The Pseudomonas aeruginosa accessory genome elements influence virulence towards Caenorhabditis elegans

Vasquez-Rifo, Alejandro; Veksler-Lublinsky, Isana; Cheng, Zhenyu; Ausubel, Frederick M.; Ambros, Victor R.

doi:10.1186/s13059-019-1890-1

Cited by 29 publications

(38 citation statements)

References 63 publications

(78 reference statements)

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“…While individual AGEs showed low importance in model predictions, it is relevant that all of the 10 most important AGEs included in our model were associated with low virulence. This supports the earlier finding that the presence of specific P. aeruginosa accessory genes can reduce virulence in C. elegans and that active CRISPR systems, which would limit acquisition of foreign DNA and new AGEs, are associated with higher virulence in that model (16). While certain AGEs enhance virulence (15), many AGEs (e.g., parasitic phages, plasmids, or ICEs) may decrease virulence through mechanisms such as dysregulation of regulatory networks, insertion into important genes, or imposition of an additional metabolic burden.…”

Section: Discussionsupporting

confidence: 90%

A Genome-Based Model to Predict the Virulence of Pseudomonas aeruginosa Isolates

Pincus

Ozer

Allen

et al. 2020

mBio

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Variation in the genome of Pseudomonas aeruginosa, an important pathogen, can have dramatic impacts on the bacterium’s ability to cause disease. We therefore asked whether it was possible to predict the virulence of P. aeruginosa isolates based on their genomic content. We applied a machine learning approach to a genetically and phenotypically diverse collection of 115 clinical P. aeruginosa isolates using genomic information and corresponding virulence phenotypes in a mouse model of bacteremia. We defined the accessory genome of these isolates through the presence or absence of accessory genomic elements (AGEs), sequences present in some strains but not others. Machine learning models trained using AGEs were predictive of virulence, with a mean nested cross-validation accuracy of 75% using the random forest algorithm. However, individual AGEs did not have a large influence on the algorithm’s performance, suggesting instead that virulence predictions are derived from a diffuse genomic signature. These results were validated with an independent test set of 25 P. aeruginosa isolates whose virulence was predicted with 72% accuracy. Machine learning models trained using core genome single-nucleotide variants and whole-genome k-mers also predicted virulence. Our findings are a proof of concept for the use of bacterial genomes to predict pathogenicity in P. aeruginosa and highlight the potential of this approach for predicting patient outcomes. IMPORTANCE Pseudomonas aeruginosa is a clinically important Gram-negative opportunistic pathogen. P. aeruginosa shows a large degree of genomic heterogeneity both through variation in sequences found throughout the species (core genome) and through the presence or absence of sequences in different isolates (accessory genome). P. aeruginosa isolates also differ markedly in their ability to cause disease. In this study, we used machine learning to predict the virulence level of P. aeruginosa isolates in a mouse bacteremia model based on genomic content. We show that both the accessory and core genomes are predictive of virulence. This study provides a machine learning framework to investigate relationships between bacterial genomes and complex phenotypes such as virulence.

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Section: Discussionsupporting

confidence: 90%

A Genome-Based Model to Predict the Virulence of Pseudomonas aeruginosa Isolates

Pincus

Ozer

Allen

et al. 2020

mBio

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“…Still, it is relevant that all of the ten most important AGEs included in our model were associated with low virulence. This supports the earlier finding that P. aeruginosa accessory genes can reduce virulence in C. elegans and that active CRISPR systems, which would limit acquisition of foreign DNA and new AGEs, are associated with higher virulence in that model 16 . While certain AGEs enhance virulence 15 , the majority of AGEs (e.g.…”

Section: Discussionsupporting

confidence: 90%

“…We recently mined the accessory genome and identified multiple novel factors important in virulence in a mouse model of bacteremia 15 . Conversely, a study using a Caenorhabditis elegans model identified several P. aeruginosa accessory genes whose presence reduced virulence 16 . Further, the presence of active CRISPR systems was associated with increased virulence 16 , supporting the hypothesis that many horizontally transferred elements are genetic parasites with respect to the host bacterium 17 .…”

Section: Introductionmentioning

confidence: 99%

“…Conversely, a study using a Caenorhabditis elegans model identified several P. aeruginosa accessory genes whose presence reduced virulence 16 . Further, the presence of active CRISPR systems was associated with increased virulence 16 , supporting the hypothesis that many horizontally transferred elements are genetic parasites with respect to the host bacterium 17 . Because of its role in both increasing and decreasing the pathogenicity of individual P. aeruginosa strains, the accessory genome may serve as a useful predictor of an isolate’s virulence.…”

Section: Introductionmentioning

confidence: 99%

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A Genome-based Model to Predict the Virulence ofPseudomonas aeruginosaIsolates

