Elevated expression of receptors for EGF, PDGF, transferrin and folate within murine and human lupus nephritis kidneys

Updates on Inflammatory Molecular Pathways Mediated by ADAM17 in Autoimmunity

ADAM17 is a member of the disintegrin and metalloproteinase (ADAM) family of transmembrane proteases with immunoregulatory activity in multiple signaling pathways. The functional ADAM17 is involved in the shedding of the ectodomain characterizing many substrates belonging to growth factors, cytokines, receptors, and adhesion molecules. The ADAM17-dependent pathways are known to be crucial in tumor development and progression and in the modulation of many pathological and physiological processes. In the last decade, ADAM17 was considered the driver of several autoimmune pathologies, and numerous substrate-mediated signal transduction pathways were identified. However, the discoveries made to date have led researchers to try to clarify the multiple mechanisms in which ADAM17 is involved and to identify any molecular gaps between the different transductional cascades. In this review, we summarize the most recent updates on the multiple regulatory activities of ADAM17, focusing on reported data in the field of autoimmunity.

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.

Identification of Immune-Related Genes as Biomarkers for Uremia

Purpose

Uremia, which is characterized by immunodeficiency, is associated with the deterioration of kidney function. Immune-related genes (IRGs) are crucial for uremia progression.

Methods

The co-expression network was constructed to identify key modular genes associated with uremia. IRGs were intersected with differentially expressed genes (DEGs) between uremia and control groups and key modular genes to obtain differentially expressed IRGs (DEIRGs). DEIRGs were subjected to functional enrichment analysis. The protein-protein interaction (PPI) network was constructed. The candidate genes were identified using the cytoHubba tool. The biomarkers were identified using various machine learning algorithms. The diagnostic value of the biomarkers was evaluated using receiver operating characteristic (ROC) analysis. The immune infiltration analysis was implemented. The biological pathways of biomarkers were identified using gene set enrichment analysis and ingenuity pathway analysis. The mRNA expression of biomarkers was validated using blood samples of patients with uremia and healthy subjects with quantitative real-time polymerase chain reaction (qRT-PCR).

Results

In total, four biomarkers (PDCD1, NGF, PDGFRB, and ZAP70) were identified by machine learning methods. ROC analysis demonstrated that the area under the curve values of individual biomarkers were > 0.9, indicating good diagnostic power. The nomogram model of biomarkers exhibited good predictive power. The proportions of six immune cells significantly varied between the uremia and control groups. ZAP70 expression was positively correlated with the proportions of resting natural killer (NK) cells, naïve B cells, and regulatory T cells. Functional enrichment analysis revealed that the biomarkers were mainly associated with translational function and neuroactive ligand-receptor interaction. ZAP70 regulated NK cell signaling. The PDCD1 and NGF expression levels determined using qRT-PCR were consistent with those determined using bioinformatics analysis.

Conclusion

PDCD1, NGF, PDGFRB, and ZAP70 were identified as biomarkers for uremia, providing a theoretical foundation for uremia diagnosis.

Transcriptome sequencing reveals novel molecular features of SLE severity

Introduction: Systemic lupus erythematosus (SLE) is an autoimmune disorder characterized by the production of autoantibodies, immune complex deposition, and tissue/organ damage. In this study, we aimed to identify molecular features and signaling pathways associated with SLE severity using RNA sequencing (RNA-seq), single-cell RNA sequencing (scRNA-seq), and clinical parameters. Methods: We analyzed transcriptome profiles of 45 SLE patients, grouped into mild (mSLE, SLEDAI ≤ 9) and severe (sSLE, SLEDAI > 9) based on SLE Disease Activity Index (SLEDAI) scores. We also collected clinical data on anti-dsDNA, ANA, ESR, CRP, snRNP, AHA, and anti-Smith antibody status for each patient. Results: By comparing gene expression across groups, we identified 12 differentially expressed genes (DEGs), including 7 upregulated (CEACAM6, UCHL1, ARFGEF3, AMPH, SERPINB10, TACSTD2, and OTX1) and 5 downregulated (SORBS2, TRIM64B, SORCS3, DRAXIN, and PCDHGA10) DEGs in sSLE compared to mSLE. Furthermore, using the CIBERSORT algorithm, we found that Treg cells were significantly decreased in sSLE and negatively correlated with AMPH expression, which was mainly expressed in Treg cells from SLE patients according to public scRNA-seq data (GSE135779). Discussion: Overall, our findings shed light on the molecular mechanisms underlying SLE severity and provide insight into potential therapeutic targets.

Pathogenic cellular and molecular mediators in lupus nephritis

Kidney involvement in patients with systemic lupus erythematosus - lupus nephritis (LN) - is one of the most important and common clinical manifestations of this disease and occurs in 40-60% of patients. Current treatment regimens achieve a complete kidney response in only a minority of affected individuals, and 10-15% of patients with LN develop kidney failure, with its attendant morbidity and considerable prognostic implications. Moreover, the medications most often used to treat LN - corticosteroids in combination with immunosuppressive or cytotoxic drugs - are associated with substantial side effects. Advances in proteomics, flow cytometry and RNA sequencing have led to important new insights into immune cells, molecules and mechanistic pathways that are instrumental in the pathogenesis of LN. These insights, together with a renewed focus on the study of human LN kidney tissue, suggest new therapeutic targets that are already being tested in lupus animal models and early-phase clinical trials and, as such, are hoped to eventually lead to meaningful improvements in the care of patients with systemic lupus erythematosus-associated kidney disease.