The magnesium transporter NIPAL1 is a pancreatic islet-expressed protein that conditionally impacts insulin secretion.

Type 2 diabetes is a chronic metabolic disease characterized by pancreatic ß-cell dysfunction and peripheral insulin resistance. Among individuals with type 2 diabetes, ∼30% exhibit hypomagnesemia. Hypomagnesemia has been linked to insulin resistance through reduced tyrosine kinase activity of the insulin receptor; however, its impact on pancreatic ß-cell function is unknown. In this study, through analysis of several single-cell RNA-sequencing data sets in tandem with quantitative PCR validation in both murine and human islets, we identified NIPAL1 (NIPA-like domain containing 1), encoding a magnesium influx transporter, as an islet-enriched gene. A series of immunofluorescence experiments confirmed NIPAL1's magnesium-dependent expression and that it specifically localizes to the Golgi in Min6-K8 cells, a pancreatic ß-cell-like cell line (mouse insulinoma 6 clone K8). Under varying magnesium concentrations, NIPAL1 knockdown decreased both basal insulin secretion and total insulin content; in contrast, its overexpression increased total insulin content. Although the expression, distribution, and magnesium responsiveness of NIPAL1 in α-TC6 glucagonoma cells (a pancreatic α-cell line) were similar to the observations in Min6-K8 cells, no effect was observed on glucagon secretion in α-TC6 cells under the conditions studied. Overall, these results suggest that NIPAL1 expression is regulated by extracellular magnesium and that down-regulation of this transporter decreases glucose-stimulated insulin secretion and intracellular insulin content, particularly under conditions of hypomagnesemia.

Correction to: The discovery of novel predictive biomarkers and early-stage pathophysiology for the transition from gestational diabetes to type 2 diabetes.

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The discovery of novel predictive biomarkers and early-stage pathophysiology for the transition from gestational diabetes to type 2 diabetes.

AIMS/HYPOTHESIS: Gestational diabetes mellitus (GDM) affects up to 20% of pregnancies, and almost half of the women affected progress to type 2 diabetes later in life, making GDM the most significant risk factor for the development of future type 2 diabetes. An accurate prediction of future type 2 diabetes risk in the early postpartum period after GDM would allow for timely interventions to prevent or delay type 2 diabetes. In addition, new targets for interventions may be revealed by understanding the underlying pathophysiology of the transition from GDM to type 2 diabetes. The aim of this study is to identify both a predictive signature and early-stage pathophysiology of the transition from GDM to type 2 diabetes. METHODS: We used a well-characterised prospective cohort of women with a history of GDM pregnancy, all of whom were enrolled at 6-9 weeks postpartum (baseline), were confirmed not to have diabetes via 2 h 75 g OGTT and tested anually for type 2 diabetes on an ongoing basis (2 years of follow-up). A large-scale targeted lipidomic study was implemented to analyse ~1100 lipid metabolites in baseline plasma samples using a nested pair-matched case-control design, with 55 incident cases matched to 85 non-case control participants. The relationships between the concentrations of baseline plasma lipids and respective follow-up status (either type 2 diabetes or no type 2 diabetes) were employed to discover both a predictive signature and the underlying pathophysiology of the transition from GDM to type 2 diabetes. In addition, the underlying pathophysiology was examined in vivo and in vitro. RESULTS: Machine learning optimisation in a decision tree format revealed a seven-lipid metabolite type 2 diabetes predictive signature with a discriminating power (AUC) of 0.92 (87% sensitivity, 93% specificity and 91% accuracy). The signature was highly robust as it includes 45-fold cross-validation under a high confidence threshold (1.0) and binary output, which together minimise the chance of data overfitting and bias selection. Concurrent analysis of differentially expressed lipid metabolite pathways uncovered the upregulation of α-linolenic/linoleic acid metabolism (false discovery rate [FDR] 0.002) and fatty acid biosynthesis (FDR 0.005) and the downregulation of sphingolipid metabolism (FDR 0.009) as being strongly associated with the risk of developing future type 2 diabetes. Focusing specifically on sphingolipids, the downregulation of sphingolipid metabolism using the pharmacological inhibitors fumonisin B1 (FB1) and myriocin in mouse islets and Min6 K8 cells (a pancreatic beta-cell like cell line) significantly impaired glucose-stimulated insulin secretion but had no significant impact on whole-body glucose homeostasis or insulin sensitivity. CONCLUSIONS/INTERPRETATION: We reveal a novel predictive signature and associate reduced sphingolipids with the pathophysiology of transition from GDM to type 2 diabetes. Attenuating sphingolipid metabolism in islets impairs glucose-stimulated insulin secretion.

Diminished Sphingolipid Metabolism, a Hallmark of Future Type 2 Diabetes Pathogenesis, Is Linked to Pancreatic ß Cell Dysfunction.

Gestational diabetes mellitus (GDM) is the top risk factor for future type 2 diabetes (T2D) development. Ethnicity profoundly influences who will transition from GDM to T2D, with high risk observed in Hispanic women. To better understand this risk, a nested 1:1 pair-matched, Hispanic-specific, case-control design was applied to a prospective cohort with GDM history. Women who were non-diabetic 6-9 weeks postpartum (baseline) were monitored for the development of T2D. Metabolomics were performed on baseline plasma to identify metabolic pathways associated with T2D risk. Notably, diminished sphingolipid metabolism was highly associated with future T2D. Defects in sphingolipid metabolism were further implicated by integrating metabolomics and genome-wide association data, which identified two significantly enriched T2D-linked genes, CERS2 and CERS4. Follow-up experiments in mice and cells demonstrated that inhibiting sphingolipid metabolism impaired pancreatic ß cell function. These data suggest early postpartum alterations in sphingolipid biosynthesis contribute to ß cell dysfunction and T2D risk.

Isoniazid and host immune system interactions: A proposal for a novel comprehensive mode of action.

The known mode of action of isoniazid (INH) is to inhibit bacterial cell wall synthesis following activation by the bacterial catalase-peroxidase enzyme KatG in Mycobacterium tuberculosis (Mtb). This simplistic model fails to explain (a) how isoniazid penetrates waxy granulomas with its very low lipophilicity, (b) how isoniazid kills latent Mtb lacking a typical cell wall, and (c) why isoniazid treatment time is remarkably long in contrast to most other antibiotics. To address these questions, a novel comprehensive mode of action of isoniazid has been proposed here. Briefly, isoniazid eradicates latent tuberculosis (TB) by prompting slow differentiation of pro-inflammatory monocytes and providing protection against reactive species-induced "self-necrosis" of phagocytes. In the case of active TB, different immune cells form INH-NAD+ adducts to inhibit Mtb's cell wall biosynthesis. This additionally suggests that the antibacterial properties of INH do not rely on KatG of Mtb. As such, isoniazid-resistant TB needs to be re-evaluated.

Unbiased data analytic strategies to improve biomarker discovery in precision medicine.

Omics technologies promised improved biomarker discovery for precision medicine. The foremost problem of discovered biomarkers is irreproducibility between patient cohorts. From a data analytics perspective, the main reason for these failures is bias in statistical approaches and overfitting resulting from batch effects and confounding factors. The keys to reproducible biomarker discovery are: proper study design, unbiased data preprocessing and quality control analyses, and a knowledgeable application of statistics and machine learning algorithms. In this review, we discuss study design and analysis considerations and suggest standards from an expert point-of-view to promote unbiased decision-making in biomarker discovery in precision medicine.