Deconvoluting the heterogeneity of SLE: The contribution of ancestry

New insight in treating autoimmune diseases by targeting autophagy

Autophagy is a highly conserved biological process in eukaryotes, which degrades cellular misfolded proteins, damaged organelles and invasive pathogens in the lysosome-dependent manner. Autoimmune diseases caused by genetic elements, environments and aberrant immune responses severely impact patients' living quality and even threaten life. Recently, numerous studies have reported autophagy can regulate immune responses, and play an important role in autoimmune diseases. In this review, we summarised the features of autophagy and autophagy-related genes, enumerated some autophagy-related genes involved in autoimmune diseases, and further overviewed how to treat autoimmune diseases through targeting autophagy. Finally, we outlooked the prospect of relieving and curing autoimmune diseases by targeting autophagy pathway.

The rs3918188 and rs1799983 loci of eNOS gene are associated with susceptibility in patients with systemic lupus erythematosus in Northeast China

To investigate the association between single nucleotide polymorphism (SNP) at the rs3918188, rs1799983 and rs1007311 loci of the endothelial nitric oxide synthase (eNOS) gene and genetic susceptibility to systemic lupus erythematosus (SLE) in northeastern China. The base distribution of eNOS gene rs3918188, rs1799983 and rs1007311 in 1712 human peripheral blood samples from Northeast China was detected by SNaPshot sequencing technology. The correlation between genotype, allele and gene model of these loci of the eNOS gene and the genetic susceptibility to SLE was investigated by logistic regression analysis. The results of the differences in the frequency distribution of their gene models were visualised using R 4.3.2 software. Finally, HaploView 4.2 software was used to analyse the relationship between the haplotypes of the three loci mentioned above and the genetic susceptibility to SLE. A multifactor dimensionality reduction (MDR) analysis was used to determine the best SNP-SNP interaction model. The CC genotype and C allele at the rs3918188 locus may be a risk factor for SLE (CC vs AA: OR = 1.827, P < 0.05; C vs A: OR = 1.558, P < 0.001), and this locus increased the risk of SLE in the dominant model and the recessive model (AC + CC vs AA: OR = 1.542, P < 0.05; CC vs AA + AC: OR = 1.707, P < 0.001), while the risk of SLE was reduced in the overdominant model (AC vs AA + CC: OR = 0.628, P < 0.001). The GT genotype and T allele at locus rs1799983 may be a protective factor for SLE (GT vs GG: OR = 0.328, P < 0.001; T vs G: OR = 0.438, P < 0.001) and this locus reduced the risk of SLE in the overdominant model (GT vs GG + TT: OR = 0.385, P < 0.001). There is a strong linkage disequilibrium between the rs1007311 and rs1799983 loci of the eNOS gene. Among them, the formed haplotype AG increased the risk of SLE compared to GG. AT and GT decreased the risk of SLE compared to GG. In this study, the eNOS gene rs3918188 and rs1799983 loci were found to be associated with susceptibility to SLE. This helps to deeply explore the mechanism of eNOS gene and genetic susceptibility to SLE. It provides a certain research basis for the subsequent exploration of the molecular mechanism of these loci and SLE, as well as the early diagnosis, treatment and prognosis of SLE.

Genetic overlap between Alzheimer's disease and immune-mediated diseases: an atlas of shared genetic determinants and biological convergence

The occurrence of immune disease comorbidities in Alzheimer's disease (AD) has been observed in both epidemiological and molecular studies, suggesting a neuroinflammatory basis in AD. However, their shared genetic components have not been systematically studied. Here, we composed an atlas of the shared genetic associations between 11 immune-mediated diseases and AD by analyzing genome-wide association studies (GWAS) summary statistics. Our results unveiled a significant genetic overlap between AD and 11 individual immune-mediated diseases despite negligible genetic correlations, suggesting a complex shared genetic architecture distributed across the genome. The shared loci between AD and immune-mediated diseases implicated several genes, including GRAMD1B, FUT2, ADAMTS4, HBEGF, WNT3, TSPAN14, DHODH, ABCB9, and TNIP1, all of which are protein-coding genes and thus potential drug targets. Top biological pathways enriched with these identified shared genes were related to the immune system and cell adhesion. In addition, in silico single-cell analyses showed enrichment of immune and brain cells, including neurons and microglia. In summary, our results suggest a genetic relationship between AD and the 11 immune-mediated diseases, pinpointing the existence of a shared however non-causal genetic basis. These identified protein-coding genes have the potential to serve as a novel path to therapeutic interventions for both AD and immune-mediated diseases and their comorbidities.

