Biology subjects

Le Viet, T.

Publications and source records attributed to Le Viet, T..

5 recordsLinked to original sources

bioRxiv2025

Case study: Genomic characteristics of the gut microbiome, Campylobacter and Salmonella genotypes in three cases of gastroenteritis co-infections

Campylobacter jejuni and Salmonella enterica co-infections in gastroenteritis cases are underexplored, particularly in relation to microbiome dysbiosis. This study uses metagenomic and culture-based approaches to investigate pathogen genotypes and stool microbiome composition. Stool samples from three patients were analysed using culture, qPCR and whole genome sequencing (WGS) of genomes and metagenomes. Strain typing and antimicrobial resistance (AMR) profiling were conducted for both pathogens. The stool microbiome structure was compared between the three patients and across three time points in one patient. C. jejuni and Salmonella enterica were of different strains and different proportions in the cases. C. jejuni sequence types were identified as ST-794, ST-50 and ST-10025, while S. enterica serovars Enteritidis ST-183 and S. Derby ST-39 were identified. C. jejuni AMR genotypes included blaOXA-447, gyrA (T86I), and tet(O). S. Enteritidis contained AMR gene mdsaA-B, and S. Derby contained mdsaA-B and fosA7 genes. Despite similarities in Bristol scales for stool composition, the three cases exhibited distinct microbiome population structures, suggesting there is no one signature microbial profile of microbiomes with a Campylobacter-Salmonella co-infection. A temporal trend towards microbial diversity was observed within a patient, shifting from Proteobacteria dominance (93.3%) during acute infection towards Bacteroidetes (89.5%) abundance post-infection, suggesting that moving towards diverse communities may play a key role in patient recovery. These findings reveal a case study of microbiome disruption for patients infected with C. jejuni and S. enterica and highlight the value of direct stool sequencing in understanding pathogen abundance in stool and microbiome dysbiosis.

microbiology↗

bioRxiv2024

hAMRonization: Enhancing antimicrobial resistance prediction using the PHA4GE AMR detection specification and tooling

The detection of antimicrobial resistance (AMR) markers directly from genomic or metagenomic data is becoming a standard clinical and public health procedure. This has resulted in the development of a number of different bioinformatic AMR prediction tools. Although many may implement similar principles, these tools differ significantly in their supported inputs, search algorithms, parameterisation, and underlying reference databases. Each of these tools generates a report of detected AMR genes or variants in a distinct, non-standard, format. This presents a huge barrier to the comparison of results and to the modularity of tools for AMR gene prediction within bioinformatic workflows. In collaboration with 17 public health laboratories across 10 countries, the Public Health Alliance for Genomic Epidemiology (PHA4GE) (https://pha4ge.org) data structures working group has developed and piloted a standardized output specification for the bioinformatic detection of AMR from microbial genomes. In this report, we discuss hAMRonization, a python package and command-line utility, which implements PHA4GEs AMR specification to combine the outputs of disparate antimicrobial resistance gene detection tools into a single unified format. hAMRonization can be easily extended and currently supports 18 different tools (both species-agnostic and species-specific) for the detection of genes and/or variants conferring AMR. The harmonized reports are available in tabular form, JSON format or through an interactive HTML file (e.g., https://maguire-lab.github.io/assets/interactive_report_demo.html) that can be opened within the browser for navigable data exploration. As of 2024-03-07 hAMRonization has been downloaded [~]12,500 times, incorporated into >9 public bioinformatic tools and workflows, and been internally adopted by several national and international public health groups. The hAMRonization tool and underlying specification are open-source and freely available through PyPI, conda and GitHub (https://github.com/pha4ge/hAMRonization).

bioinformatics↗

bioRxiv2023

A hybrid and poly-polish workflow for the complete and accurate assembly of phage genomes: a case study of ten przondoviruses

