Identification de biomarqueurs génétiques pour la détection précoce des séquelles métaboliques chez les survivants de la leucémie pédiatrique

Je tiens à remercier les bourses des IRSC en collaboration avec le programme COPSE, les Bourses du Programme de Sciences biomédicales 2014-2015 et les Bourses d’excellence de la Faculté des études supérieures et post-doctorales Hydro-Québec (2015- 2016) pour leur contribution.Introduction. Avec l’optimisation des traitements, le taux de guérison de la leucémie lymphoblastique aigüe (LLA) de l’enfant approche 90%. Cependant, 60% des survivants devront faire face à des complications à long-terme en lien avec les traitements. Ces patients ont un risque accru de complications cardiométaboliques telles que l’obésité, la résistance à l’insuline, la dyslipidémie et l’hypertension artérielle. Alors qu’il est reconnu que des facteurs génétiques contribuent au développement de ces complications, peu d’études ont observé l’impact de ces déterminants chez les survivants. Le but de cette étude est d’évaluer les associations entre les variantes rares et communes et le développement des complications cardiométaboliques chez les survivants de la LLA. Méthodes. La caractérisation du profil cardiométabolique et le séquençage de l’exome ont été réalisés dans une cohorte de 209 survivants de la LLA pédiatrique. Les variantes associées avec les complications cardiométaboliques ont été identifiées avec PLINK (commune) ou SKAT (rare et commune) et une régression logistique a été utilisée pour évaluer leur impact dans des modèles multivariés. Résultats. Nos analyses ont démontré que des variantes rares et communes dans les gènes BAD et FCRL3 sont associées au risque de présenter un phénotype dit extrême, soit trois facteurs de risque cardiométabolique et plus. Les variantes communes dans OGFOD3 et APOB et les variantes rares et communes dans BAD ont été associées à la dyslipidémie. Les variantes communes dans BAD et SERPINA6 ont été associées respectivement à l’obésité et la résistance à l’insuline. Conclusion. Notre étude a révélé une susceptibilité génétique au développement des complications cardiométaboliques chez les survivants de la LLA pédiatrique. Ces biomarqueurs pourront être utilisés pour la détection précoce et l’intervention chez cette population à haut risque.Background. While cure rates for childhood acute lymphoblastic leukemia (cALL) now exceed 80%, over 60% of survivors will face treatment-related long-term sequelae, including cardiometabolic complications such as obesity, insulin resistance, dyslipidemia and hypertension. Although genetic susceptibility contributes to the development of these problems, there are very few studies that have so far addressed this issue in a cALL survivorship context. In this study, we aimed at evaluating the associations between common and rare genetic variants and long-term cardiometabolic complications in survivors of cALL. Method. We examined the cardiometabolic profile and performed whole-exome sequencing in 209 cALL survivors from the PETALE cohort. Variants associated with cardiometabolic outcomes were identified using PLINK (common) or SKAT (common and rare) and a logistic regression was used to evaluate their impact in multivariate models. Results. Our results showed that rare and common variants in the BAD and FCRL3 genes were associated (p<0.05) with an extreme cardiometabolic phenotype (3 or more cardiometabolic risk factors). Common variants in OGFOD3 and APOB as well as rare and common BAD variants were significantly (p<0.05) associated with dyslipidemia. Common BAD and SERPINA6 variants were associated (p<0.05) with obesity and insulin resistance, respectively. Conclusion. In summary, we identified genetic susceptibility loci as contributing factors to the development of late treatment-related cardiometabolic complications in cALL survivors. These biomarkers could be used as early detection strategies to identify susceptible individuals and implement appropriate measures and follow-up to prevent the development of risk factors in this high-risk population

Genome wide analysis of gene dosage in 24,092 individuals estimates that 10,000 genes modulate cognitive ability

