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Alikhan, N.-F.

Publications and source records attributed to Alikhan, N.-F..

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Monitoring of vaccine targets and interventions using global genome data: vaccines.watch

The expansion of pathogen genome sequencing into routine disease surveillance programmes is set to bring rapidly-growing volumes of increasingly structured data on a global scale. This has the potential to deliver exciting opportunities for accelerating vaccine development and monitoring. Here we present an interactive platform, vaccines.watch (https://vaccines.watch), which aims to support decision-making around vaccine formulations and roll-out by enabling interrogation of vaccine target diversity from global genome data. We have initially focused on targets included in existing or prospective multivalent polysaccharide- based vaccines for Streptococcus pneumoniae, Klebsiella pneumoniae (and related species) and Acinetobacter baumannii. The platform currently displays data for >100k high-quality genomes with geotemporal sampling information (post-2010), with new genomes assembled, analysed and incorporated on an ongoing basis (every 4 hours) as public data are newly deposited. Crucially, users can view vaccine target information in the broader context of genotypic variants (e.g. sequence types) and antimicrobial resistance markers. The platform also enables users to review the composite serotypes of pneumococcal vaccine formulations among the available genomes. For example, using data in vaccines.watch from 3 June 2025, we observed that serotypes included in the PCV13 and PCV21 formulations accounted for 36.2% (11,907/32,918) and 87.4% (28,764/32,918) of global public genomes, respectively. The platform also enables continuous review of the global genomic landscape of the included pathogens, enabling identification of gaps (e.g. in geographic coverage) that should be targeted with increased genomic surveillance. Indeed we demonstrate that substantial geographic gaps remain in the coverage of available genomes, with over half of countries contributing no genomes for each of the three pathogens. However, while caution in interpretation is important, as global representativeness of genome data grows, vaccines.watch is positioned to support different stages of the vaccine pipeline, from selection of target antigens to post-rollout monitoring of population changes.

genomics↗

Invasive atypical non-typhoidal Salmonella in The Gambia

BackgroundInvasive non-typhoidal Salmonella (iNTS) disease continues to be a significant public health problem in sub-Saharan Africa. Common clinical misdiagnosis, antimicrobial resistance, high case fatality and lack of a vaccine make iNTS a priority for global health research. Using whole genome sequence analysis of 164 invasive Salmonella isolates obtained through population-based surveillance between 2008 and 2016, we conducted genomic analysis of the serovars causing invasive Salmonella diseases in rural Gambia. ResultsThe incidence of iNTS varied over time. The proportion of atypical serovars causing disease increased over time from 40% to 65% compared to the typical serovars Enteritidis and Typhimurium decreasing from 30% to 12%. Overall iNTS case fatality was 10% with 10% fatality in cases of atypical iNTS. Genetic virulence factors were identified in 14/70 (20%) typical serovars and 45/68 (66%) of the atypical serovars and were associated with: invasion, proliferation and/or translocation (Clade A); and host colonization and immune modulation (Clade G). Among Enteritidis isolates, 33/40 were resistant to [≥]4 the antimicrobials tested, except for ciprofloxacin, to which all isolates were susceptible. Resistance was low in Typhimurium isolates, however, all16 isolates were resistant to gentamicin. ConclusionThe increase in incidence and proportion of iNTS disease caused by atypical serovars is concerning. The increased proportion of atypical serovars and the high associated case fatality may be related to acquisition of specific genetic virulence factors. These factors may provide a selective advantage to the atypical serovars. Investigations should be conducted elsewhere in Africa to identify potential changes in the distribution iNTS serovars and the extent of these virulence elements.

genomics↗

Genomic Diversity of Escherichia coli isolates from healthy children in rural Gambia

Little is known about the genomic diversity of Escherichia coli in healthy children from sub-Saharan Africa, even though this is pertinent to understanding bacterial evolution and ecology and their role in infection. We isolated and whole-genome sequenced up to five colonies of faecal E. coli from 66 asymptomatic children aged three-to-five years in rural Gambia (n=88 isolates from 21 positive stools). We identified 56 genotypes, with an average of 2.7 genotypes per host. These were spread over 37 seven-allele sequence types and the E. coli phylogroups A, B1, B2, C, D, E, F and Escherichia cryptic clade I. Immigration events accounted for three-quarters of the diversity within our study population, while one-quarter of variants appeared to have arisen from within-host evolution. Several study strains were closely related to isolates that caused disease in humans or originated from livestock. Our results suggest that within-host evolution plays a minor role in the generation of diversity than independent immigration and the establishment of strains among our study population. Also, this study adds significantly to the number of commensal E. coli genomes, a group that has been traditionally underrepresented in the sequencing of this species.

