Date of Award

8-26-2022

Degree Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Biology

Advisor(s)

Roy Welch

Keywords

development, genotype-to-phenotype, Myxococcus xanthus, self-organization

Subject Categories

Biochemistry, Biophysics, and Structural Biology | Genetics | Genetics and Genomics | Life Sciences | Microbiology | Molecular Biology

Abstract

Genotype-to-phenotype mapping can typically involve disrupting the function of a gene and observing the impact of mutation on phenotype. While this can be a powerful tool for uncovering gene function, complicating factors such as influences between genes and the environment, epistatic interactions with other genes, and genetic redundancy could all potentially mask the phenotype of a mutation such that functional inferences cannot be made. On the level of the single gene, this may not be particularly informative, but it is possible that studying phenotype in this way at a genome scale might allow for the observation of patterns between genes and their associated mutant phenotypes that can inform the genotype-to-phenotype space. Phenotypic profiling, involving high-throughput phenotyping across different genetic backgrounds and environments, has the potential to inform our understanding of how different genes interact with the environment and each other.

Myxococcus xanthus is a soil bacterium with a relatively large genome that lives a remarkably multicellular lifestyle for a prokaryote. Under starvation, cells of M. xanthus aggregate into clusters that eventually mature into spore-filled fruiting bodies. This is a complex phenotype for which we can observe and quantify multiple features, providing a landscape of features that can be used to measure the effect of mutation on phenotype. In this thesis, I first explore how high-throughput phenotypic observations reveal a pattern of widespread genetic redundancy by demonstrating that mutant strains within the same gene family are more phenotypically similar to one another than they are to those outside of their gene family. I further show that genotypic similarity and phenotypic similarity do not correlate well on a finer scale, indicating that genotype alone is not a good predictor of genetic redundancy.

Next, I characterize a wave phenomenon that is observed during time-lapse movies of M. xanthus fruiting body formation. The oscillatory behavior seen within the swarm, which we here call pulsing, was striking and was initially thought to be a rare mutant phenotype until it was observed frequently within a database of thousands of time-lapse movies of different mutant strains. It was then quantified through image analysis and found to exist in wild-type strains as well, at the same frequency that we observed pulsing in genetic mutants. We found that pulses are waves that originate at early fruiting bodies and propagate through the starving swarm, causing individual cells to suppress reversals and travel more persistently for a longer duration. This serves as a potential mechanism to aid in the rate of aggregation and represents possible inter-aggregate communication.

Finally, I explore cell behaviors related to the coarsening phase of M. xanthus aggregation wherein larger aggregates remain stable and mature into fruiting bodies and smaller aggregates disperse back into the swarm. I collected cell tracking data that will inform a data-driven model to improve understanding of the individual cell behaviors that lead to small aggregate dispersal during the coarsening phase. These experiments culminated in an approximately 85% loss of fluorescent cells used for tracking that did not occur in samples without fruiting bodies. This cell loss co-occurs with an increase in propidium iodide staining of fruiting bodies, indicating an increase in extracellular DNA that could be associated with cell lysis.

Notably, the phenomena described in all three chapters were observed from the same set of high-throughput phenotype data. Characterizing the phenome by collecting this type of data can improve our understanding of biological patterns.

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Open Access

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