The role of multispecies social interactions in shaping Pseudomonas aeruginosa pathogenicity in the cystic fibrosis lung

O’Brien, Siobhán; Fothergill, Joanne L.

doi:10.1093/femsle/fnx128

Cited by 94 publications

(88 citation statements)

References 143 publications

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“…Because the SteadyCom method (25) used to formulate and solve the community model does not allow direct incorporation of mechanisms by which one species could inhibit the growth of another species other than by nutrient competition, the predicted community growth rate always was greater than the highest individual growth rate of the coexisting species. Consequently, the formulated model was incapable was capturing more complex interactions such as Pseudomonas secretion of diffusible toxins that inhibit the growth of other CF pathogens (64).…”

Section: Discussionmentioning

confidence: 99%

Metabolic Modeling of Cystic Fibrosis Airway Communities Predicts Mechanisms of Pathogen Dominance

Henson

Orazi

Phalak

et al. 2019

Preprint

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5Cystic fibrosis (CF) is a fatal genetic disease characterized by chronic lung infections due to aberrant 1 6 mucus production and the inability to clear invading pathogens. The traditional view that CF infections 1 7 are caused by a single pathogen has been replaced by the realization that the CF lung usually is colonized 1 8 by a complex community of bacteria, fungi and viruses. To help unravel the complex interplay between 1 9 the CF lung environment and the infecting microbial community, we developed a community metabolic 2 0 CF lung metabolomics and 16S sequencing could provide important insights into disease progression and 3 2 treatment efficacy. 3 3 Importance 3 4Cystic fibrosis (CF) is a genetic disease in which chronic airway infections and lung inflammation result 3 5 in respiratory failure. CF airway infections are usually caused by bacterial communities that are difficult 3 6 to eradicate with available antibiotics. Using species abundance data for clinically stable adult CF patients 3 7 assimilated from three published studies, we developed a metabolic model of CF airway communities to 3 8 better understand the interactions between bacterial species and between the bacterial community and the 3 9 lung environment. Our model predicted that clinically-observed CF pathogens could establish dominance 4 0 over other community members across a range of lung nutrient conditions. Heterogeneity of species 4 1 abundances across 75 patient samples could be predicted by assuming that sample-to-sample 4 2 heterogeneity was attributable to random variations in the CF nutrient environment. Our model 4 3 predictions provide new insights into the metabolic determinants of pathogen dominance in the CF lung 4 4 and could facilitate the development of improved treatment strategies. 4 5 4 6shaping community composition and behavior, and the impact of community composition on the efficacy 7 1 of antibiotic treatment regimens. 7 2In silico metabolic modeling has emerged as a powerful approach for analyzing complex microbial 7 3 communities by integrating genome-scale reconstructions of single-species metabolism within 7 4 mathematical descriptions of metabolically interacting communities (11, 12). Modeled species 7 5 interactions typically include competition for host-derived nutrients and cross-feeding of secreted 7 6 byproducts such as organic acids, alcohols and amino acids between species (13, 14). Due to challenges 7 7 in developing manually curated reconstructions of poorly studied species, including those present in the 7 8 CF lung, most in silico community models have been restricted to ~5 microbial species (15-17) and fail to 7 9adequately cover the diversity of in vivo communities. This limitation can be overcome in bacterial 8 0 communities by using semi-curated reconstructions developed through computational pipelines such as 8 1 the ModelSeed (18), AGORA (19) and other methods (20). Given the availability of suitable single-strain 8 2 metabolic reconstructions, a number of alternative methods have been develo...

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Section: Discussionmentioning

confidence: 99%

Metabolic Modeling of Cystic Fibrosis Airway Communities Predicts Mechanisms of Pathogen Dominance

Henson

Orazi

Phalak

et al. 2019

Preprint

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“…Conversely, under nutrient-limited conditions, the same strain relies on CRISPR-Cas to acquire phage resistance 4 . While these observations suggest abiotic factors are critical determinants of the evolution of phage resistance strategies, the role of biotic factors has remained unclear, even though P. aeruginosa commonly co-exists with a range of other bacterial species in both natural and clinical settings 13,14 .…”

Section: Figurementioning

confidence: 99%

“…To explore how microbial biodiversity impacts the evolution of phage resistance, we co-cultured P. aeruginosa PA14 with three other clinically relevant opportunistic pathogens that are known to co-infect with P. aeruginosa , namely Staphylococcus aureus, Burkholderia cenocepacia , and Acinetobacter baumannii 14–16 , none of which can be infected by or interact with phage DMS3 vir (Extended Data Fig. 1, Linear model: Effect of P. aeruginosa on phage titre over time; t = −3.37, p < 0.001; S. aureus ; t = 1.63, p = 0.11; A. baumannii ; t = 1.20, p = 0.23; B. cenocepacia ; t = −0.27, p = 0.79; Overall model fit; F 9,235 = 4.33, adjusted R 2 = 0.11, p < 0.001).…”

