Microbial communities play important roles in human health and disease, are essential components of terrestrial and marine ecosystems, and are crucial for producing commercially valuable molecules in industrial processes. These communities consist of hundreds of species involved in complex interactions. Mapping the interrelationships between different species in a microbial community is vital for understanding and controlling ecosystem structure and function. Advances in sequencing and other omics technologies have led to thousands of datasets containing information about microbial composition, gene expression, and metabolism in microbial communities associated with human hosts and other environments. These provide valuable information in understanding how microbes interact with each other and how their interactions affect the health of their host (e.g., human or plant). Furthermore, understanding these interactions paves the way for the rational design and modulation of synthetic communities for producing antibiotics, biofuels, and pharmaceutical products.
The first part of my thesis is focused on improving the workflow for the inference of microbial co-occurrence relationships from abundance data. Toward this goal, we developed Microbial Co-occurrence Network Explorer (MiCoNE), a pipeline that infers microbial co-occurrences from 16S ribosomal RNA (16S rRNA) amplicon data. This pipeline involves numerous complex steps that require specific tools and parameter choices, posing open questions about the robustness and uniqueness of the inferred networks. Through MiCoNE, we systematically analyzed how these choices of tools affect the final network and proposed guidelines on appropriate tool selection for a particular dataset. We envisage that this pipeline could be used to integrate multiple datasets and generate comparative analyses and consensus networks that can guide our understanding of microbial community assembly in different biomes.
The second part of my thesis focuses on microbe-host interactions rather than microbe-microbe associations. In particular, we sought to predict the effects of microbial metabolites on human receptors and their associated regulatory pathways. We specifically focus on the Aryl hydrocarbon receptor (AHR), a ligand-mediated transcription factor involved in tumorigenesis. In this project, we aimed to systematically predict the binding of diverse microbial metabolites secreted from microorganisms found in the human oral microbiome to the AHR to identify links between the microbiome and cancer initiation. We further build a mathematical model of the AHR regulatory pathway and model the effects of ligand binding on downstream molecules. We envision that these methods could be used to predict the impact of microbial dysbioses on human regulatory pathways.
In the final part of my thesis, we turn to the question of whether computational algorithms can help control microbial community growth to achieve specific objectives. In particular, we describe the development of a reinforcement learning algorithm to learn optimal environmental control strategies to steer a microbial community towards a certain goal, such as reaching a specific taxonomic distribution or producing desired metabolites. We train the reinforcement learning framework through community-level simulations of genome-scale metabolic models (GEMs) for different microbial species in bioreactor systems. In this project, we simulate a simple case study with two auxotrophic mutants to verify the algorithm's validity. Ultimately we aim to simulate the implementation of the algorithm in experimental bioreactor systems.
Overall, the work presented in this thesis demonstrates how microbe-microbe and microbe-environment (including microbe-host) interactions represent plastic system-level properties whose understanding can help unravel the role of microbial communities in specific diseases. Correspondingly, manipulating these interactions, e.g., by appropriately modifying environmental conditions, can serve as a promising strategy for steering communities towards desired states, including producing valuable molecular products. / 2024-11-06T00:00:00Z
| Identifer | oai:union.ndltd.org:bu.edu/oai:open.bu.edu:2144/47472 |
| Date | 07 November 2023 |
| Creators | Kishore, Dileep |
| Contributors | Segrè, Daniel |
| Source Sets | Boston University |
| Language | en_US |
| Detected Language | English |
| Type | Thesis/Dissertation |
| Rights | Attribution 4.0 International, http://creativecommons.org/licenses/by/4.0/ |
Page generated in 0.0025 seconds