Files in this item



application/pdfWANG-DISSERTATION-2020.pdf (4MB)Restricted Access
(no description provided)PDF


Title:Regulation and switching in bacterial gene expression networks in response to nutrients
Author(s):Wang, Xiaoyi
Director of Research:Rao, Christopher V
Doctoral Committee Chair(s):Rao, Christopher V
Doctoral Committee Member(s):Shukla, Diwakar; Kraft, Mary L; Jin, Yong-Su
Department / Program:Chemical & Biomolecular Engr
Discipline:Chemical Engineering
Degree Granting Institution:University of Illinois at Urbana-Champaign
E. coli
sugar metabolism
S. enterica
flagellar assembly
Abstract:Bacteria must respond to various types of fluctuations in surrounding environment, such as frequent alterations in temperature, salinity, osmolarity, pH, and nutrient availability. What strategies do these tiny microorganisms employ to optimize their chances of growth and survival in an ever-changing environment? It is of general interest to understand cell decision making in response to environmental cues. One well-known strategy is phenotypical heterogeneity where different individuals inside a population utilize different niches and resources within the same environment. Such strategy has the potential to increase the overall fitness of the species. The underlying genetic network plays a key role in the mechanisms that govern phenotypical variation. Many notable examples have been identified and principles and molecular mechanisms governing cell decision making have been extensively investigated. In this work, we investigated regulation and switching of two widely studied bacterial gene expression networks: sugar utilization networks in Escherichia coli and flagellar assembly networks in Salmonella enterica serovar Typhimurium in response to nutrients. Firstly, chapters 3-5 focus on sugar utilization pathways in E. coli. Bacteria rely on utilizing various carbon sources for growth and survival. When multiple carbon sources are available, bacteria make decision on which carbon source to utilize. Usually, bacteria preferentially utilize the carbon source that ensures faster growth and easier accessibility. Catabolite repression refers to the process where the metabolism of one carbon source represses the genes involved in metabolizing another carbon source. The most classic example is glucose repression, which is investigated by many studies, but much less is known about non-glucose repression. Many other sugars are also known to cause catabolite repression, but less is known about the mechanism for catabolite repression by these non-glucose sugars. In Chapter 3, we investigate the mechanism of catabolite repression in the bacterium E. coli during growth on lactose, L-arabinose, and D-xylose. The metabolism of these sugars is regulated in a hierarchical manner, where lactose is the preferred sugar, followed by arabinose, and then xylose. Previously, the preferential utilization of arabinose over xylose was found to result from transcriptional crosstalk. However, others have proposed that cAMP also plays a role in the hierarchical regulation of other non-glucose sugars. In this work, we investigate whether lactose-induced repression of arabinose and xylose gene expression is due to transcriptional crosstalk or cAMP. Our results demonstrate that reciprocal regulation by lactose is due to cAMP and not transcriptional crosstalk using fluorescent reporters to quantify the sugar metabolic gene expression. The previous chapter (Chapter 3) only focuses on the bulk expression in E.coli, in Chapter 4 and 5, we investigate the mechanism of single-cell response of several sugar utilization pathways, since single-cell response provides insights and more clues on the principles and mechanisms governed by regulatory networks in terms of expression and regulation patterns. Understanding repression microscopically helps us to understand repression macroscopically. Chapter 4 focuses on regulation and bistability for single sugar utilization and the effects of positive and negative feedback due to transporters and catabolic enzymes. To explain experimental observed single-cell response data, we have developed a Markov model that describes the intracellular sugar concentration that can be used to explain switching between graded and bistable responses and the role played by transporters and catabolic enzymes. This mathematical framework also provides insights on how the sugar utilization pathway is induced over time and how hysteresis occurs. In Chapter 5, we extend the study to single-cell response when grown on mixture of multiple sugars, in which reciprocal repression may occur. We showed how the global regulator cAMP contributes to reciprocal repression at single-cell resolution for various substrates. Intracellular cAMP concentration tunes the fraction of induced cells by changing the external sugar concentration needed to induce its utilization pathways. We also showed how the global regulation from cAMP-CRP also favors cell decision making to maximize the overall growth rate by allocating subpopulations to utilize none, one or two sugars. Collectively, the results further our understanding of metabolism, regulation and cell decision making during growth on both single sugar and multiple sugars. Chapter 6 focus on bimodality of class 3 flagellar gene expression in S. enterica. Many bacterial use flagella to swim in liquids and swarm over surface. In S. enterica, over fifty genes are required to assemble flagella. The expression of these genes is tightly regulated. Flagellar gene expression is bimodal in S. enterica. Under certain growth conditions, some cells express the flagellar genes whereas others do not. This results in mixed populations of motile and non-motile cells. In the present study, we found that two independent mechanisms control bimodal expression of the flagellar genes. One was previously found to result from a double negative-feedback loop involving the flagellar regulators RflP and FliZ. This feedback loop governs bimodal expression of class 2 genes. In this work, a second mechanism was found to govern bimodal expression of class 3 genes. In particular, class 3 gene expression is still bimodal even when class 2 gene expression is not. Using a combination of experimental and modeling approaches, we found that class 3 bimodalilty results from the σ28-FlgM developmental checkpoint. Collectively, these results further our understanding of how flagellar gene expression is regulated in S. enterica.
Issue Date:2020-12-03
Rights Information:Copyright 2020 Xiaoyi Wang
Date Available in IDEALS:2021-03-05
Date Deposited:2020-12

This item appears in the following Collection(s)

Item Statistics