Stress-Induced Premature Senescence or Stress-Induced Senescence-Like Phenotype: One In Vivo

No consensus exists so far on the definition of cellular senescence. The narrowest definition of senescence is irreversible growth arrest triggered by telomere shortening counting cell generations (definition 1). Other authors gave an enlarged functional definition encompassing any kind of irreversible arrest of proliferative cell types induced by damaging agents or cell cycle deregulations after overexpression of proto-oncogenes (definition 2). As stress increases, the proportion of cells in “stress-induced premature senescence-like phenotype” according to definition 1 or “stress-induced premature senescence,” according to definition 2, should increase when a culture reaches growth arrest, and the proportion of cells that reached telomere-dependent replicative senescence due to the end-replication problem should decrease. Stress-induced premature senescence-like phenotype and telomere-dependent replicatively senescent cells share basic similarities such as irreversible growth arrest and resistance to apoptosis, which may appear through different pathways. Irreversible growth arrest after exposure to oxidative stress and generation of DNA damage could be as efficient in avoiding immortalisation as “telomere-dependent” replicative senescence. Probabilities are higher that the senescent cells (according to definition 2) appearing in vivo are in stress-induced premature senescence rather than in telomere-dependent replicative senescence. Examples are given suggesting these cells affect in vivo tissue (patho)physiology and aging

Age-dependent impact of the major common genetic risk factor for COVID-19 on severity and mortality

