Biology subjects

David J Edwards

Publications and source records attributed to David J Edwards.

4 recordsLinked to original sources

bioRxiv2020

SNPPar: identifying convergent evolution and other homoplasies from microbial whole-genome alignments

Homoplasic single nucleotide polymorphisms (SNPs) are considered important signatures of strong (positive) selective pressure, and hence of adaptive evolution for clinically relevant traits such as antibiotic resistance and virulence. Here we present a new tool, SNPPar, for efficient detection and analysis of homoplasic SNPs from large WGS datasets (>1,000 isolates and/or >100,000 SNPs). SNPPar takes as input a SNP alignment, tree and annotated reference genome, and uses a combination of simple monophyly tests and ancestral state reconstruction (ASR, via TreeTime) to assign mutation events to branches and identify homoplasies. Mutations are annotated at the level of codon and gene, to facilitate analysis of convergent evolution. Testing on simulated data (120 Mycobacterium tuberculosis alignments representing local and global samples) showed SNPPar can detect homoplasic SNPs with very high sensitivity (zero false-positives in all tests) and high specificity (zero false-negatives in 89% of tests). SNPPar analysis of three empirically sampled datasets (E. anophelis, B. dolosa and M. tuberculosis) produced results that were in concordance with previous studies, in terms of both individual homoplasies and evidence of convergence at the codon and gene levels. SNPPar analysis of a simulated alignment of [~]64,000 genome-wide SNPs from 2000 M. tuberculosis genomes took [~]23 minutes and [~]2.6 GB of RAM to generate complete annotated results on a laptop. This analysis required ASR be conducted for only 1.25% of SNPs, and the ASR step took [~]23 seconds and 0.4 GB RAM. SNPPar automates the detection and annotation of homoplasic SNPs efficiently and accurately from large SNP alignments. As demonstrated by the examples included here, this information can be readily used to explore the role of homoplasy in parallel and/or convergent evolution at the level of nucleotide, codon and/or gene. Impact statementDNA sequences of bacterial pathogens are mutating all the time; most changes are deleterious or neutral, but sometimes a mutation leads to functional change that allows the pathogen to evade a potential threat. These random mutational changes (single nucleotide polymorphisms, or SNPs) are so very rarely beneficial, that when they do arise in parallel in distantly related isolates (known as homoplasic SNPs) this indicates that the change may be positively selected because it confers an adaptive advantage to the bacteria. Finding homoplasic SNPs in large sets of bacterial genomes is challenging as current tools require substantial time and computational resources to run. Here we present SNPPar, a software program to efficiently and accurately automate the detection and annotation of homoplasic SNPs from large whole-genome sequence data sets. We use simulated data to demonstrate accuracy of the program, and re-analyse published datasets using SNPPar to illustrate how the results can be used to gain insights into the evolution of antibiotic resistance and other traits. We envisage SNPPar will help facilitate the undertaking of long-term, real-time surveillance of bacterial pathogens, and their adaptive evolutionary response to interventions and control measures such as new drugs or vaccines. Data summaryThe authors confirm all supporting data, code and protocols have been provided within the article, through supplementary data files or other online sources as indicated in the article. New content generated for this paper is: O_LISNPPar code is available from https://github.com/d-j-e/SNPPar. The version described here is v1.0. C_LIO_LIA GitHub repository containing the full protocol, in-house code and data used to carry out the validation and performance testing is available at https://github.com/d-j-e/SNPPar_test. This repository includes all the simulated and real data sets used here. C_LI Data statementThe authors confirm all supporting data, code and protocols have been provided within the article, through supplementary data files or other online sources as indicated in the article.

