Defining the Estimated Core Genome of Bacterial Populations Using a Bayesian Decision Model

Tonder, Andries J. van; Mistry, Shilan; Bray, James E.; Hill, Dorothea M. C.; Cody, Alison J.; Farmer, Chris L.; Klugman, Keith P.; Gottberg, Anne von; Bentley, Stephen D.; Parkhill, Julian; Jolley, Keith A.; Maiden, Martin C. J.; Brueggemann, Angela B.

doi:10.1371/journal.pcbi.1003788

Cited by 57 publications

(56 citation statements)

References 39 publications

(41 reference statements)

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“…This has provided numerous novel opportunities for studying Campylobacter (Farhat et al 2014;Méric et al 2014;Van Tonder et al 2014). However, as correctly determined sequence data are absolute, the insights obtained from MLST-based analyses remain relevant and MLST data are "forward compatible" with NGS data .…”

Section: The Impact Of Sequence-based Molecular Typingmentioning

confidence: 99%

The Evolution ofCampylobacter jejuniandCampylobacter coli

Sheppard

Maiden

2015

Cold Spring Harb Perspect Biol

131

127

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The global significance of Campylobacter jejuni and Campylobacter coli as gastrointestinal human pathogens has motivated numerous studies to characterize their population biology and evolution. These bacteria are a common component of the intestinal microbiota of numerous bird and mammal species and cause disease in humans, typically via consumption of contaminated meat products, especially poultry meat. Sequence-based molecular typing methods, such as multilocus sequence typing (MLST) and whole genome sequencing (WGS), have been instructive for understanding the epidemiology and evolution of these bacteria and how phenotypic variation relates to the high degree of genetic structuring in C. coli and C. jejuni populations. Here, we describe aspects of the relatively short history of coevolution between humans and pathogenic Campylobacter, by reviewing research investigating how mutation and lateral or horizontal gene transfer (LGT or HGT, respectively) interact to create the observed population structure. These genetic changes occur in a complex fitness landscape with divergent ecologies, including multiple host species, which can lead to rapid adaptation, for example, through frame-shift mutations that alter gene expression or the acquisition of novel genetic elements by HGT. Recombination is a particularly strong evolutionary force in Campylobacter, leading to the emergence of new lineages and even large-scale genome-wide interspecies introgression between C. jejuni and C. coli. The increasing availability of large genome datasets is enhancing understanding of Campylobacter evolution through the application of methods, such as genome-wide association studies, but MLST-derived clonal complex designations remain a useful method for describing population structure.

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Section: The Impact Of Sequence-based Molecular Typingmentioning

confidence: 99%

The Evolution ofCampylobacter jejuniandCampylobacter coli

Sheppard

Maiden

2015

Cold Spring Harb Perspect Biol

131

127

View full text Add to dashboard Cite

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“…PPanGGOLiN uses a new statistical model to classify gene families into persistent, cloud, and one or several shell partitions. Unlike the few statistical methods available [24,25,26] that partitions gene families using only their frequency, our method combines the information of occurrence of gene families and the pangenome graph to make the classification. In the following sections we present an overview of the method, an illustration of a pangenome graph and then the partitioning of a large set of prokaryotic species from GenBank.…”

Section: Introductionmentioning

confidence: 99%

PPanGGOLiN: depicting microbial diversity via a partitioned pangenome graph

Gautreau

Bazin

Gachet

et al. 2019

Preprint

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The use of comparative genomics for functional, evolutionary, and epidemiological studies requires methods to classify gene families in terms of occurrence in a given species. These methods usually lack multivariate statistical models to infer the partitions and the optimal number of classes and don't account for genome organization. We introduce a graph structure to model pangenomes in which nodes represent gene families and edges represent genomic neighborhood. Our method, named PPanGGOLiN, partitions nodes using an Expectation-Maximization algorithm based on multivariate Bernoulli Mixture Model coupled with a Markov Random Field. This approach takes into account the topology of the graph and the presence/absence of genes in pangenomes to classify gene families into persistent, cloud, and one or several shell partitions. By analyzing the partitioned pangenome graphs of isolate genomes from 439 species and metagenome-assembled genomes from 78 species, we demonstrate that our method is effective in estimating the persistent genome. Interestingly, it shows that the shell genome is a key element to understand genome dynamics, presumably because it reflects how genes present at intermediate frequencies drive adaptation of species, and its proportion in genomes is independent of genome size. The graph-based approach proposed by PPanGGOLiN is useful to depict the overall genomic diversity of thousands of strains in a compact structure and provides an effective basis for very large scale comparative genomics. The software is freely available at https://github.com/labgem/PPanGGOLiN.

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“…This purely bioinformatic approach may be only the first step in elucidating genes of interest, as subsequent experimentation aimed at characterizing the role of the identified target in pathogenesis, the extent to which they are expressed during infection, and their essentiality can provide additional information necessary for identifying high value targets. The availability of numerous genome sequences for a given bacterial species permits the determination of the species’ core genome [24,25], those genes that are present in all, or the majority, of sequenced strains. Based on the idea that the presence of these genes in the majority of strains indicates that they are likely to participate in key bacterial processes, the elucidation of core genomes can provide an initial step for antibiotic target identification.…”

Section: Comparative Genomicsmentioning

confidence: 99%

Using bacterial genomes and essential genes for the development of new antibiotics

Fields

Lee

McConnell

2017

Biochemical Pharmacology

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The shrinking antibiotic development pipeline together with the global increase in antibiotic resistant infections requires that new molecules with antimicrobial activity are developed. Traditional empirical screening approaches of natural and non-natural compounds have identified the majority of antibiotics that are currently available, however this approach has produced relatively few new antibiotics over the last few decades. The vast amount of bacterial genome sequence information that has become available since the sequencing of the first bacterial genome more than 20 years ago holds potential for contributing to the discovery of novel antimicrobial compounds. Comparative genomic approaches can identify genes that are highly conserved within and between bacterial species, and thus may represent genes that participate in key bacterial processes. Whole genome mutagenesis studies can also identify genes necessary for bacterial growth and survival under different environmental conditions, making them attractive targets for the development of novel inhibitory compounds. In addition, transcriptomic and proteomic approaches can be used to characterize RNA and protein levels on a cellular scale, providing information on bacterial physiology that can be applied to antibiotic target identification. Finally, bacterial genomes can be mined to identify biosynthetic pathways that produce many intrinsic antimicrobial compounds and peptides. In this review, we provide an overview of past and current efforts aimed at using bacterial genomic data in the discovery and development of novel antibiotics.

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Defining the Estimated Core Genome of Bacterial Populations Using a Bayesian Decision Model

Cited by 57 publications

References 39 publications

The Evolution ofCampylobacter jejuniandCampylobacter coli

The Evolution ofCampylobacter jejuniandCampylobacter coli

PPanGGOLiN: depicting microbial diversity via a partitioned pangenome graph

Using bacterial genomes and essential genes for the development of new antibiotics

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