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There is now a CONTENT FREEZE for Mercury while we switch to a new platform. It began on Friday, March 10 at 6pm and will end on Wednesday, March 15 at noon. No new content can be created during this time, but all material in the system as of the beginning of the freeze will be migrated to the new platform, including users and groups. Functionally the new site is identical to the old one. webteam@gatech.edu
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In partial fulfillment of the requirements for the degree of
Doctor of Philosophy in Quantitative Biosciences
in the School of Physics
Conan Zhao
Defends his thesis:
Identifying Microbial Biomarkers of Cystic Fibrosis Health and Disease
Tuesday, June 21, 2022
10am Eastern Time
In-person: Marcus Nanotechnology Building, Room #1117-1118
Virtual: https://gatech.zoom.us/j/2903027082
Open to the Community
Dr. Sam Brown
School of Biological Sciences
Georgia Institute of Technology
Committee Members:
Dr. Joshua Weitz
School of Biological Sciences, School of Physics
Georgia Institute of Technology
Dr. Arlene Stecenko
Department of Pediatrics
Emory University School of Medicine
Dr. Peng Qiu
School of Biomedical Engineering
Georgia Institute of Technology / Emory University
Dr. Rishi Kamaleswaran
Department of Biomedical Informatics
Emory University
Summary:
Chronic, polymicrobial respiratory infections remain the primary driver of morbidity and mortality in cystic fibrosis (CF). This thesis leverages experimental data and large-scale public datasets to investigate the relationships between microbiome structure, pathogen abundance and host health.
First, using a machine learning framework, we show that off-the-shelf machine learning methods can recover known clinical and microbial predictors of lung function from a set of 77 sputum composition profiles. These methods recover known demographic predictors of lung function and further identify novel taxonomic predictors, highlighting the utility of simple machine learning methods for microbial biomarker discovery.
Second, we develop a synthetic infection microbiome model representing CF metacommunity diversity, and benchmark on clinical data. Using this synthetic microbiome system, we provide evidence that commonly used CF antibiotics can drive the expansion (via competitive release) of previously rare opportunistic pathogens and offer a path towards microbiome-informed treatment strategies.
Last, we manually curated a microbiome dataset of over 4000 sputum samples representing more than 1000 people with CF (pwCF), matching samples with corresponding metadata from 36 publications and standardizing bioinformatic analyses with a single common pipeline. We fit Sloan Neutral Community Models to each study and find a consistent set of neutral and non-neutral taxa. Using Dirichlet Multinomial Mixture modeling, we partition non-neutral CF lung microbiomes into 14 distinct pulmotypes. Integrating longitudinal data, we find that not all Pseudomonas-dominated pulmotypes are dynamically equivalent, which carries important implications for infection management in cystic fibrosis.