International audienceGenomic copy number variants (CNVs) are routinely identified and reported back to patients with neuropsychiatric disorders, but their quantitative effects on essential traits such as cognitive ability are poorly documented. We have recently shown that the effect size of deletions on cognitive ability can be statistically predicted using measures of intolerance to haploinsufficiency. However, the effect sizes of duplications remain unknown. It is also unknown if the effect of multigenic CNVs are driven by a few genes intolerant to haploinsufficiency or distributed across tolerant genes as well. Here, we identified all CNVs > 50 kilobases in 24,092 individuals from unselected and autism cohorts with assessments of general intelligence. Statistical models used measures of intolerance to haploinsufficiency of genes included in CNVs to predict their effect size on intelligence. Intolerant genes decrease general intelligence by 0.8 and 2.6 points of intelligence quotient when duplicated or deleted, respectively. Effect sizes showed no heterogeneity across cohorts. Validation analyses demonstrated that models could predict CNV effect sizes with 78% accuracy. Data on the inheritance of 27,766 CNVs showed that deletions and duplications with the same effect size on intelligence occur de novo at the same frequency. We estimated that around 10,000 intolerant and tolerant genes negatively affect intelligence when deleted, and less than 2% have large effect sizes. Genes encompassed in CNVs were not enriched in any GOterms but gene regulation and brain expression were GOterms overrepresented in the intolerant subgroup. Such pervasive effects on cognition may be related to emergent properties of the genome not restricted to a limited number of biological pathways

Crustal Deformation in the India‐Eurasia Collision Zone From 25 Years of GPS Measurements

The India‐Eurasia collision zone is the largest deforming region on the planet; direct measurements of present‐day deformation from Global Positioning System (GPS) have the potential to discriminate between competing models of continental tectonics. But the increasing spatial resolution and accuracy of observations have only led to increasingly complex realizations of competing models. Here we present the most complete, accurate, and up‐to‐date velocity field for India‐Eurasia available, comprising 2576 velocities measured during 1991–2015. The core of our velocity field is from the Crustal Movement Observation Network of China‐I/II: 27 continuous stations observed since 1999; 56 campaign stations observed annually during 1998–2007; 1000 campaign stations observed in 1999, 2001, 2004, and 2007; 260 continuous stations operating since late 2010; and 2000 campaign stations observed in 2009, 2011, 2013, and 2015. We process these data and combine the solutions in a consistent reference frame with stations from the Global Strain Rate Model compilation, then invert for continuous velocity and strain rate fields. We update geodetic slip rates for the major faults (some vary along strike), and find that those along the major Tibetan strike‐slip faults are in good agreement with recent geological estimates. The velocity field shows several large undeforming areas, strain focused around some major faults, areas of diffuse strain, and dilation of the high plateau. We suggest that a new generation of dynamic models incorporating strength variations and strain‐weakening mechanisms is required to explain the key observations. Seismic hazard in much of the region is elevated, not just near the major faults

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages

Effect of angiotensin-converting enzyme inhibitor and angiotensin receptor blocker initiation on organ support-free days in patients hospitalized with COVID-19

