EGFR Signaling Promotes β-Cell Proliferation and Survivin Expression during Pregnancy

Placental lactogen (PL) induced serotonergic signaling is essential for gestational β-cell mass expansion. We have previously shown that intact Epidermal growth factor –receptor (EGFR) function is a crucial component of this pathway. We now explored more specifically the link between EGFR and pregnancy-induced β-cell mass compensation. Islets were isolated from wild-type and β-cell-specific EGFR-dominant negative mice (E1-DN), stimulated with PL and analyzed for β-cell proliferation and expression of genes involved in gestational β-cell growth. β-cell mass dynamics were analyzed both with traditional morphometrical methods and three-dimensional optical projection tomography (OPT) of whole-mount insulin-stained pancreata. Insulin-positive volume analyzed with OPT increased 1.4-fold at gestational day 18.5 (GD18.5) when compared to non-pregnant mice. Number of islets peaked by GD13.5 (680 vs 1134 islets per pancreas, non-pregnant vs. GD13.5). PL stimulated beta cell proliferation in the wild-type islets, whereas the proliferative response was absent in the E1-DN mouse islets. Serotonin synthesizing enzymes were upregulated similarly in both the wild-type and E1-DN mice. However, while survivin (Birc5) mRNA was upregulated 5.5-fold during pregnancy in the wild-type islets, no change was seen in the E1-DN pregnant islets. PL induced survivin expression also in isolated islets and this was blocked by EGFR inhibitor gefitinib, mTOR inhibitor rapamycin and MEK inhibitor PD0325901. Our 3D-volumetric analysis of β-cell mass expansion during murine pregnancy revealed that islet number increases during pregnancy. In addition, our results suggest that EGFR signaling is required for lactogen-induced survivin expression via MAPK and mTOR pathways.     

Can long-distance dispersal shape the local and regional species pool?

Long-distance dispersal events are irregular and their role in shaping plant diversity is often discussed and modeled but rarely studied experimentally. We mimicked long-distance dispersal experimentally by sowing eleven exotic and fourteen native species into a calcareous grassland community in Estonia. Exotic species were randomly chosen from the collection of 500 herbaceous species in the Botanical Garden of the Tartu University. All exotic species were able to complete their life-cycles under the climatic and edaphic conditions in the garden. Native species originated from open dry calcareous habitats in the surroundings of the study site, but did not occur in the experimental grassland. Seven exotic species and seven native species established during the first year. In the third year, there were still three exotic species with five premature individuals, and three sown native species with sixteen individuals in the plots. These results show that long-distance dispersal both within and between regions may have an impact on species composition in target plant communities. If relatively the best established exotic speciesPhyteuma scheuchzeri would be classified as casual, one may conclude that transition among introduction and casual stages corresponds to ten’ rule. The species richness of seedlings, taking both local and sown species into account, was higher in plots with higher native established plant species richness.     

Stand characteristics and biodiversity indicators along the productivity gradient in boreal forests: Defining a critical set of indicators for the monitoring of habitat nature quality

Abstract

We quantified the effects of anthropogenic disturbances on the structure and biodiversity of boreal forests on acidic soils and created a statistically supported rational set of indicators to monitor the stand “naturalness”. For that, we surveyed various traits of tree layer, understory, herb layer, forest floor and several widely accepted biodiversity epiphytic indicators in 252 old‐aged boreal stands in Estonia, mostly dominated by Scots pine or Norway spruce. Multifactorial general linear model analyses showed that many forest characteristics and potential indicators were confounded by the gradient of soil productivity (reflected by the forest site type), local biogeographic gradients and also by stand age. Considering confounding effects, boreal forests in a near‐natural state have more large‐diameter trees (diameter at breast height >40 cm) and larger variety of diameter classes, higher proportion of spruce or deciduous trees, a larger amount of coarse woody debris in various stages, a more closed tree canopy and denser understory than managed mature forests. By increasing light availability above the field layer, forest management indirectly increases the coverage of herbs and lichens on the forest floor but reduces the alpha‐ and beta‐diversity of herbs and the proportion of graminoids. Human disturbances reduce the relative incidence of many commonly accepted biodiversity indicators such as indicator lichens, woodpeckers, wood‐dwelling insects or fungi on trees. The test for the predictive power of characteristics reacting on disturbance revealed that only a fraction of them appeared to be included in a diagnostic easy‐to‐apply set of indicators to assess the nature quality of boreal forest: the amount of dead wood, the proportion of deciduous trees, the presence of specially shaped trees and woodpeckers and, as an indicator of disturbances, the forest herb Melampyrum pratensis. Many of these indicators have already been implemented in practice.     

