Renal interferon-inducible protein 16 expression is associated with disease activity and prognosis in lupus nephritis

Ubiquitin specific peptidase 13 protects against inflammation-associated joint injury in collagen-induced rheumatoid arthritis mice by targeting TRAF6

Rheumatoid arthritis (RA) is a chronic inflammatory autoimmune disease with complex pathogenesis. Ubiquitin-specific protease 13 (USP13), a member of the deubiquitinating enzyme (DUB) superfamily, plays diverse roles in cellular events. This study investigates the role of USP13 in RA, revealing its significant downregulation in peripheral blood mononuclear cells (PBMCs) from RA patients compared to healthy individuals. USP13 expression negatively correlates with RA characteristics, including erythrocyte sedimentation rate (ESR), C-reactive protein (CRP), and rheumatoid factor (RF). Consistent with these findings, USP13 levels were reduced in knee joint synovial tissues of collagen-induced arthritis (CIA) mice and lipopolysaccharide (LPS)-stimulated bone marrow-derived macrophages (BMDMs). Mechanistically, USP13 directly interacts with tumor necrosis factor receptor-associated factor 6 (TRAF6), reducing its K63-linked polyubiquitination and thereby inhibiting TRAF6 expression. This interaction restrains nuclear factor κB (NF-κB) signaling, leading to a marked attenuation of LPS-induced inflammatory responses in BMDMs. Importantly, the anti-inflammatory effects of USP13 over-expression are largely dependent on TRAF6 suppression. In vitro, USP13 over-expression in BMDMs inhibits the proliferation of fibroblast-like synoviocytes (FLS) and osteoclastogenesis. In addition, USP13-overexpressing BMDMs in CIA mice significantly alleviates RA development, as evidenced by reduced synovial hyperplasia, inflammatory cell infiltration, cartilage destruction, and bone loss. These findings highlight the essential role of USP13 in macrophages during RA progression and reveal its therapeutic potential by targeting TRAF6 signaling.

Artificial intelligence in autoimmune diseases: a bibliometric exploration of the past two decades

Objective

Autoimmune diseases have long been recognized for their intricate nature and elusive mechanisms, presenting significant challenges in both diagnosis and treatment. The advent of artificial intelligence technology has opened up new possibilities for understanding, diagnosing, predicting, and managing autoimmune disorders. This study aims to explore the current state and emerging trends in the field through bibliometric analysis, providing guidance for future research directions.

Methods

The study employed the Web of Science Core Collection database for data acquisition and performed bibliometric analysis using CiteSpace, HistCite Pro, and VOSviewer.

Results

Over the past two decades, 1,695 publications emerged in this research field, including 1,409 research articles and 286 reviews. This investigation unveils the global development landscape predominantly led by the United States and China. The research identifies key institutions, such as Brigham & Women's Hospital, influential journals like the Annals of the Rheumatic Diseases, distinguished authors including Katherine P. Liao, and pivotal articles. It visually maps out the research clusters' evolutionary path over time and explores their applications in patient identification, risk factors, prognosis assessment, diagnosis, classification of disease subtypes, monitoring and decision support, and drug discovery.

Conclusion

AI is increasingly recognized for its potential in the field of autoimmune diseases, yet it continues to face numerous challenges, including insufficient model validation and difficulties in data integration and computational power. Significant advancements have been demanded to enhance diagnostic precision, improve treatment methodologies, and establish robust frameworks for data protection, thereby facilitating more effective management of these complex conditions.

From Gene to Intervention: NLRC4 and WIPI1 Regulate Septic Acute Lung Injury Through Autophagy

Background

Septic Acute Lung Injury (SALI)-induced severe respiratory dysfunction has been established to significantly increase patient mortality rates and socioeconomic costs. To mitigate cellular damage, autophagy -a conserved biological process in organisms -degrades damaged cellular components, such as proteins and organelles. Although autophagy is crucially involved in the inflammatory response, its precise molecular mechanisms in SALI remain unclear, forming the basis of this study.

