Integrating genome sequence and structural data for statistical learning to predict transcription factor binding sites

We report an approach to predict DNA specificity of the tetracycline repressor (TetR) family transcription regulators (TFRs). First, a genome sequence-based method was streamlined with quantitative P-values defined to filter out reliable predictions. Then, a framework was introduced to incorporate structural data and to train a statistical energy function to score the pairing between TFR and TFR binding site (TFBS) based on sequences. The predictions benchmarked against experiments, TFBSs for 29 out of 30 TFRs were correctly predicted by either the genome sequence-based or the statistical energy-based method. Using P-values or Z-scores as indicators, we estimate that 59.6% of TFRs are covered with relatively reliable predictions by at least one of the two methods, while only 28.7% are covered by the genome sequence-based method alone. Our approach predicts a large number of new TFBs which cannot be correctly retrieved from public databases such as FootprintDB. High-throughput experimental assays suggest that the statistical energy can model the TFBSs of a significant number of TFRs reliably. Thus the energy function may be applied to explore for new TFBSs in respective genomes. It is possible to extend our approach to other transcriptional factor families with sufficient structural information.

Procaspase activating compound 1 controls tetracycline repressor-regulated gene expression system

The tetracycline repressor (TetR)-regulated system is a widely used tool to study gene functions through control of its expression. Various effectors such as tetracycline (Tc) and doxycycline (Dox) quickly induce or shut down gene expression, but reversing gene expression has not been eligible due to long half-lives of such effectors. Here, we found that procaspase activating compound 1 (PAC-1) rapidly reduces transient expression of TetR-regulated green fluorescent protein (GFP) in mammalian cells. Next, we applied PAC-1 to control of expression of transient receptor potential melastatin 7 (TRPM7) protein, whose downstream cellular events can be monitored by cell morphological changes. We observed that PAC-1 quickly reduces TRPM7 expression, consequently affecting cell morphology regulated by TRPM7. The present study demonstrates the first small molecule that efficiently turns off the TetR-regulated gene expression in mammalian cells, thereby precisely regulating the expression level of target gene.

Noise Control In Gene Regulatory Networks With Negative Feedback

Genes and proteins regulate cellular functions through complex circuits of biochemical reactions. Fluctuations in the components of these regulatory networks result in noise that invariably corrupts the signal, possibly compromising function. Here, we create a practical formalism based on ideas introduced by Wiener and Kolmogorov (WK) for filtering noise in engineered communications systems to quantitatively assess the extent to which noise can be controlled in biological processes involving negative feedback. Application of the theory, which reproduces the previously proven scaling of the lower bound for noise suppression in terms of the number of signaling events, shows that a tetracycline repressor-based negative-regulatory gene circuit behaves as a WK filter. For the class of Hill-like nonlinear regulatory functions, this type of filter provides the optimal reduction in noise. Our theoretical approach can be readily combined with experimental measurements of response functions in a wide variety of genetic circuits, to elucidate the general principles by which biological networks minimize noise.