Section: Figurementioning

confidence: 99%

Bacterial biodiversity drives the evolution of CRISPR-based phage resistance in Pseudomonas aeruginosa

Alseth

Pursey

et al. 2019

Preprint

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evolution of virulence; biodiversity 19 20 21Approximately half of all bacterial species encode CRISPR-Cas adaptive immune 22 systems 1 , which provide immunological memory by inserting short DNA sequences 23 from phage and other parasitic DNA elements into CRISPR loci on the host genome 2 . 24Whereas CRISPR loci evolve rapidly in natural environments 3 , bacterial species 25 typically evolve phage resistance by the mutation or loss of phage receptors under 26 laboratory conditions 4,5 . Here, we report how this discrepancy may in part be explained 27 by differences in the biotic complexity of in vitro and natural environments 6,7 . 28 Specifically, using the opportunistic pathogen Pseudomonas aeruginosa and its phage 29DMS3vir, we show that coexistence with other human pathogens amplifies the fitness 30 trade-offs associated with phage receptor mutation, and therefore tips the balance in 31 favour of CRISPR-based resistance evolution. We also demonstrate that this has 32 important knock-on effects for P. aeruginosa virulence, which became attenuated only if 33 the bacteria evolved surface-based resistance. Our data reveal that the biotic complexity 34 of microbial communities in natural environments is an important driver of the 35 evolution of CRISPR-Cas adaptive immunity, with key implications for bacterial fitness 36 and virulence. 37 38Pseudomonas aeruginosa is a widespread opportunistic human pathogen that thrives in a 39 range of different environments, including hospitals, where it is a common source of 40 nosocomial infections. In particular, it frequently colonises the lungs of cystic fibrosis 41 patients, in whom it is the leading cause of morbidity and mortality 8 . In part fuelled by a 42 renewed interest in the therapeutic use of bacteriophages as antimicrobials (phage 43 therapy) 9,10 , many studies have examined if and how P. aeruginosa evolves resistance to 44 phage (reviewed in ref. 11). The clinical isolate P. aeruginosa strain PA14 has been reported 45 to predominantly evolve resistance against its phage DMS3vir by the modification or 46 complete loss of the phage surface receptor 12 when grown in nutrient-rich medium 4 despite 47

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“…Given the polymicrobial nature of the CF respiratory tract, interbacterial interactions likely occur in these tissues and may influence disease progression (Bisht et al, 2020;Filkins and O'Toole, 2015;O'Brien and Fothergill, 2017;Peters et al, 2012). Interbacterial competition is hypothesized to be one of the strongest determinants of ecology and evolution within polymicrobial communities (Foster and Bell, 2012).…”

Section: Introductionmentioning

confidence: 99%

Host Adaptation Predisposes Pseudomonas aeruginosa to Type VI Secretion System-Mediated Predation by the Burkholderia cepacia Complex

Perault

Chandler

Rasko

et al. 2020

Preprint

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Pseudomonas aeruginosa (Pa) and Burkholderia cepacia complex (Bcc) species are opportunistic lung pathogens of individuals with cystic fibrosis (CF). While Pa can initiate longterm infections in younger CF patients, Bcc infections only arise in teenagers and adults. Both Pa and Bcc use type VI secretion systems (T6SS) to mediate interbacterial competition. Here, we show that Pa isolates from teenage/adult CF patients, but not those from young CF patients, are outcompeted by the epidemic Bcc isolate Burkholderia cenocepacia strain AU1054 (BcAU1054) in a T6SS-dependent manner. The genomes of susceptible Pa isolates harbor T6SS-abrogating mutations, the repair of which, in some cases, rendered the isolates resistant.Moreover, seven of eight Bcc strains outcompeted Pa strains isolated from the same patients.Our findings suggest that certain mutations that arise as Pa adapts to the CF lung abrogate T6SS activity, making Pa and its human host susceptible to potentially fatal Bcc superinfection.

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The role of multispecies social interactions in shaping Pseudomonas aeruginosa pathogenicity in the cystic fibrosis lung

Cited by 94 publications

References 143 publications

Metabolic Modeling of Cystic Fibrosis Airway Communities Predicts Mechanisms of Pathogen Dominance

Metabolic Modeling of Cystic Fibrosis Airway Communities Predicts Mechanisms of Pathogen Dominance

Bacterial biodiversity drives the evolution of CRISPR-based phage resistance in Pseudomonas aeruginosa

Host Adaptation Predisposes Pseudomonas aeruginosa to Type VI Secretion System-Mediated Predation by the Burkholderia cepacia Complex

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