scholarly journals GSDMA drives the most replicated association with asthma in naïve CD4+ T cells

2019 ◽  
Author(s):  
Anne-Marie Madore ◽  
Lucile Pain ◽  
Anne-Marie Boucher-Lafleur ◽  
Jolyane Meloche ◽  
Andréanne Morin ◽  
...  
Keyword(s):  
Gene Expression ◽  
Dna Methylation ◽  
T Cells ◽  
Genetic Variants ◽  
Immune Cell ◽  
Cell Types ◽  
Sequencing Data ◽  
Opposite Pattern ◽  
Genetic Mechanisms ◽  
New Gene

AbstractBackgroundThe 17q12-21 locus is the most replicated association with asthma. However, no study had described the genetic mechanisms underlying this association considering all genes of the locus in immune cell samples isolated from asthmatic and non-asthmatic individuals.ObjectiveThis study takes benefit of samples from naïve CD4+ T cells and eosinophils isolated from the same 200 individuals to describe specific interactions between genetic variants, gene expression and DNA methylation levels for the 17q12-21 asthma locus.Methods and ResultsAfter isolation of naïve CD4+ T cells and eosinophils from blood samples, next generation sequencing was used to measure DNA methylation levels and gene expression counts. Genetic interactions were then evaluated considering genetic variants from imputed genotype data. In naïve CD4+ T cells but not eosinophils, 20 SNPs in the fourth and fifth haplotype blocks modulated both GSDMA expression and methylation levels, showing an opposite pattern of allele frequencies and expression counts in asthmatics compared to controls. Moreover, negative correlations have been measured between methylation levels of CpG sites located within the 1.5 kb region from the transcription start site of GSDMA and its expression counts.ConclusionAvailability of sequencing data from two key cell types isolated from asthmatic and non-asthmatic individuals allowed identifying a new gene in naïve CD4+ T cells that drives the association with the 17q12-21 locus, leading to a better understanding of the genetic mechanisms taking place in it.

scholarly journals T cell subset-selective IL2RA enhancers shape autoimmune diabetes risk

2020 ◽  
Author(s):  
Dimitre R. Simeonov ◽  
Harikesh S. Wong ◽  
Jessica T. Cortez ◽  
Arabella Young ◽  
Zhongmei Li ◽  
...  
Keyword(s):  
Gene Expression ◽  
T Cells ◽  
Regulatory T Cells ◽  
Disease Progression ◽  
Genetic Variants ◽  
Immune Cell ◽  
Disease Risk ◽  
Autoimmune Diabetes ◽  
Cell Subset ◽  
Regulatory Elements

The majority of genetic variants associated with complex human autoimmune diseases reside in enhancers1–3, non-coding regulatory elements that control gene expression. In contrast with variants that directly alter protein-coding sequences, enhancer variants are predicted to tune gene expression modestly and function in specific cellular contexts4, suggesting that small alterations in the functions of key immune cell populations are sufficient to shape disease risk. Here we tested this concept by experimentally perturbing distinct enhancers governing the high affinity IL-2 receptor alpha chain (IL2RA; also known as CD25). IL2RA is an immune regulator that promotes the pro- and anti-inflammatory functions of conventional T cells (Tconvs) and regulatory T cells (Tregs), respectively, and non-coding genetic variants in IL2RA have been linked to multiple autoimmune disorders4. We previously tiled across the IL2RA locus using CRISPR-activation and identified a stimulation-responsive element (CaRE4) with an enhancer that modestly affects the kinetics of IL2RA expression in Tconvs5. This enhancer is conserved across species and harbors a common human SNP associated with protection from Type 1 Diabetes (T1D)5,6. We now identified an additional conserved enhancer, termed CaRE3 enhancer, which modestly affected steady state IL2RA expression in regulatory T cells (Tregs). Despite their seemingly subtle impact on gene expression, the CaRE3 and CaRE4 enhancers had pronounced yet divergent effects on the incidence of diabetes in autoimmune prone animals. Deletion of the conserved CaRE4 enhancer completely protected against autoimmune diabetes even in animals treated with an immunostimulating anti-PD1 checkpoint inhibitor, whereas deletion of the CaRE3 enhancer accelerated spontaneous disease progression. Quantitative multiplexed imaging of the pancreatic lymph nodes (panLNs) revealed that each enhancer deletion preferentially affected the protein expression levels of IL2RA in activated Tconvs or Tregs, reciprocally tuning local competition for IL-2 input signals. In animals lacking the CaRE4 enhancer, skewed IL-2 signaling favored Tregs, increasing their local density around activated Tconvs to strongly suppress emergence of autoimmune effectors. By contrast, in animals lacking the CaRE3 enhancer, IL-2 signals were skewed towards activated Tconvs, promoting their escape from Treg control. Collectively, this work illustrates how subtle changes in gene regulation due to non-coding variation can significantly alter disease progression and how distinct enhancers controlling the same gene can have opposing effects on disease outcomes through cell type-selective activity.

