Dataset Information

From microarray to biology: an integrated experimental, statistical and in silico analysis of how the extracellular matrix modulates the phenotype of cancer cells.

ABSTRACT: A statistically robust and biologically-based approach for analysis of microarray data is described that integrates independent biological knowledge and data with a global F-test for finding genes of interest that minimizes the need for replicates when used for hypothesis generation. First, each microarray is normalized to its noise level around zero. The microarray dataset is then globally adjusted by robust linear regression. Second, genes of interest that capture significant responses to experimental conditions are selected by finding those that express significantly higher variance than those expressing only technical variability. Clustering expression data and identifying expression-independent properties of genes of interest including upstream transcriptional regulatory elements (TREs), ontologies and networks or pathways organizes the data into a biologically meaningful system. We demonstrate that when the number of genes of interest is inconveniently large, identifying a subset of "beacon genes" representing the largest changes will identify pathways or networks altered by biological manipulation. The entire dataset is then used to complete the picture outlined by the "beacon genes." This allow construction of a structured model of a system that can generate biologically testable hypotheses. We illustrate this approach by comparing cells cultured on plastic or an extracellular matrix which organizes a dataset of over 2,000 genes of interest from a genome wide scan of transcription. The resulting model was confirmed by comparing the predicted pattern of TREs with experimental determination of active transcription factors.

SUBMITTER: Dozmorov MG

PROVIDER: S-EPMC2537575 | biostudies-literature | 2008 Aug

REPOSITORIES: biostudies-literature

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From microarray to biology: an integrated experimental, statistical and in silico analysis of how the extracellular matrix modulates the phenotype of cancer cells.

Dozmorov Mikhail G MG Kyker Kimberly D KD Hauser Paul J PJ Saban Ricardo R Buethe David D DD Dozmorov Igor I Centola Michael B MB Culkin Daniel J DJ Hurst Robert E RE

BMC bioinformatics 20080812

A statistically robust and biologically-based approach for analysis of microarray data is described that integrates independent biological knowledge and data with a global F-test for finding genes of interest that minimizes the need for replicates when used for hypothesis generation. First, each microarray is normalized to its noise level around zero. The microarray dataset is then globally adjusted by robust linear regression. Second, genes of interest that capture significant responses to expe ...[more]

PMID: 18793468

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Project description:Extracellular matrix (ECM) is comprised of different types of proteins, which change in composition and ratios during morphogenesis and disease progression. ECM proteins provide cell adhesion and impart mechanical cues to the cells. Increasing substrate stiffness has been shown to induce Yes-associated protein (YAP) translocation from the cytoplasm to the nucleus, yet these mechanistic studies used fibronectin only as the biochemical cue. How varying the types of ECM modulates mechanotransduction of stem cells remains largely unknown. Using polyacrylamide hydrogels with tunable stiffness as substrates, here we conjugated four major ECM proteins commonly used for cell adhesion: fibronectin, collagen I, collagen IV and laminin, and assessed the effects of varying ECM type and density on YAP translocation in human mesenchymal stem cells (hMSCs). For all four ECM types, increasing ECM ligand density alone can induce YAP nuclear translocation without changing substrate stiffness. The ligand threshold for such biochemical ligand-induced YAP translocation differs across ECM types. While stiffness-dependent YAP translocation can be induced by all four ECM types, each ECM requires a different optimized ligand density for this to occur. Using antibody blocking, we further identified integrin subunits specifically involved in mechanotransduction of different ECM types. Finally, we demonstrated that altering ECM type further modulates hMSC osteogenesis without changing substrate stiffness. These findings highlight the important role of ECM type in modulating mechanotransduction and differentiation of stem cells, and call for future mechanistic studies to further elucidate the role of changes in ECM compositions in mediating mechanotransduction during morphogenesis and disease progression. STATEMENT OF SIGNIFICANCE: Our study addresses a critical gap of knowledge in mechanobiology. Increasing substrate stiffness has been shown to induce nuclear YAP translocation, yet only on fibronectin-coated substrates. However, extracellular matrix (ECM) is comprised of different protein types. How varying the type of ECM modulates stem cell mechanotransduction remains largely unknown. We here reveal that the choice of ECM type can directly modulate stem cell mechanotransduction, filling this critical gap. This work has broad impacts in mechanobiology and biomaterials, as it provides the first evidence that varying ECM type can impact YAP translocation independent of substrate stiffness, opening doors for a more rational biomaterials design tuning ECM properties to control cell fate for promoting normal development and for preventing disease progression.

Dataset Information

From microarray to biology: an integrated experimental, statistical and in silico analysis of how the extracellular matrix modulates the phenotype of cancer cells.

Publications

From microarray to biology: an integrated experimental, statistical and in silico analysis of how the extracellular matrix modulates the phenotype of cancer cells.

Similar Datasets

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