Biology subjects

Afolayan, A. O.

Publications and source records attributed to Afolayan, A. O..

9 recordsLinked to original sources

bioRxiv2026

Refining the Serine Protease Autotransporters of Enterobacteriaceae (SPATE) gene detection in Enteroaggregative Escherichia coli genomes uncovers differential SPATE distribution by phylogeny

BackgroundEnteroaggregative Escherichia coli (EAEC) are a heterogenous pathotype, implicated in acute and persistent diarrhoea especially in developing countries. Serine Protease Autotransporters of Enterobacteriaceae (SPATEs) are Type V Secretory System trypsin-like proteases repeatedly reported from EAEC. This study aimed to determine SPATE encoding-gene prevalence among EAEC and their association with diarrhoea. We screened 881 EAEC genomes from four recent epidemiological studies in Nigeria for 23 SPATE-encoding genes, initially using ARIBA and the Virulencefinder database. ResultsInitial screening inflated SPATE gene content, particularly in genomes with multiple SPATEs, due to cross detection of highly similar sequences and other artefacts. We developed and validated refined methodology, which detected 478 of 1,156 original SPATE calls and also identified SPATE miscalls from previous datasets in the literature. The most prevalent SPATE-encoding gene in our EAEC collection was sepA 297(33.71%), closely followed by sat 360 (29.74%). pic, encoding a SPATE with mucinase activity, was found in 65 (7.4%) genomes and associated with diarrhoea (p=0.00004). EAEC strains belonging to E. coli phylogroups A, B1 or C carried, on average, one SPATE gene per genome while >1 was typically detected in phylogroup B2 EAEC. Other EAEC carried few or no SPATE genes. ConclusionsOur study shows that multifunctional genome analysis tools may have to be refined for certain gene families to avoid overestimation. SPATEs are not as prevalent as previously thought but they remain common among EAEC, particularly among phylogroup A, B1, B2 and C, pointing to the possibility that they make lineage-specific contributions to disease.

microbiology↗

bioRxiv2025

Transmissible antimicrobial resistance in Escherichia coli isolated from household drinking water in Ibadan, Nigeria

Contaminated household water in peri-urban urban communities is a reservoir for virulent Escherichia coli, but its role in the environmental transmission of antibiotic resistance genes (ARGs) remains poorly understood. This study characterized E. coli from household water and additionally aimed to investigate the transmissibility of ARGs from drug-resistant isolates. Twenty-five E. coli from thirteen household well water sources were tested for resistance to 14 antibiotics by disc diffusion and whole-genome sequenced using the Illumina platform. The ARGs and plasmid replicon types were respectively predicted using ResFinder and PlasmidFinder. Multidrug-resistant strains carrying plasmid replicons found in unrelated strains were conjugated with nalidixic acid-resistant (NALR) E. coli C600 using the solid plate method. Fifteen isolates displayed a multi-drug resistance (MDR) phenotype with 18 possessing ARGs that confer resistance to trimethoprim-sulfamethoxazole, macrolide, sulphonamide, aminoglycoside, chloramphenicol, {beta}-lactams, and tetracycline. Fifteen of the 25 isolates belonged to sequence type detected more than once, and fourteen of these were multidrug resistant. Through solid plate mating, beta-lactam-resistant qnrS1-tet-dfrA14-positive strains bearing IncFI-, IncHI2, and IncHI2A successfully transferred ampicillin resistance to a nalidixic acid-resistant derivative of E. coli-C600. This research highlights the urgent need to safeguard household water sources against fecal contamination to curb the dissemination of ARGs among bacterial populations in this environment.

microbiology↗

bioRxiv2024

Genetic basis for antimicrobial resistance in Escherichia coli isolated from household water in municipal Ibadan, Nigeria

