Shared and Unique Patterns of DNA Methylation in Systemic Lupus Erythematosus and Primary Sjögren's Syndrome

More X's, more problems: how contributions from the X chromosomes enhance female predisposition for autoimmunity

Many autoimmune diseases exhibit a strong female bias. While sex hormones may influence sex bias in disease, recent studies suggest that the X chromosome itself directly contributes to female-biased susceptibility to autoimmunity. Females with two X chromosomes utilize X Chromosome Inactivation (XCI) to silence gene expression from one X chromosome, equalizing expression between the sexes. The X chromosome is highly enriched with immune-related genes, and recent work indicates that the fidelity of XCI maintenance in lymphocytes from female systemic lupus erythematosus patients is compromised, suggesting that aberrant X-linked gene expression contributes to autoimmune phenotypes. XCI is initiated and maintained by the long noncoding RNA XIST/Xist through its interactions with the inactive X chromosome and numerous interacting proteins, and recent studies also implicate XIST/Xist RNA in driving endosomal Toll-like receptor signaling and XIST/Xist RNA-protein complexes in serving as a source of autoantigens to respectively drive autoimmunity. Here, we will review these three distinct pathways that underscore the significance of X-linked genetics for understanding the origins of the female bias in autoimmune disease.

AI-driven feature selection and epigenetic pattern analysis: A screening strategy of CpGs validated by pyrosequencing for body fluid identification

Identification of body fluid stain at crime scene is one of the important tasks of forensic evidence analysis. Currently, body fluid-specific CpGs detected by DNA methylation microarray screening, have been widely studied for forensic body fluid identification. However, some CpGs have limited ability to distinguish certain body fluid types. The ongoing need is to discover novel methylation markers and fully validate them to enhance their evidentiary strength in complex forensic scenarios. This research gathered forensic-related DNA methylation microarrays data from the Gene Expression Omnibus (GEO) database. A novel screening strategy for marker selection was developed, combining feature selection algorithms (elastic net, information gain ratio, feature importance based on Random Forest, and mutual information coefficient) with epigenetic pattern analysis, to identify CpG markers for body fluid identification. The selected CpGs were validated through pyrosequencing on peripheral blood, saliva, semen, vaginal secretions, and menstrual blood samples, and machine learning classification models were constructed based on the sequencing results. Pyrosequencing results revealed 14 CpGs with high specificity in five types of body fluid samples. A machine learning classification model, developed based on the pyrosequencing results, could effectively distinguish five types of body fluid samples, achieving 100 % accuracy on the test set. Utilizing six CpG markers, it was also feasible to attain ideal efficacy in identifying body fluid stains. Our research proposes a systematic and scientific strategy for screening body fluid-specific CpGs, contributing new insights and methods to forensic body fluid identification.

Environmental Factor Index (EFI): A Novel Approach to Measure the Strength of Environmental Influence on DNA Methylation in Identical Twins

BACKGROUND/OBJECTIVES

The dynamic interaction between genomic DNA, epigenetic modifications, and phenotypic traits was examined in identical twins. Environmental perturbations can induce epigenetic changes in DNA methylation, influencing gene expression and phenotypes. Although DNA methylation mediates gene-environment correlations, the quantitative effects of external factors on DNA methylation remain underexplored. This study aimed to quantify these effects using a novel approach.

METHODS

A cohort study was conducted on healthy monozygotic twins to evaluate the influence of environmental stimuli on DNA methylation. We developed the Environmental Factor Index (EFI) to identify methylation sites showing statistically significant changes in response to environmental stimuli. We analyzed the identified sites for associations with disorders, DNA methylation markers, and CpG islands.

RESULTS

The EFI identified methylation sites that exhibited significant associations with genes linked to various disorders, particularly cancer. These sites were overrepresented on CpG islands compared to other genomic features, highlighting their regulatory importance.

CONCLUSIONS

The EFI is a valuable tool for understanding the molecular mechanisms underlying disease pathogenesis. It provides insights into the development of preventive and therapeutic strategies and offers a new perspective on the role of environmental factors in epigenetic regulation.

Systemic lupus in the era of machine learning medicine

Artificial intelligence and machine learning applications are emerging as transformative technologies in medicine. With greater access to a diverse range of big datasets, researchers are turning to these powerful techniques for data analysis. Machine learning can reveal patterns and interactions between variables in large and complex datasets more accurately and efficiently than traditional statistical methods. Machine learning approaches open new possibilities for studying SLE, a multifactorial, highly heterogeneous and complex disease. Here, we discuss how machine learning methods are rapidly being integrated into the field of SLE research. Recent reports have focused on building prediction models and/or identifying novel biomarkers using both supervised and unsupervised techniques for understanding disease pathogenesis, early diagnosis and prognosis of disease. In this review, we will provide an overview of machine learning techniques to discuss current gaps, challenges and opportunities for SLE studies. External validation of most prediction models is still needed before clinical adoption. Utilisation of deep learning models, access to alternative sources of health data and increased awareness of the ethics, governance and regulations surrounding the use of artificial intelligence in medicine will help propel this exciting field forward.

CXCL9 may serve as a potential biomarker for primary Sjögren's syndrome with extra-glandular manifestations

BACKGROUND

Primary Sjögren's syndrome (pSS) is an autoimmune condition that causes harm to exocrine glands and also has extra-glandular manifestations (EGM). pSS patients with EGM have a worse prognosis than those with only sicca symptoms. Previous studies have shown that the minor salivary glands (MSG) of pSS patients exhibit a unique profile of cytokines and chemokines compared to healthy controls. However, there is a lack of research comparing pSS with EGM (pSS-EGM) and pSS without EGM (pSS-non-EGM). This study aims to explore potential biomarkers associated with pSS, particularly pSS with EGM.

METHODS

By utilizing RNA sequencing, we conducted an analysis on the gene expression profiles of MSG in 63 patients diagnosed with pSS, as well as 12 non-pSS individuals. Furthermore, we also investigated the MSG of pSS patients, both with and without EGM. Through bioinformatics analysis, we identified genes with differential expression (DEGs) and determined the core hub genes using PPI network. We then analyzed the top 20 DEGs and their correlation with the patients' clinical characteristics, and validated our findings using peripheral blood plasma.