IMPORTANCE Overactivation of the renin-angiotensin system (RAS) may contribute to poor clinical outcomes in patients with COVID-19. Objective To determine whether angiotensin-converting enzyme (ACE) inhibitor or angiotensin receptor blocker (ARB) initiation improves outcomes in patients hospitalized for COVID-19. DESIGN, SETTING, AND PARTICIPANTS In an ongoing, adaptive platform randomized clinical trial, 721 critically ill and 58 non–critically ill hospitalized adults were randomized to receive an RAS inhibitor or control between March 16, 2021, and February 25, 2022, at 69 sites in 7 countries (final follow-up on June 1, 2022). INTERVENTIONS Patients were randomized to receive open-label initiation of an ACE inhibitor (n = 257), ARB (n = 248), ARB in combination with DMX-200 (a chemokine receptor-2 inhibitor; n = 10), or no RAS inhibitor (control; n = 264) for up to 10 days. MAIN OUTCOMES AND MEASURES The primary outcome was organ support–free days, a composite of hospital survival and days alive without cardiovascular or respiratory organ support through 21 days. The primary analysis was a bayesian cumulative logistic model. Odds ratios (ORs) greater than 1 represent improved outcomes. RESULTS On February 25, 2022, enrollment was discontinued due to safety concerns. Among 679 critically ill patients with available primary outcome data, the median age was 56 years and 239 participants (35.2%) were women. Median (IQR) organ support–free days among critically ill patients was 10 (–1 to 16) in the ACE inhibitor group (n = 231), 8 (–1 to 17) in the ARB group (n = 217), and 12 (0 to 17) in the control group (n = 231) (median adjusted odds ratios of 0.77 [95% bayesian credible interval, 0.58-1.06] for improvement for ACE inhibitor and 0.76 [95% credible interval, 0.56-1.05] for ARB compared with control). The posterior probabilities that ACE inhibitors and ARBs worsened organ support–free days compared with control were 94.9% and 95.4%, respectively. Hospital survival occurred in 166 of 231 critically ill participants (71.9%) in the ACE inhibitor group, 152 of 217 (70.0%) in the ARB group, and 182 of 231 (78.8%) in the control group (posterior probabilities that ACE inhibitor and ARB worsened hospital survival compared with control were 95.3% and 98.1%, respectively). CONCLUSIONS AND RELEVANCE In this trial, among critically ill adults with COVID-19, initiation of an ACE inhibitor or ARB did not improve, and likely worsened, clinical outcomes. TRIAL REGISTRATION ClinicalTrials.gov Identifier: NCT0273570

Satellite geodetic imaging reveals internal deformation of western Tibet

It is often assumed that the majority of the interseismic strain accumulates around the major mapped geological block boundaries. However, numerous recent earthquakes in the continents have occurred on faults that were previously unidentified. Existing deformation data from Tibet are insufficiently dense to map the distribution of interseismic strain. Here we combine 265 interferograms formed from 166 radar images with GPS data to constrain a high-resolution velocity field covering ∼200,000 km 2 of western Tibet. We confirm that the slip rate of the Karakoram Fault is low (<6 mm/yr), but show that areas of focused strain do not coincide with the major fault structures. Some of this strain is due to postseismic relaxation after a M w 6.8 earthquake that occurred in 1996 on a structure that is difficult to identify in satellite imagery. Models of seismic hazard that rely on imperfect knowledge of the boundaries of crustal blocks can therefore underestimate hazard from unknown faults. Copyright 2012 by the American Geophysical Union

Genomic determinants of long-term cardiometabolic complications in childhood acute lymphoblastic leukemia survivors

Abstract Background While cure rates for childhood acute lymphoblastic leukemia (cALL) now exceed 80%, over 60% of survivors will face treatment-related long-term sequelae, including cardiometabolic complications such as obesity, insulin resistance, dyslipidemia and hypertension. Although genetic susceptibility contributes to the development of these problems, there are very few studies that have so far addressed this issue in a cALL survivorship context. Methods In this study, we aimed at evaluating the associations between common and rare genetic variants and long-term cardiometabolic complications in survivors of cALL. We examined the cardiometabolic profile and performed whole-exome sequencing in 209 cALL survivors from the PETALE cohort. Variants associated with cardiometabolic outcomes were identified using PLINK (common) or SKAT (common and rare) and a logistic regression was used to evaluate their impact in multivariate models. Results Our results showed that rare and common variants in the BAD and FCRL3 genes were associated (p<0.05) with an extreme cardiometabolic phenotype (3 or more cardiometabolic risk factors). Common variants in OGFOD3 and APOB as well as rare and common BAD variants were significantly (p<0.05) associated with dyslipidemia. Common BAD and SERPINA6 variants were associated (p<0.05) with obesity and insulin resistance, respectively. Conclusions In summary, we identified genetic susceptibility loci as contributing factors to the development of late treatment-related cardiometabolic complications in cALL survivors. These biomarkers could be used as early detection strategies to identify susceptible individuals and implement appropriate measures and follow-up to prevent the development of risk factors in this high-risk population