Escherichia coli serves as an indicator of recent faecal contamination in water, signaling the potential presence of enteric pathogens. The public health impact of E. coli in water becomes more significant when strains harbor virulence genes, and may themselves be pathogenic, or antimicrobial resistance genes that can be transferred to pathogens. In this study, we used whole genome sequencing (WGS) to characterize E. coli isolated from household water in municipal Ibadan, Nigeria across two seasons. Antimicrobial susceptibility testing was performed on 97 E. coli isolates, and their genomes were assembled using SPAdes. Multi-Locus Sequence Types (MLST), virulence genes and plasmid replicons were determined using ABRicate. Antimicrobial resistance genes (ARGs) were detected using AMRFinderplus. Phylogroups and serotypes were determined using ClermonTyper and ECTyper, respectively. A phylogenetic tree was built using RAxML. Of the 97 isolates, 39(40.2%) were multidrug resistant and 13(15.9%) possessed diarrheagenic E. coli (DEC) virulence genes. Resistance to individual antibiotics was higher and DEC characteristics more frequent among isolates recovered in the dry season compared to the wet season. Thirty-seven resistance genes belonging to nine antibiotic classes were detected. Majority of the isolates belonged to phylogroup A or B1, 35unique Sequence Types (STs) were detected and there were seven expanded clones of four or more isolates. This study determined that multidrug-resistant E. coli, including DEC, were recovered from household water sources in Ibadan. Some isolates were likely derived from point-sources, highlighting the importance of improved water quality management and sanitation in preventing waterborne disease and antimicrobial resistance transmission. IMPORTANCEContamination of household drinking water sources by disease-causing microorganisms is a serious public health concern common in African settings. Escherichia coli, an indicator of faecal contamination, can also be a reservoir for resistance genes. We have previously reported high frequencies of E. coli contamination of household water in municipal Ibadan. In this study we characterized antimicrobial resistance and virulence genes harboured by contaminating isolates. We found potential diarrhoea-causing E. coli in water which often carried antimicrobial resistance genes, irrespective of whether or not they were disease causing. Resistance gene carriage was more common among isolates recovered in the dry, as compared to the wet season. This was attributable to resistant lineages of E. coli bacteria spreading in the dry season. The work shows the importance of monitoring drinking water in urban African cities like Ibadan and that treating ground water sources may be necessary, particularly in the dry season.

microbiology↗

bioRxiv2024

Whole genome Sequencing Reveals Enterobacter hormaechei as a key Bloodstream Pathogen in six tertiary care hospitals in southwestern Nigeria.

Enterobacter spp. are an important cause of healthcare-associated bloodstream infections uncommonly reported in Africa. This study used whole genome sequencing (WGS) to characterise Enterobacter spp. from hospitals in Nigerias antimicrobial resistance (AMR) surveillance system. Blood-culture isolates of Enterobacter from six such tertiary-care hospitals recovered between 2014 and 2020 were re-identified and antimicrobial susceptibility-tested using VITEK2. Illumina technology provided whole genome sequences for genome nomenclature, antimicrobial resistance gene prediction, Single Nucleotide Polymorphism (SNP) phylogeny, and multi-locus sequence typing via publicly available bioinformatics pipelines. Initial biochemical delineation often misclassified Enterobacter, necessitating whole-genome sequencing for accurate classification. Among 98 Enterobacter received, Enterobacter hormaechei subspecies xiangfangensis predominated (43), followed by other E. hormachei subspecies (18), E. cloacae (26), E. roggenkampii (4), E. bugandensis (3), E. kobei (2), E. asburiae (1) and E. cancerogenous (1). Cephalosporins, aminoglycoside, chloramphenicol, macrolide, and carbapenem resistance in E. hormaechei was attributed to known resistance genes. They belonged to clusters III, IV, and VIII based on hsp60 typing and clades A, B, C, and D according to Sutton and Cos nomenclature. These isolates and other Enterobacter species recently reported from Nigeria reveal extensive E. hormaechei diversity, as well as clusters representing potential outbreaks. Enterobacter hormaechei, often misidentified and rarely reported from Nigeria, is this studys most common blood culture isolated Enterobacter spp. Uncovering underappreciated species as important bloodstream pathogens and retrospective detection of likely outbreaks emphasise the value of genomic surveillance in resource-limited settings. DATA SUMMARYAll sequence reads were submitted to the European Nucleotide Archive (ENA) under the project ID PRJEB29739 (https://www.ebi.ac.uk/ena/browser/view/PRJEB29739). Accessions can be found in Table S1. IMPACT STATEMENTAccurate identification of Enterobacter is essential in healthcare settings as misidentification can lead to selecting antimicrobials to the genus is intrinsically resistant resistant before susceptibility testing results are available. Also, misidentification can compromise microbiology support for infection prevention and control. We show that E. hormaechei, which is never reported from clinical laboratories in Nigeria, is frequently misidentified using conventional methods like tube- or strip biochemical testing and VITEK systems. Whole genome sequence data demonstrates that E. hormaechei and E. cloacae are the most common Enterobacter isolated from bloodstream infections in Nigeria. Enhanced identification methods for surveillance play pivotal roles in improving patient care, optimising antibiotic stewardship, and combating the evolving challenges posed by this pathogen. Overall, this study reveals the effectiveness of WGS in correctly identifying this important pathogen.

microbiology↗

bioRxiv2024

Identification and Mitigation of microbial contaminant reads from long-read metagenomic samples

