Energy Signaling in the Regulation of Gene Expression during Stress

Maintenance of homeostasis is pivotal to all forms of life. In the case of plants, homeostasis is constantly threatened by the inability to escape environmental fluctuations, and therefore sensitive mechanisms must have evolved to allow rapid perception of environmental cues and concomitant modification of growth and developmental patterns for adaptation and survival. Re-establishment of homeostasis in response to environmental perturbations requires reprogramming of metabolism and gene expression to shunt energy sources from growth-related biosynthetic processes to defense, acclimation, and, ultimately, adaptation. Failure to mount an initial 'emergency' response may result in nutrient deprivation and irreversible senescence and cell death. Early signaling events largely determine the capacity of plants to orchestrate a successful adaptive response. Early events, on the other hand, are likely to be shared by different conditions through the generation of similar signals and before more specific responses are elaborated. Recent studies lend credence to this hypothesis, underpinning the importance of a shared energy signal in the transcriptional response to various types of stress. Energy deficiency is associated with most environmental perturbations due to their direct or indirect deleterious impact on photosynthesis and/or respiration. Several systems are known to have evolved for monitoring the available resources and triggering metabolic, growth, and developmental decisions accordingly. In doing so, energy-sensing systems regulate gene expression at multiple levels to allow flexibility in the diversity and the kinetics of the stress response.

Logical Modeling and Dynamical Analysis of Cellular Networks

The logical (or logic) formalism is increasingly used to model regulatory and signaling networks. Complementing these applications, several groups contributed various methods and tools to support the definition and analysis of logical models. After an introduction to the logical modeling framework and to several of its variants, we review here a number of recent methodological advances to ease the analysis of large and intricate networks. In particular, we survey approaches to determine model attractors and their reachability properties, to assess the dynamical impact of variations of external signals, and to consistently reduce large models. To illustrate these developments, we further consider several published logical models for two important biological processes, namely the differentiation of T helper cells and the control of mammalian cell cycle.

CYR61 and TAZ Upregulation and Focal Epithelial to Mesenchymal Transition May Be Early Predictors of Barrett’s Esophagus Malignant Progression

Barrett's esophagus is the major risk factor for esophageal adenocarcinoma. It has a low but non-neglectable risk, high surveillance costs and no reliable risk stratification markers. We sought to identify early biomarkers, predictive of Barrett's malignant progression, using a meta-analysis approach on gene expression data. This in silico strategy was followed by experimental validation in a cohort of patients with extended follow up from the Instituto Português de Oncologia de Lisboa de Francisco Gentil EPE (Portugal). Bioinformatics and systems biology approaches singled out two candidate predictive markers for Barrett's progression, CYR61 and TAZ. Although previously implicated in other malignancies and in epithelial-to-mesenchymal transition phenotypes, our experimental validation shows for the first time that CYR61 and TAZ have the potential to be predictive biomarkers for cancer progression. Experimental validation by reverse transcriptase quantitative PCR and immunohistochemistry confirmed the up-regulation of both genes in Barrett's samples associated with high-grade dysplasia/adenocarcinoma. In our cohort CYR61 and TAZ up-regulation ranged from one to ten years prior to progression to adenocarcinoma in Barrett's esophagus index samples. Finally, we found that CYR61 and TAZ over-expression is correlated with early focal signs of epithelial to mesenchymal transition. Our results highlight both CYR61 and TAZ genes as potential predictive biomarkers for stratification of the risk for development of adenocarcinoma and suggest a potential mechanistic route for Barrett's esophagus neoplastic progression.