Human gastrointestinal tract is complex and highly dynamic environment, harboring trillions of microbes that collectively play a pivotal role in host metabolism, immune function, and the maintenance of general health. However, studying the gut microbes and their interplay with host requires measurements at multiple dimensions is challenging since we currently lack the tools to directly measure these microorganisms at many different levels, which hinders our ability to unravel the ecology of gut microbes and their mechanistic underpinnings in human health and disease.
Here, I present a set of novel techniques to study gut microbe and host-microbe interactions at unprecedented resolution, providing tremendous innovative insights to comprehensively understand the gut microbes and their environment. First, I leverage a high-throughput automation system to build a machine learning guided culturomics platform, enabling rapid isolation and culturing of personalized gut strains.
Second, to better characterize the functions of these non-model microorganisms, I describe a ribonuclease-based ribosomal RNA depletion approach for microbe, paving the way for high-throughput bacterial transcriptome profiling. Next, shifting from gut microbes to the interaction with the host, I develop a technique of fecal exfoliome sequencing to robustly profile host gastrointestinal transcriptome in a non-invasive way, providing a powerful approach to study the temporal behaviors of gut cells in health and disease.
Finally, to study massive genetic variants in microbiome arena, I describe a template-mediated synthesis approach for rapid and efficient generation of genetic variants sequences and demonstrate its utility in a few cases. Taken together, these techniques provide in-depth novel measurements into the ecology and physiology of human gut microbes, collectively making up an exciting set of tools for future studies.
| Identifer | oai:union.ndltd.org:columbia.edu/oai:academiccommons.columbia.edu:10.7916/q6vv-9p65 |
| Date | January 2022 |
| Creators | Huang, Yiming |
| Source Sets | Columbia University |
| Language | English |
| Detected Language | English |
| Type | Theses |
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