Goutham Atla, Silvia Bonàs-Guarch, Mirabai Cuenca-Ardura, Anthony Beucher, Daniel J M Crouch, Javier Garcia-Hurtado, Ignasi Moran, T2DSystems Consortium, Manuel Irimia, Rashmi B Prasad, Anna L Gloyn, Lorella Marselli, Mara Suleiman, Thierry Berney, Eelco J P de Koning, Julie Kerr-Conte, Francois Pattou, John A Todd, Lorenzo Piemonti, Jorge Ferrer <<<

BACKGROUND: Non-coding genetic variants that influence gene transcription in pancreatic islets play a major role in the susceptibility to type 2 diabetes (T2D), and likely also contribute to type 1 diabetes (T1D) risk. For many loci, however, the mechanisms through which non-coding variants influence diabetes susceptibility are unknown.RESULTS: We examine splicing QTLs (sQTLs) in pancreatic islets from 399 human donors and observe that common genetic variation has a widespread influence on the splicing of genes with established roles in islet biology and diabetes. In parallel, we profile expression QTLs (eQTLs) and use transcriptome-wide association as well as genetic co-localization studies to assign islet sQTLs or eQTLs to T2D and T1D susceptibility signals, many of which lack candidate effector genes. This analysis reveals biologically plausible mechanisms, including the association of T2D with an sQTL that creates a nonsense isoform in ERO1B, a regulator of ER-stress and proinsulin biosynthesis. The expanded list of T2D risk effector genes reveals overrepresented pathways, including regulators of G-protein-mediated cAMP production. The analysis of sQTLs also reveals candidate effector genes for T1D susceptibility such as DCLRE1B, a senescence regulator, and lncRNA MEG3.CONCLUSIONS: These data expose widespread effects of common genetic variants on RNA splicing in pancreatic islets. The results support a role for splicing variation in diabetes susceptibility, and offer a new set of genetic targets with potential therapeutic benefit. <<<

Takahiro Seki, Yunlong Yang, Xiaoting Sun, Sharon Lim, Sisi Xie, Ziheng Guo, Wenjing Xiong, Masashi Kuroda, Hiroshi Sakaue, Kayoko Hosaka, Xu Jing, Masahito Yoshihara, Lili Qu, Xin Li, Yuguo Chen, Yihai Cao <<<

Glucose uptake is essential for cancer glycolysis and is involved in non-shivering thermogenesis of adipose tissues. Most cancers use glycolysis to harness energy for their infinite growth, invasion and metastasis. Activation of thermogenic metabolism in brown adipose tissue (BAT) by cold and drugs instigates blood glucose uptake in adipocytes. However, the functional effects of the global metabolic changes associated with BAT activation on tumour growth are unclear. Here we show that exposure of tumour-bearing mice to cold conditions markedly inhibits the growth of various types of solid tumours, including clinically untreatable cancers such as pancreatic cancers. Mechanistically, cold-induced BAT activation substantially decreases blood glucose and impedes the glycolysis-based metabolism in cancer cells. The removal of BAT and feeding on a high-glucose diet under cold exposure restore tumour growth, and genetic deletion of Ucp1-the key mediator for BAT-thermogenesis-ablates the cold-triggered anticancer effect. In a pilot human study, mild cold exposure activates a substantial amount of BAT in both healthy humans and a patient with cancer with mitigated glucose uptake in the tumour tissue. These findings provide a previously undescribed concept and paradigm for cancer therapy that uses a simple and effective approach. We anticipate that cold exposure and activation of BAT through any other approach, such as drugs and devices either alone or in combination with other anticancer therapeutics, will provide a general approach for the effective treatment of various cancers. <<<

Single-cell Hi-C (scHi-C) can identify cell-to-cell variability of three-dimensional (3D) chromatin organization, but the sparseness of measured interactions poses an analysis challenge. Here we report Higashi, an algorithm based on hypergraph representation learning that can incorporate the latent correlations among single cells to enhance overall imputation of contact maps. Higashi outperforms existing methods for embedding and imputation of scHi-C data and is able to identify multiscale 3D genome features in single cells, such as compartmentalization and TAD-like domain boundaries, allowing refined delineation of their cell-to-cell variability. Moreover, Higashi can incorporate epigenomic signals jointly profiled in the same cell into the hypergraph representation learning framework, as compared to separate analysis of two modalities, leading to improved embeddings for single-nucleus methyl-3C data. In an scHi-C dataset from human prefrontal cortex, Higashi identifies connections between 3D genome features and cell-type-specific gene regulation. Higashi can also potentially be extended to analyze single-cell multiway chromatin interactions and other multimodal single-cell omics data. <<<