Biology subjects

Walker, A. S.

Publications and source records attributed to Walker, A. S..

At least 19 recordsLinked to original sources

bioRxiv2026

Gut microbiome derived folate metabolite suppresses colorectal cancer progression

The gut microbiota influences colorectal cancer (CRC) progression, primarily through the secretion of small molecule metabolites. While numerous microbial products are known to drive CRC, endogenous protective mechanisms remain largely uncharacterized. Utilizing a folate metabolomics platform, we demonstrate that the healthy gut microbiota produces folinic acid (FA), a known chemotherapeutic adjuvant also known as leucovorin. This microbially derived folinic acid is progressively depleted in mouse models of colitis-associated CRC and in human clinical metagenomic cohorts with advancing disease severity. Mechanistically, folinic acid acts as a signaling molecule that directly binds and inhibits the intracellular protease calpain-2. This interaction stabilizes epithelial E-cadherin protein expression and suppresses CRC epithelial-to-mesenchymal transition driving metastasis. Genetically manipulating gut microbial production of FA is sufficient to modulate CRC in vivo, even in the presence of chronic inflammation. This study reframes folinic acid from a chemotherapeutic enhancer to an endogenous microbial metabolite that actively suppresses CRC progression.

cancer biology↗

bioRxiv2026

UTILIZING SEQUENCE SIMILARITY NETWORKS FOR CROSS SPECIES ELICITOR IDENTIFICATION OF STREPTOMYCES REGULATORY PROTIENS

Streptomyces bacteria produce a variety of secondary metabolites that hold clinical and agricultural value, yet their biosynthetic potential remains unrealized as many biosynthetic gene clusters are not expressed under standard laboratory conditions. Expression of these clusters is tightly regulated, often by cluster situated transcription factors. The TetR family are regulators whose activity is modulated by small molecule elicitors. Although many TetRs have been characterized, elicitors have only been identified for a small fraction of them. This lack of data presents a limitation in our ability to exploit elicitor-regulator pairs for activation of silent clusters and underscores the need for predictive and testable strategies for elicitor identification. In this work, we test the use of sequence similarity networks (SSNs) as a predictor of elicitor identity using the well characterized TetR protein, JadR2, that has a known elicitor, chloramphenicol. We utilized SSNs to identify JadR2 homologs that may also be elicited by chloramphenicol. We developed a heterologous Escherichia coli reporter system in which regulator activity was monitored using an EGFP readout of DNA binding activity. Using this system, we screened JadR2 and four homologs for responsiveness to chloramphenicol. We found that 3 homologs were elicited by chloramphenicol, all of which were formerly uncharacterized. These results demonstrate that TetR-family proteins can share elicitor responsiveness and that SSNs can be used to prioritize regulators for functional screening. This work establishes a genomics-informed and bioinformatics-guided framework for linking elicitors to their regulator, expanding the toolkit for natural product discovery by unlocking regulatory information across Streptomyces.

microbiology↗

bioRxiv2026

OxBreaker: species-agnostic pipeline for the analysis of outbreaks using nanopore sequencing

Real-time genomic surveillance may mitigate the spread of health-care-associated infections, but whole-genome sequencing costs and the need for specialised expertise constrain its wide implementation in public health. Here we present OxBreaker, an automated and species-agnostic pipeline optimised for the high-resolution analysis of bacterial and plasmid genomes sequenced via Oxford Nanopore Technologies (ONT). OxBreaker streamlines the transition from raw reads to phylogenetic inference through automated reference selection and high-accuracy variant calling. It is accessible via a graphical user interface (GUI) that can be easily installed locally and operated by non-specialists. Benchmarking against technical and biological replicates of high-priority pathogens demonstrates high accuracy, with false positive variant rates reduced to 0-4 single-nucleotide polymorphisms (SNPs) for common species. We further validated the pipeline by accurately characterising previously published clonal and plasmid-mediated outbreaks, reproducing established phylogenies with improved accessibility. By providing a stable, scalable, open-source offline-compatible solution that matches the resolution of short-read platforms while maintaining the speed of long-read technology, OxBreaker is designed to facilitate the adoption of local, real-time genomic surveillance for frontline infection prevention and control.

genomics↗

bioRxiv2026

Identification and Masking of Artefactual and Misleading Within-Host Variants in Deep-Sequencing SARS-CoV-2 Data