Amniotic Fluid Derived Stem Cells with a Renal Progenitor Phenotype Inhibit Interstitial Fibrosis in Renal Ischemia and Reperfusion Injury in Rats

Objectives

Mesenchymal stem cells derived from human amniotic fluid (hAFSCs) are a promising source for cellular therapy, especially for renal disorders, as a subpopulation is derived from the fetal urinary tract. The purpose of this study was to evaluate if hAFSCs with a renal progenitor phenotype demonstrate a nephroprotective effect in acute ischemia reperfusion (I/R) model and prevent late stage fibrosis.

Methods

A total of 45 male 12-wk-old Wistar rats were divided into three equal groups;: rats subjected to I/R injury and treated with Chang Medium, rats subjected to I/R injury and treated with hAFSCs and sham-operated animals. In the first part of this study, hAFSCs that highly expressed CD24, CD117, SIX2 and PAX2 were isolated and characterized. In the second part, renal I/R injury was induced in male rats and cellular treatment was performed 6 hours later via arterial injection. Functional and histological analyses were performed 24 hours, 48 hours and 2 months after treatment using serum creatinine, urine protein to creatinine ratio, inflammatory and regeneration markers and histomorphometric analysis of the kidney. Statistical analysis was performed by analysis of variance followed by the Tukey’s test for multiple comparisons or by nonparametric Kruskal-Wallis followed by Dunn. Statistical significance level was defined as p <0.05.

Results

hAFSCs treatment resulted in significantly reduced serum creatinine level at 24 hours, less tubular necrosis, less hyaline cast formation, higher proliferation index, less inflammatory cell infiltration and less myofibroblasts at 48h. The treated group had less fibrosis and proteinuria at 2 months after injury.

Conclusion

hAFSCs contain a renal progenitor cell subpopulation that has a nephroprotective effect when delivered intra-arterially in rats with renal I/R injury, and reduces interstitial fibrosis on long term follow-up.     

Improving the phenotype predictions of a yeast genome‐scale metabolic model by incorporating enzymatic constraints

Genome‐scale metabolic models (GEMs) are widely used to calculate metabolic phenotypes. They rely on defining a set of constraints, the most common of which is that the production of metabolites and/or growth are limited by the carbon source uptake rate. However, enzyme abundances and kinetics, which act as limitations on metabolic fluxes, are not taken into account. Here, we present GECKO, a method that enhances a GEM to account for enzymes as part of reactions, thereby ensuring that each metabolic flux does not exceed its maximum capacity, equal to the product of the enzyme's abundance and turnover number. We applied GECKO to a Saccharomyces cerevisiae GEM and demonstrated that the new model could correctly describe phenotypes that the previous model could not, particularly under high enzymatic pressure conditions, such as yeast growing on different carbon sources in excess, coping with stress, or overexpressing a specific pathway. GECKO also allows to directly integrate quantitative proteomics data; by doing so, we significantly reduced flux variability of the model, in over 60% of metabolic reactions. Additionally, the model gives insight into the distribution of enzyme usage between and within metabolic pathways. The developed method and model are expected to increase the use of model‐based design in metabolic engineering.