scholarly journals Comprehensive analysis of immune cell enrichment in the tumor microenvironment of head and neck squamous cell carcinoma

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ikko Mito ◽  
Hideyuki Takahashi ◽  
Reika Kawabata-Iwakawa ◽  
Shota Ida ◽  
Hiroe Tada ◽  
...  
Keyword(s):  
Gene Expression ◽  
T Cells ◽  
Tumor Microenvironment ◽  
Head And Neck ◽  
Gene Expression Data ◽  
Immune Cell ◽  
Cell Types ◽  
Expression Data ◽  
Immune Microenvironment ◽  
Cancer Tissues

AbstractHead and neck squamous carcinoma (HNSCC) is highly infiltrated by immune cells, including tumor-infiltrating lymphocytes and myeloid lineage cells. In the tumor microenvironment, tumor cells orchestrate a highly immunosuppressive microenvironment by secreting immunosuppressive mediators, expressing immune checkpoint ligands, and downregulating human leukocyte antigen expression. In the present study, we aimed to comprehensively profile the immune microenvironment of HNSCC using gene expression data obtained from public database. We calculated enrichment scores of 33 immune cell types based on gene expression data of HNSCC tissues and adjacent non-cancer tissues. Based on these scores, we performed non-supervised clustering and identified three immune signatures—cold, lymphocyte, and myeloid/dendritic cell (DC)—based on the clustering results. We then compared the clinical and biological features of the three signatures. Among HNSCC and non-cancer tissues, human papillomavirus (HPV)-positive HNSCCs exhibited the highest scores in various immune cell types, including CD4+ T cells, CD8+ T cells, B cells, plasma cells, basophils, and their subpopulations. Among the three immune signatures, the proportions of HPV-positive tumors, oropharyngeal cancers, early T tumors, and N factor positive cases were significantly higher in the lymphocyte signature than in other signatures. Among the three signatures, the lymphocyte signature showed the longest overall survival (OS), especially in HPV-positive patients, whereas the myeloid/DC signature demonstrated the shortest OS in these patients. Gene set enrichment analysis revealed the upregulation of several pathways related to inflammatory and proinflammatory responses in the lymphocyte signature. The expression of PRF1, IFNG, GZMB, CXCL9, CXCL10, PDCD1, LAG3, CTLA4, HAVCR2, and TIGIT was the highest in the lymphocyte signature. Meanwhile, the expression of PD-1 ligand genes CD274 and PDCD1LG2 was highest in the myeloid/DC signature. Herein, our findings revealed the transcriptomic landscape of the immune microenvironment that closely reflects the clinical and biological significance of HNSCC, indicating that molecular profiling of the immune microenvironment can be employed to develop novel biomarkers and precision immunotherapies for HNSCC.

scholarly journals Integrative transcriptomic analysis of SLE reveals IFN-driven cross-talk between immune cells

