MEMP PhD Thesis Defense (1:30pm): Jennifer Dawkins

Computational prediction of health status from the human gut microbiome and metabolome

A healthy gut microbiome is crucial to overall human well-being. Gut microbiome dysfunction, or dysbiosis, has been implicated in a broad range of diseases, including inflammatory bowel diseases (IBDs), cardiovascular diseases, kidney diseases, metabolic diseases, and gastrointestinal infections like Clostridioides difficile infection (CDI). Often, microbiome-linked illnesses arise after the microbiome is disrupted, such as by antibiotic treatment. However, because the microbiome is so diverse and individual-specific, very little is known about the specific microbial changes that may lead to it human disease. Thus, it is extremely difficult to predict whether a given disruption to the microbiome will result in disease. 

Of the diseases linked to gut microbial disfunction, dysbiosis is perhaps most prominently linked to CDI. As the most common health-care associate infection, CDI is thought to occur when an individual has had both exposure to the C. difficile pathogen and gut dysbiosis caused by a past perturbation, such as antibiotic treatment. Infection recurrence, with an estimated rate of 15.5%, is a particularly insidious problem, and there is currently no reliable method to predict which individuals will recur. There is a need for early prediction of CDI after a perturbation, as this can allow physicians to start or restart more effective treatments immediately and prevent further sickness and risk of death.

Current research into the microbiome and microbiome dysbiosis, including CDI, focuses heavily on identifying the microbial taxonomic composition using next generation sequencing. However, there is growing evidence that the gut metabolome may provide crucial information that cannot be gained from microbial composition alone, as metabolites provide the means by which host cells and microbe cells communicate with each-other. Predictive analysis is especially useful for uncovering links between metabolic or microbial composition features and host disease state as it models all input covariates simultaneously. However, current predictive methods often fall short when applied to the microbiome, as simpler methods lack the capabilities to model this complex system, whereas highly non-linear “black box” methods lack interpretability. When predicting from biological or medical data with the goals of clinical utility and advancement of scientific knowledge, a model that can explain its decisions is crucial for increasing physician trust and uncovering avenues for future investigation. There is a need for interpretable computational models that can learn non-linear relationships between host outcome and paired microbial composition and metabolomic profiles.

This thesis addresses these two challenges. First, we present the analysis of a novel longitudinal study of CDI recurrence in patients, including predictive analyses, which demonstrate that a small set of metabolites can accurately predict future recurrence. Our findings have clinical utility in the development of diagnostic tests and treatments that could ultimately short-circuit the cycle of CDI recurrence. Secondly, we present a novel predictive model developed specifically for making interpretable predictions on paired microbial composition and untargeted metabolic profiles. We demonstrate our model’s ability to predict a variety of host disease states accurately while providing clear and biologically compelling explanations of its decisions, thereby demonstrating high clinical and scientific utility.

Thesis Supervisor:
Georg K. Gerber, MD, PhD
Associate Professor of Pathology, HMS; Member of the Faculty, Harvard-MIT Program in Health Sciences and Technology

Thesis Committee Chair:
Emery Brown, MD, PhD
Warren M. Zapol Professor of Anesthesia, HMS, MGH; Edward Hood Taplin Professor of Medical Engineering and of Computational Neuroscience, MIT 

Thesis Readers:
Eric Alm, PhD
Professor of Biological Engineering, MIT

Emily Balskus, PhD
Thomas Dudley Cabot Professor of Chemistry and Chemical Biology, HU; Howard Hughes Medical Institute Investigator
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Zoom invitation – 
Jennifer Dawkins is inviting you to a scheduled Zoom meeting.

Topic: Jennifer Dawkins MEMP PhD Thesis Defense
Time: Tuesday, April 30, 2024, 1:30 PM Eastern Time (US and Canada)

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