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Metabolite exchange between stromal and tumor cells or among tumor cells themselves accompanies metabolic reprogramming in cancer including pancreatic adenocarcinoma (PDAC). Some tumor cells import and utilize lactate for oxidative energy production (reverse Warburg-metabolism) and the presence of these “reverse Warburg“ cells associates with a more aggressive phenotype and worse prognosis, though the underlying mechanisms are poorly understood. We now show that PDAC cells (BxPc3, A818-6, T3M4) expressing the lactate-importer monocarboxylate transporter-1 (MCT1) are protected by lactate against gemcitabine-induced apoptosis in a MCT1-dependent fashion, contrary to MCT1-negative PDAC cells (Panc1, Capan2). Moreover, lactate administration under glucose starvation, resembling reverse Warburg co a phenotype of BxPc3 and T3M4 cells that confers greater potential of clonal growth upon re-exposure to glucose, along with drug resistance and elevated expression of the stemness marker Nestin and reprogramming factors (Oct4, KLF4, Nanog). These lactate dependent effects on stemness properties are abrogated by the MCT1/lactate-uptake inhibitor 7ACC2 or MCT1 knock-down. Furthermore, the clinical relevance of these observations was supported by detecting co-expression of MCT1 and reprogramming factors in human PDAC tissues. In conclusion, the MCT1-dependent import of lactate supplies “reverse Warburg “PDAC cells with an efficient driver of metabostemness. This condition may essentially contribute to malignant traits including therapy resistance.

Background Previously, we identified six prediabetes clusters, three at moderate and three at high-risk for type 2 diabetes and/or complications. While this novel classification could enable earlier and improved disease prevention, it relies on intensive clinical phenotyping. Here, we developed a machine learning workflow to identify blood-based epigenetic markers to distinguish between prediabetes clusters. Methods DNA methylation was profiled in blood cells of different cohorts including individuals that belong to clusters 2 (low-risk), 3, 5, and 6 (each high-risk) and data was subjected to a machine learning workflow. Results In a discovery cohort ( n  = 187), we identified 1,557 CpG sites as predictors for clusters 2, 3, 5, and 6. These CpGs were sufficient to distinguish between individuals belonging to the high-risk clusters 3, 5 and 6 in an independent replication cohort ( n  = 146) with an accuracy of 92%. Between 300 and 339 CpG sites were specific for each cluster and the corresponding genes linked to TGF-β receptor and calcium signaling (cluster 3), MAPK cascade and ECM organization (cluster 5), and Wnt/SMAD signaling (cluster 6), mirroring the metabolic deterioration observed in each cluster. Conclusions Without the need for complex clinical measurements, the identified blood-based epigenetic signatures may improve the detection of individuals at high-risk of developing diabetes and complications and point to the potential molecular mechanism responsible for the heterogeneity in prediabetes. These markers highlight the potential of the blood epigenome as an effective proxy for predicting future complications and make extensive clinical assessments obsolete, enabling the identification of clusters in larger populations.

The menstrual cycle impacts food intake, peripheral metabolism, and brain function. One well-known central regulator of eating behavior is the hormone insulin. Here, we show that the responsiveness of functional brain networks to central insulin varies dynamically across the menstrual cycle in premenopausal women. Intranasal insulin (INI) administration increases functional connectivity within networks that support decision-making processes (namely the default mode and salience network) in the follicular compared to the luteal phase of the menstrual cycle. In contrast, INI decreases functional connectivity within the somatosensory network during the follicular phase relative to the luteal phase. In response to visual food cues, hippocampus and dorsal striatum activity are higher in the luteal compared to the follicular phase, particularly to sweet food. Estradiol and progesterone levels predict these changes. This could contribute to higher food craving and food intake observed in the luteal phase. Our findings emphasize sex hormones’ role in modulating brain sensitivity to hormonal signals and external stimuli. Central insulin effects on brain networks and food cue reactivity in the hippocampus and striatum vary dynamically across the menstrual cycle in premenopausal women, with estradiol and progesterone predicting these changes.

Brain insulin responsiveness is linked to long-term weight gain and unhealthy body fat distribution. Here we show that short-term overeating with calorie-rich sweet and fatty foods triggers liver fat accumulation and disrupted brain insulin action that outlasted the time-frame of its consumption in healthy weight men. Hence, brain response to insulin can adapt to short-term changes in diet before weight gain and may facilitate the development of obesity and associated diseases. Short-term overeating with calorie-rich snacks is shown to disrupt insulin action in the brain of men, which outlasted the time-frame of overeating.