Biology subjects

Pritchard, L.

Publications and source records attributed to Pritchard, L..

12 recordsLinked to original sources

bioRxiv2026

genomeRxiv: a genome-based identification web server that bridges the gap between taxonomy and strain typing

Precise identification depends on highly resolved classification. Therefore, whole genome-based strain typing schemes have been implemented for identification of the most important microbial human pathogens. However, there are no dedicated typing schemes for the majority of prokaryotic human, animal, and plant pathogens, or for non-pathogenic microbes, often limiting their identification to taxonomic classification tools that have the species as the smallest unit. To address this deficiency, we created the genomeRxiv web server which contains over 350,000 genomes and leverages the average nucleotide identity (ANI)-based Life Identification Number (LIN) approach, allowing users to identify prokaryotic genomes in real time from approximately genus-rank to near strain-level resolution. LINs are overlaid with circumscriptions of species and infraspecies classes, integrating taxonomy with strain typing. Metadata and distance trees are visualized to complement inferred phylogenetic relationships with epidemiological and ecological information. A command line tool is provided for users to upload k-mer sketches of unpublished genomes and associated metadata, facilitating epidemiological investigations without the need to share complete DNA sequences. In conclusion, genomeRxiv constitutes a much-needed resource for precise, rapid identification of the many prokaryotes not included in any pathogen-specific typing scheme.

bioinformatics↗

bioRxiv2026

A bacterial Rhesus transporter retunes a structurally conserved ammonium pore into a reversible nitrogen valve

How conserved proteins acquire new physiological functions is a central question in molecular evolution. Rather than inventing new architectures, evolution often repurposes existing scaffolds, preserving core structural features while retuning the molecular logic that connects mechanism to physiology. Membrane transporters offer a powerful test of this principle because substrate selectivity, directionality, flux and energetic cost must be coordinated at the interface between the cell and its environment. The Amt/Mep/Rh superfamily controls one of the most fundamental requirements of cellular life: the movement of reduced nitrogen across biological membranes. Despite sharing a highly conserved ammonium-conducting pore, these proteins support distinct physiological roles, including nitrogen acquisition, sensing and homeostatic control. Here, we combined targeted mutagenesis, electrophysiology, yeast complementation and molecular dynamics simulations to define the transport logic of NeRh50, a bacterial Rhesus protein from the ammonia-oxidising bacterium Nitrosomonas europaea. We show that NeRh50 is not simply an AmtB-like ammonium importer. Instead, it uses the conserved Amt/Mep/Rh pore as a branched transport system in which inward uptake and export-linked transport can be separated genetically and mechanistically. Two conserved pore residues define this division of labour. A residue at the external entrance couples ammonium recruitment to productive inward uptake, whereas a second residue deeper in the pore enables a distinct transport mode required for substrate release when intracellular nitrogen accumulates. Thus, conserved Amt/Mep/Rh pore landmarks do not impose a single mechanism. Their local chemistry can be reassigned to generate different transport outputs, allowing NeRh50 to function as a reversible nitrogen valve. These findings reveal how minimal retuning within an ancient membrane-protein scaffold can rewire transport directionality and adapt nitrogen handling to ecological and physiological demand.

biochemistry↗

bioRxiv2026

Lipid-associated architecture and divergent ammonium transport distinguish a bacterial Rhesus protein

Nitrification is a major process in the global nitrogen cycle, initiated by the oxidation of ammonia to nitrite. The ammonia-oxidising bacterium Nitrosomonas europaea catalyses this first and rate-limiting step and depends on ammonium both as an energy substrate and as a nitrogen source, making ammonium acquisition central to its physiology. Cellular ammonium transport is mediated by the Amt/Mep/Rh superfamily, whose members share a conserved fold despite divergent physiological roles. Remarkably, N. europaea lacks a canonical prokaryotic Amt transporter and instead expresses NeRh50, a Rh-family protein proposed to have been acquired from a eukaryotic lineage. Here, we determine a cryo-EM structure of NeRh50 that reveals tightly associated lipids at the monomer-monomer interfaces, including density consistent with an unusual inverted lipid orientation. Native mass spectrometry and molecular dynamics simulations further support persistent lipid association at these interfaces. NeRh50 also differs functionally from canonical AmtB, selectively mediating electrogenic ammonium transport with ~70-fold lower apparent affinity. Transport is enhanced under alkaline conditions but not by an imposed proton gradient, suggesting membrane-potential dependence. Together, these findings reveal divergence from canonical Amt proteins in both membrane-associated architecture and transport properties, providing insight into the functional and evolutionary diversification of the Amt/Mep/Rh superfamily.

