Integrating autoimmune risk loci with gene-expression data identifies specific pathogenic immune cell subsets

Functional genomics implicates natural killer cells in the pathogenesis of ankylosing spondylitis

Objective

Multiple lines of evidence indicate that ankylosing spondylitis (AS) is a lymphocyte-driven disease. However, which lymphocyte populations are critical in AS pathogenesis is not known. In this study, we aimed to identify the key cell types mediating the genetic risk in AS using an unbiased functional genomics approach.

Methods

We integrated genome-wide association study (GWAS) data with epigenomic and transcriptomic datasets of human immune cells. To quantify enrichment of cell type-specific open chromatin or gene expression in AS risk loci, we used three published methods that have successfully identified relevant cell types in other diseases. We performed co-localization analyses between GWAS risk loci and genetic variants associated with gene expression (eQTL) to find putative target genes.

Results

Natural killer (NK) cell-specific open chromatin regions are significantly enriched in heritability for AS, compared to other immune cell types such as T cells, B cells, and monocytes. This finding was consistent between two AS GWAS. Using RNA-seq data, we validated that genes in AS risk loci are enriched in NK cell-specific gene expression. Using the human Space-Time Gut Cell Atlas, we also found significant upregulation of AS-associated genes predominantly in NK cells. Co-localization analysis revealed four AS risk loci affecting regulation of candidate target genes in NK cells: two known loci, ERAP1 and TNFRSF1A, and two under-studied loci, ENTR1 (aka SDCCAG3) and B3GNT2.

Conclusion

Our findings suggest that NK cells may play a crucial role in AS development and highlight four putative target genes for functional follow-up in NK cells.

Non-coding autoimmune risk variant defines role for ICOS in T peripheral helper cell development

Fine-mapping and functional studies implicate rs117701653, a non-coding single nucleotide polymorphism in the CD28/CTLA4/ICOS locus, as a risk variant for rheumatoid arthritis and type 1 diabetes. Here, using DNA pulldown, mass spectrometry, genome editing and eQTL analysis, we establish that the disease-associated risk allele is functional, reducing affinity for the inhibitory chromosomal regulator SMCHD1 to enhance expression of inducible T-cell costimulator (ICOS) in memory CD4+ T cells from healthy donors. Higher ICOS expression is paralleled by an increase in circulating T peripheral helper (Tph) cells and, in rheumatoid arthritis patients, of blood and joint fluid Tph cells as well as circulating plasmablasts. Correspondingly, ICOS ligation and carriage of the rs117701653 risk allele accelerate T cell differentiation into CXCR5-PD-1high Tph cells producing IL-21 and CXCL13. Thus, mechanistic dissection of a functional non-coding variant in human autoimmunity discloses a previously undefined pathway through which ICOS regulates Tph development and abundance.

The genetic basis of autoimmunity seen through the lens of T cell functional traits

Autoimmune disease heritability is enriched in T cell-specific regulatory regions of the genome. Modern-day T cell datasets now enable association studies between single nucleotide polymorphisms (SNPs) and a myriad of molecular phenotypes, including chromatin accessibility, gene expression, transcriptional programs, T cell antigen receptor (TCR) amino acid usage, and cell state abundances. Such studies have identified hundreds of quantitative trait loci (QTLs) in T cells that colocalize with genetic risk for autoimmune disease. The key challenge facing immunologists today lies in synthesizing these results toward a unified understanding of the autoimmune T cell: which genes, cell states, and antigens drive tissue destruction?

Statistical integration of multi-omics and drug screening data from cell lines

Data integration methods are used to obtain a unified summary of multiple datasets. For multi-modal data, we propose a computational workflow to jointly analyze datasets from cell lines. The workflow comprises a novel probabilistic data integration method, named POPLS-DA, for multi-omics data. The workflow is motivated by a study on synucleinopathies where transcriptomics, proteomics, and drug screening data are measured in affected LUHMES cell lines and controls. The aim is to highlight potentially druggable pathways and genes involved in synucleinopathies. First, POPLS-DA is used to prioritize genes and proteins that best distinguish cases and controls. For these genes, an integrated interaction network is constructed where the drug screen data is incorporated to highlight druggable genes and pathways in the network. Finally, functional enrichment analyses are performed to identify clusters of synaptic and lysosome-related genes and proteins targeted by the protective drugs. POPLS-DA is compared to other single- and multi-omics approaches. We found that HSPA5, a member of the heat shock protein 70 family, was one of the most targeted genes by the validated drugs, in particular by AT1-blockers. HSPA5 and AT1-blockers have been previously linked to α-synuclein pathology and Parkinson's disease, showing the relevance of our findings. Our computational workflow identified new directions for therapeutic targets for synucleinopathies. POPLS-DA provided a larger interpretable gene set than other single- and multi-omic approaches. An implementation based on R and markdown is freely available online.

Dynamic regulatory elements in single-cell multimodal data implicate key immune cell states enriched for autoimmune disease heritability

In autoimmune diseases such as rheumatoid arthritis, the immune system attacks the body's own cells. Developing a precise understanding of the cell states where noncoding autoimmune risk variants impart causal mechanisms is critical to developing curative therapies. Here, to identify noncoding regions with accessible chromatin that associate with cell-state-defining gene expression patterns, we leveraged multimodal single-nucleus RNA and assay for transposase-accessible chromatin (ATAC) sequencing data across 28,674 cells from the inflamed synovial tissue of 12 donors. Specifically, we used a multivariate Poisson model to predict peak accessibility from single-nucleus RNA sequencing principal components. For 14 autoimmune diseases, we discovered that cell-state-dependent ('dynamic') chromatin accessibility peaks in immune cell types were enriched for heritability, compared with cell-state-invariant ('cs-invariant') peaks. These dynamic peaks marked regulatory elements associated with T peripheral helper, regulatory T, dendritic and STAT1+CXCL10+ myeloid cell states. We argue that dynamic regulatory elements can help identify precise cell states enriched for disease-critical genetic variation.

Modeling of horizontal pleiotropy identifies possible causal gene expression in systemic lupus erythematosus

Background

Systemic lupus erythematosus (SLE) is a chronic autoimmune condition with complex causes involving genetic and environmental factors. While genome-wide association studies (GWASs) have identified genetic loci associated with SLE, the functional genomic elements responsible for disease development remain largely unknown. Mendelian Randomization (MR) is an instrumental variable approach to causal inference based on data from observational studies, where genetic variants are employed as instrumental variables (IVs).

Methods

This study utilized a two-step strategy to identify causal genes for SLE. In the first step, the classical MR method was employed, assuming the absence of horizontal pleiotropy, to estimate the causal effect of gene expression on SLE. In the second step, advanced probabilistic MR methods (PMR-Egger, MRAID, and MR-MtRobin) were applied to the genes identified in the first step, considering horizontal pleiotropy, to filter out false positives. PMR-Egger and MRAID analyses utilized whole blood expression quantitative trait loci (eQTL) and SLE GWAS summary data, while MR-MtRobin analysis used an independent eQTL dataset from multiple immune cell types along with the same SLE GWAS data.

