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Access Type

Open Access Thesis

Document Type


Degree Program

Chemical Engineering

Degree Type

Master of Science in Chemical Engineering (M.S.Ch.E.)

Year Degree Awarded


Month Degree Awarded



Cystic Fibrosis (CF) is a genetic disorder, found with higher prevalence in the Caucasian population, affecting > 30,000 individuals in the United States and > 70,000 worldwide. Due to the astoundingly high rate of mortality among CF patients being attributed to respiratory failure brought on by chronic bacterial infections and subsequent airway inflammation, there has been a lot of focus on systematically analyzing CF lung airway communities. While it is observed traditionally that Pseudomonas aeruginosa is the most threatening and persistent CF colonizer due to high antibiotic resistance, recent studies have elicited the roles of other pathogens and it has been widely accepted the CF lung airway consists of a complex codependent community of bacteria, viruses, and fungi.

To elucidate the interplay among the members of this community, within the constraint of lung uptake regime, I developed a community metabolic network model comprising of >380 metabolites obtained after modeling 39 most abundant bacterial genera across 279 sputum specimens collected from 79 individuals over 10 years from a study by LiPuma et. al. by 16S rRNA gene sequencing, accounting for >89% of reads across samples. The community metabolic model was contrasted with the 16S relative abundance data through standard data mining techniques employed for the analysis of multidimensional data. I further attempted to quantitatively analyze and elucidate the correlations among patient lung function, disease progression, community diversity, microbial compositions, and metabolic capabilities by standard classical hypothesis testing methods.

Comparison through linear dimensionality reduction (PCA) of the 16S data and the model data revealed slightly higher variance explained by the model, indicating presence of relatively smaller number of metabolite-based than the 16S-based polymicrobial communities. A deeper analysis elucidated both the phenomena, consolidation of compositionally different communities due to metabolic closeness, as well as splitting of other communities into metabolically distinct clusters due to minor changes in composition and increase in diversity. Clustering of 16S-based relative abundance data and the model data revealed that the rare Burkholderia infections are metabolically distinct from other CF communities, and are heavily dominated by this genus. It was also reiterated that Achromobacter infections are highly resilient to treatment. Linear regression analysis between lung function and microbiota diversity revealed no strong correlation across the population, however, diversity was found to first increase and then subsequently decrease drastically with disease severity.


First Advisor

Michael A. Henson

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.