Modelling microbiome recovery after antibiotics using a stability landscape framework

Shaw, Liam P.; Bassam, Hassan; Cp, Barnes; Walker, A. Sarah; Klein, Nigel; Balloux, François

doi:10.1038/s41396-019-0392-1

Cited by 102 publications

(89 citation statements)

References 51 publications

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“…Finally, a hallmark of the healthy adult microbial community is a stable microbial strain population as described by Shaw et al as a stability landscape (40). However, the results of our analysis using both data sets highlight that microbial strain change is inherent in developing the infant gut microbial ecosystem.…”

Section: Discussionmentioning

confidence: 64%

“…Numerous studies have shown that antibiotics cause a disruption of the gut microbial composition with a long-term impact on the community structure (37)(38)(39). In a recent study, we have used our WSS strain tracking analysis to show that antibiotics can also impact the gut microbial strain stability in adults that we correlated with alterations in the "stability landscape" profile of the microbial community (28,40). In the current study, we found that the strain tracking analysis of the Yassour et al data set revealed that antibiotics had resulted in a unique strain change pattern for certain infants (19).…”

Section: Discussionmentioning

confidence: 99%

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Strain Tracking to Identify Individualized Patterns of Microbial Strain Stability in the Developing Infant Gut Ecosystem

2020

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Stable microbe and host interactions are established during the development of the infant gut microbial community that provide essential functions for the efficient digestion of food, immune development, and resistance to colonization with pathogens. To further delineate the stability of the gut microbial community during this time, we have used microbial strain tracking analysis with published longitudinal metagenomic data sets to identify strains that persist in the developing infant gut ecosystem. In the first study, 17 infants were evaluated that had not received antibiotics for 3 years after birth. An infant specific pattern was seen for stable and unstable microbial strains during this time, with only one infant having no stable strains identified out of available strains during the first 3 years. Strain tracking was also applied to follow microbes in a separate set of 14 infants that had multiple doses of antibiotics over the 3 years. In 10 out of 14 infants given multiple antibiotics during the first 3 years, we identified a unique pattern of transient strains that appeared after multiple antibiotic treatments for a short time compared to that in infants not on antibiotics. In a second, independent study, we selected a subset of 9 infants from a previously published study consisting of high-density longitudinal fecal sampling to analyze the gut microbial strain stability of Bacteroides vulgatus and Bifidobacterium adolescentis for up to 6 years following birth. Individual specific patterns were found consisting of varying dominant microbial strains that were independent of antibiotic exposure and birth mode. Our analysis demonstrates an individual specific inherent variability of extinction and persistence of microbial strains in the infant gut community during a time of development that is critical for interactions necessary for establishing normal metabolism and the development of the host immune response.

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Section: Discussionmentioning

confidence: 64%

Section: Discussionmentioning

confidence: 99%

Strain Tracking to Identify Individualized Patterns of Microbial Strain Stability in the Developing Infant Gut Ecosystem

2020

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“…For the case of AMR evolution, our result that intermediate dosage therapy is optimal is particularly interesting as it may also reduce detrimental off-target species effects, such as enriching for resistance in off-target species [12] or compromising community resilience and functioning [34]. Such effects are prevalent in bacterial communities and possibly important to the AMR problem as a whole [12].…”

