gbpA and chiA genes are not uniformly distributed amongst diverse Vibrio cholerae

AbstractMembers of the bacterial genus Vibrio utilise chitin both as a metabolic substrate and a signal to activate natural competence. Vibrio cholerae is a bacterial enteric pathogen, sub-lineages of which can cause pandemic cholera. However, the chitin metabolic pathway in V. cholerae has been dissected using only a limited number of laboratory strains of this species. Here, we survey the complement of key chitin metabolism genes amongst 195 diverse V. cholerae. We show that the gene encoding GbpA, known to be an important colonisation and virulence factor in pandemic isolates, is not ubiquitous amongst V. cholerae. We also identify a putatively novel chitinase, and present experimental evidence in support of its functionality. Our data indicate that the chitin metabolic pathway within the V. cholerae species is more complex than previously thought, and emphasise the importance of considering genes and functions in the context of a species in its entirety, rather than simply relying on traditional reference strains.Impact statementIt is thought that the ability to metabolise chitin is ubiquitous amongst Vibrio spp., and that this enables these species to survive in aqueous and estuarine environmental contexts. Although chitin metabolism pathways have been detailed in several members of this genus, little is known about how these processes vary within a single Vibrio species. Here, we present the distribution of genes encoding key chitinase and chitin-binding proteins across diverse Vibrio cholerae, and show that our canonical understanding of this pathway in this species is challenged when isolates from non-pandemic V. cholerae lineages are considered alongside those linked to pandemics. Furthermore, we show that genes previously thought to be species core genes are not in fact ubiquitous, and we identify novel components of the chitin metabolic cascade in this species, and present functional validation for these observations.Data summaryThe authors confirm that all supporting data, code, and protocols have been provided within the article or through supplementary data files.No whole-genome sequencing data were generated in this study. Accession numbers for the publicly-available sequences used for these analyses are listed in Supplementary Table 1, Table 2, and the Methods.All other data which underpin the figures in this manuscript, including pangenome data matrices, modified and unmodified sequence alignments and phylogenetic trees, original images of gels and immunoblots, raw fluorescence data, amplicon sequencing reads, and the R code used to generate Figure 7, are available in Figshare: https://dx.doi.org/10.6084/m9.figshare.13169189(Note for peer-review: Figshare DOI is inactive but will be activated upon publication, please use temporary URL https://figshare.com/s/7795a2d80c13f694f8fa for review).

Download Full-text

gbpA and chiA genes are not uniformly distributed amongst diverse Vibrio cholerae

Microbial Genomics ◽

10.1099/mgen.0.000594 ◽

2021 ◽

Vol 7 (6) ◽

Author(s):

Thea G. Fennell ◽

Grace A. Blackwell ◽

Nicholas R. Thomson ◽

Matthew J. Dorman

Keyword(s):

Vibrio Cholerae ◽

Metabolic Pathway ◽

Type Species ◽

Gene Encoding ◽

Natural Competence ◽

Content Type ◽

Metabolic Substrate ◽

Link Type ◽

Reference Strains ◽

Present Experimental Evidence

Members of the bacterial genus Vibrio utilize chitin both as a metabolic substrate and a signal to activate natural competence. Vibrio cholerae is a bacterial enteric pathogen, sub-lineages of which can cause pandemic cholera. However, the chitin metabolic pathway in V. cholerae has been dissected using only a limited number of laboratory strains of this species. Here, we survey the complement of key chitin metabolism genes amongst 195 diverse V. cholerae . We show that the gene encoding GbpA, known to be an important colonization and virulence factor in pandemic isolates, is not ubiquitous amongst V. cholerae . We also identify a putatively novel chitinase, and present experimental evidence in support of its functionality. Our data indicate that the chitin metabolic pathway within V. cholerae is more complex than previously thought, and emphasize the importance of considering genes and functions in the context of a species in its entirety, rather than simply relying on traditional reference strains.

Download Full-text

Natrix: a Snakemake-based workflow for processing, clustering, and taxonomically assigning amplicon sequencing reads

BMC Bioinformatics ◽

10.1186/s12859-020-03852-4 ◽

2020 ◽

Vol 21 (1) ◽

Author(s):

Marius Welzel ◽

Anja Lange ◽

Dominik Heider ◽

Michael Schwarz ◽

Bernd Freisleben ◽

...

