Urinary Cellular Profile as a Biomarker for Lupus Nephritis

Noninvasive biomarkers for lupus nephritis

Lupus nephritis (LN) is one of the most severe clinical manifestations of systemic lupus erythematosus (SLE). Notably, the clinical manifestations of LN are not always consistent with the histopathological findings. Therefore, the diagnosis and activity monitoring of this disease are challenging and largely depend on invasive renal biopsy. Renal biopsy has side effects and is associated with the risk of bleeding and infection. There is a growing interest in the development of novel noninvasive biomarkers for LN. In this review, we summarize most of the LN biomarkers discovered so far by correlating current knowledge with future perspectives. These biomarkers fundamentally reflect the biological processes of kidney damage and repair during disease. Furthermore, this review highlights the role of urinary cell phenotype detection in the diagnosis, monitoring, and treatment of LN and summarizes the limitations and countermeasures of this test.

Applying 12 machine learning algorithms and Non-negative Matrix Factorization for robust prediction of lupus nephritis

Lupus nephritis (LN) is a challenging condition with limited diagnostic and treatment options. In this study, we applied 12 distinct machine learning algorithms along with Non-negative Matrix Factorization (NMF) to analyze single-cell datasets from kidney biopsies, aiming to provide a comprehensive profile of LN. Through this analysis, we identified various immune cell populations and their roles in LN progression and constructed 102 machine learning-based immune-related gene (IRG) predictive models. The most effective models demonstrated high predictive accuracy, evidenced by Area Under the Curve (AUC) values, and were further validated in external cohorts. These models highlight six hub IRGs (CD14, CYBB, IFNGR1, IL1B, MSR1, and PLAUR) as key diagnostic markers for LN, showing remarkable diagnostic performance in both renal and peripheral blood cohorts, thus offering a novel approach for noninvasive LN diagnosis. Further clinical correlation analysis revealed that expressions of IFNGR1, PLAUR, and CYBB were negatively correlated with the glomerular filtration rate (GFR), while CYBB also positively correlated with proteinuria and serum creatinine levels, highlighting their roles in LN pathophysiology. Additionally, protein-protein interaction (PPI) analysis revealed significant networks involving hub IRGs, emphasizing the importance of the interleukin family and chemokines in LN pathogenesis. This study highlights the potential of integrating advanced genomic tools and machine learning algorithms to improve diagnosis and personalize management of complex autoimmune diseases like LN.

Investigating the value of urinary biomarkers in relation to lupus nephritis histopathology: present insights and future prospects

Lupus nephritis (LN), a leading cause of death in Systemic Lupus Erythematosus (SLE) patients, presents significant diagnostic and prognostic challenges. Although renal pathology offers critical insights regarding the diagnosis, classification, and therapy for LN, its clinical utility is constrained by the invasive nature and limited reproducibility of renal biopsies. Moreover, the continuous monitoring of renal pathological changes through repeated biopsies is impractical. Consequently, there is a growing interest in exploring urine as a non-invasive, easily accessible, and dynamic "liquid biopsy" alternative to guide clinical management. This paper examines novel urinary biomarkers from a renal pathology perspective, encompassing cellular components, cytokines, adhesion molecules, auto-antibodies, soluble leukocyte markers, light chain fragments, proteins, small-molecule peptides, metabolomics, urinary exosomes, and ribonucleic acids. We also discuss the application of combined models comprising multiple biomarkers in assessing lupus activity. These innovative biomarkers and models offer insights into LN disease activity, acute and chronic renal indices, fibrosis, thrombotic microangiopathy, podocyte injury, and other pathological changes, potentially improving the diagnosis, management, and prognosis of LN. These urinary biomarkers or combined models may serve as viable alternatives to traditional renal pathology, potentially revolutionizing the method for future LN diagnosis and observation.

Analysis of m6A-regulated genes and subtype classification in lupus nephritis

BACKGROUND

Lupus nephritis (LN) is the most common and severe clinical manifestation of systemic lupus erythematosus (SLE). N6-methyladenosine (m6A) is a reversible RNA modification and has been implicated in various biological processes. However, the roles of m6A regulators in LN are not fully demonstrated.

METHODS

We downloaded the kidney tissue transcriptome dataset of LN patients and normal controls from the GEO database and extracted the expression levels of m6A regulators. We constructed and compared Random Forest (RF) and Support Vector Machine (SVM) models, and subsequently selected featured genes to develop nomogram models. The m6A subtypes were identified based on significantly differentially expressed m6A regulators, and the m6A gene subtypes were identified based on m6A-associated differential genes, and the two m6A modification patterns were comprehensively evaluated.

RESULTS

We obtained the GSE32591 and GSE112943 datasets from the GEO database, including 78 LN samples and 36 normal control samples. We extracted the expression levels of 20 m6A regulators. By RF analysis we identified 7 characteristic m6A regulators and constructed nomogramh models with these 7 genes. We identified two m6A subtypes based on these seven important m6A regulators, and the immune cell infiltration levels of the two subtype clusters were significantly different. We identified two more m6A gene subtypes based on m6A-associated DEGs. We calculated the m6A scores using the principal component analysis (PCA) algorithm and found that the m6A scores of m6A cluster A and gene cluster A were lower than those of m6A cluster B and gene cluster B. In addition, we found that the levels of inflammatory factors were also significantly different between m6A clusters and gene clusters.

