Scoring personalized molecular portraits identify Systemic Lupus Erythematosus subtypes and predict individualized drug responses, symptomatology and disease progression.

OBJECTIVES: Systemic Lupus Erythematosus is a complex autoimmune disease that leads to significant worsening of quality of life and mortality. Flares appear unpredictably during the disease course and therapies used are often only partially effective. These challenges are mainly due to the molecular heterogeneity of the disease, and in this context, personalized medicine-based approaches offer major promise. With this work we intended to advance in that direction by developing MyPROSLE, an omic-based analytical workflow for measuring the molecular portrait of individual patients to support clinicians in their therapeutic decisions. METHODS: Immunological gene-modules were used to represent the transcriptome of the patients. A dysregulation score for each gene-module was calculated at the patient level based on averaged z-scores. Almost 6100 Lupus and 750 healthy samples were used to analyze the association among dysregulation scores, clinical manifestations, prognosis, flare and remission events and response to Tabalumab. Machine learning-based classification models were built to predict around 100 different clinical parameters based on personalized dysregulation scores. RESULTS: MyPROSLE allows to molecularly summarize patients in 206 gene-modules, clustered into nine main lupus signatures. The combination of these modules revealed highly differentiated pathological mechanisms. We found that the dysregulation of certain gene-modules is strongly associated with specific clinical manifestations, the occurrence of relapses or the presence of long-term remission and drug response. Therefore, MyPROSLE may be used to accurately predict these clinical outcomes. CONCLUSIONS: MyPROSLE (https://myprosle.genyo.es) allows molecular characterization of individual Lupus patients and it extracts key molecular information to support more precise therapeutic decisions.

Distinct gene dysregulation patterns herald precision medicine potentiality in systemic lupus erythematosus.

OBJECTIVES: We aimed at investigating the whole-blood transcriptome, expression quantitative trait loci (eQTLs), and levels of selected serological markers in patients with SLE versus healthy controls (HC) to gain insight into pathogenesis and identify drug targets. METHODS: We analyzed differentially expressed genes (DEGs) and dysregulated gene modules in a cohort of 350 SLE patients and 497 HC from the European PRECISESADS project (NTC02890121), split into a discovery (60%) and a replication (40%) set. Replicated DEGs qualified for eQTL, pathway enrichment, regulatory network, and druggability analysis. For validation purposes, a separate gene module analysis was performed in an independent cohort (GSE88887). RESULTS: Analysis of 521 replicated DEGs identified multiple enriched interferon signaling pathways through Reactome. Gene module analysis yielded 18 replicated gene modules in SLE patients, including 11 gene modules that were validated in GSE88887. Three distinct gene module clusters were defined i.e., "interferon/plasma cells", "inflammation", and "lymphocyte signaling". Predominant downregulation of the lymphocyte signaling cluster denoted renal activity. By contrast, upregulation of interferon-related genes indicated hematological activity and vasculitis. Druggability analysis revealed several potential drugs interfering with dysregulated genes within the "interferon" and "PLK1 signaling events" modules. STAT1 was identified as the chief regulator in the most enriched signaling molecule network. Drugs annotated to 15 DEGs associated with cis-eQTLs included bortezomib for its ability to modulate CTSL activity. Belimumab was annotated to TNFSF13B (BAFF) and daratumumab was annotated to CD38 among the remaining replicated DEGs. CONCLUSIONS: Modulation of interferon, STAT1, PLK1, B and plasma cell signatures showed promise as viable approaches to treat SLE, pointing to their importance in SLE pathogenesis.

COPD GWAS variant at 19q13.2 in relation with DNA methylation and gene expression.