biochemistry↗

bioRxiv2026

Carbohydrate active enzymes in Pectobacteriaceae: coevolving enzyme sets and host adaptation

Many phytopathogenic bacteria have evolved large, diverse arsenals of Carbohydrate Active enZymes (CAZymes) that liberate simple sugars, and thus nutrition and energy, from the complex lignocellulosic matrices of their plant hosts. The CAZyme arsenals of these phytopathogens are expected to be influenced by and adapted to the cell wall composition of their plant hosts. The solutions these organisms have reached for the problem of degrading plant material may help us understand their host ranges and present a rich source of novel CAZymes for exploitation in industrial bioprocessing. Here we catalogue and analyse CAZyme complements (CAZomes) of publicly-available Enterobacterial phytopathogen genomes, including those of the economically significant and widely-studied Pectobacterium and Dickeya genera. These comprise a broad diversity of CAZymes, providing insight into host adaptation and a resource for bio-prospection of industrially-relevant enzymes. We find evidence supporting coevolution of sets of CAZymes specific to bacterial genus and species and, notably, CAZymes associated with pathogen preference for either woody or soft plant tissue, suggesting adaptation of CAZomes to host plant cell wall composition.

bioinformatics↗

bioRxiv2024

Genomic diversity of novel strains of mammalian gut microbiome derived Clostridium XIVa strains is driven by mobile genetic element acquisition

Despite advances in sequencing technologies that enable a greater understanding of mammalian gut microbiome composition, our ability to determine a role for individual strains is hampered by our inability to isolate, culture and study such microbes. Here we describe highly unusual Clostridium XIVa group strains isolated from the murine gut. Genome sequencing indicates that these strains, Clostridium symbiosum LM19B and LM19R and Clostridium clostridioforme LM41 and LM42, have significantly larger genomes than most closely related strains. Genomic evidence indicates that the isolated LM41 and LM42 strains diverge from most other Clostridium XIVa strains and supports reassignment of these groups at genus-level. We attribute increased C. clostridioforme LM41 and LM42 genome size to acquisition of mobile genetic elements including dozens of prophages, integrative elements, putative group II introns and numerous transposons including 29 identical copies of the IS66 transposase, and a very large 192 Kb plasmid. antiSmash analysis determines a greater number of biosynthetic gene clusters within LM41 and LM42 than in related strains, encoding a diverse array of potential novel antimicrobial compounds. Together these strains highlight the potential untapped microbial diversity that remains to be discovered within the gut microbiome and indicate that, despite our ability to get a top down view of microbial diversity, we remain significantly blinded to microbe capabilities at the strain level.

microbiology↗

bioRxiv2023

16S rRNA phylogeny and clustering is not a reliable proxy for genome-based taxonomy in Streptomyces

