Pujan Joshi

Analysis of αSMA-Labeled Progenitor Cell Commitment Identifies Notch Signaling as an Important Pathway in Fracture Healing

Fracture healing is a regenerative process that involves coordinated responses of many cell types, but characterization of the roles of specific cell populations in this process has been limited. We have identified alpha smooth muscle actin (αSMA) as a marker of a population of mesenchymal progenitor cells in the periosteum that contributes to osteochondral elements during fracture healing. Using a lineage tracing approach, we labeled αSMA‐expressing cells, and characterized changes in the periosteal population during the early stages of fracture healing by histology, flow cytometry, and gene expression profiling. In response to fracture, the αSMA‐labeled population expanded and began to differentiate toward the osteogenic and chondrogenic lineages. The frequency of mesenchymal progenitor cell markers such as Sca1 and PDGFRα increased after fracture. By 6 days after fracture, genes involved in matrix production and remodeling were elevated. In contrast, genes associated with muscle contraction and Notch signaling were downregulated after fracture. We confirmed that activating Notch signaling in αSMA‐labeled cells inhibited differentiation into osteogenic and adipogenic lineages in vitro and ectopic bone formation in vivo. By characterizing changes in a selected αSMA‐labeled progenitor cell population during fracture callus formation, we have shown that modulation of Notch signaling may determine osteogenic potential of αSMA‐expressing progenitor cells during bone healing.

GABRA2 Alcohol Dependence Risk Allele is Associated with Reduced Expression of Chromosome 4p12 GABAA Subunit Genes in Human Neural Cultures

BACKGROUND Genetic variation in a region of chromosome 4p12 that includes the GABAA subunit gene GABRA2 has been reproducibly associated with alcohol dependence (AD). However, the molecular mechanisms underlying the association are unknown. This study examined correlates of in vitro gene expression of the AD-associated GABRA2 rs279858*C-allele in human neural cells using an induced pluripotent stem cell (iPSC) model system. METHODS We examined mRNA expression of chromosome 4p12 GABAA subunit genes (GABRG1, GABRA2, GABRA4, and GABRB1) in 36 human neural cell lines differentiated from iPSCs using quantitative polymerase chain reaction and next-generation RNA sequencing. mRNA expression in adult human brain was examined using the BrainCloud and BRAINEAC data sets. RESULTS We found significantly lower levels of GABRA2 mRNA in neural cell cultures derived from rs279858*C-allele carriers. Levels of GABRA2 RNA were correlated with those of the other 3 chromosome 4p12 GABAA genes, but not other neural genes. Cluster analysis based on the relative RNA levels of the 4 chromosome 4p12 GABAA genes identified 2 distinct clusters of cell lines, a low-expression cluster associated with rs279858*C-allele carriers and a high-expression cluster enriched for the rs279858*T/T genotype. In contrast, there was no association of genotype with chromosome 4p12 GABAA gene expression in postmortem adult cortex in either the BrainCloud or BRAINEAC data sets. CONCLUSIONS AD-associated variation in GABRA2 is associated with differential expression of the entire cluster of GABAA subunit genes on chromosome 4p12 in human iPSC-derived neural cell cultures. The absence of a parallel effect in postmortem human adult brain samples suggests that AD-associated genotype effects on GABAA expression, although not present in mature cortex, could have effects on regulation of the chromosome 4p12 GABAA cluster during neural development.

Diversity and complexity in chromatin recognition by TFII-I transcription factors in pluripotent embryonic stem cells and embryonic tissues.

GTF2I and GTF2IRD1 encode a family of closely related transcription factors TFII-I and BEN critical in embryonic development. Both genes are deleted in Williams-Beuren syndrome, a complex genetic disorder associated with neurocognitive, craniofacial, dental and skeletal abnormalities. Although genome-wide promoter analysis has revealed the existence of multiple TFII-I binding sites in embryonic stem cells (ESCs), there was no correlation between TFII-I occupancy and gene expression. Surprisingly, TFII-I recognizes the promoter sequences enriched for H3K4me3/K27me3 bivalent domain, an epigenetic signature of developmentally important genes. Moreover, we discovered significant differences in the association between TFII-I and BEN with the cis-regulatory elements in ESCs and embryonic craniofacial tissues. Our data indicate that in embryonic tissues BEN, but not the highly homologous TFII-I, is primarily recruited to target gene promoters. We propose a “feed-forward model” of gene regulation to explain the specificity of promoter recognition by TFII-I factors in eukaryotic cells.