Deep sequencing data are increasingly used to study within-host viral diversity and to inform evolutionary inference. For SARS-CoV-2, analyses based on intra-host single-nucleotide variants (iSNVs) have been widely applied to quantify within-host diversity and infer transmission dynamics. However, these applications critically depend on the reliable identification of low-frequency variants, which remain vulnerable to systematic and technical artefacts. In this study, we show that recurrent artefactual iSNVs are common in large-scale SARS-CoV-2 sequencing data and can persist even under conservative minor allele frequency (MAF) thresholds. Using data from the UKs Office for National Statistics COVID-19 Infection Survey, we demonstrate that such artefacts are predominantly sequencing centre-rather than protocol-specific. Each centre exhibits a modest, distinct set of recurrent artefactual variants showing little overlap with sites routinely masked at the consensus level. To address this, we developed a systematic, dataset-aware framework that uses recurrence within sequencing datasets to identify small, noise-adapted sets of artefactual iSNVs to mask. Applying this framework reduces spurious sharing of low-frequency variants between samples and qualitatively alters downstream inferences, including estimates of within-host diversity and transmission bottleneck sizes. Together, these findings highlight the importance of explicit, dataset-aware artefact control for robust inference from within-host variation, particularly as genomic studies increasingly seek to exploit sub-consensus diversity in rapidly evolving pathogens such as SARS-CoV-2.

genomics↗

bioRxiv2025

Nanopore long-read only genome assembly of clinical Enterobacterales isolates is complete and accurate

Whole bacterial genome sequence reconstruction using Oxford Nanopore Technologies ("Nanopore") long-read only sequencing may offer a lower-cost, higher-throughput alternative for pathogen surveillance to hybrid assembly with recent improvements in Nanopore sequencing accuracy. We evaluated the accuracy, including plasmid reconstruction, of Nanopore long-read only genome assemblies of Enterobacterales. We sequenced 92 genomes from clinical Enterobacterales isolates, collected in England under a national surveillance program, with long-read Nanopore (R10.4.1, Dorado v5.0.0 super-high-accuracy basecalled) and short-read Illumina (NovaSeq) sequencing approaches. Genomes were assembled using three long-read only (Flye; Hybracter long; Autocycler), and three hybrid assemblers (Hybracter hybrid; Unicycler normal; bold). Three polishing modalities (Medaka v2 with subsampled or un-subsampled long-reads; Polypolish + Pypolca with short-reads) were investigated. Autocycler circularised the most chromosomes (87/92 [95%]). Plasmid sequence reconstruction was comparable between all assemblers except Flye, all recovering 90-96% of plasmids, although the ground truth was uncertain. Flye performed worse than other assemblers on almost all metrics. Autocycler + Medaka (un-subsampled long-reads) was the most accurate long-read only assembler/polisher combination, comparable to hybrid assemblies (median 0 [IQR:0-0] SNPs and 0 [IQR:0-1] indels per genome; quality value/Q score, 100 [IQR: 64-100]), with only 4/92 genome sequences having >10 SNPs/indels. Medaka polishing with un-subsampled long-reads resulted in small improvements in indels but not SNPs for both Flye and Autocycler assemblies. Seven-locus MLST, antimicrobial resistance, virulence, and stress gene annotation was equivalent across assembler/polisher combinations. Nanopore long-read only bacterial genome assembly with Autocycler combined with Medaka polishing (using un-subsampled reads) is similarly accurate and possibly more complete than hybrid assemblies, representing a viable alternative for incorporating high-quality genomic data, including plasmids, into Enterobacterales surveillance. Data SummaryNanopore long-reads and Illumina short-reads from the 92 Enterobacterales isolates from this study have been uploaded to ENA (BioProject accession: PRJEB93885). Code for the Nextflow assembly pipeline, downstream analysis scripts, and R statistical analysis scripts are available on GitHub (https://github.com/oxfordmmm/NEKSUS_ont_hybrid_assembly_comparison). The following supplementary data tables are available on FigShare (https://figshare.com/account/home#/projects/253775): O_LIENA Sample accessions and sample metadata (accessions_and_metadata.csv) C_LIO_LISeqkit stats summaries of the Illumina and Nanopore reads (raw_qc_sup.cav) C_LIO_LISummary of assembly contig features (contigs_summary_sup_cleaned.csv) C_LIO_LIPairwise mash distances between contigs (mash_cleaned.csv) C_LIO_LIPlasmids matching across different assemblers compared to the Hybracter (hybrid) and manually-curated reference sets (plasmids_match_hybracter_mash.csv; plasmids_match_manual_mash.csv, respectively) C_LIO_LISeven-locus multi-locus sequence type annotation (mlst_cleaned.csv) C_LIO_LICheckM2 summaries of assemblies (checkm2_cleaned.csv) C_LIO_LINucleotide-level accuracy of assemblies (SNP, Indels, and Quality value compared to short-read mapping; assembly_nucleotide_accuracy_cleaned.csv) C_LIO_LIBakta annotation (bakta_by_contig_cleaned.csv) C_LIO_LIAMRFinderPlus annotations of contigs (amrfinder_plus_cleaned.csv) C_LIO_LIMOB-suite annotation summaries of contigs (mobsuite_cleaned.csv) C_LI Impact StatementNanopore long-reads have historically been too error-prone to use alone for accurate bacterial genome assembly, necessitating additional Illumina short-reads to achieve structurally complete and accurate hybrid genome assemblies for public health surveillance. This increases cost and complexity. Previous studies have shown that recent improvements in Nanopore chemistry (R10.4.1 flowcell) and basecalling (super-high accuracy) allow high-quality long-read only assemblies on a small number of laboratory reference strains. This is the first evaluation, to our knowledge, to assess Nanopore long-read only genome assembly compared with hybrid assembly on a large number of clinical isolates. In addition, this is the first large-scale evaluation of the recently released automated consensus long-read assembly tool, Autocycler. We show that Autocycler long-read only assemblies are more structurally complete for chromosomal sequences, while reconstructing a similar number of plasmids to other long-read and hybrid assemblers. Most long-read polished, Autocycler-assembled genome sequences have 0 errors (median: 0 SNPs/indels) relative to a short-read polished (hybrid) Autocycler assemblies, enabling accurate annotation of key genes.