ecology↗

CoronaHiT: large scale multiplexing of SARS-CoV-2 genomes using Nanopore sequencing

The COVID-19 pandemic has spread to almost every country in the world since it started in China in late 2019. Controlling the pandemic requires a multifaceted approach including whole genome sequencing to support public health interventions at local and national levels. One of the most widely used methods for sequencing is the ARTIC protocol, a tiling PCR approach followed by Oxford Nanopore sequencing (ONT) of up to 96 samples at a time. There is a need, however, for a flexible, platform agnostic, method that can provide multiple throughput options depending on changing requirements as the pandemic peaks and troughs. Here we present CoronaHiT, a method capable of multiplexing up to 96 small genomes on a single MinION flowcell or >384 genomes on Illumina NextSeq, using transposase mediated addition of adapters and PCR based addition of barcodes to ARTIC PCR products. We demonstrate the method by sequencing 95 and 59 SARS-CoV-2 genomes for routine and rapid outbreak response runs, respectively, on Nanopore and Illumina platforms and compare to the standard ARTIC LoCost nanopore method. Of the 154 samples sequenced using the three approaches, genomes with [≥] 90% coverage (GISAID criteria) were generated for 64.3% of samples for ARTIC LoCost, 71.4% for CoronaHiT-ONT, and 76.6% for CoronaHiT-Illumina and have almost identical clustering on a maximum likelihood tree. In conclusion, we demonstrate that CoronaHiT can multiplex up to 96 SARS-CoV-2 genomes per MinION flowcell and that Illumina sequencing can be performed on the same libraries, which will allow significantly higher throughput. CoronaHiT provides increased coverage for higher Ct samples, thereby increasing the number of high quality genomes that pass the GISAID QC threshold. This protocol will aid the rapid expansion of SARS-CoV-2 genome sequencing globally, to help control the pandemic.

genomics↗

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↗

Genomic diversity of Escherichia coli isolates from backyard chickens and guinea fowl in the Gambia

Chickens and guinea fowl are commonly reared in Gambian homes as affordable sources of protein. Using standard microbiological techniques, we obtained 68 caecal isolates of Escherichia coli from ten chickens and nine guinea fowl in rural Gambia. After Illumina whole-genome sequencing, 28 sequence types were detected in the isolates (four of them novel), of which ST155 was the most common (22/68, 32%). These strains span four of the eight main phylogroups of E. coli, with phylogroups B1 and A being most prevalent. Nearly a third of the isolates harboured at least one antimicrobial resistance gene, while most of the ST155 isolates (14/22, 64%) encoded resistance to [≥]3 classes of clinically relevant antibiotics, as well as putative virulence factors, suggesting pathogenic potential in humans. Furthermore, hierarchical clustering revealed that several Gambian poultry strains were closely related to isolates from humans. Although the ST155 lineage is common in poultry from Africa and South America, the Gambian ST155 isolates belong to a unique cgMLST cluster comprised of closely related (38-39 alleles differences) isolates from poultry and livestock from sub-Saharan Africa--suggesting that strains can be exchanged between poultry and livestock in this setting. Continued surveillance of E. coli and other potential pathogens in rural backyard poultry from sub-Saharan Africa is warranted. Author notesAll supporting data and protocols have been provided within the article or as supplementary data files. Eleven supplementary figures and eight supplementary files are available with the online version of this article. Data summaryThe genomic assemblies for the isolates reported here are available for download from EnteroBase (http://enterobase.warwick.ac.uk/species/index/ecoli) and the EnteroBase assembly barcodes are provided in File S2. Sequences have been deposited in the NCBI SRA, under the BioProject ID: PRJNA616250 and accession numbers SAMN14485281 to SAMN14485348 (File S2). Assemblies have been deposited in GenBank under the BioProject ID: PRJNA616250 and accession numbers CP053258 and CP053259. Impact statementDomestic birds play a crucial role in human society, in particular contributing to food security in low-income countries. Many households in Sub-Saharan Africa rear free-range chickens and guinea fowl, which are often left to scavenge for feed in and around the family compound, where they are frequently exposed to humans, other animals and the environment. Such proximity between backyard poultry and humans is likely to facilitate transmission of pathogens such as Escherichia coli or antimicrobial resistance between the two host species. Little is known about the population structure of E. coli in rural chickens and guinea fowl, although this information is needed to contextualise the potential risks of transmission of bacterial strains between humans and rural backyard poultry. Thus, we sought to investigate the genomic diversity of E. coli in backyard poultry from rural Gambia.

genomics↗

Within-Host Diversity and Vertical Transmission of Group B Streptococcus Among Mother-infant Dyads in The Gambia

