Classification of systemic lupus erythematosus: From the development of classification criteria to a new taxonomy?

SLE is a highly variable systemic autoimmune disease. Its immunopathological effector phase is partly understood. However, the background of its variability is not. SLE classification criteria have been relying on the clinical manifestations and standard autoimmune serology. This still holds true for the 2019 EULAR/ACR classification criteria. On one hand, this has led to significant precision in defining patients with SLE. On the other hand, the information in the criteria neither helps understanding the individual patient's pathophysiology, nor does it predict the efficacy of the available immunomodulatory therapies. Chances of further improvement of clinical criteria are most likely limited. This is where new multi-omic approaches have started to make an impact. While not yet able to differentiate diseases with the same precision as the classification criteria, the results of these studies go far beyond the scope of the criteria with regard to immune dysregulation. Looking at both sides in detail, we here try to synthesize the available data, aiming at a better understanding of SLE and its immune pathophysiology.

Local Ancestry at the Major Histocompatibility Complex Region is Not a Major Contributor to Disease Heterogeneity in a Multiethnic Lupus Cohort

OBJECTIVE

Systemic lupus erythematosus (SLE) is an autoimmune disease resulting in debilitating clinical manifestations that vary in severity by race and ethnicity with a disproportionate burden in African American, Mestizo, and Asian populations compared with populations of European descent. Differences in global and local genetic ancestry may shed light on the underlying mechanisms contributing to these disparities, including increased prevalence of lupus nephritis, younger age of symptom onset, and presence of autoantibodies.

METHODS

A total of 1,139 European, African American, and Mestizos patients with SLE were genotyped using the Affymetrix LAT1 World array. Global ancestry proportions were estimated using ADMIXTURE, and local ancestry was estimated using RFMIXv2.0. We investigated associations between lupus nephritis, age at onset, and autoantibody status with both global and local ancestry proportions within the Major Histocompatibility Complex region.

RESULTS

Our results showed small effect sizes that did not meet the threshold for statistical significance for global or local ancestry proportions in either African American or Mestizo patients with SLE who presented with the clinical manifestations of interest compared with those who did not.

CONCLUSION

These findings suggest that local genetic ancestry within the Major Histocompatibility Complex region is not a major contributor to these SLE manifestations among patients with SLE from admixed populations.

Molecular characterisation of lupus low disease activity state (LLDAS) and DORIS remission by whole-blood transcriptome-based pathways in a pan-European systemic lupus erythematosus cohort

OBJECTIVES

To unveil biological milieus underlying low disease activity (LDA) and remission versus active systemic lupus erythematosus (SLE).

METHODS

We determined differentially expressed pathways (DEPs) in SLE patients from the PRECISESADS project (NTC02890121) stratified into patients fulfilling and not fulfilling the criteria of (1) Lupus LDA State (LLDAS), (2) Definitions of Remission in SLE remission, and (3) LLDAS exclusive of remission.

RESULTS

We analysed data from 321 patients; 40.8% were in LLDAS, and 17.4% in DORIS remission. After exclusion of patients in remission, 28.3% were in LLDAS. Overall, 604 pathways differed significantly in LLDAS versus non-LLDAS patients with an false-discovery rate-corrected p (q)<0.05 and a robust effect size (dr)≥0.36. Accordingly, 288 pathways differed significantly between DORIS remitters and non-remitters (q<0.05 and dr≥0.36). DEPs yielded distinct molecular clusters characterised by differential serological, musculoskeletal, and renal activity. Analysis of partially overlapping samples showed no DEPs between LLDAS and DORIS remission. Drug repurposing potentiality for treating SLE was unveiled, as were important pathways underlying active SLE whose modulation could aid attainment of LLDAS/remission, including toll-like receptor (TLR) cascades, Bruton tyrosine kinase (BTK) activity, the cytotoxic T lymphocyte antigen 4 (CTLA-4)-related inhibitory signalling, and the nucleotide-binding oligomerization domain leucine-rich repeat-containing protein 3 (NLRP3) inflammasome pathway.

CONCLUSIONS

We demonstrated for the first time molecular signalling pathways distinguishing LLDAS/remission from active SLE. LLDAS/remission was associated with reversal of biological processes related to SLE pathogenesis and specific clinical manifestations. DEP clustering by remission better grouped patients compared with LLDAS, substantiating remission as the ultimate treatment goal in SLE; however, the lack of substantial pathway differentiation between the two states justifies LLDAS as an acceptable goal from a biological perspective.

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.

Approaching Mass Cytometry Translational Studies by Experimental and Data Curation Settings

Clinical studies are conducted to better understand the pathological mechanism of diseases and to find biomarkers associated with disease activity, drug response, or outcome prediction. Mass cytometry (MC) is a high-throughput single-cell technology that measures hundreds of cells per second with more than 40 markers per cell. Thus, it is a suitable tool for immune monitoring and biomarker discovery studies. Working in translational and clinical settings requires a careful experimental design to minimize, monitor, and correct the variations introduced during sample collection, preparation, acquisition, and analysis. In this review, we will focus on these important aspects of MC-related experiments and data curation in the context of translational clinical research projects.

A genome-wide association study suggests new susceptibility loci for primary antiphospholipid syndrome

Objectives

Primary antiphospholipid syndrome (PAPS) is a rare autoimmune disease characterized by the presence of antiphospholipid antibodies and the occurrence of thrombotic events and pregnancy complications. Our study aimed to identify novel genetic susceptibility loci associated with PAPS.

Methods

We performed a genome-wide association study comprising 5,485 individuals (482 affected individuals) of European ancestry. Significant and suggestive independent variants from a meta-analysis of approximately 7 million variants were evaluated for functional and biological process enrichment. The genetic risk variability for PAPS in different populations was also assessed. Hierarchical clustering, Mahalanobis distance, and Dirichlet Process Mixtures with uncertainty clustering methods were used to assess genetic similarities between PAPS and other immune-mediated diseases.

Results

We revealed genetic associations with PAPS in a regulatory locus within the HLA class II region near HLA-DRA and in STAT4 with a genome-wide level of significance. 34 additional suggestive genetic susceptibility loci for PAPS were also identified. The disease risk allele in the HLA class II locus is associated with overexpression of HLA-DRB6 , HLA-DRB9 , HLA-DPB2 , HLA-DQA2 and HLA-DQB2 , and is independent of the association between PAPS and HLA-DRB1*1302 . Functional analyses highlighted immune and nervous system related pathways in PAPS-associated loci. The comparison with other immune-mediated diseases revealed a close genetic relatedness to neuromyelitis optica, systemic sclerosis, and Sjögren's syndrome, suggesting colocalized causal variations close to STAT4 , TNPO3 , and BLK .