bioinformatics↗

bioRxiv2024

Yatakemycin biosynthesis requires two deoxyribonucleases for toxin self-resistance

The highly active natural product yatakemycin (YTM) from Streptomyces sp. TP-A0356 is a potent DNA damaging agent with antimicrobial and antitumor properties. The YTM biosynthesis gene cluster (ytk) contains several toxin self-resistance genes. Of these, ytkR2 encodes a DNA glycosylase that is important for YTM production and host survival by excising lethal YTM-adenine lesions from the genome, presumably initiating a base excision repair (BER) pathway. However, the genes involved in repair of the resulting apurinic/apyrimidinic (AP) site as the second BER step have not been identified. Here, we show that ytkR4 and ytkR5 are essential for YTM production and encode deoxyribonucleases related to other known DNA repair nucleases. Purified YtkR4 and YtkR5 exhibit AP endonuclease activity specific for YtkR2-generated AP sites, providing a basis for BER of the toxic AP intermediate produced from YTM-adenine excision and consistent with co-evolution of ytkR2, ytkR4, and ytkR5. YtkR4 and YtkR5 also exhibit 3'-5' exonuclease activity with differing substrate specificities. The YtkR5 exonuclease is capable of digesting through a YTM-DNA lesion and may represent an alternative repair mechanism to BER. We also show that ytkR4 and ytkR5 homologs are often clustered together in putative gene clusters related to natural product production, consistent with non-redundant roles in repair of other DNA adducts derived from genotoxic natural products.

biochemistry↗

bioRxiv2024

E. coli phylogeny drives co-amoxiclav resistance through variable expression of blaTEM-1

Co-amoxiclav resistance in E. coli is a clinically important phenotype associated with increased mortality. The class A beta-lactamase blaTEM-1 is often carried by co- amoxiclav-resistant pathogens, but exhibits high phenotypic heterogeneity, making genotype-phenotype predictions challenging. We present a curated dataset of n=377 E. coli isolates representing all 8 known phylogroups, where the only acquired beta- lactamase is blaTEM-1. For all isolates, we generate hybrid assemblies and co-amoxiclav MICs, and for a subset (n=67/377), blaTEM-1 qPCR expression data. First, we test whether certain E. coli lineages are intrinsically better or worse at expressing blaTEM-1, for example, due to lineage differences in regulatory systems, which are challenging to directly quantify. Using genotypic features of the isolates (blaTEM-1 promoter variants and copy number), we develop a hierarchical Bayesian model for blaTEM-1 expression that controls for phylogeny. We establish that blaTEM-1 expression intrinsically varies across the phylogeny, with some lineages (e.g. phylogroups B1 and C, ST12) better at expression than others (e.g. phylogroups E and F, ST372). Next, we test whether phylogenetic variation in expression influences the resistance of the isolates. With a second model, we use genotypic features (blaTEM-1 promoter variants, copy number, duplications; ampC promoter variants; efflux pump AcrF presence) to predict isolate MIC, again controlling for phylogeny. Lastly, we use a third model to demonstrate that the phylogenetic influence on blaTEM-1 expression causally drives the variation in co- amoxiclav MIC. This underscores the importance of incorporating phylogeny into genotype-phenotype predictions, and the study of resistance more generally.

microbiology↗

bioRxiv2024

Statistical Coupling Analysis Predicts Correlated Motions in Dihydrofolate Reductase

