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Monteith, W.

Publications and source records attributed to Monteith, W..

3 recordsLinked to original sources

Development and Implementation of a Core Genome Multilocus Sequence Typing (cgMLST) scheme for Haemophilus influenzae

2.Haemophilus influenzae is part of the human nasopharyngeal microbiota and a pathogen causing invasive disease. The extensive genetic diversity observed in H. influenzae necessitates discriminatory analytical approaches to evaluate its population structure. This study developed a core genome MLST (cgMLST) scheme for H. influenzae using pangenome analysis tools and validated the cgMLST scheme using datasets consisting of complete reference genomes (N=14) and high-quality draft H. influenzae genomes (N=2,297). The draft genome dataset was divided into a development (N=921) and a validation dataset (N=1,376). The development dataset was used to identify potential core genes with the validation dataset used to refine the final core gene list to ensure the reliability of the proposed cgMLST scheme. Functional classifications were made for all resulting core genes. Phylogenetic analyses were performed using both allelic profiles and nucleotide sequence alignments of the core genome to test congruence, as assessed by Spearmans correlation and Ordinary Least Square linear regression tests. Preliminary analyses using the development dataset identified 1,067 core genes, which were refined to 1,037 with the validation dataset. More than 70% of core genes were predicted to encode proteins essential for metabolism or genetic information processing. Phylogenetic and statistical analyses indicated that the core genome allelic profile accurately represented phylogenetic relatedness among the isolates (R2 = 0.945). We used this cgMLST scheme to define a high-resolution population structure for H. influenzae, which enhances the genomic analysis of this clinically relevant human pathogen. 3. Impact statementDiscriminating H. influenzae variants and evaluating population structure has been challenging and largely unstandardised. To address this, we have developed a cgMLST scheme for H. influenzae. Since an accurate typing approach relies on precise reflection of the underlying population structure, we explored various methods to define the scheme. The core genes included in this scheme were predicted to encode functions in essential biological pathways, such as metabolism and genetic information processing, and could be reliably assembled from short-read sequence data. Single-linkage clustering, based on core genome allelic profiles, showed high congruence to genealogy reconstructed by Maximum-Likelihood (ML) methods from the core genome nucleotide alignment. The cgMLST scheme v1 enables rapid and accurate depiction of high-resolution H. influenzae population structure, and making this scheme accessible via the PubMLST database, ensures that microbiology reference laboratories and public health authorities worldwide can use it for genomic surveillance. 4. Data summaryThe H. influenzae cgMLST scheme is accessible via https://pubmlst.org/organisms/haemophilus-influenzae. The list of isolate IDs available publicly from pubmlst.org is provided in Supplementary File 1. The pipeline for cgMLST scheme development and validation is published at https://www.protocols.io/private/EF6DB7FE429311EEB8630A58A9FEAC02. All in-house R and Python scripts for data processing and analysis are available from https://gitfront.io/r/user-4399403/ZHt8DArALHcY/cgmlst-hinf/.

genomics↗

Contrasting genes conferring short and long-term biofilm adaptation in Listeria

Listeria monocytogenes is an opportunistic food-borne bacterium that is capable of infecting humans with high rates of hospitalisation and mortality. Natural populations are genotypically and phenotypically variable, with some lineages being responsible for most human infections. The success of L. monocytogenes is linked to its capacity to persist on food and in the environment. Biofilms are an important feature that allow these bacteria to persist and infect humans, therefore, understanding the genetic basis of biofilm formation is key to understanding transmission. We sought to investigate the biofilm forming ability of L. monocytogenes by identifying genetic variation that underlies biofilm formation in natural populations using genome-wide association studies. Changes in gene expression of specific strains during biofilm formation were then investigated using RNAseq. Genetic variation associated with enhanced biofilm formation was identified in 273 genes by GWAS and differential expression in 220 genes by RNAseq. Statistical analyses show that number of overlapping genes flagged by either type of experiment is less than expected by random sampling. This is consistent with an evolutionary scenario where rapid adaptation is driven by variation in gene expression of pioneer genes, and this is followed by slower adaptation driven by nucleotide changes within the core genome. Impact statementListeria monocytogenes is a problematic food-borne bacterium that can cause severe illness and even death in humans. Some strains are known to be more common in disease and biofilms are crucial for survival in the environment and transmission to humans. To unravel the genetic basis of biofilm formation, we undertook a study employing genome-wide association studies (GWAS) and gene transcription profiling. We identified 273 genes associated with robust biofilm formation through GWAS and discovered differential expression in 220 genes through RNAseq. Statistical analysis revealed fewer overlapping genes than expected by chance, supporting an evolutionary scenario where initial adaptation relies on gene expression variation, followed by slower adaptation through genetic changes within the core genome. Data summaryShort read genome data are available from the NCBI (National Center for Biotechnology Information) SRA (Sequence Read Archive), associated with BioProject PRJNA971143 (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA971143). Assembled genomes and supplementary material are available from FigShare: doi: 10.6084/m9.figshare.23148029. RNA sequence data and differential gene expression profiles have been deposited in the NCBI Gene Expression Omnibus.

microbiology↗

Multiple clones of colistin-resistant Salmonella enterica carrying mcr-1 plasmids in meat products and patients in Northern Thailand

Salmonella spp. is an important foodborne pathogen associated with consumption of contaminated food, especially livestock products. Antimicrobial resistance (AMR) in Salmonella has been reported globally and increasing AMR in food production is a major public health issue worldwide. The objective of this study was to describe the genetic relatedness among Salmonella enterica isolates, which displayed identical DNA fingerprint profiles. Ten S. enterica isolates were selected from meat and human cases with an identical rep-PCR profile of serovars Rissen (n=4), Weltevreden (n=4), and Stanley (n=2). We used long-read whole genome sequencing (WGS) on the MinION sequencing platform to type isolates and investigate in silico the presence of specific AMR genes. Antimicrobial susceptibility testing was tested by disk diffusion and gradient diffusion method to corroborate the AMR phenotype. Multidrug resistance and resistance to more than one antimicrobial agent were observed in eight and nine isolates, respectively. Resistance to colistin with an accompanying mcr-1 gene was observed among the Salmonella isolates. The analysis of core genome and whole genome MLST revealed that the Salmonella from meat and human salmonellosis were closely genetic related. Hence, it could be concluded that meat is one of the important sources for Salmonella infection in human. HighlightsO_LIColistin resistance detected in 2 clones from 2 different Salmonella enterica serovars (Rissen and Weltevreden) with accompanying plasmid-borne mcr-1 gene from the food production chain and human clinical salmonellosis. C_LIO_LIHigh prevalence of multidrug resistant isolates and resistance to more than one antimicrobial agent. C_LIO_LIMinION has potential for mobile, rapid and accurate application in veterinary genomic epidemiology studies. C_LI

microbiology↗