genomics↗

bioRxiv2016

Genome-scale rates of evolutionary change in bacteria

Estimating the rates at which bacterial genomes evolve is critical to understanding major evolutionary and ecological processes such as disease emergence, long-term host-pathogen associations, and short-term transmission patterns. The surge in bacterial genomic data sets provides a new opportunity to estimate these rates and reveal the factors that shape bacterial evolutionary dynamics. For many organisms estimates of evolutionary rate display an inverse association with the time-scale over which the data are sampled. However, this relationship remains unexplored in bacteria due to the difficulty in estimating genome-wide evolutionary rates, which are impacted by the extent of temporal structure in the data and the prevalence of recombination. We collected 36 whole genome sequence data sets from 16 species of bacterial pathogens to systematically estimate and compare their evolutionary rates and assess the extent of temporal structure in the absence of recombination. The majority (28/36) of data sets possessed sufficient clock-like structure to robustly estimate evolutionary rates. However, in some species reliable estimates were not possible even with \"ancient DNA\" data sampled over many centuries, suggesting that they evolve very slowly or that they display extensive rate variation among lineages. The robustly estimated evolutionary rates spanned several orders of magnitude, from 10-6 to 10-8 nucleotide substitutions site-1 year-1. This variation was largely attributable to sampling time, which was strongly negatively associated with estimated evolutionary rates, with this relationship best described by an exponential decay curve. To avoid potential estimation biases such time-dependency should be considered when inferring evolutionary time-scales in bacteria.

Evolutionary Biology↗

bioRxiv2015

ISMapper: Identifying insertion sequences in bacterial genomes from short read sequence data

BackgroundInsertion sequences (IS) are small transposable elements, commonly found in bacterial genomes. Identifying the location of IS in bacterial genomes can be useful for a variety of purposes including epidemiological tracking and predicting antibiotic resistance. However IS are commonly present in multiple copies in a single genome, which complicates genome assembly and the identification of IS insertion sites. Here we present ISMapper, a mapping-based tool for identification of the site and orientation of IS insertions in bacterial genomes, direct from paired-end short read data.\n\nResultsISMapper was validated using three types of short read data: (i) simulated reads from a variety of species, (ii) Illumina reads from 5 isolates for which finished genome sequences were available for comparison, and (iii) Illumina reads from 7 Acinetobacter baumannii isolates for which predicted IS locations were tested using PCR. A total of 20 genomes, including 13 species and 32 distinct IS, were used for validation. ISMapper correctly identified 96% of known IS insertions in the analysis of simulated reads, and 98% in real Illumina reads. Subsampling of real Illumina reads to lower depths indicated ISMapper was reliable for average genome-wide read depths >20x. All ISAba1 insertions identified by ISMapper in the A. baumannii genomes were confirmed by PCR. In each A. baumannii genome, ISMapper successfully identified an IS insertion upstream of the ampC beta-lactamase that could explain phenotypic resistance to third-generation cephalosporins. The utility of ISMapper was further demonstrated by profiling genome-wide IS6110 insertions in 138 publicly available Mycobacterium tuberculosis genomes, revealing lineage-speific inserction and multi inserction hotspot.\n\nConclusionsISMapper provides a rapid and robust method for identifying IS insertion sites direct from short read data, with a high degree of accuracy demonstrated across a wide range of bacteria.

Bioinformatics↗

bioRxiv2014

Extensive capsule locus variation and large-scale genomic recombination within the Klebsiella pneumoniae clonal complex 258/11.

Klebsiella pneumoniae clonal complex (CC) 258/11, comprising sequence types (STs) 258, 11 and closely related STs, is associated with dissemination of the K. pneumoniae carbapenemase (KPC). Hospital outbreaks of KPC CC258/11 infections have been observed globally and are very difficult to treat. As a consequence there is renewed interest in alternative infection control measures such as vaccines and phage or depolymerase treatments targeting the K pneumoniae polysaccharide capsule. To date, 78 immunologically distinct capsule variants have been described in K. pneumoniae. Previous investigations of ST258 and a small number of closely related strains suggested capsular variation was limited within this clone; only two distinct ST258 capsular synthesis (cps) loci have been identified, both acquired through large-scale recombination events (>50 kbp). Here we report comparative genomic analysis of the broader K. pneumoniae CC258/11. Our data indicate that several large-scale recombination events have shaped the genomes of CC258/11, and that definition of the complex should be broadened to include ST395 (also reported to harbour KPC). We identified 11 different cps loci within CC258/11, suggesting that capsular switching is actually common within the complex. We also observed several insertion sequences (IS) within the cps loci, and show further diversification of two loci through IS activity. These findings suggest the capsular loci of clinically important K. pneumoniae are under diversifying selection, which alters our understanding of the evolution of this important clone and has implications for the design of control measures targeting the capsule.

Genomics↗