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High-order interactions distort the functional landscape of microbial consortia.

ABSTRACT: Understanding the link between community composition and function is a major challenge in microbial population biology, with implications for the management of natural microbiomes and the design of synthetic consortia. Specifically, it is poorly understood whether community functions can be quantitatively predicted from traits of species in monoculture. Inspired by the study of complex genetic interactions, we have examined how the amylolytic rate of combinatorial assemblages of six starch-degrading soil bacteria depend on the separate functional contributions from each species and their interactions. Filtering our results through the theory of biochemical kinetics, we show that this simple function is additive in the absence of interactions among community members. For about half of the combinatorially assembled consortia, the amylolytic function is dominated by pairwise and higher-order interactions. For the other half, the function is additive despite the presence of strong competitive interactions. We explain the mechanistic basis of these findings and propose a quantitative framework that allows us to separate the effect of behavioral and population dynamics interactions. Our results suggest that the functional robustness of a consortium to pairwise and higher-order interactions critically affects our ability to predict and bottom-up engineer ecosystem function in complex communities.

SUBMITTER: Sanchez-Gorostiaga A

PROVIDER: S-EPMC6932822 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature

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High-order interactions distort the functional landscape of microbial consortia.

Sanchez-Gorostiaga Alicia A Bajić Djordje D Osborne Melisa L ML Poyatos Juan F JF Sanchez Alvaro A

PLoS biology 20191212 12

Understanding the link between community composition and function is a major challenge in microbial population biology, with implications for the management of natural microbiomes and the design of synthetic consortia. Specifically, it is poorly understood whether community functions can be quantitatively predicted from traits of species in monoculture. Inspired by the study of complex genetic interactions, we have examined how the amylolytic rate of combinatorial assemblages of six starch-degra ...[more]

PMID: 31830028

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Project description:BackgroundMixed microbial cultures, in which bacteria and fungi interact, have been proposed as an efficient way to deconstruct plant waste. The characterization of specific microbial consortia could be the starting point for novel biotechnological applications related to the efficient conversion of lignocellulose to cello-oligosaccharides, plastics and/or biofuels. Here, the diversity, composition and predicted functional profiles of novel bacterial-fungal consortia are reported, on the basis of replicated aerobic wheat straw enrichment cultures.ResultsIn order to set up biodegradative microcosms, microbial communities were retrieved from a forest soil and introduced into a mineral salt medium containing 1% of (un)treated wheat straw. Following each incubation step, sequential transfers were carried out using 1 to 1,000 dilutions. The microbial source next to three sequential batch cultures (transfers 1, 3 and 10) were analyzed by bacterial 16S rRNA gene and fungal ITS1 pyrosequencing. Faith's phylogenetic diversity values became progressively smaller from the inoculum to the sequential batch cultures. Moreover, increases in the relative abundances of Enterobacteriales, Pseudomonadales, Flavobacteriales and Sphingobacteriales were noted along the enrichment process. Operational taxonomic units affiliated with Acinetobacter johnsonii, Pseudomonas putida and Sphingobacterium faecium were abundant and the underlying strains were successfully isolated. Interestingly, Klebsiella variicola (OTU1062) was found to dominate in both consortia, whereas K. variicola-affiliated strains retrieved from untreated wheat straw consortia showed endoglucanase/xylanase activities. Among the fungal players with high biotechnological relevance, we recovered members of the genera Penicillium, Acremonium, Coniochaeta and Trichosporon. Remarkably, the presence of peroxidases, alpha-L-fucosidases, beta-xylosidases, beta-mannases and beta-glucosidases, involved in lignocellulose degradation, was indicated by predictive bacterial metagenome reconstruction. Reassuringly, tests for specific (hemi)cellulolytic enzymatic activities, performed on the consortial secretomes, confirmed the presence of such gene functions.ConclusionIn an in-depth characterization of two wheat straw degrading microbial consortia, we revealed the enrichment and selection of specific bacterial and fungal taxa that were presumably involved in (hemi) cellulose degradation. Interestingly, the microbial community composition was strongly influenced by the wheat straw pretreatment. Finally, the functional bacterial-metagenome prediction and the evaluation of enzymatic activities (at the consortial secretomes) revealed the presence and enrichment of proteins involved in the deconstruction of plant biomass.

Project description:There has been increased interest in discovering combinations of single-nucleotide polymorphisms (SNPs) that are strongly associated with a phenotype even if each SNP has little individual effect. Efficient approaches have been proposed for searching two-locus combinations from genome-wide datasets. However, for high-order combinations, existing methods either adopt a brute-force search which only handles a small number of SNPs (up to few hundreds), or use heuristic search that may miss informative combinations. In addition, existing approaches lack statistical power because of the use of statistics with high degrees-of-freedom and the huge number of hypotheses tested during combinatorial search. Due to these challenges, functional interactions in high-order combinations have not been systematically explored. We leverage discriminative-pattern-mining algorithms from the data-mining community to search for high-order combinations in case-control datasets. The substantially improved efficiency and scalability demonstrated on synthetic and real datasets with several thousands of SNPs allows the study of several important mathematical and statistical properties of SNP combinations with order as high as eleven. We further explore functional interactions in high-order combinations and reveal a general connection between the increase in discriminative power of a combination over its subsets and the functional coherence among the genes comprising the combination, supported by multiple datasets. Finally, we study several significant high-order combinations discovered from a lung-cancer dataset and a kidney-transplant-rejection dataset in detail to provide novel insights on the complex diseases. Interestingly, many of these associations involve combinations of common variations that occur in small fractions of population. Thus, our approach is an alternative methodology for exploring the genetics of rare diseases for which the current focus is on individually rare variations.

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High-order interactions distort the functional landscape of microbial consortia.

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High-order interactions distort the functional landscape of microbial consortia.

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