The role of dynamics in enzymatic function is a highly debated topic. Dihydrofolate reductase (DHFR), due to its universality and the depth with which it has been studied, is a model system in this debate. Myriad previous works have identified networks of residues in positions near to and remote from the active site that are involved in dynamics and others that are important for catalysis. For example, specific mutations on the Met20 loop in E. coli DHFR (N23PP/S148A) are known to disrupt millisecond-timescale motions and reduce catalytic activity. However, how and if networks of dynamically coupled residues influence the evolution of DHFR is still an unanswered question. In this study, we first identify, by statistical coupling analysis and molecular dynamic simulations, a network of coevolving residues, which possess increased correlated motions. We then go on to show that allosteric communication in this network is selectively knocked down in N23PP/S148A mutant E. coli DHFR. Finally, we identify two sites in the human DHFR sector which may accommodate the Met20 loop double proline mutation while preserving dynamics. These findings strongly implicate protein dynamics as a driving force for evolution.

biochemistry↗

bioRxiv2024

Robust self-supervised denoising of voltage imaging data using CellMincer

Voltage imaging enables high-throughput investigation of neuronal activity, yet its utility is often constrained by a low signal-to-noise ratio (SNR). Conventional denoising algorithms, such as those based on matrix factorization, impose limiting assumptions about the noise process and the spatiotemporal structure of the signal. While deep learning based denoising techniques offer greater adaptability, existing approaches fail to fully exploit the fast temporal dynamics and unique short- and long-range dependencies within voltage imaging datasets. Here, we introduce CellMincer, a novel self-supervised deep learning method designed specifically for denoising voltage imaging datasets. CellMincer operates on the principle of masking and predicting sparse sets of pixels across short temporal windows and conditions the denoiser on precomputed spatiotemporal auto-correlations to effectively model long-range dependencies without the need for large temporal denoising contexts. We develop and utilize a physics-based simulation framework to generate realistic datasets for rigorous hyperparameter optimization and ablation studies, highlighting the key role of conditioning the denoiser on precomputed spatiotemporal auto-correlations to achieve 3-fold further reduction in noise. Comprehensive benchmarking on both simulated and real voltage imaging datasets, including those with paired patch-clamp electrophysiology (EP) as ground truth, demonstrates CellMincers state-of-the-art performance. It achieves substantial noise reduction across the entire frequency spectrum, enhanced detection of subthreshold events, and superior cross-correlation with ground-truth EP recordings. Finally, we demonstrate how CellMincers addition to a typical voltage imaging data analysis workflow improves neuronal segmentation, peak detection, and ultimately leads to significantly enhanced separation of functional phenotypes.

neuroscience↗

bioRxiv2023

Machine learning-based exploration, expansion and definition of the atropopeptide family of ribosomally synthesized and posttranslationally modified peptides

Ribosomally synthesized and posttranslationally modified peptides (RiPPs) constitute a diverse class of natural products. Atropopeptides are a recent addition to the fast-growing number of RiPP families. Characterized members of the peptide family feature a particular intricate three-dimensional shape. Here we developed AtropoFinder, a machine learning-based algorithm to chart the biosynthetic landscape of the atropopeptides. AtropoFinder identified more than 650 atropopeptide biosynthetic gene clusters (BGCs). Through bioinformatics and modeling analyses, we pinpointed crucial motifs and residues in leader and core peptide sequences, prompting a refined definition of the atropopeptide RiPP family. Our study revealed that a substantial subset of atropopeptide BGCs harbors multiple tailoring genes, thus suggesting a broader structural diversity than previously anticipated. To verify AtropoFinder, we heterologously expressed four atropopeptide BGCs, which resulted in the identification of novel atropopeptides with varying peptide lengths, number and type of modifications. Most notably, our study resulted in the characterization of an atropopeptide that is more extensively modified than previously identified members, resulting in an even more rigid 3-dimensional shape. Moreover, one characterized atropopeptide BGC encoding a single P450 is involved in the biosynthesis of two peptides with the same sequence but distinct and non-overlapping modification patterns. This work expands the atropopeptide chemical space, advances our understanding of atropopeptide biosynthesis and underscores the potential of machine learning in uncovering the uncharted biosynthetic diversity encoded in RiPP biosynthetic blueprints.

bioinformatics↗

bioRxiv2023

A workflow for the detection of antibiotic residues, measurement of water chemistry and preservation of hospital sink drain samples for metagenomic sequencing

