Impact of Physical Activity and Exercise on the Epigenome in Skeletal Muscle and Effects on Systemic Metabolism

Julio Plaza-Diaz and Concepcion M. Aguilera are part of the "UGR Plan Propio de Investigacion 2016" and the "Excellence actions: Unit of Excellence on Exercise and Health (UCEES), University of Granada". Julio Plaza-Diaz is supported by a fellowship to postdoctoral researchers at foreign universities and research centers from the "Fundacion Ramon Areces", Madrid, Spain. Francisco Javier Ruiz-Ojeda is supported by a fellowship from Spanish Government "Agencia Estatal de Investigacion-Juan de la Cierva-Incorporacion" program (IJC2020-042739-I). Alvaro TorresMartos is supported by the Project "Transductores Moleculares del Ejercicio Fisico y la Activacion del Tejido Adiposo Pardo: en Busca de Nuevas Dianas Terapeuticas en la Comunicacion Intercelular" funded by "Consejeria de Economia, Conocimiento, Empresas y Universidad (PY18-4455), Junta de Andalucia", Spain.Exercise and physical activity induces physiological responses in organisms, and adaptations in skeletal muscle, which is beneficial for maintaining health and preventing and/or treating most chronic diseases. These adaptations are mainly instigated by transcriptional responses that ensue in reaction to each individual exercise, either resistance or endurance. Consequently, changes in key metabolic, regulatory, and myogenic genes in skeletal muscle occur as both an early and late response to exercise, and these epigenetic modifications, which are influenced by environmental and genetic factors, trigger those alterations in the transcriptional responses. DNA methylation and histone modifications are the most significant epigenetic changes described in gene transcription, linked to the skeletal muscle transcriptional response to exercise, and mediating the exercise adaptations. Nevertheless, other alterations in the epigenetics markers, such as epitranscriptomics, modifications mediated by miRNAs, and lactylation as a novel epigenetic modification, are emerging as key events for gene transcription. Here, we provide an overview and update of the impact of exercise on epigenetic modifications, including the well-described DNA methylations and histone modifications, and the emerging modifications in the skeletal muscle. In addition, we describe the effects of exercise on epigenetic markers in other metabolic tissues; also, we provide information about how systemic metabolism or its metabolites influence epigenetic modifications in the skeletal muscle."Fundacion Ramon Areces", Madrid, SpainSpanish Government "Agencia Estatal de Investigacion-Juan de la Cierva-Incorporacion" program IJC2020-042739-IProject "Transductores Moleculares del Ejercicio Fisico y la Activacion del Tejido Adiposo Pardo: en Busca de Nuevas Dianas Terapeuticas en la Comunicacion Intercelular" - "Consejeria de Economia, Conocimiento, Empresas y Universidad, Junta de Andalucia", PY18-445

Extraction of thermal characteristics of surrounding geological layers of a geothermal heat exchanger by 3D numerical simulations

Ground thermal conductivity and borehole thermal resistance are key parameters for the design of closed Ground-Source Heat Pump (GSHP) systems. The standard method to determine these parameters is the Thermal Response Test (TRT). This test analyses the ground thermal response to a constant heat power injection or extraction by measuring inlet and outlet temperatures of the fluid at the top of the borehole heat exchanger. These data are commonly evaluated by models considering the ground being homogeneous and isotropic. This approach estimates an effective ground thermal conductivity representing an average of the thermal conductivity of the different layers crossed by perforation. In order to obtain a thermal conductivity profile of the ground as a function of depth, two additional inputs are needed; first, a measurement of the borehole temperature profile and, second, an analysis procedure taking into account ground is not homogeneous. This work presents an analysis procedure, complementing the standard TRT analysis, estimating the thermal conductivity profile from a temperature profile along the borehole during the test. The analysis procedure is implemented by a 3D Finite Element Model (FEM) in which depth depending thermal conductivity of the subsoil is estimated by fitting simulation results with experimental data. The methodology is evaluated by the recorded temperature profiles throughout a TRT in a BHE (Borehole Heat Exchanger) monitored facility, which allowed the detection of a highly conductive layer at 25 meters depth. © 2015 Elsevier Ltd. All rights reserved.This work has been supported by the EIT Climate-KIC, a body of the European Union inside the PhD Programme of TBE Platform.Aranzabal, N.; Martos, J.; Montero Reguera, ÁE.; Monreal Mengual, L.; Soret, J.; Torres, J.; García Olcina, R. (2016). Extraction of thermal characteristics of surrounding geological layers of a geothermal heat exchanger by 3D numerical simulations. Applied Thermal Engineering. 99:92-102. doi:10.1016/j.applthermaleng.2015.12.109921029