RESULTS

A total of 725 differentially expressed genes (DEGs) were identified in the comparison between pSS and non-pSS groups, and 727 DEGs were observed between pSS-EGM and pSS-non-EGM. It is noteworthy that the expression levels of CXCL9 were higher in both pSS patients and pSS-EGM when compared to the control group. Taking into consideration the significance of the top 20 DEGs in relation to clinical parameters and the central hub genes, we ultimately chose CXCL9. In comparison to the non-pSS group, pSS patients exhibited notably greater expression of the CXCL9 gene in the MSG, as well as higher levels of CXCL9 protein in their plasma (p < 0.001). Furthermore, the expression of the CXCL9 gene and levels of CXCL9 protein were notably higher in pSS patients accompanied by EGM and those with SSA antibodies. Additionally, a correlation was found between the expression of the CXCL9 gene and the EULAR Sjogren's Syndrome Disease Activity Index (ESSDAI), as well as with immunoglobulin G (IgG) levels and erythrocyte sedimentation rate (ESR). Meanwhile, the protein levels of CXCL9 were found to be correlated with IgG levels and ESSDAI.

CONCLUSION

CXCL9 proves to be a valuable biomarker in pSS, specifically due to its strong ability to differentiate between pSS patients with EGM and those without EGM. There is a significant correlation between CXCL9 and various clinical parameters both at the gene and protein level. Therefore, CXCL9 could be a potential target for future treatment of pSS.

Transcriptional profiling upon T cell stimulation reveals down-regulation of inflammatory pathways in T and B cells in SLE versus Sjögren's syndrome

Systemic lupus erythematosus (SLE) and primary Sjögren's syndrome (pSS) share clinical as well as pathogenic similarities. Although previous studies suggest various abnormalities in different immune cell compartments, dedicated cell-type specific transcriptomic signatures are often masked by patient heterogeneity. Here, we performed transcriptional profiling of isolated CD4, CD8, CD16 and CD19 lymphocytes from pSS and SLE patients upon T cell stimulation, in addition to a steady-state condition directly after blood drawing, in total comprising 581 sequencing samples. T cell stimulation, which induced a pronounced inflammatory response in all four cell types, gave rise to substantial re-modulation of lymphocyte subsets in the two autoimmune diseases compared to healthy controls, far exceeding the transcriptomic differences detected at steady-state. In particular, we detected cell-type and disease-specific down-regulation of a range of pro-inflammatory cytokine and chemokine pathways. Such differences between SLE and pSS patients are instrumental for selective immune targeting by future therapies.

Integrated DNA Methylation and Transcriptomics Analyses of Lacrimal Glands Identify the Potential Genes Implicated in the Development of Sjögren's Syndrome-Related Dry Eye

Purpose

Sjögren's syndrome-related dry eye (SS-related dry eye) is an intractable autoimmune disease characterized by chronic inflammation of lacrimal glands (LGs), where epigenetic factors are proven to play a crucial role in the pathogenesis of this disease. However, the alteration of DNA methylation in LGs and its role in the pathogenesis of SS-related dry eye is still unknown. Here, we performed an integrated analysis of DNA methylation and RNA-Seq data in LGs to identify novel DNA methylation-regulated differentially expressed genes (MeDEGs) in the pathogenesis of SS-related dry eye.

Methods

The DNA methylation and transcription profiles of LGs in NOD mice at different stages of SS-related dry eye (4-, 8-, 12- and 16 weeks old) were generated by reduced representation bisulfite sequencing (RRBS) and RNA-Seq. The differentially methylated genes (DMGs) and differentially expressed genes (DEGs) were analyzed by MethylKit R package and edgeR. Correlation analysis between methylation level and mRNA expression was conducted with R software. The functional correlation of DMGs and DEGs was analyzed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Finally, LG tissues from another litter of NOD mice were collected for methylation-specific polymerase chain reaction (MSP) and quantitative real-time PCR (qRT-PCR) to validate the methylation and expression levels of key genes. CD4+ cell infiltration of LGs was detected by immunofluorescence staining.

Results

Hypermethylation of LGs was identified in NOD mice with the progression of SS-related dry eye and the DMGs were mainly enriched in the GTPases activation and Ras signaling pathway. RNA-seq analysis revealed 1321, 2549, and 3712 DEGs in the 8-, 12- and 16-week-old NOD mice compared with 4-week-old normal control mice. For GO analysis, the DEGs were mainly enriched in T cell immune responses. Further, a total of 140 MeDEGs were obtained by integrated analysis of methylome and transcriptome, which were primarily enriched in T cell activation, proliferation and differentiation. Based on the main GO terms and KEGG pathways of MeDEGs, 8 genes were screened out. The expression levels of these key genes, especially Itgal, Vav1, Irf4 and Icosl, were verified to elevate after the onset of SS-related dry eye in NOD mice and positively correlated with the extent of inflammatory cell infiltration in LGs. Immunofluorescence assay revealed that CD4+ cell infiltration dramatically increased in LGs of SS-related dry eye mice compared with the control mice. And the expression levels of four genes showed significantly positive correlation with the extent of CD4+ cell infiltration in LGs. MSP showed the hypomethylation of the Irf4 and Itgal promoters in NOD mice with SS-related dry eye compared to control group.

Conclusion

Our study revealed the critical role of epigenetic regulation of T cell immunity-related genes in the progression of SS-related dry eye and reminded us that DNA methylation-regulated genes such as Itgal, Vav1, Irf4 and Icosl may be used as new targets for SS-related dry eye therapy.

Exploring the shared molecular mechanisms between systemic lupus erythematosus and primary Sjögren's syndrome based on integrated bioinformatics and single-cell RNA-seq analysis

Background

Systemic lupus erythematosus (SLE) and primary Sjögren's syndrome (pSS) are common systemic autoimmune diseases that share a wide range of clinical manifestations and serological features. This study investigates genes, signaling pathways, and transcription factors (TFs) shared between SLE and pSS.

Methods

Gene expression profiles of SLE and pSS were obtained from the Gene Expression Omnibus (GEO). Weighted gene co-expression network analysis (WGCNA) and differentially expressed gene (DEG) analysis were conducted to identify shared genes related to SLE and pSS. Overlapping genes were then subject to Gene Ontology (GO) and protein-protein interaction (PPI) network analyses. Cytoscape plugins cytoHubba and iRegulon were subsequently used to screen shared hub genes and predict TFs. In addition, gene set variation analysis (GSVA) and CIBERSORTx were used to calculate the correlations between hub genes and immune cells as well as related pathways. To confirm these results, hub genes and TFs were verified in microarray and single-cell RNA sequencing (scRNA-seq) datasets.