Structured summaryO_ST_ABSBackgroundC_ST_ABSHospital sinks are environmental reservoirs that harbour healthcare-associated (HCA) pathogens. Selective pressures in sink environments, such as antibiotic residues, nutrient waste and hardness ions, may promote antibiotic resistance gene (ARG) exchange between bacteria. However, cheap and accurate sampling methods to characterise these factors are lacking. AimTo validate a workflow to detect antibiotic residues and evaluate water chemistry using dipsticks. Secondarily, to validate boric acid to preserve the taxonomic and ARG ("resistome") composition of sink trap samples for metagenomic sequencing. MethodsAntibiotic residue dipsticks were validated against serial dilutions of ampicillin, doxycycline, sulfamethoxazole and ciprofloxacin, and water chemistry dipsticks against serial dilutions of chemical calibration standards. Sink trap aspirates were used for a "real-world" pilot evaluation of dipsticks. To assess boric acid as a preservative of microbial diversity, the impact of incubation with and without boric acid at [~]22{degrees}C on metagenomic sequencing outputs was evaluated at Day 2 and Day 5 compared with baseline (Day 0). FindingsThe limits of detection for each antibiotic were: 3{micro}g/L (ampicillin), 10{micro}g/L (doxycycline), 20{micro}g/L (sulfamethoxazole) and 8{micro}g/L (ciprofloxacin). The best performing water chemistry dipstick correctly characterised 34/40 (85%) standards in a concentration-dependent manner. One trap sample tested positive for the presence of tetracyclines and sulfonamides. Taxonomic and resistome composition were largely maintained after storage with boric acid at [~]22{degrees}C for up to five days. ConclusionsDipsticks can be used to detect antibiotic residues and characterise water chemistry in sink trap samples. Boric acid was an effective preservative of trap sample composition, representing a low-cost alternative to cold-chain transport.

microbiology↗

bioRxiv2023

Predicting fungal secondary metabolite activity from biosynthetic gene cluster data using machine learning

Fungal secondary metabolites (SMs) play a significant role in the diversity of ecological communities, niches, and lifestyles in the fungal kingdom. Many fungal SMs have medically and industrially important properties including antifungal, antibacterial, and antitumor activity, and a single metabolite can display multiple types of bioactivities. The genes necessary for fungal SM biosynthesis are typically found in a single genomic region forming biosynthetic gene clusters (BGCs). However, whether fungal SM bioactivity can be predicted from specific attributes of genes in BGCs remains an open question. We adapted previously used machine learning models for predicting SM bioactivity from bacterial BGC data to fungal BGC data. We trained our models to predict antibacterial, antifungal, and cytotoxic/antitumor bioactivity on two datasets: 1) fungal BGCs (dataset comprised of 314 BGCs), and 2) fungal (314 BGCs) and bacterial BGCs (1,003 BGCs); the second dataset was our control since a previous study using just the bacterial BGC data yielded prediction accuracies as high as 80%. We found that the models trained only on fungal BGCs had balanced accuracies between 51-68%, whereas training on bacterial and fungal BGCs yielded balanced accuracies between 61-74%. The lower accuracy of the predictions from fungal data likely stems from the small number of BGCs and SMs with known bioactivity; this lack of data currently limits the application of machine learning approaches in studying fungal secondary metabolism. However, our data also suggest that machine learning approaches trained on bacterial and fungal data can predict SM bioactivity with good accuracy. With more than 15,000 characterized fungal SMs, millions of putative BGCs present in fungal genomes, and increased demand for novel drugs, efforts that systematically link fungal SM bioactivity to BGCs are urgently needed.

microbiology↗

bioRxiv2022

High-resolution characterisation of short-term temporal variability in the taxonomic and resistome composition of wastewater influent