Conclusions

This study represents a comprehensive large-scale genetic analysis for PAPS and provides new insights into the genetic basis and pathophysiology of this rare disease.

Serum profiling identifies CCL8, CXCL13, and IL-1RA as markers of active disease in patients with systemic lupus erythematosus

Introduction

Systemic lupus erythematosus (SLE) is a clinically heterogeneous disease that presents a challenge for clinicians. To identify potential biomarkers for diagnosis and disease activity in SLE, we investigated a selected yet broad panel of cytokines and autoantibodies in patients with SLE, healthy controls (HC), and patients with other autoimmune diseases (AIDs).

Methods

Serum samples from 422 SLE patients, 546 HC, and 1223 other AIDs were analysed within the frame of the European PRECISESADS project (NTC02890121). Cytokine levels were determined using Luminex panels, and autoantibodies using different immunoassays.

Results

Of the 83 cytokines analysed, 29 differed significantly between patients with SLE and HC. Specifically, CCL8, CXCL13, and IL-1RA levels were elevated in patients with active, but not inactive, SLE versus HC, as well as in patients with SLE versus other AIDs. The levels of these cytokines also correlated with SLE Disease Activity Index 2000 (SLEDAI-2K) scores, among five other cytokines. Overall, the occurrence of autoantibodies was similar across SLEDAI-2K organ domains, and the correlations between autoantibodies and activity in different organ domains were weak.

Discussion

Our findings suggest that, upon validation, CCL8, CXCL13, and IL-1RA could serve as promising serum biomarkers of activity in SLE.

Gut epithelial barrier dysfunction in lupus triggers a differential humoral response against gut commensals

Introduction

Systemic lupus erythematosus is an autoimmune disease with multisystemic involvement including intestinal inflammation. Lupus-associated intestinal inflammation may alter the mucosal barrier where millions of commensals have a dynamic and selective interaction with the host immune system. Here, we investigated the consequences of the intestinal inflammation in a TLR7-mediated lupus model.

Methods

IgA humoral and cellular response in the gut was measured. The barrier function of the gut epithelial layer was characterised. Also, microbiota composition in the fecal matter was analysed as well as the systemic humoral response to differential commensals.

Results

The lupus-associated intestinal inflammation modifies the IgA+ B cell response in the gut-associated lymphoid tissue in association with dysbiosis. Intestinal inflammation alters the tight junction protein distribution in the epithelial barrier, which correlated with increased permeability of the intestinal barrier and changes in the microbiota composition. This permeability resulted in a differential humoral response against intestinal commensals.

Discussion

Lupus development can cause alterations in microbiota composition, allowing specific species to colonize only the lupus gut. Eventually, these alterations and the changes in gut permeability induced by intestinal inflammation could lead to bacterial translocation.

Lack of strong innate immune reactivity renders macrophages alone unable to control productive Varicella-Zoster Virus infection in an isogenic human iPSC-derived neuronal co-culture model

With Varicella-Zoster Virus (VZV) being an exclusive human pathogen, human induced pluripotent stem cell (hiPSC)-derived neural cell culture models are an emerging tool to investigate VZV neuro-immune interactions. Using a compartmentalized hiPSC-derived neuronal model allowing axonal VZV infection, we previously demonstrated that paracrine interferon (IFN)-α2 signalling is required to activate a broad spectrum of interferon-stimulated genes able to counteract a productive VZV infection in hiPSC-neurons. In this new study, we now investigated whether innate immune signalling by VZV-challenged macrophages was able to orchestrate an antiviral immune response in VZV-infected hiPSC-neurons. In order to establish an isogenic hiPSC-neuron/hiPSC-macrophage co-culture model, hiPSC-macrophages were generated and characterised for phenotype, gene expression, cytokine production and phagocytic capacity. Even though immunological competence of hiPSC-macrophages was shown following stimulation with the poly(dA:dT) or treatment with IFN-α2, hiPSC-macrophages in co-culture with VZV-infected hiPSC-neurons were unable to mount an antiviral immune response capable of suppressing a productive neuronal VZV infection. Subsequently, a comprehensive RNA-Seq analysis confirmed the lack of strong immune responsiveness by hiPSC-neurons and hiPSC-macrophages upon, respectively, VZV infection or challenge. This may suggest the need of other cell types, like T-cells or other innate immune cells, to (co-)orchestrate an efficient antiviral immune response against VZV-infected neurons.

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.

Machine Learning for the Identification of a Common Signature for Anti-SSA/Ro 60 Antibody Expression Across Autoimmune Diseases

OBJECTIVE

Anti-Ro autoantibodies are among the most frequently detected extractable nuclear antigen autoantibodies, mainly associated with primary Sjögren's syndrome (SS), systemic lupus erythematosus (SLE), and undifferentiated connective tissue disease (UCTD). This study was undertaken to determine if there is a common signature for all patients expressing anti-Ro 60 autoantibodies regardless of their disease phenotype.

METHODS

Using high-throughput multiomics data collected from the cross-sectional cohort in the PRECISE Systemic Autoimmune Diseases (PRECISESADS) study Innovative Medicines Initiative (IMI) project (genetic, epigenomic, and transcriptomic data, combined with flow cytometry data, multiplexed cytokines, classic serology, and clinical data), we used machine learning to assess the integrated molecular profiling of 520 anti-Ro 60+ patients compared to 511 anti-Ro 60- patients with primary SS, patients with SLE, and patients with UCTD, and 279 healthy controls.

RESULTS

The selected clinical features for RNA-Seq, DNA methylation, and genome-wide association study data allowed for a clear distinction between anti-Ro 60+ and anti-Ro 60- patients. The different features selected using machine learning from the anti-Ro 60+ patients constituted specific signatures when compared to anti-Ro 60- patients and healthy controls. Remarkably, the transcript Z score of 3 genes (ATP10A, MX1, and PARP14), presenting with overexpression associated with hypomethylation and genetic variation and independently identified using the Boruta algorithm, was clearly higher in anti-Ro 60+ patients compared to anti-Ro 60- patients regardless of disease type. Our findings demonstrated that these signatures, enriched in interferon-stimulated genes, were also found in anti-Ro 60+ patients with rheumatoid arthritis and those with systemic sclerosis and remained stable over time and were not affected by treatment.