Methods

Herein, two microarray datasets (GSE33118 and GSE131761) and three single-cell sequencing datasets (SCP43, SCP548, and SCP2156) derived from human samples were used to ascertain the interrelationship between Differentially Expressed Autophagy-Related Genes (DEARGs) and SALI. The relationship between key DEARGs and SALI was validated both in vitro and in vivo using various techniques, including flow cytometry, Immunofluorescence (IF), Quantitative Polymerase Chain Reaction (qPCR), Western Blotting (WB), and small interfering RNA (siRNA).

Results

Herein, we found that autophagy activation attenuated SALI, with NLRC4 and WIPI1 as the two key DEARGs involved. Specifically, NLRC4 and WIPI1 downregulation mitigated SALI via autophagy activation. Compared to NLRC4, WIPI1 was more closely associated with noncanonical autophagic flux in SALI. Furthermore, immune infiltration analysis and single-cell data showed a close relationship between NLRC4, WIPI1, and immune cells.

Conclusion

Our findings revealed that SALI correlated strongly with autophagy, with the downregulation of the two key DEARGs, NLRC4 and WIPI1, attenuating sepsis lung injury via autophagy regulation, highlighting their therapeutic significance in SALI.

Macrophage Infiltration Correlated with IFI16, EGR1 and MX1 Expression in Renal Tubular Epithelial Cells Within Lupus Nephritis-Associated Tubulointerstitial Injury via Bioinformatics Analysis

Objective

A comprehensive bioinformatics analysis was conducted to investigate potential new diagnostic biomarkers and immune infiltration characteristics associated with tubulointerstitial injury in lupus nephritis (LN), and to examine possible correlations between key genes and infiltrating immune cells.

Methods

The GSE32591, GSE113342, and GSE200306 datasets were downloaded from the Gene Expression Omnibus database and differentially expressed genes (DEGs) were identified in the pooled dataset. Support vector machine-recursive feature elimination analysis and the least absolute shrinkage and selection operator regression model were used to screen for possible markers, and the compositional patterns of the 22 types of immune cell fractions in LN were determined using CIBERSORT. Finally, Western blotting, quantitative real-time polymerase chain reaction, and multiple immunofluorescence methods were used to confirm the significance of these feature genes in MRL/lpr mice and patients with LN.

Results

Seventeen DEGs were identified, of which 11 were considerably upregulated and six were markedly downregulated. Kyoto Encyclopedia of Genes and Genomes pathway analysis revealed significant enrichment in pertussis, complement and coagulation cascades, systemic lupus erythematosus, and other pathways. Based on the machine learning results, we identified IFI16, EGR1 and MX1 were key diagnostic genes for tubulointerstitial injury associated with LN. Immune cell infiltration analysis revealed that IFI16, EGR1 and MX1 were associated with M1 macrophages. Finally, the association between IFI16, EGR1, MX1 and macrophages in MRL/lpr mice and patients with LN were verified.

Conclusion

This study suggests that IFI16, EGR1 and MX1 which are highly expressed in renal tubular epithelial cells in LN and are associated with macrophage infiltration, may be a novel diagnostic and therapeutic target.

Changes in DNA methylation are associated with systemic lupus erythematosus flare remission and clinical subtypes

BACKGROUND

Systemic lupus erythematosus (SLE) has numerous symptoms across organs and an unpredictable flare-remittance pattern. This has made it challenging to understand drivers of long-term SLE outcomes. Our objective was to identify whether changes in DNA methylation over time, in an actively flaring SLE cohort, were associated with remission and whether these changes meaningfully subtype SLE patients.

METHODS

Fifty-nine multi-ethnic SLE patients had clinical visits and DNA methylation profiles at a flare and approximately 3 months later. Methylation was measured using the Illumina EPIC array. We identified sites where methylation change between visits was associated with remission at the follow-up visit using limma package and a time x remission interaction term. Models adjusted for batch, age at diagnosis, time between visits, age at flare, sex, medications, and cell-type proportions. Separately, a paired T-test identified Bonferroni significant methylation sites with ≥ 3% change between visits (n = 546). Methylation changes at these sites were used for unsupervised consensus hierarchical clustering. Associations between clusters and patient features were assessed.

