Biology subjects

Martinez-Perez, A.

Publications and source records attributed to Martinez-Perez, A..

5 recordsLinked to original sources

bioRxiv2025

Evolution of SARS-CoV-2 T cell responses as a function of multiple COVID-19 boosters

The long-term effects of repeated COVID-19 vaccinations on adaptive immunity remain incompletely understood. Here, we conducted a comprehensive three-year longitudinal study examining T cell and antibody responses in 78 vaccinated individuals without reported symptomatic infections. We observed distinct dynamics in Spike-specific humoral and cellular immune responses across multiple vaccine doses. While antibody titers incrementally increased and stabilized with each booster, T cell responses rapidly plateaued, maintaining remarkable stability across CD4+ and CD8+ subsets. Notably, approximately 30% of participants showed CD4+ T cell reactivity to non-Spike antigens, consistent with asymptomatic infections. Single-cell RNA sequencing revealed a diverse landscape of Spike-specific T cell phenotypes, with no evidence of increased exhaustion or significant functional impairment. However, qualitative changes were observed in individuals with evidence of asymptomatic infection, exhibiting unique immunological characteristics, including increased frequencies of Th17-like CD4+ T cells and GZMKhi/IFNR CD8+ T cell subsets. Remarkably, repeated vaccinations in this group were associated with a progressive increase in regulatory T cells, potentially indicating a balanced immune response that may mitigate immunopathology. By regularly stimulating T cell memory, boosters contribute to a stable and enhanced immune response, which may provide better protection against symptomatic infections.

immunology↗

bioRxiv2024

Identification of immunogenic and cross-reactive chikungunya virus-specific CD4+ T cell epitopes in chronic chikungunya viral arthritic disease in humans

Chikungunya virus (CHIKV), a mosquito-borne alphavirus, causes acute febrile illness that can progress into chronic chikungunya virus disease (CHIKVD) marked by persistent debilitating arthralgia. At present, the exact cause of chronic CHIKVD is not understood, and in humans, the targets of CD4+ T cells in CHIKV are currently unknown. Here, by stimulating peripheral blood mononuclear cells (PBMCs) collected from patients suffering from chronic CHIKVD with peptides spanning the entire CHIKV genome, we provide a comprehensive landscape of CHIKV CD4+ T cell epitopes. We identified 123 novel CD4+ T cell epitopes and three immunodominant regions in E1, nsP1 and CP proteins. The immunodominance of these E1, nsP1 and CP regions was mapped to optimal epitopes, characterized by the capacity to bind to many common HLA class II allelic variants. In addition, we designed and validated a new CHIKV-specific CD4+ T cell epitope megapool, spanning both structural and non-structural proteins, which can be a useful tool to study CHIKV-specific T cell responses in small blood volumes, typically available in pediatric or clinical samples. Finally, by in silico assessment of the conservation of the CHIKV proteome in a diverse set of alphaviruses, we defined CHIKV epitopes conserved across arthritogenic and encephalitic viruses. Overall, our work is the first to identify CD4+ T cell targets of CHIKV in humans, expanding our capacity to study the role of T cells in CHIKV pathogenesis and mapping targets of alphaviruses for vaccine design.

immunology↗

bioRxiv2024

A genome wide search for non-additive allele effects identifies PSKH2 as involved in the variability of Factor V activity.

BackgroundFactor V (FV) is a key molecular player in the coagulation cascade. FV plasma levels have been associated with several human diseases, including thrombosis, bleeding and diabetic complications. So far, two genes have been robustly found through genome wide association analyses to contribute in the inter-individual variability of plasma FV levels: structural F5 gene and PLXDC2. MethodsWe used the underestimated Brown-Forsythe methodology implemented in the Quicktest software to search for non-additive genetic effects that could contribute to the inter-individual variability of FV plasma activity. QUICKTEST was applied to 4 independent GWAS studies (LURIC, MARTHA, MEGA and RETROVE) totaling 4,505 participants of European ancestry with measured FV plasma levels. Results obtained in the 4 cohorts were meta-analyzed using a fixed-effect model. Additional analyses involved exploring haplotype and genexgene interactions in downstream investigations. ResultsWe observed a genome-wide significant signal at PSKH2 locus, on chr8q21.3 with lead variant rs75463553 with no evidence for heterogeneity across cohorts (p = 0.518). Although rs75463553 did not show association with mean FV levels (p = 0.49), it demonstrated a robust significant (p = 8.4 10-9) association with the variance of FV plasma levels. Further analyses confirmed the reported association of PSKH2 with neutrophil biology and revealed that rs75463553 likely interact with two loci, GRIN2A and POM121L12, known for their involvement in smoking biology. ConclusionsThis comprehensive approach identifies the role of PSKH2 as a novel molecular player in the genetic regulation of FV, shedding light on the contribution of neutrophils to FV biology.

genetics↗

bioRxiv2019

Cell type specific genetic regulation of gene expression across human tissues

The Genotype-Tissue Expression (GTEx) project has identified expression and splicing quantitative trait loci (cis-QTLs) for the majority of genes across a wide range of human tissues. However, the interpretation of these QTLs has been limited by the heterogeneous cellular composition of GTEx tissue samples. Here, we map interactions between computational estimates of cell type abundance and genotype to identify cell type interaction QTLs for seven cell types and show that cell type interaction eQTLs provide finer resolution to tissue specificity than bulk tissue cis-eQTLs. Analyses of genetic associations to 87 complex traits show a contribution from cell type interaction QTLs and enables the discovery of hundreds of previously unidentified colocalized loci that are masked in bulk tissue.\n\nOne Sentence SummaryEstimated cell type abundances from bulk RNA-seq across tissues reveal the cellular specificity of quantitative trait loci.

genomics↗

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↗