Biology subjects

Cobat, A.

Publications and source records attributed to Cobat, A..

7 recordsLinked to original sources

bioRxiv2024

Polyunsaturated fatty acid-derived lipid mediator networks characterize COVID-19 severity and risk for critical illness

Severe COVID-19, caused by SARS-CoV-2 infection, is characterized by excessive inflammation leading to the development of pneumonia and acute respiratory distress syndrome. Bioactive lipid mediators (LMs) derived from {omega}6 and {omega}3 polyunsaturated fatty acids are central to the regulation of inflammation, controlling both its initiation and resolution. Still, their role in COVID-19 remains underexplored. By employing a holistic approach involving the analysis of white blood cell transcriptomes, targeted lipidomics, cytokine and immune cell profiling, across the spectrum of disease severity groups, including mild non-hospitalized patients and healthy individuals, we now show that LM networks are profoundly altered in COVID-19, correlate with inflammatory patterns, and stratify patients according to disease severity. Central to this are CYP450-derived LMs such as 20-HETE, lipid peroxidation metabolites such as iPF2a-VI, and lipoxygenase-derived LMs such as 12-HETE, all of which are major vasoactive mediators of inflammation. Among them, 20-HETE appears to be a promising prognostic biomarker for ICU admission and a potential therapeutic target for severe COVID-19 disease. Our study thus underscores the significance of LM networks in COVID-19 pathophysiology and sheds light into the broader mechanisms driving viral pneumonia in humans. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=158 SRC="FIGDIR/small/610123v1_ufig1.gif" ALT="Figure 1"> View larger version (69K): org.highwire.dtl.DTLVardef@6e406dorg.highwire.dtl.DTLVardef@47c9deorg.highwire.dtl.DTLVardef@1162488org.highwire.dtl.DTLVardef@b86ba3_HPS_FORMAT_FIGEXP M_FIG C_FIG

immunology↗

bioRxiv2020

Basal Expression of Interferon-Stimulated Genes Drives Population Differences in Monocyte Susceptibility to Influenza Infection

There is considerable inter-individual and inter-population variability in response to viruses. The potential of monocytes to elicit type-I interferon responses has attracted attention to their role in viral infections. Here, we use an ex vivo model to characterize the role of cellular heterogeneity in human variation of monocyte responses to influenza A virus (IAV) exposure. Using single-cell RNA-sequencing, we show widespread inter-individual variability in the percentage of IAV-infected monocytes. We show that cells escaping viral infection display increased mRNA expression of type-I interferon stimulated genes and decreased expression of ribosomal genes, relative to both infected cells and those never exposed to IAV. While this host defense strategy is shared between CD16+/CD16- monocytes, we also uncover CD16+-specific mRNA expression of IL6 and TNF in response to IAV, and a stronger resistance of CD16+ monocytes to IAV infection. Notably, individuals with high cellular susceptibility to IAV are characterized by a lower activation at basal state of an IRF/STAT-induced transcriptional network, which includes antiviral genes such as IFITM3, MX1, and OAS3. Finally, using flow cytometry and bulk RNA-sequencing across 200 individuals of African and European ancestry, we observe a higher number of CD16+ monocytes and lower susceptibility to IAV infection among monocytes from individuals of African-descent. Collectively, our results reveal the effects of IAV infection on the transcriptional landscape of human monocytes and highlight previously unappreciated differences in cellular susceptibility to IAV infection between individuals of African and European ancestry, which may account for the greater susceptibility of Africans to severe influenza. Significance StatementMonocytes may play a critical role during severe viral infections. Our study tackles how heterogeneity in monocyte subsets and activation contributes to shape individual differences in the transcriptional response to viral infections. Using single-cell RNA-sequencing, we reveal heterogeneity in monocyte susceptibility to IAV infection, both between CD16+/CD16- monocytes and across individuals, driven by differences in basal activation of an IRF/STAT-induced antiviral program. Furthermore, we show a decreased ability of IAV to infect and replicate in monocytes from African-ancestry individuals, with possible implications for antigen presentation and lymphocyte activation. These results highlight the importance of early cellular activation in determining an individuals innate immune response to viral infection.