RESULTS

Nineteen patients fully remitted at the follow-up visit. For 1,953 CpG sites, methylation changed differently for remitters vs. non-remitters (Bonferroni p < 0.05). Nearly half were within genes regulated by interferon. The largest effect was at cg22873177; on average, remitters had 23% decreased methylation between visits while non-remitters had no change. Three SLE patient clusters were identified using methylation differences agnostic of clinical outcomes. All Cluster 1 subjects (n = 12) experienced complete flare remission, despite similar baseline disease activity scores, medications, and demographics as other clusters. Methylation changes at six CpG sites, including within immune-related CD45 and IFI genes, were particularly distinct for each cluster, suggesting these may be good candidates for stratifying patients in the future.

CONCLUSIONS

Changes in DNA methylation during active SLE were associated with remission status and identified subgroups of SLE patients with several distinct clinical and biological characteristics. DNA methylation patterns might help inform SLE subtypes, leading to targeted therapies based on relevant underlying biological pathways.

Therapeutically targeting proinflammatory type I interferons in systemic lupus erythematosus: efficacy and insufficiency with a specific focus on lupus nephritis

Type I interferons (IFN-Is) are important players in the immunopathogenesis of systemic lupus erythematosus (SLE). Pathogenic events in patients with SLE are potent triggers of IFN-I induction, yet IFN-I may induce or initiate the immunopathogenesis leading to these events. Because blocking IFN-I is effective in some clinical manifestations of SLE patients, concerns about the efficacy of anti-IFN-I therapy in patients with lupus nephritis remain. Tissues from kidney biopsies of patients with lupus nephritis revealed infiltration of various immune cells and activation of inflammatory signals; however, their correlation with renal damage is not clear, which raises serious concerns about how critical the role of IFN-I is among the potential contributors to the pathogenesis of lupus nephritis. This review addresses several issues related to the roles of IFN-I in SLE, especially in lupus nephritis, including (1) the contribution of IFN-I to the development and immunopathogenesis of SLE; (2) evidence supporting the association of IFN-I with lupus nephritis; (3) therapies targeting IFN-I and IFN-I downstream signaling molecules in SLE and lupus nephritis; (4) findings challenging the therapeutic benefits of anti-IFN-I in lupus nephritis; and (5) a perspective associated with anti-IFN-I biologics for lupus nephritis treatment. In addition to providing clear pictures of the roles of IFN-I in SLE, especially in lupus nephritis, this review addresses the lately published observations and clinical trials on this topic.

Biomarkers for systemic lupus erythematosus: A scoping review

BACKGROUND

In recent years, newly discovered potential biomarkers have great research potential in the diagnosis, disease activity prediction, and treatment of systemic lupus erythematosus (SLE).

OBJECTIVE

In this study, a scoping review of potential biomarkers for SLE over several years has identified the extent to which studies on biomarkers for SLE have been conducted, the specificity, sensitivity, and diagnostic value of potential biomarkers of SLE, the research potential of these biomarkers in disease diagnosis, and activity detection is discussed.

METHODS

In PubMed and Google Scholar databases, "SLE," "biomarkers," "predictor," "autoimmune diseases," "lupus nephritis," "neuropsychiatric SLE," "diagnosis," "monitoring," and "disease activity" were used as keywords to systematically search for SLE molecular biomarkers published from 2020 to 2024. Analyze and summarize the literature that can guide the article.

CONCLUSIONS

Recent findings suggest that some potential biomarkers may have clinical application prospects. However, to date, many of these biomarkers have not been subjected to repeated clinical validation. And no single biomarker has sufficient sensitivity and specificity for SLE. It is not scientific to choose only one or several biomarkers to judge the complex disease of SLE. It may be a good direction to carry out a meta-analysis of various biomarkers to find SLE biomarkers suitable for clinical use, or to evaluate SLE by combining multiple biomarkers through mathematical models. At the same time, advanced computational methods are needed to analyze large data sets and discover new biomarkers, and strive to find biomarkers that are sensitive and specific enough to SLE and can be used in clinical practice, rather than only staying in experimental research and data analysis.