Although Streptomyces is one of the most extensively studied genera of bacteria, their taxonomy remains contested and is suspected to contain significant species-level misclassification. Resolving the classification of Streptomyces would benefit many areas of study and applied microbiology that rely heavily on having an accurate ground truth classification of similar and dissimilar organisms, including comparative genomics-based searches for novel antimicrobials in the fight against the ongoing antimicrobial resistance (AMR) crisis. To attempt a resolution, we investigate taxonomic conflicts between 16S rRNA and whole genome classifications using all available 48,981 full-length 16S rRNA Streptomyces sequences from the combined SILVA, Greengenes, Ribosomal Database Project (RDP) and NCBI (National Center for Biotechnology Information) databases, and 2,276 publicly available Streptomyces genome assemblies. We construct a 16S gene tree for 14,239 distinct Streptomyces 16S rRNA sequences, identifying three major lineages of Streptomyces, and find that existing taxonomic classifications are inconsistent with the tree topology. We also use these data to delineate 16S and whole genome landscapes for Streptomyces, finding that 16S and whole-genome classifications of Streptomyces strains are frequently in disagreement, and in particular that 16S zero-radius Operational Taxonomic Units (zOTUs) are often inconsistent with Average Nucleotide Identity (ANI)-based taxonomy. Our results strongly imply that 16S rRNA sequence data does not map to taxonomy sufficiently well to delineate Streptomyces species reliably, and we propose that alternative markers should instead be adopted by the community for classification and metabarcoding. As much of current Streptomyces taxonomy has been determined or supported by historical 16S sequence data and may in parts be in error, we also propose that reclassification of the genus by alternative approaches is required. Impact StatementAccurate classification of microbes, usually in the form of taxonomic assignments, provides a fundamental ground truth or reference point for many aspects of applied microbiology including comparative genomics, identification of strains for natural product discovery, and dereplication of strains. Bacteria belonging to the genus Streptomyces are an important source of bioactive metabolites and enzymes in biotechnology, and proper understanding of their phylogeny aids understanding of the evolution of industrially important gene products and metabolites, and prioritization of strains for industrial exploitation. Taxonomic classification in the genus Streptomyces is complex and contested, and there are clear conflicts between taxonomies inferred from 16S rRNA and from whole genome sequences. Despite this, 16S sequence-based classifications are still widely used to infer taxonomic identity, to determine community composition, and to prioritise strains for study. We investigate a diverse and comprehensive set of Streptomyces genomes using whole-genome Average Nucleotide Identity (ANI) and 16S sequence analysis to delineate and compare classifications made using these approaches. We outline the genomic and 16S sequence landscape of Streptomyces, demonstrating that (i) distinct taxonomic species may share identical full-length 16S sequences, and (ii) in some instances, isolates representing the same taxonomic species do not share any common 16S rRNA sequence. Our results strongly imply that 16S rRNA sequence variation does not map to taxonomy sufficiently well to delineate Streptomyces species reliably, and that alternative markers should instead be adopted by the community. Much of current Streptomyces taxonomy has been determined or supported by historical 16S sequence data, and we therefore propose that reclassification within this group by alternative approaches is required. Data summaryAll code, raw and supporting data are publicly available from GitHub (https://github.com/kiepczi/Kiepas_et_al_2023_16S) and Zenodo (https://doi.org/10.5281/zenodo.8223787). The flowchart provided in Supplementary File 28 provides an overview of analysis steps and serves as a guide through Supplementary Files generated during reconstruction of the 16S phylogeny. The flowchart in Supplementary File 29 outlines the workflow processes and supplementary materials used for analysis of 16S rRNA sequences from Streptomyces genomes. Supplementary DataSupplementary File 1: Generate figures