Results

The initial MR analysis identified 142 genes, including 43 outside of chromosome 6. Subsequently, applying the advanced MR methods reduced the number of genes with significant causal effects on SLE to 66. PMR-Egger, MRAID, and MR-MtRobin, respectively, identified 13, 7, and 16 non-chromosome 6 genes with significant causal effects. All methods identified expression of PHRF1 gene as causal for SLE. A comprehensive literature review was conducted to enhance understanding of the functional roles and mechanisms of the identified genes in SLE development.

Conclusions

The findings from the three MR methods exhibited overlapping genes with causal effects on SLE, demonstrating consistent results. However, each method also uncovered unique genes due to different modelling assumptions and technical factors, highlighting the complementary nature of the approaches. Importantly, MRAID demonstrated a reduced percentage of causal genes from the Major Histocompatibility complex (MHC) region on chromosome 6, indicating its potential in minimizing false positive findings. This study contributes to unraveling the mechanisms underlying SLE by employing advanced probabilistic MR methods to identify causal genes, thereby enhancing our understanding of SLE pathogenesis.

Polygenic regression uncovers trait-relevant cellular contexts through pathway activation transformation of single-cell RNA sequencing data

Advances in single-cell RNA sequencing (scRNA-seq) techniques have accelerated functional interpretation of disease-associated variants discovered from genome-wide association studies (GWASs). However, identification of trait-relevant cell populations is often impeded by inherent technical noise and high sparsity in scRNA-seq data. Here, we developed scPagwas, a computational approach that uncovers trait-relevant cellular context by integrating pathway activation transformation of scRNA-seq data and GWAS summary statistics. scPagwas effectively prioritizes trait-relevant genes, which facilitates identification of trait-relevant cell types/populations with high accuracy in extensive simulated and real datasets. Cellular-level association results identified a novel subpopulation of naive CD8+ T cells related to COVID-19 severity and oligodendrocyte progenitor cell and microglia subsets with critical pathways by which genetic variants influence Alzheimer's disease. Overall, our approach provides new insights for the discovery of trait-relevant cell types and improves the mechanistic understanding of disease variants from a pathway perspective.

Genetics of SLE: mechanistic insights from monogenic disease and disease-associated variants

The past few years have provided important insights into the genetic architecture of systemic autoimmunity through aggregation of findings from genome-wide association studies (GWAS) and whole-exome or whole-genome sequencing studies. In the prototypic systemic autoimmune disease systemic lupus erythematosus (SLE), monogenic disease accounts for a small fraction of cases but has been instrumental in the elucidation of disease mechanisms. Defects in the clearance or digestion of extracellular or intracellular DNA or RNA lead to increased sensing of nucleic acids, which can break B cell tolerance and induce the production of type I interferons leading to tissue damage. Current data suggest that multiple GWAS SLE risk alleles act in concert with rare functional variants to promote SLE development. Moreover, introduction of orthologous variant alleles into mice has revealed that pathogenic X-linked dominant and recessive SLE can be caused by novel variants in TLR7 and SAT1, respectively. Such bespoke models of disease help to unravel pathogenic pathways and can be used to test targeted therapies. Cell type-specific expression data revealed that most GWAS SLE risk genes are highly expressed in age-associated B cells (ABCs), which supports the view that ABCs produce lupus autoantibodies and contribute to end-organ damage by persisting in inflamed tissues, including the kidneys. ABCs have thus emerged as key targets of promising precision therapeutics.

GWAS and autoimmunity: What have we learned and what next

Autoimmune diseases are common conditions characterized by loss of tolerance, female predominance and a remarkable heterogeneity among different populations. Most often they are polygenic and several genetic loci have been linked with the risk of developing autoimmune diseases. However, causal inference is difficult. When the genomic revolution began there were high hopes of translating fast genetic analyses to the bedside but this has proven to be challenging. Nonetheless, over the last decade, fine-mapping strategies have greatly improved; one of the most significant research lines focuses on the in vivo and ex vivo definition of the effect of genetic variants within the target tissues and within specific subpopulations of immune cells that are involved in the disease pathogenesis. This strategy also includes the longitudinal tracking of a large number of immunophenotypes in many individuals to build a large reference atlas for variant characterization. In this review, we discuss the results obtained by GWAS in autoimmune diseases and review recent advances in fine mapping strategies. More importantly, we discuss gaps and future directions.

Polygenic enrichment distinguishes disease associations of individual cells in single-cell RNA-seq data

Single-cell RNA sequencing (scRNA-seq) provides unique insights into the pathology and cellular origin of disease. We introduce single-cell disease relevance score (scDRS), an approach that links scRNA-seq with polygenic disease risk at single-cell resolution, independent of annotated cell types. scDRS identifies cells exhibiting excess expression across disease-associated genes implicated by genome-wide association studies (GWASs). We applied scDRS to 74 diseases/traits and 1.3 million single-cell gene-expression profiles across 31 tissues/organs. Cell-type-level results broadly recapitulated known cell-type-disease associations. Individual-cell-level results identified subpopulations of disease-associated cells not captured by existing cell-type labels, including T cell subpopulations associated with inflammatory bowel disease, partially characterized by their effector-like states; neuron subpopulations associated with schizophrenia, partially characterized by their spatial locations; and hepatocyte subpopulations associated with triglyceride levels, partially characterized by their higher ploidy levels. Genes whose expression was correlated with the scDRS score across cells (reflecting coexpression with GWAS disease-associated genes) were strongly enriched for gold-standard drug target and Mendelian disease genes.

ACPA-negative rheumatoid arthritis: From immune mechanisms to clinical translation

The presence of anti-citrullinated protein autoantibodies (ACPA) is a hallmark feature of rheumatoid arthritis (RA), which causes chronic joint destruction and systemic inflammation. Based on ACPA status, RA patients can be sub-grouped into two major subsets: ACPA-positive RA (ACPA⁺ RA) and ACPA-negative RA (ACPA^– RA). Accumulating evidence have suggested that ACPA⁺ RA and ACPA^– RA are two distinct disease entities with different underlying pathophysiology. In contrast to the well-characterized pathogenic mechanisms of ACPA⁺ RA, the etiology of ACPA^– RA remains largely unknown. In this review, we summarized current knowledge about the primary drivers of ACPA^– RA, particularly focusing on the serological, cellular, and molecular aspects of immune mechanisms. A better understanding of the immunopathogenesis in ACPA^– RA will help in designing more precisely targeting strategies, and paving the road to personalized treatment. In addition, identification of novel biomarkers in ACPA^– RA will substantially promote early treatment and improve the outcomes.

In rheumatoid arthritis inflamed joints share dominant patient-specific B-cell clones

Background

In patients with rheumatoid arthritis (RA) different joints were shown to share the same dominant T-cell clones, suggesting shared characteristics of the inflammatory process and indicating that strategies to selectively target the antigen receptor might be feasible. Since T- and B-lymphocytes closely interact in adaptive responses, we analysed to what extent different joints also share dominant B-cell clones.

