PathoFact: a pipeline for the prediction of virulence factors and antimicrobial resistance genes in metagenomic data

Sara Lopes,Patrick May,Laura De Nies,Anna Heintz-Buschart,Valentina Galata,Paul Wilmes,Susheel Bhanu Busi,Cedric Christian Laczny

doi:10.1186/s40168-020-00993-9

Abstract

BackgroundPathogenic 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.ResultsHere, 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.ConclusionPathoFact 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.C2FGScqUUFdTvmU6TgDvEJVideo abstract

Highlights

Pathogenic microorganisms cause disease by invading, colonizing, and damaging their host
We propose the primary usage of PathoFact for metagenomic analyses, as seen with these three case-control metagenomic datasets, it can be applied to single genome assemblies
We found that in all datasets the majority of mobile genetic elements (MGEs) were found to be both linked to virulence factors as well as antimicrobial resistance (AMR) (~ 50%), closely followed by MGEs linked solely to virulence factors, including bacterial toxins, with AMR contributing to the remaining MGEs (Fig. 6b)

Summary

Introduction

Pathogenic microorganisms cause disease by invading, colonizing, and damaging their host. Virulence factors, including bacterial toxins among others, contribute to this pathogenicity by enhancing the infectivity of pathogenic bacteria and by exacerbating antimicrobial resistance which in turn restricts treatment options [1]. Virulence factors enable pathogenic microorganisms to colonize host niches resulting in tissue damage as well as local and systemic inflammation. These factors are important for pathogens to establish an infection and span a wide range, contributing both directly and indirectly to disease processes [2]. These virulence traits include cell-surface structures, secretion machineries, siderophores, regulators, etc. Exotoxins are typically polypeptides and proteins that act to stimulate a variety of host responses either through direct action with cell receptors or via enzymatic modulation [5, 6]

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