using Python and R. ZIP file containing all data, Python and R scripts to generate figures for this manuscript. (ZIP 40.9MB) Supplementary File 2: Raw 16S rRNA public databases. Zip file containing four separate txt files with sequence IDs for public 16S rRNA databases used in this manuscript, and an additional txt file with Greengenes sequence taxonomy information, and a Python script used to map taxonomy information to sequences found in Greengenes v13.5. (ZIP 34.8MB) Supplementary File 3: Filtration of 16S rRNA public databases. Zip file containing Python script used for filtration of the raw databases, and generated outputs. (ZIP 7.2MB) Supplementary File 4: Cleaning of the filtrated 16S rRNA local. Zip file containing all bash and Python scripts used to clean the local full-length 16S rRNA local databases by removing redundant and poor quality 16S rRNA sequences. (ZIP 9MB) Supplementary File 5: Sequence Clustering. Zip file containing a bash script used to cluster the full-length cleaned local 16S rRNA Streptomyces local databases at various thresholds, and provides txt files with accessions for representative sequences, and cluster members for each clustering threshold. (ZIP 40.8MB) Supplementary File 6: Analysis of taxonomic composition for each clustering threshold. Zip file containing Python scripts, NCBI taxonomy input and all outputs generated used to determine the taxonomic composition for each clustering threshold. (ZIP 49.6) Supplementary File 7. Cluster sizes. Empirical cumulative frequency plot showing cluster sizes generated for all clustering thresholds. (PDF 44KB) Supplementary File 8. Cluster taxID abundance. Empirical cumulative frequency plot for unique numbers of taxID present at all clustering thresholds. (PDF 9KB) Supplementary File 9. MSA. Zip file containing all Python and bash scripts, and additional data needed to generate and clean MSA for phylogenetic analysis. (ZIP 4.2MB) Supplementary File 10. Phylogenetic reconstruction. ZIP file containing bash scripts used for phylogenetic reconstruction, and all generated outputs and log files. (ZIP 16.8MB). Supplementary File 11. Collapse branches. ZIP file containing jupyter notebook used for collapsing branches with the same species names, and the collapsed tree in newick format. (ZIP 385KB) Supplementary File 12. Phylogenetic tree. PDF file showing collapsed phylogenetic tree with marked branches with transfer bootstrap expectation support of >= 50%. (PDF 224KB) Supplementary File 13. Phylogenetic tree. PDF file showing collapsed phylogenetic tree showing distribution of Streptomyces albus and Streptomyces griseus. (PDF 229KB) Supplementary File 14. Phylogenetic tree. PDF file showing collapsed phylogenetic tree showing distribution of Streptomyces albulus, Streptomyces lydicus and Streptomyces venezuelae. (PDF 228KB) Supplementary File 15. Phylogenetic tree. PDF file showing collapsed phylogenetic tree showing distribution of Streptomyces clavuligerus and Streptomyces coelicolor. (PDF 227KB) Supplementary File 16. Phylogenetic tree. PDF file showing collapsed phylogenetic tree showing distribution of Streptomyces lavendulae, Streptomyces rimosus and Streptomyces scabiei. (PDF 228KB) Supplementary File 17. Streptomyces genomes. Zip file containing bash scripts used to download Streptomyces genomes, and Python scripts used to check assembly status. The ZIP file also contains two separate txt files with Streptomyces genomes used in this manuscript: one file with all initial candidates, and a second file with replaced genomes. (ZIP 2.6MB) Supplementary File 18. Extraction of full-length and ambiguity free 16S rRNA sequences from Streptomyces genomes. Zip file containing all Python and bash scripts used to extract full-length sequences from the filtered Streptomyces genomes. A single FASTA file with all extracted 16S rRNA sequences, and a single FASTA file with filtered sequences. A txt file with accession of genomes retained in the analysis. (ZIP 742KB) Supplementary File 19. ANI analysis among Streptomyces genomes with identical 16S rRNA sequences. ZIP file containing all Bash and Python scripts used to determine taxonomic boundaries among Streptomyces genomes sharing identical full-length 16S rRNA sequences. All output and pyANI log files. (ZIP 37.1MB) Supplementary File 20. Network analysis of genomes based on shared 16S sequences. ZIP file containing jupyter notebook with NetworkX analysis and all associated output files including. bash