Pincus

Ozer

Allen

et al. 2020

Preprint

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25Variation in the genome of Pseudomonas aeruginosa, an important pathogen, can have 26 dramatic impacts on the bacterium's ability to cause disease. We therefore asked whether it 27 was possible to predict the virulence of P. aeruginosa isolates based upon their genomic 54 Pseudomonas aeruginosa is a ubiquitous gram-negative opportunistic pathogen that can 55 infect a variety of hosts. Its ability to cause severe acute infections in susceptible patients and 56 chronic infections in individuals with cystic fibrosis, coupled with increasing rates of antimicrobial 57 resistance, make it an organism of particular concern to the medical community 1-3 . The P. 58 aeruginosa species, however, is not monolithic but rather shows a large amount of genomic 59 diversity both through polymorphisms and differences in gene content 4-6 . As routine whole 60 genome sequencing becomes increasingly feasible, understanding how these genomic 61 differences impact the pathogenicity of P. aeruginosa may allow clinicians to rapidly identify 62 high-risk infections and researchers to select the most high-yield strains for further study. 63As with other bacteria, the genome of P. aeruginosa can be divided into a core genome, 64 made up of sequences common to the species, and an accessory genome, made up of 65 sequences present in some strains but not others 6,7 . While only 10-15% of a typical strain's 66 genome is accessory, these sequences when combined from all strains comprise the vast 67 majority of the P. aeruginosa pangenome 4,7,8 . Variations in both the core and accessory 68 genomes play an important role in the virulence of any given P. aeruginosa strain. Core genome 69 mutations that accumulate in P. aeruginosa strains during chronic infection of cystic fibrosis 70 patients lead to decreased markers of in vitro virulence 9 , and these strains have attenuated 71 virulence in animal models of acute infection 10 . Genomic islands, major components of the 72 accessory genome, are enriched for predicted virulence factors 11 . Several genomic islands in P. 73 aeruginosa, including those containing the type III secretion system (T3SS) effector gene exoU, 74 have been shown to enhance pathogenicity in multiple infection models [12][13][14] . We recently mined 75 the accessory genome and identified multiple novel factors important in virulence in a mouse 76 model of bacteremia 15 . Conversely, a study using a Caenorhabditis elegans model identified 77 several P. aeruginosa accessory genes whose presence reduced virulence 16 . Further, the 78 presence of active CRISPR systems was associated with increased virulence 16 , supporting the 79 5 hypothesis that many horizontally transferred elements are genetic parasites with respect to the 80 host bacterium 17 . Because of its role in both increasing and decreasing the pathogenicity of 81 individual P. aeruginosa strains, the accessory genome may serve as a useful predictor of an 82 isolate's virulence. This is not necessarily as simple as detecting individual virulence or anti-83 virulence...

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“…For instance, in Pseudomonas aeruginosa, accessory gene elements have enhanced the virulence toward invertebrate Caenorhabditis elegans [6]. GIs' another class of accessory gene elements contribute to the evolution and adaptation to the niche.…”

Section: Introductionmentioning

confidence: 99%

Genomic islands and the evolution of livestock-associatedStaphylococcus aureusgenomes

et al. 2020

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Background: Genomic Islands (GIs) are commonly believed to be relics of horizontal transfer and associated with specific metabolic capacities, including virulence of the strain. Horizontal gene transfer (HGT) plays a vital role in the acquisition of GIs and the evolution and adaptation of bacterial genomes. Objective: The present study was designed to predict the GIs and role of HGT in evolution of livestock-associated Staphylococcus aureus (LA-SA). Methods: GIs were predicted with two methods namely, Ensemble algorithm for Genomic Island Detection (EGID) tool, and Seq word Sniffer script. Functional characterization of GI elements was performed with clustering of orthologs. The putative donor predictions of GIs was done with the aid of the pre_GI database. Results: The present study predicted a pan of 46 GIs across the LA-SA genomes. Functional characterization of GI sequences revealed few unique results like the presence of metabolic operons like leuABCD and folPK genes in GIs and showed the importance of GIs in the adaptation to the host niche. The developed framework for GI donor prediction results revealed Rickettsia and Mycoplasma as the major donors of GI elements. Conclusions: The role of GIs during the evolutionary race of LA-SA could be concluded from the present study. Niche adaptation of LA-SA enhanced presumably due to these GIs. Future studies could focus on the evolutionary relationships between Rickettsia and Mycoplasma sp. with S. aureus and also the evolution of Leucine/Isoleucine mosaic operon (leuABCD).

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The Pseudomonas aeruginosa accessory genome elements influence virulence towards Caenorhabditis elegans

Cited by 29 publications

References 63 publications

A Genome-Based Model to Predict the Virulence of Pseudomonas aeruginosa Isolates

A Genome-Based Model to Predict the Virulence of Pseudomonas aeruginosa Isolates

A Genome-based Model to Predict the Virulence ofPseudomonas aeruginosaIsolates

Genomic islands and the evolution of livestock-associatedStaphylococcus aureusgenomes

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