CONCLUSION

Anti-Ro 60+ patients present with a specific inflammatory signature regardless of their disease type, suggesting that a dual therapeutic approach targeting both Ro-associated RNAs and anti-Ro 60 autoantibodies should be considered.

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.

A Summary on the Genetics of Systemic Lupus Erythematosus, Rheumatoid Arthritis, Systemic Sclerosis, and Sjögren's Syndrome

Systemic lupus erythematosus, systemic sclerosis, rheumatoid arthritis, and Sjögren's syndrome are four major autoimmune rheumatic diseases characterized by the presence of autoantibodies, caused by a dysregulation of the immune system that leads to a wide variety of clinical manifestations. These conditions present complex etiologies strongly influenced by multiple environmental and genetic factors. The human leukocyte antigen (HLA) region was the first locus identified to be associated and still represents the strongest susceptibility factor for each of these conditions, particularly the HLA class II genes, including DQA1, DQB1, and DRB1, but class I genes have also been associated. Over the last two decades, the genetic component of these disorders has been extensively investigated and hundreds of non-HLA risk genetic variants have been uncovered. Furthermore, it is widely accepted that autoimmune rheumatic diseases share molecular disease pathways, such as the interferon (IFN) type I pathways, which are reflected in a common genetic background. Some examples of well-known pleiotropic loci for autoimmune rheumatic diseases are the HLA region, DNASEL13, TNIP1, and IRF5, among others. The identification of the causal molecular mechanisms behind the genetic associations is still a challenge. However, recent advances have been achieved through mouse models and functional studies of the loci. Here, we provide an updated overview of the genetic architecture underlying these four autoimmune rheumatic diseases, with a special focus on the HLA region.

Novel genes and sex differences in COVID-19 severity

Here we describe the results of a genome-wide study conducted in 11 939 COVID-19 positive cases with an extensive clinical information that were recruited from 34 hospitals across Spain (SCOURGE consortium). In sex-disaggregated genome-wide association studies for COVID-19 hospitalization, genome-wide significance (p < 5x10-8) was crossed for variants in 3p21.31 and 21q22.11 loci only among males (p = 1.3x10-22 and p = 8.1x10-12, respectively), and for variants in 9q21.32 near TLE1 only among females (p = 4.4x10-8). In a second phase, results were combined with an independent Spanish cohort (1598 COVID-19 cases and 1068 population controls), revealing in the overall analysis two novel risk loci in 9p13.3 and 19q13.12, with fine-mapping prioritized variants functionally associated with AQP3 (p = 2.7x10-8) and ARHGAP33 (p = 1.3x10-8), respectively. The meta-analysis of both phases with four European studies stratified by sex from the Host Genetics Initiative confirmed the association of the 3p21.31 and 21q22.11 loci predominantly in males and replicated a recently reported variant in 11p13 (ELF5, p = 4.1x10-8). Six of the COVID-19 HGI discovered loci were replicated and an HGI-based genetic risk score predicted the severity strata in SCOURGE. We also found more SNP-heritability and larger heritability differences by age (<60 or ≥ 60 years) among males than among females. Parallel genome-wide screening of inbreeding depression in SCOURGE also showed an effect of homozygosity in COVID-19 hospitalization and severity and this effect was stronger among older males. In summary, new candidate genes for COVID-19 severity and evidence supporting genetic disparities among sexes are provided.

Transcriptome Studies in Lupus Nephritis

The present review is aimed at describing the main works that have used gene expression to analyze tissue kidney samples of lupus nephritis patients. Most studies used the gene expression arrays, which enormously advanced our knowledge on the possible mechanisms behind lupus nephritis. However, using bulk gene expression platforms, either as arrays, or as sequencing of RNA is not enough to go into detail of the cells and their molecular patterns and single cell mechanisms of disease. More recently, the first single cell RNA Sequencing study was published and this will also be discussed in the context of lupus nephritis. Single cell RNA sequencing allows to retrieve the genes expressed in each cell in the tissue of interest or in blood. In this context, the results of such studies give us a first glimpse of how a lupus nephritis kidney looks like, but much is still to be done to understand the changes that occur with treatment or with the different pathological subtypes of lupus nephritis and their cellular content.

A glimpse into the future of systemic lupus erythematosus

This viewpoint article on a forecast of clinically meaningful changes in the management of systemic lupus erythematosus (SLE) in the next 10 years is based on a review of the current state of the art. The groundwork has been laid by a robust series of classification criteria and treatment recommendations that have all been published since 2019. Building on this strong foundation, SLE management predictably will take significant steps forward. Assessment for lupus arthritis will presumably include musculoskeletal sonography. Large-scale polyomics studies are likely to unravel more of the central immune mechanisms of the disease. Biomarkers predictive of therapeutic success may enter the field; the type I interferon signature, as a companion for use of anifrolumab, an antibody against the common type I interferon receptor, is one serious candidate. Besides anifrolumab for nonrenal SLE and the new calcineurin inhibitor voclosporin in lupus nephritis, both of which are already approved in the United States and likely to become available in the European Union in 2022, several other approaches are in advanced clinical trials. These include advanced B cell depletion, inhibition of costimulation via CD40 and CD40 ligand (CD40L), and Janus kinase 1 (Jak1) and Tyrosine kinase 2 (Tyk2) inhibition. At the same time, essentially all of our conventional therapeutic armamentarium will continue to be used. The ability of patients to have successful SLE pregnancies, which has become much better in the last decades, should further improve, with approaches including tumor necrosis factor blockade and self-monitoring of fetal heart rates. While we hope that the COVID-19 pandemic will soon be controlled, it has highlighted the risk of severe viral infections in SLE, with increased risk tied to certain therapies. Although there are some data that a cure might be achievable, this likely will remain a challenge beyond 10 years from now.

SIDT1 plays a key role in type I IFN responses to nucleic acids in plasmacytoid dendritic cells and mediates the pathogenesis of an imiquimod-induced psoriasis model

Background

Type I IFN (IFN-I) is a family of cytokines involved in the pathogenesis of autoimmune and autoinflammatory diseases such as psoriasis. SIDT1 is an ER-resident protein expressed in the lymphoid lineage, and involved in anti-viral IFN-I responses in vivo, through an unclear mechanism. Herein we have dissected the role of SIDT1 in the natural IFN-producing cells, the plasmacytoid dendritic cells (pDC).