Chronic obstructive pulmonary disease (COPD) is among the major health burdens in adults. While cigarette smoking is the leading risk factor, a growing number of genetic variations have been discovered to influence disease susceptibility. Epigenetic modifications may mediate the response of the genome to smoking and regulate gene expression. Chromosome 19q13.2 region is associated with both smoking and COPD, yet its functional role is unclear. Our study aimed to determine whether rs7937 (RAB4B, EGLN2), a top genetic variant in 19q13.2 region identified in genome-wide association studies of COPD, is associated with differential DNA methylation in blood (N = 1490) and gene expression in blood (N = 721) and lungs (N = 1087). We combined genetic and epigenetic data from the Rotterdam Study (RS) to perform the epigenome-wide association analysis of rs7937. Further, we used genetic and transcriptomic data from blood (RS) and from lung tissue (Lung expression quantitative trait loci mapping study), to perform the transcriptome-wide association study of rs7937. Rs7937 was significantly (FDR < 0.05) and consistently associated with differential DNA methylation in blood at 4 CpG sites in cis, independent of smoking. One methylation site (cg11298343-EGLN2) was also associated with COPD (P = 0.001). Additionally, rs7937 was associated with gene expression levels in blood in cis (EGLN2), 42% mediated through cg11298343, and in lung tissue, in cis and trans (NUMBL, EGLN2, DNMT3A, LOC101929709 and PAK2). Our results suggest that changes of DNA methylation and gene expression may be intermediate steps between genetic variants and COPD, but further causal studies in lung tissue should confirm this hypothesis.

Collapsed methylation quantitative trait loci analysis for low frequency and rare variants.

BACKGROUND: Single variant approaches have been successful in identifying DNA methylation quantitative trait loci (mQTL), although as with complex traits they lack the statistical power to identify the effects from rare genetic variants. We have undertaken extensive analyses to identify regions of low frequency and rare variants that are associated with DNA methylation levels. METHODS: We used repeated measurements of DNA methylation from five different life stages in human blood, taken from the Avon Longitudinal Study of Parents and Children (ALSPAC) cohort. Variants were collapsed across CpG islands and their flanking regions to identify variants collectively associated with methylation, where no single variant was individually responsible for the observed signal. All analyses were undertaken using the sequence kernel association test. RESULTS: For loci where no individual variant mQTL was observed based on a single variant analysis, we identified 95 unique regions where the combined effect of low frequency variants (MAF ≤ 5%) provided strong evidence of association with methylation. For loci where there was previous evidence of an individual variant mQTL, a further 3 regions provided evidence of association between multiple low frequency variants and methylation levels. Effects were observed consistently across 5 different time points in the lifecourse and evidence of replication in the TwinsUK and Exeter cohorts was also identified. CONCLUSION: We have demonstrated the potential of this novel approach to mQTL analysis by analysing the combined effect of multiple low frequency or rare variants. Future studies should benefit from applying this approach as a complementary follow up to single variant analyses.

Epigenome-wide association studies for systemic autoimmune diseases: The road behind and the road ahead.

Epigenetics is known to be an important mechanism in the pathogenesis of autoimmune diseases. Epigenetic variations can act as integrators of environmental and genetic exposures and propagate activated states in immune cells. Studying epigenetic alterations by means of genome-wide approaches promises to unravel novel molecular mechanisms related to disease etiology, disease progression, clinical manifestations and treatment responses. This paper reviews what we have learned in the last five years from epigenome-wide studies for three systemic autoimmune diseases, namely systemic lupus erythematosus, primary Sjögren's syndrome, and rheumatoid arthritis. We examine the degree of epigenetic sharing between different diseases and the possible mediating role of epigenetic associations in genetic and environmental risks. Finally, we also shed light into the use of epigenetic markers towards a better precision medicine regarding disease prediction, prevention and personalized treatment in systemic autoimmunity.

IgG glycosylation and DNA methylation are interconnected with smoking.