Genetic polymorphisms in genes involved in the type I interferon system (STAT4 and IRF5): association with Asian SLE patients

Systemic lupus erythematosus (SLE) is a common autoimmune disease with a polymorphic clinical presentation involving multisystem damages with significant differences in prevalence and disease severity among different ethnic groups. Although genetic, hormonal, and environmental factors have been demonstrated to contribute a lot to SLE, the pathogenesis of SLE is still unknown. Numerous evidence revealed that gene variants within the type I interferons (IFN) signaling pathway performed the great genetic associations with autoimmune diseases including SLE. To date, through genome-wide association studies (GWAS), genetic association studies showed that more than 100 susceptibility genes have been linked to the pathogenesis of SLE, among which TYK2, STAT1, STAT4, and IRF5 are important molecules directly connected to the type I interferon signaling system. The review summarized the genetic associations and the detailed risk loci of STAT4 and IRF5 with Asian SLE patients, explored the genotype distributions associated with the main clinical manifestations of SLE, and sorted out the potential reasons for the differences in susceptibility in Asia and Europe. Moreover, the therapies targeting STAT4 and IRF5 were also evaluated in order to propose more personalized and targeted treatment plans in SLE.

Emerging role of gut microbiota in autoimmune diseases

Accumulating studies have indicated that the gut microbiota plays a pivotal role in the onset of autoimmune diseases by engaging in complex interactions with the host. This review aims to provide a comprehensive overview of the existing literatures concerning the relationship between the gut microbiota and autoimmune diseases, shedding light on the complex interplay between the gut microbiota, the host and the immune system. Furthermore, we aim to summarize the impacts and potential mechanisms that underlie the interactions between the gut microbiota and the host in autoimmune diseases, primarily focusing on systemic lupus erythematosus, rheumatoid arthritis, Sjögren's syndrome, type 1 diabetes mellitus, ulcerative colitis and psoriasis. The present review will emphasize the clinical significance and potential applications of interventions based on the gut microbiota as innovative adjunctive therapies for autoimmune diseases.

Examination of the shared genetic architecture between multiple sclerosis and systemic lupus erythematosus facilitates discovery of novel lupus risk loci

Systemic Lupus Erythematosus (SLE) is an autoimmune disease with heterogeneous manifestations, including neurological and psychiatric symptoms. Genetic association studies in SLE have been hampered by insufficient sample size and limited power compared to many other diseases. Multiple Sclerosis (MS) is a chronic relapsing autoimmune disease of the central nervous system (CNS) that also manifests neurological and immunological features. Here, we identify a method of leveraging large-scale genome wide association studies (GWAS) in MS to identify novel genetic risk loci in SLE. Statistical genetic comparison methods including linkage disequilibrium score regression (LDSC) and cross-phenotype association analysis (CPASSOC) to identify genetic overlap in disease pathophysiology, traditional 2-sample and novel PPI-based mendelian randomization to identify causal associations and Bayesian colocalization were applied to association studies conducted in MS to facilitate discovery in the smaller, more limited datasets available for SLE. Pathway analysis using SNP-to-gene mapping identified biological networks composed of molecular pathways with causal implications for CNS disease in SLE specifically, as well as pathways likely causal of both pathologies, providing key insights for therapeutic selection.

LILRB4 Checkpoint for Immunotherapy: Structure, Mechanism and Disease Targets

LILRB4, a myeloid inhibitory receptor belonging to the family of leukocyte immunoglobulin-like receptors (LILRs/LIRs), plays a pivotal role in the regulation of immune tolerance. LILRB4 primarily mediates suppressive immune responses by transmitting inhibitory signals through immunoreceptor tyrosine-based inhibitory motifs (ITIMs). This immune checkpoint molecule has gained considerable attention due to its potent regulatory functions. Its ability to induce effector T cell dysfunction and promote T suppressor cell differentiation has been demonstrated, indicating the therapeutic potential of LILRB4 for modulating excessive immune responses, particularly in autoimmune diseases or the induction of transplant tolerance. Additionally, through intervening with LILRB4 molecules, immune system responsiveness can be adjusted, representing significant value in areas such as cancer treatment. Thus, LILRB4 has emerged as a key player in addressing autoimmune diseases, transplant tolerance induction, and other medical issues. In this review, we provide a comprehensive overview of LILRB4, encompassing its structure, expression, and ligand molecules as well as its role as a tolerance receptor. By exploring the involvement of LILRB4 in various diseases, its significance in disease progression is emphasized. Furthermore, we propose that the manipulation of LILRB4 represents a promising immunotherapeutic strategy and highlight its potential in disease prevention, treatment and diagnosis.

