Metformin Decreases the Incidence of Pancreatic Ductal Adenocarcinoma Promoted by Diet-induced Obesity in the Conditional KrasG12D Mouse Model.

Pancreatic ductal adenocarcinoma (PDAC) is a particularly deadly disease. Chronic conditions, including obesity and type-2 diabetes are risk factors, thus making PDAC amenable to preventive strategies. We aimed to characterize the chemo-preventive effects of metformin, a widely used anti-diabetic drug, on PDAC development using the KrasG12D mouse model subjected to a diet high in fats and calories (HFCD). LSL-KrasG12D/+;p48-Cre (KC) mice were given control diet (CD), HFCD, or HFCD with 5 mg/ml metformin in drinking water for 3 or 9 months. After 3 months, metformin prevented HFCD-induced weight gain, hepatic steatosis, depletion of intact acini, formation of advanced PanIN lesions, and stimulation of ERK and mTORC1 in pancreas. In addition to reversing hepatic and pancreatic histopathology, metformin normalized HFCD-induced hyperinsulinemia and hyperleptinemia among the 9-month cohort. Importantly, the HFCD-increased PDAC incidence was completely abrogated by metformin (p < 0.01). The obesogenic diet also induced a marked increase in the expression of TAZ in pancreas, an effect abrogated by metformin. In conclusion, administration of metformin improved the metabolic profile and eliminated the promoting effects of diet-induced obesity on PDAC formation in KC mice. Given the established safety profile of metformin, our findings have a strong translational potential for novel chemo-preventive strategies for PDAC

The Ursinus Weekly, October 13, 1905

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Altitude and life-history shape the evolution of Heliconius wings.

Phenotypic divergence between closely related species has long interested biologists. Taxa that inhabit a range of environments and have diverse natural histories can help understand how selection drives phenotypic divergence. In butterflies, wing color patterns have been extensively studied but diversity in wing shape and size is less well understood. Here, we assess the relative importance of phylogenetic relatedness, natural history, and habitat on shaping wing morphology in a large dataset of over 3500 individuals, representing 13 Heliconius species from across the Neotropics. We find that both larval and adult behavioral ecology correlate with patterns of wing sexual dimorphism and adult size. Species with solitary larvae have larger adult males, in contrast to gregarious Heliconius species, and indeed most Lepidoptera, where females are larger. Species in the pupal-mating clade are smaller than those in the adult-mating clade. Interestingly, we find that high-altitude species tend to have rounder wings and, in one of the two major Heliconius clades, are also bigger than their lowland relatives. Furthermore, within two widespread species, we find that high-altitude populations also have rounder wings. Thus, we reveal novel adaptive wing morphological divergence among Heliconius species beyond that imposed by natural selection on aposematic wing coloration

Tracing Sub-Structure in the European American Population with PCA-Informative Markers

Genetic structure in the European American population reflects waves of migration and recent gene flow among different populations. This complex structure can introduce bias in genetic association studies. Using Principal Components Analysis (PCA), we analyze the structure of two independent European American datasets (1,521 individuals–307,315 autosomal SNPs). Individual variation lies across a continuum with some individuals showing high degrees of admixture with non-European populations, as demonstrated through joint analysis with HapMap data. The CEPH Europeans only represent a small fraction of the variation encountered in the larger European American datasets we studied. We interpret the first eigenvector of this data as correlated with ancestry, and we apply an algorithm that we have previously described to select PCA-informative markers (PCAIMs) that can reproduce this structure. Importantly, we develop a novel method that can remove redundancy from the selected SNP panels and show that we can effectively remove correlated markers, thus increasing genotyping savings. Only 150–200 PCAIMs suffice to accurately predict fine structure in European American datasets, as identified by PCA. Simulating association studies, we couple our method with a PCA-based stratification correction tool and demonstrate that a small number of PCAIMs can efficiently remove false correlations with almost no loss in power. The structure informative SNPs that we propose are an important resource for genetic association studies of European Americans. Furthermore, our redundancy removal algorithm can be applied on sets of ancestry informative markers selected with any method in order to select the most uncorrelated SNPs, and significantly decreases genotyping costs