Wastewater-based epidemiology (WBE) for population-level surveillance of antimicrobial resistance (AMR) is gaining significant traction, but the impact of wastewater sampling methods on results is unclear. In this study we characterised taxonomic and resistome differences between single-timepoint-grab and 24H-composites of wastewater influent from a large UK-based wastewater treatment work (WWTW [population equivalent:223,435]). We autosampled hourly influent grab samples (n=72) over three consecutive weekdays, and prepared additional 24H-composites (n=3) from respective grabs. For taxonomic profiling, metagenomic DNA was extracted from all samples and 16S-rRNA gene sequenced. One composite and six grabs from day one underwent metagenomic sequencing for metagenomic dissimilarity estimation and resistome profiling. Taxonomic abundances of phyla varied significantly across hourly grab samples but followed a repeating diurnal pattern for all three days. Hierarchical clustering grouped grab samples into four time periods dissimilar in both 16S rRNA gene-based profiles and metagenomic distances. 24H-composites resembled mean daily phyla abundances and showed low variability of taxonomic profiles. Of the 122 AMR gene families (AGFs) identified across all day one samples, single grab samples identified a median of 6 (IQR:5-8) AGFs not seen in the composite. However, 36/36 of these hits were at lateral coverage <0.5 (median:0.19; IQR:0.16-0.22) and potential false positives. Conversely, the 24H-composite identified three AGFs not seen in any grab with higher lateral coverage (0.82; 0.55-0.84). Additionally, several clinically significant human AGFs (blaVIM, blaIMP, blaKPC) were intermittently or completely missed by grab sampling but captured by the 24H-composite. Wastewater influent undergoes significant taxonomic and resistome changes on short timescales potentially affecting interpretation of results based on sampling strategy. Grab samples are more convenient and potentially capture low-prevalence/transient targets but are less comprehensive and temporally variable. Therefore, we recommend 24H-composite sampling where feasible. Further validation and optimisation of WBE methods is vital for its development into a robust AMR surveillance approach. HighlightsO_LIInfluent undergoes significant taxonomic/resistome changes over short timescales. C_LIO_LITaxonomic abundances fluctuate diurnally but repeat for the 3 weekdays sampled. C_LIO_LIDetection of less prevalent AMR determinants is time-dependent for grab sampling. C_LIO_LISingle timepoint grab samples may produce temporally variable metagenomic profiles. C_LIO_LI24H-composites reflect mean daily taxa and more reliably captured AMR determinants. C_LI

genomics↗

bioRxiv2022

Penicillin Binding Protein Substitutions Co-occur with Fluoroquinolone Resistance in 'Epidemic Lineages of Multi Drug-Resistant Clostridioides difficile

Clostridioides difficile remains a key cause of healthcare-associated infection, with multi-drug-resistant (MDR) lineages causing high mortality ([≥]20%) outbreaks. Cephalosporin treatment is a long-established risk factor, and antimicrobial stewardship a key control. A mechanism underlying raised cephalosporin MICs has not been identified in C. difficile, but among other species resistance is often acquired via amino acid substitutions in cell wall transpeptidases (penicillin binding proteins, PBPs). Here, we investigated five C. difficile transpeptidases (PBP1-5) for recent substitutions. Previously published genome assemblies (n=7096) were obtained, representing sixteen geographically widespread lineages, including healthcare-associated MDR ST1(027), ST3(001) and ST17(018). Recent amino acid substitutions were found within PBP1 (n=50) and PBP3 (n=48), ranging from 1-10 substitutions per genome. {beta}-lactam MICs were measured for closely related pairs of wild-type and PBP substituted isolates separated by 20-273 SNPs. Recombination-corrected, dated phylogenies were constructed to date substitution acquisition. Key substitutions such as PBP3 V497L and PBP1 T674I/N/V emerged independently across multiple lineages. They were associated with extremely high cephalosporin MICs; 1-4 doubling dilutions >wild-type up to [≤]1506g/ml. Substitution patterns varied by lineage and clade, showed geographic structure, and notably occurred post-1990, coincident with the acquisition of gyrA/B substitutions conferring fluoroquinolone resistance. In conclusion, recent PBP1 and PBP3 substitutions are associated with raised cephalosporin MICs in C. difficile. The co-occurrence of resistance to cephalosporins and fluoroquinolones hinders attempts to understand their relative importance in the dissemination of epidemic lineages. Further controlled studies of cephalosporin and fluoroquinolone stewardship are needed to determine their relative effectiveness in outbreak control. IMPORTANCEFluoroquinolone and cephalosporin prescribing in healthcare settings have triggered outbreaks of high-mortality, multi-drug resistant C. difficile infection. Here, we identify a mechanism of acquired cephalosporin resistance in C. difficile, comprising amino acid substitutions in two cell-wall transpeptidase enzymes (penicillin binding proteins). The higher the number of substitutions, the greater the impact on phenotype. Dated phylogenies revealed that resistance to both cephalosporins and fluoroquinolones was co-acquired immediately before clinically important, outbreak strains emerged. PBP substitutions were geographically structured within genetic lineages, suggesting adaptation to local antimicrobial prescribing. Antimicrobial stewardship of cephalosporins and fluoroquinolones is an effective means of C. difficile outbreak control. Genetic changes conferring resistance likely impart a fitness-cost after antibiotic withdrawal. Our study identifies a mechanism that may explain the contribution of cephalosporin stewardship to resolving outbreak conditions. However, due to the co-occurrence of cephalosporin and fluoroquinolone resistance, further work is needed to determine the relative importance of each.

microbiology↗

bioRxiv2022

Evaluation of metagenomic, 16S rRNA gene and ultra-plexed PCR-based sequencing approaches for profiling antimicrobial resistance gene and bacterial taxonomic composition of polymicrobial samples