Metagenomic sequencing of clinical samples has significantly enhanced our understanding of microbial communities. However, microbial contamination and host-derived DNA remain a major obstacle to accurate data interpretation. Here, we present a methodology called Stop-Check-Go for detecting and mitigating contaminants in metagenomic datasets obtained from neonatal patient samples (nasal and rectal swabs). This method incorporates laboratory and bioinformatics work combining a prevalence method, coverage estimation, and microbiological reports. We compared the Stop-Check-Go decontamination system with other published decontamination tools, and commonly found poor performance in decontaminating microbiologically negative patients (false positives). We emphasize that host DNA decreased by an average of 76% per sample using a lysis method and was further reduced during post-sequencing analysis. Microbial species were classified as putative contaminants and assigned to Stop in nearly 60% of the dataset. The Stop-Check-Go system was developed to address the specific need of decontaminating low-biomass samples, where existing tools primarily designed for short-read metagenomic data showed limited performance. Impact StatementMetagenomics has gained popularity due to its diverse applications in the multi-omics research field and the improvements in sequencing performance of technologies such as Nanopore. However, challenges in biological interpretation remain because of the complexity of the data structure and the potential of contamination occurring at multiple steps during sample processing, which can lead to incorrect conclusions. We aim to raise awareness of contamination, which can be host-associated, cross-contamination, or library-derived, any of which may be introduced at any stage from sample collection. Existing decontamination tools are largely designed for short-read sequencing and thus present limitations when applied to long-read datasets. We propose a direct comparison of species in samples with species in weekly negative controls that progressively accumulate both external and kit-reagent contaminants. Additionally, we recommend incorporating read-depth coverage and read-prevalence metrics, particularly in studies involving low-biomass or non-culturable microorganisms. Whenever possible, validation with microbiological reports is strongly advised. Our code is available on GitHub and can be executed locally in RStudio. It outputs species classifications labeled Stop, Check, or Go, as well as BIOM format files clean of identified contaminants, ready for downstream analysis with R packages such as phyloseq, vegan, or metagenomeSeq. Data summaryThe complete source code and documentation are available from GitHub (https://github.com/SAM81221/Stop-Check-Go_TAPIR). Metagenomic sequences including controls have been deposited in the ENA in project PRJEB82667; and isolate sequences of control samples in PRJEB95992. Information on samples and sequences can be found in Supplementary Table S1.

bioinformatics↗

bioRxiv2024

Assessing the performance of current strain resolution tools on long-read metagenomes

Recent advances in long-read sequencing-based methods have greatly enhanced genomics and public health applications. However, the challenge of effectively distinguishing strains within microbial communities from clinical samples using these technologies restricts their widespread use. We assessed the strain resolution capabilities of three currently available bioinformatics tools--TRACS, Strainy, and Strainberry--using both mock communities and authentic metagenomic datasets. Following sample preparation and long-read sequencing using the GridION sequencing platform, raw reads were processed using TRACS, aligning them to a custom reference database, while Strainberry and Strainy mapped reads to metagenome assemblies for strain resolution. Performance on mock microbial community was assessed by comparing predicted microbiota composition to the expected composition, and on both mock and authentic datasets by evaluating strain-resolved genome assemblies. Computational efficiency was measured in terms of task execution time, single-core CPU usage, and physical memory usage. TRACS demonstrated substantial agreement with the known composition, achieving a median score of 86.7% for Escherichia coli-dominant communities and 94.7% for Klebsiella pneumoniae-dominant communities. Strainberry and Strainy exhibited improved concordance after excluding strains with a genome size below 1 Mb, thus showcasing comparable performance metrics to TRACS. In mock and real metagenomic datasets, TRACS demonstrated the highest haplotype completeness compared to the other two tools, while Strainy demonstrated the highest haplotype accuracy. All tools were able to allocate strains to their respective transmission clusters (< 20 SNPs), albeit with varying degrees of success. Except for single core CPU usage, TRACS outperformed Strainy and Strainberry in terms of speed and computational efficiency. Our study underscores the utility of TRACS, Strainy, and Strainberry in resolving strains within microbial communities from clinical samples. TRACS stands out for its better haplotype completeness and computational efficiency, suggesting its potential to streamline advanced genomic analyses and public health initiatives.

bioinformatics↗

bioRxiv2024

Typhi Mykrobe: fast and accurate lineage identification and antimicrobial resistance genotyping directly from sequence reads for the typhoid fever agent Salmonella Typhi