Results

Following WGCNA and limma analysis, 152 shared genes were identified. These genes were involved in interferon (IFN) response and cytokine-mediated signaling pathway. Moreover, we screened six shared genes, namely IFI44L, ISG15, IFIT1, USP18, RSAD2 and ITGB2, out of which three genes, namely IFI44L, ISG15 and ITGB2 were found to be highly expressed in both microarray and scRNA-seq datasets. IFN response and ITGB2 signaling pathway were identified as potentially relevant pathways. In addition, STAT1 and IRF7 were identified as common TFs in both diseases.

Conclusion

This study revealed IFI44L, ISG15 and ITGB2 as the shared genes and identified STAT1 and IRF7 as the common TFs of SLE and pSS. Notably, the IFN response and ITGB2 signaling pathway played vital roles in both diseases. Our study revealed common pathogenetic characteristics of SLE and pSS. The particular roles of these pivotal genes and mutually overlapping pathways may provide a basis for further mechanistic research.

Understanding the role and adoption of artificial intelligence techniques in rheumatology research: An in-depth review of the literature

The major and upward trend in the number of published research related to rheumatic and musculoskeletal diseases, in which artificial intelligence plays a key role, has exhibited the interest of rheumatology researchers in using these techniques to answer their research questions. In this review, we analyse the original research articles that combine both worlds in a five- year period (2017-2021). In contrast to other published papers on the same topic, we first studied the review and recommendation articles that were published during that period, including up to October 2022, as well as the publication trends. Secondly, we review the published research articles and classify them into one of the following categories: disease identification and prediction, disease classification, patient stratification and disease subtype identification, disease progression and activity, treatment response, and predictors of outcomes. Thirdly, we provide a table with illustrative studies in which artificial intelligence techniques have played a central role in more than twenty rheumatic and musculoskeletal diseases. Finally, the findings of the research articles, in terms of disease and/or data science techniques employed, are highlighted in a discussion. Therefore, the present review aims to characterise how researchers are applying data science techniques in the rheumatology medical field. The most immediate conclusions that can be drawn from this work are: multiple and novel data science techniques have been used in a wide range of rheumatic and musculoskeletal diseases including rare diseases; the sample size and the data type used are heterogeneous, and new technical approaches are expected to arrive in the short-middle term.

Pathogenesis of systemic lupus erythematosus: risks, mechanisms and therapeutic targets

Research elucidating the pathogenesis of systemic lupus erythematosus (SLE) has defined two critical families of mediators, type I interferon (IFN-I) and autoantibodies targeting nucleic acids and nucleic acid-binding proteins, as fundamental contributors to the disease. On the fertile background of significant genetic risk, a triggering stimulus, perhaps microbial, induces IFN-I, autoantibody production or most likely both. When innate and adaptive immune system cells are engaged and collaborate in the autoimmune response, clinical SLE can develop. This review describes recent data from genetic analyses of patients with SLE, along with current studies of innate and adaptive immune function that contribute to sustained IFN-I pathway activation, immune activation and autoantibody production, generation of inflammatory mediators and tissue damage. The goal of these studies is to understand disease mechanisms, identify therapeutic targets and stimulate development of therapeutics that can achieve improved outcomes for patients.

Altered DNA methylation and gene expression predict disease severity in patients with Aicardi-Goutières syndrome

Aicardi-Goutières Syndrome (AGS) is a rare neuro-inflammatory disease characterized by increased expression of interferon-stimulated genes (ISGs). Disease-causing mutations are present in genes associated with innate antiviral responses. Disease presentation and severity vary, even between patients with identical mutations from the same family. This study investigated DNA methylation signatures in PBMCs to understand phenotypic heterogeneity in AGS patients with mutations in RNASEH2B. AGS patients presented hypomethylation of ISGs and differential methylation patterns (DMPs) in genes involved in "neutrophil and platelet activation". Patients with "mild" phenotypes exhibited DMPs in genes involved in "DNA damage and repair", whereas patients with "severe" phenotypes had DMPs in "cell fate commitment" and "organ development" associated genes. DMPs in two ISGs (IFI44L, RSAD2) associated with increased gene expression in patients with "severe" when compared to "mild" phenotypes. In conclusion, altered DNA methylation and ISG expression as biomarkers and potential future treatment targets in AGS.

DNA methylation alterations in systemic lupus erythematosus: A systematic review of case-control studies

BACKGROUND

Traditionally, the diagnosis and monitoring of disease activity in systemic lupus erythematosus (SLE) are contingent upon clinical manifestations and serological markers. However, researchers are struggling to find biomarkers with higher sensitivity and specificity. DNA methylation has been the most studied epigenetic feature in SLE. So, in this study, we performed a systematic review of studies about DNA methylation alterations in SLE patients compared to healthy controls.

METHODS

By searching PubMed, Scopus, and Google Scholar up to July 2022, all case-control studies in which DNA methylation of specific genes was assessed by a non-high-throughput technique and passed the quality of bias assessment were included.

RESULTS

In total, 44 eligible studies underwent a data extraction process. In all, 3471 SLE patients and 1028 healthy individuals were included. Among the studies that reported the patients' gender (n = 2853), 89.41% were female and 10.59% were male. Forty studies have been conducted on adult patients. The number of works on fractionated and unfractionated blood cells was almost equal. In this regard, 22 studies were conducted on whole blood or peripheral blood mononuclear cells and two studies on unfractionated white blood cells. Sorted blood cells were biological sources in 20 studies. The most investigated gene was IFI44L. Sensitivity, specificity, and diagnostic power of methylation levels were only reported for IFI44L in five studies. The most employed methylation profiling method was bisulfite sequencing polymerase chain reaction. The correlation between methylation patterns and clinical parameters was explored in 22 studies, which of them 16 publications displayed a remarkable association between DNA methylation status and clinical indices.

CONCLUSIONS

The methylation status of some genes especially IFI44L, FOXP3, and MX1 has been suggested as promising SLE biomarkers. However, given the conflicting findings between studies because of potential confounders such as different sample types, methylation profiling methods, and ethnicity as well as shared DNA methylation patterns of SLE and other autoimmune diseases, DNA methylation biomarkers are currently not reliable diagnostic biomarkers and do not represent surrogate markers of SLE disease activity. Future investigations on a larger scale with the discarding of limitations of previous studies would probably lead to a consensus.

Type I interferon pathway assays in studies of rheumatic and musculoskeletal diseases: a systematic literature review informing EULAR points to consider

OBJECTIVES

To systematically review the literature for assay methods that aim to evaluate type I interferon (IFN-I) pathway activation and to harmonise-related terminology.

