Adipocyte-specific deletion of Tcf7l2 induces dysregulated lipid metabolism and impairs glucose tolerance in mice.

Aims/hypothesis: Transcription factor 7-like 2 (TCF7L2) is a downstream effector of the Wnt/β-catenin signalling pathway implicated in type 2 diabetes risk through genome-wide association studies. Although its expression is critical for adipocyte development, the potential roles of changes in adipose tissue TCF7L2 levels in diabetes risk are poorly defined. Here, we investigated whether forced changes in Tcf7l2 expression in adipocytes affect whole body glucose or lipid metabolism and crosstalk between disease-relevant tissues.

Patient-derived xenograft models-the future of personalised cancer treatment.

For many tumours there is a lack of randomised data from which we can guide systemic treatments. Although gene expression profiling along with proteomics has led to advances in diagnosis, classification and prognosis, our ability to target many cancers has been further limited due to a lack of therapeutic options. The use of patient-derived xenograft (PDX) models in the setting of a rare malignancy is discussed here by Kamili et al, with the successful establishment of new model systems.

KSR1 regulates BRCA1 degradation and inhibits breast cancer growth.

Kinase suppressor of Ras-1 (KSR1) facilitates signal transduction in Ras-dependent cancers, including pancreatic and lung carcinomas but its role in breast cancer has not been well studied. Here, we demonstrate for the first time it functions as a tumor suppressor in breast cancer in contrast to data in other tumors. Breast cancer patients (n>1000) with high KSR1 showed better disease-free and overall survival, results also supported by Oncomine analyses, microarray data (n=2878) and genomic data from paired tumor and cell-free DNA samples revealing loss of heterozygosity.

Leveraging gene co-expression networks to pinpoint the regulation of complex traits and disease, with a focus on cardiovascular traits.

Over the past decade, the number of genome-scale transcriptional datasets in publicly available databases has climbed to nearly one million, providing an unprecedented opportunity for extensive analyses of gene co-expression networks. In systems-genetic studies of complex diseases researchers increasingly focus on groups of highly interconnected genes within complex transcriptional networks (referred to as clusters, modules or subnetworks) to uncover specific molecular processes that can inform functional disease mechanisms and pathological pathways. Here, we outline the basic paradigms underlying gene co-expression network analysis and critically review the most commonly used computational methods.