Methods

In 11 RA patients, quantitative B-cell receptor (BCR) repertoire analysis was performed in simultaneously obtained samples from inflamed synovial tissue (ST) from distinct locations within one joint, from multiple joints, from synovial fluid (SF) and peripheral blood (PB).

Results

ST biopsies from different locations in the same joint showed clear overlap in the top-25 dominant BCR clones (16.7%, SD 12.5), in the same range as the overlap between ST and SF in the same joint (8.0%, SD 8.8) and the overlap between ST-ST between different joints (9.1%, SD 8.2), but clearly higher than the overlap between ST and PB (1.7%, SD 2.4; p<0.05) and SF and PB (2.7%, SD 4.1; p<0.05). Interestingly, these figures were substantially lower than the overlap observed in previous T-cell clonality studies.

Conclusions

We conclude that in RA BCR clonal responses may be more localized than TCR clonal responses, pointing to antigen-selective influx, proliferation and/or maturation of B-cells. B lineage cells in the SF may adequately represent the dominant BCR clones of the ST, which is in contrast to T-cells. Collectively, the presence of shared B- and especially T-cells in different joints from the same patient suggests that approaches might be feasible that aim to develop antigen-receptor specific targeting of lymphocyte clones in RA as an alternative to more generalized immunosuppressive strategies.

Single-cell RNA-seq reveals cell type-specific molecular and genetic associations to lupus

Systemic lupus erythematosus (SLE) is a heterogeneous autoimmune disease. Knowledge of circulating immune cell types and states associated with SLE remains incomplete. We profiled more than 1.2 million peripheral blood mononuclear cells (162 cases, 99 controls) with multiplexed single-cell RNA sequencing (mux-seq). Cases exhibited elevated expression of type 1 interferon-stimulated genes (ISGs) in monocytes, reduction of naïve CD4+ T cells that correlated with monocyte ISG expression, and expansion of repertoire-restricted cytotoxic GZMH+ CD8+ T cells. Cell type-specific expression features predicted case-control status and stratified patients into two molecular subtypes. We integrated dense genotyping data to map cell type-specific cis-expression quantitative trait loci and to link SLE-associated variants to cell type-specific expression. These results demonstrate mux-seq as a systematic approach to characterize cellular composition, identify transcriptional signatures, and annotate genetic variants associated with SLE.

Genomic Multiple Sclerosis Risk Variants Modulate the Expression of the ANKRD55-IL6ST Gene Region in Immature Dendritic Cells

Intronic single-nucleotide polymorphisms (SNPs) in the ANKRD55 gene are associated with the risk for multiple sclerosis (MS) and rheumatoid arthritis by genome-wide association studies (GWAS). The risk alleles have been linked to higher expression levels of ANKRD55 and the neighboring IL6ST (gp130) gene in CD4+ T lymphocytes of healthy controls. The biological function of ANKRD55, its role in the immune system, and cellular sources of expression other than lymphocytes remain uncharacterized. Here, we show that monocytes gain capacity to express ANKRD55 during differentiation in immature monocyte-derived dendritic cells (moDCs) in the presence of interleukin (IL)-4/granulocyte-macrophage colony-stimulating factor (GM-CSF). ANKRD55 expression levels are further enhanced by retinoic acid agonist AM580 but downregulated following maturation with interferon (IFN)-γ and lipopolysaccharide (LPS). ANKRD55 was detected in the nucleus of moDC in nuclear speckles. We also analyzed the adjacent IL6ST, IL31RA, and SLC38A9 genes. Of note, in healthy controls, MS risk SNP genotype influenced ANKRD55 and IL6ST expression in immature moDC in opposite directions to that in CD4+ T cells. This effect was stronger for a partially correlated SNP, rs13186299, that is located, similar to the main MS risk SNPs, in an ANKRD55 intron. Upon analysis in MS patients, the main GWAS MS risk SNP rs7731626 was associated with ANKRD55 expression levels in CD4+ T cells. MoDC-specific ANKRD55 and IL6ST mRNA levels showed significant differences according to the clinical form of the disease, but, in contrast to healthy controls, were not influenced by genotype. We also measured serum sgp130 levels, which were found to be higher in homozygotes of the protective allele of rs7731626. Our study characterizes ANKRD55 expression in moDC and indicates monocyte-to-dendritic cell (Mo-DC) differentiation as a process potentially influenced by MS risk SNPs.

Genetic and transcriptomic analyses support a switch to lytic phase in Epstein Barr virus infection as an important driver in developing Systemic Lupus Erythematosus

To investigate the molecular mechanisms through which Epstein-Barr virus (EBV) may contribute to Systemic Lupus Erythematosus (SLE) pathogenesis, we interrogated SLE genetic risk loci for signatures of EBV infection. We first compared the gene expression profile of SLE risk genes across 459 different cell/tissue types. EBV-infected B cells (LCLs) had the strongest representation of highly expressed SLE risk genes. By determining an SLE risk allele effect on gene expression (expression quantitative trait loci, eQTL) in LCLs and 16 other immune cell types, we identified 79 SLE risk locus:gene pairs putatively interacting with EBV infection. A total of 10 SLE risk genes from this list (CD40, LYST, JAZF1, IRF5, BLK, IKZF2, IL12RB2, FAM167A, PTPRC and SLC15A) were targeted by the EBV transcription factor, EBNA2, differentially expressed between LCLs and B cells, and the majority were also associated with EBV DNA copy number, and expression level of EBV encoded genes. Our final gene network model based on these genes is suggestive of a nexus involving SLE risk loci and EBV latency III and B cell proliferation signalling pathways. Collectively, our findings provide further evidence to support the interaction between SLE risk loci and EBV infection that is in part mediated by EBNA2. This interplay may increase the tendency towards EBV lytic switching dependent on the presence of SLE risk alleles. These results support further investigation into targeting EBV as a therapeutic strategy for SLE.

Molecular remission at T cell level in patients with rheumatoid arthritis

While numerous disease-modifying anti-rheumatic drugs (DMARDs) have brought about a dramatic paradigm shift in the management of rheumatoid arthritis (RA), unmet needs remain, such as the small proportion of patients who achieve drug-free status. The aim of this study was to explore key molecules for remission at the T cell level, which are known to be deeply involved in RA pathogenesis, and investigate the disease course of patients who achieved molecular remission (MR). We enrolled a total of 46 patients with RA and 10 healthy controls (HCs). We performed gene expression profiling and selected remission signature genes in CD4⁺ T cells and CD8⁺ T cells from patients with RA using machine learning methods. In addition, we investigated the benefits of achieving MR on disease control. We identified 9 and 23 genes that were associated with clinical remission in CD4⁺ and CD8⁺ T cells, respectively. Principal component analysis (PCA) demonstrated that their expression profiling was similar to those in HCs. For the remission signature genes in CD4⁺ T cells, the PCA result was reproduced using a validation cohort, indicating the robustness of these genes. A trend toward better disease control was observed during 12 months of follow-up in patients treated with tocilizumab in deep MR compared with those in non-deep MR, although the difference was not significant. The current study will promote our understanding of the molecular mechanisms necessary to achieve deep remission during the management of RA.