Methods

The function of SIDT1 in pDC was determined by silencing its expression in human primary pDC and GEN2.2 cell line. SIDT1 role in vivo was assessed using the imiquimod-induced psoriasis model in the SIDT1-deficient mice (sidt1^−/−).

Findings

Silencing of SIDT1 in GEN2.2 led to a blockade of the IFN-I response after stimulation of TLR7 and TLR9, without affecting the pro-inflammatory responses or upregulation of maturation markers. We found that SIDT1 migrates from the ER to the endosomal and lysosomal compartments together with TLR9 after CpG stimulation, participating in the access of the TLR9-CpG complex to lysosome-related vesicles, and therefore mediating the activation of TBK1 and the nuclear migration of IRF7, but not of NF-κB. sidt1^−/− mice showed a significant decrease in severity parameters of the imiquimod-induced acute psoriasis-like model, associated with a decrease in the production of IFN-I and IFN-dependent chemokines.

Interpretation

Our findings indicate that SIDT1 is at the cross-road between the IFN-I and the proinflammatory pathways and constitutes a promising drug target for psoriasis and other diseases mediated by IFN-I responses.

Funding

This work was supported by the Consejería de Salud y Familias de la Junta de Andalucía (PIER_S1149 and C2_S0050) and Instituto de Salud Carlos III (PI18/00082 and PI21/01151), partly supported by European FEDER funds, and prior funding to MEAR from the Alliance for Lupus Research and the Swedish Research Council.

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.

Evaluating the Impact of Sex-Biased Genetic Admixture in the Americas through the Analysis of Haplotype Data

A general imbalance in the proportion of disembarked males and females in the Americas has been documented during the Trans-Atlantic Slave Trade and the Colonial Era and, although less prominent, more recently. This imbalance may have left a signature on the genomes of modern-day populations characterised by high levels of admixture. The analysis of the uniparental systems and the evaluation of continental proportion ratio of autosomal and X chromosomes revealed a general sex imbalance towards males for European and females for African and Indigenous American ancestries. However, the consistency and degree of this imbalance are variable, suggesting that other factors, such as cultural and social practices, may have played a role in shaping it. Moreover, very few investigations have evaluated the sex imbalance using haplotype data, containing more critical information than genotypes. Here, we analysed genome-wide data for more than 5000 admixed American individuals to assess the presence, direction and magnitude of sex-biased admixture in the Americas. For this purpose, we applied two haplotype-based approaches, ELAI and NNLS, and we compared them with a genotype-based method, ADMIXTURE. In doing so, besides a general agreement between methods, we unravelled that the post-colonial admixture dynamics show higher complexity than previously described.

Age-dependent impact of the major common genetic risk factor for COVID-19 on severity and mortality

BACKGROUND

There is considerable variability in COVID-19 outcomes amongst younger adults-and some of this variation may be due to genetic predisposition.

METHODS

We combined individual level data from 13,888 COVID-19 patients (N=7,185 hospitalized) from 17 cohorts in nine countries to assess the association of the major common COVID-19 genetic risk factor (chromosome 3 locus tagged by rs10490770) with mortality, COVID-19-related complications and laboratory values. We next performed meta-analyses using FinnGen and the Columbia University COVID-19 Biobank.

RESULTS

We found that rs10490770 risk allele carriers experienced an increased risk of all-cause mortality (HR 1.4, 95%CI 1.2-1.7). Risk allele carriers had increased odds of several COVID-19 complications: severe respiratory failure (OR 2.1, 95%CI 1.6-2.6), venous thromboembolism (OR 1.7, 95%CI 1.2-2.4), and hepatic injury (OR 1.5, 95%CI 1.2-2.0). Risk allele carriers ≤60 years had higher odds of death or severe respiratory failure (OR 2.7, 95%CI 1.8-3.9) compared to those >60 years (OR 1.5, 95%CI 1.2-1.8, interaction-p=0.038). Amongst individuals ≤60 years who died or experienced severe respiratory failure, 32.3% were risk variant carriers, compared to 13.9% of those not experiencing these outcomes. The genetic risk improved the prediction of death or severe respiratory failure similarly to, or better than, most established clinical risk factors.

CONCLUSIONS

The major common COVID-19 genetic risk factor is associated with increased risks of morbidity and mortality, which are more pronounced amongst individuals ≤60 years. The effect was similar in magnitude and more common than most established clinical risk factors, suggesting potential implications for future clinical risk management.

PeacoQC: Peak-based selection of high quality cytometry data

In cytometry analysis, a large number of markers is measured for thousands or millions of cells, resulting in high-dimensional datasets. During the measurement of these samples, erroneous events can occur such as clogs, speed changes, slow uptake of the sample etc., which can influence the downstream analysis and can even lead to false discoveries. As these issues can be difficult to detect manually, an automated approach is recommended. In order to filter these erroneous events out, we created a novel quality control algorithm, Peak Extraction And Cleaning Oriented Quality Control (PeacoQC), that allows for automated cleaning of cytometry data. The algorithm will determine density peaks per channel on which it will remove low quality events based on their position in the isolation tree and on their mean absolute deviation distance to these density peaks. To evaluate PeacoQC's cleaning capability, it was compared to three other existing quality control algorithms (flowAI, flowClean and flowCut) on a wide variety of datasets. In comparison to the other algorithms, PeacoQC was able to filter out all different types of anomalies in flow, mass and spectral cytometry data, while the other methods struggled with at least one type. In the quantitative comparison, PeacoQC obtained the highest median balanced accuracy and a similar running time compared to the other algorithms while having a better scalability for large files. To ensure that the parameters chosen in the PeacoQC algorithm are robust, the cleaning tool was run on 16 public datasets. After inspection, only one sample was found where the parameters should be further optimized. The other 15 datasets were analyzed correctly indicating a robust parameter choice. Overall, we present a fast and accurate quality control algorithm that outperforms existing tools and ensures high-quality data that can be used for further downstream analysis. An R implementation is available.

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.

Downregulation of exhausted cytotoxic T cells in gene expression networks of multisystem inflammatory syndrome in children

Multisystem inflammatory syndrome in children (MIS-C) presents with fever, inflammation and pathology of multiple organs in individuals under 21 years of age in the weeks following severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Although an autoimmune pathogenesis has been proposed, the genes, pathways and cell types causal to this new disease remain unknown. Here we perform RNA sequencing of blood from patients with MIS-C and controls to find disease-associated genes clustered in a co-expression module annotated to CD56dimCD57+ natural killer (NK) cells and exhausted CD8+ T cells. A similar transcriptome signature is replicated in an independent cohort of Kawasaki disease (KD), the related condition after which MIS-C was initially named. Probing a probabilistic causal network previously constructed from over 1,000 blood transcriptomes both validates the structure of this module and reveals nine key regulators, including TBX21, a central coordinator of exhausted CD8+ T cell differentiation. Together, this unbiased, transcriptome-wide survey implicates downregulation of NK cells and cytotoxic T cell exhaustion in the pathogenesis of MIS-C.

