Microbial community design: methods, applications, and opportunities

Eng, Alexander; Borenstein, Elhanan

doi:10.1016/j.copbio.2019.03.002

Cited by 74 publications

(62 citation statements)

References 118 publications

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“…Artificial selection of an amylolytic consortium. Designing communities for efficient biodegradation has long been an aspiration of synthetic ecology and community engineering (Gilbert et al 2003;Yoshida et al 2009;Zanaroli et al 2010;Piccardi et al 2019) , and it has also been a problem of interest in artificial community-level selection and microbial bioprospecting (Swenson et al 2000a;Zanaroli et al 2010;Cortes-Tolalpa et al 2016;Eng and Borenstein 2019) .…”

Section: Resultsmentioning

confidence: 99%

“…Manipulating community composition towards desirable collective functions, and away from undesirable ones, has become an important aspiration in microbiome biology and biotechnology (Mueller and Sachs 2015;Eng and Borenstein 2016;Rillig et al 2016;Sheth et al 2016) . One approach has been to engineer communities from the bottom-up, by mixing species with known functionality towards a desired community function (Gilbert et al 2003;Regot et al 2011;Minty et al 2013;Smith et al 2013;Wang et al 2016;Eng and Borenstein 2019) . An important challenge of this approach is the combinatorial explosion in the number of interactions in the community, with high-order interactions posing significant challenges for the predictability of even mid-size consortia (Gould et al 2018;Mickalide and Kuehn 2019;Sanchez-Gorostiaga et al 2019;Senay et al 2019) .…”

Section: Introductionmentioning

confidence: 99%

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Artificially selecting microbial communities using propagule strategies

Chang

Osborne

Bajić

et al. 2020

Preprint

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Artificial selection is a promising approach to manipulate the function of microbial communities. Here, we report the outcome of two artificial selection experiments at the microbial community level. Both experiments used "propagule" strategies, in which a set of the best-performing communities are used as the inocula to form a new generation of communities. In both cases, the selected communities are compared to a control treatment where communities are randomly selected. The first experiment used a defined set of strains as the starting inoculum, and the function under selection was the amylolytic activity of the consortia. The second experiment used a diverse set of natural communities as the inoculum, and the function under selection was the cross-feeding potential of the resulting communities towards a reference bacterial strain. In both experiments, the selected communities reached a higher mean and a higher maximum function than the control. In the first experiment this is caused by a decline in function of the control, rather than an improvement of the selected line. In the second experiment, the strong response of the mean is caused by the large initial variance in function across communities, and is the immediate consequence of the spread of the top-performing community in the starting group, whose function does not increase. Our results are in agreement with basic expectations of artificial selection theory, pointing out some of the limitations of community-level selection experiments which can inform the design of future studies.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Artificially selecting microbial communities using propagule strategies

Chang

Osborne

Bajić

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…There is an increasing interest in combining GEMs to simulate microbial communities [12], with the objectives of understanding community interactions and dynamics in experimentally hard-to-reproduce/manipulate environments (e.g. the innate gut microbiome [71]) and developing comprehensive computational tools to engineer synthetic consortia (e.g.…”

Section: Genome Scale Metabolic Models (Gem)mentioning

confidence: 99%

“…External perturbations could modulate the soil microbiome to allow plants to grow in new conditions, such as those provoked by biotic or abiotic stresses such as climate change [6], salt-stress [7], fungal infection [8], and soil over-exploitation [9]. Industrial applications of microbiome engineering will increase the performance of addedvalue chemicals production, such as food (yogurt, beverages), probiotics, enzymes or biofuels [10,11], by modulation of the strain ratio (community enrichment or reduction) or identification of the optimal medium composition [12].…”

Section: Introductionmentioning

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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“…One could also artificially assemble different combinations of species or genotypes to screen for high community function (e.g. [8,9,10]). However, some species may not be culturable in isolation.…”

Section: Introductionmentioning

confidence: 99%

Simulations reveal challenges to artificial community selection and possible strategies for success

2019

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Multispecies microbial communities often display "community functions" arising from interactions of member species. Interactions are often difficult to decipher, making it challenging to design communities with desired functions. Alternatively, similar to artificial selection for individuals in agriculture and industry, one could repeatedly choose communities with the highest community functions to reproduce by randomly partitioning each into multiple "Newborn" communities for the next cycle. However, previous efforts in selecting complex communities have generated mixed outcomes that are difficult to interpret. To understand how to effectively enact community selection, we simulated community selection to improve a community function that requires 2 species and imposes a fitness cost on one or both species. Our simulations predict that improvement could be easily stalled unless various aspects of selection are carefully considered. These aspects include promoting species coexistence, suppressing noncontributors, choosing additional communities besides the highest functioning ones to reproduce, and reducing stochastic fluctuations in the biomass of each member species in Newborn communities. These considerations can be addressed experimentally. When executed effectively, community selection is predicted to improve costly community function, and may even force species to evolve slow growth to achieve species coexistence. Our conclusions hold under various alternative model assumptions and are therefore applicable to a variety of communities.

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Microbial community design: methods, applications, and opportunities

Cited by 74 publications

References 118 publications

Artificially selecting microbial communities using propagule strategies

Artificially selecting microbial communities using propagule strategies

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

Simulations reveal challenges to artificial community selection and possible strategies for success

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