Emerging bacterial pathogens: the past and beyond

Vouga, Manon; Greub, Gilbert

doi:10.1016/j.cmi.2015.10.010

Cited by 171 publications

(108 citation statements)

References 111 publications

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“…In infectious diseases, the niche of a given pathogen is determined by host range and the agent’s capacity to survive in the environment2. Some microbes are obligate pathogens restricted to one or several host species3,4, others are mainly free-living and only occasionally pathogenic5. Little is known on the niche width of obligate human pathogens3.…”

Section: Introductionmentioning

confidence: 99%

Mycobacterium tuberculosis lineage 4 comprises globally distributed and geographically restricted sublineages

et al. 2016

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Generalist and specialist species differ in the breadth of their ecological niche. Little is known about the niche width of obligate human pathogens. Here we analyzed a global collection of Mycobacterium tuberculosis Lineage 4 clinical isolates, the most geographically widespread cause of human tuberculosis. We show that Lineage 4 comprises globally distributed and geographically restricted sublineages, suggesting a distinction between generalists and specialists. Population genomic analyses showed that while the majority of human T cell epitopes were conserved in all sublineages, the proportion of variable epitopes was higher in generalists. Our data further support a European origin for the most common generalist sublineage. Hence, the global success of Lineage 4 reflects distinct strategies adopted by different sublineages and the influence of human migration.

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Section: Introductionmentioning

confidence: 99%

Mycobacterium tuberculosis lineage 4 comprises globally distributed and geographically restricted sublineages

et al. 2016

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“…This, coupled with the large percentage of bacterial pathogens (38%, Taylor et al 2001), makes understanding associations of different bacteria and bird species valuable to public health efforts to combat infectious disease (Kruse et al 2004; Vouga and Greub 2016). In a recent literature survey, 122 studies documented associations between wildlife and transmission of bacteria to our food chain (Greig et al 2014).…”

Section: Introductionmentioning

confidence: 99%

Patterns of Bird–Bacteria Associations

Chung¹,

Ferree

Simon

et al. 2018

EcoHealth

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Birds, with their broad geographic ranges and close association with humans, have historically played an important role as carriers of human disease and as reservoirs for drug-resistant bacteria. Here, we examine scientific literature over a 15-year timespan to identify reported avian-bacterial associations and factors that may impact zoonotic disease emergence by classifying traits of bird species and their bacteria. We find that the majority of wild birds studied were migratory, in temperate habitats, and in the order Passeriformes. The highest diversity of bacteria was found on birds in natural habitats. The most frequently reported bacteria were Escherichia coli, Salmonella enterica, and Campylobacter jejuni. Of the bacteria species reported, 54% have shown pathogenicity toward humans. Percentage-wise, more pathogens were found in tropical (vs. temperate) habitats and natural (vs. suburban, urban, or agricultural) habitats. Yet, only 22% were tested for antibiotic resistance, and of those tested, 75% of bacteria species were resistant to at least one antibiotic. There were no significant patterns of antibiotic resistance in migratory versus non-migratory birds, temperate versus tropical areas, or different habitats. We discuss biases in detection and representation, and suggest a need for increased sampling in non-temperate zones and in a wider range of avian species.

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“…The vast amount and diversity of bacteria on Earth, together with ever increasing human exposure1, suggests that we will be continuously confronted with novel bacterial pathogens, too. Encouragingly, next-generation sequencing (NGS) has emerged as a novel, powerful diagnostic tool in this regard.…”

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confidence: 99%

PaPrBaG: A machine learning approach for the detection of novel pathogens from NGS data

Deneke

Rentzsch

Renard

2017

Sci Rep

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The reliable detection of novel bacterial pathogens from next-generation sequencing data is a key challenge for microbial diagnostics. Current computational tools usually rely on sequence similarity and often fail to detect novel species when closely related genomes are unavailable or missing from the reference database. Here we present the machine learning based approach PaPrBaG (Pathogenicity Prediction for Bacterial Genomes). PaPrBaG overcomes genetic divergence by training on a wide range of species with known pathogenicity phenotype. To that end we compiled a comprehensive list of pathogenic and non-pathogenic bacteria with human host, using various genome metadata in conjunction with a rule-based protocol. A detailed comparative study reveals that PaPrBaG has several advantages over sequence similarity approaches. Most importantly, it always provides a prediction whereas other approaches discard a large number of sequencing reads with low similarity to currently known reference genomes. Furthermore, PaPrBaG remains reliable even at very low genomic coverages. CombiningPaPrBaG with existing approaches further improves prediction results.The vast amount and diversity of bacteria on Earth, together with ever increasing human exposure 1 , suggests that we will be continuously confronted with novel bacterial pathogens, too. Encouragingly, next-generation sequencing (NGS) has emerged as a novel, powerful diagnostic tool in this regard. However, the direct NGS-based characterisation of novel pathogenic strains or even species is still problematic when closely related genomes are unavailable or missing from the respective reference database. Here we introduce a machine learning based approach, PaPrBaG, which overcomes genetic divergence in predicting bacterial pathogenicity by training on a wide range of species with known pathogenicity phenotype. Importantly, even if this is avoided for practical reasons at some points throughout this (and related) work, one may more cautiously speak of pathogenic potential than pathogenicity, given that the latter is ultimately governed by the complex interplay between host (state) and pathogen. Existing MethodsExisting approaches amenable to pathogenicity prediction broadly fall into two classes: protein content based and whole-genome based. Where assembled genomes are available, the presence/absence pattern of certain protein families can be expected to correlate with complex phenotypes, e.g. pathogenicity. This is primarily based on the presence of virulence factors (VFs) -often acquired through horizontal gene transfer 2 -or the absence of more common genes (functions) that become dispensable when e.g. host-specific pathogens evolve from commensal ancestors 3 . Three recent studies rely on these considerations.The BacFier method by Iraola et al. 4 was the first to apply the described approach on a large scale. The authors defined eight VF categories and obtained 814 related VF protein families from KEGG 5 . They further used a set of 848 human-pathogenic (HP) and generally n...

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Emerging bacterial pathogens: the past and beyond

Cited by 171 publications

References 111 publications

Mycobacterium tuberculosis lineage 4 comprises globally distributed and geographically restricted sublineages

Mycobacterium tuberculosis lineage 4 comprises globally distributed and geographically restricted sublineages

Patterns of Bird–Bacteria Associations

PaPrBaG: A machine learning approach for the detection of novel pathogens from NGS data

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