BACKGROUND: Glycosylation is one of the most common post-translation modifications with large influences on protein structure and function. The effector function of immunoglobulin G (IgG) alters between pro- and anti-inflammatory, based on its glycosylation. IgG glycan synthesis is highly complex and dynamic. METHODS: With the use of two different analytical methods for assessing IgG glycosylation, we aim to elucidate the link between DNA methylation and glycosylation of IgG by means of epigenome-wide association studies. In total, 3000 individuals from 4 cohorts were analyzed. RESULTS: The overlap of the results from the two glycan measurement panels yielded DNA methylation of 7 CpG-sites on 5 genomic locations to be associated with IgG glycosylation: cg25189904 (chr.1, GNG12); cg05951221, cg21566642 and cg01940273 (chr.2, ALPPL2); cg05575921 (chr.5, AHRR); cg06126421 (6p21.33); and cg03636183 (chr.19, F2RL3). Mediation analyses with respect to smoking revealed that the effect of smoking on IgG glycosylation may be at least partially mediated via DNA methylation levels at these 7 CpG-sites. CONCLUSION: Our results suggest the presence of an indirect link between DNA methylation and IgG glycosylation that may in part capture environmental exposures. GENERAL SIGNIFICANCE: An epigenome-wide analysis conducted in four population-based cohorts revealed an association between DNA methylation and IgG glycosylation patterns. Presumably, DNA methylation mediates the effect of smoking on IgG glycosylation.

2SNP heritability and effects of genetic variants for neutrophil-to-lymphocyte and platelet-to-lymphocyte ratio.

Neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) are important biomarkers for disease development and progression. To gain insight into the genetic causes of variance in NLR and PLR in the general population, we conducted genome-wide association (GWA) analyses and estimated SNP heritability in a sample of 5901 related healthy Dutch individuals. GWA analyses identified a new genome-wide significant locus on the HBS1L-MYB intergenic region for PLR, which replicated in a sample of 2538 British twins. For platelet count, we replicated three known genome-wide significant loci in our cohort (at CCDC71L-PIK3CG, BAK1 and ARHGEF3). For neutrophil count, we replicated the PSMD3 locus. For the identified top SNPs, we found significant cis and trans expression quantitative trait loci effects for several loci involved in hematological and immunological pathways. Linkage Disequilibrium score (LD) regression analyses for PLR and NLR confirmed that both traits are heritable, with a polygenetic SNP heritability for PLR of 14.1%, and for NLR of 2.4%. Genetic correlations were present between ratios and the constituent counts, with the genetic correlation (r=0.45) of PLR with platelet count reaching statistical significance. In conclusion, we established that two important biomarkers have a significant heritable SNP component, and identified the first genome-wide locus for PLR.

Extreme population differences in the human zinc transporter ZIP4 (SLC39A4) are explained by positive selection in Sub-Saharan Africa.

Extreme differences in allele frequency between West Africans and Eurasians were observed for a leucine-to-valine substitution (Leu372Val) in the human intestinal zinc uptake transporter, ZIP4, yet no further evidence was found for a selective sweep around the ZIP4 gene (SLC39A4). By interrogating allele frequencies in more than 100 diverse human populations and resequencing Neanderthal DNA, we confirmed the ancestral state of this locus and found a strong geographical gradient for the derived allele (Val372), with near fixation in West Africa. In extensive coalescent simulations, we show that the extreme differences in allele frequency, yet absence of a classical sweep signature, can be explained by the effect of a local recombination hotspot, together with directional selection favoring the Val372 allele in Sub-Saharan Africans. The possible functional effect of the Leu372Val substitution, together with two pathological mutations at the same codon (Leu372Pro and Leu372Arg) that cause acrodermatitis enteropathica (a disease phenotype characterized by extreme zinc deficiency), was investigated by transient overexpression of human ZIP4 protein in HeLa cells. Both acrodermatitis mutations cause absence of the ZIP4 transporter cell surface expression and nearly absent zinc uptake, while the Val372 variant displayed significantly reduced surface protein expression, reduced basal levels of intracellular zinc, and reduced zinc uptake in comparison with the Leu372 variant. We speculate that reduced zinc uptake by the ZIP4-derived Val372 isoform may act by starving certain pathogens of zinc, and hence may have been advantageous in Sub-Saharan Africa. Moreover, these functional results may indicate differences in zinc homeostasis among modern human populations with possible relevance for disease risk.

Interferon and B-cell Signatures Inform Precision Medicine in Lupus Nephritis.