2020 ◽  
Author(s):  
Bharat Panwar ◽  
Benjamin J. Schmiedel ◽  
Shu Liang ◽  
Brandie White ◽  
Enrique Rodriguez ◽  
...  
Keyword(s):  
Gene Expression ◽  
T Cells ◽  
Autoimmune Disease ◽  
Cross Talk ◽  
Immune Cell ◽  
Cell Types ◽  
Cell Populations ◽  
Multiple Cell ◽  
Classical Monocytes ◽  
Multiple Immune Cell

ABSTRACTThe systemic lupus erythematosus (SLE) is an incurable autoimmune disease disproportionately affecting women and may lead to damage in multiple different organs. The marked heterogeneity in its clinical manifestations is a major obstacle in finding targeted treatments and involvement of multiple immune cell types further increases this complexity. Thus, identifying molecular subtypes that best correlate with disease heterogeneity and severity as well as deducing molecular cross-talk among major immune cell types that lead to disease progression are critical steps in the development of more informed therapies for SLE. Here we profile and analyze gene expression of six major circulating immune cell types from patients with well-characterized SLE (classical monocytes (n=64), T cells (n=24), neutrophils (n=24), B cells (n=20), conventional (n=20) and plasmacytoid (n=22) dendritic cells) and from healthy control subjects. Our results show that the interferon (IFN) response signature was the major molecular feature that classified SLE patients into two distinct groups: IFN-signature negative (IFNneg) and positive (IFNpos). We show that the gene expression signature of IFN response was consistent (i) across all immune cell types, (ii) all single cells profiled from three IFNpos donors using single-cell RNA-seq, and (iii) longitudinal samples of the same patient. For a better understanding of molecular differences of IFNpos versus IFNneg patients, we combined differential gene expression analysis with differential Weighted Gene Co-expression Network Analysis (WGCNA), which revealed a relatively small list of genes from classical monocytes including two known immune modulators, one the target of an approved therapeutic for SLE (TNFSF13B/BAFF: belimumab) and one itself a therapeutic for Rheumatoid Arthritis (IL1RN: anakinra). For a more integrative understanding of the cross-talk among different cell types and to identify potentially novel gene or pathway connections, we also developed a novel gene co-expression analysis method for joint analysis of multiple cell types named integrated WGNCA (iWGCNA). This method revealed an interesting cross-talk between T and B cells highlighted by a significant enrichment in the expression of known markers of T follicular helper cells (Tfh), which also correlate with disease severity in the context of IFNpos patients. Interestingly, higher expression of BAFF from all myeloid cells also shows a strong correlation with enrichment in the expression of genes in T cells that may mark circulating Tfh cells or related memory cell populations. These cell types have been shown to promote B cell class-switching and antibody production, which are well-characterized in SLE patients. In summary, we generated a large-scale gene expression dataset from sorted immune cell populations and present a novel computational approach to analyze such data in an integrative fashion in the context of an autoimmune disease. Our results reveal the power of a hypothesis-free and data-driven approach to discover drug targets and reveal novel cross-talk among multiple immune cell types specific to a subset of SLE patients. This approach is immediately useful for studying autoimmune diseases and is applicable in other contexts where gene expression profiling is possible from multiple cell types within the same tissue compartment.

scholarly journals Genetic determinants of chromatin accessibility in T cell activation across humans

2016 ◽  
Author(s):  
Rachel E. Gate ◽  
Christine S. Cheng ◽  
Aviva P. Aiden ◽  
Atsede Siba ◽  
Marcin Tabaka ◽  
...  
Keyword(s):  
Gene Expression ◽  
T Cells ◽  
T Cell ◽  
T Cell Activation ◽  
Genetic Variants ◽  
Cell Activation ◽  
Cell Types ◽  
Regulatory Elements ◽  
Specific Cell ◽  
Accessible Chromatin