The impact of cell type and context-dependent regulatory variants on human immune traits

BACKGROUND

The vast majority of trait-associated variants identified using genome-wide association studies (GWAS) are noncoding, and therefore assumed to impact gene regulation. However, the majority of trait-associated loci are unexplained by regulatory quantitative trait loci (QTLs).

RESULTS

We perform a comprehensive characterization of the putative mechanisms by which GWAS loci impact human immune traits. By harmonizing four major immune QTL studies, we identify 26,271 expression QTLs (eQTLs) and 23,121 splicing QTLs (sQTLs) spanning 18 immune cell types. Our colocalization analyses between QTLs and trait-associated loci from 72 GWAS reveals that genetic effects on RNA expression and splicing in immune cells colocalize with 40.4% of GWAS loci for immune-related traits, in many cases increasing the fraction of colocalized loci by two fold compared to previous studies. Notably, we find that the largest contributors of this increase are splicing QTLs, which colocalize on average with 14% of all GWAS loci that do not colocalize with eQTLs. By contrast, we find that cell type-specific eQTLs, and eQTLs with small effect sizes contribute very few new colocalizations. To investigate the 60% of GWAS loci that remain unexplained, we collect H3K27ac CUT&Tag data from rheumatoid arthritis and healthy controls, and find large-scale differences between immune cells from the different disease contexts, including at regions overlapping unexplained GWAS loci.

CONCLUSION

Altogether, our work supports RNA splicing as an important mediator of genetic effects on immune traits, and suggests that we must expand our study of regulatory processes in disease contexts to improve functional interpretation of as yet unexplained GWAS loci.

Global discovery of lupus genetic risk variant allelic enhancer activity

Genome-wide association studies of Systemic Lupus Erythematosus (SLE) nominate 3073 genetic variants at 91 risk loci. To systematically screen these variants for allelic transcriptional enhancer activity, we construct a massively parallel reporter assay (MPRA) library comprising 12,396 DNA oligonucleotides containing the genomic context around every allele of each SLE variant. Transfection into the Epstein-Barr virus-transformed B cell line GM12878 reveals 482 variants with enhancer activity, with 51 variants showing genotype-dependent (allelic) enhancer activity at 27 risk loci. Comparison of MPRA results in GM12878 and Jurkat T cell lines highlights shared and unique allelic transcriptional regulatory mechanisms at SLE risk loci. In-depth analysis of allelic transcription factor (TF) binding at and around allelic variants identifies one class of TFs whose DNA-binding motif tends to be directly altered by the risk variant and a second class of TFs that bind allelically without direct alteration of their motif by the variant. Collectively, our approach provides a blueprint for the discovery of allelic gene regulation at risk loci for any disease and offers insight into the transcriptional regulatory mechanisms underlying SLE.

Thousands of genetic variants have been associated with lupus, but causal variants and mechanisms are unknown. Here, the authors combine a massively parallel reporter assay with genome-wide ChIP experiments to identify risk variants with allelic enhancer activity mediated through transcription factor binding.

Editing GWAS: experimental approaches to dissect and exploit disease-associated genetic variation

Genome-wide association studies (GWAS) have uncovered thousands of genetic variants that influence risk for human diseases and traits. Yet understanding the mechanisms by which these genetic variants, mainly noncoding, have an impact on associated diseases and traits remains a significant hurdle. In this review, we discuss emerging experimental approaches that are being applied for functional studies of causal variants and translational advances from GWAS findings to disease prevention and treatment. We highlight the use of genome editing technologies in GWAS functional studies to modify genomic sequences, with proof-of-principle examples. We discuss the challenges in interrogating causal variants, points for consideration in experimental design and interpretation of GWAS locus mechanisms, and the potential for novel therapeutic opportunities. With the accumulation of knowledge of functional genetics, therapeutic genome editing based on GWAS discoveries will become increasingly feasible.

A Review of Statistical Methods for Identifying Trait-Relevant Tissues and Cell Types

Genome-wide association studies (GWASs) have identified and replicated many genetic variants that are associated with diseases and disease-related complex traits. However, the biological mechanisms underlying these identified associations remain largely elusive. Exploring the biological mechanisms underlying these associations requires identifying trait-relevant tissues and cell types, as genetic variants likely influence complex traits in a tissue- and cell type-specific manner. Recently, several statistical methods have been developed to integrate genomic data with GWASs for identifying trait-relevant tissues and cell types. These methods often rely on different genomic information and use different statistical models for trait-tissue relevance inference. Here, we present a comprehensive technical review to summarize ten existing methods for trait-tissue relevance inference. These methods make use of different genomic information that include functional annotation information, expression quantitative trait loci information, genetically regulated gene expression information, as well as gene co-expression network information. These methods also use different statistical models that range from linear mixed models to covariance network models. We hope that this review can serve as a useful reference both for methodologists who develop methods and for applied analysts who apply these methods for identifying trait relevant tissues and cell types.

Genetic variants shape rheumatoid arthritis-specific transcriptomic features in CD4+ T cells through differential DNA methylation, explaining a substantial proportion of heritability

OBJECTIVE

CD4⁺ T cells have been suggested as the most disease-relevant cell type in rheumatoid arthritis (RA) in which RA-risk non-coding variants exhibit allele-specific effects on regulation of RA-driving genes. This study aimed to understand RA-specific signatures in CD4⁺ T cells using multi-omics data, interpreting inter-omics relationships in shaping the RA transcriptomic landscape.

METHODS

We profiled genome-wide variants, gene expression and DNA methylation in CD4⁺ T cells from 82 patients with RA and 40 healthy controls using high-throughput technologies. We investigated differentially expressed genes (DEGs) and differential methylated regions (DMRs) in RA and localised quantitative trait loci (QTLs) for expression and methylation. We then integrated these based on individual-level correlations to inspect DEG-regulating sources and investigated the potential regulatory roles of RA-risk variants by a partitioned-heritability enrichment analysis with RA genome-wide association summary statistics.

RESULTS

A large number of RA-specific DEGs were identified (n=2575), highlighting T cell differentiation and activation pathways. RA-specific DMRs, preferentially located in T cell regulatory regions, were correlated with the expression levels of 548 DEGs mostly in the same topologically associating domains. In addition, expressional variances in 771 and 83 DEGs were partially explained by expression QTLs for DEGs and methylation QTLs (meQTLs) for DEG-correlated DMRs, respectively. A large number of RA variants were moderately to strongly correlated with meQTLs. DEG-correlated DMRs, enriched with meQTLs, had strongly enriched heritability of RA.

CONCLUSION

Our findings revealed that the methylomic changes, driven by RA heritability-explaining variants, shape the differential expression of a substantial fraction of DEGs in CD4⁺ T cells in patients with RA, reinforcing the importance of a multidimensional approach in disease-relevant tissues.