METHODS

Three databases were searched for reports of IFN-I and rheumatic musculoskeletal diseases. Information about the performance metrics of assays measuring IFN-I and measures of truth were extracted and summarised. A EULAR task force panel assessed feasibility and developed consensus terminology.

RESULTS

Of 10 037 abstracts, 276 fulfilled eligibility criteria for data extraction. Some reported more than one technique to measure IFN-I pathway activation. Hence, 276 papers generated data on 412 methods. IFN-I pathway activation was measured using: qPCR (n=121), immunoassays (n=101), microarray (n=69), reporter cell assay (n=38), DNA methylation (n=14), flow cytometry (n=14), cytopathic effect assay (n=11), RNA sequencing (n=9), plaque reduction assay (n=8), Nanostring (n=5), bisulphite sequencing (n=3). Principles of each assay are summarised for content validity. Concurrent validity (correlation with other IFN assays) was presented for n=150/412 assays. Reliability data were variable and provided for 13 assays. Gene expression and immunoassays were considered most feasible. Consensus terminology to define different aspects of IFN-I research and practice was produced.

CONCLUSIONS

Diverse methods have been reported as IFN-I assays and these differ in what elements or aspects of IFN-I pathway activation they measure and how. No 'gold standard' represents the entirety of the IFN pathway, some may not be specific for IFN-I. Data on reliability or comparing assays were limited, and feasibility is a challenge for many assays. Consensus terminology should improve consistency of reporting.

Association between type I interferon pathway activation and clinical outcomes in rheumatic and musculoskeletal diseases: a systematic literature review informing EULAR points to consider

BACKGROUND

Type I interferons (IFN-I) contribute to a broad range of rheumatic and musculoskeletal diseases (RMDs). Compelling evidence suggests that the measurement of IFN-I pathway activation may have clinical value. Although several IFN-I pathway assays have been proposed, the exact clinical applications are unclear. We summarise the evidence on the potential clinical utility of assays measuring IFN-I pathway activation.

METHODS

A systematic literature review was conducted across three databases to evaluate the use of IFN-I assays in diagnosis and monitor disease activity, prognosis, response to treatment and responsiveness to change in several RMDs.

RESULTS

Of 366 screened, 276 studies were selected that reported the use of assays reflecting IFN-I pathway activation for disease diagnosis (n=188), assessment of disease activity (n=122), prognosis (n=20), response to treatment (n=23) and assay responsiveness (n=59). Immunoassays, quantitative PCR (qPCR) and microarrays were reported most frequently, while systemic lupus erythematosus (SLE), rheumatoid arthritis, myositis, systemic sclerosis and primary Sjögren's syndrome were the most studied RMDs. The literature demonstrated significant heterogeneity in techniques, analytical conditions, risk of bias and application in diseases. Inadequate study designs and technical heterogeneity were the main limitations. IFN-I pathway activation was associated with disease activity and flare occurrence in SLE, but their incremental value was uncertain. IFN-I pathway activation may predict response to IFN-I targeting therapies and may predict response to different treatments.

CONCLUSIONS

Evidence indicates potential clinical value of assays measuring IFN-I pathway activation in several RMDs, but assay harmonisation and clinical validation are urged. This review informs the EULAR points to consider for the measurement and reporting of IFN-I pathway assays.

Differential diagnosis of systemic lupus erythematosus and Sjögren's syndrome using machine learning and multi-omics data

Systemic lupus erythematosus and primary Sjogren's syndrome are complex systemic autoimmune diseases that are often misdiagnosed. In this article, we demonstrate the potential of machine learning to perform differential diagnosis of these similar pathologies using gene expression and methylation data from 651 individuals. Furthermore, we analyzed the impact of the heterogeneity of these diseases on the performance of the predictive models, discovering that patients assigned to a specific molecular cluster are misclassified more often and affect to the overall performance of the predictive models. In addition, we found that the samples characterized by a high interferon activity are the ones predicted with more accuracy, followed by the samples with high inflammatory activity. Finally, we identified a group of biomarkers that improve the predictions compared to using the whole data and we validated them with external studies from other tissues and technological platforms.

TLR7 agonism accelerates disease in a mouse model of primary Sjögren's syndrome and drives expansion of T-bet+ B cells

Primary Sjögren's syndrome (pSS) is a systemic autoimmune disease characterized by chronic inflammation of exocrine tissue, resulting in loss of tears and saliva. Patients also experience many extra-glandular disease manifestations. Treatment for pSS is palliative, and there are currently no treatments available that target disease etiology. Previous studies in our lab demonstrated that MyD88 is crucial for pSS pathogenesis in the NOD.B10Sn-H2^b (NOD.B10) pSS mouse model, although the way in which MyD88-dependent pathways become activated in disease remains unknown. Based on its importance in other autoimmune diseases, we hypothesized that TLR7 activation accelerates pSS pathogenesis. We administered the TLR7 agonist Imiquimod (Imq) or sham treatment to pre-disease NOD.B10 females for 6 weeks. Parallel experiments were performed in age and sex-matched C57BL/10 controls. Imq-treated pSS animals exhibited cervical lymphadenopathy, splenomegaly, and expansion of TLR7-expressing B cells. Robust lymphocytic infiltration of exocrine tissues, kidney and lung was observed in pSS mice following treatment with Imq. TLR7 agonism also induced salivary hypofunction in pSS mice, which is a hallmark of disease. Anti-nuclear autoantibodies, including Ro (SSA) and La (SSB) were increased in pSS mice following Imq administration. Cervical lymph nodes from Imq-treated NOD.B10 animals demonstrated an increase in the percentage of activated/memory CD4+ T cells. Finally, T-bet⁺ B cells were expanded in the spleens of Imq-treated pSS mice. Thus, activation of TLR7 accelerates local and systemic disease and promotes expansion of T-bet-expressing B cells in pSS.

Machine Learning for Diagnosis of Systemic Lupus Erythematosus: A Systematic Review and Meta-Analysis

Background

Application of machine learning (ML) for identification of systemic lupus erythematosus (SLE) has been recently drawing increasing attention, while there is still lack of evidence-based support.

Methods

Systematic review and meta-analysis are conducted to evaluate its diagnostic accuracy and application prospect. PubMed, Embase, Cochrane Library, and Web of Science libraries are searched, in combination with manual searching and literature retrospection, for studies regarding machine learning for identifying SLE and neuropsychiatric systemic lupus erythematosus (NPSLE). Quality Assessment of Diagnostic Accuracy Studies (QUADA-2) is applied to assess the quality of included studies. Diagnostic accuracy of the SLE model and NPSLE model is assessed using the bivariate fixed-effect model, and the data are pooled. Summary receiver operator characteristic curve (SROC) is plotted, and area under the curve (AUC) is calculated.