PBC: A Software Framework Facilitating Pattern-Based Clustering for Microarray Data Analysis

Microarray data produces expression pattern of thousands of genes at once. Grouping these gene expression patterns to have each group convey some biologically meaningful sight entails use of a clustering method. Two problems exist when attempting to use conventional clustering methods for the microarray data analysis. Presence of outliers skews the mean value computation which, in turn influences placement of inconsistent gene expression patterns into one group. The clustering algorithms themselves generally cannot determine the right size of the clusters. We present a new method which approaches to the clustering problem from a different angle. That is, the clustering of gene expression patterns is better dealt with within a software framework that is conducive to helping biologists derive the right size of clusters utilizing their understanding of the experimental context once the baseline clusters are computed using the fold changes of gene expression levels. We discuss our experiences of using the framework in analyzing numerous microarray data experiments.

Deep pathway analysis incorporating mutation information and gene expression data

We propose a new way of analyzing biological pathways in which the analysis combines both transcriptome data and mutation information and uses the outcome to identify routes of aberrant pathways potentially responsible for the etiology of disease. Each pathway route is encoded as a Bayesian Network which is initialized with a sequence of conditional probabilities which are designed to encode directionality of regulatory relationships encoded in the pathways, i.e. activation and inhibition relationships. First, we demonstrate the effectiveness of our model through simulation in which the model is able to discern patients in Test Group from ones in Control Group. Second, we apply our model to analyze the Breast Cancer data set, available from TCGA, against some pathways available from KEGG. Our experiment with this published data reaffirms the claims reported from the original breast cancer PAM50 subtype study. Our model can further analyze the patients of each subtype based on the identified route of aberration. For example, our analysis shows that complex biological process patterns are presented for HER2+ patients potentially suggesting our methods use for producing refined subtyping. We manage to find commonly perturbed pathway routes for HER2+ patients. We claim such “deep” pathway analysis could be very useful in designing a personalized therapy.

The LG/J murine strain exhibits near-normal tendon biomechanical properties following a full-length central patellar tendon defect.

ABSTRACT Purpose of the study: Identifying biological success criteria is needed to improve therapies, and one strategy for identifying them is to analyze the RNA transcriptome for successful and unsuccessful models of tendon healing. We have characterized the MRL/MpJ murine strain and found improved mechanical outcomes following a central patellar tendon (PT) injury. In this study, we evaluate the healing of the LG/J murine strain, which comprises 75% of the MRL/MpJ background, to determine if the LG/J also exhibits improved biomechanical properties following injury and to determine differentially expressed transcription factors across the C57BL/6, MRL/MpJ and the LG/J strains during the early stages of healing. Materials and Methods: A full-length, central PT defect was created in 16–20 week old MRL/MpJ, LG/J, and C57BL/6 murine strains. Mechanical properties were assessed at 2, 5, and 8 weeks post surgery. Transcriptomic expression was assessed at 3, 7, and 14 days following injury using a novel clustering software program to evaluate differential expression of transcription factors. Results: Average LG/J structural properties improved to 96.7% and 97.2% of native LG/J PT stiffness and ultimate load by 8 weeks post surgery, respectively. We found the LG/J responded by increasing expression of transcription factors implicated in the inflammatory response and collagen fibril organization. Conclusions: The LG/J strain returns to normal structural properties by 8 weeks, with steadily increasing properties at each time point. Future work will characterize the cell populations responding to injury and investigate the role of the differentially expressed transcription factors during healing.

Extending Biological Pathways by Utilizing Conditional Mutual Information Extracted from RNA-SEQ Gene Expression Data

We propose a method of constructing a gene/protein regulatory network specifically tailored for assessing the disease state of a patient by combining generally known gene/protein pathways with transcription level changes obtained by comparing the patient’s data with the average gene expression data of the disease population. This approach uses histogram estimation with conditional mutual information to identify if some genes/proteins may more likely interact with each other. We applied our method to the Cancer Genome Atlas (TCGA) cancer data, specifically, RNA-Seq gene expression data of 110 breast cancer, 141 colorectal cancer, 445 gastric cancer and 105 rectal cancer samples, which are publicly available. We focused on examining transcription factors such as SNAI1, SNAI2, ZEB2, and TWIST1 and their downstream targets in EMT pathway (e.g., OCLN, DSP, VIM and CDH2…). We discovered that although the participating biological entities of the EMT pathway are generally known, our approach can extend their regulatory relationships through new discoveries. Our approach could form a basis for inventing a novel way of constructing a gene regulation pathway specifically tailored for each individual cancer patient.