Translational Control of FOG-2 Expression in Cardiomyocytes by MicroRNA-130a

MicroRNAs are increasingly being recognized as regulators of embryonic development; however, relatively few microRNAs have been identified to regulate cardiac development. FOG-2 (also known as zfpm2) is a transcriptional co-factor that we have previously shown is critical for cardiac development. In this report, we demonstrate that FOG-2 expression is controlled at the translational level by microRNA-130a. We identified a conserved region in the FOG-2 3′ untranslated region predicted to be a target for miR-130a. To test the functional significance of this site, we generated an expression construct containing the luciferase coding region fused with the 3′ untranslated region of FOG-2 or a mutant version lacking this microRNA binding site. When these constructs were transfected into NIH 3T3 fibroblasts (which are known to express miR-130a), we observed a 3.3-fold increase in translational efficiency when the microRNA target site was disrupted. Moreover, knockdown of miR-130a in fibroblasts resulted in a 3.6-fold increase in translational efficiency. We also demonstrate that cardiomyocytes express miR-130a and can attenuate translation of mRNAs with a FOG-2 3′ untranslated region. Finally, we generated transgenic mice with cardiomyocyte over-expression of miR-130a. In the hearts of these mice, FOG-2 protein levels were reduced by as much as 80%. Histological analysis of transgenic embryos revealed ventricular wall hypoplasia and ventricular septal defects, similar to that seen in FOG-2 deficient hearts. These results demonstrate the importance of miR-130a for the regulation of FOG-2 protein expression and suggest that miR-130a may also play a role in the regulation of cardiac development

Estimation of Lattice Enthalpies of Ionic Liquids Supported by Hirshfeld Analysis

© 2015 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim. New measurements of vaporization enthalpies for 15 1:1 ionic liquids are performed by using a quartz-crystal microbalance. Collection and analysis of 33 available crystal structures of organic salts, which comprise 13 different cations and 12 anions, is performed. Their dissociation lattice enthalpies are calculated by a combination of experimental and quantum chemical quantities and are divided into the relaxation and Coulomb components to give an insight into elusive short-range interaction enthalpies. An empirical equation is developed, based on interaction-specific Hirshfeld surfaces and solvation enthalpies, which enables the estimation of the lattice enthalpy by using only the crystal-structure data. A compound view: A combination of newly collected experimental and computational data delivers the lattice enthalpies of ionic compounds. By using Hirshfeld surfaces and COSMO solvation enthalpies (see figure), a simple equation for the estimation of lattice enthalpies that requires only lattice data can be established. This paves the way to understand short-range interactions in the solid state

Multi-orbital and density-induced tunneling of bosons in optical lattices

We show that multi-orbital and density-induced tunneling have a significant impact on the phase diagram of bosonic atoms in optical lattices. Off-site interactions lead to density-induced hopping, the so-called bond-charge interactions, which can be identified with an effective tunneling potential and can reach the same order of magnitude as conventional tunneling. In addition, interaction-induced higher-band processes also give rise to strongly modified tunneling, on-site and bond-charge interactions. We derive an extended occupation-dependent Hubbard model with multi-orbitally renormalized processes and compute the corresponding phase diagram. It substantially deviates from the single-band Bose-Hubbard model and predicts strong changes of the superfluid to Mott-insulator transition. In general, the presented beyond-Hubbard physics plays an essential role in bosonic lattice systems and has an observable influence on experiments with tunable interactions.Comment: 21 pages, 7 figure

Agri-Environmental Policy Measures in Israel: The Potential of Using Market-Oriented Instruments

This paper examines the possibilities of developing agri-environmental policy measures in Israel, focusing on market-oriented instruments. A conceptual framework for developing agri-environmental policy measures is presented, first in very broad lines (mandatory regulations, economic instruments and advisory measures) and subsequently focusing on economic instruments, and specifically, on market-oriented ones. Two criteria of choice between the measures are suggested: their contribution to improving the effectiveness of the policy; and the feasibility of their implementation. This is the framework used for analyzing agri-environmental measures in Israel. Israel currently implements a mix of mandatory regulations, economic instruments and advisory measures to promote the agri-environment. The use of additional economic instruments may improve the effectiveness of the policy. When comparing the effectiveness of various economic measures, we found that the feasibility of implementation of market-oriented instruments is greater, due to the Israeli public’s preference for strengthening market orientation in the agricultural sector. Four market-oriented instruments were practiced in a pilot project conducted in an Israeli rural area. We found that in this case study, the institutional feasibility and acceptance by stakeholders were the major parameters influencing the implementation of the market-oriented instruments, whereas the instruments’ contribution to enhancing the ecological or economic effectiveness were hardly considered by the stakeholders as arguments in favor of their use

