Context-dependent transcriptional regulations between signal transduction pathways

BACKGROUND: Cells coordinate their metabolism, proliferation, and cellular communication according to environmental cues through signal transduction. Because signal transduction has a primary role in cellular processes, many experimental techniques and approaches have emerged to discover the molecular components and dynamics that are dependent on cellular contexts. However, omics approaches based on genome-wide expression analysis data comparing one differing condition (e.g. complex disease patients and normal subjects) did not investigate the dynamics and inter-pathway cross-communication that are dependent on cellular contexts. Therefore, we introduce a new computational omics approach for discovering signal transduction pathways regulated by transcription and transcriptional regulations between pathways in signaling networks that are dependent on cellular contexts, especially focusing on a transcription-mediated mechanism of inter-pathway cross-communication. RESULTS: Applied to dendritic cells treated with lipopolysaccharide, our analysis well depicted how dendritic cells respond to the treatment through transcriptional regulations between signal transduction pathways in dendritic cell maturation and T cell activation. CONCLUSIONS: Our new approach helps to understand the underlying biological phenomenon of expression data (e.g. complex diseases such as cancer) by providing a graphical network which shows transcriptional regulations between signal transduction pathways. The software programs are available upon request.ope

Yes-associated protein (YAP) in pancreatic cancer: at the epicenter of a targetable signaling network associated with patient survival.

Pancreatic ductal adenocarcinoma (PDAC) is generally a fatal disease with no efficacious treatment modalities. Elucidation of signaling mechanisms that will lead to the identification of novel targets for therapy and chemoprevention is urgently needed. Here, we review the role of Yes-associated protein (YAP) and WW-domain-containing Transcriptional co-Activator with a PDZ-binding motif (TAZ) in the development of PDAC. These oncogenic proteins are at the center of a signaling network that involves multiple upstream signals and downstream YAP-regulated genes. We also discuss the clinical significance of the YAP signaling network in PDAC using a recently published interactive open-access database (www.proteinatlas.org/pathology) that allows genome-wide exploration of the impact of individual proteins on survival outcomes. Multiple YAP/TEAD-regulated genes, including AJUBA, ANLN, AREG, ARHGAP29, AURKA, BUB1, CCND1, CDK6, CXCL5, EDN2, DKK1, FOSL1,FOXM1, HBEGF, IGFBP2, JAG1, NOTCH2, RHAMM, RRM2, SERP1, and ZWILCH, are associated with unfavorable survival of PDAC patients. Similarly, components of AP-1 that synergize with YAP (FOSL1), growth factors (TGFα, EPEG, and HBEGF), a specific integrin (ITGA2), heptahelical receptors (P2Y2R, GPR87) and an inhibitor of the Hippo pathway (MUC1), all of which stimulate YAP activity, are associated with unfavorable survival of PDAC patients. By contrast, YAP inhibitory pathways (STRAD/LKB-1/AMPK, PKA/LATS, and TSC/mTORC1) indicate a favorable prognosis. These associations emphasize that the YAP signaling network correlates with poor survival of pancreatic cancer patients. We conclude that the YAP pathway is a major determinant of clinical aggressiveness in PDAC patients and a target for therapeutic and preventive strategies in this disease

Emerging connections between small RNAs and phytohormones

Small RNAs (sRNAs), mainly including miRNAs and siRNAs, are ubiquitous in eukaryotes. sRNAs mostly negatively regulate gene expression via (post-)transcriptional gene silencing through DNA methylation, mRNA cleavage, or translation inhibition. The mechanisms of sRNA biogenesis and function in diverse biological processes, as well as the interactions between sRNAs and environmental factors, like (a)biotic stress, have been deeply explored. Phytohormones are central in the plant’s response to stress, and multiple recent studies highlight an emerging role for sRNAs in the direct response to, or the regulation of, plant hormonal pathways. In this review, we discuss recent progress on the unraveling of crossregulation between sRNAs and nine plant hormones

When kinases meet mathematics: the systems biology of MAPK signalling

The mitogen activated protein kinase/extracellular signal regulated kinase pathway regulates fundamental cellular function such as cell proliferation, survival, differentiation and motility, raising the question how these diverse functions are specified and coordinated. They are encoded through the activation kinetics of the pathway, a multitude of feedback loops, scaffold proteins, subcellular compartmentalisation, and crosstalk with other pathways. These regulatory motifs alone or in combination can generate a multitude of complex behaviour. Systems biology tries to decode this complexity through mathematical modelling and prediction in order to gain a deeper insight into the inner works of signalling networks

Shh production and Gli signaling is activated in vivo in lung, enhancing the Th2 response during a murine model of allergic asthma

The pathophysiology of allergic asthma is driven by T-helper 2 (Th2) immune responses following aeroallergen inhalation. The mechanisms that initiate, potentiate and regulate airways allergy are incompletely characterized. We have previously shown that Hedgehog (Hh) signaling to T-cells, via downstream Gli transcription factors, enhances T-cell conversion to a Th2 phenotype. Here, we show for the first time that Gli-dependent transcription is activated in T-cells in vivo during murine allergic airways disease (AAD) a model for the immunopathology of asthma; and that genetic repression of Gli signaling in Tcells decreases the differentiation and/or recruitment of Th2 cells to the lung. We report that T-cells are not the only cells capable of expressing activated Gli during AAD. A substantial proportion of eosinophils and lung epithelial cells, both central mediators of the immunopathology of asthma, are also able to undergo Hh/Gli signaling. Finally, we show that Shh increases Il4 expression in eosinophils. We therefore propose that Hh signaling during AAD is complex, involving multiple cell types, signaling in an auto- or paracrine fashion. Improved understanding of the role of this major morphogenetic pathway in asthma may give rise to new drug targets for this chronic condition

Mathematical modelling plant signalling networks

During the last two decades, molecular genetic studies and the completion of the sequencing of the Arabidopsis thaliana genome have increased knowledge of hormonal regulation in plants. These signal transduction pathways act in concert through gene regulatory and signalling networks whose main components have begun to be elucidated. Our understanding of the resulting cellular processes is hindered by the complex, and sometimes counter-intuitive, dynamics of the networks, which may be interconnected through feedback controls and cross-regulation. Mathematical modelling provides a valuable tool to investigate such dynamics and to perform in silico experiments that may not be easily carried out in a laboratory. In this article, we firstly review general methods for modelling gene and signalling networks and their application in plants. We then describe specific models of hormonal perception and cross-talk in plants. This sub-cellular analysis paves the way for more comprehensive mathematical studies of hormonal transport and signalling in a multi-scale setting

An integrative, multi-scale, genome-wide model reveals the phenotypic landscape of Escherichia coli.

Given the vast behavioral repertoire and biological complexity of even the simplest organisms, accurately predicting phenotypes in novel environments and unveiling their biological organization is a challenging endeavor. Here, we present an integrative modeling methodology that unifies under a common framework the various biological processes and their interactions across multiple layers. We trained this methodology on an extensive normalized compendium for the gram-negative bacterium Escherichia coli, which incorporates gene expression data for genetic and environmental perturbations, transcriptional regulation, signal transduction, and metabolic pathways, as well as growth measurements. Comparison with measured growth and high-throughput data demonstrates the enhanced ability of the integrative model to predict phenotypic outcomes in various environmental and genetic conditions, even in cases where their underlying functions are under-represented in the training set. This work paves the way toward integrative techniques that extract knowledge from a variety of biological data to achieve more than the sum of their parts in the context of prediction, analysis, and redesign of biological systems