Results

Eighteen (18) studies are included, in which ten (10) focused on SLE and eight (8) on NPSLE. The AUC of SLE identification is 0.95, the sensitivity is 0.90, the specificity is 0.89, the PLR is 8.4, the NLR is 0.12, and the DOR is 73. AUC of NPSLE identification is 0.89, the sensitivity is 0.83, the specificity is 0.83, the PLR is 5.0, the NLR is 0.20, and the DOR is 25.

Conclusion

Machine learning presented remarkable performance in identification of SLE and NPSLE. Based on the convenience for inclusion factor collection and non-invasiveness of detection, machine learning is expected to be widely applied in clinical practice to assist medical decision making.

Recent advances in the use of machine learning and artificial intelligence to improve diagnosis, predict flares, and enrich clinical trials in lupus

PURPOSE OF REVIEW

Machine learning is a computational tool that is increasingly used for the analysis of medical data and has provided the promise of more personalized care.

RECENT FINDINGS

The frequency with which machine learning analytics are reported in lupus research is comparable with that of rheumatoid arthritis and cancer, yet the clinical application of these computational tools has yet to be translated into better care. Considerable work has been applied to the development of machine learning models for lupus diagnosis, flare prediction, and classification of disease using histology or other medical images, yet few models have been tested in external datasets and independent centers. Application of machine learning has yet to be reported for lupus clinical trial enrichment and automated identification of eligible patients. Integration of machine learning into lupus clinical care and clinical trials would benefit from collaborative development between clinicians and data scientists.

SUMMARY

Although the application of machine learning to lupus data is at a nascent stage, initial results suggest a promising future.

Pesticide use and risk of systemic autoimmune diseases in the Agricultural Health Study

BACKGROUND

Systemic lupus erythematosus (SLE) risk has been associated with pesticide use, but evidence on specific pesticides or other agricultural exposures is lacking. We investigated history of pesticide use and risk of SLE and a related disease, Sjögren's syndrome (SS), in the Agricultural Health Study.

METHODS

The study sample (N = 54,419, 52% male, enrolled in 1993-1997) included licensed pesticide applicators from North Carolina and Iowa and spouses who completed any of the follow-up questionnaires (1999-2003, 2005-2010, 2013-2015). Self-reported cases were confirmed by medical records or medication use (total: 107 incident SLE or SS, 79% female). We examined ever use of 31 pesticides and farm tasks and exposures reported at enrollment in association with SLE/SS, using Cox regression to estimate hazard ratios (HR) and 95% confidence intervals (CI), with age as the timescale and adjusting for gender, state, and correlated pesticides.

RESULTS

In older participants (>62 years), SLE/SS was associated with ever use of the herbicide metribuzin (HR 5.33; 95%CI 2.19, 12.96) and applying pesticides 20+ days per year (2.97; 1.20, 7.33). Inverse associations were seen for petroleum oil/distillates (0.39; 0.18, 0.87) and the insecticide carbaryl (0.56; 0.36, 0.87). SLE/SS was inversely associated with having a childhood farm residence (0.59; 0.39, 0.91), but was not associated with other farm tasks/exposures (except welding, HR 2.65; 95%CI 0.96, 7.35).

CONCLUSIONS

These findings suggest that some agricultural pesticides may be associated with higher or lower risk of SLE/SS. However, the overall risk associated with farming appears complex, involving other factors and childhood exposures.

Exploration of the pathogenesis of Sjögren's syndrome via DNA methylation and transcriptome analyses

OBJECTIVES

Sjögren's syndrome (SS), a systemic autoimmune disorder, is characterized by dry mouth and eyes. However, SS pathogenesis is poorly understood. We performed bioinformatics analysis to investigate the potential targets and molecular pathogenesis of SS.

METHODS

Gene expression profiles (GSE157159) and methylation data (GSE110007) associated with SS patients were obtained from the Gene Expression Omnibus (GEO) database. Differentially methylated positions (DMPs) and differentially expressed genes (DEGs) were identified by the R package limma. The potential biological functions of DEGs were determined using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. Key DMPs were selected by overlap and the shrunken centroid algorithm, and corresponding genes were identified as hub genes, with their diagnostic value assessed by receiver operating characteristic (ROC) curves. The potential molecular mechanisms of hub genes were analyzed by protein-protein interaction (PPI) networks and single-gene gene set enrichment analysis (GSEA). Peripheral blood mononuclear cells (PBMCs) were collected from control and SS patients at The Affiliated Hospital of Southwest Medical University and Dazhou Central Hospital. The mRNA levels of hub genes were verified by quantitative real-time polymerase chain reaction (qRT-PCR).

RESULTS

We identified 788 DMPs and 2457 DEGs between the two groups. Functional enrichment analysis suggested that the DEGs were significantly enriched in T cell activation, leukocyte cell-cell adhesion, and cytokine-cytokine receptor interaction. TSS200, TSS1500, and 1stExon were identified as highly enriched areas of differentially methylated promoter CpG islands (DMCIs). In total, 61 differentially methylated genes (DMGs) were identified by the overlap of 2457 DEGs and 507 genes related to DMPs (DMPGs), of which 21 genes located near TSS200, TSS1500, and 1stExon were selected. Then, three key DMPs and the corresponding hub genes (RUNX3, HLA-DPA1, and CD6) were screened by the shrunken centroid algorithm and calculated to have areas under the ROC curve of 1.000, 0.931, and 0.986, respectively, indicating good diagnostic value. The GSEA results suggested that all three hub genes were highly associated with the immune response. Finally, positive mRNA expression of the three hub genes in clinical SS samples was verified by qRT-PCR, consistent with the GSE157159 data.

CONCLUSIONS

The identification of three hub genes provides novel insight into molecular mechanisms and therapeutic targets for SS. Key Points • Hub genes were screened by DNA methylation and transcriptome analyses. • The relative expression of hub genes in peripheral blood samples was verified by qRT-PCR. • HLA-DPA1 was correlated with the pathogenic mechanism of SS.

Association of primary Sjögren’s syndrome with incident heart failure: a secondary analysis of health claims data in Taiwan

Objective:

Mounting evidence has demonstrated that various chronic inflammatory diseases are associated with incident heart failure (HF). However, there is scarce evidence about the association between primary Sjögren’s syndrome (pSS) and HF. We aimed to explore this association using a nationwide database in Taiwan.