A randomized controlled trial of metformin on left ventricular hypertrophy in patients with coronary artery disease without diabetes:the MET-REMODEL trial

Aim We tested the hypothesis that metformin may regress left ventricular hypertrophy (LVH) in patients who have coronary artery disease (CAD), with insulin resistance (IR) and/or pre-diabetes. Methods and results We randomly assigned 68 patients (mean age 65 ± 8 years) without diabetes who have CAD with IR and/or pre-diabetes to receive either metformin XL (2000 mg daily dose) or placebo for 12 months. Primary endpoint was change in left ventricular mass indexed to height1.7 (LVMI), assessed by magnetic resonance imaging. In the modified intention-to-treat analysis (n = 63), metformin treatment significantly reduced LVMI compared with placebo group (absolute mean difference −1.37 (95% confidence interval: −2.63 to −0.12, P = 0.033). Metformin also significantly reduced other secondary study endpoints such as: LVM (P = 0.032), body weight (P = 0.001), subcutaneous adipose tissue (P = 0.024), office systolic blood pressure (BP, P = 0.022) and concentration of thiobarbituric acid reactive substances, a biomarker for oxidative stress (P = 0.04). The glycated haemoglobin A1C concentration and fasting IR index did not differ between study groups at the end of the study. Conclusion Metformin treatment significantly reduced LVMI, LVM, office systolic BP, body weight, and oxidative stress. Although LVH is a good surrogate marker of cardiovascular (CV) outcome, conclusive evidence for the cardio-protective role of metformin is required from large CV outcomes trials

Whole Transcriptome Sequencing Reveals Gene Expression and Splicing Differences in Brain Regions Affected by Alzheimer's Disease

Recent studies strongly indicate that aberrations in the control of gene expression might contribute to the initiation and progression of Alzheimer's disease (AD). In particular, alternative splicing has been suggested to play a role in spontaneous cases of AD. Previous transcriptome profiling of AD models and patient samples using microarrays delivered conflicting results. This study provides, for the first time, transcriptomic analysis for distinct regions of the AD brain using RNA-Seq next-generation sequencing technology. Illumina RNA-Seq analysis was used to survey transcriptome profiles from total brain, frontal and temporal lobe of healthy and AD post-mortem tissue. We quantified gene expression levels, splicing isoforms and alternative transcript start sites. Gene Ontology term enrichment analysis revealed an overrepresentation of genes associated with a neuron's cytological structure and synapse function in AD brain samples. Analysis of the temporal lobe with the Cufflinks tool revealed that transcriptional isoforms of the apolipoprotein E gene, APOE-001, -002 and -005, are under the control of different promoters in normal and AD brain tissue. We also observed differing expression levels of APOE-001 and -002 splice variants in the AD temporal lobe. Our results indicate that alternative splicing and promoter usage of the APOE gene in AD brain tissue might reflect the progression of neurodegeneration

Using Expression and Genotype to Predict Drug Response in Yeast

Personalized, or genomic, medicine entails tailoring pharmacological therapies according to individual genetic variation at genomic loci encoding proteins in drug-response pathways. It has been previously shown that steady-state mRNA expression can be used to predict the drug response (i.e., sensitivity or resistance) of non-genotyped mammalian cancer cell lines to chemotherapeutic agents. In a real-world setting, clinicians would have access to both steady-state expression levels of patient tissue(s) and a patient's genotypic profile, and yet the predictive power of transcripts versus markers is not well understood. We have previously shown that a collection of genotyped and expression-profiled yeast strains can provide a model for personalized medicine. Here we compare the predictive power of 6,229 steady-state mRNA transcript levels and 2,894 genotyped markers using a pattern recognition algorithm. We were able to predict with over 70% accuracy the drug sensitivity of 104 individual genotyped yeast strains derived from a cross between a laboratory strain and a wild isolate. We observe that, independently of drug mechanism of action, both transcripts and markers can accurately predict drug response. Marker-based prediction is usually more accurate than transcript-based prediction, likely reflecting the genetic determination of gene expression in this cross