Repression Mechanisms in Bacterial Transcription

Abstract

Gene expression in bacteria is subject to strict regulation. This allows cells to timely tune their behavior to tackle challenging environmental conditions. For example, when subject to heat shock, E. <i>coli</i> induce the expression of σ<SUP>32</SUP>, which triggers the synthesis of several proteins that counteract protein denaturation. Simultaneously, their membrane lipid composition is readjusted, to beneficially alter its fluidity. Such complex coordinated behaviors are made possible by a global regulatory network of gene expression.

Transcription factors (TFs) are one mode of gene regulation. These are specialized proteins expressed by genes to directly (via TF-DNA interactions) or indirectly (via TF-TF interactions) activate or repress RNA production from a specific gene(s).

Another influential dynamic regulator of gene expression is DNA supercoiling. In normal conditions, the DNA is kept negatively supercoiled. When the RNA polymerase (RNAP) elongates along the DNA it unwinds it. This causes positive supercoiling downstream the RNAP and negative supercoiling upstream it. When the supercoiling levels of the DNA are significantly disturbed, elongation rates are affected and, eventually, transcription initiation can be halted, until specialized proteins intervene. Since the DNA is organized in topologically constrained segments, these effects are not homogenously spread throughout the DNA. Namely, DNA regions with high rates of transcription will have significantly more positive supercoiling buildup (PSB).

Finally, specific spatial arrangements of regulatory elements in the DNA can also be influential, by allowing genes to co-express. These arrangements include operons and transcription units, terminator sequences, and closely spaced promoters. On the latter, while most pairs of neighboring promoters in the DNA are separated by distances long enough to make them dynamically independent, some pairs are sufficiently close to have interdependent dynamics.

In this project, from empirical, genome-wide data, as well as from single-cell, single-gene data, we studied regulatory mechanisms of gene expression, in optimal and in non-optimal conditions. In detail, we delve into the kinetics of transcription when subject to repression by regulatory molecules as well as by changes in supercoiling levels, and when controlled by promoter pairs in tandem formation.

We first developed two methods to characterize the dynamics of transcription repression by TFs and PSB. In the former, we dissected the kinetic rates constants controlling TF regulation, while in the latter we showed that the dynamics of transcription locking due to PSB is promoter-strength dependent.

Next, we identified all native genes controlled by tandem promoters and studied how the distance between two promoters makes their transcription kinetics interdependent. We found evidence that, when the distance between the promoters is less than the length of DNA occupied by an RNAP in open complex formation, a phenomenon of promoter occlusion becomes influential. Otherwise, the only form of transcription interference is due to collision between RNAPs, causing a mild decrease in rate, along with a mild increase in noise in RNA production.

Finally, we studied the kinetics of natural genes, when repressed, following shifts in temperature during cold shock. To identify the nature of the mechanism responsible for their repression, we also subjected the same genes to the effects of Gyrase inhibition. Our study revealed that high sensitivity to supercoiling is one of the natural triggers of short-term responsiveness to cold shock.

In each of the aforementioned studies, we proposed new analytical and/or stochastic models. These models were designed to mimic how each mechanism operates and influences the single-cell statistics of RNA and/or protein expression. Subsequently, they were confronted to empirical data, to test hypotheses of which steps in transcription are most affected by the changing conditions.

We found that all the mechanisms of regulation studied were of similar nature, in that they all made possible an ON-OFF kinetics of RNA production. And while they were made possible by distinct physical processes, in all cases the reduction in expression rate occurred at the expense of increased noise.

Overall, our findings and models contribute to the knowledge on the mechanisms by which gene networks self-regulate and, thus, about how cells adapt to changing environments. Further, they provide insight on how to engineer synthetic gene circuits that make use of promoter-promoter interactions and/or PSB.