Methods:

We selected patients with incident pSS and no history of HF. Using propensity score matching based on age, sex, cohort entry time, comorbidities, and concomitant medications, cohorts of patients with and without pSS (as controls) were created in a 1:1 ratio and the groups were compared. The cumulative incidence of HF was calculated using Kaplan–Meier estimation. Cox proportional hazard regression analysis was used to measure the hazard ratio (HR) of HF-related hospitalization for the pSS cohort compared with the comparison group.

Results:

A total of 16,466 pairs of patients with pSS and those without pSS were identified. The cumulative incidence of HF-related hospitalization at 3, 5, and 10 years in patients with pSS was 1.05%, 1.89%, and 4.33%, respectively. The risk of HF-related hospitalization was not higher in patients with pSS than in the general population (HR: 0.98, 95% confidence interval [CI]: 0.84–1.14). There was no difference in survival probability after the first episode of HF-related hospitalization between pSS and non-pSS groups.

Conclusion:

Our results suggest that distinct inflammatory spectrums in each chronic inflammatory disease might have differential impacts on cardiac function and subsequent risk of HF. Future studies are needed to elucidate the complex and diverse mechanisms of HF in various chronic autoimmune diseases.

Gender-affirming hormone therapy induces specific DNA methylation changes in blood

BACKGROUND

DNA methylation is an epigenetic mark that is influenced by underlying genetic profile, environment, and ageing. In addition to X-linked DNA methylation, sex-specific methylation patterns are widespread across autosomal chromosomes and can be present from birth or arise over time. In individuals where gender identity and sex assigned at birth are markedly incongruent, as in the case of transgender people, feminization or masculinization may be sought through gender-affirming hormone therapy (GAHT). GAHT is a cornerstone of transgender care, yet no studies to date have investigated its effect on genome-wide methylation. We profiled genome-wide DNA methylation in blood of transgender women (n = 13) and transgender men (n = 13) before and during GAHT (6 months and 12 months into feminizing or masculinizing hormone therapy).

RESULTS

We identified several thousand differentially methylated CpG sites (DMPs) (Δβ ≥ 0.02, unadjusted p value < 0.05) and several differentially methylated regions (DMRs) in both people undergoing feminizing and masculinizing GAHT, the vast majority of which were progressive changes over time. X chromosome and sex-specific autosomal DNA methylation patterns established in early development are largely refractory to change in association with GAHT, with only 3% affected (Δβ ≥ 0.02, unadjusted p value < 0.05). The small number of sex-specific DMPs that were affected by GAHT were those that become sex-specific during the lifetime, known as sex-and-age DMPs, including DMRs in PRR4 and VMP1 genes. The GAHT-induced changes at these sex-associated probes consistently demonstrated a shift towards the methylation signature of the GAHT-naïve opposite sex, and we observed enrichment of previously reported adolescence-associated methylation changes.

CONCLUSION

We provide evidence for GAHT inducing a unique blood methylation signature in transgender people. This study advances our understanding of the complex interplay between sex hormones, sex chromosomes, and DNA methylation in the context of immunity. We highlight the need to broaden the field of 'sex-specific' immunity beyond cisgender males and cisgender females, as transgender people on GAHT exhibit a unique molecular profile.

Accurate Measurement of DNA Methylation: Challenges and Bias Correction

DNA methylation is a key epigenetic modification involved in gene regulation whose contribution to disease susceptibility is still not fully understood. As the cost of genome sequencing technologies continues to drop, it will soon become commonplace to perform genome-wide quantification of DNA methylation at a single base-pair resolution. However, the demand for its accurate quantification might vary across studies. When the scope of the analysis is to detect regions of the genome with different methylation patterns between two or more conditions, e.g., case vs control; treatments vs placebo, accuracy is not crucial. This is the case in epigenome-wide association studies used as genome-wide screening of methylation changes to detect new candidate genes and regions associated with a specific disease or condition. If the aim of the analysis is to use DNA methylation measurements as a biomarker for diseases diagnosis and treatment (Laird, Nat Rev Cancer 3:253-266, 2003; Bock, Epigenomics 1:99-110, 2009), it is instead recommended to produce accurate methylation measurements. Furthermore, if the objective is the detection of DNA methylation in subclonal tumor cell populations or in circulating tumor DNA or in any case of mosaicism, the importance of accuracy becomes critical. The aim of this chapter is to describe the factors that could affect the precise measurement of methylation levels and a recent Bayesian statistical method called MethylCal and its extension that have been proposed to minimize this problem.

The Complex Role of C-Reactive Protein in Systemic Lupus Erythematosus

C-reactive protein (CRP) is well-known as a sensitive albeit unspecific biomarker of inflammation. In most rheumatic conditions, the level of this evolutionarily highly conserved pattern recognition molecule conveys reliable information regarding the degree of ongoing inflammation, driven mainly by interleukin-6. However, the underlying causes of increased CRP levels are numerous, including both infections and malignancies. In addition, low to moderate increases in CRP predict subsequent cardiovascular events, often occurring years later, in patients with angina and in healthy individuals. However, autoimmune diseases characterized by the Type I interferon gene signature (e.g., systemic lupus erythematosus, primary Sjögren’s syndrome and inflammatory myopathies) represent exceptions to the general rule that the concentrations of CRP correlate with the extent and severity of inflammation. In fact, adequate levels of CRP can be beneficial in autoimmune conditions, in that they contribute to efficient clearance of cell remnants and immune complexes through complement activation/modulation, opsonization and phagocytosis. Furthermore, emerging data indicate that CRP constitutes an autoantigen in systemic lupus erythematosus. At the same time, the increased risks of cardiovascular and cerebrovascular diseases in patients diagnosed with systemic lupus erythematosus and rheumatoid arthritis are well-established, with significant impacts on quality of life, accrual of organ damage, and premature mortality. This review describes CRP-mediated biological effects and the regulation of CRP release in relation to aspects of cardiovascular disease and mechanisms of autoimmunity, with particular focus on systemic lupus erythematosus.

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.

Contribution of Dysregulated DNA Methylation to Autoimmunity

Epigenetic mechanisms, such as DNA methylation, histone modifications, and non-coding RNAs are known regulators of gene expression and genomic stability in cell growth, development, and differentiation. Because epigenetic mechanisms can regulate several immune system elements, epigenetic alterations have been found in several autoimmune diseases. The purpose of this review is to discuss the epigenetic modifications, mainly DNA methylation, involved in autoimmune diseases in which T cells play a significant role. For example, Rheumatoid Arthritis and Systemic Lupus Erythematosus display differential gene methylation, mostly hypomethylated 5′-C-phosphate-G-3′ (CpG) sites that may associate with disease activity. However, a clear association between DNA methylation, gene expression, and disease pathogenesis must be demonstrated. A better understanding of the impact of epigenetic modifications on the onset of autoimmunity will contribute to the design of novel therapeutic approaches for these diseases.

