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Boolean analysis reveals systematic interactions among low-abundance species in the human gut microbiome.

ABSTRACT: The analysis of microbiome compositions in the human gut has gained increasing interest due to the broader availability of data and functional databases and substantial progress in data analysis methods, but also due to the high relevance of the microbiome in human health and disease. While most analyses infer interactions among highly abundant species, the large number of low-abundance species has received less attention. Here we present a novel analysis method based on Boolean operations applied to microbial co-occurrence patterns. We calibrate our approach with simulated data based on a dynamical Boolean network model from which we interpret the statistics of attractor states as a theoretical proxy for microbiome composition. We show that for given fractions of synergistic and competitive interactions in the model our Boolean abundance analysis can reliably detect these interactions. Analyzing a novel data set of 822 microbiome compositions of the human gut, we find a large number of highly significant synergistic interactions among these low-abundance species, forming a connected network, and a few isolated competitive interactions.

SUBMITTER: Claussen JC

PROVIDER: S-EPMC5480827 | biostudies-literature | 2017 Jun

REPOSITORIES: biostudies-literature

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Boolean analysis reveals systematic interactions among low-abundance species in the human gut microbiome.

Claussen Jens Christian JC Skiecevičienė Jurgita J Wang Jun J Rausch Philipp P Karlsen Tom H TH Lieb Wolfgang W Baines John F JF Franke Andre A Hütt Marc-Thorsten MT

PLoS computational biology 20170622 6

The analysis of microbiome compositions in the human gut has gained increasing interest due to the broader availability of data and functional databases and substantial progress in data analysis methods, but also due to the high relevance of the microbiome in human health and disease. While most analyses infer interactions among highly abundant species, the large number of low-abundance species has received less attention. Here we present a novel analysis method based on Boolean operations appli ...[more]

PMID: 28640804

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Project description:The gut microbiome is a complex microbial community that plays a key role in human health. Diet is an important factor dictating gut microbiome composition. This is mediated by multiple microbe-microbe interactions that result in the fermentation of nondigestible carbohydrates and the production of short-chain fatty acids. Certain species play key metabolic roles in the microbiome, and their disappearance could result in dysbiosis. In this work, a synthetic consortium of 14 gut microbes was studied during the utilization of prebiotic inulin in batch bioreactors. Fermentations were repeated leaving one species out every time, in order to evaluate the impact of their elimination on the system. Substrate consumption, microbial composition, and metabolite production were determined. Single deletions never resulted in a complete loss of bacterial growth or inulin consumption, suggesting functional redundancy. Deletions of Bacteroides dorei and Lachnoclostridium clostridioforme resulted in lower biomass and higher residual inulin. The absence of B. dorei impacted the abundance of the other 10 species negatively. Lachnoclostridium symbiosum, a butyrate producer, appeared to be the most sensitive species to deletions, being stimulated by the presence of Escherichia coli, Bifidobacterium adolescentis, B. dorei, and Lactobacillus plantarum Conversely, bioreactors without these species did not show butyrate production. L. clostridioforme was observed to be essential for propionate production, and B. dorei for lactate production. Our analysis identified specific members that were essential for the function of the consortium. In conclusion, species deletions from microbial consortia could be a useful approach to identify relevant interactions between microorganisms and defining metabolic roles in the gut microbiome.IMPORTANCE Gut microbes associate, compete for, and specialize in specific metabolic tasks. These interactions are dictated by the cross-feeding of degradation or fermentation products. However, the individual contribution of microbes to the function of the gut microbiome is difficult to evaluate. It is essential to understand the complexity of microbial interactions and how the presence or absence of specific microorganisms affects the stability and functioning of the gut microbiome. The experimental approach of this study could be used for identifying keystone species, in addition to redundant functions and conditions that contribute to community stability. Redundancy is an important feature of the microbiome, and its reduction could be useful for the design of microbial consortia with desired metabolic properties enhancing the tasks of the keystone species.

Project description:BackgroundIntestinal microbiota plays an important role in the human health. It is involved in the digestion and protects the host against external pathogens. Examination of the intestinal microbiome interactions is required for understanding of the community influence on host health. Studies of the microbiome can provide insight on methods of improving health, including specific clinical procedures for individual microbial community composition modification and microbiota correction by colonizing with new bacterial species or dietary changes.Methodology/principal findingsIn this work we report an agent-based model of interactions between two bacterial species and between species and the gut. The model is based on reactions describing bacterial fermentation of polysaccharides to acetate and propionate and fermentation of acetate to butyrate. Antibiotic treatment was chosen as disturbance factor and used to investigate stability of the system. System recovery after antibiotic treatment was analyzed as dependence on quantity of feedback interactions inside the community, therapy duration and amount of antibiotics. Bacterial species are known to mutate and acquire resistance to the antibiotics. The ability to mutate was considered to be a stochastic process, under this suggestion ratio of sensitive to resistant bacteria was calculated during antibiotic therapy and recovery.Conclusion/significanceThe model confirms a hypothesis of feedbacks mechanisms necessity for providing functionality and stability of the system after disturbance. High fraction of bacterial community was shown to mutate during antibiotic treatment, though sensitive strains could become dominating after recovery. The recovery of sensitive strains is explained by fitness cost of the resistance. The model demonstrates not only quantitative dynamics of bacterial species, but also gives an ability to observe the emergent spatial structure and its alteration, depending on various feedback mechanisms. Visual version of the model shows that spatial structure is a key factor, which helps bacteria to survive and to adapt to changed environmental conditions.

Dataset Information

Boolean analysis reveals systematic interactions among low-abundance species in the human gut microbiome.

Publications

Boolean analysis reveals systematic interactions among low-abundance species in the human gut microbiome.

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