Section: Discussionmentioning

confidence: 99%

Drug-induced resistance evolution necessitates less aggressive treatment

Kuosmanen

Cairns

Noble

et al. 2020

Preprint

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Evolution of drug resistance to anticancer, antimicrobial and antiviral therapies is widespread among cancer and pathogen cell populations. Classical theory posits strictly that genetic and phenotypic variation is generated in evolving populations independently of the selection pressure. However, recent experimental findings among antimicrobial agents, traditional cytotoxic chemotherapies and targeted cancer therapies suggest that treatment not only imposes selection but also affects the rate of adaptation via altered mutational processes. Here we analyze a model with drug-induced increase in mutation rate and explore its consequences for treatment optimization. We argue that the true biological cost of treatment is not limited to the harmful side-effects, but instead realizes even more profoundly by fundamentally changing the underlying eco-evolutionary dynamics within the microenvironment. Factoring in such costs (or collateral damage) of control is at the core of successful therapy design and can unify different evolution-based approaches to therapy optimization. Using the concept of evolutionary rescue, we formulate the treatment as an optimal control problem and solve the optimal elimination strategy, which minimizes the probability of evolutionary rescue. Our solution exploits a trade-off, where increasing the drug concentration has two opposing effects. On the one hand, it reduces de novo mutations by decreasing the size of the target cell population faster; on the other hand, a higher dosage generates a surplus of treatment-induced mutations. We show that aggressive elimination strategies, which aim at eradication as fast as possible and which represent the current standard of care, can be detrimental even with modest drug-induced increases (fold change ≤10) to the baseline mutation rate. Our findings highlight the importance of dose dependencies in resistance evolution and motivate further investigation of the mutagenicity and other hidden collateral costs of therapies that promote resistance.Author summaryThe evolution of drug resistance is a particularly problematic and frequent outcome of cancer and antimicrobial therapies. Recent research suggests that these treatments may enhance the evolvability of the target population not only via inducing intense selection pressures but also via altering the underlying mutational processes. Here we investigate the consequences of such drug-induced evolution by considering a mathematical model with explicitly dose-dependent mutation rate. We identify, characterize and exploit a trade-off between decreasing the target population size as fast as possible and generating a surplus of treatment-induced de novo mutations. By formulating the treatment as an optimal control problem over the evolution of the target population, we find the optimal treatment strategy, which minimizes the probability of evolutionary rescue. We show that this probability changes non-monotonically with the cumulative drug concentration and is minimized at an intermediate dosage. Our results are immediately amenable to experimental investigation and motivate further study of the various mutagenic and other hidden collateral costs of treatment. Taken together, our results add to the ongoing criticism of the standard practice of administering aggressive, high-dose therapies and stimulate further clinical trials on alternative treatment strategies.

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“…Of the remaining dynamic studies, most are not GEM-based, but rather Bayes [47,48], Lotka-Volterra [49] or Machine Learning approaches [50]. Shaw et al [47] models microbiomes experiencing perturbations, but considers only 2 states ('healthy' vs an alternative stable state). This contrasts with the MDPbiomeGEM system which can model >= 2 states undergoing perturbations.…”

Section: Comparison With Peer Approachesmentioning

confidence: 99%

Dynamic simulations of microbial communities under perturbations: opportunities for microbiome engineering

García-Jiménez

Carrasco

Medina

et al. 2020

Preprint

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Background : There are few large longitudinal microbiome studies, and fewer that include controlled, well-annotated perturbations between sampling-points. Thus, there are few opportunities to employ data-driven computational analyses of perturbed microbial communities over time. Results : Our novel computational system simulates the dynamics of microbial communities under perturbations using genome-scale metabolic models (GEMs). Perturbations include modifications to a) the nutrients available in the medium, allowing modelling of prebiotics; and/or b) the microorganisms present in the community to model, for example, probiotics or pathogen infection. These simulations generate the quantity and types of information required by MDPbiome, an AI system which builds predictive models suggesting the perturbation(s) required to engineer microbial communities to a desired state. We call this novel combination of technologies "MDPbiomeGEM"'. We demonstrate, in a Crohn’s disease microbiome, that MDPbiomeGEM correctly models the influence of both prebiotic fiber and a probiotic, resulting in a recommendation to consume inulin to recover from dysbiosis, consistent with prior biomedical knowledge. When used to model the soil microbiome's ability to degrade the herbicide Atrazine, differing recommendations arise depending on the highly variable state of the initial soil microbial composition, highlighting the relevance of both phosphate and microbes (i.e. Halobacillus sp. and H.stevensii ) in a directed microbiome engineering strategy, consistent with previously published observations. Conclusions : MDPbiomeGEM generates large volumes of longitudinal data of complex microbial communities experiencing perturbations. Machine learning on these data reveal patterns consistent with existing biological knowledge, supporting the validity of the approach. MDPbiomeGEM could save research resources by optimizing sample collection in metagenomics studies through identification of "informative" scenarios/time-points, or by predicting optimal in-vitro culture formulations for generating performant synthetic microbial communities. Finally, MDPbiomeGEM outputs include detailed information about the metabolic state of the community, which can be used to further interpret the impact of perturbations, and potentially could be used to predict novel metabolic biomarkers of a microbiome's state.

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Modelling microbiome recovery after antibiotics using a stability landscape framework

Cited by 102 publications

References 51 publications

Strain Tracking to Identify Individualized Patterns of Microbial Strain Stability in the Developing Infant Gut Ecosystem

Strain Tracking to Identify Individualized Patterns of Microbial Strain Stability in the Developing Infant Gut Ecosystem

Drug-induced resistance evolution necessitates less aggressive treatment

Dynamic simulations of microbial communities under perturbations: opportunities for microbiome engineering

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