Planeamiento territorial sostenible: un reto para el futuro de nuestras sociedades; criterios aplicados

In a large part of the 17 sustainable development objectives set as goals for humanity by the UN, sustainability can be glimpsed. As a result of the dominant socio-productive model, the only way to head towards more sustainable territories that allow achieving and maintaining the well-being of the world's population is to bear in mind the need to properly plan territorial development. This work reflects on this need and takes a step forward in the definition of the main criteria to achieve territorial sustainability at regional and local scales

Mutational spectrum of GNAL, THAP1 and TOR1A genes in isolated dystonia: study in a population from Spain and systematic literature review

[Objective] We aimed to investigate the prevalence of TOR1A, GNAL and THAP1 variants as the cause of dystonia in a cohort of Spanish patients with isolated dystonia and in the literature.[Methods] A population of 2028 subjects (including 1053 patients with different subtypes of isolated dystonia and 975 healthy controls) from southern and central Spain was included. The genes TOR1A, THAP1 and GNAL were screened using a combination of high-resolution melting analysis and direct DNA resequencing. In addition, an extensive literature search to identify original articles (published before 10 August 2020) reporting mutations in TOR1A, THAP1 or GNAL associated to dystonia was performed.[Results] Pathogenic or likely pathogenic variants in TOR1A, THAP1 and GNAL were identified in 0.48%, 0.57% and 0.29% of our patients, respectively. Five patients carried the variation p.Glu303del in TOR1A. A very rare variant in GNAL (p.Ser238Asn) was found as a putative risk factor for dystonia. In the literature, variations in TOR1A, THAP1 and GNAL accounted for about 6%, 1.8% and 1.1% of published dystonia patients, respectively.[Conclusions] There is a different genetic contribution to dystonia of these three genes in our patients (about 1.3% of patients) and in the literature (about 3.6% of patients), probably due the high proportion of adult-onset cases in our cohort. As regards age at onset, site of dystonia onset, and final distribution, in our population there is a clear differentiation between DYT-TOR1A and DYT-GNAL, with DYT-THAP1 likely to be an intermediate phenotype.This work was supported by the Carlos III Health Institute-European Regional Development Fund (ISCIII-FEDER) [PI14/01823, PI16/01575, PI18/01898, PI19/01576], the Andalusian Regional Ministry of Economics, Innovation, Science and Employment [CVI-02526, CTS-7685], the Andalusian Regional Ministry of Health and Welfare [PI-0741-2010, PI-0471-2013, PE-0210-2018, PI-0459-2018, PE-0186-2019], and the Alicia Koplowitz and Mutua Madrileña Foundations. Pilar Gómez-Garre was supported by the "Miguel Servet" program [MSII14/00018] (from ISCIII-FEDER) and “Nicolás Monardes” program [C-0048-2017] (from the Andalusian Regional Ministry of Health). Silvia Jesús was supported by the "Juan Rodés" program [B-0007-2019] and Daniel Macías-García by the “Río Hortega” program [CM18/00142] (both from ISCIII-FEDER). María Teresa Periñán was supported by the Spanish Ministry of Education [FPU16/05061]. Cristina Tejera was supported by VPPI-US from the University of Seville.Peer reviewe

Identification of sixteen novel candidate genes for late onset Parkinson’s disease

Background Parkinson’s disease (PD) is a neurodegenerative movement disorder affecting 1–5% of the general population for which neither effective cure nor early diagnostic tools are available that could tackle the pathology in the early phase. Here we report a multi-stage procedure to identify candidate genes likely involved in the etiopathogenesis of PD. Methods The study includes a discovery stage based on the analysis of whole exome data from 26 dominant late onset PD families, a validation analysis performed on 1542 independent PD patients and 706 controls from different cohorts and the assessment of polygenic variants load in the Italian cohort (394 unrelated patients and 203 controls). Results Family-based approach identified 28 disrupting variants in 26 candidate genes for PD including PARK2, PINK1, DJ-1(PARK7), LRRK2, HTRA2, FBXO7, EIF4G1, DNAJC6, DNAJC13, SNCAIP, AIMP2, CHMP1A, GIPC1, HMOX2, HSPA8, IMMT, KIF21B, KIF24, MAN2C1, RHOT2, SLC25A39, SPTBN1, TMEM175, TOMM22, TVP23A and ZSCAN21. Sixteen of them have not been associated to PD before, were expressed in mesencephalon and were involved in pathways potentially deregulated in PD. Mutation analysis in independent cohorts disclosed a significant excess of highly deleterious variants in cases (p = 0.0001), supporting their role in PD. Moreover, we demonstrated that the co-inheritance of multiple rare variants (≥ 2) in the 26 genes may predict PD occurrence in about 20% of patients, both familial and sporadic cases, with high specificity (> 93%; p = 4.4 × 10− 5). Moreover, our data highlight the fact that the genetic landmarks of late onset PD does not systematically differ between sporadic and familial forms, especially in the case of small nuclear families and underline the importance of rare variants in the genetics of sporadic PD. Furthermore, patients carrying multiple rare variants showed higher risk of manifesting dyskinesia induced by levodopa treatment. Conclusions Besides confirming the extreme genetic heterogeneity of PD, these data provide novel insights into the genetic of the disease and may be relevant for its prediction, diagnosis and treatment

Identification of novel risk loci, causal insights, and heritable risk for Parkinson's disease: a meta-analysis of genome-wide association studies

