High-throughput technologies have led to the generation of an increasing amount of data in different areas of biology. Datasets capturing the cell’s response to its intra- and extra-cellular microenvironment allows such data to be incorporated as signed and directed graphs or influence networks. These prior knowledge networks (PKNs) represent our current knowledge of the causality of cellular signal transduction. New signalling data is often examined and interpreted in conjunction with PKNs. However, different biological contexts, such as cell type or disease states, may have distinct variants of signalling pathways, resulting in the misinterpretation of new data. The identification of inconsistencies between measured data and signalling topologies, as well as the training of PKNs using context specific datasets (PKN contextualization), are necessary conditions to construct reliable, predictive models, which are current challenges in the systems biology of cell signalling. Here we present PRUNET, a user-friendly software tool designed to address the contextualization of a PKNs to specific experimental conditions. As the input, the algorithm takes a PKN and the expression profile of two given stable steady states or cellular phenotypes. The PKN is iteratively pruned using an evolutionary algorithm to perform an optimization process. This optimization rests in a match between predicted attractors in a discrete logic model (Boolean) and a Booleanized representation of the phenotypes, within a population of alternative subnetworks that evolves iteratively. We validated the algorithm applying PRUNET to four biological examples and using the resulting contextualized networks to predict missing expression values and to simulate well-characterized perturbations. PRUNET constitutes a tool for the automatic curation of a PKN to make it suitable for describing biological processes under particular experimental conditions. The general applicability of the implemented algorithm makes PRUNET suitable for a variety of biological processes, for instance cellular reprogramming or transitions between healthy and disease states.

In the pathogenesis of type 2 diabetes, development of insulin resistance triggers an increase in pancreatic β-cell insulin secretion capacity and β-cell number. Failure of this compensatory mechanism is caused by a dedifferentiation of β-cells, which leads to insufficient insulin secretion and diabetic hyperglycemia. The β-cell factors that normally protect against dedifferentiation remain poorly defined. Here, through a systems biology approach, we identify the transcription factor Klf6 as a regulator of β-cell adaptation to metabolic stress. We used a β-cell specific Klf6 knockout mouse model to investigate whether Klf6 may be a potential regulator of β-cell adaptation to a metabolic stress. We show that inactivation of Klf6 in β-cells blunts their proliferation induced by the insulin resistance of pregnancy, high-fat high-sucrose feeding, and insulin receptor antagonism. Transcriptomic analysis showed that Klf6 controls the expression of β-cell proliferation genes and, in the presence of insulin resistance, it prevents the down-expression of genes controlling mature β-cell identity and the induction of disallowed genes that impair insulin secretion. Its expression also limits the transdifferentiation of β-cells into α-cells. Our study identifies a new transcription factor that protects β-cells against dedifferentiation, and which may be targeted to prevent diabetes development.

To find plasma biomarkers prognostic of type 2 diabetes, which could also inform on pancreatic β-cell deregulations or defects in the function of insulin target tissues. We conducted a systems biology approach to characterize the plasma lipidomes of C57Bl/6J, DBA/2J, and BALB/cJ mice under different nutritional conditions, as well as their pancreatic islet and liver transcriptomes. We searched for correlations between plasma lipids and tissue gene expression modules. We identified strong correlation between plasma triacylglycerols (TAGs) and islet gene modules that comprise key regulators of glucose- and lipid-regulated insulin secretion and of the insulin signaling pathway, the two top hits were Gck and Abhd6 for negative and positive correlations, respectively. Correlations were also found between sphingomyelins and islet gene modules that overlapped in part with the gene modules correlated with TAGs. In the liver, the gene module most strongly correlated with plasma TAGs was enriched in mRNAs encoding fatty acid and carnitine transporters as well as multiple enzymes of the β-oxidation pathway. In humans, plasma TAGs also correlated with the expression of several of the same key regulators of insulin secretion and the insulin signaling pathway identified in mice. This cross-species comparative analysis further led to the identification of PITPNC1 as a candidate regulator of glucose-stimulated insulin secretion. TAGs emerge as biomarkers of a liver-to-β-cell axis that links hepatic β-oxidation to β-cell functional mass and insulin secretion.

Objective: Type 2 diabetes (T2D) occurs by deterioration in pancreatic β-cell function and/or progressive loss of pancreatic β-cell mass under the context of insulin resistance. α7 nicotinic acetylcholine receptor (nAChR) may contribute to insulin sensitivity but its role in the pathogenesis of T2D remains undefined. We investigated whether the systemic lack of α7 nAChR was sufficient to impair glucose homeostasis. Methods: We used an α7 nAChR knock-out (α7^−/−) mouse model fed a standard chow diet. The effects of the lack of α7 nAChR on islet mass, insulin secretion, glucose and insulin tolerance, body composition, and food behaviour were assessed in vivo and ex vivo experiments. Results: Young α7^−/− mice display a chronic mild high glycemia combined with an impaired glucose tolerance and a marked deficit in β-cell mass. In addition to these metabolic disorders, old mice developed adipose tissue inflammation, elevated plasma free fatty acid concentrations and presented glycolytic muscle insulin resistance in old mice. Finally, α7^−/− mice, fed a chow diet, exhibited a late-onset excessive gain in body weight through increased fat mass associated with higher food intake. Conclusion: Our work highlights the important role of α7 nAChR in glucose homeostasis. The constitutive lack of α7 nAChR suggests a novel pathway influencing the pathogenesis of T2D.