Introduction: Current therapeutic management of lupus nephritis (LN) fails to induce long-term remission in over 50% of patients, highlighting the urgent need for additional options. Methods: We analyzed differentially expressed genes (DEGs) in peripheral blood from patients with active LN (n = 41) and active nonrenal lupus (n = 62) versus healthy controls (HCs) (n = 497) from the European PRECISESADS project (NTC02890121), and dysregulated gene modules in a discovery (n = 26) and a replication (n = 15) set of active LN cases. Results: Replicated gene modules qualified for correlation analyses with serologic markers, and regulatory network and druggability analysis. Unsupervised coexpression network analysis revealed 20 dysregulated gene modules and stratified the active LN population into 3 distinct subgroups. These subgroups were characterized by low, intermediate, and high interferon (IFN) signatures, with differential dysregulation of the "B cell" and "plasma cells/Ig" modules. Drugs annotated to the IFN network included CC-motif chemokine receptor 1 (CCR1) inhibitors, programmed death-ligand 1 (PD-L1) inhibitors, and irinotecan; whereas the anti-CD38 daratumumab and proteasome inhibitor bortezomib showed potential for counteracting the "plasma cells/Ig" signature. In silico analysis demonstrated the low-IFN subgroup to benefit from calcineurin inhibition and the intermediate-IFN subgroup from B-cell targeted therapies. High-IFN patients exhibited greater anticipated response to anifrolumab whereas daratumumab appeared beneficial to the intermediate-IFN and high-IFN subgroups. Conclusion: IFN upregulation and B and plasma cell gene dysregulation patterns revealed 3 subgroups of LN, which may not necessarily represent distinct disease phenotypes but rather phases of the inflammatory processes during a renal flare, providing a conceptual framework for precision medicine in LN.

A genome-wide association study for survival from a multi-centre European study identified variants associated with COVID-19 risk of death.

The clinical manifestations of SARS-CoV-2 infection vary widely among patients, from asymptomatic to life-threatening. Host genetics is one of the factors that contributes to this variability as previously reported by the COVID-19 Host Genetics Initiative (HGI), which identified sixteen loci associated with COVID-19 severity. Herein, we investigated the genetic determinants of COVID-19 mortality, by performing a case-only genome-wide survival analysis, 60 days after infection, of 3904 COVID-19 patients from the GEN-COVID and other European series (EGAS00001005304 study of the COVID-19 HGI). Using imputed genotype data, we carried out a survival analysis using the Cox model adjusted for age, age2, sex, series, time of infection, and the first ten principal components. We observed a genome-wide significant (P-value < 5.0 × 10-8) association of the rs117011822 variant, on chromosome 11, of rs7208524 on chromosome 17, approaching the genome-wide threshold (P-value = 5.19 × 10-8). A total of 113 variants were associated with survival at P-value < 1.0 × 10-5 and most of them regulated the expression of genes involved in immune response (e.g., CD300 and KLR genes), or in lung repair and function (e.g., FGF19 and CDH13). Overall, our results suggest that germline variants may modulate COVID-19 risk of death, possibly through the regulation of gene expression in immune response and lung function pathways.

Evolutionary and functional evidence for positive selection at the human CD5 immune receptor gene.

CD5 is a lymphocyte surface coreceptor of still incompletely understood function. Currently available information indicates that CD5 participates not only in cell-to-cell immune interactions through still poorly defined endogenous ligands expressed on hemopoietic and nonhemopoietic cells but also in recognition of exogenous and highly conserved microbial structures such as fungal ß-glucans. Preceding single nucleotide polymorphism (SNP) data analysis provided evidence for a recent selective sweep in East Asia and suggested a nonsynonymous substitution at position 471 (A471V; rs2229177) of the cytoplasmatic region of the CD5 receptor as the most plausible target of selection. The present report further investigates the role of natural selection in the CD5 gene by a resequencing approach in 60 individuals representing populations from 3 different continents (20 Africans, 20 Europeans and 20 East Asians) and by functionally assaying the relevance of the A471V replacement on CD5 signaling. The high differentiation pattern found at the nonsynonymous A471V site together with the low diversity, most of the performed neutrality tests (Tajima's D, Fu and Li's F* and D*, and Fu's Fs) and the predominance of a major haplotype in East Asians strongly argue in favor of positive selection for the A471V site. Importantly, anti-CD5 monoclonal antibody cross-linking unveiled significant differences among A471V variants regarding the mitogen-activated protein kinase (MAPK) cascade activation on COS7 and on human peripheral blood mononuclear cells. Similar differences on MAPK activation and IL-8 cytokine release were also observed upon exposure of HEK293 cell transfectants expressing the A471V variants to Zymosan, a ß-glucan-rich fungal particle. Taken together, the results provide evidence for the hypothesis of an adaptive role of the A471V substitution to environmental challenges, most likely infectious pathogens, in East Asian populations.

