Article (Scientific journals)
PathoFact: a pipeline for the prediction of virulence factors and antimicrobial resistance genes in metagenomic data
de Nies, Laura; Lopes, Sara; Busi, Susheel Bhanu et al.
2021In Microbiome
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Abstract :
[en] Background Pathogenic microorganisms cause disease by invading, colonizing, and damaging their host. Virulence factors including bacterial toxins contribute to pathogenicity. Additionally, antimicrobial resistance genes allow pathogens to evade otherwise curative treatments. To understand causal relationships between microbiome compositions, functioning, and disease, it is essential to identify virulence factors and antimicrobial resistance genes in situ. At present, there is a clear lack of computational approaches to simultaneously identify these factors in metagenomic datasets. Results Here, we present PathoFact, a tool for the contextualized prediction of virulence factors, bacterial toxins, and antimicrobial resistance genes with high accuracy (0.921, 0.832 and 0.979, respectively) and specificity (0.957, 0.989 and 0.994). We evaluate the performance of PathoFact on simulated metagenomic datasets and perform a comparison to two other general workflows for the analysis of metagenomic data. PathoFact outperforms all existing workflows in predicting virulence factors and toxin genes. It performs comparably to one pipeline regarding the prediction of antimicrobial resistance while outperforming the others. We further demonstrate the performance of PathoFact on three publicly available case-control metagenomic datasets representing an actual infection as well as chronic diseases in which either pathogenic potential or bacterial toxins are hypothesized to play a role. In each case, we identify virulence factors and AMR genes which differentiated between the case and control groups, thereby revealing novel gene associations with the studied diseases. Conclusion PathoFact is an easy-to-use, modular, and reproducible pipeline for the identification of virulence factors, bacterial toxins, and antimicrobial resistance genes in metagenomic data. Additionally, our tool combines the prediction of these pathogenicity factors with the identification of mobile genetic elements. This provides further depth to the analysis by considering the genomic context of the pertinent genes. Furthermore, PathoFact’s modules for virulence factors, toxins, and antimicrobial resistance genes can be applied independently, thereby making it a flexible and versatile tool. PathoFact, its models, and databases are freely available at https://pathofact.lcsb.uni.lu.
Research center :
Luxembourg Centre for Systems Biomedicine (LCSB)
Disciplines :
Microbiology
Author, co-author :
de Nies, Laura ;  University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > Systems Ecology
Lopes, Sara
Busi, Susheel Bhanu ;  University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > Systems Ecology
Galata, Valentina ;  University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > Systems Ecology
Heintz-Buschart, Anna
Laczny, Cedric Christian  ;  University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > Systems Ecology
May, Patrick  ;  University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > Bioinformatics Core
Wilmes, Paul ;  University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > Systems Ecology
External co-authors :
no
Language :
English
Title :
PathoFact: a pipeline for the prediction of virulence factors and antimicrobial resistance genes in metagenomic data
Publication date :
17 February 2021
Journal title :
Microbiome
ISSN :
2049-2618
Publisher :
BioMed Central, London, United Kingdom
Peer reviewed :
Peer Reviewed verified by ORBi
Name of the research project :
PathoFact
Funders :
FNR - Fonds National de la Recherche [LU]
Available on ORBilu :
since 27 April 2021

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