Novel Wireless Sensor System for Dynamic Characterization of Borehole Heat Exchangers

The design and field test of a novel sensor system based in autonomous wireless sensors to measure the temperature of the heat transfer fluid along a borehole heat exchanger (BHE) is presented. The system, by means of two specials valves, inserts and extracts miniaturized wireless sensors inside the pipes of the borehole, which are carried by the thermal fluid. Each sensor is embedded in a small sphere of just 25 mm diameter and 8 gr weight, containing a transceiver, a microcontroller, a temperature sensor and a power supply. A wireless data processing unit transmits to the sensors the acquisition configuration before the measurements, and also downloads the temperature data measured by the sensor along its way through the BHE U-tube. This sensor system is intended to improve the conventional thermal response test (TRT) and it allows the collection of information about the thermal characteristics of the geological structure of subsurface and its influence in borehole thermal behaviour, which in turn, facilitates the implementation of TRTs in a more cost-effective and reliable way

Body mass index interacts with a genetic-risk score for depression increasing the risk of the disease in high-susceptibility individuals

This study was funded by the Spanish Ministry of Health, the Institute of Health Carlos III (ISCIII), and the European Regional Development Fund (grants PS09/02272, PS09/02147, PS09/01095, PS09/00849, PS09/00461, and PI12-02755); the Andalusian Council of Health (grant PI-0569-2010); the Spanish Network of Primary Care Research, redIAPP (grant RD06/ 0018); the Aragon group (grant RD06/0018/0020); the Bizkaya group (grant RD06/0018/0018); the Castilla-Leon group (grant RD06/0018/0027); the Mental Health Barcelona Group (grant RD06/0018/0017); the Mental Health, Services and Primary Care Malaga group (grant RD06/0018/0039); and the projects "PI18/00238" and "PI18/00467" funded by the Institute of Health Carlos III (Co-funded by European Regional Development Fund/European Social Fund "A way tomake Europe"/"Investing in your future"). This study was performed as part of a PhD thesis conducted within the Official Doctoral Programme in Biomedicine of the University of Granada, Spain. Augusto Anguita-Ruiz was supported by a Ministry of Economy and Competitiveness and Institute of Health Carlos III fellowship (IFI17/00048). Juan Antonio Zarza-Rebollo received financial support from the Spanish Ministry of Economy and Competitiveness (BES-2017-082698). Ana M. Perez-Gutierrez was supported by a grant from the Ministry of Economy and Competitiveness and Institute of Health Carlos III (FI19/00228). Elena Lopez-Isac received financial support from the Spanish Ministry of Science and Innovation Juan de la Cierva Incorporacion Program (IJC2019040080-I), and Margarita Rivera was supported by the Ministry of Economy and Competitiveness Ramon y Cajal Program (RYC-2014-15774). The authors thank the Institute of Health Carlos III (ISCIII), the European Regional Development Fund (FEDER), the Andalusian Council of Health and Andalusian Health Service (SAS), the Primary Care Prevention and Health Promotion Research Network (redIAPP), the Biomedical Research Institute of Malaga (IBIMA), and the Biomedical Research Centre (CIBM) from the University of Granada for their economic and logistic support. The authors thank all the patients and General Practitioners who participated in the trial.Depression is strongly associated with obesity among other chronic physical diseases. The latest mega- and meta-analysis of genome-wide association studies have identified multiple risk loci robustly associated with depression. In this study, we aimed to investigate whether a genetic-risk score (GRS) combining multiple depression risk single nucleotide polymorphisms (SNPs) might have utility in the prediction of this disorder in individuals with obesity. A total of 30 depression-associated SNPs were included in a GRS to predict the risk of depression in a large case-control sample from the Spanish PredictD-CCRT study, a national multicentre, randomized controlled trial, which included 104 cases of depression and 1546 controls. An unweighted GRS was calculated as a summation of the number of risk alleles for depression and incorporated into several logistic regression models with depression status as the main outcome. Constructed models were trained and evaluated in the whole recruited sample. Non-genetic-risk factors were combined with the GRS in several ways across the five predictive models in order to improve predictive ability. An enrichment functional analysis was finally conducted with the aim of providing a general understanding of the biological pathways mapped by analyzed SNPs. We found that an unweighted GRS based on 30 risk loci was significantly associated with a higher risk of depression. Although the GRS itself explained a small amount of variance of depression, we found a significant improvement in the prediction of depression after including some non-genetic-risk factors into the models. The highest predictive ability for depression was achieved when the model included an interaction term between the GRS and the body mass index (BMI), apart from the inclusion of classical demographic information as marginal terms (AUC = 0.71, 95% CI = [0.65, 0.76]). Functional analyses on the 30 SNPs composing the GRS revealed an over-representation of the mapped genes in signaling pathways involved in processes such as extracellular remodeling, proinflammatory regulatory mechanisms, and circadian rhythm alterations. Although the GRS on its own explained a small amount of variance of depression, a significant novel feature of this study is that including non-genetic-risk factors such as BMI together with a GRS came close to the conventional threshold for clinical utility used in ROC analysis and improves the prediction of depression. In this study, the highest predictive ability was achieved by the model combining the GRS and the BMI under an interaction term. Particularly, BMI was identified as a trigger-like risk factor for depression acting in a concerted way with the GRS component. This is an interesting finding since it suggests the existence of a risk overlap between both diseases, and the need for individual depression genetics-risk evaluation in subjects with obesity. This research has therefore potential clinical implications and set the basis for future research directions in exploring the link between depression and obesityassociated disorders. While it is likely that future genome-wide studies with large samples will detect novel genetic variants associated with depression, it seems clear that a combination of genetics and non-genetic information (such is the case of obesity status and other depression comorbidities) will still be needed for the optimization prediction of depression in high-susceptibility individuals.Instituto de Salud Carlos III Spanish Government Institute of Health Carlos III (ISCIII) European Commission PS09/02272 PS09/02147 PS09/01095 PS09/00849 PS09/00461 PI12-02755Andalusian Council of Health PI-0569-2010Spanish Network of Primary Care Research, redIAPP RD06/ 0018Gobierno de Aragon RD06/0018/0020Bizkaya group RD06/0018/0018Castilla-Leon group RD06/0018/0027Mental Health Barcelona Group RD06/0018/0017Mental Health, Services and Primary Care Malaga group RD06/0018/0039Instituto de Salud Carlos III PI18/00238 PI18/00467 FI19/00228European Regional Development Fund/European Social Fund "A way tomake Europe"/"Investing in your future"Ministry of Economy and CompetitivenessInstitute of Health Carlos III fellowship IFI17/00048Spanish Government BES-2017-082698Spanish Ministry of Science and Innovation Juan de la Cierva Incorporacion Program IJC2019040080-IMinistry of Economy and Competitiveness Ramon y Cajal Program RYC-2014-15774Andalusian Council of HealthAndalusian Health Service (SAS)Primary Care Prevention and Health Promotion Research Network (redIAPP)Biomedical Research Institute of Malaga (IBIMA)Biomedical Research Centre (CIBM) from the University of GranadaEuropean Commissio