A Proinflammatory Cytokine Network Profile in Th1/Type 1 Effector B Cells Delineates a Common Group of Patients in Four Systemic Autoimmune Diseases

OBJECTIVE

The effector T cell and B cell cytokine networks have been implicated in the pathogenesis of systemic autoimmune diseases, but the association of these cytokine networks with the heterogeneity of clinical manifestations and immune profiles has not been carefully examined. This study was undertaken to examine whether cytokine profiles can delineate distinct groups of patients in 4 systemic autoimmune diseases (systemic lupus erythematosus, Sjögren's syndrome, rheumatoid arthritis, and systemic sclerosis).

METHODS

A total of 179 patients and 48 healthy volunteers were enrolled in the multicenter cross-sectional PRECISE Systemic Autoimmune Diseases (PRECISESADS) study. Multi-low-dimensional omics data (cytokines, autoantibodies, circulating immune cells) were examined. Coculture experiments were performed to test the impact of the cytokine microenvironment on T cell/B cell cross-talk.

RESULTS

A proinflammatory cytokine profile defined by high levels of CXCL10, interleukin-6 (IL-6), IL-2, and tumor necrosis factor characterized a distinct group of patients in the 4 systemic autoimmune diseases. In each disease, this proinflammatory cluster was associated with a specific circulating immune cell signature, more severe disease, and higher levels of autoantibodies, suggesting an uncontrolled proinflammatory Th1 immune response. We observed in vitro that B cells reinforce Th1 differentiation and naive T cell proliferation, leading to the induction of type 1 effector B cells and IgG production. This process was associated with an increase in CXCL10, IL-6, IL-2, and interferon-γ production.

CONCLUSION

This composite analysis brings new insights into human B cell functional heterogeneity based on T cell/B cell cross-talk, and proposes a better stratification of patients with systemic autoimmune diseases, suggesting that combined biomarkers would be of great value for the design of personalized treatments.

Continental-scale genomic analysis suggests shared post-admixture adaptation in the Americas

American populations are one of the most interesting examples of recently admixed groups, where ancestral components from three major continental human groups (Africans, Eurasians and Native Americans) have admixed within the last 15 generations. Recently, several genetic surveys focusing on thousands of individuals shed light on the geography, chronology and relevance of these events. However, even though gene flow could drive adaptive evolution, it is unclear whether and how natural selection acted on the resulting genetic variation in the Americas.

In this study, we analysed the patterns of local ancestry of genomic fragments in genome-wide data for ~ 6000 admixed individuals from 10 American countries. In doing so, we identified regions characterized by a divergent ancestry profile (DAP), in which a significant over or under ancestral representation is evident.

Our results highlighted a series of genomic regions with DAPs associated with immune system response and relevant medical traits, with the longest DAP region encompassing the human leukocyte antigen locus. Furthermore, we found that DAP regions are enriched in genes linked to cancer-related traits and autoimmune diseases. Then, analysing the biological impact of these regions, we showed that natural selection could have acted preferentially towards variants located in coding and non-coding transcripts and characterized by a high deleteriousness score.

Taken together, our analyses suggest that shared patterns of post admixture adaptation occurred at a continental scale in the Americas, affecting more often functional and impactful genomic variants.

A comprehensive database for integrated analysis of omics data in autoimmune diseases

BACKGROUND

Autoimmune diseases are heterogeneous pathologies with difficult diagnosis and few therapeutic options. In the last decade, several omics studies have provided significant insights into the molecular mechanisms of these diseases. Nevertheless, data from different cohorts and pathologies are stored independently in public repositories and a unified resource is imperative to assist researchers in this field.

RESULTS

Here, we present Autoimmune Diseases Explorer ( https://adex.genyo.es ), a database that integrates 82 curated transcriptomics and methylation studies covering 5609 samples for some of the most common autoimmune diseases. The database provides, in an easy-to-use environment, advanced data analysis and statistical methods for exploring omics datasets, including meta-analysis, differential expression or pathway analysis.

CONCLUSIONS

This is the first omics database focused on autoimmune diseases. This resource incorporates homogeneously processed data to facilitate integrative analyses among studies.

A new molecular classification to drive precision treatment strategies in primary Sjögren's syndrome

There is currently no approved treatment for primary Sjögren's syndrome, a disease that primarily affects adult women. The difficulty in developing effective therapies is -in part- because of the heterogeneity in the clinical manifestation and pathophysiology of the disease. Finding common molecular signatures among patient subgroups could improve our understanding of disease etiology, and facilitate the development of targeted therapeutics. Here, we report, in a cross-sectional cohort, a molecular classification scheme for Sjögren's syndrome patients based on the multi-omic profiling of whole blood samples from a European cohort of over 300 patients, and a similar number of age and gender-matched healthy volunteers. Using transcriptomic, genomic, epigenetic, cytokine expression and flow cytometry data, combined with clinical parameters, we identify four groups of patients with distinct patterns of immune dysregulation. The biomarkers we identify can be used by machine learning classifiers to sort future patients into subgroups, allowing the re-evaluation of response to treatments in clinical trials.

The trans-ancestral genomic architecture of glycemic traits

Glycemic traits are used to diagnose and monitor type 2 diabetes and cardiometabolic health. To date, most genetic studies of glycemic traits have focused on individuals of European ancestry. Here we aggregated genome-wide association studies comprising up to 281,416 individuals without diabetes (30% non-European ancestry) for whom fasting glucose, 2-h glucose after an oral glucose challenge, glycated hemoglobin and fasting insulin data were available. Trans-ancestry and single-ancestry meta-analyses identified 242 loci (99 novel; P < 5 × 10-8), 80% of which had no significant evidence of between-ancestry heterogeneity. Analyses restricted to individuals of European ancestry with equivalent sample size would have led to 24 fewer new loci. Compared with single-ancestry analyses, equivalent-sized trans-ancestry fine-mapping reduced the number of estimated variants in 99% credible sets by a median of 37.5%. Genomic-feature, gene-expression and gene-set analyses revealed distinct biological signatures for each trait, highlighting different underlying biological pathways. Our results increase our understanding of diabetes pathophysiology by using trans-ancestry studies for improved power and resolution.