script for pyANI analysis runs on each connected component and all associated matrices, heatmaps and log files. (ZIP 29.3MB) Supplementary File 21. Interactive network graph. HTML file containing interactive network graph of genomes sharing common full-length 16S sequences with each node colour corresponding to the number of connections/degrees. (HTML 4.7MB) Supplementary File 22. Interactive network graph. HTML file containing interactive network graph of genomes sharing common full-length 16S sequences showing clique (blue) and non-clique (green) components. (HTML 4.7MB) Supplementary File 23. Interactive network graph. HTML file containing interactive network graph of genomes sharing common full-length 16S sequences showing number of unique genera within each connected component. Each candidate genus is represented as a single node colour within a connected component. (HTML 4.7MB) Supplementary File 24. Interactive network graph. HTML file containing interactive network graph of genomes sharing common full-length 16S rRNA sequences showing number of unique species within each connected component. Each candidate species is represented as a single node colour within a connected component. (HTML 4.7MB) Supplementary File 25 Interactive network graph. HTML file containing interactive network graph of genomes sharing common full-length 16S rRNA sequences showing number of unique NCBI names within each connected component. Each NCBI assigned name is represented as a single node colour within a connected component. Gray nodes represent genomes currently lacking assigned species names. (HTML 4.7MB) Supplementary file 26. Intragenomic 16S rRNA heterogeneity within 1,369 Streptomyces genomes which exclusively contain only full-length and ambiguity symbol-free 16S rRNA sequences. A total of 811 genomes containing single 16S rRNA sequences are not shown. (PDF 8KB) Supplementary File 27. Distribution of 16S copies per genome with a distinction between unique and total copies for genomes at assembly level complete and chromosome. (PDF 7KB) Supplementary File 28. Schematic workflow for construction of the full-length 16S rRNA Streptomyces phylogeny. Each arrow represents a process and is annotated with script used and corresponding supplementary file. Output/data files, and the number of remaining sequences after each step, are indicated by rectangles. The green shading represents a single processing step of collecting and collating 16S database sequences. (PDF 91KB) Supplementary File 29. Schematic representation of the pipeline used to filter publicly available Streptomyces genomes. (PDF 59KB) Supplementary File 30. Sankey plot showing counts of taxonomic names in source databases, assigned at ranks from phylum to genus, to sequences identified with a key word Streptomyces in the taxonomy field. Note that Actinobacteria and Actinobacteriota are synonyms in LPSN for the correct Phylum name Actinomycetota, but that Actinomycetales and Streptomycetales are not taxonomic synonyms for each other. Streptomycetales is synonymous in LPSN with the correct name Kitasatosporales; Actinomycetales is a distinct taxonomic Order. The parent order of the Family Streptomycetaceae in LPSN is Kitasatosporales. (PDF 64KB) Supplementary File 31. Rectangular phylogram of the comprehensive maximum-likelihood tree of the genus Streptomyces based on the 16S sequence diversity of all 5,064 full-length 16S rRNA sequences with 100 TBE values. (PDF 194KB) Supplementary file 32. Genomes sharing identical 16S rRNA sequences are assigned different names in NCBI. A total of 1,030 singleton clusters are not shown. (PDF 8KB) Supplementary File 33. Phylogenetic tree. PDF file showing collapsed phylogenetic tree showing distribution of members of the novel Acintacidiphila genus. (PDF 228KB) Supplementary File 34. Phylogenetic tree. PDF file showing collapsed phylogenetic tree showing distribution of members of the novel Phaeacidiphilus genus. (PDF 228KB) Supplementary File 35. Phylogenetic tree. PDF file showing collapsed phylogenetic tree showing distribution of members of the novel Mangrovactinospora genus. (PDF 228KB) Supplementary File 36. Phylogenetic tree. PDF file showing collapsed phylogenetic tree showing distribution of members of the novel Wenjunlia genus. (PDF 228KB) Supplementary File 37. Phylogenetic tree. PDF file showing collapsed phylogenetic tree showing distribution of members of the novel Streptantibioticus genus. (PDF 228KB)

