Multi-dimensional experimental and computational exploration of metabolism pinpoints complex probiotic interactions

Sustainable Food Waste Management in Anaerobic Digesters: Prediction of the Organic Load Impact by Metagenome-Scale Metabolic Modeling

The increasing urbanization has led to rising waste and energy demands, necessitating innovative solutions. A sustainable food waste management approach involves anaerobic codigestion with sewage sludge, enhancing biogas production while managing waste. Although this technology has been successfully tested, the biological mechanisms determining its efficiency are still poorly understood. This study leverages genome-scale metabolic modeling of 138 metagenome-assembled genomes to explore species interactions in lab-scale anaerobic reactors fed with sewage sludge to increasing proportions of food waste. The models showed positive correlations with experimental biogas production (CH4: r = 0.54, CO2: r = 0.66), validating their reliability. The dominant methanogen, Methanothrix sp., adapted its metabolism based on feedstock, affecting methane yields, which ranged from 2.5 to 3 mmol/g of volatile solids·h with sewage sludge to 10-14 mmol/g of VS·h with high food waste. The integration of extracellular enzymes into the models highlighted the role in methane production of pectin degradation, protein hydrolysis, and lipid metabolism, mediated by Proteiniphilum sp., Kiritimatiellae sp., and Olb16 sp. The study identified 475 mutualistic interactions involving amino acid, hydrogen, acetate, and phosphate exchange and 44 competitive interactions in hydrolytic and fermentative processes. These insights can help optimize anaerobic digestion and sustainable waste management in urban settings.

Genome-Scale Community Model-Guided Development of Bacterial Coculture for Lignocellulose Bioconversion

Microbial communities have shown promising potential in degrading complex biopolymers, producing value-added products through collaborative metabolic functionality. Hence, developing synthetic microbial consortia has become a predominant technique for various biotechnological applications. However, diverse microbial entities in a consortium can engage in distinct biochemical interactions that pose challenges in developing mutualistic communities. Therefore, a systems-level understanding of the inter-microbial metabolic interactions, growth compatibility, and metabolic synergisms is essential for developing effective synthetic consortia. This study demonstrated a genome-scale community modeling approach to assess the inter-microbial interaction pattern and screen metabolically compatible bacterial pairs for designing the lignocellulolytic coculture system. Here, we have investigated the pairwise growth and biochemical synergisms among six termite gut bacterial isolates by implementing flux-based parameters, i.e., pairwise growth support index (PGSI) and metabolic assistance (PMA). Assessment of the PGSI and PMA helps screen nine beneficial bacterial pairs that were validated by designing a coculture experiment with lignocellulosic substrates. For the cocultured bacterial pairs, the experimentally measured enzymatic synergisms (DES) showed good coherence with model-derived biochemical compatibility (PMA), which explains the fidelity of the in silico predictions. The highest degree of enzymatic synergisms has been observed in C. denverensis P3 and Brevibacterium sp P5 coculture, where the total cellulase activity has been increased by 53%. Hence, the flux-based assessment of inter-microbial interactions and metabolic compatibility helps select the best bacterial coculture system with enhanced lignocellulolytic functionality. The flux-based parameters (PGSI and PMA) in the proposed community modeling strategy will help optimize the composition of microbial consortia for developing synthetic microcosms for bioremediation, bioengineering, and biomedical applications.

Genome-Scale Metabolic Modeling of Halomonas elongata 153B Explains Polyhydroxyalkanoate and Ectoine Biosynthesis in Hypersaline Environments

Halomonas elongata thrives in hypersaline environments producing polyhydroxyalkanoates (PHAs) and osmoprotectants such as ectoine. Despite its biotechnological importance, several aspects of the dynamics of its metabolism remain elusive. Here, we construct and validate a genome-scale metabolic network model for H. elongata 153B. Then, we investigate the flux distribution dynamics during optimal growth, ectoine, and PHA biosynthesis using statistical methods, and a pipeline based on shadow prices. Lastly, we use optimization algorithms to uncover novel engineering targets to increase PHA production. The resulting model (iEB1239) includes 1534 metabolites, 2314 reactions, and 1239 genes. iEB1239 can reproduce growth on several carbon sources and predict growth on previously unreported ones. It also reproduces biochemical phenotypes related to Oad and Ppc gene functions in ectoine biosynthesis. A flux distribution analysis during optimal ectoine and PHA biosynthesis shows decreased energy production through oxidative phosphorylation. Furthermore, our analysis unveils a diverse spectrum of metabolic alterations that extend beyond mere flux changes to encompass heightened precursor production for ectoine and PHA synthesis. Crucially, these findings capture other metabolic changes linked to adaptation in hypersaline environments. Bottlenecks in the glycolysis and fatty acid metabolism pathways are identified, in addition to PhaC, which has been shown to increase PHA production when overexpressed. Overall, our pipeline demonstrates the potential of genome-scale metabolic models in combination with statistical approaches to obtain insights into the metabolism of H. elongata. Our platform can be exploited for researching environmental adaptation, and for designing and optimizing metabolic engineering strategies for bioproduct synthesis.

Emerging methods for genome-scale metabolic modeling of microbial communities

Genome-scale metabolic models (GEMs) are consolidating as platforms for studying mixed microbial populations, by combining biological data and knowledge with mathematical rigor. However, deploying these models to answer research questions can be challenging due to the increasing number of available computational tools, the lack of universal standards, and their inherent limitations. Here, we present a comprehensive overview of foundational concepts for building and evaluating genome-scale models of microbial communities. We then compare tools in terms of requirements, capabilities, and applications. Next, we highlight the current pitfalls and open challenges to consider when adopting existing tools and developing new ones. Our compendium can be relevant for the expanding community of modelers, both at the entry and experienced levels.

The Potential of Meta-Proteomics and Artificial Intelligence to Establish the Next Generation of Probiotics for Personalized Healthcare

The symbiosis of probiotic bacteria with humans has rendered various health benefits while providing nutrition and a suitable environment for their survival. However, the probiotics must survive unfavorable gut conditions to exert beneficial effects. The intrinsic resistance of probiotics to survive harsh conditions results from a myriad of proteins. Interaction of microbial proteins with the host is indispensable for modulating the gut microbiome, such as interaction with cell receptors and protective action against pathogens. The complex interplay of proteins should be unraveled by utilizing metaproteomic strategies. The contribution of probiotics to health is now widely accepted. However, due to the inconsistency of generalized probiotics, contemporary research toward precision probiotics has gained momentum for customized treatment. This review explores the application of metaproteomics and AI/ML algorithms in resolving multiomics data analysis and in silico prediction of microbial features for screening specific beneficial probiotic organisms. Implementing these integrative strategies could augment the potential of precision probiotics for personalized healthcare.