Data processing workflow for large-scale immune monitoring studies by mass cytometry

Mass cytometry is a powerful tool for deep immune monitoring studies. To ensure maximal data quality, a careful experimental and analytical design is required. However even in well-controlled experiments variability caused by either operator or instrument can introduce artifacts that need to be corrected or removed from the data. Here we present a data processing pipeline which ensures the minimization of experimental artifacts and batch effects, while improving data quality. Data preprocessing and quality controls are carried out using an R pipeline and packages like CATALYST for bead-normalization and debarcoding, flowAI and flowCut for signal anomaly cleaning, AOF for files quality control, flowClean and flowDensity for gating, CytoNorm for batch normalization and FlowSOM and UMAP for data exploration. As proper experimental design is key in obtaining good quality events, we also include the sample processing protocol used to generate the data. Both, analysis and experimental pipelines are easy to scale-up, thus the workflow presented here is particularly suitable for large-scale, multicenter, multibatch and retrospective studies.

"Precision Medicine in Autoimmune Diseases: Fact or Fiction"

Much is said about precision medicine, but its real significance and the possibility of making it a real possibility is far from certain. Several studies in each of the autoimmune diseases have provided important insight into molecular pathways but the use of molecular studies, particularly those looking into transcriptome pathways, have seldom approached the possibility of using the data for disease stratification and then for prediction, or diagnosis. Only the type I interferon signature has been considered in the use of this signature for therapeutic purposes, particularly in the case of systemic lupus erythematosus. Here, the authors provide an update on precision medicine, what can be translated into clinical practice, and what do single-cell molecular studies provide to our knowledge in autoimmune diseases, focusing on a few examples. The main message being that we should try to move from precision medicine of established disease to preventive medicine in order to predict the development of disease.

The Role of BANK1 in B Cell Signaling and Disease

The B cell scaffold protein with ankyrin repeats (BANK1) is expressed primarily in B cells and with multiple but discrete roles in B cell signaling, including B cell receptor signaling, CD40-related signaling, and Toll-like receptor (TLR) signaling. The gene for BANK1, located in chromosome 4, has been found to contain genetic variants that are associated with several autoimmune diseases and also other complex phenotypes, in particular, with systemic lupus erythematosus. Common genetic variants are associated with changes in BANK1 expression in B cells, while rare variants modify their capacity to bind efferent effectors during signaling. A BANK1-deficient model has shown the importance of BANK1 during TLR7 and TLR9 signaling and has confirmed its role in the disease. Still, much needs to be done to fully understand the function of BANK1, but the main conclusion is that it may be the link between different signaling functions within the B cells and they may act to synergize the various pathways within a cell. With this review, we hope to enhance the interest in this molecule.

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.

Age-dependent impact of the major common genetic risk factor for COVID-19 on severity and mortality

BACKGROUND

There is considerable variability in COVID-19 outcomes amongst younger adults-and some of this variation may be due to genetic predisposition. We characterized the clinical implications of the major genetic risk factor for COVID-19 severity, and its age-dependent effect, using individual-level data in a large international multi-centre consortium.

METHOD

The major common COVID-19 genetic risk factor is a chromosome 3 locus, tagged by the marker rs10490770. We combined individual level data for 13,424 COVID-19 positive patients (N=6,689 hospitalized) from 17 cohorts in nine countries to assess the association of this genetic marker with mortality, COVID-19-related complications and laboratory values. We next examined if the magnitude of these associations varied by age and were independent from known clinical COVID-19 risk factors.

FINDINGS

We found that rs10490770 risk allele carriers experienced an increased risk of all-cause mortality (hazard ratio [HR] 1·4, 95% confidence interval [CI] 1·2-1·6) and COVID-19 related mortality (HR 1·5, 95%CI 1·3-1·8). Risk allele carriers had increased odds of several COVID-19 complications: severe respiratory failure (odds ratio [OR] 2·0, 95%CI 1·6-2·6), venous thromboembolism (OR 1·7, 95%CI 1·2-2·4), and hepatic injury (OR 1·6, 95%CI 1·2-2·0). Risk allele carriers ≤ 60 years had higher odds of death or severe respiratory failure (OR 2·6, 95%CI 1·8-3·9) compared to those > 60 years OR 1·5 (95%CI 1·3-1·9, interaction p-value=0·04). Amongst individuals ≤ 60 years who died or experienced severe respiratory COVID-19 outcome, we found that 31·8% (95%CI 27·6-36·2) were risk variant carriers, compared to 13·9% (95%CI 12·6-15·2%) of those not experiencing these outcomes. Prediction of death or severe respiratory failure among those ≤ 60 years improved when including the risk allele (AUC 0·82 vs 0·84, p=0·016) and the prediction ability of rs10490770 risk allele was similar to, or better than, most established clinical risk factors.

INTERPRETATION

The major common COVID-19 risk locus on chromosome 3 is associated with increased risks of morbidity and mortality-and these are more pronounced amongst individuals ≤ 60 years. The effect on COVID-19 severity was similar to, or larger than most established risk factors, suggesting potential implications for clinical risk management.

FUNDING

Funding was obtained by each of the participating cohorts individually.

Stabilization of Human Whole Blood Samples for Multicenter and Retrospective Immunophenotyping Studies

Whole blood is often collected for large-scale immune monitoring studies to track changes in cell frequencies and responses using flow (FC) or mass cytometry (MC). In order to preserve sample composition and phenotype, blood samples should be analyzed within 24 h after bleeding, restricting the recruitment, analysis protocols, as well as biobanking. Herein, we have evaluated two whole blood preservation protocols that allow rapid sample processing and long-term stability. Two fixation buffers were used, Phosphoflow Fix and Lyse (BD) and Proteomic Stabilizer (PROT) to fix and freeze whole blood samples for up to 6 months. After analysis by an 8-plex panel by FC and a 26-plex panel by MC, manual gating of circulating leukocyte populations and cytokines was performed. Additionally, we tested the stability of a single sample over a 13-months period using 45 consecutive aliquots and a 34-plex panel by MC. We observed high correlation and low bias toward any cell population when comparing fresh and 6 months frozen blood with FC and MC. This correlation was confirmed by hierarchical clustering. Low coefficients of variation (CV) across studied time points indicate good sample preservation for up to 6 months. Cytokine detection stability was confirmed by low CVs, with some differences between fresh and fixed conditions. Thirteen months regular follow-up of PROT samples showed remarkable sample stability. Whole blood can be preserved for phenotyping and cytokine-response studies provided the careful selection of a compatible antibody panel. However, possible changes in cell morphology, differences in antibody affinity, and changes in cytokine-positive cell frequencies when compared to fresh blood should be considered. Our setting constitutes a valuable tool for multicentric and retrospective studies. © 2020 International Society for Advancement of Cytometry.

