecone can be used to discriminate between lineages of bacteria from LV populations where other methods fail, enabling a deeper understanding of infectious disease epidemiology for public health purposes.\n\nDATA SUMMARYO_LISource code for rPinecone is available on GitHub under the open source licence GNU GPL 3; (url: https://github.com/alexwailan/rpinecone).\nC_LIO_LINewick format files for both phylogenetic trees have been deposited in Figshare; (url: https://doi.org/10.6084/m9.figshare.7022558)\nC_LIO_LIGeographical analysis of the S. Typhi Dataset using Microreact is available at https://microreact.org/project/r1IqkrN1X.\nC_LIO_LIAccession numbers, meta data and sample lineage results of both datasets used in this paper are listed in the supplementary tables.\nC_LI\n\nI/We confirm all supporting data, code and protocols have been provided within the article or through supplementary data files. {boxtimes}\n\nIMPACT STATEMENTWhole genome sequence data from bacterial pathogens is increasingly used in the epidemiological investigation of infectious disease, both in outbreak and endemic situations. However, distinguishing bacterial species which are both very similar and which are likely to come from a small geographical and temporal range presents a major technical challenge for epidemiologists. rPinecone was designed to address this challenge and utilises phylogenetic data to define lineages within bacterial populations that have limited variation. This approach is therefore of great interest to epidemiologists as it adds a further level of clarity above and beyond that which is offered by existing approaches which have not been designed to consider bacterial isolates containing variation that only transiently exist, but which is epidemiologically informative. rPinecone has the flexibility to be applied to multiple pathogens and has direct application for investigations of clinical outbreaks and endemic disease to understand transmission dynamics or geographical hotspots of disease.

genomics↗

bioRxiv2017

Population-based analysis of ocular Chlamydia trachomatis in trachoma-endemic West African communities identifies genomic markers of disease severity

Chlamydia trachomatis (Ct) is the most common infectious cause of blindness and bacterial sexually transmitted infection worldwide. Using Ct whole genome sequences obtained directly from conjunctival swabs, we studied Ct genomic diversity and associations between Ct genetic polymorphisms with ocular localization and disease severity in a treatment-naive trachoma-endemic population in Guinea Bissau, West Africa. All sequences fall within the T2 ocular clade phylogenetically. This is consistent with the presence of the characteristic deletion in trpA resulting in a truncated non-functional protein and the ocular tyrosine repeat regions present in tarP associated with ocular tissue localization. We have identified twenty-one Ct non-synonymous single nucleotide polymorphisms (SNPs) associated with ocular localization, including SNPs within pmpD (OR=4.07, p*=0.001) and tarP (OR=0.34, p*=0.009). Eight SNPs associated with disease severity were found in yjfH (rlmB) (OR=0.13, p*=0.037), CTA0273 (OR=0.12, p*=0.027), trmD (OR=0.12, p*=0.032), CTA0744 (OR=0.12, p*=0.041), glgA (OR=0.10, p*=0.026), alaS (OR=0.10, p*=0.032), pmpE (OR=0.08, p*=0.001) and the intergenic region CTA0744-CTA0745 (OR=0.13, p*=0.043). This study demonstrates the extent of genomic diversity within a naturally circulating population of ocular Ct, and the first to describe novel genomic associations with disease severity. These findings direct investigation of host-pathogen interactions that may be important in ocular Ct pathogenesis and disease transmission.

genomics↗