Whole blood DNA methylation analysis reveals respiratory environmental traits involved in COVID-19 severity following SARS-CoV-2 infection.

SARS-CoV-2 infection can cause an inflammatory syndrome (COVID-19) leading, in many cases, to bilateral pneumonia, severe dyspnea, and in ~5% of these, death. DNA methylation is known to play an important role in the regulation of the immune processes behind COVID-19 progression, however it has not been studied in depth. In this study, we aim to evaluate the implication of DNA methylation in COVID-19 progression by means of a genome-wide DNA methylation analysis combined with DNA genotyping. The results reveal the existence of epigenomic regulation of functional pathways associated with COVID-19 progression and mediated by genetic loci. We find an environmental trait-related signature that discriminates mild from severe cases and regulates, among other cytokines, IL-6 expression via the transcription factor CEBP. The analyses suggest that an interaction between environmental contribution, genetics, and epigenetics might be playing a role in triggering the cytokine storm described in the most severe cases.

Expression Quantitative Trait Locus Analysis in Systemic Sclerosis Identifies New Candidate Genes Associated With Multiple Aspects of Disease Pathology.

OBJECTIVE: To identify the genetic variants that affect gene expression (expression quantitative trait loci [eQTLs]) in systemic sclerosis (SSc) and to investigate their role in the pathogenesis of the disease. METHODS: We performed an eQTL analysis using whole-blood sequencing data from 333 SSc patients and 524 controls and integrated them with SSc genome-wide association study (GWAS) data. We integrated our findings from expression modeling, differential expression analysis, and transcription factor binding site enrichment with key clinical features of SSc. RESULTS: We detected 49,123 validated cis-eQTLs from 4,539 SSc-associated single-nucleotide polymorphisms (SNPs) (PGWAS < 10-5 ). A total of 1,436 genes were within 1 Mb of the 4,539 SSc-associated SNPs. Of those 1,436 genes, 565 were detected as having ≥1 eQTL with an SSc-associated SNP. We developed a strategy to prioritize disease-associated genes based on their expression variance explained by SSc eQTLs (r2 > 0.05). As a result, 233 candidates were identified, 134 (58%) of them associated with hallmarks of SSc and 105 (45%) of them differentially expressed in the blood cells, skin, or lung tissue of SSc patients. Transcription factor binding site analysis revealed enriched motifs of 24 transcription factors (5%) among SSc eQTLs, 5 of which were found to be differentially regulated in the blood cells (ELF1 and MGA), skin (KLF4 and ID4), and lungs (TBX4) of SSc patients. Ten candidate genes (4%) can be targeted by approved medications for immune-mediated diseases, of which only 3 have been tested in clinical trials in patients with SSc. CONCLUSION: The findings of the present study indicate a new layer to the molecular complexity of SSc, contributing to a better understanding of the pathogenesis of the disease.

Integrative epigenomics in Sjögren´s syndrome reveals novel pathways and a strong interaction between the HLA, autoantibodies and the interferon signature.

