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Dubreuil, P.

Publications and source records attributed to Dubreuil, P..

3 recordsLinked to original sources

Absence of severe COVID-19 in patients with clonal mast cells activation disorders: effective anti-SARS-CoV-2 immune response.

Mast cells are key actors of innate immunity and Th2 adaptive immune response which counterbalance Th1 response, critical for anti-viral immunity. Clonal Mast Cells Activation Disorders (cMCADs) such as mastocytosis and clonal mast cells activation syndrome are characterized by an abnormal mast cells accumulation and/or activation. No data have been published on the anti-viral immune response of patients with cMCADs. The aims of the study were to collected, in a comprehensive way, outcomes of cMCADs patients who experienced a biologically-proven COVID-19 and to characterize both anti-endemic coronaviruses and specific anti-SARS-CoV-2 immune responses in these patients. Clinical follow-up and outcome data were collected prospectively for one year within the French rare disease network CEREMAST encompassing patients from all over the country. Anti-SARS-CoV-2 and anti-endemic coronaviruses specific T-cells were assessed with an enzyme-linked immunospot assay (EliSpot) and anti-SARS-CoV-2 humoral response with dosage of circulating levels of specific IgG, IgA and neutralizing antibodies. Overall, 32 cMCADs patients were identified. None of them required non-invasive or mechanical ventilation; two patients were hospitalized to receive oxygen and steroid therapy. In 21 patients, a characterization of the SARS-CoV-2-specific immune response has been performed. A majority of patients showed a high proportion of circulating SARS-CoV-2-specific interferon (IFN)-{gamma} producing T-cells and high levels of anti-Spike IgG antibodies with neutralizing activity. In addition, no defects in anti-endemic coronaviruses responses were found in patients with cMCADs compared to non-cMCADs controls. Patients with cMCADs frequently showed a spontaneous IFN-{gamma} T-cell production in absence of any stimulation that correlated with circulating basal tryptase levels, a marker of mast cells burden. These findings underscore that patients with cMCADs might be not at risk of severe COVID-19 and the spontaneous IFN-{gamma} production might explain this observation. Author SummaryMast cells are immune cells involved in many biological processes including the anti-microbial response. However, previous studies suggest that mast cells may have a detrimental role in the response against viruses such as SARS-CoV-2, responsible for COVID-19. When a mutation occurs in mast cells, it can lead to a group of diseases called clonal mast cells activation disorders (cMCADs), characterized by deregulated activation of these cells. Hence, patients with cMCADs might be more susceptible to severe COVID-19 than general population. We therefore conducted a 1-year study in France to collect data from all cMCADs patients included in the CEREMAST rare disease French network and who experienced COVID-19. Interestingly, we did not find any severe COVID-19 (i.e. requiring non-invasive or mechanical ventilation) in spite of well-known risk factors for severe COVID-19 in a part of cMCADs patients. We then have studied the immune response against SARS-CoV-2 and other endemic coronaviruses in these patients. We did not observe any abnormalities in the immune response either at the level of T and B lymphocytes. These findings underscore that these patients might not be at risk of severe COVID-19 as one might have feared.

immunology↗

Analysis of genotype by environment interactions in a maize mapping population

Genotype by environment interactions are a significant challenge for crop breeding as well as being important for understanding the genetic basis of environmental adaptation. In this study, we analyzed genotype by environment interaction in a maize multi-parent advanced generation intercross population grown across five environments. We found that genotype by environment interactions contributed as much as genotypic effects to the variation in some agronomically important traits. In order to understand how genetic correlations between traits change across environments, we estimated the genetic variance-covariance matrix in each environment. Changes in genetic covariances between traits across environments were common, even among traits that show low genotype by environment variance. We also performed a genome-wide association study to identify markers associated with genotype by environment interactions but found only a small number of significantly associated markers, possibly due to the highly polygenic nature of genotype by environment interactions in this population.

plant biology↗

Modeling Allelic Diversity of Multi-parent Mapping Populations Affects Detection of Quantitative Trait Loci

The search for quantitative trait loci (QTL) that explain complex traits such as yield and flowering time has been ongoing in all crops. Methods such as bi-parental QTL mapping and genome-wide association studies (GWAS) each have their own advantages and limitations. Multi-parent advanced generation intercross (MAGIC) populations contain more recombination events and genetic diversity than bi-parental mapping populations and reduce the confounding effect of population structure that is an issue in association mapping populations. Here we discuss the results of using a MAGIC population of doubled haploid (DH) maize lines created from 16 diverse founders to perform QTL mapping. We compare three models that assume bi-allelic, founder, and ancestral haplotype allelic states for QTL. The three methods have different power to detect QTL for a variety of agronomic traits. Although the founder approach finds the most QTL, there are also QTL unique to each method, suggesting that each model has advantages for traits with different genetic architectures. A closer look at a well-characterized flowering time QTL, qDTA8, which contains vgt1, suggests a potential epistatic interaction and highlights the strengths and weaknesses of each method. Overall, our results reinforce the importance of considering different approaches to analyzing genotypic datasets, and show the limitations of binary SNP data for identifying multi-allelic QTL.9

plant biology↗