genomics↗

bioRxiv2023

THAPBI PICT - a fast, cautious, and accurate metabarcoding analysis pipeline

THAPBI PICT is an open source software pipeline for metabarcoding analysis with multiplexed Illumina paired-end reads, including where different amplicons are sequenced together. We demonstrate using worked examples with our own and public data sets how, with appropriate primer settings and a custom database, THAPBI PICT can be applied to other amplicons and organisms, and used for reanalysis of existing datasets. The core dataflow of the implementation is (i) data reduction to unique marker sequences, often called amplicon sequence variants (ASVs), (ii) dynamic thresholds for discarding low abundance sequences to remove noise and artifacts (rather than error correction by default), before (iii) classification using a curated reference database. The default classifier assigns a label to each query sequence based on a database match that is either perfect, or a single base pair edit away (substitution, deletion or insertion). Abundance thresholds for inclusion can be set by the user or automatically using per-batch negative or synthetic control samples. Output is designed for practical interpretation by nonspecialists and includes a read report (ASVs with classification and counts per sample), sample report (samples with counts per species classification), and a topological graph of ASVs as nodes with short edit distances as edges. Source code available from https://github.com/peterjc/thapbi-pict/with documentation including installation instructions.

bioinformatics↗

bioRxiv2022

cazy_webscraper: local compilation and interrogation of comprehensive CAZyme datasets

Carbohydrate Active enZymes (CAZymes) are pivotal in biological processes including energy metabolism, cell structure maintenance, signalling and pathogen recognition. Bioinformatic prediction and mining of CAZymes improves our understanding of these activities, and enables discovery of candidates of interest for industrial biotechnology, particularly the processing of organic waste for biofuel production. CAZy (www.cazy.org) is a high-quality, manually-curated and authoritative database of CAZymes that is often the starting point for these analyses. Automated querying, and integration of CAZy data with other public datasets would constitute a powerful resource for mining and exploring CAZyme diversity. However, CAZy does not itself provide methods to automate queries, or integrate annotation data from other sources (except by following hyperlinks) to support further analysis. To overcome these limitations we developed cazy_webscraper, a command-line tool that retrieves data from CAZy and other online resources to build a local, shareable, and reproducible database that augments and extends the authoritative CAZy database. cazy_webscrapers integration of curated CAZyme annotations with their corresponding protein sequences, up to date taxonomy assignments, and protein structure data facilitates automated large-scale and targeted bioinformatic CAZyme family analysis and candidate screening. This tool has found widespread uptake in the community, with over 20,000 downloads. We demonstrate the use and application of cazy_webscraper to: (i) augment, update and correct CAZy database accessions; (ii) explore taxonomic distribution of CAZymes recorded in CAZy, identifying underrepresented taxa and unusual CAZy class distributions; and (iii) investigate three CAZymes having potential biotechnological application for degradation of biomass, but lacking a representative structure in the PDB database. We describe in general how cazy_webscraper facilitates functional, structural and evolutionary studies to aid identification of candidate enzymes for further characterisation, and specifically note that CAZy provides supporting evidence for recent expansion of the Auxiliary Activities (AA) CAZy family in eukaryotes, consistent with functions potentially specific to eukaryotic lifestyles. Supplementary informationcazy_webscraper source code is available at https://github.com/HobnobMancer/cazy_webscraper, and online documentation is provided at https://cazywebscraper.readthedocs.io.

bioinformatics↗

bioRxiv2021

Mining underutilized whole-genome sequencing projects to improve 16S rRNA databases

Current approaches to interpreting 16S rDNA amplicon data are hampered by several factors. Among these are database inaccuracy or incompleteness, sequencing error, and biased DNA/RNA extraction. Existing 16S rRNA databases source the majority of sequences from deposited amplicon sequences, draft genomes, and complete genomes. Most of the draft genomes available are assembled from short reads. However, repeated ribosomal regions are notoriously difficult to assemble well from short reads, and as a consequence the short-read-assembled 16S rDNA region may be an amalgamation of different loci within the genome. This complicates high-resolution community analysis, as a draft genomes 16S rDNA sequence may be a chimera of multiple loci; in such cases, the draft-derived sequences in a database may not represent a 16S rRNA sequence as it occurs in biology. We present Focus16, a pipeline for improving 16S rRNA databases by mining NCBIs Sequence Read Archive for whole-genome sequencing runs that could be reassembled to yield additional 16S rRNA sequences. Using riboSeed (a genome assembly tool for correcting rDNA misassembly), Focus16 provides a way to augment 16S rRNA databases with high-quality re-assembled sequences. In this study, we augmented the widely-used SILVA 16S rRNA database with the novel sequences disclosed by Focus16 and re-processed amplicon sequences from several benchmarking datasets with DADA2. Using this augmented SILVA database increased the number of amplicon sequence variants that could be assigned taxonomic annotations. Further, fine-scale classification was improved by revealing ambiguities. We observed, for example, that amplicon sequence variants (ASVs) may be assigned to a specific genus where Focus16-correction would indicate that the ASV is represented in two or more genera. Thus, we demonstrate that improvements can be made to taxonomic classification by incorporating these carefully re-assembled 16S rRNA sequences, and we invite the community to expand our work to augment existing 16S rRNA reference databases such as SILVA, GreenGenes, and RDP.