Host methylation predicts SARS-CoV-2 infection and clinical outcome

BACKGROUND

Since the onset of the SARS-CoV-2 pandemic, most clinical testing has focused on RT-PCR1. Host epigenome manipulation post coronavirus infection2-4 suggests that DNA methylation signatures may differentiate patients with SARS-CoV-2 infection from uninfected individuals, and help predict COVID-19 disease severity, even at initial presentation.

METHODS

We customized Illumina's Infinium MethylationEPIC array to enhance immune response detection and profiled peripheral blood samples from 164 COVID-19 patients with longitudinal measurements of disease severity and 296 patient controls.

RESULTS

Epigenome-wide association analysis revealed 13,033 genome-wide significant methylation sites for case-vs-control status. Genes and pathways involved in interferon signaling and viral response were significantly enriched among differentially methylated sites. We observe highly significant associations at genes previously reported in genetic association studies (e.g. IRF7, OAS1). Using machine learning techniques, models built using sparse regression yielded highly predictive findings: cross-validated best fit AUC was 93.6% for case-vs-control status, and 79.1%, 80.8%, and 84.4% for hospitalization, ICU admission, and progression to death, respectively.

CONCLUSIONS

In summary, the strong COVID-19-specific epigenetic signature in peripheral blood driven by key immune-related pathways related to infection status, disease severity, and clinical deterioration provides insights useful for diagnosis and prognosis of patients with viral infections.

Abnormalities of the type I interferon signaling pathway in lupus autoimmunity

Type I interferons (IFNs), mostly IFNα and IFNβ, and the type I IFN Signature are important in the pathogenesis of Systemic Lupus Erythematosus (SLE), an autoimmune chronic condition linked to inflammation. Both IFNα and IFNβ trigger a signaling cascade that, through the activation of JAK1, TYK2, STAT1 and STAT2, initiates gene transcription of IFN stimulated genes (ISGs). Noteworthy, other STAT family members and IFN Responsive Factors (IRFs) can also contribute to the activation of the IFN response. Aberrant type I IFN signaling, therefore, can exacerbate SLE by deregulated homeostasis leading to unnecessary persistence of the biological effects of type I IFNs. The etiopathogenesis of SLE is partially known and considered multifactorial. Family-based and genome wide association studies (GWAS) have identified genetic and transcriptional abnormalities in key molecules directly involved in the type I IFN signaling pathway, namely TYK2, STAT1 and STAT4, and IRF5. Gain-of-function mutations that heighten IFNα/β production, which in turn maintains type I IFN signaling, are found in other pathologies like the interferonopathies. However, the distinctive characteristics have yet to be determined. Signaling molecules activated in response to type I IFNs are upregulated in immune cell subsets and affected tissues of SLE patients. Moreover, Type I IFNs induce chromatin remodeling leading to a state permissive to transcription, and SLE patients have increased global and gene-specific epigenetic modifications, such as hypomethylation of DNA and histone acetylation. Epigenome wide association studies (EWAS) highlight important differences between SLE patients and healthy controls in Interferon Stimulated Genes (ISGs). The combination of environmental and genetic factors may stimulate type I IFN signaling transiently and produce long-lasting detrimental effects through epigenetic alterations. Substantial evidence for the pathogenic role of type I IFNs in SLE advocates the clinical use of neutralizing anti-type I IFN receptor antibodies as a therapeutic strategy, with clinical studies already showing promising results. Current and future clinical trials will determine whether drugs targeting molecules of the type I IFN signaling pathway, like non-selective JAK inhibitors or specific TYK2 inhibitors, may benefit people living with lupus.

Stratification of Patients With Sjögren's Syndrome and Patients With Systemic Lupus Erythematosus According to Two Shared Immune Cell Signatures, With Potential Therapeutic Implications

OBJECTIVE

Similarities in the clinical and laboratory features of primary Sjögren's syndrome (SS) and systemic lupus erythematosus (SLE) have led to attempts to treat patients with primary SS or SLE with similar biologic therapeutics. However, the results of many clinical trials are disappointing, and no biologic treatments are licensed for use in primary SS, while only a few biologic agents are available to treat SLE patients whose disease has remained refractory to other treatments. With the aim of improving treatment selections, this study was undertaken to identify distinct immunologic signatures in patients with primary SS and patients with SLE, using a stratification approach based on immune cell endotypes.

METHODS

Immunophentyping of 29 immune cell subsets was performed using flow cytometry in peripheral blood from patients with primary SS (n = 45), patients with SLE (n = 29), and patients with secondary SS associated with SLE (SLE/SS) (n = 14), all of whom were considered to have low disease activity or be in clinical remission, and sex-matched healthy controls (n = 31). Data were analyzed using supervised machine learning (balanced random forest, sparse partial least squares discriminant analysis), logistic regression, and multiple t-tests. Patients were stratified by K-means clustering and clinical trajectory analysis.

RESULTS

Patients with primary SS and patients with SLE had a similar immunologic architecture despite having different clinical presentations and prognoses. Stratification of the combined primary SS, SLE, and SLE/SS patient cohorts by K-means cluster analysis revealed 2 endotypes, characterized by distinct immune cell profiles spanning the diagnoses. A signature of 8 T cell subsets that distinctly differentiated the 2 endotypes with high accuracy (area under the curve 0.9979) was identified in logistic regression and machine learning models. In clinical trajectory analyses, the change in damage scores and disease activity levels from baseline to 5 years differed between the 2 endotypes.

CONCLUSION

These findings identify an immune cell toolkit that may be useful for differentiating, with high accuracy, the immunologic profiles of patients with primary SS and patients with SLE as a way to achieve targeted therapeutic approaches.

