Title:
IDENTIFYING MICROBIAL BIOMARKERS OF CYSTIC FIBROSIS HEALTH AND DISEASE

dc.contributor.author Zhao, Conan Ying-Yi
dc.contributor.department Biology
dc.date.accessioned 2022-08-25T13:34:25Z
dc.date.available 2022-08-25T13:34:25Z
dc.date.created 2022-08
dc.date.issued 2022-07-06
dc.date.submitted August 2022
dc.date.updated 2022-08-25T13:34:25Z
dc.description.abstract 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
dc.description.degree Ph.D.
dc.format.mimetype application/pdf
dc.identifier.uri http://hdl.handle.net/1853/67232
dc.language.iso en_US
dc.publisher Georgia Institute of Technology
dc.subject Cystic fibrosis
dc.subject Microbiome
dc.title IDENTIFYING MICROBIAL BIOMARKERS OF CYSTIC FIBROSIS HEALTH AND DISEASE
dc.type Text
dc.type.genre Dissertation
dspace.entity.type Publication
local.contributor.corporatename College of Sciences
local.contributor.corporatename School of Biological Sciences
relation.isOrgUnitOfPublication 85042be6-2d68-4e07-b384-e1f908fae48a
relation.isOrgUnitOfPublication c8b3bd08-9989-40d3-afe3-e0ad8d5c72b5
thesis.degree.level Doctoral
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