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Wagh, Kaustubh
Upadhyaya, Arpita
The genetic information encoded within our DNA is converted into RNA in a process called transcription. This is a tightly regulated process where multiple proteins act in concert to activate appropriate gene expression programs. Transcription factors (TFs) are key players in this process, with TF binding being the first step in the assembly of the transcriptional machinery. TFs are sequence-specific DNA binding proteins that bind specific motifs within chromatin. How TFs navigate the complex nuclear microenvironment to rapidly find their target sites remains poorly understood. Technological advances over the past 20 years have enabled us to follow single TF molecules within live cells as they interact with chromatin. Most TFs have been shown to exhibit power law distributed residence times, which arise from the broad distribution of binding affinities within the nucleus. This blurs the line between specific and non-specific binding and renders it impossible to distinguish between different binding modes based on residence times alone. In this dissertation, I combine single molecule tracking (SMT) with statistical algorithms to identify two distinct low-mobility states for chromatin (histone H2B) and bound transcriptional regulators within the nucleus. On our timescales, the TF mobility states represent the mobility of the piece of chromatin that they are bound to. Ligand activation results in a dramatic increase in the proportion of steroid receptors in the lowest mobility state. Mutational analysis revealed that only chromatin interactions in the lowest mobility state require an intact DNA-binding domain as well as oligomerization domains. Importantly, these states are not spatially separated as previously believed but in fact, individual H2B and chromatin-bound TF molecules can dynamically switch between them. Single molecules presenting different mobilities exhibit different residence time distributions, suggesting that the mobility of a TF is intimately coupled with their temporal dynamics. This provides a way to identify different binding modes that cannot be detected by measuring residence times alone. Together, these results identify two unique and distinct low-mobility states of chromatin that appear to represent common pathways for transcription activation in mammalian cells. Next, I demonstrate how SMT can complement genome wide assays to paint a complete picture of gene regulation by TFs using two case studies: corticosteroid signaling and endocrine therapy resistance in breast cancer. Finally, I conclude with a roadmap for future work on examining the role of mechanical cues within the cellular microenvironment (such as stiffness and topography) in regulating TF dynamics and gene expression.