Biology subjects

Esparza-Gordillo, J.

Publications and source records attributed to Esparza-Gordillo, J..

6 recordsLinked to original sources

bioRxiv2023

Mapping the functional impact of non-coding regulatory elements in primary T cells through single-cell CRISPR screens

Drug targets with human genetic evidence are expected to increase clinical success by at least two-fold. Yet, translating disease-associated genetic variants into functional knowledge remains a fundamental challenge of early drug discovery. A key issue is that, currently, the vast majority of complex disease associations cannot be cleanly mapped to a gene. Immune disease-associated variants are enriched within regulatory elements, such as distal enhancers, found in T cell-specific open chromatin regions. To identify the genes and thus the molecular programs modulated by these regulatory elements, we developed a CRISPRi-based single-cell functional screening approach in primary human CD4+ T cells. Our pipeline enables the interrogation of transcriptomic changes induced by the perturbation of regulatory elements at scale. We first optimised a highly efficient CRISPRi protocol in primary human CD4+ T cells via CROPseq vectors. Subsequently, we performed a proof-of-concept screen targeting 45 non-coding regulatory elements and 35 transcription start sites and profiled approximately 250,000 CD4+ T cell single-cell transcriptomes. We developed a bespoke analytical pipeline for element-to-gene (E2G) mapping and demonstrate that our method can identify both previously annotated and novel E2G links. Lastly, we integrated genetic association data for immune-related traits and demonstrate how our platform can aid in the identification of effector genes for GWAS loci.

genomics↗

bioRxiv2021

Immune disease risk variants regulate gene expression dynamics during CD4+ T cell activation

During activation, T cells undergo extensive changes in gene expression which shape the properties of cells to exert their effector function. Therefore, understanding the genetic regulation of gene expression during T cell activation provides essential insights into how genetic variants influence the response to infections and immune diseases. We generated a single-cell map of expression quantitative trait loci (eQTL) across a T cell activation time-course. We profiled 655,349 CD4+ naive and memory T cells, capturing transcriptional states of unstimulated cells and three time points of cell activation in 119 healthy individuals. We identified 38 cell clusters, including stable clusters such as central and effector memory T cells and transient clusters that were only present at individual time points of activation, such as interferon-responding cells. We mapped eQTLs using a T cell activation trajectory and identified 6,407 eQTL genes, of which a third (2,265 genes) were dynamically regulated during T cell activation. We integrated this information with GWAS variants for immune-mediated diseases and observed 127 colocalizations, with significant enrichment in dynamic eQTLs. Immune disease loci colocalized with genes that are involved in the regulation of T cell activation, and genes with similar functions tended to be perturbed in the same direction by disease risk alleles. Our results emphasize the importance of mapping context-specific gene expression regulation, provide insights into the mechanisms of genetic susceptibility of immune diseases, and help prioritize new therapeutic targets.

genomics↗

bioRxiv2019

Identification of putative effector genes across the GWAS Catalog using molecular quantitative trait loci from 68 tissues and cell types

Identifying the effector genes from genome-wide association studies (GWAS) is a crucial step towards understanding the biological mechanisms underlying complex traits and diseases. Colocalization of expression and protein quantitative trait loci (eQTL and pQTL, hereafter collectively called \"xQTL\") can be effective for mapping associations to genes in many loci. However, existing colocalization methods require full single-variant summary statistics which are often not readily available for many published GWAS or xQTL studies. Here, we present PICCOLO, a method that uses minimum SNP p-values within a locus to determine if pairs of genetic associations are colocalized. This method greatly expands the number of GWAS and xQTL datasets that can be tested for colocalization. We applied PICCOLO to 10,759 genome-wide significant associations across the NHGRI-EBI GWAS Catalog with xQTLs from 28 studies. We identified at least one colocalized gene-xQTL in at least one tissue for 30% of associations, and we pursued multiple lines of evidence to demonstrate that these mappings are biologically meaningful. PICCOLO genes are significantly enriched for biologically relevant tissues, and 4.3-fold enriched for targets of approved drugs.

genetics↗

bioRxiv2019

Single-cell transcriptomics identifies an effectorness gradient shaping the response of CD4+ T cells to cytokines

Naive CD4+ T cells coordinate the immune response by acquiring an effector phenotype in response to cytokines. However, the cytokine responses in memory T cells remain largely understudied. We used quantitative proteomics, bulk RNA-seq and single-cell RNA-seq of over 40,000 human naive and memory CD4+ T cells to generate a detailed map of cytokine-regulated gene expression programs. We demonstrated that cytokine response differs substantially between naive and memory T cells and showed that memory cells are unable to differentiate into the Th2 phenotype. Moreover, memory T cells acquire a Th17-like phenotype in response to iTreg polarization. At the single-cell level, we demonstrated that T cells form a continuum which progresses from naive to effector memory T cells. This continuum is accompanied by a gradual increase in the expression levels of chemokines and cytokines and thus represents an effectorness gradient. Finally, we found that T cell cytokine responses are determined by where the cells lie in the effectorness gradient and identified genes whose expression is controlled by cytokines in an effectorness-dependent manner. Our results shed light on the heterogeneity of T cells and their responses to cytokines, provide insight into immune disease inflammation and could inform drug development.

immunology↗

bioRxiv2019

Chromatin activity at GWAS loci identifies T cell states driving complex immune diseases

Complex immune disease variants are enriched in active chromatin regions of T cells and macrophages. However, whether these variants function in specific cell states or stages of cell activation is unknown. We stimulated T cells and macrophages in the presence of thirteen different cytokine cocktails linked to immune diseases and profiled active enhancers and promoters together with regions of open chromatin. We observed that T cell activation induced major chromatin remodelling, while additional exposure to cytokines fine-tuned the magnitude of these changes. Therefore, we developed a new statistical method that accounts for subtle changes in chromatin landscape to identify SNP enrichment across cell states. Our results point towards the role of immune disease variants in early rather than late activation of memory CD4+ T cells, and with limited differences across polarizing cytokines. Furthermore, we demonstrate that inflammatory bowel disease variants are enriched in chromatin regions active in Th1 cells, while asthma variants overlap regions active in Th2 cells. We also show that Alzheimers disease variants are enriched in different macrophage cell states. Our results represent the first in-depth analysis of immune disease variants across a comprehensive panel of activation states of T cells and macrophages.

genetics↗

bioRxiv2018

Identification of new therapeutic targets for osteoarthritis through genome-wide analyses of UK Biobank

Osteoarthritis is the most common musculoskeletal disease and the leading cause of disability globally. Here, we perform the largest genome-wide association study for osteoarthritis to date (77,052 cases and 378,169 controls), analysing 4 phenotypes: knee osteoarthritis, hip osteoarthritis, knee and/or hip osteoarthritis, and any osteoarthritis. We discover 64 signals, 52 of them novel, more than doubling the number of established disease loci. Six signals fine map to a single variant. We identify putative effector genes by integrating eQTL colocalization, fine-mapping, human rare disease, animal model, and osteoarthritis tissue expression data. We find enrichment for genes underlying monogenic forms of bone development diseases, and for the collagen formation and extracellular matrix organisation biological pathways. Ten of the likely effector genes, including TGFB1, FGF18, CTSK and IL11 have therapeutics approved or in clinical trials, with mechanisms of action supportive of evaluation for efficacy in osteoarthritis.

genetics↗