In The Blood: Connecting Variant to Function In Human Hematopoiesis

Genome-wide association studies (GWAS) have identified thousands of genetic variants associated with a range of human diseases and traits. However, understanding the mechanisms by which these genetic variants have an impact on associated diseases and traits, often referred to as the variant-to-function (V2F) problem, remains a significant hurdle. Solving the V2F challenge requires us to identify causative genetic variants, relevant cell types/states, target genes, and mechanisms by which variants can cause diseases or alter phenotypic traits. We discuss emerging functional approaches that are being applied to tackle the V2F problem for blood cell traits, illuminating how human genetic variation can impact on key mechanisms in hematopoiesis, as well as highlighting future prospects for this nascent field.

The differential expression patterns and co-expression networks of paralogs as an indicator of the TNM stages of lung adenocarcinoma and squamous cell carcinoma

Cancers constitute a severe threat to human health. Elucidating the association between the expression patterns of the paralogous genes and transcription factors (TF) and the progression of cancers by comprehensively investigating the expression patterns and co-expression networks will contribute to the in-depth understanding of the pathogenesis of cancers. Here, we identified the paralogous gene pairs and systematically analyzed the expression patterns of these paralogs and the known TFs to elucidate the associations with Tumor, Node, Metastasis (TNM) staging information across ten cancers. We found that the expression of ~60% paralogs was cancer-dependent, and more than 50% of the differentially expressed TFs pairs showed positive expression correlations. The down-regulation patterns of paralogs and TFs were closely associated with the M and N developmental stages of lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC). Our results will help to understand the roles of paralogs and TFs in cancer progression and to screen prognostic biomarkers for early cancer diagnosis.

From GWAS to Function: Using Functional Genomics to Identify the Mechanisms Underlying Complex Diseases

Genome-wide association studies (GWAS) have successfully mapped thousands of loci associated with complex traits. These associations could reveal the molecular mechanisms altered in common complex diseases and result in the identification of novel drug targets. However, GWAS have also left a number of outstanding questions. In particular, the majority of disease-associated loci lie in non-coding regions of the genome and, even though they are thought to play a role in gene expression regulation, it is unclear which genes they regulate and in which cell types or physiological contexts this regulation occurs. This has hindered the translation of GWAS findings into clinical interventions. In this review we summarize how these challenges have been addressed over the last decade, with a particular focus on the integration of GWAS results with functional genomics datasets. Firstly, we investigate how the tissues and cell types involved in diseases can be identified using methods that test for enrichment of GWAS variants in genomic annotations. Secondly, we explore how to find the genes regulated by GWAS loci using methods that test for colocalization of GWAS signals with molecular phenotypes such as quantitative trait loci (QTLs). Finally, we highlight potential future research avenues such as integrating GWAS results with single-cell sequencing read-outs, designing functionally informed polygenic risk scores (PRS), and validating disease associated genes using genetic engineering. These tools will be crucial to identify new drug targets for common complex diseases.

The importance of differences; On environment and its interactions with genes and immunity in the causation of rheumatoid arthritis

The current review uses rheumatoid arthritis (RA) as a prominent example for how studies on the interplay between environmental and genetic factors in defined subsets of a disease can be used to formulate aetiological hypotheses that subsequently can be tested for causality using molecular and functional studies. Major discussed findings are that exposures to airways from many different noxious agents including cigarette smoke, silica dust and more interact with major susceptibility genes, mainly HLA-DR genetic variants in triggering antigen-specific immune reactions specific for RA. We also discuss how several other environmental and lifestyle factors, including microbial, neural and metabolic factors, can influence risk for RA in ways that are different in different subsets of RA.The description of these processes in RA provides the best example so far in any immune-mediated disease of how triggering of immunity at one anatomical site in the context of known environmental and genetic factors subsequently can lead to symptoms that precede the classical inflammatory disease symptoms and later contribute also to the classical RA joint inflammation. The findings referred to in the review have led to a change of paradigms for very early therapy and prevention of RA and to efforts towards what we have named 'personalized prevention'. We believe that the progress described here for RA will be of relevance for research and practice also in other immune-mediated diseases.

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

Naïve 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. Here we use quantitative proteomics, bulk RNA-seq, and single-cell RNA-seq of over 40,000 human naïve and memory CD4⁺ T cells to show that responses to cytokines differ substantially between these cell types. Memory T cells are unable to differentiate into the Th2 phenotype, and acquire a Th17-like phenotype in response to iTreg polarization. Single-cell analyses show that T cells constitute a transcriptional continuum that progresses from naïve to central and effector memory T cells, forming an effectorness gradient accompanied by an increase in the expression of chemokines and cytokines. Finally, we show that T cell activation and cytokine responses are influenced by the effectorness gradient. Our results illustrate the heterogeneity of T cell responses, furthering our understanding of inflammation.

Cytokines critically control the differentiation and functions of activated naïve and memory T cells. Here the authors show, using multi-omics and single-cell analyses, that naïve and memory T cells exhibit distinct cytokine responses, in which an ‘effectorness gradient’ is depicted by a transcriptional continuum, which shapes the downstream genetic programs.

The Ubiquitin Ligase Adaptor NDFIP1 Selectively Enforces a CD8+ T Cell Tolerance Checkpoint to High-Dose Antigen

Escape from peripheral tolerance checkpoints that control cytotoxic CD8⁺ T cells is important for cancer immunotherapy and autoimmunity, but pathways enforcing these checkpoints are mostly uncharted. We reveal that the HECT-type ubiquitin ligase activator, NDFIP1, enforces a cell-intrinsic CD8⁺ T cell checkpoint that desensitizes TCR signaling during in vivo exposure to high antigen levels. Ndfip1-deficient OT-I CD8⁺ T cells responding to high exogenous tolerogenic antigen doses that normally induce anergy aberrantly expanded and differentiated into effector cells that could precipitate autoimmune diabetes in RIP-OVA^hi mice. In contrast, NDFIP1 was dispensable for peripheral deletion to low-dose exogenous or pancreatic islet-derived antigen and had little impact upon effector responses to Listeria or acute LCMV infection. These data provide evidence that NDFIP1 mediates a CD8⁺ T cell tolerance checkpoint, with a different mechanism to CD4⁺ T cells, and indicates that CD8⁺ T cell deletion and anergy are molecularly separable checkpoints.

Advances in genetics toward identifying pathogenic cell states of rheumatoid arthritis

Rheumatoid arthritis (RA) risk has a large genetic component (~60%) that is still not fully understood. This has hampered the design of effective treatments that could promise lifelong remission. RA is a polygenic disease with 106 known genome-wide significant associated loci and thousands of small effect causal variants. Our current understanding of RA risk has suggested cell-type-specific contexts for causal variants, implicating CD4 + effector memory T cells, as well as monocytes, B cells and stromal fibroblasts. While these cellular states and categories are still mechanistically broad, future studies may identify causal cell subpopulations. These efforts are propelled by advances in single cell profiling. Identification of causal cell subpopulations may accelerate therapeutic intervention to achieve lifelong remission.

