Date of Award
Doctor of Philosophy (PhD)
bacteria, biofilm, genotype-to-phenotype, mechanosensing
Mechanobiology is an emerging field investigating mechanical signals as a necessary component of cellular and developmental regulation. These mechanical signals play a well-established role in the differentiation of animal cells, whereby cells with identical genes specialize their function and create distinct tissues depending on the physical properties of their environment, such as shear stiffness. These differences arise from the cell’s ability to use those incoming signals to inform which genes it expresses and what molecular machinery it builds and activates. Understanding the various missing factors that cause cells with specific genes to express an emergent phenotype is termed the genotype-to-phenotype problem, and mechanical signaling pathways present themselves as a significant piece of this puzzle. Despite the strong evidence for mechanosensing in eukaryotes, the pathways by which prokaryotes respond to mechanical stimuli are still largely unknown. Bacteria are among the simplest and yet most abundant forms of life. Many of their survival strategies depend on multicellular development and the coordinated formation of a colony into functional structures that may also feature cellular differentiation. This dissertation employs bacteria as a model system to investigate multiple biophysical questions of collective motion through novel experimental and analytical techniques. This work addresses the understudied mechanical relationship between a bacterial colony and the substrate it colonizes by asking “what is the effect of substrate stiffness on colony growth?” This is done by measuring bacterial growth on hydrogel substrates that decouple the effects of substrate stiffness from other material properties of the substrate that vary with stiffness. We report a previously unobserved effect in which bacteria colonize stiffer substrates faster than softer substrates, in opposition to previous studies done on agar, where permeability, viscoelasticity, and other material properties vary with stiffness.A second theme of this work probes the genetic inputs to the genotype-to-phenotype problem in multicellular development. The bacterial species Myxococcus xanthus producing macroscopic aggregates called fruiting bodies is used as a model organism for these studies. It has long been conjectured that genes may stand in for each other functionally, allowing for development to be more consistent and stable, but the extent of this redundancy has resisted measurement. We approach the question “how does redundancy among related genes lead to robust collective behavior?” by quantifying developmental phenotype in a large dataset of time lapse microscopy videos that show development in many mutant strains. We observe that when knocking out multiple genes that have a common origin (i.e. homologous genes), the resulting phenotypes differ from wild-type in a similar way. These phenotype clusters also differ from knockouts from other homologous gene families. These distinct phenotypic clusters provide evidence for the existence of networks of redundant genes that are larger than could previously be tested directly. Because of this robustness, the effects of individual gene mutations can be hidden or damped. We thus develop our analytical techniques further to address the question “how can subtle changes in phenotype be measured?” This involves quantifying the breadth of variation observed in wild-type development and creating a statistical technique to distinguish probabilistic distributions of phenotypic outcomes. We present a coherent method of visualizing large phenotypic datasets that include multiple metrics that we use to distinguish small developmental differences from wild-type, giving each mutant strain a phenotypic fingerprint that can be used in future studies on gene annotation and environmental impacts on phenotype.
Asp, Merrill Einar, "The biophysics of bacterial collective motion: Measuring responses to mechanical and genetic cues" (2023). Dissertations - ALL. 1681.