Insulin Promotes Glycogen Storage and Cell Proliferation in Primary Human Astrocytes

In the human brain, there are at least as many astrocytes as neurons. Astrocytes are known to modulate neuronal function in several ways. Thus, they may also contribute to cerebral insulin actions. Therefore, we examined whether primary human astrocytes are insulin-responsive and whether their metabolic functions are affected by the hormone.Commercially available Normal Human Astrocytes were grown in the recommended medium. Major players in the insulin signaling pathway were detected by real-time RT-PCR and Western blotting. Phosphorylation events were detected by phospho-specific antibodies. Glucose uptake and glycogen synthesis were assessed using radio-labeled glucose. Glycogen content was assessed by histochemistry. Lactate levels were measured enzymatically. Cell proliferation was assessed by WST-1 assay.We detected expression of key proteins for insulin signaling, such as insulin receptor β-subunit, insulin receptor substrat-1, Akt/protein kinase B and glycogen synthase kinase 3, in human astrocytes. Akt was phosphorylated and PI-3 kinase activity increased following insulin stimulation in a dose-dependent manner. Neither increased glucose uptake nor lactate secretion after insulin stimulation could be evidenced in this cell type. However, we found increased insulin-dependent glucose incorporation into glycogen. Furthermore, cell numbers increased dose-dependently upon insulin treatment.This study demonstrated that human astrocytes are insulin-responsive at the molecular level. We identified glycogen synthesis and cell proliferation as biological responses of insulin signaling in these brain cells. Hence, this cell type may contribute to the effects of insulin in the human brain

Evidence for a Pathogenic Role of Different Mutations at Codon 188 of PRNP

Clinical and pathological changes in familial Creutzfeldt-Jakob disease (CJD) cases may be similar or indistinguishable from sporadic CJD. Therefore determination of novel mutations in PRNP remains of major importance

Digitalisierung beruflicher Lern- und Arbeitsprozesse. Impulse aus der Bauwirtschaft und anderen gewerblich-technischen Sektoren

Der Sammelband stellt aktuelle Ansätze zum digital unterstützten beruflichen Lernen dar. Die Beiträge geben Einblicke in die dynamische Entwicklung der Schnittstellen von Erwerbsarbeit und beruflicher Aus-, Fort- und Weiterbildung im Kontext der Digitalisierung Arbeits- und Lernmitteln. Der Band schließt damit an die 2019 ebenfalls im Universitätsverlag der Technischen Universität Berlin erschienene Publikation „Berufsbildung am Bau digital“ (hrsg. von Bernd Mahrin und Johannes Meyser) an. Das erste Kapitel erörtert grundsätzliche didaktische Fragen zu digital unterstütztem Lernen und Arbeiten einschließlich der Rahmenbedingungen. Im zweiten Kapitel schließen sich Beiträge zur Kapazitätsentwicklung, zu Standards und zu digitalen Werkzeugen an. Das dritte Kapitel widmet sich konkreten Einzellösungen mit starkem Praxisbezug und hohem Transferpotenzial zum digitalisierten Arbeiten und Lernen im Bausektor und im Metallbereich. Das abschließende vierte Kapitel präsentiert übergreifend nutzbare und frei zugängliche Online-Angebote wie einen Medienpool für Bildungszwecke, eine Lernmedien-Datenbank und ein hybrides Lernsystem mit virtuellem 3D-Gebäudemodell. Das Buch ist entstanden im Rahmen des durch das Bundesministerium für Bildung und Forschung und den Europäischen Sozialfonds geförderten Projektes DigiBAU – Digitales Bauberufliches Lernen und Arbeiten. (DIPF/Orig.)The anthology presents current approaches to digitally supported professional learning. The articles provide insights into the dynamic development of the interfaces between gainful employment and vocational training and further education in the context of digitization of work and learning aids. The volume is thus connected to the publication “Berufsbildung am Bau digital” (edited by Bernd Mahrin and Johannes Meyser), which was published in 2019 by the University Press of the Technische Universität Berlin. The first chapter discusses fundamental didactic questions about digitally supported learning and working, including the framework conditions. The second chapter picks contributions on capacity development, standards, and digital tools out as central themes. The third chapter is dedicated to concrete specific solutions with strong practical relevance and high transfer potential for digitized work and learning in the construction sector and in the metal sector. The final fourth chapter presents comprehensive and freely accessible online offers such as a media pool for educational purposes, a learning media database and a hybrid learning system with a virtual 3D building model. The book was created as part of the DigiBAU project - digital vocational learning and working in the field of construction - funded by the German Federal Ministry of Education and Research and the European Social Fund. (DIPF/Orig.

Dissecting the Shared Genetic Architecture of Suicide Attempt, Psychiatric Disorders, and Known Risk Factors

Background Suicide is a leading cause of death worldwide, and nonfatal suicide attempts, which occur far more frequently, are a major source of disability and social and economic burden. Both have substantial genetic etiology, which is partially shared and partially distinct from that of related psychiatric disorders. Methods We conducted a genome-wide association study (GWAS) of 29,782 suicide attempt (SA) cases and 519,961 controls in the International Suicide Genetics Consortium (ISGC). The GWAS of SA was conditioned on psychiatric disorders using GWAS summary statistics via multitrait-based conditional and joint analysis, to remove genetic effects on SA mediated by psychiatric disorders. We investigated the shared and divergent genetic architectures of SA, psychiatric disorders, and other known risk factors. Results Two loci reached genome-wide significance for SA: the major histocompatibility complex and an intergenic locus on chromosome 7, the latter of which remained associated with SA after conditioning on psychiatric disorders and replicated in an independent cohort from the Million Veteran Program. This locus has been implicated in risk-taking behavior, smoking, and insomnia. SA showed strong genetic correlation with psychiatric disorders, particularly major depression, and also with smoking, pain, risk-taking behavior, sleep disturbances, lower educational attainment, reproductive traits, lower socioeconomic status, and poorer general health. After conditioning on psychiatric disorders, the genetic correlations between SA and psychiatric disorders decreased, whereas those with nonpsychiatric traits remained largely unchanged. Conclusions Our results identify a risk locus that contributes more strongly to SA than other phenotypes and suggest a shared underlying biology between SA and known risk factors that is not mediated by psychiatric disorders.Peer reviewe

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 ancestry(1,2). 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 analysis(3), 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 approach(4), 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 ancestry(5). 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.</p

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