Personalised analytics for rare disease diagnostics

Whole genome and exome sequencing is a standard tool for the diagnosis of patients suffering from rare and other genetic disorders. The interpretation of the tens of thousands of variants returned from such tests remains a major challenge. Here we focus on the problem of prioritising variants with respect to the observed disease phenotype. We hypothesise that linking patterns of gene expression across multiple tissues to the phenotypes will aid in discovering disease causing variants. To test this, we construct classifiers that learn associations between tissue-specific gene expression and disease phenotypes. We find that using Genotype-Tissue Expression project (GTEx) expression data in conjunction with disease agnostic variant prioritisation methods (CADD or MetaSVM) results in consistent improvements in classification accuracy. Our method represents a previously overlooked avenue of utilising existing expression data for clinical diagnostics, and also opens the door to use of other functional genomic data sets in the same manner.

Rare variants in non-coding regulatory regions of the genome that affect gene expression in systemic lupus erythematosus

Personalized medicine approaches are increasingly sought for diseases with a heritable component. Systemic lupus erythematosus (SLE) is the prototypic autoimmune disease resulting from loss of immunologic tolerance, but the genetic basis of SLE remains incompletely understood. Genome wide association studies (GWAS) identify regions associated with disease, based on common single nucleotide polymorphisms (SNPs) within them, but these SNPs may simply be markers in linkage disequilibrium with other, causative mutations. Here we use an hierarchical screening approach for prediction and testing of true functional variants within regions identified in GWAS; this involved bioinformatic identification of putative regulatory elements within close proximity to SLE SNPs, screening those regions for potentially causative mutations by high resolution melt analysis, and functional validation using reporter assays. Using this approach, we screened 15 SLE associated loci in 143 SLE patients, identifying 7 new variants including 5 SNPs and 2 insertions. Reporter assays revealed that the 5 SNPs were functional, altering enhancer activity. One novel variant was linked to the relatively well characterized rs9888739 SNP at the ITGAM locus, and may explain some of the SLE heritability at this site. Our study demonstrates that non-coding regulatory elements can contain private sequence variants affecting gene expression, which may explain part of the heritability of SLE.

Mixed-effects association of single cells identifies an expanded effector CD4+ T cell subset in rheumatoid arthritis

High-dimensional single-cell analyses have improved the ability to resolve complex mixtures of cells from human disease samples; however, identifying disease-associated cell types or cell states in patient samples remains challenging because of technical and interindividual variation. Here, we present mixed-effects modeling of associations of single cells (MASC), a reverse single-cell association strategy for testing whether case-control status influences the membership of single cells in any of multiple cellular subsets while accounting for technical confounders and biological variation. Applying MASC to mass cytometry analyses of CD4⁺ T cells from the blood of rheumatoid arthritis (RA) patients and controls revealed a significantly expanded population of CD4⁺ T cells, identified as CD27^- HLA-DR⁺ effector memory cells, in RA patients (odds ratio, 1.7; P = 1.1 × 10^-3). The frequency of CD27^- HLA-DR⁺ cells was similarly elevated in blood samples from a second RA patient cohort, and CD27^- HLA-DR⁺ cell frequency decreased in RA patients who responded to immunosuppressive therapy. Mass cytometry and flow cytometry analyses indicated that CD27^- HLA-DR⁺ cells were associated with RA (meta-analysis P = 2.3 × 10^-4). Compared to peripheral blood, synovial fluid and synovial tissue samples from RA patients contained about fivefold higher frequencies of CD27^- HLA-DR⁺ cells, which comprised ~10% of synovial CD4⁺ T cells. CD27^- HLA-DR⁺ cells expressed a distinctive effector memory transcriptomic program with T helper 1 (T_H1)- and cytotoxicity-associated features and produced abundant interferon-γ (IFN-γ) and granzyme A protein upon stimulation. We propose that MASC is a broadly applicable method to identify disease-associated cell populations in high-dimensional single-cell data.

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

Immune-disease-associated variants are enriched in active chromatin regions of T cells and macrophages. However, whether these variants function in specific cell states is unknown. Here we stimulated T cells and macrophages in the presence of 13 cytokines and profiled active and open chromatin regions. T cell activation induced major chromatin remodeling, while the presence of cytokines fine-tuned the magnitude of changes. We developed a statistical method that accounts for subtle changes in the chromatin landscape to identify SNP enrichment across cell states. Our results point towards the role of immune-disease-associated variants in early rather than late activation of memory CD4+ T cells, with modest differences across cytokines. Furthermore, variants associated with inflammatory bowel disease are enriched in type 1 T helper (TH1) cells, whereas variants associated with Alzheimer's disease are enriched in different macrophage cell states. Our results represent an in-depth analysis of immune-disease-associated variants across a comprehensive panel of activation states of T cells and macrophages.

Landscape of stimulation-responsive chromatin across diverse human immune cells

A hallmark of the immune system is the interplay among specialized cell types transitioning between resting and stimulated states. The gene regulatory landscape of this dynamic system has not been fully characterized in human cells. Here, we collected ATAC-seq and RNA-seq data under resting and stimulated conditions for up to 32 immune cell populations. Stimulation caused widespread chromatin remodeling, including response elements shared between stimulated B and T cells. Furthermore, several autoimmune traits showed significant heritability in stimulation-responsive elements from distinct cell types, highlighting the importance of these cell states in autoimmunity. Use of allele-specific read-mapping identified variants that alter chromatin accessibility in particular conditions, allowing us to observe evidence of function for a candidate causal variant that is undetected by existing large-scale studies in resting cells. Our results provide a resource of chromatin dynamics and highlight the need for characterization of effects of genetic variation in stimulated cells.

Analysis of gene expression and open chromatin regions in up to 32 immune cell populations under resting and stimulated conditions identifies widespread chromatin remodeling and shared response elements between stimulated B and T cells.

Multiple sclerosis genomic map implicates peripheral immune cells and microglia in susceptibility

We analyzed genetic data of 47,429 multiple sclerosis (MS) and 68,374 control subjects and established a reference map of the genetic architecture of MS that includes 200 autosomal susceptibility variants outside the major histocompatibility complex (MHC), one chromosome X variant, and 32 variants within the extended MHC. We used an ensemble of methods to prioritize 551 putative susceptibility genes that implicate multiple innate and adaptive pathways distributed across the cellular components of the immune system. Using expression profiles from purified human microglia, we observed enrichment for MS genes in these brain-resident immune cells, suggesting that these may have a role in targeting an autoimmune process to the central nervous system, although MS is most likely initially triggered by perturbation of peripheral immune responses.

PD-L1- and calcitriol-dependent liposomal antigen-specific regulation of systemic inflammatory autoimmune disease

Autoimmune diseases resulting from MHC class II-restricted autoantigen-specific T cell immunity include the systemic inflammatory autoimmune conditions rheumatoid arthritis and vasculitis. While currently treated with broad-acting immunosuppressive drugs, a preferable strategy is to regulate antigen-specific effector T cells (Teffs) to restore tolerance by exploiting DC antigen presentation. We targeted draining lymph node (dLN) phagocytic DCs using liposomes encapsulating 1α,25-dihydroxyvitamin D3 (calcitriol) and antigenic peptide to elucidate mechanisms of tolerance used by DCs and responding T cells under resting and immunized conditions. PD-L1 expression was upregulated in dLNs of immunized relative to naive mice. Subcutaneous administration of liposomes encapsulating OVA323-339 and calcitriol targeted dLN PD-L1hi DCs of immunized mice and reduced their MHC class II expression. OVA323-339/calcitriol liposomes suppressed expansion, differentiation, and function of Teffs and induced Foxp3+ and IL-10+ peripheral Tregs in an antigen-specific manner, which was dependent on PD-L1. Peptide/calcitriol liposomes modulated CD40 expression by human DCs and promoted Treg induction in vitro. Liposomes encapsulating calcitriol and disease-associated peptides suppressed the severity of rheumatoid arthritis and Goodpasture's vasculitis models with suppression of antigen-specific memory T cell differentiation and function. Accordingly, peptide/calcitriol liposomes leverage DC PD-L1 for antigen-specific T cell regulation and induce antigen-specific tolerance in inflammatory autoimmune diseases.

