Transcriptional profiling of mesenchymal stromal cells from young and old rats in response to Dexamethasone

BACKGROUND: Marrow-derived stromal cells (MSCs) maintain the capability of self-renewal and differentiation into multiple lineages in adult life. Age-related changes are recognized by a decline in the stemness potential that result in reduced regeneration potential of the skeleton. To explore the molecular events that underline skeletal physiology during aging we catalogued the profile of gene expression in ex vivo cultured MSCs derived from 3 and 15 month old rats. The ex vivo cultured cells were analyzed following challenge with or without Dexamethasone (Dex). RNA retrieved from these cells was analyzed using Affymetrix Gene Chips to compare the effect of Dex on gene expression in both age groups. RESULTS: The molecular mechanisms that underline skeletal senescence were studied by gene expression analysis of RNA harvested from MSCs. The analysis resulted in complex profiles of gene expression of various differentiation pathways. We revealed changes of lineage-specific gene expression; in general the pattern of expression included repression of proliferation and induction of differentiation. The functional analysis of genes clustered were related to major pathways; an increase in bone remodeling, osteogenesis and muscle formation, coupled with a decrease in adipogenesis. We demonstrated a Dex-related decrease in immune response and in genes that regulate bone resorption and an increase in osteoblastic differentiation. Myogenic-related genes and genes that regulate cell cycle were induced by Dex. While Dex repressed genes related to adipogenesis and catabolism, this decrease was complementary to an increase in expression of genes related to osteogenesis. CONCLUSION: This study summarizes the genes expressed in the ex vivo cultured mesenchymal cells and their response to Dex. Functional clustering highlights the complexity of gene expression in MSCs and will advance the understanding of major pathways that trigger the natural changes underlining physiological aging. The high throughput analysis shed light on the anabolic effect of Dex and the relationship between osteogenesis, myogenesis and adipogenesis in the bone marrow cells

NFkB Disrupts Tissue Polarity in 3D by Preventing Integration of Microenvironmental Signals

The microenvironment of cells controls their phenotype, and thereby the architecture of the emerging multicellular structure or tissue. We have reported more than a dozen microenvironmental factors whose signaling must be integrated in order to effect an organized, functional tissue morphology. However, the factors that prevent integration of signaling pathways that merge form and function are still largely unknown. We have identified nuclear factor kappa B (NFkB) as a transcriptional regulator that disrupts important microenvironmental cues necessary for tissue organization. We compared the gene expression of organized and disorganized epithelial cells of the HMT-3522 breast cancer progression series: the non-malignant S1 cells that form polarized spheres (\u27acini\u27), the malignant T4-2 cells that form large tumor-like clusters, and the \u27phenotypically reverted\u27 T4-2 cells that polarize as a result of correction of the microenvironmental signaling. We identified 180 genes that display an increased expression in disorganized compared to polarized structures. Network, GSEA and transcription factor binding site analyses suggested that NFkB is a common activator for the 180 genes. NFkB was found to be activated in disorganized breast cancer cells, and inhibition of microenvironmental signaling via EGFR, beta1 integrin, MMPs, or their downstream signals suppressed its activation. The postulated role of NFkB was experimentally verified: Blocking the NFkB pathway with a specific chemical inhibitor or shRNA induced polarization and inhibited invasion of breast cancer cells in 3D cultures. These results may explain why NFkB holds promise as a target for therapeutic intervention: Its inhibition can reverse the oncogenic signaling involved in breast cancer progression and integrate the essential microenvironmental control of tissue architecture

JISTIC: Identification of Significant Targets in Cancer

<p>Abstract</p> <p>Background</p> <p>Cancer is caused through a multistep process, in which a succession of genetic changes, each conferring a competitive advantage for growth and proliferation, leads to the progressive conversion of normal human cells into malignant cancer cells. Interrogation of cancer genomes holds the promise of understanding this process, thus revolutionizing cancer research and treatment. As datasets measuring copy number aberrations in tumors accumulate, a major challenge has become to distinguish between those mutations that drive the cancer versus those passenger mutations that have no effect.</p> <p>Results</p> <p>We present JISTIC, a tool for analyzing datasets of genome-wide copy number variation to identify driver aberrations in cancer. JISTIC is an improvement over the widely used GISTIC algorithm. We compared the performance of JISTIC versus GISTIC on a dataset of glioblastoma copy number variation, JISTIC finds 173 significant regions, whereas GISTIC only finds 103 significant regions. Importantly, the additional regions detected by JISTIC are enriched for oncogenes and genes involved in cell-cycle and proliferation.</p> <p>Conclusions</p> <p>JISTIC is an easy-to-install platform independent implementation of GISTIC that outperforms the original algorithm detecting more relevant candidate genes and regions. The software and documentation are freely available and can be found at: <url>http://www.c2b2.columbia.edu/danapeerlab/html/software.html</url></p