AbstractOver 90% of genetic variants associated with complex human traits map to non-coding regions, but little is understood about how they modulate gene regulation in health and disease. One possible mechanism is that genetic variants affect the activity of one or more cis-regulatory elements leading to gene expression variation in specific cell types. To identify such cases, we analyzed Assay for Transposase-Accessible Chromatin sequencing (ATAC-seq) and RNA-seq profiles from activated CD4+ T cells of up to 105 healthy donors. We found that regions of accessible chromatin (ATAC-peaks) are co-accessible at kilobase and megabase resolution, in patterns consistent with the 3D organization of chromosomes measured by in situ Hi-C in T cells. 15% of genetic variants located within ATAC-peaks affected the accessibility of the corresponding peak through disrupting binding sites for transcription factors important for T cell differentiation and activation. These ATAC quantitative trait nucleotides (ATAC-QTNs) have the largest effects on co-accessible peaks, are associated with gene expression from the same aliquot of cells, are rarely affecting core binding motifs, and are enriched for autoimmune disease variants. Our results provide insights into how natural genetic variants modulate cis- regulatory elements, in isolation or in concert, to influence gene expression in primary immune cells that play a key role in many human diseases.

scholarly journals Exploring inflammatory signatures in arthritic joint biopsies with Spatial Transcriptomics

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Konstantin Carlberg ◽  
Marina Korotkova ◽  
Ludvig Larsson ◽  
Anca I. Catrina ◽  
Patrik L. Ståhl ◽  
...  
Keyword(s):  
Gene Expression ◽  
T Cells ◽  
Immune Cell ◽  
Memory T Cells ◽  
Inflammatory Diseases ◽  
Adaptive Immune Response ◽  
Cell Types ◽  
Effector Memory ◽  
Cellular Mechanisms ◽  
Chronic Inflammatory Diseases

AbstractLately it has become possible to analyze transcriptomic profiles in tissue sections with retained cellular context. We aimed to explore synovial biopsies from rheumatoid arthritis (RA) and spondyloarthritis (SpA) patients, using Spatial Transcriptomics (ST) as a proof of principle approach for unbiased mRNA studies at the site of inflammation in these chronic inflammatory diseases. Synovial tissue biopsies from affected joints were studied with ST. The transcriptome data was subjected to differential gene expression analysis (DEA), pathway analysis, immune cell type identification using Xcell analysis and validation with immunohistochemistry (IHC). The ST technology allows selective analyses on areas of interest, thus we analyzed morphologically distinct areas of mononuclear cell infiltrates. The top differentially expressed genes revealed an adaptive immune response profile and T-B cell interactions in RA, while in SpA, the profiles implicate functions associated with tissue repair. With spatially resolved gene expression data, overlaid on high-resolution histological images, we digitally portrayed pre-selected cell types in silico. The RA displayed an overrepresentation of central memory T cells, while in SpA effector memory T cells were most prominent. Consequently, ST allows for deeper understanding of cellular mechanisms and diversity in tissues from chronic inflammatory diseases.

scholarly journals Insights Into the Role of DNA Methylation in Immune Cell Development and Autoimmune Disease

2021 ◽  
Vol 9 ◽  
Author(s):  
Jiaqi Li ◽  
Lifang Li ◽  
Yimeng Wang ◽  
Gan Huang ◽  
Xia Li ◽  
...  
Keyword(s):  
Gene Expression ◽  
Dna Methylation ◽  
Autoimmune Disease ◽  
Autoimmune Diseases ◽  
Immune Cell ◽  
Gene Expression Regulation ◽  
Cell Types ◽  
Basic Function ◽  
Detailed Knowledge ◽  
Disease Pathogenesis

