Archerparrish3595

The process of transcription initiation and elongation are primary points of control in the regulation of gene expression. Although biochemical studies have uncovered the mechanisms involved in controlling transcription at each step, how these mechanisms manifest in vivo at the level of individual genes is still unclear. Recent experimental advances have enabled single-cell measurements of RNA polymerase (RNAP) molecules engaged in the process of transcribing a gene of interest. In this article, we use Gillespie simulations to show that measurements of cell-to-cell variability of RNAP numbers and interpolymerase distances can reveal the prevailing mode of regulation of a given gene. Mechanisms of regulation at each step, from initiation to elongation dynamics, produce qualitatively distinct signatures, which can further be used to discern between them. Most intriguingly, depending on the initiation kinetics, stochastic elongation can either enhance or suppress cell-to-cell variability at the RNAP level. To demonstrate the value of this framework, we analyze RNAP number distribution data for ribosomal genes in Saccharomyces cerevisiae from three previously published studies and show that this approach provides crucial mechanistic insights into the transcriptional regulation of these genes. Epithelial-mesenchymal transition (EMT) is a fundamental biological process that plays a central role in embryonic development, tissue regeneration, and cancer metastasis. Transforming growth factor-β (TGFβ) is a potent inducer of this cellular transition, which is composed of transitions from an epithelial state to intermediate or partial EMT state(s) to a mesenchymal state. Using computational models to predict cell state transitions in a specific experiment is inherently difficult for reasons including model parameter uncertainty and error associated with experimental observations. In this study, we demonstrate that a data-assimilation approach using an ensemble Kalman filter, which combines limited noisy observations with predictions from a computational model of TGFβ-induced EMT, can reconstruct the cell state and predict the timing of state transitions. We used our approach in proof-of-concept "synthetic" in silico experiments, in which experimental observations were produced from a known computational ion. Our study demonstrates the feasibility and utility of a data-assimilation approach to forecasting the fate of cells undergoing EMT. The ubiquitin (Ub) proteolysis pathway uses an E1, E2, and E3 enzyme cascade to label substrate proteins with ubiquitin and target them for degradation. The mechanisms of ubiquitin chain formation remain unclear and include a sequential addition model, in which polyubiquitin chains are built unit by unit on the substrate, or a preassembly model, in which polyubiquitin chains are preformed on the E2 or E3 enzyme and then transferred in one step to the substrate. The E2 conjugating enzyme UBE2K has a 150-residue catalytic core domain and a C-terminal ubiquitin-associated (UBA) domain. Polyubiquitin chains anchored to the catalytic cysteine and free in solution are formed by UBE2K supporting a preassembly model. To study how UBE2K might assemble polyubiquitin chains, we synthesized UBE2K-Ub and UBE2K-Ub2 covalent complexes and analyzed E2 interactions with the covalently attached Ub and Ub2 moieties using NMR spectroscopy. The UBE2K-Ub complex exists in multiple conformations, including the catalytically competent closed state independent of the UBA domain. In contrast, the UBE2K-Ub2 complex takes on a more extended conformation directed by interactions between the classic I44 hydrophobic face of the distal Ub and the conserved MGF hydrophobic patch of the UBA domain. Our results indicate there are distinct differences between the UBE2K-Ub and UBE2K-Ub2 complexes and show how the UBA domain can alter the position of a polyubiquitin chain attached to the UBE2K active site. These observations provide structural insights into the unique Ub chain-building capacity for UBE2K. Individual cells in a solution display variable uptake of nanomaterials, peptides, and nutrients. Such variability reflects their heterogeneity in endocytic capacity. In a recent work, we have shown that the endocytic capacity of a cell depends on its size and surface density of endocytic components (transporters). We also demonstrated that in MDA-MB-231 breast cancer cells, the cell-surface transporter density (n) may decay with cell radius (r) following the power rule n ∼ rα, where α ≈ -1. In this work, we investigate how n and r may independently contribute to the endocytic heterogeneity of a cell population. Our analysis indicates that the smaller cells display more heterogeneity because of the higher stochastic variations in n. By contrast, the larger cells display a more uniform uptake, reflecting less-stochastic variations in n. We provide analyses of these dependencies by establishing a stochastic model. Our analysis reveals that the exponent α in the above relationship is not a constant; rather, it is a random variable whose distribution depends on cell size r. Using Bayesian analysis, we characterize the cell-size-dependent distributions of α that accurately capture the particle uptake heterogeneity of MDA-MB-231 cells. Transcription factor (TF) recognition is dictated by the underlying DNA motif sequence specific for each TF. Here, we reveal that DNA sequence repeat symmetry plays a central role in defining TF-DNA-binding preferences. In particular, we find that different TFs bind similar symmetry patterns in the context of different developmental layers. Most TFs possess dominant preferences for similar DNA repeat symmetry types. However, in some cases, preferences of specific TFs are changed during differentiation, suggesting the importance of information encoded outside of known motif regions. Histone modifications also exhibit strong preferences for similar DNA repeat symmetry patterns unique to each type of modification. βGlycerophosphate Next, using an in vivo reporter assay, we show that gene expression in embryonic stem cells can be positively modulated by the presence of genomic and computationally designed DNA oligonucleotides containing identified nonconsensus-repetitive sequence elements. This supports the hypothesis that certain nonconsensus-repetitive patterns possess a functional ability to regulate gene expression.