AG has received support by NordForsk Nordic Trial Alliance (NTA) grant, by Academy of Finland Fellow grant N. 323116 and the Academy of Finland for PREDICT consortium N. 340541. The Richards research group is supported by the Canadian Institutes of Health Research (CIHR) (365825 and 409511), the Lady Davis Institute of the Jewish General Hospital, the Canadian Foundation for Innovation (CFI), the NIH Foundation, Cancer Research UK, Genome Québec, the Public Health Agency of Canada, the McGill Interdisciplinary Initiative in Infection and Immunity and the Fonds de Recherche Québec Santé (FRQS). TN is supported by a research fellowship of the Japan Society for the Promotion of Science for Young Scientists. GBL is supported by a CIHR scholarship and a joint FRQS and Québec Ministry of Health and Social Services scholarship. JBR is supported by an FRQS Clinical Research Scholarship. Support from Calcul Québec and Compute Canada is acknowledged. TwinsUK is funded by the Welcome Trust, the Medical Research Council, the European Union, the National Institute for Health Research-funded BioResource and the Clinical Research Facility and Biomedical Research Centre based at Guy’s and St. Thomas’ NHS Foundation Trust in partnership with King’s College London. The Biobanque Québec COVID19 is funded by FRQS, Genome Québec and the Public Health Agency of Canada, the McGill Interdisciplinary Initiative in Infection and Immunity and the Fonds de Recherche Québec Santé. These funding agencies had no role in the design, implementation or interpretation of this study. The COVID19-Host(a)ge study received infrastructure support from the DFG Cluster of Excellence 2167 “Precision Medicine in Chronic Inflammation (PMI)” (DFG Grant: “EXC2167”). The COVID19-Host(a)ge study was supported by the German Federal Ministry of Education and Research (BMBF) within the framework of the Computational Life Sciences funding concept (CompLS grant 031L0165). Genotyping in COVID19-Host(a)ge was supported by a philantropic donation from Stein Erik Hagen. The COVID GWAs, Premed COVID-19 study (COVID19-Host(a)ge_3) was supported by "Grupo de Trabajo en Medicina Personalizada contra el COVID-19 de Andalucia"and also by the Instituto de Salud Carlos III (CIBERehd and CIBERER). Funding comes from COVID-19-GWAS, COVID-PREMED initiatives. Both of them are supported by "Consejeria de Salud y Familias" of the Andalusian Government. DMM is currently funded by the the Andalussian government (Proyectos Estratégicos-Fondos Feder PE-0451-2018). The Columbia University Biobank was supported by Columbia University and the National Center for Advancing Translational Sciences, NIH, through Grant Number UL1TR001873. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH or Columbia University. The SPGRX study was supported by the Consejería de Economía, Conocimiento, Empresas y Universidad #CV20-10150. The GEN-COVID study was funded by: the MIUR grant “Dipartimenti di Eccellenza 2018-2020” to the Department of Medical Biotechnologies University of Siena, Italy; the “Intesa San Paolo 2020 charity fund” dedicated to the project NB/2020/0119; and philanthropic donations to the Department of Medical Biotechnologies, University of Siena for the COVID-19 host genetics research project (D.L n.18 of March 17, 2020). Part of this research project is also funded by Tuscany Region “Bando Ricerca COVID-19 Toscana” grant to the Azienda Ospedaliero Universitaria Senese (CUP I49C20000280002). Authors are grateful to: the CINECA consortium for providing computational resources; the Network for Italian Genomes (NIG) (http://www.nig.cineca.it) for its support; the COVID-19 Host Genetics Initiative (https://www.covid19hg.org/); the Genetic Biobank of Siena, member of BBMRI-IT, Telethon Network of Genetic Biobanks (project no. GTB18001), EuroBioBank, and RD-Connect, for managing specimens. Genetics against coronavirus (GENIUS), Humanitas University (COVID19-Host(a)ge_4) was supported by Ricerca Corrente (Italian Ministry of Health), intramural funding (Fondazione Humanitas per la Ricerca). The generous contribution of Banca Intesa San Paolo and of the Dolce&Gabbana Fashion Firm is gratefully acknowledged. Data acquisition and sample processing was supported by COVID-19 Biobank, Fondazione IRCCS Cà Granda Milano; LV group was supported by MyFirst Grant AIRC n.16888, Ricerca Finalizzata Ministero della Salute RF-2016-02364358, Ricerca corrente Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, the European Union (EU) Programme Horizon 2020 (under grant agreement No. 777377) for the project LITMUS- “Liver Investigation: Testing Marker Utility in Steatohepatitis”, Programme “Photonics” under grant agreement “101016726” for the project “REVEAL: Neuronal microscopy for cell behavioural examination and manipulation”, Fondazione Patrimonio Ca’ Granda “Liver Bible” PR-0361. DP was supported by Ricerca corrente Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, CV PREVITAL “Strategie di prevenzione primaria nella popolazione Italiana” Ministero della Salute, and Associazione Italiana per la Prevenzione dell’Epatite Virale (COPEV). Genetic modifiers for COVID-19 related illness (BeLCovid_1) was supported by the "Fonds Erasme". The Host genetics and immune response in SARS-Cov-2 infection (BelCovid_2) study was supported by grants from Fondation Léon Fredericq and from Fonds de la Recherche Scientifique (FNRS). The INMUNGEN-CoV2 study was funded by the Consejo Superior de Investigaciones Científicas. KUL is supported by the German Research Foundation (LU 1944/3-1) SweCovid is funded by the SciLifeLab/KAW national COVID-19 research program project grant to Michael Hultström (KAW 2020.0182) and the Swedish Research Council to Robert Frithiof (2014-02569 and 2014-07606). HZ is supported by