Metabolic Dependencies Drive Species Interactions in the Microbial Communities

Abstract

Microbial interactions are fundamental to ecosystem dynamics, influencing processes such as nutrient cycling, energy flow, and ecosystem stability. This dissertation explores the complexity of microbial communities across diverse contexts, from environmental ecosystems to human health, using advanced multi-omics technologies and computational modeling. By examining both natural and synthetic microbial consortia, the research presented here highlights how metabolic dependencies, such as cross-feeding and syntrophy, shape community structure, stability, and functionality.A key focus of the research is on microbial metabolic dependencies and their role in ecological processes such as nutrient cycling and organic matter decomposition. Recent advances in multi-omics approaches have revealed how these dependencies influence the assembly, stability, and adaptation of microbial communities, with implications for agriculture, environmental management, and biotechnology. Further investigations into synthetic microbial consortia reveal how higher-order interactions lead to emergent properties that are not present in simpler systems. A synthetic quad-culture, composed of microorganisms responsible for cellulose degradation and methane production, exhibited both positive and negative synergies, demonstrating enhanced methane production and reduced cellulose degradation compared to simpler consortia. These findings highlight the potential for engineering microbial communities for industrial applications such as bioenergy production and waste management. The dissertation also examines the effects of environmental stressors, such as oxygen and sulfate exposure, on microbial communities in wetland soils, key players in global carbon cycling. The research reveals that elevated sulfate and oxygen levels suppress methane emissions while increasing carbon dioxide production, providing insights into how microbial communities respond to climate change and influence greenhouse gas dynamics. In the clinical context, this work explores the effects of dietary interventions on the gut microbiomes of women with gestational diabetes mellitus (GDM) and their infants. The results demonstrate that targeted dietary modifications can significantly alter microbial diversity and function, with potential implications for managing metabolic disorders through personalized nutrition and microbiome-based therapies. Overall, this dissertation contributes to a deeper understanding of microbial interactions, emphasizing their role in ecosystem stability, industrial processes, and human health. By leveraging the power of multi-omics technologies and computational models, this work provides a framework for predicting microbial community behavior and designing targeted interventions for both environmental and clinical applications.