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Metabolic syndrome is a cluster of risk factors, such as obesity, insulin resistance, and hyperlipidemia that increases the individual’s likelihood of developing cardiovascular diseases. Patients inflicted with metabolic disorders also suffer from tissue repair defect. Mitsugumin 53 (MG53) is a protein essential to cellular membrane repair. It facilitates the nucleation of intracellular vesicles to sites of membrane disruption to create repair patches, contributing to the regenerative capacity of skeletal and cardiac muscle tissues upon injury. Since individuals suffering from metabolic syndrome possess tissue regeneration deficiency and MG53 plays a crucial role in restoring membrane integrity, we studied MG53 activity in mice models exhibiting metabolic disorders induced by a 6 month high-fat diet (HFD) feeding. Western blotting showed that MG53 expression is not altered within the skeletal and cardiac muscles of mice with metabolic syndrome. Rather, we found that MG53 levels in blood circulation were actually reduced. This data directly contradicts findings presented by Song et. al that indict MG53 as a causative factor for metabolic syndrome (Nature 494, 375-379). The diminished MG53 serum level observed may contribute to the inadequate tissue repair aptitude exhibited by diabetic patients. Furthermore, immunohistochemical analyses reveal that skeletal muscle fibers of mice with metabolic disorders experience localization of subcellular MG53 around mitochondria. This clustering may represent an adaptive response to oxidative stress resulting from HFD feeding and may implicate MG53 as a guardian to protect damaged mitochondria. Therapeutic approaches that elevate MG53 expression in serum circulation may be a novel method to treat the degenerative tissue repair function of diabetic patients. [ABSTRACT FROM AUTHOR]

There has been much effort to prioritize genomic variants with respect to their impact on “function”. However, function is often not precisely defined: sometimes it is the disease association of a variant; on other occasions, it reflects a molecular effect on transcription or epigenetics. Here, we coupled multiple genomic predictors to build GRAM, a GeneRAlized Model, to predict a well-defined experimental target: the expression-modulating effect of a non-coding variant on its associated gene, in a transferable, cell-specific manner. Firstly, we performed feature engineering: using LASSO, a regularized linear model, we found transcription factor (TF) binding most predictive, especially for TFs that are hubs in the regulatory network; in contrast, evolutionary conservation, a popular feature in many other variant-impact predictors, has almost no contribution. Moreover, TF binding inferred from in vitro SELEX is as effective as that from in vivo ChIP-Seq. Second, we implemented GRAM integrating only SELEX features and expression profiles; thus, the program combines a universal regulatory score with an easily obtainable modifier reflecting the particular cell type. We benchmarked GRAM on large-scale MPRA datasets, achieving AUROC scores of 0.72 in GM12878 and 0.66 in a multi-cell line dataset. We then evaluated the performance of GRAM on targeted regions using luciferase assays in the MCF7 and K562 cell lines. We noted that changing the insertion position of the construct relative to the reporter gene gave very different results, highlighting the importance of carefully defining the exact prediction target of the model. Finally, we illustrated the utility of GRAM in fine-mapping causal variants and developed a practical software pipeline to carry this out. In particular, we demonstrated in specific examples how the pipeline could pinpoint variants that directly modulate gene expression within a larger linkage-disequilibrium block associated with a phenotype of interest (e.g., for an eQTL). [ABSTRACT FROM AUTHOR]