Jeansson Stiftelser, Magnus Bergvalls Stiftelse. The COMRI cohort is funded by Technical University of Munich, Munich, Germany. Genotyping for the COMRI cohort was performed and funded by the Genotyping Laboratory of Institute for Molecular Medicine Finland FIMM Technology Centre, University of Helsinki, Helsinki, Finland. These funding agencies had no role in the design, implementation or interpretation of this study.Background: There is considerable variability in COVID-19 outcomes amongst younger adults—and some of this variation may be due to genetic predisposition. We characterized the clinical implications of the major genetic risk factor for COVID-19 severity, and its age-dependent effect, using individual-level data in a large international multi-centre consortium. Method: The major common COVID-19 genetic risk factor is a chromosome 3 locus, tagged by the marker rs10490770. We combined individual level data for 13,424 COVID-19 positive patients (N=6,689 hospitalized) from 17 cohorts in nine countries to assess the association of this genetic marker with mortality, COVID-19-related complications and laboratory values. We next examined if the magnitude of these associations varied by age and were independent from known clinical COVID-19 risk factors. Findings: We found that rs10490770 risk allele carriers experienced an increased risk of all-cause mortality (hazard ratio [HR] 1·4, 95% confidence interval [CI] 1·2–1·6) and COVID-19 related mortality (HR 1·5, 95%CI 1·3–1·8). Risk allele carriers had increased odds of several COVID-19 complications: severe respiratory failure (odds ratio [OR] 2·0, 95%CI 1·6-2·6), venous thromboembolism (OR 1·7, 95%CI 1·2-2·4), and hepatic injury (OR 1·6, 95%CI 1·2-2·0). Risk allele carriers ≤ 60 years had higher odds of death or severe respiratory failure (OR 2·6, 95%CI 1·8-3·9) compared to those > 60 years OR 1·5 (95%CI 1·3-1·9, interaction p-value=0·04). Amongst individuals ≤ 60 years who died or experienced severe respiratory COVID-19 outcome, we found that 31·8% (95%CI 27·6-36·2) were risk variant carriers, compared to 13·9% (95%CI 12·6-15·2%) of those not experiencing these outcomes. Prediction of death or severe respiratory failure among those ≤ 60 years improved when including the risk allele (AUC 0·82 vs 0·84, p=0·016) and the prediction ability of rs10490770 risk allele was similar to, or better than, most established clinical risk factors. Interpretation: The major common COVID-19 risk locus on chromosome 3 is associated with increased risks of morbidity and mortality—and these are more pronounced amongst individuals ≤ 60 years. The effect on COVID-19 severity was similar to, or larger than most established risk factors, suggesting potential implications for clinical risk management.Academy of Finland Fellow grant N. 323116Academy of Finland for PREDICT consortium N. 340541.Canadian Institutes of Health Research (CIHR) (365825 and 409511)Lady Davis Institute of the Jewish General HospitalCanadian Foundation for Innovation (CFI)NIH FoundationCancer Research UKGenome QuébecPublic Health Agency of CanadaMcGill Interdisciplinary Initiative in Infection and Immunity and the Fonds de Recherche Québec Santé (FRQS)Japan Society for the Promotion of Science for Young ScientistsCIHR scholarship and a joint FRQS and Québec Ministry of Health and Social Services scholarshipFRQS Clinical Research ScholarshipCalcul QuébecCompute CanadaWelcome TrustMedical Research CouncEuropean UnionNational Institute for Health Research-funded BioResourceClinical Research Facility and Biomedical Research Centre based at Guy’s and St. Thomas’ NHS Foundation TrustKing’s College LondonGenome QuébecPublic Health Agency of CanadaMcGill Interdisciplinary Initiative in Infection and ImmunityFonds de Recherche Québec Santé(DFG Grant: “EXC2167”)(CompLS grant 031L0165)Stein Erik Hagen"Grupo de Trabajo en Medicina Personalizada contra el COVID-19 de Andalucia"Instituto de Salud Carlos III (CIBERehd and CIBERER)COVID-19-GWASCOVID-PREMED initiatives"Consejeria de Salud y Familias" of the Andalusian GovernmentAndalusian government (Proyectos Estratégicos-Fondos Feder PE-0451-2018)Columbia UniversityNational Center for Advancing Translational SciencesNIH Grant Number UL1TR001873Consejería de Economía, Conocimiento, Empresas y Universidad #CV20-10150MIUR grant “Dipartimenti di Eccellenza 2018-2020”“Intesa San Paolo 2020 charity fund” dedicated to the project NB/2020/0119Tuscany Region “Bando Ricerca COVID-19 Toscana”CINECA consortiumNetwork for Italian Genomes (NIG)COVID-19 Host Genetics InitiativeGenetic Biobank of SienaEuroBioBankRD-ConnectRicerca Corrente (Italian Ministry of Health)Fondazione Humanitas per la RicercaBanca Intesa San PaoloDolce&Gabbana Fashion FirmCOVID-19 BiobankFondazione IRCCS Cà Granda MilanoMyFirst Grant AIRC n.16888Ricerca Finalizzata Ministero della Salute RF-2016-02364358Ricerca corrente Fondazione IRCCS Ca’ Granda Ospedale Maggiore PoliclinicoEuropean Union (EU) Programme Horizon 2020 (under grant agreement No. 777377)“Photonics” “101016726”Fondazione Patrimonio Ca’ Granda “Liver Bible” PR-0361CV PREVITAL “Strategie di prevenzione primaria nella popolazione Italiana” Ministero della Salute, and Associazione Italiana per la Prevenzione dell’Epatite Virale (COPEV)"Fonds Erasme"Fondation Léon FredericqFonds de la Recherche Scientifique (FNRS)Consejo Superior de Investigaciones CientíficasGerman Research Foundation (LU 1944/3-1)SciLifeLab/KAW national COVID-19 research program project (KAW 2020.0182)Swedish Research Council (2014-02569 and 2014-07606)Jeansson Stiftelser, Magnus Bergvalls StiftelseTechnical University of Munich, Munich, GermanyGenotyping Laboratory of Institute for Molecular Medicine Finland FIMM Technology Centre, University of Helsinki, Helsinki, Finlan