CONCLUSION

This study confirms that m6A regulators are involved in the LN process through different modes of action and provide new diagnostic and therapeutic targets for LN.

The role of N6-methyladenosine (m6A) in kidney diseases

Chemical modifications are a specific and efficient way to regulate the function of biological macromolecules. Among them, RNA molecules exhibit a variety of modifications that play important regulatory roles in various biological processes. More than 170 modifications have been identified in RNA molecules, among which the most common internal modifications include N6-methyladenine (m6A), n1-methyladenosine (m1A), 5-methylcytosine (m5C), and 7-methylguanine nucleotide (m7G). The most widely affected RNA modification is m6A, whose writers, readers, and erasers all have regulatory effects on RNA localization, splicing, translation, and degradation. These functions, in turn, affect RNA functionality and disease development. RNA modifications, especially m6A, play a unique role in renal cell carcinoma disease. In this manuscript, we will focus on the biological roles of m6A in renal diseases such as acute kidney injury, chronic kidney disease, lupus nephritis, diabetic kidney disease, and renal cancer.

Proteomic research on new urinary biomarkers of renal disease in canine leishmaniosis: Survival and monitoring response to treatment

The objective of our study was to search for survival biomarkers (SB) and treatment response monitoring biomarkers (TRMB) in the urinary proteome of dogs with renal disease secondary to canine leishmaniosis (CanL), using UHPLC-MS/MS. The proteomic data are available via ProteomeXchange with identifier PXD042578. Initially, a group of 12 dogs was evaluated and divided into survivors (SG; n = 6) and nonsurvivors (NSG; n = 6). A total of 972 proteins were obtained from the evaluated samples. Then, bioinformatic analysis reduced them to 6 proteins like potential SB increased in the NSG, specifically, Haemoglobin subunit Alpha 1, Complement Factor I, Complement C5, Fibrinogen beta chain (fragment), Peptidase S1 domain-containing protein, and Fibrinogen gamma chain. Afterwards, SG was used to search for TRMB, studying their urine at 0, 30, and 90 days, and 9 proteins that decreased after treatment were obtained: Apolipoprotein E, Cathepsin B, Cystatin B, Cystatin-C-like, Lysozyme, Monocyte differentiation CD14, Pancreatitis-associated precursor protein, Profilin, and Protein FAM3C. Finally, enrichment analysis provided information about the biological mechanisms in which these proteins are involved. In conclusion, this study provides 15 new candidate urinary biomarkers and an improved understanding of the pathogenesis of kidney disease in CanL.

Transcriptome Studies in Lupus Nephritis

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

Exploration of Potential Integrated Models of N6-Methyladenosine Immunity in Systemic Lupus Erythematosus by Bioinformatic Analyses

Systemic lupus erythematosus (SLE) is a prototypical systemic autoimmune disease of unknown etiology. The epigenetic regulation of N6-methyladenosine (m6A) modification in immunity is emerging. However, few studies have focused on SLE and m6A immune regulation. In this study, we aimed to explore a potential integrated model of m6A immunity in SLE. The models were constructed based on RNA-seq data of SLE. A consensus clustering algorithm was applied to reveal the m6A-immune signature using principal component analysis (PCA). Univariate and multivariate Cox regression analyses and Kaplan–Meier analysis were used to evaluate diagnostic differences between groups. The effects of m6A immune-related characteristics were investigated, including risk evaluation of m6A immune phenotype-related characteristics, immune cell infiltration profiles, diagnostic value, and enrichment pathways. CIBERSORT, ESTIMATE, and single-sample gene set enrichment analysis (ssGSEA) were used to evaluate the relative immune cell infiltrations (ICIs) of the samples. Conventional bioinformatics methods were used to identify key m6A regulators, pathways, gene modules, and the coexpression network of SLE. In summary, our study revealed that IGFBP3 (as a key m6A regulator) and two pivotal immune genes (CD14 and IDO1) may aid in the diagnosis and treatment of SLE. The potential integrated models of m6A immunity that we developed could guide clinical management and may contribute to the development of personalized immunotherapy strategies.

Immune-Related Urine Biomarkers for the Diagnosis of Lupus Nephritis

The kidney is one of the main organs affected by the autoimmune disease systemic lupus erythematosus. Lupus nephritis (LN) concerns 30-60% of adult SLE patients and it is significantly associated with an increase in the morbidity and mortality. The definitive diagnosis of LN can only be achieved by histological analysis of renal biopsies, but the invasiveness of this technique is an obstacle for early diagnosis of renal involvement and a proper follow-up of LN patients under treatment. The use of urine for the discovery of non-invasive biomarkers for renal disease in SLE patients is an attractive alternative to repeated renal biopsies, as several studies have described surrogate urinary cells or analytes reflecting the inflammatory state of the kidney, and/or the severity of the disease. Herein, we review the main findings in the field of urine immune-related biomarkers for LN patients, and discuss their prognostic and diagnostic value. This manuscript is focused on the complement system, antibodies and autoantibodies, chemokines, cytokines, and leukocytes, as they are the main effectors of LN pathogenesis.