genomics↗

bioRxiv2020

Detection of homozygous and hemizygous partial exon deletions by whole-exome sequencing

The detection of copy number variations (CNVs) in whole-exome sequencing (WES) data is important, as CNVs may underlie a number of human genetic disorders. The recently developed HMZDelFinder algorithm can detect rare homozygous and hemizygous (HMZ) deletions in WES data more effectively than other widely used tools. Here, we present HMZDelFinder_opt, an approach that outperforms HMZDelFinder for the detection of HMZ deletions, including partial exon deletions in particular, in typical laboratory cohorts that are generated over time under different experimental conditions. We show that using an optimized reference control set of WES data, based on a PCA-derived Euclidean distance for coverage, strongly improves the detection of HMZ deletions both in real patients carrying validated disease-causing deletions and in simulated data. Furthermore, we develop a sliding window approach enabling HMZDelFinder-opt to identify HMZ partial deletions of exons that are otherwise undiscovered by HMZDelFinder. HMZDelFinder_opt is a timely and powerful approach for detecting HMZ deletions, particularly partial exon deletions, in laboratory cohorts, which are typically heterogeneous.

bioinformatics↗

bioRxiv2020

Controlling for Human Population Stratification in Rare Variant Association Studies

Population stratification is a strong confounding factor in human genetic association studies. In analyses of rare variants, the main correction strategies based on principal components (PC) and linear mixed models (LMM), may yield conflicting conclusions, due to both the specific type of structure induced by rare variants and the particular statistical features of association tests. Studies evaluating these approaches generally focused on specific situations with limited types of simulated structure and large sample sizes. We investigated the properties of several correction methods in the context of a large simulation study using real exome data, and several within- and between- continent stratification scenarios. We also considered different sample sizes, with situations including as few as 50 cases, to account for the analysis of rare disorders. In this context, we focused on a genetic model with a phenotype driven by rare deleterious variants well suited for a burden test. For analyses of large samples, we found that accounting for stratification was more difficult with a continental structure than with a worldwide structure. LMM failed to maintain a correct type I error in many scenarios, whereas PCs based on common variants failed only in the presence of extreme continental stratification. When a sample of 50 cases was considered, an inflation of type I errors was observed with PC for small numbers of controls ([≤]100), and with LMM for large numbers of controls ([≥]1000). We also tested a promising novel adapted local permutation method (LocPerm), which maintained a correct type I error in all situations. All approaches capable of correcting for stratification properly had similar powers for detecting actual associations pointing out that the key issue is to properly control type I errors. Finally, we found that adding a large panel of external controls (e.g. extracted from publicly available databases) was an efficient way to increase the power of analyses including small numbers of cases, provided an appropriate stratification correction was used. Author SummaryGenetic association studies focusing on rare variants using next generation sequencing (NGS) data have become a common strategy to overcome the shortcomings of classical genome-wide association studies for the analysis of rare and common diseases. The issue of population stratification remains however a substantial question that has not been fully resolved when analyzing NGS data. In this work, we propose a comprehensive evaluation of the main strategies to account for stratification, that are principal components and linear mixed model, along with a novel approach based on local permutations (LocPerm). We compared these correction methods in many different settings, considering several types of population structures, sample sizes or types of variants. Our results highlighted important limitations of some classical methods as those using principal components (in particular in small samples) and linear mixed models (in several situations). In contrast, LocPerm maintained a correct type I error in all situations. Also, we showed that adding a large panel of external controls, e.g coming from publicly available databases, is an efficient strategy to increase the power of an analysis including a low number of cases, as long as an appropriate stratification correction is used. Our findings provide helpful guidelines for many researchers working on rare variant association studies.

genetics↗

bioRxiv2020

A genome-wide case-only test for the detection of digenic inheritance in human exomes

