Date of Award
Doctor of Philosophy (PhD)
Bacteria can survive and adapt to various environments by changing their gene expression. This process is controlled by both transcription factors and shared cellular resources. Elucidating the regulatory responses in bacteria at the genome scale is crucial for biomedical and biotechnology applications. To date, even for the best-characterized bacterium, Escherichia coli, the regulatory interactions of more than 60% of operons are still unclear. In this thesis, I described two synthetic biology methods that allow the perturbation of key component concentrations and the measurement of gene expression response systematically. I characterized two types of regulatory processes: transcriptional regulation and shared resource competition. For individual genes, transcription factors (TFs) recognize specific DNA sequences that are often present in promoter regions to regulate gene expression. In Chapter 2, I developed a technique named pooled promoter responses to TF perturbations via sequencing (PPTP-seq). Using PPTP-seq, I measured the activity of 1373 E. coli promoters under single knockdowns of 183 TFs, illustrating more than 200,000 possible TF-gene responses in one experiment. From this data set, I identified new TF auto-regulatory responses and complex transcriptional control on one-carbon metabolism. I also found context-dependent promoter regulation by multiple TFs whose relative binding strengths determined promoter activities. At the system level, the availability of gene expression resources, e.g., RNA polymerase and ribosomes, determine the expression levels of all genes in the cells. Overexpressing one gene can cause repression of other genes by reducing the finite pool of shared resources. In Chapter 3, I designed two competing gene overexpression modules with fluorescent proteins as outputs and investigated resource competition in single cells. I demonstrated a Simpson’s paradox during the overexpression of multiple genes: two competing proteins in single cells correlated positively for every induction condition, but the overall correlation was negative. However, this phenomenon was not observed between two competing mRNAs in single cells. I developed an analytical framework showing that the phenomenon arises from competition for translational resources, with the correlation modulated by both mRNA and ribosome variability.
Michael Brent, Barak Cohen, Fangqiong Ling, Yinjie Tang,
Available for download on Sunday, March 03, 2024