GC/MS and LC/MS serum metabolomic analysis of Chinese LN patients

China, being a densely populated nation, faces a substantial economic burden due to a high incidence of lupus nephritis (LN) cases. The concealed onset of LN has resulted in many individuals have missed the optimal timing for treatment. The aim of the research is to study the serum metabolomics of Chinese LN patients using gas chromatography (GC)/mass spectrometry (MS) and liquid chromatography (LC)/MS to identify potential diagnostic markers. Fifty LN patients and fifty normal controls, matched for Body Mass Index (BMI) and age, were selected. Serum analysis was conducted using GC/MS and LC/MS, followed by multivariate statistical analysis. Various multidimensional analyses, including principal component analysis, partial least squares discrimination analysis, and orthogonal partial least squares discrimination analysis, along with one-dimensional analyses such as t-tests, were performed. Metabolites with variable importance in projection value > 1 and a p-value < 0.05 were considered critical biomarkers for LN. Furthermore, identified biomarkers delineated relevant metabolic pathways, and a metabolic pathway map was obtained from the database. Forty-one metabolites were identified as potential LN biomarkers, primarily associated with immune regulation, energy metabolism, intestinal microbial metabolism, renal damage, and oxidative stress. The potential for diagnosing LN and other diseases through metabolomics is demonstrated. Future research should explore larger sample sizes, metabolomic comparisons across different diseases and health states, and integration of metabolomics with clinical diagnostics. Such studies will enhance the understanding of metabolomics in medical diagnosis and provide robust support for its practical application.

Circular RNAs: Potential biomarkers and therapeutic targets for autoimmune diseases

The outcomes and prognosis of autoimmune diseases depend on early diagnosis and effective treatments. However, symptoms of early autoimmune diseases are often remarkably similar to many inflammatory diseases, leading to difficulty in precise diagnosis. Circular RNAs (circRNAs) belong to a novel class of endogenous RNAs, functioning as microRNA (miRNA) sponges or participating in protein coding. It has been shown in many studies that patients with autoimmune diseases have aberrant circRNA expression in liquid biopsy samples (such as plasma, saliva, and urine). Thus, circRNAs are potential biomarkers for the diagnosis and prognosis of autoimmune diseases. Moreover, overexpression and depletion of target circRNAs can be utilized as possible therapeutic approaches for treating autoimmune diseases. In this review, we summarized recent progress in the roles of circRNAs in the pathogenesis of autoimmune diseases, including rheumatoid arthritis, systemic lupus erythematosus, multiple sclerosis, and type 1 diabetes. We also discussed their potential as biomarkers and therapeutic targets.

Genetic overlap between Alzheimer's disease and immune-mediated diseases: An atlas of shared genetic determinants and biological convergence

The occurrence of immune disease comorbidities in Alzheimer's disease (AD) has been observed in both epidemiological and molecular studies, suggesting a neuroinflammatory basis in AD. However, their shared genetic components have not been systematically studied. Here, we composed an atlas of the shared genetic associations between 11 immune-mediated diseases and AD by analyzing genome-wide association studies (GWAS) summary statistics. Our results unveiled a significant genetic overlap between AD and 11 individual immune-mediated diseases despite negligible genetic correlations, suggesting a complex shared genetic architecture distributed across the genome. The shared loci between AD and immune-mediated diseases implicated several genes, including GRAMD1B, FUT2, ADAMTS4, HBEGF, WNT3, TSPAN14, DHODH, ABCB9 and TNIP1, all of which are protein-coding genes and thus potential drug targets. Top biological pathways enriched with these identified shared genes were related to the immune system and cell adhesion. In addition, in silico single-cell analyses showed enrichment of immune and brain cells, including neurons and microglia. In summary, our results suggest a genetic relationship between AD and the 11 immune-mediated diseases, pinpointing the existence of a shared however non-causal genetic basis. These identified protein-coding genes have the potential to serve as a novel path to therapeutic interventions for both AD and immune-mediated diseases and their comorbidities.

