Biology subjects

Brunel, H.

Publications and source records attributed to Brunel, H..

3 recordsLinked to original sources

bioRxiv2024

Argon neuroprotection in a non-human primate model of transient endovascular ischemic stroke.

BackgroundPrevious studies have demonstrated the efficacy of argon neuroprotection in rodent models of cerebral ischemia. The objective of the present study was to confirm a potential neuroprotective effect of argon in a non-human primate model of endovascular ischemic stroke as an essential step before considering the use of argon as a neuroprotective agent in humans. MethodsThirteen adult monkeys (Macaca mulatta) were allocated to two groups: a control group (n=8) without neuroprotection and an argon group (n=5) in which argon inhalation (90 min) was initiated 30 minutes after onset of ischemia. Animals in both groups underwent brain MRI (pre-ischemic) at least 7 days before the intervention. The monkeys were subjected to focal cerebral ischemia induced by a transient (90 min) middle cerebral artery occlusion (tMCAO). After tMCAO, MRI was performed 1 hour after cerebral reperfusion. The ischemic core volume was defined by the apparent diffusion coefficient (aDC) and edema in fluid attenuated inversion recovery (FLAIR) acquisitions. MRI masks were applied to distinguish between cortical and subcortical abnormalities. In addition, a modified version of the Rankin scale was used to neurologically assess post-tMCAO. ResultsDespite variability in the ischemic core and edema volumes in the control group, argon significantly reduced ischemic core volume after ischemia compared to the control group (1.1{+/-}1.6 cm3 vs. 8.5{+/-}8.1 cm3; p=0.03). This effect was limited to cortical structures (0.6{+/-}1.1 cm3 vs. 7.4{+/-}7.2 cm3; p=0.03). No significant differences were observed in the edema volumes. Measures of neurological clinical outcome suggested a better prognosis in argon-treated animals. ConclusionsIn the tMCAO macaque model, argon induced effective neuroprotective effects, leading to a reduced ischemic core in cortical areas. These results support the potential use of this therapeutic approach for future clinical studies in stroke patients.

neuroscience↗

bioRxiv2018

Integrated genetic and methylomic analyses identify shared biology between autism and autistic traits

Previous studies have identified differences in DNA methylation in autistic individuals compared to neurotypical individuals. Yet, it is unclear if this extends to autistic traits - subclinical manifestation of autism features in the general population. Here, we investigate the association between DNA methylation at birth (cord blood), and scores on the Social and Communication Disorders Checklist (SCDC), a measure of autistic traits, in 701 8-year olds, by conducting a methylome-wide association study (MWAS) using DNA methylation data from cord-blood. Whilst did not identify significant loci demonstrating differential methylation, we observe a degree of overlap between the SCDC MWAS and post-mortem brain methylation signature in autism. Validating this, we observe an enrichment for genes that are dysregulated in the post-mortem autism brain. Finally, integrating genome-wide data from more than 40,000 individuals and mQTL maps from cord-blood, we demonstrate that mQTLs of CpGs associated with SCDC scores at different P-value thresholds are significantly shifted towards lower P-values in a GWAS for autism. We validate this using a GWAS of SCDC, and demonstrate a lack of enrichment in a GWAS of Alzheimers disease. Our results highlight the shared cross-tissue epigenetic architecture of autism and autistic traits, and demonstrate that mQTLs associated with methylation changes in childhood autistic traits are enriched for common genetic variants associated with autism and autistic traits.

genetics↗

bioRxiv2017

lme4qtl: Linear Mixed Models With Flexible Covariance Structure For Genetic Studies Of Related Individuals

BackgroundQuantitative trait locus (QTL) mapping in genetic data often involves analysis of correlated observations, which need to be accounted for to avoid false association signals. This is commonly performed by modeling such correlations as random effects in linear mixed models (LMMs). The R package lme4 is a well-established tool that implements major LMM features using sparse matrix methods; however, it is not fully adapted for QTL mapping association and linkage studies. In particular, two LMM features are lacking in the base version of lme4: the definition of random effects by custom covariance matrices; and parameter constraints, which are essential in advanced QTL models. Apart from applications in linkage studies of related individuals, such functionalities are of high interest for association studies in situations where multiple covariance matrices need to be modeled, a scenario not covered by many genome-wide association study (GWAS) software.\n\nResultsTo address the aforementioned limitations, we developed a new R package lme4qtl as an extension of lme4. First, lme4qtl contributes new models for genetic studies within a single tool integrated with lme4 and its companion packages. Second, lme4qtl offers a flexible framework for scenarios with multiple levels of relatedness and becomes efficient when covariance matrices are sparse. We showed the value of our package using real family-based data in the Genetic Analysis of Idiopathic Thrombophilia 2 (GAIT2) project.\n\nConclusionsOur software lme4qtl enables QTL mapping models with a versatile structure of random effects and efficient computation for sparse covariances. lme4qtl is available at https://github.com/variani/lme4qtl.

genetics↗