To date, nearly 100 autoimmune diseases have been an area of focus, and these diseases bring health challenges to approximately 5% of the population worldwide. As a type of disease caused by tolerance breakdown, both environmental and genetic risk factors contribute to autoimmune disease development. However, in most cases, there are still gaps in our understanding of disease pathogenesis, diagnosis, and treatment. Therefore, more detailed knowledge of disease pathogenesis and potential therapies is indispensable. DNA methylation, which does not affect the DNA sequence, is one of the key epigenetic silencing mechanisms and has been indicated to play a key role in gene expression regulation and to participate in the development of certain autoimmune diseases. Potential epigenetic regulation via DNA methylation has garnered more attention as a disease biomarker in recent years. In this review, we clarify the basic function and distribution of DNA methylation, evaluate its effects on gene expression and discuss related key enzymes. In addition, we summarize recent aberrant DNA methylation modifications identified in the most important cell types related to several autoimmune diseases and then provide potential directions for better diagnosing and monitoring disease progression driven by epigenetic control, which may broaden our understanding and contribute to further epigenetic research in autoimmune diseases.

scholarly journals COVID-19 genetic risk variants are associated with expression of multiple genes in diverse immune cell types

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Benjamin J. Schmiedel ◽  
Job Rocha ◽  
Cristian Gonzalez-Colin ◽  
Sourya Bhattacharyya ◽  
Ariel Madrigal ◽  
...  
Keyword(s):  
Gene Expression ◽  
T Cells ◽  
Genetic Risk ◽  
Immune Cell ◽  
Severe Disease ◽  
Cell Types ◽  
Chromatin Interaction ◽  
Risk Variants ◽  
Wide Range ◽  
Genetic Risk Variants

AbstractCommon genetic polymorphisms associated with COVID-19 illness can be utilized for discovering molecular pathways and cell types driving disease pathogenesis. Given the importance of immune cells in the pathogenesis of COVID-19 illness, here we assessed the effects of COVID-19-risk variants on gene expression in a wide range of immune cell types. Transcriptome-wide association study and colocalization analysis revealed putative causal genes and the specific immune cell types where gene expression is most influenced by COVID-19-risk variants. Notable examples include OAS1 in non-classical monocytes, DTX1 in B cells, IL10RB in NK cells, CXCR6 in follicular helper T cells, CCR9 in regulatory T cells and ARL17A in TH2 cells. By analysis of transposase accessible chromatin and H3K27ac-based chromatin-interaction maps of immune cell types, we prioritized potentially functional COVID-19-risk variants. Our study highlights the potential of COVID-19 genetic risk variants to impact the function of diverse immune cell types and influence severe disease manifestations.

scholarly journals GEM-DeCan: Improving tumor immune microenvironment profiling by the integration of novel gene expression and DNA methylation deconvolution signatures

2021 ◽  
Author(s):  
Ting XIE ◽  
Julien Pernet ◽  
Nina Verstraete ◽  
Miguel Madrid-Mencia ◽  
Mei-Shiue Kuo ◽  
...  
Keyword(s):  
Gene Expression ◽  
Dna Methylation ◽  
Immune Cell ◽  
Checkpoint Inhibitors ◽  
Gene Expression Signature ◽  
Gene Promoters ◽  
Sequencing Data ◽  
Data Types ◽  
Tumour Sample

Quantifying the proportion of the different cell types present in tumor biopsies remains a priority in cancer research. So far, a number of deconvolution methods have emerged for estimating cell composition using reference signatures either based on gene expression or on DNA methylation from purified cells. These two deconvolution approaches could be complementary to each other, leading to even more performant signatures, in cases where both data types are available. However, the potential relationship between signatures based on gene expression and those based on DNA methylation remains underexplored. Here we present five new deconvolution signature matrices, based on DNA methylation or RNAseq data, which can estimate the proportion of immune cells and cancer cells in a tumour sample. We test these signature matrices on available datasets for in-silico and in-vitro mixtures, peripheral blood, cancer samples from TCGA, bone marrow from multiple myeloma patients and a single-cell melanoma dataset. Cell proportions estimates based on deconvolution performed using our signature matrices, implemented within the EpiDISH framework, show comparable or better correlation with FACS measurements of immune cell-type abundance and with various estimates of cancer sample purity and composition than existing methods. Finally, using publicly available data of 3D chromatin structure in haematopoietic cells, we expanded the list of genes to be included in the RNAseq signature matrices by considering the presence of methylated CpGs in gene promoters or in genomic regions which are in 3D contact with these promoters. Our expanded signature matrices have improved performance compared to our initial RNAseq signature matrix. Finally, we show the value of our signature matrices in predicting patient response to immune checkpoint inhibitors in three melanoma and one bladder cancer cohort, based on bulk tumour sample gene expression data. We also provide GEM-DeCan: a snakemake pipeline, able to run an analysis from raw sequencing data to deconvolution based on various gene expression signature matrices, both for bulk RNASeq and DNA methylation data. The code for producing the signature matrices and reproducing all the figures of this paper is available on GitHub: https://github.com/VeraPancaldiLab/GEMDeCan.