Cytotoxic lymphocytes are dysregulated in multisystem inflammatory syndrome in children

Multisystem inflammatory syndrome in children (MIS-C) presents with fever, inflammation and multiple organ involvement in individuals under 21 years following severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. To identify genes, pathways and cell types driving MIS-C, we sequenced the blood transcriptomes of MIS-C cases, pediatric cases of coronavirus disease 2019, and healthy controls. We define a MIS-C transcriptional signature partially shared with the transcriptional response to SARS-CoV-2 infection and with the signature of Kawasaki disease, a clinically similar condition. By projecting the MIS-C signature onto a co-expression network, we identified disease gene modules and found genes downregulated in MIS-C clustered in a module enriched for the transcriptional signatures of exhausted CD8 + T-cells and CD56 dim CD57 + NK cells. Bayesian network analyses revealed nine key regulators of this module, including TBX21 , a central coordinator of exhausted CD8 + T-cell differentiation. Together, these findings suggest dysregulated cytotoxic lymphocyte response to SARS-Cov-2 infection in MIS-C.

Proteomic analysis in lupus mice identifies Coronin-1A as a potential biomarker for lupus nephritis

BACKGROUND

Approximately 50% of systemic lupus erythematosus (SLE) patients develop nephritis, which is among the most severe and frequent complications of the disease and a leading cause of morbidity and mortality. Despite intensive research, there are still no reliable lupus nephritis (LN) markers in clinical use that can assess renal damage and activity with a high sensitivity and specificity. To this end, the aim of this study was to identify new clinically relevant tissue-specific protein biomarkers and possible underlying molecular mechanisms associated with renal involvement in SLE, using mass spectrometry (MS)-based proteomics.

METHODS

Kidneys were harvested from female triple congenic B6.NZMsle1/sle2/sle3 lupus mice model, and the respective sex- and age-matched C57BL/6 control mice at 12, 24 and 36 weeks of age, representing pre-symptomatic, established and end-stage LN, respectively. Proteins were extracted from kidneys, purified, reduced, alkylated and digested by trypsin. Purified peptides were separated by liquid chromatography and analysed by high-resolution MS. Data were processed by the Progenesis QIp software, and functional annotation analysis was performed using DAVID bioinformatics resources. Immunofluorescence and multiple reaction monitoring (MRM) MS methods were used to confirm prospective biomarkers in SLE mouse strains as well as human serum samples.

RESULTS

Proteomic profiling of kidney tissues from SLE and control mice resulted in the identification of more than 3800 unique proteins. Pathway analysis revealed a number of dysregulated molecular pathways that may be mechanistically involved in renal pathology, including phagosome and proximal tubule bicarbonate reclamation pathways. Proteomic analysis supported by human transcriptomic data and pathway analysis revealed Coronin-1A, Ubiquitin-like protein ISG15, and Rho GDP-dissociation inhibitor 2, as potential LN biomarkers. These results were further validated in other SLE mouse strains using MRM-MS. Most importantly, experiments in humans showed that measurement of Coronin-1A in human sera using MRM-MS can segregate LN patients from SLE patients without nephritis with a high sensitivity (100%) and specificity (100%).

CONCLUSIONS

These preliminary findings suggest that serum Coronin-1A may serve as a promising non-invasive biomarker for LN and, upon validation in larger cohorts, may be employed in the future as a screening test for renal disease in SLE patients.

Key steps and methods in the experimental design and data analysis of highly multi-parametric flow and mass cytometry

High-dimensional, single-cell cell technologies revolutionized the way to study biological systems, and polychromatic flow cytometry (FC) and mass cytometry (MC) are two of the drivers of this revolution. As up to 30-50 dimensions respectively can be measured per single-cell, they allow deep phenotyping combined with cellular functions studies, like cytokine production or protein phosphorylation. In parallel, the bioinformatics field develops algorithms that are able to process incoming data and extract the most useful and meaningful biological information. However, the success of automated analysis tools depends on the generation of high-quality data. In this review we present the most recent FC and MC computational approaches that are used to prepare, process and interpret high-content cytometry data. We also underscore proper experimental design as a key step for obtaining good quality data.

A survey of gene expression meta-analysis: methods and applications

The increasing use of high-throughput gene expression quantification technologies over the last two decades and the fact that most of the published studies are stored in public databases has triggered an explosion of studies available through public repositories. All this information offers an invaluable resource for reuse to generate new knowledge and scientific findings. In this context, great interest has been focused on meta-analysis methods to integrate and jointly analyze different gene expression datasets. In this work, we describe the main steps in the gene expression meta-analysis, from data preparation to the state-of-the art statistical methods. We also analyze the main types of applications and problems that can be approached in gene expression meta-analysis studies and provide a comparative overview of the available software and bioinformatics tools. Moreover, a practical guide for choosing the most appropriate method in each case is also provided.

ImaGEO: integrative gene expression meta-analysis from GEO database

SUMMARY

The Gene Expression Omnibus (GEO) database provides an invaluable resource of publicly available gene expression data that can be integrated and analyzed to derive new hypothesis and knowledge. In this context, gene expression meta-analysis (geMAs) is increasingly used in several fields to improve study reproducibility and discovering robust biomarkers. Nevertheless, integrating data is not straightforward without bioinformatics expertise. Here, we present ImaGEO, a web tool for geMAs that implements a complete and comprehensive meta-analysis workflow starting from GEO dataset identifiers. The application integrates GEO datasets, applies different meta-analysis techniques and provides functional analysis results in an easy-to-use environment. ImaGEO is a powerful and useful resource that allows researchers to integrate and perform meta-analysis of GEO datasets to lead robust findings for biomarker discovery studies.