Bacteriophages (phages) within the Przondovirus genus are T7-like podoviruses belonging to the Studiervirinae subfamily, within the Autographiviridae family and have a highly conserved genome organisation. The genome size of these phages ranges from 37 kb to 42 kb, encode 50-60 genes and are characterised by the presence of direct terminal repeats (DTRs) flanking the linear chromosome. These DTRs are often deleted during short-read-only and hybrid assemblies. Moreover, long-read-only assemblies are often littered with sequencing and/or assembly errors and require additional curation. Here, we present the isolation and characterisation of ten novel przondoviruses targeting Klebsiella spp. We describe HYPPA - a HYbrid and Poly-polish Phage Assembly workflow, which utilises long-read assemblies in combination with short-read sequencing to resolve phage DTRs and correcting errors, negating the need for laborious primer walking and Sanger sequencing validation. Our data demonstrate the importance of careful curation of phage assemblies before publication, and prior to using them for comparative genomics. IMPACT STATEMENTThe current workflows employed for phage genome assembly are often error-prone and can lead to many incomplete phage genomes being deposited within databases. This can create challenges when performing comparative genomics, and may also lead to incorrect taxonomic assignment. To overcome these challenges we proposed HYPPA, a workflow that can produce complete and high-quality phage genomes without the need for laborious lab-based validation. DATA SUMMARYPhage raw reads are available from the National Centre for Biotechnology Information Sequence Read Archive (NCBI-SRA) under the BioProject number PRJNA914245. Phage annotated genomes have been deposited at GenBank under the accessions OQ579023-OQ579032 (Table 1). Bacterial WGS data for clinical preterm infant samples have been deposited at GenBank under BioProject accession PRJNA471164 (Table S1). Bacterial raw reads for food samples are available from NCBI-SRA with individual accessions (SAMN33593347-SAMN33593351), and can be found under the BioProject number PRJNA941224 (Table S1). Strain-specific details for bacteria and publicly-available phages used in these analyses, along with accessions for the latter can be found in Table S1 and Table S6, respectively. The CL1-CL8 clinical Klebsiella strains (Table S1) were under a Materials Transfer Agreement, for which sequencing data and strain information is not available. O_TBL View this table: org.highwire.dtl.DTLVardef@8cae53org.highwire.dtl.DTLVardef@8e512org.highwire.dtl.DTLVardef@1f71a67org.highwire.dtl.DTLVardef@1e6947dorg.highwire.dtl.DTLVardef@11ebc58_HPS_FORMAT_FIGEXP M_TBL O_FLOATNOTable 1.C_FLOATNO O_TABLECAPTIONPrzondoviruses within the collection to date and data relating to the closest database relative. C_TABLECAPTION C_TBL

microbiology↗

bioRxiv2020

Rapid Mycobacterium tuberculosis spoligotyping from uncorrected long reads using Galru

Spoligotyping of Mycobacterium tuberculosis provides a subspecies classification of this major human pathogen. Spoligotypes can be predicted from short read genome sequencing data; however, no methods exist for long read sequence data such as from Nanopore or PacBio. We present a novel software package Galru, which can rapidly detect the spoligotype of a Mycobacterium tuberculosis sample from as little as a single uncorrected long read. It allows for near real-time spoligotyping from long read data as it is being sequenced, giving rapid sample typing. We compare it to the existing state of the art software and find it performs identically to the results obtained from short read sequencing data. Galru is freely available from https://github.com/quadram-institute-bioscience/galru under the GPLv3 open source licence.

bioinformatics↗

bioRxiv2019

AlbaTraDIS: Comparative analysis of large datasets from parallel transposon mutagenesis experiments

BackgroundBacteria have evolved over billions of years to survive in a wide range of environments. Currently, there is an incomplete understanding of the genetic basis for mechanisms underpinning survival in stressful conditions, such as the presence of anti-microbials. Transposon mutagenesis has been proven to be a powerful tool to identify genes and networks which are involved in survival and fitness under a given condition by simultaneously assaying the fitness of millions of mutants, thereby relating genotype to phenotype and contributing to an understanding of bacterial cell biology. A recent refinement of this approach allows the roles of essential genes in conditional stress survival to be inferred by altering their expression. These advancements combined with the rapidly falling costs of sequencing now allows comparisons between multiple experiments to identify commonalities in stress responses to different conditions. This capacity however poses a new challenge for analysis of multiple data sets in conjunction.\n\nResultsTo address this analysis need, we have developed AlbaTraDIS; a software application for rapid large-scale comparative analysis of TraDIS experiments that predicts the impact of transposon insertions on nearby genes. AlbaTraDIS can identify genes which are up or down regulated, or inactivated, between multiple conditions, producing a filtered list of genes for further experimental validation as well as several accompanying data visualisations. We demonstrate the utility of our new approach by applying it to identify genes used by Escherichia coli to survive in a wide range of different concentrations of the biocide Triclosan. AlbaTraDIS automatically identified all well characterised Triclosan resistance genes, including the primary target, fabI. A number of new loci were also implicated in Triclosan resistance and the predicted phenotypes for a selection of these were validated experimentally and results showed high consistency with predictions.\n\nConclusionsAlbaTraDIS provides a simple and rapid method to analyse multiple transposon mutagenesis data sets allowing this technology to be used at large scale. To our knowledge this is the only tool currently available that can perform these tasks. AlbaTraDIS is written in Python 3 and is available under the open source licence GNU GPL 3 from https://github.com/quadram-institute-bioscience/albatradis.

bioinformatics↗