Identification of Shared Immune Cells and Immune-Related Co-Disease Genes in Chronic Heart Failure and Systemic Lupus Erythematosus Based on Transcriptome Sequencing

Purpose

The purpose was to identify shared immune cells and co-disease genes in chronic heart failure (HF) and systemic lupus erythematosus (SLE), as well as explore the potential mechanisms of action between HF and SLE.

Methods

A collection of peripheral blood mononuclear cells (PBMCs) from ten patients with HF and SLE and ten normal controls (NC) was used for transcriptome sequencing. Differentially expressed genes (DEGs) analysis, enrichment analysis, immune infiltration analysis, weighted gene co-expression network analysis (WGCNA), protein-protein interaction (PPI) analysis, and machine learning were applied for the screening of shared immune cells and co-disease genes in HF and SLE. Gene expression analysis and correlation analysis were used to explore the potential mechanisms of co-disease genes and immune cells in HF and SLE.

Results

In this study, it was found that two immune cells, T cells CD4 naïve and Monocytes, displayed similar expression patterns in HF and SLE at the same time. By taking intersection of the above immune cell-associated genes with the DEGs common to both HF and SLE, four immune-associated co-disease genes, CCR7, RNASE2, RNASE3 and CXCL10, were finally identified. CCR7, as one of the four key genes, was significantly down-regulated in HF and SLE, while the rest three key genes were all significantly up-regulated in both diseases.

Conclusion

T cells CD4 naïve and Monocytes were first revealed as possible shared immune cells of HF and SLE, and CCR7, RNASE2, RNASE3 and CXCL10 were identified as possible key genes common to HF and SLE as well as potential biomarkers or therapeutic targets for HF and SLE.

SLE is not a one-size-fits-all disease

In this Viewpoint we discuss how experimental medicine applied in the setting of clinical trials can address unmet need in the prototypic autoimmune disease systemic lupus erythematosus (SLE) to improve outcomes for patients.

APOL1 and the risk of adverse renal outcomes in patients of African ancestry with systemic lupus erythematosus

BACKGROUND

Systemic lupus erythematosus (SLE) disproportionately affects individuals of African ancestry (AA) compared to European ancestry (EA). In the general population, high risk (HR) variants in the apolipoprotein L1 (APOL1) gene increase the risk of renal and hypertensive disorders in individuals of AA. Since SLE is characterized by an interferon signature and APOL1 expression is driven by interferon, we examined the hypothesis that APOL1 HR genotypes predominantly drive higher rates of renal and hypertensive-related comorbidities observed in SLE patients of AA versus those of EA.

METHODS

We performed a retrospective cohort study in patients with SLE of EA and AA using a genetic biobank linked to de-identified electronic health records. APOL1 HR genotypes were defined as G1/G1, G2/G2, or G1/G2 and low risk (LR) genotypes as 1 or 0 copies of the G1 and G2 alleles. To identify renal and hypertensive-related disorders that differed in prevalence by ancestry, we used a phenome-wide association approach. We then used logistic regression to compare the prevalence of renal and hypertensive-related disorders in EA and AA patients, both including and excluding patients with the APOL1 HR genotype. In a sensitivity analysis, we examined the association of end stage renal disease secondary to lupus nephritis (LN-related ESRD) with ancestry and the APOL1 genotype.

RESULTS

We studied 784 patients with SLE; 195 (24.9%) were of AA, of whom 27 (13.8%) had APOL1 HR genotypes. Eighteen renal and hypertensive-related phenotypes were more common in AA than EA patients (p-value ≤ 1.4E-4). All phenotypes remained significantly different after exclusion of patients with APOL1 HR genotypes, and most point odds ratios (ORs) decreased only slightly. Even among ORs with the greatest decrease, risk for AA patients without the APOL1 HR genotype remained significantly elevated compared to EA patients. In the sensitivity analysis, LN-related ESRD was more prevalent in SLE patients of AA versus EA and AA patients with the APOL1 HR genotype versus LR (p-value < .05 for both).

CONCLUSION

The higher prevalence of renal and hypertensive disorders in SLE patients of AA compared to those of EA is not fully explained by the presence of APOL1 high risk variants.

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.