BackgroundShotgun metagenomic sequencing is increasingly popular in taxonomic and resistome-profiling of polymicrobial samples due to its agnostic nature and data versatility. However, caveats include high- cost, sequencing depth/sensitivity trade-offs, and challenging bioinformatic deconvolution. Targeted PCR-based profiling optimises sensitivity and cost-effectiveness, but can only identify prespecified targets and may introduce amplification biases. Ultra-high multiplexed PCR is a potential compromise. As no comprehensive comparative evaluation exists, we evaluated performance of each method in taxonomic/resistome-profiling of a well-defined DNA mock sample and seven "real- world" wastewater samples. ResultsWe tested three sequencing approaches (short-read shotgun metagenomics, Illumina Ampliseq ultra-plexed AMR Research Panel, 16S rRNA gene sequencing) with seven bioinformatic pipelines (ResPipe, Illumina DNA Amplicon App, One Codex Metagenomic-/Targeted Loci classification and Ampliseq Report, DADA2, and an in-house pipeline for AmpliSeq data [AmpliSeek]). Metagenomics outperformed 16S rRNA gene sequencing in accurately reconstituting a mock taxonomic profile and optimising the identification of diverse wastewater taxa, while 16S rRNA gene sequencing produced more even taxonomic outputs which may be quantitatively inaccurate but enhance detection of low abundance taxa. Shotgun metagenomic and AmpliSeq sequencing performed equally well in profiling AMR genes present in a mock sample, but AmpliSeq identified more genes in more complex, "real-world" samples, likely related to sensitivity of detection at the metagenomic sequencing depth used. ConclusionsA complementary sequencing approach employing 16S rRNA gene or shallow-metagenomic sequencing for taxonomic profiling, and the AmpliSeq AMR panel for high-resolution resistome profiling represents a potential lower-cost alternative to deep shotgun sequencing and may also be more sensitive for the detection of low-prevalence AMR genes. However, our evaluation highlights that both the sequencing and bioinformatics approach used can significantly influence results; for AmpliSeq AMR gene profiling, we developed AmpliSeek which outperformed the other pipelines tested and is open source. Sequencing approach and bioinformatic pipeline should be considered in the context of study goals and sample type, with study-specific validation when feasible.

genomics↗

bioRxiv2021

Discordance between different bioinformatic methods for identifying resistance genes from short-read genomic data, with a focus on Escherichia coli

2.Several bioinformatics genotyping algorithms are now commonly used to characterise antimicrobial resistance (AMR) gene profiles in whole genome sequencing (WGS) data, with a view to understanding AMR epidemiology and developing resistance prediction workflows using WGS in clinical settings. Accurately evaluating AMR in Enterobacterales, particularly Escherichia coli, is of major importance, because this is a common pathogen. However, robust comparisons of different genotyping approaches on relevant simulated and large real-life WGS datasets are lacking. Here, we used both simulated datasets and a large set of real E. coli WGS data (n=1818 isolates) to systematically investigate genotyping methods in greater detail. Simulated constructs and real sequences were processed using four different bioinformatic programs (ABRicate, ARIBA, KmerResistance, and SRST2, run with the ResFinder database) and their outputs compared. For simulations tests where 3,079 AMR gene variants were inserted into random sequence constructs, KmerResistance was correct for 3,076 (99.9%) simulations, ABRicate for 3,054 (99.2%), ARIBA for 2,783 (90.4%) and SRST2 for 2,108 (68.5%). For simulations tests where two closely related gene variants were inserted into random sequence constructs, KmerResistance the correct alleles in 35,338/46,318 (76.3%) ABRicate identified in 11,842/46,318 (25.6%) of simulations, ARIBA in 1679/46,318 (3.6%), and SRST2 in 2000/46,318 (4.3%). In real data, across all methods, 1392/1818 (76%) isolates had discrepant allele calls for at least one gene. Our evaluations revealed poor performance in scenarios that would be expected to be challenging (e.g. identification of AMR genes at <10x coverage, discriminating between closely related AMR gene sequences), but also identified systematic sequence classification (i.e. naming) errors even in straightforward circumstances, which contributed to 1081/3092 (35%) errors in our most simple simulations and at least 2530/4321 (59%) discrepancies in real data. Further, many of the remaining discrepancies were likely "artefactual" with reporting cut-off differences accounting for at least 1430/4321 (33%) discrepants. Comparing outputs generated by running multiple algorithms on the same dataset can help identify and resolve these artefacts, but ideally new and more robust genotyping algorithms are needed. 3. Impact statementWhole-genome sequencing is widely used for studying the epidemiology of antimicrobial resistance (AMR) genes in bacteria; however, there is some concern that outputs are highly dependent on the bioinformatics methods used. This work evaluates these concerns in detail by comparing four different, commonly used AMR gene typing methods using large simulated and real datasets. The results highlight performance issues for most methods in at least one of several simulated and real-life scenarios. However most discrepancies between methods were due to differential labelling of the same sequences related to the assumptions made regarding the underlying structure of the reference resistance gene database used (i.e. that resistance genes can be easily classified in well-defined groups). This study represents a major advance in quantifying and evaluating the nature of discrepancies between outputs of different AMR typing algorithms, with relevance for historic and future work using these algorithms. Some of the discrepancies can be resolved by choosing methods with fewer assumptions about the reference AMR gene database and manually resolving outputs generated using multiple programs. However, ideally new and better methods are needed.

