Histone deacetylase inhibitors (HDACIs) are known to alter gene expression by both up- and down-regulation of protein-coding genes in normal and cancer cells. However, the exact regulatory mechanisms of action remain uncharacterized. Here we investigated genome wide dose-dependent epigenetic and transcriptome changes in response to HDACI largazole in a transformed and a non-transformed cell line. Exposure to low nanomolar largazole concentrations (

Sustained cell migration is essential for wound healing and cancer metastasis. The epidermal growth factor receptor (EGFR) signaling cascade is known to drive cell migration and proliferation. While the signal transduction downstream of EGFR has been extensively investigated, our knowledge of the initiation and maintenance of EGFR signaling during cell migration remains limited. The metalloprotease TACE is responsible for producing active EGFR family ligands in the via ligand shedding. Sustained TACE activity may perpetuate EGFR signaling and reduce a cell's reliance on exogenous growth factors. Using a cultured keratinocyte model system, we show that depletion of α-catenin perturbs adherens junctions, enhances cell proliferation and motility, and decreases dependence on exogenous growth factors. We show that the underlying mechanism for these observed phenotypical changes depends on enhanced autocrine/paracrine release of the EGFR ligand TGF-α in a TACE-dependent manner. We demonstrate that proliferating keratinocyte epithelial cell clusters display waves of oscillatory extracellular signal-regulated kinase (ERK) activity, which can be eliminated by TACE knockout, suggesting that these waves of oscillatory ERK activity depend on autocrine/paracrine signals produced by TACE. These results provide new insights into the regulatory role of adherens junctions in initiating and maintaining autocrine/paracrine signaling with relevance to wound healing and cellular transformation.

Interacting particle system (IPS) models have proven to be highly successful for describing the spatial movement of organisms. However, it is challenging to infer the interaction rules directly from data. In the field of equation discovery, the weak-form sparse identification of nonlinear dynamics (WSINDy) methodology has been shown to be computationally efficient for identifying the governing equations of complex systems from noisy data. Motivated by the success of IPS models to describe the spatial movement of organisms, we develop WSINDy for the second-order IPS to learn equations for communities of cells. Our approach learns the directional interaction rules for each individual cell that in aggregate govern the dynamics of a heterogeneous population of migrating cells. To sort a cell according to the active classes present in its model, we also develop a novel ad hoc classification scheme (which accounts for the fact that some cells do not have enough evidence to accurately infer a model). Aggregated models are then constructed hierarchically to simultaneously identify different species of cells present in the population and determine best-fit models for each species. We demonstrate the efficiency and proficiency of the method on several test scenarios, motivated by common cell migration experiments.

A key aspect in defining cell state is the complex choreography of DNA binding events in a given cell type, which in turn establishes a cell-specific gene-expression program. Here we wanted to take a deep analysis of DNA binding events and transcriptional output of a single cell state (K562 cells). To this end we re-analyzed 195 DNA binding proteins contained in ENCODE data. We used standardized analysis pipelines, containerization, and literate programming with R Markdown for reproducibility and rigor. Our approach validated many findings from previous independent studies, underscoring the importance of ENCODE’s goals in providing these reproducible data resources. We also had several new findings including: (i) 1,362 promoters, which we refer to as ‘reservoirs,’ that are defined by having up to 111 different DNA binding-proteins localized on one promoter, yet do not have any expression of steady-state RNA (ii) Reservoirs do not overlap super-enhancer annotations and distinct have distinct properties from super-enhancers. (iii) The human specific SVA repeat element may have been co-opted for enhancer regulation and is highly transcribed in PRO-seq and RNA-seq. Collectively, this study performed by the students of a CU Boulder computational biology class (BCHM 5631 –Spring 2020) demonstrates the value of reproducible findings and how resources like ENCODE that prioritize data standards can foster new findings with existing data in a didactic environment.

A key aspect in defining cell state is the complex choreography of DNA binding events in a given cell type, which in turn establishes a cell-specific gene-expression program. In the past two decades since the sequencing of the human genome there has been a deluge of genome-wide experiments which have measured gene-expression and DNA binding events across numerous cell-types and tissues. Here we re-analyze ENCODE data in a highly reproducible manner by utilizing standardized analysis pipelines, containerization, and literate programming with Rmarkdown. Our approach validated many findings from previous independent studies, underscoring the importance of ENCODE’s goals in providing these reproducible data resources. This approach also revealed several new findings: (i) 1,362 promoters, termed ‘reservoirs,’ have up to 111 different DNA binding-proteins localized on one promoter yet do not have any expression of steady-state RNA (ii) The human specific SVA repeat element may have been co-opted for enhancer regulation. Collectively, this study performed by the students of a CU Boulder computational biology class (BCHM 5631 – Spring 2020) demonstrates the value of reproducible findings and how resources like ENCODE that prioritize data standards can foster new findings with existing data in a didactic environment.