Whole-exome sequencing (WES) has facilitated the discovery of genetic lesions underlying monogenic disorders. Incomplete penetrance and variable expressivity suggest a contribution of additional genetic lesions to clinical manifestations and outcome. Some monogenic disorders may therefore actually be digenic. However, only a few digenic disorders have been reported, all discovered by candidate gene approaches applied to at least one locus. We propose here a novel two-locus genome-wide test for detecting digenic inheritance in WES data. This approach uses the gene as the unit of analysis and tests all pairs of genes to detect pairwise gene x gene interactions underlying disease. It is a case-only method, which has several advantages over classic case-control tests, in particular by avoiding recruitment and bias of controls. Our simulation studies based on real WES data identified two major sources of type I error inflation in this case-only test: linkage disequilibrium and population stratification. Both were corrected by specific procedures. Moreover, our case-only approach is more powerful than the corresponding case-control test for detecting digenic interactions in various population stratification scenarios. Finally, we validated our unbiased, genome-wide approach by successfully identifying a previously reported digenic lesion in patients with craniosynostosis. Our case-only test is a powerful and timely tool for detecting digenic inheritance in WES data from patients. Significance statementDespite a growing number of reports of rare disorders not fully explained by monogenic lesions, digenic inheritance has been reported for only 54 diseases to date. The very few existing methods for detecting gene x gene interactions from next-generation sequencing data were generally studied in rare-variant association studies with limited simulation analyses for short genomic regions, under a case-control design. We describe the first case-only approach designed specifically to search for digenic inheritance, which avoids recruitment and bias related to controls. We show, through both extensive simulation studies on real WES datasets and application to a real example of craniosynostosis, that our method is robust and powerful for the genome-wide identification of digenic lesions.

genetics↗

bioRxiv2020

Taking population stratification into account by local permutations in rare-variant association studies on small samples

Many methods for rare variant association studies require permutations to assess the significance of tests. Standard permutations assume that all individuals are exchangeable and do not take population stratification (PS), a known confounding factor in genetic studies, into account. We propose a novel strategy, LocPerm, in which individuals are permuted only with their closest ancestry-based neighbors. We performed a simulation study, focusing on small samples, to evaluate and compare LocPerm with standard permutations and classical adjustment on first principal components. Under the null hypothesis, LocPerm was the only method providing an acceptable type I error, regardless of sample size and level of stratification. The power of LocPerm was similar to that of standard permutation in the absence of PS, and remained stable in different PS scenarios. We conclude that LocPerm is a method of choice for taking PS and/or small sample size into account in rare variant association studies.

genomics↗

bioRxiv2019

Pleiotropic effects for Parkin and LRRK2 in leprosy type-1 reactions and Parkinson’s disease

Type-1 reactions (T1Rs) are pathological inflammatory episodes and main contributors to nerve damage in leprosy. Here, we evaluate the gene-wise enrichment of rare protein altering variants in seven genes where common variants were previously associated with T1R. We selected 474 Vietnamese leprosy-patients of which 237 were T1R-affected and 237 were T1R-free matched controls. Gene-wise enrichment of nonsynonymous variants was tested with both kernel based (SKAT) and burden methods. Of the seven genes tested two showed statistical evidence of association with T1R. For the LRRK2 gene an enrichment of nonsynonymous variants was observed in T1R-free controls (p O_SCPCAPSKAT-OC_SCPCAP= 1.6x10-4). This gene-wise association was driven almost entirely by the gain of function variant R1628P (p = 0.004; OR = 0.29). The second gene-wise association was found for the Parkin coding gene PRKN (formerly PARK2) where seven rare variants were enriched in T1R-affected cases (p O_SCPCAPSKAT-OC_SCPCAP = 7.4x10-5). Mutations in both PRKN and LRRK2 are known causes of Parkinsons Disease (PD). Hence, we evaluated to what extent such rare amino acid changes observed in T1R are shared with PD. We observed that nonsynonymous T1R-risk mutations in Parkin were enriched for amino acid mutations implicated in PD (p = 1.5x10-4). Hence, neuro-inflammation in PD and peripheral nerve damage due to inflammation in T1R share overlapping mechanisms of pathogenicity.

genetics↗