Repression of RNA Polymerase II Transcription by a Drosophila Oligopeptide

Background: Germline progenitors resist signals that promote differentiation into somatic cells. This occurs through the transient repression in primordial germ cells of RNA polymerase II, specifically by disrupting Ser2 phosphorylation on its C-terminal domain. Methodology/Principal Findings: Here we show that contrary to expectation the Drosophila polar granule component (pgc) gene functions as a protein rather than a non-coding RNA. Surprisingly, pgc encodes a 71-residue, dimeric, alphahelical oligopeptide repressor. In vivo data show that Pgc ablates Ser2 phosphorylation of the RNA polymerase II C-terminal domain and completely suppresses early zygotic transcription in the soma. Conclusions/Significance: We thus identify pgc as a novel oligopeptide that readily inhibits gene expression. Germ cell repression of transcription in Drosophila is thus catalyzed by a small inhibitor protein

Leaders, leadership and future primary care clinical research

Background: A strong and self confident primary care workforce can deliver the highest quality care and outcomes equitably and cost effectively. To meet the increasing demands being made of it, primary care needs its own thriving research culture and knowledge base. Methods: Review of recent developments supporting primary care clinical research. Results: Primary care research has benefited from a small group of passionate leaders and significant investment in recent decades in some countries. Emerging from this has been innovation in research design and focus, although less is known of the effect on research output. Conclusion: Primary care research is now well placed to lead a broad re-vitalisation of academic medicine, answering questions of relevance to practitioners, patients, communities and Government. Key areas for future primary care research leaders to focus on include exposing undergraduates early to primary care research, integrating this early exposure with doctoral and postdoctoral research career support, further expanding cross disciplinary approaches, and developing useful measures of output for future primary care research investment

Structural Heterogeneity and Quantitative FRET Efficiency Distributions of Polyprolines through a Hybrid Atomistic Simulation and Monte Carlo Approach

Förster Resonance Energy Transfer (FRET) experiments probe molecular distances via distance dependent energy transfer from an excited donor dye to an acceptor dye. Single molecule experiments not only probe average distances, but also distance distributions or even fluctuations, and thus provide a powerful tool to study biomolecular structure and dynamics. However, the measured energy transfer efficiency depends not only on the distance between the dyes, but also on their mutual orientation, which is typically inaccessible to experiments. Thus, assumptions on the orientation distributions and averages are usually made, limiting the accuracy of the distance distributions extracted from FRET experiments. Here, we demonstrate that by combining single molecule FRET experiments with the mutual dye orientation statistics obtained from Molecular Dynamics (MD) simulations, improved estimates of distances and distributions are obtained. From the simulated time-dependent mutual orientations, FRET efficiencies are calculated and the full statistics of individual photon absorption, energy transfer, and photon emission events is obtained from subsequent Monte Carlo (MC) simulations of the FRET kinetics. All recorded emission events are collected to bursts from which efficiency distributions are calculated in close resemblance to the actual FRET experiment, taking shot noise fully into account. Using polyproline chains with attached Alexa 488 and Alexa 594 dyes as a test system, we demonstrate the feasibility of this approach by direct comparison to experimental data. We identified cis-isomers and different static local environments as sources of the experimentally observed heterogeneity. Reconstructions of distance distributions from experimental data at different levels of theory demonstrate how the respective underlying assumptions and approximations affect the obtained accuracy. Our results show that dye fluctuations obtained from MD simulations, combined with MC single photon kinetics, provide a versatile tool to improve the accuracy of distance distributions that can be extracted from measured single molecule FRET efficiencies

The Emergence of Emotions

Emotion is conscious experience. It is the affective aspect of consciousness. Emotion arises from sensory stimulation and is typically accompanied by physiological and behavioral changes in the body. Hence an emotion is a complex reaction pattern consisting of three components: a physiological component, a behavioral component, and an experiential (conscious) component. The reactions making up an emotion determine what the emotion will be recognized as. Three processes are involved in generating an emotion: (1) identification of the emotional significance of a sensory stimulus, (2) production of an affective state (emotion), and (3) regulation of the affective state. Two opposing systems in the brain (the reward and punishment systems) establish an affective value or valence (stimulus-reinforcement association) for sensory stimulation. This is process (1), the first step in the generation of an emotion. Development of stimulus-reinforcement associations (affective valence) serves as the basis for emotion expression (process 2), conditioned emotion learning acquisition and expression, memory consolidation, reinforcement-expectations, decision-making, coping responses, and social behavior. The amygdala is critical for the representation of stimulus-reinforcement associations (both reward and punishment-based) for these functions. Three distinct and separate architectural and functional areas of the prefrontal cortex (dorsolateral prefrontal cortex, orbitofrontal cortex, anterior cingulate cortex) are involved in the regulation of emotion (process 3). The regulation of emotion by the prefrontal cortex consists of a positive feedback interaction between the prefrontal cortex and the inferior parietal cortex resulting in the nonlinear emergence of emotion. This positive feedback and nonlinear emergence represents a type of working memory (focal attention) by which perception is reorganized and rerepresented, becoming explicit, functional, and conscious. The explicit emotion states arising may be involved in the production of voluntary new or novel intentional (adaptive) behavior, especially social behavior

Stroke genetics informs drug discovery and risk prediction across ancestries

Previous genome-wide association studies (GWASs) of stroke — the second leading cause of death worldwide — were conducted predominantly in populations of European ancestry1,2. Here, in cross-ancestry GWAS meta-analyses of 110,182 patients who have had a stroke (five ancestries, 33% non-European) and 1,503,898 control individuals, we identify association signals for stroke and its subtypes at 89 (61 new) independent loci: 60 in primary inverse-variance-weighted analyses and 29 in secondary meta-regression and multitrait analyses. On the basis of internal cross-ancestry validation and an independent follow-up in 89,084 additional cases of stroke (30% non-European) and 1,013,843 control individuals, 87% of the primary stroke risk loci and 60% of the secondary stroke risk loci were replicated (P < 0.05). Effect sizes were highly correlated across ancestries. Cross-ancestry fine-mapping, in silico mutagenesis analysis3, and transcriptome-wide and proteome-wide association analyses revealed putative causal genes (such as SH3PXD2A and FURIN) and variants (such as at GRK5 and NOS3). Using a three-pronged approach4, we provide genetic evidence for putative drug effects, highlighting F11, KLKB1, PROC, GP1BA, LAMC2 and VCAM1 as possible targets, with drugs already under investigation for stroke for F11 and PROC. A polygenic score integrating cross-ancestry and ancestry-specific stroke GWASs with vascular-risk factor GWASs (integrative polygenic scores) strongly predicted ischaemic stroke in populations of European, East Asian and African ancestry5. Stroke genetic risk scores were predictive of ischaemic stroke independent of clinical risk factors in 52,600 clinical-trial participants with cardiometabolic disease. Our results provide insights to inform biology, reveal potential drug targets and derive genetic risk prediction tools across ancestries