Modelling microbial communities: Harnessing consortia for biotechnological applications

Ibrahim, Maziya; Raajaraam, Lavanya; Raman, Karthik

doi:10.1016/j.csbj.2021.06.048

Cited by 45 publications

(31 citation statements)

References 173 publications

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“…Modeling and understanding of microbial metabolism can disclose vital knowledge of how microorganisms form communities, interact, and can be employed in biotechnological processes as a result. Several methods for modeling microbial metabolism have been developed in recent decades, and some of them are being extended to represent microbial communities ( Ibrahim et al, 2021 ). While there are alternative approaches to modeling microbial metabolism, the constraint-based modeling paradigm is, by far, the most used.…”

Section: Discussionmentioning

confidence: 99%

A Computational Framework for Studying Gut-Brain Axis in Autism Spectrum Disorder

et al. 2022

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IntroductionThe integrity of the intestinal epithelium is crucial for human health and is harmed in autism spectrum disorder (ASD). An aberrant gut microbial composition resulting in gut-derived metabolic toxins was found to damage the intestinal epithelium, jeopardizing tissue integrity. These toxins further reach the brain via the gut-brain axis, disrupting the normal function of the brain. A mechanistic understanding of metabolic disturbances in the brain and gut is essential to design effective therapeutics and early intervention to block disease progression. Herein, we present a novel computational framework integrating constraint based tissue specific metabolic (CBM) model and whole-body physiological pharmacokinetics (PBPK) modeling for ASD. Furthermore, the role of gut microbiota, diet, and oxidative stress is analyzed in ASD.MethodsA representative gut model capturing host-bacteria and bacteria-bacteria interaction was developed using CBM techniques and patient data. Simultaneously, a PBPK model of toxin metabolism was assembled, incorporating multi-scale metabolic information. Furthermore, dynamic flux balance analysis was performed to integrate CBM and PBPK. The effectiveness of a probiotic and dietary intervention to improve autism symptoms was tested on the integrated model.ResultsThe model accurately highlighted critical metabolic pathways of the gut and brain that are associated with ASD. These include central carbon, nucleotide, and vitamin metabolism in the host gut, and mitochondrial energy and amino acid metabolisms in the brain. The proposed dietary intervention revealed that a high-fiber diet is more effective than a western diet in reducing toxins produced inside the gut. The addition of probiotic bacteria Lactobacillus acidophilus, Bifidobacterium longum longum, Akkermansia muciniphila, and Prevotella ruminicola to the diet restores gut microbiota balance, thereby lowering oxidative stress in the gut and brain.ConclusionThe proposed computational framework is novel in its applicability, as demonstrated by the determination of the whole-body distribution of ROS toxins and metabolic association in ASD. In addition, it emphasized the potential for developing novel therapeutic strategies to alleviate autism symptoms. Notably, the presented integrated model validates the importance of combining PBPK modeling with COBRA -specific tissue details for understanding disease pathogenesis.

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Section: Discussionmentioning

confidence: 99%

A Computational Framework for Studying Gut-Brain Axis in Autism Spectrum Disorder

et al. 2022

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“…They use experimental time series of microbiome compositions. Unfortunately, data on microbial interactions at a large scale is usually scarce, and this information is indispensable for this type of modeling [134] . Other approaches help to understand the composition and functions of different communities but do not simulate their dynamical properties.…”

Section: Other Approaches For Community Modelingmentioning

confidence: 99%

Modeling approaches for probing cross-feeding interactions in the human gut microbiome

Saa

Urrutia

Silva-Andrade

et al. 2022

Computational and Structural Biotechnology Journal

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Microbial communities perform emergent activities that are essentially different from those carried by their individual members. The gut microbiome and its metabolites have a significant impact on the host, contributing to homeostasis or disease. Food molecules shape this community, being fermented through cross-feeding interactions of metabolites such as lactate, acetate, and amino acids, or products derived from macromolecule degradation. Mathematical and experimental approaches have been applied to understand and predict the interactions between microorganisms in complex communities such as the gut microbiota. Rational and mechanistic understanding of microbial interactions is essential to exploit their metabolic activities and identify keystone taxa and metabolites. The latter could be used in turn to modulate or replicate the metabolic behavior of the community in different contexts. This review aims to highlight recent experimental and modeling approaches for studying cross-feeding interactions within the gut microbiome. We focus on short-chain fatty acid production and fiber fermentation, which are fundamental processes in human health and disease. Special attention is paid to modeling approaches, particularly kinetic and genome-scale stoichiometric models of metabolism, to integrate experimental data under different diet and health conditions. Finally, we discuss limitations and challenges for the broad application of these modeling approaches and their experimental verification for improving our understanding of the mechanisms of microbial interactions.

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“…These models capture the metabolic capabilities of an organism. Metabolic models of all known members of a community allow us to study community interactions using various graph-based and constraint-based approaches [17][18][19] .…”

Section: Introductionmentioning

confidence: 99%

Metagenome-based metabolic modelling predicts unique microbial interactions in deep-sea hydrothermal plume microbiomes

Baskaran

Umale

Zhou

et al. 2022

Preprint

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Deep sea hydrothermal vents are abundant on the ocean floor and play important roles in ocean biogeochemistry. In vent ecosystems such as hydrothermal plumes, microorganisms rely on reduced chemicals and gases in hydrothermal fluids to fuel primary production and form diverse and complex microbial communities. However, microbial interactions that drive these complex microbiomes remain poorly understood. Here, we use microbiomes from the Guaymas Basin hydrothermal system in the Pacific Ocean to shed more light on the key species in these communities and their interactions. We built metabolic models from metagenomically assembled genomes (MAGs) and infer possible metabolic exchanges and horizontal gene transfer (HGT) events within the community. We highlight possible archaea-archaea and archaea-bacteria interactions and their contributions to robustness of the community. Cellobiose, D-Mannose 1-phosphate, O2, CO2, and H2S were among the most exchanged metabolites. Ten microbes, including eight bacteria and two archaea, were identified as key contributors. These microorganisms uniquely enhanced the metabolic capabilities of the community by donating metabolites that cannot be produced by any other community member. Archaea from the DPANN group stood out as key microbes, benefiting significantly from accepting metabolites from other members of the microbiome. Amino acids were the key auxotrophy driving metabolic interactions in the community. Finally, over 200 horizontal gene transfer events were predicted in the community, the majority of which were between Gammaproteobacteria and Alphaproteobacteria. Overall, our study provides key insights into the microbial interactions that drive community structure and organisation in complex hydrothermal plumes and deep sea microbiomes.

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Modelling microbial communities: Harnessing consortia for biotechnological applications

Abstract: Graphical abstract

Cited by 45 publications

References 173 publications

A Computational Framework for Studying Gut-Brain Axis in Autism Spectrum Disorder

A Computational Framework for Studying Gut-Brain Axis in Autism Spectrum Disorder

Modeling approaches for probing cross-feeding interactions in the human gut microbiome

Metagenome-based metabolic modelling predicts unique microbial interactions in deep-sea hydrothermal plume microbiomes

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