BackgroundTyphoid fever results from systemic infection with Salmonella enterica serovar Typhi (Typhi) and causes 10 million illnesses annually. Disease control relies on prevention (water, sanitation, and hygiene interventions or vaccination) and effective antimicrobial treatment. Antimicrobial resistant (AMR) Typhi lineages have emerged and become established in many parts of the world. Knowledge of local pathogen populations informed by genomic surveillance, including of lineages (defined by the GenoTyphi scheme) and AMR determinants, is increasingly used to inform local treatment guidelines and to inform vaccination strategy. Current tools for genotyping Typhi require multiple read alignment or assembly steps and have not been validated for analysis of data generated with Oxford Nanopore Technologies (ONT) long-read sequencing devices. Here, we introduce Typhi Mykrobe, a command line software tool for rapid genotyping of Typhi lineages, AMR determinants, and plasmid replicons direct from sequencing reads. ResultsWe validated Typhi Mykrobe lineage genotyping by comparison with the current standard read mapping-based approach and demonstrated 99.8% concordance across nearly 13,000 genomes sequenced with Illumina platforms. For the few isolates with discordant calls, we show that Typhi Mykrobe results are better supported by the evidence from raw sequence read data than the results generated using the mapping-based approach. We also demonstrate 99.9% concordance for detection of AMR determinants compared with the current standard assembly-based approach, with similar results for plasmid marker detection. Typhi Mykrobe predicts clinical resistance categorisation (S/I/R) for eight drug classes, and we show strong agreement with phenotypic categorisations generated from reference laboratory minimum inhibitory concentration (MIC) data for n=1,572 Illumina-sequenced isolates (>99% agreement within one doubling dilution). We show strong concordance (>96% for genotype and >98% for AMR and plasmid) between calls made from ONT reads and those made from Illumina reads for isolates sequenced on both platforms (n =93 genomes). Typhi Mykrobe takes less than a minute per sample and is available at https://github.com/typhoidgenomics/genotyphi. ConclusionsTyphi Mykrobe provides rapid and sensitive genotyping of Typhi genomes direct from Illumina and ONT reads, although lower accuracy was observed for R9 ONT data. It demonstrated accurate assignment of GenoTyphi lineage, detection of AMR determinants and prediction of corresponding AMR phenotypes, and identification of plasmid replicons.

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↗

bioRxiv2021

Clones and Clusters of Antimicrobial-Resistant Klebsiella from Southwestern Nigeria

IntroductionKlebsiella pneumoniae is a World Health Organization high-priority antibiotic-resistant pathogen. However, little is known about the population structure and evolution of Klebsiella circulating in Nigeria. MethodsWe performed whole genome sequencing (WGS) of 141 Klebsiella isolated between 2016 and 2018 from clinical specimens at 3 antimicrobial-resistance (AMR) sentinel surveillance tertiary hospitals in southwestern Nigeria. We conducted in silico multilocus sequence typing, AMR gene, virulence gene, plasmid, and K and O loci profiling, as well as phylogenetic analyses, using publicly available tools and Nextflow pipelines. ResultsPhylogenetic analysis revealed that the majority of the 134 K. pneumoniae and 5 K. quasipneumoniae isolates from Nigeria characterized are closely related to globally disseminated multidrug-resistant clones. Of the 39 K. pneumoniae sequence types (STs) identified, the most common were ST307 (15%), ST5241 (12%), ST15 (~9%), and ST25 (~6%). ST5241, one of 10 novel STs detected, is a single locus variant of ST636 carrying dfrA14, tetD, qnrS, and oqxAB resistance genes. The extended-spectrum {beta} lactamase (ESBL) gene blaCTX_M-15 was seen in 72 % of K. pneumoniae genomes, while 8% encoded a carbapenemase. Four likely outbreak clusters from one facility, within STs 17, 25, 307, and 5241, were ESBL but not carbapenemase-bearing clones. ConclusionThis study uncovered known and novel K. pneumoniae lineages circulating in Nigeria that include multidrug-resistant ESBL producers. Carbapenemase-producing isolates remain uncommon. WGS retrospectively identified outbreak clusters, pointing to the value of genomic approaches in AMR surveillance for improving infection prevention and control in Nigerian hospitals. summaryWe performed whole genome sequencing (WGS) of 141 Klebsiella isolated in 2016-2018 at 3 antimicrobial-resistance (AMR) sentinel surveillance tertiary hospitals in southwestern Nigeria. This study uncovered known and novel K. pneumoniae lineages circulating in Nigeria that include multidrug-resistant ESBL producers. FUNDINGThis work was supported by Official Development Assistance (ODA) funding from the National Institute of Health Research [16/136/111: NIHR Global Health Research Unit on Genomic Surveillance of Antimicrobial Resistance]. This research was commissioned by the National Institute of Health Research using Official Development Assistance (ODA) funding. INO is an African Research Leader supported by the UK Medical Research Council (MRC) and the UK Department for International Development (DFID) under the MRC/DFID Concordat agreement that is also part of the EDCTP2 program supported by the European Union. The funders had no role in the content, crafting or submission of this paper. The views expressed in this publication are those of the authors and not necessarily those of the funders or their affiliates. CONFLICT OF INTERESTThe authors: No reported conflicts of interest. All authors have submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest.

genomics↗