Background Genome-wide association studies (GWAS) in Parkinson's disease have increased the scope of biological knowledge about the disease over the past decade. We aimed to use the largest aggregate of GWAS data to identify novel risk loci and gain further insight into the causes of Parkinson's disease. Methods We did a meta-analysis of 17 datasets from Parkinson's disease GWAS available from European ancestry samples to nominate novel loci for disease risk. These datasets incorporated all available data. We then used these data to estimate heritable risk and develop predictive models of this heritability. We also used large gene expression and methylation resources to examine possible functional consequences as well as tissue, cell type, and biological pathway enrichments for the identified risk factors. Additionally, we examined shared genetic risk between Parkinson's disease and other phenotypes of interest via genetic correlations followed by Mendelian randomisation. Findings Between Oct 1, 2017, and Aug 9, 2018, we analysed 7·8 million single nucleotide polymorphisms in 37 688 cases, 18 618 UK Biobank proxy-cases (ie, individuals who do not have Parkinson's disease but have a first degree relative that does), and 1·4 million controls. We identified 90 independent genome-wide significant risk signals across 78 genomic regions, including 38 novel independent risk signals in 37 loci. These 90 variants explained 16–36% of the heritable risk of Parkinson's disease depending on prevalence. Integrating methylation and expression data within a Mendelian randomisation framework identified putatively associated genes at 70 risk signals underlying GWAS loci for follow-up functional studies. Tissue-specific expression enrichment analyses suggested Parkinson's disease loci were heavily brain-enriched, with specific neuronal cell types being implicated from single cell data. We found significant genetic correlations with brain volumes (false discovery rate-adjusted p=0·0035 for intracranial volume, p=0·024 for putamen volume), smoking status (p=0·024), and educational attainment (p=0·038). Mendelian randomisation between cognitive performance and Parkinson's disease risk showed a robust association (p=8·00 × 10−7). Interpretation These data provide the most comprehensive survey of genetic risk within Parkinson's disease to date, to the best of our knowledge, by revealing many additional Parkinson's disease risk loci, providing a biological context for these risk factors, and showing that a considerable genetic component of this disease remains unidentified. These associations derived from European ancestry datasets will need to be followed-up with more diverse data. Funding The National Institute on Aging at the National Institutes of Health (USA), The Michael J Fox Foundation, and The Parkinson's Foundation (see appendix for full list of funding sources)

Copernicus Marine Service ocean state report, issue 4

This is the final version. Available from Taylor & Francis via the DOI in this record. FCT/MCTE

Hepatic insulin-degrading enzyme regulates glucose and insulin homeostasis in diet-induced obese mice

© 2020 The Authors.The insulin-degrading enzyme (IDE) is a metalloendopeptidase with a high affinity for insulin. Human genetic polymorphisms in Ide have been linked to increased risk for T2DM. In mice, hepatic Ide ablation causes glucose intolerance and insulin resistance when mice are fed a regular diet. [Objective]: These studies were undertaken to further investigate its regulatory role in glucose homeostasis and insulin sensitivity in diet-induced obesity. [Methods]: To this end, we have compared the metabolic effects of loss versus gain of IDE function in mice fed a high-fat diet (HFD). [Results]: We demonstrate that loss of IDE function in liver (L-IDE-KO mouse) exacerbates hyperinsulinemia and insulin resistance without changes in insulin clearance but in parallel to an increase in pancreatic β-cell function. Insulin resistance was associated with increased FoxO1 activation and a ~2-fold increase of GLUT2 protein levels in the liver of HFD-fed mice in response to an intraperitoneal injection of insulin. Conversely, gain of IDE function (adenoviral delivery) improves glucose tolerance and insulin sensitivity, in parallel to a reciprocal ~2-fold reduction in hepatic GLUT2 protein levels. Furthermore, in response to insulin, IDE co-immunoprecipitates with the insulin receptor in liver lysates of mice with adenoviral-mediated liver overexpression of IDE. [Conclusions]: We conclude that IDE regulates hepatic insulin action and whole-body glucose metabolism in diet-induced obesity via insulin receptor levels.This work was supported by grants from the Ministerio de Economía, Industria y Competitividad: SAF2016-77871-C2-1-R to IC; SAF2016-77871-C2-2-R to GP; This work was supported by grants from the Ministerio de Ciencia e Innovación PID2019-110496RB-C21 to IC; PID2019-110496RB-C22 to GP. European Foundation for the Study of Diabetes (European Diabetes Research Programme on New Targets for Type 2 Diabetes supported by MSD-2017) to IC and GP. The project leading to these results has received funding from “la Caixa” Foundation, under agreement LCF/PR/PR18/51130007 to GP. This work was suppoted by grant from NIH GM115617 to ML

Finding genetically-supported drug targets for Parkinson’s disease using Mendelian randomization of the druggable genome

Parkinson’s disease is a neurodegenerative movement disorder that currently has no disease-modifying treatment, partly owing to inefficiencies in drug target identification and validation. We use Mendelian randomization to investigate over 3,000 genes that encode druggable proteins and predict their efficacy as drug targets for Parkinson’s disease. We use expression and protein quantitative trait loci to mimic exposure to medications, and we examine the causal effect on Parkinson’s disease risk (in two large cohorts), age at onset and progression. We propose 23 drug-targeting mechanisms for Parkinson’s disease, including four possible drug repurposing opportunities and two drugs which may increase Parkinson’s disease risk. Of these, we put forward six drug targets with the strongest Mendelian randomization evidence. There is remarkably little overlap between our drug targets to reduce Parkinson’s disease risk versus progression, suggesting different molecular mechanisms. Drugs with genetic support are considerably more likely to succeed in clinical trials, and we provide compelling genetic evidence and an analysis pipeline to prioritise Parkinson’s disease drug development