Upscaling of solute transport in heterogeneous media : theories and experiments to compare and validate Fickian and non-Fickian approaches/

The classical Fickian model for solute transport in porous media cannot correctly predict the spreading (the dispersion) of contaminant plumes in a heterogeneous subsoil unless its structure is completely characterized. Although the required precision is outside the reach of current field characterization methods, the classical Fickian model remains the most widely used model among practitioners. Two approaches can be adopted to solve the effect of physical heterogeneity on transport. First, upscaling methods allow one to compute "apparent" scale-dependent parameters to be used in the classical Fickian model. In the second approach, upscaled (non-Fickian) transport equations with scale-independent parameters are used. This research aims at comparing upscaling methods for Fickian transport parameters with non-Fickian upscaled transport equations, and evaluate their capabilities to predict solute transport in heterogeneous media. The models were tested using simplified numerical examples (perfectly stratified aquifers and bidimensional heterogeneous media). Hypothetical lognormal permeability fields were investigated, for different values of variance, correlation length and anisotropy ratio. Examples exhibiting discrete and multimodal permeability distributions were also investigated using both numerical examples and a physical laboratory experiment. It was found that non-Fickian transport equations involving fractional derivatives have higher upscaling capabilities regarding the prediction of contaminant plume migration and spreading, although their key parameters can only be inferred from inverse modelling of test data.(FSA 3)--UCL, 200

Upscaling of solute transport in mildly heterogeneous media. Comparison of Fickian and non-Fickian approaches

Non-Fickian solute transport models were investigated in the case of mildly heterogeneous porous media using a synthetic two-dimensional In(K) field, generated randomly. Transport was fully solved using MT3D and exhibited scale-dependence. Monte Carlo simulations were used to quantify uncertainty in the absence of a priori data. Then, inverse and direct modelling using the continuous time random walk approach and the fractional advection-dispersion equation were performed in order to assess the upscaling capacities of these models. A sensitive improvement could be observed compared to the classical advection-dispersion model

A comparative review of upscaling methods for solute transport in heterogeneous porous media

The classical Fickian model for solute transport in porous media cannot correctly predict the spreading (the dispersion) of contaminant plumes in a heterogeneous subsurface unless its structure is completely characterized. Although the required precision is outside the reach of current field characterization methods, the advection-dispersion model remains the most widely used model among practitioners. Two approaches can be adopted to solve the effect of physical heterogeneity on transport. First, based on a given characterization of the spatial structure of the subsurface, upscaling methods allow the computation of apparent scale-dependent parameters (especially longitudinal dispersivity) to be used in the classical Fickian model. In the second approach, upscaled (non-Fickian) transport equations with scale-independent parameters are used. In this paper, efforts are made to classify and review upscaling methods for Fickian transport parameters and non-Fickian upscaled transport equations for solute transport, with an emphasis on their mathematical properties and their (one-dimensional) analytical formulations. In particular, their capacity to mode( scale effects in apparent longitudinal dispersion is investigated. Upscaling methods and upscaled models are illustrated in the case of two three-dimensional synthetic aquifers, with lognormal hydraulic conductivity distributions characterized by variance values of 2 and 8. (C) 2008 Etsevier B.V. All rights reserved