Body mass index interacts with a genetic-risk score for depression increasing the risk of the disease in high-susceptibility individuals

Depression is strongly associated with obesity among other chronic physical diseases. The latest mega- and meta-analysis of genome-wide association studies have identified multiple risk loci robustly associated with depression. In this study, we aimed to investigate whether a genetic-risk score (GRS) combining multiple depression risk single nucleotide polymorphisms (SNPs) might have utility in the prediction of this disorder in individuals with obesity. A total of 30 depression-associated SNPs were included in a GRS to predict the risk of depression in a large case-control sample from the Spanish PredictD-CCRT study, a national multicentre, randomized controlled trial, which included 104 cases of depression and 1546 controls. An unweighted GRS was calculated as a summation of the number of risk alleles for depression and incorporated into several logistic regression models with depression status as the main outcome. Constructed models were trained and evaluated in the whole recruited sample. Non-genetic-risk factors were combined with the GRS in several ways across the five predictive models in order to improve predictive ability. An enrichment functional analysis was finally conducted with the aim of providing a general understanding of the biological pathways mapped by analyzed SNPs. We found that an unweighted GRS based on 30 risk loci was significantly associated with a higher risk of depression. Although the GRS itself explained a small amount of variance of depression, we found a significant improvement in the prediction of depression after including some non-genetic-risk factors into the models. The highest predictive ability for depression was achieved when the model included an interaction term between the GRS and the body mass index (BMI), apart from the inclusion of classical demographic information as marginal terms (AUC = 0.71, 95% CI = [0.65, 0.76]). Functional analyses on the 30 SNPs composing the GRS revealed an over-representation of the mapped genes in signaling pathways involved in processes such as extracellular remodeling, proinflammatory regulatory mechanisms, and circadian rhythm alterations. Although the GRS on its own explained a small amount of variance of depression, a significant novel feature of this study is that including non-genetic-risk factors such as BMI together with a GRS came close to the conventional threshold for clinical utility used in ROC analysis and improves the prediction of depression. In this study, the highest predictive ability was achieved by the model combining the GRS and the BMI under an interaction term. Particularly, BMI was identified as a trigger-like risk factor for depression acting in a concerted way with the GRS component. This is an interesting finding since it suggests the existence of a risk overlap between both diseases, and the need for individual depression genetics-risk evaluation in subjects with obesity. This research has therefore potential clinical implications and set the basis for future research directions in exploring the link between depression and obesity-associated disorders. While it is likely that future genome-wide studies with large samples will detect novel genetic variants associated with depression, it seems clear that a combination of genetics and non-genetic information (such is the case of obesity status and other depression comorbidities) will still be needed for the optimization prediction of depression in high-susceptibility individuals