AVAILABILITY AND IMPLEMENTATION

ImaGEO is accessible at http://bioinfo.genyo.es/imageo/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Discovering new metabolite alterations in primary sjögren's syndrome in urinary and plasma samples using an HPLC-ESI-QTOF-MS methodology

Sjögren's Syndrome (SjS) is a complex autoimmune disease characterized by the affection of the exocrine glands and the involvement of multiple organs. Although a greater number of biomarker studies have been carried out in recent years, the origin and pathogenesis are not yet well known and therefore there is a need to continue studying this pathology. This work aims to find metabolic changes in biological samples (plasma and urine), which could help identify the metabolic pathways affected by the SjS pathogenesis. The samples collected from SjS patients and healthy volunteers were analyzed by a fingerprinting metabolomic approach based on HPLC-ESI-QTOF-MS methodology. After feature pre-selection by univariate statistical tests, an integrated PLS-DA model using data from urine and plasma was constructed obtaining a good classification between cases and controls (AUROC = 0.839 ± 0.021). 31 and 38 metabolites in plasma and urine, respectively, showed significant differences between healthy volunteers and SjS patients and were proposed for their identification. From them, 12 plasma and 24 urinary metabolites could be annotated. In general, the main metabolic pathways altered in SjS patients were related to the metabolism of phospholipids, fatty acids, and amino acids, specially tryptophan, proline and phenylalanine.

The Genomic Impact of European Colonization of the Americas

The human genetic diversity of the Americas has been affected by several events of gene flow that have continued since the colonial era and the Atlantic slave trade. Moreover, multiple waves of migration followed by local admixture occurred in the last two centuries, the impact of which has been largely unexplored. Here, we compiled a genome-wide dataset of ∼12,000 individuals from twelve American countries and ∼6,000 individuals from worldwide populations and applied haplotype-based methods to investigate how historical movements from outside the New World affected (1) the genetic structure, (2) the admixture profile, (3) the demographic history, and (4) sex-biased gene-flow dynamics of the Americas. We revealed a high degree of complexity underlying the genetic contribution of European and African populations in North and South America, from both geographic and temporal perspectives, identifying previously unreported sources related to Italy, the Middle East, and to specific regions of Africa.

Differential Treatments Based on Drug-induced Gene Expression Signatures and Longitudinal Systemic Lupus Erythematosus Stratification

Systemic lupus erythematosus (SLE) is a heterogeneous disease with unpredictable patterns of activity. Patients with similar activity levels may have different prognosis and molecular abnormalities. In this study, we aimed to measure the main differences in drug-induced gene expression signatures across SLE patients and to evaluate the potential for clinical data to build a machine learning classifier able to predict the SLE subset for individual patients. SLE transcriptomic data from two cohorts were compared with drug-induced gene signatures from the CLUE database to compute a connectivity score that reflects the capability of a drug to revert the patient signatures. Patient stratification based on drug connectivity scores revealed robust clusters of SLE patients identical to the clusters previously obtained through longitudinal gene expression data, implying that differential treatment depends on the cluster to which patients belongs. The best drug candidates found, mTOR inhibitors or those reducing oxidative stress, showed stronger cluster specificity. We report that drug patterns for reverting disease gene expression follow the cell-specificity of the disease clusters. We used 2 cohorts to train and test a logistic regression model that we employed to classify patients from 3 independent cohorts into the SLE subsets and provide a clinically useful model to predict subset assignment and drug efficacy.

Epigenome-Wide Comparative Study Reveals Key Differences Between Mixed Connective Tissue Disease and Related Systemic Autoimmune Diseases

Mixed Connective Tissue Disease (MCTD) is a rare complex systemic autoimmune disease (SAD) characterized by the presence of increased levels of anti-U1 ribonucleoprotein autoantibodies and signs and symptoms that resemble other SADs such as systemic sclerosis (SSc), rheumatoid arthritis (RA), and systemic lupus erythematosus (SLE). Due to its low prevalence, this disease has been very poorly studied at the molecular level. We performed for the first time an epigenome-wide association study interrogating DNA methylation data obtained with the Infinium MethylationEPIC array from whole blood samples in 31 patients diagnosed with MCTD and 255 healthy subjects. We observed a pervasive hypomethylation involving 170 genes enriched for immune-related function such as those involved in type I interferon signaling pathways or in negative regulation of viral genome replication. We mostly identified epigenetic signals at genes previously implicated in other SADs, for example MX1, PARP9, DDX60, or IFI44L, for which we also observed that MCTD patients exhibit higher DNA methylation variability compared with controls, suggesting that these sites might be involved in plastic immune responses that are relevant to the disease. Through methylation quantitative trait locus (meQTL) analysis we identified widespread local genetic effects influencing DNA methylation variability at MCTD-associated sites. Interestingly, for IRF7, IFI44 genes, and the HLA region we have evidence that they could be exerting a genetic risk on MCTD mediated through DNA methylation changes. Comparison of MCTD-associated epigenome with patients diagnosed with SLE, or Sjögren's Syndrome, reveals a common interferon-related epigenetic signature, however we find substantial epigenetic differences when compared with patients diagnosed with rheumatoid arthritis and systemic sclerosis. Furthermore, we show that MCTD-associated CpGs are potential epigenetic biomarkers with high diagnostic value. Our study serves to reveal new genes and pathways involved in MCTD, to illustrate the important role of epigenetic modifications in MCTD pathology, in mediating the interaction between different genetic and environmental MCTD risk factors, and as potential biomarkers of SADs.

The TREX1 Dinosaur Bites the Brain through the LINE

In this issue of Cell Stem Cell, Thomas et al. (2017) define the nature of accumulated ssDNA present in the neuron and astrocyte cytoplasm of TREX1 mutated stem cell-derived organoids. Accumulated ssDNAs are derived from LINE-1 endogenous retroelements, providing new clues as to the development of Aicardi-Goutières syndrome in the neural system.

Exploring Impact of Rare Variation in Systemic Lupus Erythematosus by a Genome Wide Imputation Approach

The importance of low frequency and rare variation in complex disease genetics is difficult to estimate in patient populations. Genome-wide association studies are therefore, underpowered to detect rare variation. We have used a combined approach of genome-wide-based imputation with a highly stringent sequence kernel association (SKAT) test and a case-control burden test. We identified 98 candidate genes containing rare variation that in aggregate show association with SLE many of which have recognized immunological function, but also function and expression related to relevant tissues such as the joints, skin, blood or central nervous system. In addition we also find that there is a significant enrichment of genes annotated for disease-causing mutations in the OMIM database, suggesting that in complex diseases such as SLE, such mutations may be involved in subtle or combined phenotypes or could accelerate specific organ abnormalities found in the disease. We here provide an important resource of candidate genes for SLE.