microbiology↗

bioRxiv2021

BashTheBug: a crowd of volunteers reproducibly and accurately measure the minimum inhibitory concentrations of 13 antitubercular drugs from photographs of 96-well broth microdilution plates.

Tuberculosis is a respiratory disease that is treatable with antibiotics. An increasing prevalence of resistance means that to ensure a good treatment outcome it is desirable to test the susceptibility of each infection to different antibiotics. Conventionally this is done by culturing a clinical sample and then exposing aliquots to a panel of antibiotics, thereby determining the minimum inhibitory concentration (MIC) of each drug. Using 96-well broth micro dilution plates with each well containing a lyophilised pre-determined amount of an antibiotic is a convenient and cost-effective way to measure the MICs of several drugs at once for a clinical sample. Although accurate, this is an expensive and slow process that requires highly-skilled and experienced laboratory scientists. Here we show that, through the BashTheBug project hosted on the Zooniverse citizen science platform, a crowd of volunteers can reproducibly and accurately determine the MICs for 13 drugs and that simply taking the median or mode of 11-17 independent classifications is sufficient. There is therefore a potential role for crowds to support (but not supplant) the role of experts in antibiotic susceptibility testing.

microbiology↗

bioRxiv2021

K-mer based prediction of Clostridioides difficile relatedness and ribotypes

Comparative analysis of Clostridioides difficile whole-genome sequencing (WGS) data enables fine scaled investigation of transmission and is increasingly becoming part of routine surveillance. However, these analyses are constrained by the computational requirements of the large volumes of data involved. By decomposing WGS reads or assemblies into k-mers and using the dimensionality reduction technique MinHash, it is possible to rapidly approximate genomic distances without alignment. Here we assessed the performance of MinHash, as implemented by sourmash, in predicting single nucleotide differences between genomes (SNPs) and C. difficile ribotypes (RTs). For a set of 1,905 diverse C. difficile genomes (differing by 0-168,519 SNPs), using sourmash to screen for closely related genomes, at a sensitivity of 100% for pairs [≤]10 SNPs, sourmash reduced the number of pairs from 1,813,560 overall to 161,934, i.e., by 91%, with a positive predictive value of 32% to correctly identify pairs [≤]10 SNPs (maximum SNP distance 4,144). At a sensitivity of 95%, pairs were reduced by 94% to 108,266 and PPV increased to 45% (maximum SNP distance 1,009). Increasing the MinHash sketch size above 2000 produced minimal performance improvement. We also explored a MinHash similarity-based ribotype prediction method. Genomes with known ribotypes (n=3,937) were split into a training set (2,937) and test set (1,000) randomly. The training set was used to construct a sourmash index against which genomes from the test set were compared. If the closest 5 genomes in the index had the same ribotype this was taken to predict the searched genomes ribotype. Using our MinHash ribotype index, predicted ribotypes were correct in 780/1000 (78%) genomes, incorrect in 20 (2%), and indeterminant in 200 (20%). Relaxing the classifier to 4/5 closest matches with the same RT improved the correct predictions to 87%. Using MinHash it is possible to subsample C. difficile genome k-mer hashes and use them to approximate small genomic differences within minutes, significantly reducing the search space for further analysis. Impact statementThe genetic code, or DNA, of bacteria is increasingly used to track how infection spreads and to guide infection control interventions, as similar or identical DNA sequences are expected in samples from pair of individuals related by transmission. While obtaining the DNA sequence for bacteria is increasingly straightforward, comparing thousands or even millions of sequences requires substantial computing power and time using current approaches. Here we describe how a method for summarising sequencing data, MinHash, can be used to rapidly reduce the number of possible close sequence matches in Clostridioides difficile, an important healthcare-associated pathogen. It can also be used to approximate traditional schemes used to classify C. difficile into smaller subgroups in transmission analyses, such as ribotyping. Data summaryThe authors confirm all supporting data, code and protocols have been provided within the article or through supplementary data files.

genomics↗