Keyword(s):

High Throughput Sequencing ◽

Workflow Management ◽

Amplicon Sequencing ◽

Version Control ◽

Marker Genes ◽

Sequencing Data ◽

Taxonomic Assignment ◽

Ecological Processes ◽

Link Type ◽

User Friendly

Abstract Background Sequencing of marker genes amplified from environmental samples, known as amplicon sequencing, allows us to resolve some of the hidden diversity and elucidate evolutionary relationships and ecological processes among complex microbial communities. The analysis of large numbers of samples at high sequencing depths generated by high throughput sequencing technologies requires efficient, flexible, and reproducible bioinformatics pipelines. Only a few existing workflows can be run in a user-friendly, scalable, and reproducible manner on different computing devices using an efficient workflow management system. Results We present Natrix, an open-source bioinformatics workflow for preprocessing raw amplicon sequencing data. The workflow contains all analysis steps from quality assessment, read assembly, dereplication, chimera detection, split-sample merging, sequence representative assignment (OTUs or ASVs) to the taxonomic assignment of sequence representatives. The workflow is written using Snakemake, a workflow management engine for developing data analysis workflows. In addition, Conda is used for version control. Thus, Snakemake ensures reproducibility and Conda offers version control of the utilized programs. The encapsulation of rules and their dependencies support hassle-free sharing of rules between workflows and easy adaptation and extension of existing workflows. Natrix is freely available on GitHub (https://github.com/MW55/Natrix) or as a Docker container on DockerHub (https://hub.docker.com/r/mw55/natrix). Conclusion Natrix is a user-friendly and highly extensible workflow for processing Illumina amplicon data.

Download Full-text

To rarefy or not to rarefy: Enhancing microbial community analysis through next-generation sequencing

10.1101/2020.09.09.290049 ◽

2020 ◽

Author(s):

Ellen S. Cameron ◽

Philip J. Schmidt ◽

Benjamin J.-M. Tremblay ◽

Monica B. Emelko ◽

Kirsten M. Müller

Keyword(s):

Microbial Community ◽

Community Analysis ◽

Amplicon Sequencing ◽

Microbial Community Analysis ◽

List Type ◽

Sequencing Data ◽

Water And Wastewater Treatment ◽

Library Size ◽

Size Selection ◽

The Impact

AbstractThe application of amplicon sequencing in water research provides a rapid and sensitive technique for microbial community analysis in a variety of environments ranging from freshwater lakes to water and wastewater treatment plants. It has revolutionized our ability to study DNA collected from environmental samples by eliminating the challenges associated with lab cultivation and taxonomic identification. DNA sequencing data consist of discrete counts of sequence reads, the total number of which is the library size. Samples may have different library sizes and thus, a normalization technique is required to meaningfully compare them. The process of randomly subsampling sequences to a selected normalized library size from the sample library—rarefying—is one such normalization technique. However, rarefying has been criticized as a normalization technique because data can be omitted through the exclusion of either excess sequences or entire samples, depending on the rarefied library size selected. Although it has been suggested that rarefying should be avoided altogether, we propose that repeatedly rarefying enables (i) characterization of the variation introduced to diversity analyses by this random subsampling and (ii) selection of smaller library sizes where necessary to incorporate all samples in the analysis. Rarefying may be a statistically valid normalization technique, but researchers should evaluate their data to make appropriate decisions regarding library size selection and subsampling type. The impact of normalized library size selection and rarefying with or without replacement in diversity analyses were evaluated herein.Highlights▪ Amplicon sequencing technology for environmental water samples is reviewed▪ Sequencing data must be normalized to allow comparison in diversity analyses▪ Rarefying normalizes library sizes by subsampling from observed sequences▪ Criticisms of data loss through rarefying can be resolved by rarefying repeatedly▪ Rarefying repeatedly characterizes errors introduced by subsampling sequences

Download Full-text

A high-throughput amplicon screen for somatic UBA1 variants in Cytopenic and Giant Cell Arteritis cohorts

10.1101/2021.12.08.21266919 ◽

2021 ◽

Author(s):

James A. Poulter ◽

Alesia Khan ◽

Stephen Martin ◽

Mark Grey ◽

Bosko Andjelic ◽

...

Keyword(s):