IntroductionUnderstanding mother-to-infant transmission of Group B Streptococcus (GBS) is vital to the prevention and control of GBS disease. We investigated the transmission and phylogenetic relationships of mothers colonised by GBS and their infants in a peri-urban setting in The Gambia.\n\nMethodsWe collected nasopharyngeal swabs from 35 mother-infant dyads at weekly intervals from birth until six weeks post-partum. GBS was isolated by conventional microbiology techniques. Whole-genome sequencing was performed on GBS isolates from one mother-infant dyad (dyad 17).\n\nResultsWe recovered 85 GBS isolates from the 245 nasopharyngeal swabs. GBS was isolated from 16.33% and 18.37% of sampled mothers and infants, respectively. In 87% of cultured swabs, the culture status of an infant agreed with that of the mother (Kappa p-value <0.001). In dyad 17, phylogenetic analysis revealed within-host strain diversity in the mother and clone to her infant.\n\nConclusionGBS colonisation in mothers presents a significant risk of colonisation in their infants. We confirm vertical transmission from mother to child in dyad 17, accompanied by within-host diversity.

microbiology↗

The user’s guide to comparative genomics with EnteroBase. Three case studies: micro-clades within Salmonella enterica serovar Agama, ancient and modern populations of Yersinia pestis, and core genomic diversity of all Escherichia

EnteroBase is an integrated software environment which supports the identification of global population structures within several bacterial genera that include pathogens. Here we provide an overview on how EnteroBase works, what it can do, and its future prospects. EnteroBase has currently assembled more than 300,000 genomes from Illumina short reads from Salmonella, Escherichia, Yersinia, Clostridiodes, Helicobacter, Vibrio, and Moraxella, and genotyped those assemblies by core genome Multilocus Sequence Typing (cgMLST). Hierarchical clustering of cgMLST sequence types allows mapping, a new bacterial strain to predefined population structures at multiple levels of resolution within a few hours after uploading its short reads. Case study 1 illustrates this process for local transmissions of Salmonella enterica serovar Agama between neighboring social groups of badgers and humans. EnteroBase also supports SNP calls from both genomic assemblies and after extraction from metagenomic sequences, as illustrated by case study 2 which summarizes the microevolution of Yersinia pestis over the last 5,000 years of pandemic plague. EnteroBase can also provide a global overview of the genomic diversity within an entire genus, as illustrated by case study 3 which presents a novel, global overview of the population structure of all of the species, subspecies and clades within Escherichia.

microbiology↗

Multiple evolutionary trajectories for non-O157 Shiga toxigenic Escherichia coli

BackgroundShiga toxigenic Escherichia coli (STEC) is an emerging global pathogen and remains a major cause of food-borne illness with more severe symptoms including hemorrhagic colitis and hemolytic-uremic syndrome. Since the characterization of the archetypal STEC serotype, E. coli O157:H7, more than 250 STEC serotypes have been defined. Many of these non-O157 STEC are associated with clinical cases of equal severity as O157. In this study, we utilize whole genome sequencing of 44 STEC strains from eight serogroups associated with human infection to establish their evolutionary relationships and contrast this with their virulence gene profiles and established typing methods. ResultsOur phylogenomic analysis delineated these STEC strains into seven distinct lineages, each with a characteristic repertoire of virulence factors. Some lineages included commensal or other E. coli pathotypes. Multiple independent acquisitions of the Locus for Enterocyte Effacement were identified, each associated with a distinct repertoire of effector genes. Lineages were inconsistent with O-antigen typing in several instances, consistent with lateral gene transfer within the O-antigen locus. STEC lineages could be defined by the conservation of clustered regularly interspaced short palindromic repeats (CRISPRs), however, no CRISPR profile could differentiate STEC from other E. coli strains. Six genomic regions (ranging from 500 bp - 10 kbp) were found to be conserved across all STEC in this dataset and may dictate interactions with Stx phage lysogeny. ConclusionsThe genomic analyses reported here present non-O157 STEC as a diverse group of pathogenic E. coli emerging from multiple lineages that independently acquired mobile genetic elements that promote pathogenesis.

microbiology↗

GrapeTree: Visualization of core genomic relationships among 100,000 bacterial pathogens