Meta-analysis and profiling of cardiac expression modules

Heart failure is a complex, complicated disease that is not yet fully understood. We used the Module Map algorithm to uncover groups of genes that have a similar pattern of expression under various conditions of heart stress. These groups of genes are called modules and may serve as computational predictions of biological pathways for the various clinical situations. The Module Map algorithm allows a large-scale analysis of genes expressed. We applied this algorithm to 700 different mouse experiments downloaded from the Gene Expression Omnibus database, which identified 884 modules. The analysis reconstructed partially known principles that play a role in governing the response of heart to stress, thus demonstrating the strength of the method. We have shown a role of genes related to the immune system in conditions of heart remodeling and failure. We have also shown changes in the expression of genes involved with energy metabolism and changes in the expression of contractile proteins of the heart following myocardial infarction. When focusing on another module we noted a new correlation between genes related to osteogenesis and heart failure, including Runx2 and Ahsg, whose role in heart failure was unknown so far. Despite a lack of prior biological knowledge, the Module Map algorithm has reconstructed known pathways, which demonstrates the strength of this new method for analyzing gene profiles related to clinical phenomenon. The method and the analysis presented are a new avenue to uncover the correlation of clinical conditions to the molecular level

Modulatory profiling identifies mechanisms of small molecule-induced cell death

Cell death is a complex process that plays a vital role in development, homeostasis, and disease. Our understanding of and ability to control cell death is impeded by an incomplete characterization of the full range of cell death processes that occur in mammalian systems, especially in response to exogenous perturbations. We present here a general approach to address this problem, which we call modulatory profiling. Modulatory profiles are composed of the changes in potency and efficacy of lethal compounds produced by a second cell death-modulating agent in human cell lines. We show that compounds with the same characterized mechanism of action have similar modulatory profiles. Furthermore, clustering of modulatory profiles revealed relationships not evident when clustering lethal compounds based on gene expression profiles alone. Finally, modulatory profiling of compounds correctly predicted three previously uncharacterized compounds to be microtubule-destabilizing agents, classified numerous compounds that act nonspecifically, and identified compounds that cause cell death through a mechanism that is morphologically and biochemically distinct from previously established ones

PGC-1α supports glutamine metabolism in breast cancer

Background: Glutamine metabolism is a central metabolic pathway in cancer. Recently, reductive carboxylation of glutamine for lipogenesis has been shown to constitute a key anabolic route in cancer cells. However, little is known regarding central regulators of the various glutamine metabolic pathways in cancer cells. Methods: The impact of PGC-1α and ERRα on glutamine enzyme expression was assessed in ERBB2+ breast cancer cell lines with quantitative RT-PCR, chromatin immunoprecipitation, and immunoblotting experiments. Glutamine flux was quantified using 13C-labeled glutamine and GC/MS analyses. Functional assays for lipogenesis were performed using 14C-labeled glutamine. The expression of glutamine metabolism genes in breast cancer patients was determined by bioinformatics analyses using The Cancer Genome Atlas. Results: We show that the transcriptional coactivator PGC-1α, along with the transcription factor ERRα, is a positive regulator of the expression of glutamine metabolism genes in ERBB2+ breast cancer. Indeed, ERBB2+ breast cancer cells with increased expression of PGC-1α display elevated expression of glutamine metabolism genes. Furthermore, ERBB2+ breast cancer cells with reduced expression of PGC-1α or when treated with C29, a pharmacological inhibitor of ERRα, exhibit diminished expression of glutamine metabolism genes. The biological relevance of the control of glutamine metabolism genes by the PGC-1α/ERRα axis is demonstrated by consequent regulation of glutamine flux through the citric acid cycle. PGC-1α and ERRα regulate both the canonical citric acid cycle (forward) and the reductive carboxylation (reverse) fluxes; the latter can be used to support de novo lipogenesis reactions, most notably in hypoxic conditions. Importantly, murine and human ERBB2+ cells lines display a significant dependence on glutamine availability for their growth. Finally, we show that PGC-1α expression is positively correlated with that of the glutamine pathway in ERBB2+ breast cancer patients, and high expression of this pathway is associated with reduced patient survival. Conclusions: These data reveal that the PGC-1α/ERRα axis is a central regulator of glutamine metabolism in ERBB2+ breast cancer. This novel regulatory link, as well as the marked reduction in patient survival time associated with increased glutamine pathway gene expression, suggests that targeting glutamine metabolism may have therapeutic potential in the treatment of ERBB2+ breast cancer