Expression2Kinases: mRNA profiling linked to multiple upstream regulatory layers

Chen, Edward Y.; Xu, Huilei; Gordonov, Simon; Lim, Maribel P.; Perkins, Matthew H.; Ma’ayan, Avi

doi:10.1093/bioinformatics/btr625

Cited by 155 publications

(163 citation statements)

References 43 publications

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“…Clustering parameters such as Fuzzification, memberhip threshold and number of clusters were set at 1.45, 0.5 and 9, respectively. The protein members of each cluster were then subjected for functional analyses (that is, enrichment of GO terms, transcription factor binding and miRNAs prediction) using X2K software 55 . Statistical tests combining P value and z-score were used to detect overrepresentation of the annotations included in either GO, KEGG and CheA database.…”

Section: Methodsmentioning

confidence: 99%

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Proteome adaptation in cell reprogramming proceeds via distinct transcriptional networks

et al. 2014

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The ectopic expression of Oct4, Klf4, c-Myc and Sox2 (OKMS) transcription factors allows reprogramming of somatic cells into induced pluripotent stem cells (iPSCs). The reprogramming process, which involves a complex network of molecular events, is not yet fully characterized. Here we perform a quantitative mass spectrometry-based analysis to probe in-depth dynamic proteome changes during somatic cell reprogramming. Our data reveal defined waves of proteome resetting, with the first wave occurring 48 h after the activation of the reprogramming transgenes and involving specific biological processes linked to the c-Myc transcriptional network. A second wave of proteome reorganization occurs in a later stage of reprogramming, where we characterize the proteome of two distinct pluripotent cellular populations. In addition, the overlay of our proteome resource with parallel generated -omics data is explored to identify post-transcriptionally regulated proteins involved in key steps during reprogramming.

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Section: Methodsmentioning

confidence: 99%

“…Pearson's correlation heatmaps were generated using the MeV software. Functional analyses (that is, enrichment of GO terms, transcription factor binding) were done with the X2K software 55 . Statistical tests combining P value and z-score were used to detect overrepresentation of the annotations included in either GO, KEGG and CheA databases.…”

Section: Methodsmentioning

confidence: 99%

Proteome adaptation in cell reprogramming proceeds via distinct transcriptional networks

et al. 2014

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“…sets of targeted genes) assembled from public available resources, such as ChEA, Transfac, and Jaspar. 10,53,54 All connections from TFs to their target genes are modeled with a positive sign (i.e. TF up-regulation leads to target gene over-expression and TF down-regulation leads to target gene under-expression).…”

Section: Construction Of Prior Knowledge Network (Pkn)mentioning

confidence: 99%

Identification of drug-specific pathways based on gene expression data: application to drug induced lung injury

et al. 2015

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Identification of signaling pathways that are functional in a specific biological context is a major challenge in systems biology, and could be instrumental to the study of complex diseases and various aspects of drug discovery. Recent approaches have attempted to combine gene expression data with prior knowledge of protein connectivity in the form of a PPI network, and employ computational methods to identify subsets of the protein-protein-interaction (PPI) network that are functional, based on the data at hand. However, the use of undirected networks limits the mechanistic insight that can be drawn, since it does not allow for following mechanistically signal transduction from one node to the next. To address this important issue, we used a directed, signaling network as a scaffold to represent protein connectivity, and implemented an Integer Linear Programming (ILP) formulation to model the rules of signal transduction from one node to the next in the network. We then optimized the structure of the network to best fit the gene expression data at hand. We illustrated the utility of ILP modeling with a case study of drug induced lung injury. We identified the modes of action of 200 lung toxic drugs based on their gene expression profiles and, subsequently, merged the drug specific pathways to construct a signaling network that captured the mechanisms underlying Drug Induced Lung Disease (DILD). We further demonstrated the predictive power and biological relevance of the DILD network by applying it to identify drugs with relevant pharmacological mechanisms for treating lung injury. Insight, innovation, integrationIn this manuscript we introduce a novel approach for the identification of signaling pathways that are functional in a specific biological context, by leveraging gene expression data and prior knowledge of protein connectivity. In more detail, we introduce a linear programming formulation to model signal transduction from one node to the next in a Prior Knowledge Network (PKN), and by minimizing the mismatch between model predictions and experimental data, we are able to identify subsets of the PKN that are most probably functional in the specific biological context. More specifically, we address the problem of identifying the modes of action of drugs that have been reported to induce respiratory side effects, based on their gene expression profiles, and subsequently, merge the drug specific pathways together to construct a signaling network that captures the signaling mechanisms underlying Drug Induced Lung Disease (DILD). Moreover, to demonstrate the predictive power and biological relevance of the DILD network, we use it to suggest potential drug repositioning for treating lung injury.

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“…Mouse target genes in the Expression2Kinases (X2K) databases [25] were replaced by their human homologs based on the Jackson Laboratories mouse informatics database (Mouse Genome Informatics [MGI], http://www.informatics.jax.org) and the National Center for Biotechnology Information homologene (http://www.ncbi.nlm.nih.gov/homologene/) database. To secure accurate computation of statistical significance, the populated GO association list and the TRANSFAC-background and ChEA-background databases were further modified.…”

Section: Biological Process Enrichment Analysismentioning

confidence: 99%

“…Next, we used computational approaches to identify potential upstream regulatory mechanisms that are responsible for observed changes in gene expression based on two transcription factor gene target association databases of the X2K suite [25]. We identified 10 transcription factors at each time point as putative upstream regulators of the identified differentially expressed gene sets (Fig.…”

Section: Molecular Analysis Of Cardiomyocyte Differentiationmentioning

confidence: 99%

Small Molecule-Mediated Directed Differentiation of Human Embryonic Stem Cells Toward Ventricular Cardiomyocytes

Karakikes

Senyei

Hansen

et al. 2013

Stem Cells Translational Medicine

148

127

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The generation of human ventricular cardiomyocytes from human embryonic stem cells and/or induced pluripotent stem cells could fulfill the demand for therapeutic applications and in vitro pharmacological research; however, the production of a homogeneous population of ventricular cardiomyocytes remains a major limitation. By combining small molecules and growth factors, we developed a fully chemically defined, directed differentiation system to generate ventricular-like cardiomyocytes (VCMs) from human embryonic stem cells and induced pluripotent stem cells with high efficiency and reproducibility. Molecular characterization revealed that the differentiation recapitulated the developmental steps of cardiovascular fate specification. Electrophysiological analyses further illustrated the generation of a highly enriched population of VCMs. These chemically induced VCMs exhibited the expected cardiac electrophysiological and calcium handling properties as well as the appropriate chronotropic responses to cardioactive compounds. In addition, using an integrated computational and experimental systems biology approach, we demonstrated that the modulation of the canonical Wnt pathway by the small molecule IWR-1 plays a key role in cardiomyocyte subtype specification. In summary, we developed a reproducible and efficient experimental platform that facilitates a chemical genetics-based interrogation of signaling pathways during cardiogenesis that bypasses the limitations of genetic approaches and provides a valuable source of ventricular cardiomyocytes for pharmacological screenings as well as cell replacement therapies. STEM CELLS

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Expression2Kinases: mRNA profiling linked to multiple upstream regulatory layers

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 155 publications

References 43 publications

Proteome adaptation in cell reprogramming proceeds via distinct transcriptional networks

Proteome adaptation in cell reprogramming proceeds via distinct transcriptional networks

Identification of drug-specific pathways based on gene expression data: application to drug induced lung injury

Small Molecule-Mediated Directed Differentiation of Human Embryonic Stem Cells Toward Ventricular Cardiomyocytes

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