The zinc finger transcription factor Snail is a known effector of epithelial-to-mesenchymal transition (EMT), a process that underlies the enhanced invasiveness and chemoresistance of common to cancerous cells. Induction of Snail-driven EMT has also been shown to drive a range of pro-survival metabolic adaptations in different cancers. In the present study, we sought to determine the specific role that Snail has in driving EMT and adaptive metabolic programming in pancreatic ductal adenocarcinoma (PDAC) by overexpressing Snail in a PDAC cell line, Panc1, and in immortalized, non-tumorigenic human pancreatic ductal epithelial (HPDE) cells. Snail overexpression was able to induce EMT in both pancreatic cell lines through suppression of epithelial markers and upregulation of mesenchymal markers alongside changes in cell morphology and enhanced migratory capacity. Snail-overexpressed pancreatic cells additionally displayed increased glucose uptake and lactate production with concomitant reduction in oxidative metabolism measurements. Snail overexpression reduced maximal respiration in both Panc1 and HPDE cells, with further reductions seen in ATP production, spare respiratory capacity and non-mitochondrial respiration in Snail overexpressing Panc1 cells. Accordingly, lower expression of mitochondrial electron transport chain proteins was observed with Snail overexpression, particularly within Panc1 cells. Modelling of 13C metabolite flux within both cell lines revealed decreased carbon flux from glucose in the TCA cycle in snai1-overexpressing Panc1 cells only. This work further highlights the role that Snail plays in EMT and demonstrates its specific effects on metabolic reprogramming of glucose metabolism in PDAC.

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Understanding the neurogenic causes of obesity may reveal novel drug targets to counter the obesity crisis and associated sequelae. Here, we investigate whether the deletion of GPR37L1, an astrocyte-specific orphan G protein-coupled receptor, affects whole-body energy homeostasis in mice. We subjected male Gpr37l1−/− mice and littermate wildtype (Gpr37l1+/+, C57BL/6J background) controls to either 12 weeks of high-fat diet (HFD) or chow feeding, or to 1 year of chow diet, with body composition quantified by EchoMRI, glucose handling by glucose tolerance test and metabolic rate by indirect calorimetry. Following an HFD, Gpr37l1−/− mice had similar glucose handling, body weight and fat mass compared with wildtype controls. Interestingly, we observed a significantly elevated respiratory exchange ratio in HFD- and chow-fed Gpr37l1−/− mice during daylight hours. After 1 year of chow feeding, we again saw no differences in glucose and insulin tolerance or body weight between genotypes, nor in energy expenditure or respiratory exchange ratio. However, there was significantly lower fat mass accumulation, and higher ambulatory activity in the Gpr37l1−/− mice during night hours. Overall, these results indicate that while GPR37L1 may play a minor role in whole-body metabolism, it is not a viable clinical target for the treatment of obesity.

Recent advances in single-cell genomics and transcriptomics technologies have transformed our understanding of cellular heterogeneity in growth, development, ageing and disease. Despite these advances, however, single-cell metabolite profiling (i.e., metabolomics) methods have comparatively lagged behind due to limitations in technology. We have developed a high-throughput method for the detection and quantification of a wide range of phosphatidylcholine (PC) and sphingomyelin (SM) species from single cells that combines fluorescence-assisted cell sorting (FACS) with automated chip-based nanoelectrospray ionisation (nanoESI) and shotgun lipidomics. We show herein that our method is capable of quantifying more than 50 different PC and SM species from single cells and can easily distinguish between cells of different lineages or cells treated with exogenous fatty acids. Moreover, our method can detect more subtle differences in the lipidome between cell lines of the same cancer type. Our approach has the capacity to be run in parallel with other single-cell technologies to deliver near-complete multi-omics data on cells with a similar phenotype and has the capacity to significantly advance our current knowledge on cellular heterogeneity.