Primary Sjögren's syndrome (SS) is a systemic autoimmune disease characterized by lymphocytic infiltration and damage of exocrine salivary and lacrimal glands. The etiology of SS is complex with environmental triggers and genetic factors involved. By conducting an integrated multi-omics study, we confirmed a vast coordinated hypomethylation and overexpression effects in IFN-related genes, what is known as the IFN signature. Stratified and conditional analyses suggest a strong interaction between SS-associated HLA genetic variation and the presence of Anti-Ro/SSA autoantibodies in driving the IFN epigenetic signature and determining SS. We report a novel epigenetic signature characterized by increased DNA methylation levels in a large number of genes enriched in pathways such as collagen metabolism and extracellular matrix organization. We identified potential new genetic variants associated with SS that might mediate their risk by altering DNA methylation or gene expression patterns, as well as disease-interacting genetic variants that exhibit regulatory function only in the SS population. Our study sheds new light on the interaction between genetics, autoantibody profiles, DNA methylation and gene expression in SS, and contributes to elucidate the genetic architecture of gene regulation in an autoimmune population.

Integrative Analysis Reveals a Molecular Stratification of Systemic Autoimmune Diseases.

OBJECTIVE: Clinical heterogeneity, a hallmark of systemic autoimmune diseases, impedes early diagnosis and effective treatment, issues that may be addressed if patients could be classified into groups defined by molecular pattern. This study was undertaken to identify molecular clusters for reclassifying systemic autoimmune diseases independently of clinical diagnosis. METHODS: Unsupervised clustering of integrated whole blood transcriptome and methylome cross-sectional data on 955 patients with 7 systemic autoimmune diseases and 267 healthy controls was undertaken. In addition, an inception cohort was prospectively followed up for 6 or 14 months to validate the results and analyze whether or not cluster assignment changed over time. RESULTS: Four clusters were identified and validated. Three were pathologic, representing "inflammatory," "lymphoid," and "interferon" patterns. Each included all diagnoses and was defined by genetic, clinical, serologic, and cellular features. A fourth cluster with no specific molecular pattern was associated with low disease activity and included healthy controls. A longitudinal and independent inception cohort showed a relapse-remission pattern, where patients remained in their pathologic cluster, moving only to the healthy one, thus showing that the molecular clusters remained stable over time and that single pathogenic molecular signatures characterized each individual patient. CONCLUSION: Patients with systemic autoimmune diseases can be jointly stratified into 3 stable disease clusters with specific molecular patterns differentiating different molecular disease mechanisms. These results have important implications for future clinical trials and the study of nonresponse to therapy, marking a paradigm shift in our view of systemic autoimmune diseases.

A multi-ethnic epigenome-wide association study of leukocyte DNA methylation and blood lipids.

Here we examine the association between DNA methylation in circulating leukocytes and blood lipids in a multi-ethnic sample of 16,265 subjects. We identify 148, 35, and 4 novel associations among Europeans, African Americans, and Hispanics, respectively, and an additional 186 novel associations through a trans-ethnic meta-analysis. We observe a high concordance in the direction of effects across racial/ethnic groups, a high correlation of effect sizes between high-density lipoprotein and triglycerides, a modest overlap of associations with epigenome-wide association studies of other cardio-metabolic traits, and a largely non-overlap with lipid loci identified to date through genome-wide association studies. Thirty CpGs reached significance in at least 2 racial/ethnic groups including 7 that showed association with the expression of an annotated gene. CpGs annotated to CPT1A showed evidence of being influenced by triglycerides levels. DNA methylation levels of circulating leukocytes show robust and consistent association with blood lipid levels across multiple racial/ethnic groups.

Genomic and phenotypic insights from an atlas of genetic effects on DNA methylation.

Characterizing genetic influences on DNA methylation (DNAm) provides an opportunity to understand mechanisms underpinning gene regulation and disease. In the present study, we describe results of DNAm quantitative trait locus (mQTL) analyses on 32,851 participants, identifying genetic variants associated with DNAm at 420,509 DNAm sites in blood. We present a database of >270,000 independent mQTLs, of which 8.5% comprise long-range (trans) associations. Identified mQTL associations explain 15-17% of the additive genetic variance of DNAm. We show that the genetic architecture of DNAm levels is highly polygenic. Using shared genetic control between distal DNAm sites, we constructed networks, identifying 405 discrete genomic communities enriched for genomic annotations and complex traits. Shared genetic variants are associated with both DNAm levels and complex diseases, but only in a minority of cases do these associations reflect causal relationships from DNAm to trait or vice versa, indicating a more complex genotype-phenotype map than previously anticipated.