Giant Cell Arteritis ◽

Giant Cell ◽

Somatic Mutations ◽

Late Onset ◽

Relapsing Polychondritis ◽

Amplicon Sequencing ◽

List Type ◽

Gene Encoding ◽

Monosomy X ◽

Female Case

AbstractSomatic mutations in the gene encoding the major E1 ubiquitin ligase, UBA1, were recently identified as a cause of VEXAS, a late-onset acquired auto-inflammatory syndrome. Differential diagnoses for patients subsequently found to have VEXAS include relapsing polychondritis, Sweet’s syndrome, myelodysplastic syndrome (MDS), giant cell arteritis (GCA) and undifferentiated systemic autoinflammatory disease (uSAID). We therefore sought to screen DNA from individuals with a non-diagnostic cytopenia or GCA, for known VEXAS-associated mutations. To this end, we developed a multiplexed UBA1 amplicon sequencing assay, allowing quick screening of large cohorts while also providing sufficient sequencing depth to identify somatic mutations to an allele frequency <1%. Using this assay, we screened genomic DNA from 612 males diagnosed with GCA, and bone marrow DNA from 1,055 cases with an undiagnosed cytopenia. No GCA cases were found to have UBA1 mutations, however 4 different mutations in the cytopenic cohort were identified in 7 individuals. Furthermore, we describe a female case identified in the screen with a UBA1 mutation and all VEXAS-associated phenotypes, but without Monosomy X. Our study suggests that, despite the overlap in clinical features, VEXAS is rarely misdiagnosed as GCA, but identified in 1.0% of males with an undiagnosed cytopenia. The identification of a UBA1 variant in a female case adds further evidence that VEXAS should not be ruled out as a differential diagnosis in females with VEXAS-like symptoms.Key points-Mutations in UBA1 exon 3 have been associated with VEXAS syndrome-UBA1 exon 3 was screened in 1650 patients with cytopenia or GCA by amplicon sequencing.-6 males were identified from the non-diagnostic cytopenia cohort (1.0%) with UBA1 mutations.-A female with a somatic UBA1 mutation was identified without Monosomy X

Download Full-text

MutantHuntWGS: A Pipeline for Identifying Saccharomyces cerevisiae Mutations

G3 Genes|Genome|Genetics ◽

10.1534/g3.120.401396 ◽

2020 ◽

Vol 10 (9) ◽

pp. 3009-3014 ◽

Cited By ~ 1

Author(s):

Mitchell A Ellison ◽

Jennifer L Walker ◽

Patrick J Ropp ◽

Jacob D Durrant ◽

Karen M Arndt

Keyword(s):

Saccharomyces Cerevisiae ◽

Whole Genome Sequencing ◽

Genome Sequencing ◽

Whole Genome Sequencing Data ◽

Whole Genome ◽

Sequencing Data ◽

Sequence Alignments ◽

Causal Variants ◽

User Friendly ◽

Analyze Data

Abstract MutantHuntWGS is a user-friendly pipeline for analyzing Saccharomyces cerevisiae whole-genome sequencing data. It uses available open-source programs to: (1) perform sequence alignments for paired and single-end reads, (2) call variants, and (3) predict variant effect and severity. MutantHuntWGS outputs a shortlist of variants while also enabling access to all intermediate files. To demonstrate its utility, we use MutantHuntWGS to assess multiple published datasets; in all cases, it detects the same causal variants reported in the literature. To encourage broad adoption and promote reproducibility, we distribute a containerized version of the MutantHuntWGS pipeline that allows users to install and analyze data with only two commands. The MutantHuntWGS software and documentation can be downloaded free of charge from https://github.com/mae92/MutantHuntWGS.

Download Full-text

MutantHuntWGS: A Pipeline for Identifying Saccharomyces cerevisiae Mutations

10.1101/2020.05.16.099259 ◽

2020 ◽

Author(s):

Mitchell A. Ellison ◽

Jennifer L. Walker ◽

Patrick J. Ropp ◽

Jacob D. Durrant ◽

Karen M. Arndt

Keyword(s):

Saccharomyces Cerevisiae ◽

Whole Genome Sequencing ◽

Genome Sequencing ◽

Whole Genome Sequencing Data ◽

Whole Genome ◽

Sequencing Data ◽

Sequence Alignments ◽

Causal Variants ◽

User Friendly ◽

Analyze Data

ABSTRACTMutantHuntWGS is a user-friendly pipeline for analyzing Saccharomyces cerevisiae whole-genome sequencing data. It uses available open-source programs to: (1) perform sequence alignments for paired and single-end reads, (2) call variants, and (3) predict variant effect and severity. MutantHuntWGS outputs a shortlist of variants while also enabling access to all intermediate files. To demonstrate its utility, we use MutantHuntWGS to assess multiple published datasets; in all cases, it detects the same causal variants reported in the literature. To encourage broad adoption and promote reproducibility, we distribute a containerized version of the MutantHuntWGS pipeline that allows users to install and analyze data with only two commands. The MutantHuntWGS software and documentation can be downloaded free of charge from https://github.com/mae92/MutantHuntWGS.

Download Full-text

Discovery of tandem and interspersed segmental duplications using high throughput sequencing

10.1101/393694 ◽

2018 ◽

Cited By ~ 1

Author(s):

Arda Soylev ◽

Thong Le ◽

Hajar Amini ◽

Can Alkan ◽

Fereydoun Hormozdiari

Keyword(s):

