Person:

Hansen, Anders Sejr

Numerous transcription factors (TFs) encode information about upstream signals in the dynamics of their activation, but how downstream genes decode these dynamics remains poorly understood. Using microfluidics to control the nucleocytoplasmic translocation dynamics of the budding yeast TF Msn2, we elucidate the principles that govern how different promoters convert dynamical Msn2 input into gene expression output in single cells. Combining modeling and experiments, we classify promoters according to their signal-processing behavior and reveal that multiple, distinct gene expression programs can be encoded in the dynamics of Msn2. We show that both oscillatory TF dynamics and slow promoter kinetics lead to higher noise in gene expression. Furthermore, we show that the promoter activation timescale is related to nucleosome remodeling. Our findings imply a fundamental trade-off: although the cell can exploit different promoter classes to differentially control gene expression using TF dynamics, gene expression noise fundamentally limits how much information can be encoded in the dynamics of a single TF and reliably decoded by promoters.

Many cellular signaling pathways exhibit a bowtie topology: multiple distinct signal inputs converge on a single master transcription factor, which controls the expression of downstream genes. Recent evidence suggests that information about signal inputs can be encoded by regulating the activation dynamics of the master transcription factor. However, it was unclear whether this is sufficient to obtain specificity in gene expression, such that each input induces a specific set of output genes. Using the budding yeast transcription factor Msn2 as a model system, we address this question.

We systematically dissect how different promoters decode transcription factor translocation dynamics in single cells (Chapter 2). We find that promoters fall into four main classes depending on the threshold of signal required for activation and on the timescale of activation. Furthermore, we provide insight into the mechanistic basis for promoter class. We show that it is possible to differentially control expression of the different promoter classes by control of Msn2 dynamics. We find that slow promoters exhibit dramatically higher noise in gene expression, but are able to filter out Msn2 oscillations. This highlights a general trade-off: for promoters, implementing a high-pass temporal filter comes at the cost of much higher noise in gene expression.

Applying tools from information theory and focusing on gene expression, we rigorously quantify the limits on information transduction through regulation of Msn2 dynamics (Chapter 3). Although we find that the amount of information transduced by Msn2 to target genes is only sufficient for reliable binary decisions, information transduction can be improved by modulating promoter cis-elements or by integrating information from multiple genes. We find amplitude-encoding to be more reliable than frequency-encoding. Taken together, our results suggest that information transduction through regulation of Msn2 dynamics is limited to reliable transduction of signal identity, but not signal intensity.

The work we describe in this dissertation would not have been possible without the development of high-throughput microfluidic technologies (Chapter 4). We describe our development of a multiplexed microfluidic device, which we combine with four-color quantitative time-lapse microscopy to control nucleocytoplasmic shuttling of Msn2 and measure gene expression dynamics in single cells. We provide a detailed protocol for future studies.

Our work demonstrates that it is possible to encode multiple distinct gene expression programs in the dynamics of a single transcription factor. Nonetheless, at the level of individual genes, noise in the decoding step places an upper limit on information transduction.