Genetic mapping of cell type specificity for complex traits

Single-cell RNA sequencing (scRNA-seq) data allows to create cell type specific transcriptome profiles. Such profiles can be aligned with genome-wide association studies (GWASs) to implicate cell type specificity of the traits. Current methods typically rely only on a small subset of available scRNA-seq datasets, and integrating multiple datasets is hampered by complex batch effects. Here we collated 43 publicly available scRNA-seq datasets. We propose a 3-step workflow with conditional analyses within and between datasets, circumventing batch effects, to uncover associations of traits with cell types. Applying this method to 26 traits, we identify independent associations of multiple cell types. These results lead to starting points for follow-up functional studies aimed at gaining a mechanistic understanding of these traits. The proposed framework as well as the curated scRNA-seq datasets are made available via an online platform, FUMA, to facilitate rapid evaluation of cell type specificity by other researchers.

Tissue- and cell type-specific information helps to interpret findings from genome-wide association studies. Here, the authors leverage multiple single cell expression datasets to infer cell type specificity of traits.

Multi-dimensional analysis identified rheumatoid arthritis-driving pathway in human T cell

Objectives

Rheumatoid arthritis (RA) is an autoimmune disease accompanied by lymphocyte infiltration into joint synovium. While T cells are considered to be important for its pathogenesis, the features that are the most relevant to disease and how they change after treatment remain unclear. The aim of this study was to clarify the characteristics of T cells in RA, comprehensively.

Methods

We enrolled a total of 311 patients with RA and 73 healthy participants, and carefully classified them by disease state, constructed multiple cohorts and analysed clinical samples from them in a stepwise manner. We performed immunophenotyping with multiple evaluation axes, and two independent transcriptome analyses complementary to each other.

Results

We identified that ‘effector memory-Tfh’ subset was specifically expanded in the peripheral blood (PB) of patients with RA in correlation with disease activity, and reverted after treatment. Besides, we revealed distinct features of T cells in synovial fluid (SF) that the expression of Tfh/Tph-related genes and pro-inflammatory cytokines and chemokines, including CXCL13, were significantly enriched, whereas these phenotype were Th1-like. Finally, we identified specific pathways, such as mTORC1, IL-2-stat5, E2F, cell cycle and interferon-related genes, that were significantly enriched in SF, in particular, as well as PB of untreated patients with RA, and notably, these features reverted after treatment.

Conclusion

Our multi-dimensional investigation identified disease relevant T-cell subsets and gene signatures deeply involved in pathogenesis of RA. These findings could aid in our understanding of essential roles of T cells in RA and will facilitate to development better diagnostic and therapeutic interventions.

Disease classification: from phenotypic similarity to integrative genomics and beyond

A fundamental challenge of modern biomedical research is understanding how diseases that are similar on the phenotypic level are similar on the molecular level. Integration of various genomic data sets with the traditionally used phenotypic disease similarity revealed novel genetic and molecular mechanisms and blurred the distinction between monogenic (Mendelian) and complex diseases. Network-based medicine has emerged as a complementary approach for identifying disease-causing genes, genetic mediators, disruptions in the underlying cellular functions and for drug repositioning. The recent development of machine and deep learning methods allow for leveraging real-life information about diseases to refine genetic and phenotypic disease relationships. This review describes the historical development and recent methodological advancements for studying disease classification (nosology).

IMPACT: Genomic Annotation of Cell-State-Specific Regulatory Elements Inferred from the Epigenome of Bound Transcription Factors

Despite significant progress in annotating the genome with experimental methods, much of the regulatory noncoding genome remains poorly defined. Here we assert that regulatory elements may be characterized by leveraging local epigenomic signatures where specific transcription factors (TFs) are bound. To link these two features, we introduce IMPACT, a genome annotation strategy that identifies regulatory elements defined by cell-state-specific TF binding profiles, learned from 515 chromatin and sequence annotations. We validate IMPACT using multiple compelling applications. First, IMPACT distinguishes between bound and unbound TF motif sites with high accuracy (average AUPRC 0.81, SE 0.07; across 8 tested TFs) and outperforms state-of-the-art TF binding prediction methods, MocapG, MocapS, and Virtual ChIP-seq. Second, in eight tested cell types, RNA polymerase II IMPACT annotations capture more cis-eQTL variation than sequence-based annotations, such as promoters and TSS windows (25% average increase in enrichment). Third, integration with rheumatoid arthritis (RA) summary statistics from European (N = 38,242) and East Asian (N = 22,515) populations revealed that the top 5% of CD4+ Treg IMPACT regulatory elements capture 85.7% of RA h2, the most comprehensive explanation for RA h2 to date. In comparison, the average RA h2 captured by compared CD4+ T histone marks is 42.3% and by CD4+ T specifically expressed gene sets is 36.4%. Lastly, we find that IMPACT may be used in many different cell types to identify complex trait associated regulatory elements.

Mesenchymal Stem Cells Improve Rheumatoid Arthritis Progression by Controlling Memory T Cell Response

In the last years, mesenchymal stem cell (MSC)-based therapies have become an interesting therapeutic opportunity for the treatment of rheumatoid arthritis (RA) due to their capacity to potently modulate the immune response. RA is a chronic autoimmune inflammatory disorder with an incompletely understood etiology. However, it has been well described that peripheral tolerance defects and the subsequent abnormal infiltration and activation of diverse immune cells into the synovial membrane, are critical for RA development and progression. Moreover, the imbalance between the immune response of pro-inflammatory and anti-inflammatory cells, in particular between memory Th17 and memory regulatory T cells (Treg), respectively, is well admitted to be associated to RA immunopathogenesis. In this context, MSCs, which are able to alter the frequency and function of memory lymphocytes including Th17, follicular helper T (Tfh) cells and gamma delta (γδ) T cells while promoting Treg cell generation, have been proposed as a candidate of choice for RA cell therapy. Indeed, given the plasticity of memory CD4⁺ T cells, it is reasonable to think that MSCs will restore the balance between pro-inflammatory and anti-inflammatory memory T cells populations deregulated in RA leading to prompt their therapeutic function. In the present review, we will discuss the role of memory T cells implicated in RA pathogenesis and the beneficial effects exerted by MSCs on the phenotype and functions of these immune cells abnormally regulated in RA and how this regulation could impact RA progression.