False Discovery Rate ◽

High Throughput ◽

High Throughput Sequencing ◽

Real Data ◽

Whole Genome Sequencing Data ◽

Data Sets ◽

Segmental Duplications ◽

Sequencing Data ◽

Link Type ◽

False Discovery

AbstractMotivationSeveral algorithms have been developed that use high throughput sequencing technology to characterize structural variations. Most of the existing approaches focus on detecting relatively simple types of SVs such as insertions, deletions, and short inversions. In fact, complex SVs are of crucial importance and several have been associated with genomic disorders. To better understand the contribution of complex SVs to human disease, we need new algorithms to accurately discover and genotype such variants. Additionally, due to similar sequencing signatures, inverted duplications or gene conversion events that include inverted segmental duplications are often characterized as simple inversions; and duplications and gene conversions in direct orientation may be called as simple deletions. Therefore, there is still a need for accurate algorithms to fully characterize complex SVs and thus improve calling accuracy of more simple variants.ResultsWe developed novel algorithms to accurately characterize tandem, direct and inverted interspersed segmental duplications using short read whole genome sequencing data sets. We integrated these methods to our TARDIS tool, which is now capable of detecting various types of SVs using multiple sequence signatures such as read pair, read depth and split read. We evaluated the prediction performance of our algorithms through several experiments using both simulated and real data sets. In the simulation experiments, using a 30× coverage TARDIS achieved 96% sensitivity with only 4% false discovery rate. For experiments that involve real data, we used two haploid genomes (CHM1 and CHM13) and one human genome (NA12878) from the Illumina Platinum Genomes set. Comparison of our results with orthogonal PacBio call sets from the same genomes revealed higher accuracy for TARDIS than state of the art methods. Furthermore, we showed a surprisingly low false discovery rate of our approach for discovery of tandem, direct and inverted interspersed segmental duplications prediction on CHM1 (less than 5% for the top 50 predictions).AvailabilityTARDIS source code is available at https://github.com/BilkentCompGen/tardis, and a corresponding Docker image is available at https://hub.docker.com/r/alkanlab/tardis/[email protected] and [email protected]

Download Full-text

Iroki: automatic customization and visualization of phylogenetic trees

10.1101/106138 ◽

2017 ◽

Cited By ~ 7

Author(s):

Ryan M. Moore ◽

Amelia O. Harrison ◽

Sean M. McAllister ◽

Shawn W. Polson ◽

K. Eric Wommack

Keyword(s):

Web Application ◽

Phylogenetic Trees ◽

Evolutionary History ◽

Phylogenetic Analyses ◽

Community Diversity ◽

Sequencing Data ◽

Link Type ◽

Large Trees ◽

History Of ◽

Microbial Groups

ABSTRACTPhylogenetic trees are an important analytical tool for evaluating community diversity and evolutionary history. In the case of microorganisms, the decreasing cost of sequencing has enabled researchers to generate ever-larger sequence datasets, which in turn have begun to fill gaps in the evolutionary history of microbial groups. However, phylogenetic analyses of these types of datasets create complex trees that can be challenging to interpret. Scientific inferences made by visual inspection of phylogenetic trees can be simplified and enhanced by customizing various parts of the tree. Yet, manual customization is time-consuming and error prone, and programs designed to assist in batch tree customization often require programming experience or complicated file formats for annotation. Iroki, a user-friendly web interface for tree visualization, addresses these issues by providing automatic customization of large trees based on metadata contained in tab-separated text files. Iroki’s utility for exploring biological and ecological trends in sequencing data was demonstrated through a variety of microbial ecology applications in which trees with hundreds to thousands of leaf nodes were customized according to extensive collections of metadata. The Iroki web application and documentation are available at https://www.iroki.net or through the VIROME portal (http://virome.dbi.udel.edu). Iroki’s source code is released under the MIT license and is available at https://github.com/mooreryan/iroki.

Download Full-text

Whole-Genome Sequencing Data for Serotyping Escherichia coli—It's Time for a Change!

Journal of Clinical Microbiology ◽

10.1128/jcm.01448-15 ◽

2015 ◽

Vol 53 (8) ◽

pp. 2402-2403 ◽

Cited By ~ 9

Author(s):

Claire Jenkins

Keyword(s):

Escherichia Coli ◽

Whole Genome Sequencing ◽

Genome Sequencing ◽

Whole Genome Sequencing Data ◽

Whole Genome ◽

Sequencing Data ◽

Content Type ◽

Link Type ◽

Article Type ◽

User Friendly

The accessibility of whole-genome sequencing (WGS) presents the opportunity for national reference laboratories to provide a state-of-the-art public health surveillance service. The replacement of traditional serology-based typing ofEscherichia coliby WGS is supported by user-friendly, freely available data analysis Web tools. Anarticle in this issueof theJournal of Clinical Microbiology(K. G. Joensen, A. M. M. Tetzschner, A. Iguchi, F. M. Aarestrup, and F. Scheutz, J Clin Microbiol, 53:2410–2426, 2015,http://dx.doi.org/10.1128/JCM.00008-15) describes SerotypeFinder, an essential guide to serotypingE. coliin the 21st century.

Download Full-text