Treatment with tocilizumab or corticosteroids for COVID-19 patients with hyperinflammatory state: a multicentre cohort study (SAM-COVID-19)

Objectives: The objective of this study was to estimate the association between tocilizumab or corticosteroids and the risk of intubation or death in patients with coronavirus disease 19 (COVID-19) with a hyperinflammatory state according to clinical and laboratory parameters. Methods: A cohort study was performed in 60 Spanish hospitals including 778 patients with COVID-19 and clinical and laboratory data indicative of a hyperinflammatory state. Treatment was mainly with tocilizumab, an intermediate-high dose of corticosteroids (IHDC), a pulse dose of corticosteroids (PDC), combination therapy, or no treatment. Primary outcome was intubation or death; follow-up was 21 days. Propensity score-adjusted estimations using Cox regression (logistic regression if needed) were calculated. Propensity scores were used as confounders, matching variables and for the inverse probability of treatment weights (IPTWs). Results: In all, 88, 117, 78 and 151 patients treated with tocilizumab, IHDC, PDC, and combination therapy, respectively, were compared with 344 untreated patients. The primary endpoint occurred in 10 (11.4%), 27 (23.1%), 12 (15.4%), 40 (25.6%) and 69 (21.1%), respectively. The IPTW-based hazard ratios (odds ratio for combination therapy) for the primary endpoint were 0.32 (95%CI 0.22-0.47; p < 0.001) for tocilizumab, 0.82 (0.71-1.30; p 0.82) for IHDC, 0.61 (0.43-0.86; p 0.006) for PDC, and 1.17 (0.86-1.58; p 0.30) for combination therapy. Other applications of the propensity score provided similar results, but were not significant for PDC. Tocilizumab was also associated with lower hazard of death alone in IPTW analysis (0.07; 0.02-0.17; p < 0.001). Conclusions: Tocilizumab might be useful in COVID-19 patients with a hyperinflammatory state and should be prioritized for randomized trials in this situatio

Methodology Aspects of Colony Maintain for a Murine Model of Amyotrophic Lateral Sclerosis (ALS) TDP-43 Proteinopathy

The use of genetically engineered mouse (GEMs) models provides an unprecedented opportunity to study the genetic basis of diseases and gene function, therefore it is paramount to determine reproductive parameters that guarantee proper colony maintenance. We studied the reproductive parameters of mice hemizygous for TDP-43A315T transgene, which are viable, fertile, and express a mutant human TAR DNA binding protein (hTDP-43) cDNA harboring an amino acid substitution associated with familial amyotrophic lateral sclerosis (fALS). TDP43A315T mice were backcrossed to a C57Bl6/J pure background for four consecutive generations. The Tg offspring genotype were then confirmed by PCR assays. Our statistical analysis indicated there were no differences in the sex and number of pups per offspring when hemizygous female and male TDP43A315T mice were backcrossed to C57Bl6/J mice. Interestingly, our results showed significant differences in the number of offspring expressing the transgene when hemizygous TDP43A315T male mice were used as breeders. Therefore, our findings suggest that male TDP43A315T mice transfer the transgene with a greater genetic strengths. Such is an important breeding consideration to ensure the principle of reduction in animal experimentation considering most basic research with models focuses on males and excludes female mice