A mechanistic study on the role of combinatorial gene regulation in controlling gene expression noise through direct observation on the transcriptional activities in single cells

Gene transcriptions are inherently random due to the stochastic activities of the molecules. Random fluctuations in gene expression can potentially affect the functioning of genetic circuits and downstream signalling pathways, thus may lead to transcriptional noise in a population of cells. An appealing view on transcriptional noise is that it may not be a kind of dysregulation of the central dogma, instead, it could be utilized by cells to achieve certain functions. Recent studies have addressed this fascinating point, suggesting that gene fluctuations may be under the direct control of cells in responding to intracellular and extracellular signals.

Despite great details of the spatial and temporal patterns have been revealed by direct observations on gene transcriptions in living cells, the mechanism that cells utilize to control the gene expression noise and phenotypic heterogeneity is still poorly understood. Recently, an experiment on single cells suggested that cells can control the diverse patterns of gene expression through combinatorial gene regulation by modulating the relative timing of two TFs. Another single-cell study found that two TFs can form dynamic logic gates in regulating their target genes. However, a mechanistic explanation on how the cells control transcriptional noise through combinatorial gene regulation is still missing.

In this project, we plan to examine the role and mechanism of combinatorial gene regulation in modulating transcriptional noise in bacteria. We started by formulating a single-TF model that can describe transcriptional bursting in single bacterial cells. Next, we extended the single- TF model to the two-TFs model by introducing combinatorial logic to represent the interactions between TFs. Then, based on the models and recent experimental findings, we generated three hypotheses to interrogate the influences of combinatorial gene regulation on gene expression noise. We hypothesized that two-TF regulation can cause larger noise of gene expression comparing to single-TF regulation. In fact, it has been observed from experiments that the large-amplitude oscillatory signals due to combinatorial gene regulation can produce higher level of noise. Validating this hypothesis in this proposal will help to answer whether combinatorial regulation is responsible for the additional noise in bacterial cells.

Then, we plan to examine whether the noises are different for different regulatory logics by testing the second hypothesis. To conduct this test, we developed a theoretical framework of combinatorial gene regulation. Solving the computational model can determine the dominant regulatory logic in a cell. Then, by clustering the cells with the same logic in the same group, we are able to investigate whether the noises are different in different logic groups. Further, we ask whether there is additional mechanism of noise control beyond the regulatory logic. Through a simulation study, we found that variations in the phases of TF binding can alter the bursting patterns for genes regulated by two or multiple TFs. Therefore, we hypothesized that the out-of-synchronization on TF binding in cells can be leveraged by gene regulatory logics to generate extra variations in gene expression.

To test these hypotheses, we plan to construct a single-lac system, a single-tet system, and several hybrid tet-lac systems in E. coli cells, using lacZ as the target gene. The time-series fluorescence signals of gene expression and TF binding activities will be measured using super- resolution microscopy.

The main novelties of this project include: (i) Deciphering the influence of combinatorial gene regulatory logic to transcriptional noise in single bacterial cells through direct observations; (ii) Describing the mechanism of out-of-synchronization of TF binding in modulating the gene expression noise; (iii) Developing a quantitative framework of modelling combinatorial gene regulation in single cells, which can characterize the bursting patterns dynamic behaviours of gene transcription.