O_LICurrent methods struggle to reconstruct and visualise the genomic relationships of [&ge;]100,000 bacterial genomes.\nC_LIO_LIGrapeTree facilitates the analyses of allelic profiles from 10,000s of core genomes within a web browser window.\nC_LIO_LIGrapeTree implements a novel minimum spanning tree algorithm to reconstruct genetic relationships despite missing data together with a static \"GrapeTree Layout\" algorithm to render interactive visualisations of large trees.\nC_LIO_LIGrapeTree is a stand-along package for investigating Newick trees plus associated metadata and is also integrated into EnteroBase to facilitate cutting edge navigation of genomic relationships among >160,000 genomes from bacterial pathogens.\nC_LIO_LIThe GrapeTree package was released under the GPL v3.0 Licence.\nC_LI

bioinformatics↗

Accurate Reconstruction of Microbial Strains Using Representative Reference Genomes

Exploring the genetic diversity of microbes within the environment through metagenomic sequencing first requires classifying these reads into taxonomic groups. Current methods compare these sequencing data with existing biased and limited reference databases. Several recent evaluation studies demonstrate that current methods either lack sufficient sensitivity for species-level assignments or suffer from false positives, overestimating the number of species in the metagenome. Both are especially problematic for the identification of low-abundance microbial species, e. g. detecting pathogens in ancient metagenomic samples. We present a new method, SPARSE, which improves taxonomic assignments of metagenomic reads. SPARSE balances existing biased reference databases by grouping reference genomes into similarity-based hierarchical clusters, implemented as an efficient incremental data structure. SPARSE assigns reads to these clusters using a probabilistic model, which specifically penalizes non-specific mappings of reads from unknown sources and hence reduces false-positive assignments. Our evaluation on simulated datasets from two recent evaluation studies demonstrated the improved precision of SPARSE in comparison to other methods for species-level classification. In a third simulation, our method successfully differentiated multiple co-existing Escherichia coli strains from the same sample. In real archaeological datasets, SPARSE identified ancient pathogens with[&le;] 0.02% abundance, consistent with published findings that required additional sequencing data. In these datasets, other methods either missed targeted pathogens or reported non-existent ones. SPARSE and all evaluation scripts are available at https://github.com/zheminzhou/SPARSE.

bioinformatics↗

Comparison Of Multi-locus Sequence Typing Software For Next Generation Sequencing Data

Multi-locus sequence typing (MLST) is a widely used method for categorising bacteria. Increasingly MLST is being performed using next generation sequencing data by reference labs and for clinical diagnostics. Many software applications have been developed to calculate sequence types from NGS data; however, there has been no comprehensive review to date on these methods. We have compared six of these applications against real and simulated data and present results on: 1. the accuracy of each method against traditional typing methods, 2. the performance on real outbreak datasets, 3. in the impact of contamination and varying depth of coverage, and 4. the computational resource requirements.\n\nDATA SUMMARYO_LISimulated reads for datasets testing coverage and mixed samples have been deposited in Figshare; DOI: https://doi.org/10.6084/m9.figshare.4602301.vl\nC_LIO_LIOutbreak databases are available from Github; url - https://github.com/WGS-standards-and-analysis/datasets\nC_LIO_LIDocker containers used to run each of the applications are available from Github; url - https://tinyurl.com/z7ks2ft\nC_LIO_LIAccession numbers for the data used in this paper are available in the Supplementary material.\nC_LI\n\nWe confirm all supporting data, code and protocols have been provided within the article or through supplementary data files. {ballotx}\n\nIMPACT STATEMENTSequence typing is rapidly transitioning from traditional sequencing methods to using whole genome sequencing. A number of in silico prediction methods have been developed on an ad hoc basis and aim to replicate Multi-locus sequence typing (MLST). This is the first study to comprehensively evaluate multiple MLST software applications on real validated datasets and on common simulated difficult cases. It will give researchers a clearer understanding of the accuracy, limitations and computational performance of the methods they use, and will assist future researchers to choose the most appropriate method for their experimental goals.

bioinformatics↗

Millennia of genomic stability within the invasive Para C Lineage of Salmonella enterica

Salmonella enterica serovar Paratyphi C is the causative agent of enteric (paratyphoid) fever. While today a potentially lethal infection of humans that occurs in Africa and Asia, early 20th century observations in Eastern Europe suggest it may once have had a wider-ranging impact on human societies. We recovered a draft Paratyphi C genome from the 800-year-old skeleton of a young woman in Trondheim, Norway, who likely died of enteric fever. Analysis of this genome against a new, significantly expanded database of related modern genomes demonstrated that Paratyphi C is descended from the ancestors of swine pathogens, serovars Choleraesuis and Typhisuis, together forming the Para C Lineage. Our results indicate that Paratyphi C has been a pathogen of humans for at least 1,000 years, and may have evolved after zoonotic transfer from swine during the Neolithic period.\n\nOne Sentence SummaryThe combination of an 800-year-old Salmonella enterica Paratyphi C genome with genomes from extant bacteria reshapes our understanding of this pathogens origins and evolution.

microbiology↗