CD72 is a Negative Regulator of B Cell Responses to Nuclear Lupus Self-antigens and Development of Systemic Lupus Erythematosus

Systemic lupus erythematosus (SLE) is the prototypic systemic autoimmune disease characterized by production of autoantibodies to various nuclear antigens and overexpression of genes regulated by IFN-I called IFN signature. Genetic studies on SLE patients and mutational analyses of mouse models demonstrate crucial roles of nucleic acid (NA) sensors in development of SLE. Although NA sensors are involved in induction of anti-microbial immune responses by recognizing microbial NAs, recognition of self NAs by NA sensors induces production of autoantibodies to NAs in B cells and production of IFN-I in plasmacytoid dendritic cells. Among various NA sensors, the endosomal RNA sensor TLR7 plays an essential role in development of SLE at least in mouse models. CD72 is an inhibitory B cell co-receptor containing an immunoreceptor tyrosine-based inhibition motif (ITIM) in the cytoplasmic region and a C-type lectin like-domain (CTLD) in the extracellular region. CD72 is known to regulate development of SLE because CD72 polymorphisms associate with SLE in both human and mice and CD72^−/− mice develop relatively severe lupus-like disease. CD72 specifically recognizes the RNA-containing endogenous TLR7 ligand Sm/RNP by its extracellular CTLD, and inhibits B cell responses to Sm/RNP by ITIM-mediated signal inhibition. These findings indicate that CD72 inhibits development of SLE by suppressing TLR7-dependent B cell response to self NAs. CD72 is thus involved in discrimination of self-NAs from microbial NAs by specifically suppressing autoimmune responses to self-NAs.

The Human Transcription Factors

Transcription factors (TFs) recognize specific DNA sequences to control chromatin and transcription, forming a complex system that guides expression of the genome. Despite keen interest in understanding how TFs control gene expression, it remains challenging to determine how the precise genomic binding sites of TFs are specified and how TF binding ultimately relates to regulation of transcription. This review considers how TFs are identified and functionally characterized, principally through the lens of a catalog of over 1,600 likely human TFs and binding motifs for two-thirds of them. Major classes of human TFs differ markedly in their evolutionary trajectories and expression patterns, underscoring distinct functions. TFs likewise underlie many different aspects of human physiology, disease, and variation, highlighting the importance of continued effort to understand TF-mediated gene regulation.

T-cell Composition in Ileal and Colonic Creeping Fat - Separating Ileal from Colonic Crohn's Disease

BACKGROUND AND AIMS

Creeping fat [CF] is a hyperplasia of adipose tissue adjacent to inflamed intestine in Crohn's disease [CD]. Data from genome-wide association studies [GWAS] distinguished Crohn's colitis from ileal CD and ulcerative colitis [UC]. This study analysed the T-cell compartments of ileal and colonic mesenteric fat and corresponding mucosa to provide cellular proof for the suggested GWAS classification.

METHODS

Samples were obtained from 34 CD or UC patients. Cells were analysed by immunohistochemistry and flow cytometry, and tissue cytokine release was assessed by cytometric bead array.

RESULTS

Only ileal CF revealed the distinct adipocyte hyperplasia combined with dense T-cell infiltration and fibrosis; colonic fat from CD and UC patients lacked these findings. T-cell subpopulations differed between mesenteric fat in ileal CD, colonic CD and UC: ileal CF had nearly 10 times more T-cells than colonic fat. The proportions of regulatory and central memory T-cells were significantly higher in ileal CF compared with colonic fat in CD and UC. In all groups, the mucosal T-cell compartment was distinct from the mesenteric fat. Remarkably, correlation between disease activity and proportion of pro- and anti-inflammatory T-cell subpopulations was inverse, comparing ileal and colonic fat in CD.

CONCLUSIONS

This first in-depth analysis of the T-cell compartment in ileal and colonic mesenteric adipose tissue in CD and UC identifies a unique T-cell niche in the ileal mesenteric fat tissue in CD. From a clinical point of view, our findings underscore the novel concept of colonic and ileal CD as distinct IBD entities.

Fine-mapping and functional studies highlight potential causal variants for rheumatoid arthritis and type 1 diabetes

To define potentially causal variants for autoimmune disease, we fine-mapped^1,2 76 rheumatoid arthritis (11,475 cases, 15,870 controls)³ and type 1 diabetes loci (9,334 cases, 11,111 controls)⁴. After sequencing 799 1-kilobase regulatory (H3K4me3) regions within these loci in 568 individuals, we observed accurate imputation for 89% of common variants. We defined credible sets of ≤5 causal variants at 5 rheumatoid arthritis and 10 type 1 diabetes loci. We identified potentially causal missense variants at DNASE1L3, PTPN22, SH2B3, and TYK2, and noncoding variants at MEG3, CD28-CTLA4, and IL2RA. We also identified potential candidate causal variants at SIRPG and TNFAIP3. Using functional assays, we confirmed allele-specific protein binding and differential enhancer activity for three variants: the CD28-CTLA4 rs117701653 SNP, MEG3 rs34552516 indel, and TNFAIP3 rs35926684 indel.

Genetics of Multiple Sclerosis: An Overview and New Directions

The contribution of genetic inheritance in multiple sclerosis was established early on. Although multiple sclerosis is not a Mendelian disease, its incidence and prevalence is higher in family members of affected individuals compared with the general population. Throughout the last decade, several small studies failed to identify any robust genetic associations besides the classic associations in the major histocompatibility complex region. During the past few years, genome-wide association studies (GWAS) have revolutionized the genetics of multiple sclerosis, uncovering more than 200 implicated genetic loci. Here, we describe these main findings and discuss the new avenues that these discoveries lay open.

A plausibly causal functional lupus-associated risk variant in the STAT1-STAT4 locus

Systemic lupus erythematosus (SLE or lupus) (OMIM: 152700) is a chronic autoimmune disease with debilitating inflammation that affects multiple organ systems. The STAT1-STAT4 locus is one of the first and most highly replicated genetic loci associated with lupus risk. We performed a fine-mapping study to identify plausible causal variants within the STAT1-STAT4 locus associated with increased lupus disease risk. Using complementary frequentist and Bayesian approaches in trans-ancestral Discovery and Replication cohorts, we found one variant whose association with lupus risk is supported across ancestries in both the Discovery and Replication cohorts: rs11889341. In B cell lines from patients with lupus and healthy controls, the lupus risk allele of rs11889341 was associated with increased STAT1 expression. We demonstrated that the transcription factor HMGA1, a member of the HMG transcription factor family with an AT-hook DNA-binding domain, has enriched binding to the risk allele compared with the non-risk allele of rs11889341. We identified a genotype-dependent repressive element in the DNA within the intron of STAT4 surrounding rs11889341. Consistent with expression quantitative trait locus (eQTL) analysis, the lupus risk allele of rs11889341 decreased the activity of this putative repressor. Altogether, we present a plausible molecular mechanism for increased lupus risk at the STAT1-STAT4 locus in which the risk allele of rs11889341, the most probable causal variant, leads to elevated STAT1 expression in B cells due to decreased repressor activity mediated by increased binding of HMGA1.