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PhD Defense by Conan Zhao

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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

 

Advisor:

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.

 

Status

  • Workflow Status:Published
  • Created By:Tatianna Richardson
  • Created:06/13/2022
  • Modified By:Tatianna Richardson
  • Modified:06/13/2022

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