Decoding the connection between SLE and DNA Sensors: A comprehensive review

Systemic lupus erythematosus (SLE) is recognized as a prevalent autoimmune disorder characterized by a multifaceted pathogenesis potentially influenced by a combination of environmental factors, genetic predisposition, and hormonal regulation. The continuous study of immune system activation is especially intriguing. Analysis of blood samples from individuals with SLE reveals an abnormal increase in interferon levels, along with the existence of anti-double-stranded DNA antibodies. This evidence suggests that the development of SLE may be initiated by innate immunity. The presence of abnormal dsDNA fragments can activate DNA sensors within cells, particularly immune cells, leading to the initiation of downstream signaling cascades that result in the upregulation of relevant cytokines and the subsequent initiation of adaptive immune responses, such as B cell differentiation and T cell activation. The intricate pathogenesis of SLE results in DNA sensors exhibiting a wide range of functions in innate immune responses that are subject to variation based on cell types, developmental processes, downstream effector signaling pathways and other factors. The review aims to reorganize how DNA sensors influence signaling pathways and contribute to the development of SLE according to current studies, with the aspiration of furnishing valuable insights for future investigations into the pathological mechanisms of SLE and potential treatment approaches.

LATS2 degradation promoted fibrosis damage and rescued by vitamin K3 in lupus nephritis

BACKGROUND

Lupus nephritis (LN) is the most common complication of systemic lupus erythematosus (SLE). The limited treatment options for LN increase the economic burdens on patients. Because fibrotic progression leads to irreversible renal damage in LN patients and further progresses to chronic kidney disease (CKD) and the end stage of renal disease (ESRD), developing new targets to prevent LN fibrotic progression could lead to a feasible treatment strategy for LN patients.

METHODS

In this study, we examined YAP activation and LATS2 downregulation in LN kidney biopsy samples (LN: n = 8, normal: n = 2) and lupus-prone MRL/lpr mice (n = 8 for each disease stage). The function of LATS2 was further investigated by in situ injection of Ad-LATS2 into mice with LN (n = 6 mice per group). We examined the role of SIAH2-LATS2 regulation by IP-MS and co-IP, and the protective effect of the SIAH2 inhibitor was investigated in mice with LN.

RESULTS

Restoring LATS2 by an adenovirus in vivo alleviated renal fibrotic damage in mice with LN. Moreover, we found that LATS2 was degraded by a K48 ubiquitination-proteasome pathway mediated by SIAH2 and promoted YAP activation to worsen fibrosis progression in LN. The H150 region of the substrate binding domain (SBD) is an important site for SIAH2-LATS2 binding. The SIAH2-specific inhibitor vitamin K3 protected against LN-associated fibrotic damage in vivo.

CONCLUSION

In summary, we identified the SIAH2-LATS2 axis as an attractive intervention target in LN to alter the resistance to fibrosis.

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.

Lupus Nephritis Risk Factors and Biomarkers: An Update

Lupus nephritis (LN) represents the most severe organ manifestation of systemic lupus erythematosus (SLE) in terms of morbidity and mortality. To reduce these risks, tremendous efforts have been made in the last decade to characterize the different steps of the disease and to develop biomarkers in order to better (i) unravel the pre-SLE stage (e.g., anti-nuclear antibodies and interferon signature); (ii) more timely initiation of therapy by improving early and accurate LN diagnosis (e.g., pathologic classification was revised); (iii) monitor disease activity and therapeutic response (e.g., recommendation to re-biopsy, new urinary biomarkers); (iv) prevent disease flares (e.g., serologic and urinary biomarkers); (v) mitigate the deterioration in the renal function; and (vi) reduce side effects with new therapeutic guidelines and novel therapies. However, progress is poor in terms of improvement with early death attributed to active SLE or infections, while later deaths are related to the chronicity of the disease and the use of toxic therapies. Consequently, an individualized treat-to-target strategy is mandatory, and for that, there is an unmet need to develop a set of accurate biomarkers to be used as the standard of care and adapted to each stage of the disease.