scholarly journals Rare genetic variants affecting urine metabolite levels link population variation to inborn errors of metabolism

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Yurong Cheng ◽  
◽  
Pascal Schlosser ◽  
Johannes Hertel ◽  
Peggy Sekula ◽  
...  
Keyword(s):  
Genetic Variants ◽  
Inborn Errors Of Metabolism ◽  
Cell Types ◽  
Population Variation ◽  
Whole Body ◽  
Sequencing Data ◽  
Inborn Errors ◽  
Data Links ◽  
Liver And Kidney ◽  
Constraint Based Modeling

AbstractMetabolite levels in urine may provide insights into genetic mechanisms shaping their related pathways. We therefore investigate the cumulative contribution of rare, exonic genetic variants on urine levels of 1487 metabolites and 53,714 metabolite ratios among 4864 GCKD study participants. Here we report the detection of 128 significant associations involving 30 unique genes, 16 of which are known to underlie inborn errors of metabolism. The 30 genes are strongly enriched for shared expression in liver and kidney (odds ratio = 65, p-FDR = 3e−7), with hepatocytes and proximal tubule cells as driving cell types. Use of UK Biobank whole-exome sequencing data links genes to diseases connected to the identified metabolites. In silico constraint-based modeling of gene knockouts in a virtual whole-body, organ-resolved metabolic human correctly predicts the observed direction of metabolite changes, highlighting the potential of linking population genetics to modeling. Our study implicates candidate variants and genes for inborn errors of metabolism.

484 Bioturing browser: interactively explore public single cell sequencing data

2020 ◽  
Vol 8 (Suppl 3) ◽  
pp. A520-A520
Author(s):  
Son Pham ◽  
Tri Le ◽  
Tan Phan ◽  
Minh Pham ◽  
Huy Nguyen ◽  
...  
Keyword(s):  
Single Cell ◽  
Immune Cell ◽  
Expression Profiles ◽  
Meta Analysis ◽  
Cell Types ◽  
Sequencing Data ◽  
Single Cell Sequencing ◽  
Data Formats ◽  
Cancer Types ◽  
Cell Data

BackgroundSingle-cell sequencing technology has opened an unprecedented ability to interrogate cancer. It reveals significant insights into the intratumoral heterogeneity, metastasis, therapeutic resistance, which facilitates target discovery and validation in cancer treatment. With rapid advancements in throughput and strategies, a particular immuno-oncology study can produce multi-omics profiles for several thousands of individual cells. This overflow of single-cell data poses formidable challenges, including standardizing data formats across studies, performing reanalysis for individual datasets and meta-analysis.MethodsN/AResultsWe present BioTuring Browser, an interactive platform for accessing and reanalyzing published single-cell omics data. The platform is currently hosting a curated database of more than 10 million cells from 247 projects, covering more than 120 immune cell types and subtypes, and 15 different cancer types. All data are processed and annotated with standardized labels of cell types, diseases, therapeutic responses, etc. to be instantly accessed and explored in a uniform visualization and analytics interface. Based on this massive curated database, BioTuring Browser supports searching similar expression profiles, querying a target across datasets and automatic cell type annotation. The platform supports single-cell RNA-seq, CITE-seq and TCR-seq data. BioTuring Browser is now available for download at www.bioturing.com.ConclusionsN/A

Sign in / Sign up

Export Citation Format

Share Document