bioinformatics↗

bioRxiv2020

A high-throughput genomic screen identifies a role for the plasmid-borne Type II secretion system of Escherichia coli O157:H7 (Sakai) in plant-microbe interactions

Food-borne illness arising from Shiga-toxigenic Escherichia coli (STEC) is often linked to consumption of fruit and vegetables as the bacteria have the ability to interact with plants and use them as alternative or secondary hosts. The initial stages of the interaction involve chemotaxis, attachment and potentially, responding to the early stages of microbe perception by the plant host. We used a high-throughput positive-selection approach to identify early interaction factors of E. coli O157:H7 isolate Sakai to spinach. A bacterial artificial chromosome (BAC) clone library was quantified by microarray hybridisation, and gene loci enrichment measured using a Bayesian hierarchical model. The screen of four successive rounds of short-term (2 hour) interaction with spinach roots produced in 115 CDS credible candidates, comprising seven contiguous genomic regions. Two candidate regions were selected for functional assessment: a chaperone-usher fimbrial gene cluster (loc6) and the pO157 plasmid-encoded type two secretion system (T2SS). Interaction of bacteria with spinach tissue was reduced in the absence of the pO157 plasmid, which was appeared to involve the T2SS EtpD secretin protein, whereas loss of loc6 did not impact interactions. The T2SS genes, etpD and etpC, were expressed at a plant-relevant temperature of 18 {degrees}C, and etpD expressed in planta by E. coli Sakai on spinach plants. Thus, a whole genome screening approach using a combination of computational modelling and functional assays has identified a novel function for STEC T2SS in interactions with plant tissue.

microbiology↗

bioRxiv2018

Easily phylotyping E. coli via the EzClermont web app and command-line tool.

The Clermont PCR method of phylotyping Escherichia coli has remained a useful classification scheme despite the proliferation of higher-resolution sequence typing schemes. We have implemented an in silico Clermont PCR method as both a web app and as a command-line tool to allow researchers to easily apply this phylotyping scheme to genome assemblies easily. Availability and ImplementationEzClermont is available as a web app at http://www.ezclermont.org. For local use, EzClermont can be installed with pip or installed from the source code at https://github.com/nickp60/ezclermont. All analysis was done with version 0.4.0. Contactn.waters4@nuigalway.ie, leighton.pritchard@strath.ac.uk Supplementary informationTable S1: test dataset; S2: validation dataset; S3: results.

bioinformatics↗

bioRxiv2017

riboSeed: leveraging prokaryotic genomic architecture to assemble across ribosomal regions

The vast majority of bacterial genome sequencing has been performed using Illumina short reads. Because of the inherent difficulty of resolving repeated regions with short reads alone, only {approx}10% of sequencing projects have resulted in a closed genome. The most common repeated regions are those coding for ribosomal operons (rDNAs), which occur in a bacterial genome between 1 and 15 times, and are typically used as sequence markers to classify and identify bacteria. Here, we exploit conservation in the genomic context in which rDNAs occur across taxa to improve assembly of these regions relative to de novo sequencing by using the conserved nature of rDNAs across taxa and the uniqueness of their flanking regions within a genome. We describe a method to construct targeted pseudocontigs generated by iteratively assembling reads that map to a reference genomes rDNAs. These pseudocontigs are then used to more accurately assemble the newly-sequenced chromosome. We show that this method, implemented as riboSeed, correctly bridges across adjacent contigs in bacterial genome assembly and, when used in conjunction with other genome polishing tools, can assist in closure of a genome.

bioinformatics↗