Tissue-specific activation of Myd88-dependent pathways governs disease severity in primary Sjögren's syndrome

Myd88 activation is an important driver of autoimmunity. Primary Sjögren's syndrome (pSS) is an autoimmune disease characterized by exocrine gland dysfunction in combination with serious systemic disease manifestations. Myd88-dependent signaling networks remain incompletely understood in the context of pSS. The objective of this study was to establish the contribution of tissue-specific Myd88 activation to local (exocrine) and systemic pSS manifestations. To this end, we generated two novel conditional knockout pSS mouse models; one lacking Myd88 in hematopoietic cells and a second strain in which Myd88 was deleted in the stromal compartment. Spontaneous production of inflammatory mediators was altered in salivary tissue, and nephritis was diminished in both conditional knockout strains. In contrast, pulmonary inflammation was increased in mice lacking Myd88 in hematopoietic cells and was reduced when Myd88 was ablated in stromal cells. Finally, anti-nuclear autoantibodies (ANAs) were attenuated in pSS mice lacking Myd88 in immune cells. Additionally, the ANA-specific B cell repertoire was skewed in the Myd88-deficient strains. Collectively, these data demonstrate that Myd88 activation in specific cell types is essential for distinct aspects of pSS pathology.

Clinical value of DNA methylation markers in autoimmune rheumatic diseases

Methylation of cytosine residues in DNA, the best studied epigenetic modification, is associated with gene transcription and nuclear organization, and ultimately the function of a cell. DNA methylation can be influenced by various factors, including changes in neighbouring genomic sites such as those induced by transcription factor binding. The DNA methylation profiles in relevant cell types are altered in most human diseases compared with the healthy state. Given the physical stability of DNA and methylated DNA compared with other epigenetic modifications, DNA methylation is an ideal marker for clinical purposes. However, few DNA methylation-based markers have made it into clinical practice, with the notable exception of some markers used in the field of oncology. Autoimmune rheumatic diseases are genetically complex entities that can vary widely in terms of prognosis, subtypes, progression and treatment responses. Increasing reports showing strong links between DNA methylation profiles and different clinical outcomes and other clinical aspects in autoimmune rheumatic diseases reinforce the usefulness of DNA methylation profiles as novel clinical markers. In this Review, we provide an updated discussion on DNA methylation alterations in autoimmune rheumatic diseases and the advantages and disadvantages of using these markers in clinical practice.

The multifaceted functional role of DNA methylation in immune-mediated rheumatic diseases

Genomic predisposition cannot explain the onset of complex diseases, as well illustrated by the largely incomplete concordance among monozygotic twins. Epigenetic mechanisms, including DNA methylation, chromatin remodelling and non-coding RNA, are considered to be the link between environmental stimuli and disease onset on a permissive genetic background in autoimmune and chronic inflammatory diseases. The paradigmatic cases of rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), systemic sclerosis (SSc), Sjogren's syndrome (SjS) and type-1 diabetes (T1D) share the loss of immunological tolerance to self-antigen influenced by several factors, with a largely incomplete role of individual genomic susceptibility. The most widely investigated epigenetic mechanism is DNA methylation which is associated with gene silencing and is due to the binding of methyl-CpG binding domain (MBD)-containing proteins, such as MECP2, to 5-methylcytosine (5mC). Indeed, a causal relationship occurs between DNA methylation and transcription factors occupancy and recruitment at specific genomic locus. In most cases, the results obtained in different studies are controversial in terms of DNA methylation comparison while fascinating evidence comes from the comparison of the epigenome in clinically discordant monozygotic twins. In this manuscript, we will review the mechanisms of epigenetics and DNA methylation changes in specific immune-mediated rheumatic diseases to highlight remaining unmet needs and to identify possible shared mechanisms beyond different tissue involvements with common therapeutic opportunities. Key Points • DNA methylation has a crucial role in regulating and tuning the immune system. • Evidences suggest that dysregulation of DNA methylation is pivotal in the context of immune-mediated rheumatic diseases. • DNA methylation dysregulation in FOXP3 and interferons-related genes is shared within several autoimmune diseases. • DNA methylation is an attractive marker for diagnosis and therapy.

Renal disorders in rheumatologic diseases: the spectrum is changing (Part 1: connective tissue diseases)

The kidney is frequently involved by autoimmune rheumatic diseases. The renal manifestations may be variable, ranging from asymptomatic proteinuria and microscopic haematuria to nephrotic syndrome and rapidly progressive glomerulonephritis or vasculitis. In a number of cases the kidney involvement is related to the treatment of the original disease and may represent a major cause of morbidity and mortality. Thus, it is important for nephrologists and rheumatologists to remember that dysfunction of the kidney may be part of the primary systemic disorder or consequence of its pharmacotherapy. In the first part of this review we will analyse the kidney involvement in four autoimmune connective tissue diseases: systemic lupus erythematosus, Sjögren syndrome, polymyositis/dermatomyositis, and systemic sclerosis. Renal disease is common in lupus and is a main cause of morbidity and mortality. About 10% of patients with Sjögren syndrome may present interstitial nephritis or, more rarely, glomerulonephritis. Myoglobinuria and acute kidney injury is a frequent complication of polymyositis. Renal disease is one of the most serious complications of systemic sclerosis and may present with a dramatic renal crisis, characterized by malignant hypertension, oligo-anuria, and microangiopathic thrombocytopenic anaemia.

Decrease in alpha-1 antiproteinase antitrypsin is observed in primary Sjogren's syndrome condition

Primary Sjogren's syndrome (pSS) is a systemic autoimmune disease that is characterized by the infiltration of immune cells. Although the loss of salivary gland function is a major manifestation observed in pSS, the factors that could promote these changes in salivary gland tissue in pSS is not yet determined. Herein, we provide evidence that loss of alpha-1 antiproteinase antitrypsin could contribute to the induction of pSS. Alpha-1 antiproteinase antitrypsin belongs to the family of serpin proteins that function as protease inhibitors and protect secretory cells against proteases, especially to elastases that is secreted from lymphocytes. Importantly, expression of alpha-1 antiproteinase antitrypsin was decreased (more than 3-fold), along with an increase in elastase expression, in pSS samples when compared with age-matched non-SS-SICCA patients. Consistent with the human data, loss of alpha-1 antiproteinase antitrypsin, as well as an increase in immune infiltration, was observed in IL14α transgenic mice that exhibit SS like symptoms. Moreover, an age-dependent increase in elastase expression was observed in IL14α transgenic mice along with a decrease in total saliva secretion. Importantly, a 4-fold increase in microRNA132 expression, but not in other microRNAs, and increased DNA methylation in the promoter/noncoding region of serpina gene was observed in pSS, which could be responsible for the inhibition of alpha-1 antiproteinase antitrypsin expression in salivary gland cells of pSS patients. Together, these findings demonstrate that epigenetic regulations that include DNA methylation and microRNAs that could modulate the expression of alpha-1 antiproteinase antitrypsin in salivary glands and could be involved in the onset of pSS.