Gene expression profiling in skin reveals strong similarities between subacute and chronic cutaneous lupus that are distinct from lupus nephritis

Machine learning-based identification of novel hub genes associated with oxidative stress in lupus nephritis: implications for diagnosis and therapeutic targets

BACKGROUND

Lupus nephritis (LN) is a complication of SLE characterised by immune dysfunction and oxidative stress (OS). Limited options exist for LN. We aimed to identify LN-related OS, highlighting the need for non-invasive diagnostic and therapeutic approaches.

METHODS

LN-differentially expressed genes (DEGs) were extracted from Gene Expression Omnibus datasets (GSE32591, GSE112943 and GSE104948) and Molecular Signatures Database for OS-associated DEGs (OSEGs). Functional enrichment analysis was performed for OSEGs related to LN. Weighted gene co-expression network analysis identified hub genes related to OS-LN. These hub OSEGs were refined as biomarker candidates via least absolute shrinkage and selection operator. The predictive value was validated using receiver operating characteristic (ROC) curves and nomogram for LN prognosis. We evaluated LN immune cell infiltration using single-sample gene set enrichment analysis and CIBERSORT. Additionally, gene set enrichment analysis explored the functional enrichment of hub OSEGs in LN.

RESULTS

The study identified four hub genes, namely STAT1, PRODH, TXN2 and SETX, associated with OS related to LN. These genes were validated for their diagnostic potential, and their involvement in LN pathogenesis was elucidated through ROC and nomogram. Additionally, alterations in immune cell composition in LN correlated with hub OSEG expression were observed. Immunohistochemical analysis reveals that the hub gene is most correlated with activated B cells and CD8 T cells. Finally, we uncovered that the enriched pathways of OSEGs were mainly involved in the PI3K-Akt pathway and the Janus kinase-signal transducer and activator of transcription pathway.

CONCLUSION

These findings contribute to advancing our understanding of the complex interplay between OS, immune dysregulation and molecular pathways in LN, laying a foundation for the identification of potential diagnostic biomarkers and therapeutic targets.

Screening of Genes Associated with Immune Infiltration of Discoid Lupus Erythematosus Based on Weighted Gene Co-expression Network Analysis

Discoid lupus erythematosus (DLE) is a disorder of the immune system commonly seen in women of childbearing age. The pathophysiology and aetiology are still poorly understood, and no cure is presently available. Therefore, there is an urgent need to explore the underlying molecular mechanisms, as well as search for new therapeutic targets. Gene expression data from skin biopsies samples of DLE patients and healthy controls were downloaded from the Gene Expression Omnibus database. The differentially expressed genes (DEGs) between DLE and healthy control samples were identified by differential expression analysis. Samples were analysed using CIBERSORT to examine the proportion of immune infiltration. Weighted gene co-expression network analysis was used to screen for the module most relevant to immune infiltration. Candidate genes were uploaded to the TRRUST database to obtain the potential transcription factors regulating these genes. Protein-protein interaction (PPI) analysis was performed to obtain the hub genes most associated with immune infiltration among the candidate genes. A total of 273 DEGs were identified between the DLE and healthy control samples. The results of immunoinfiltration analysis showed that the abundances of resting memory CD4 T cells, activated memory CD4 T cells and M1 macrophages were significantly higher, while those of resting infiltration of plasma cells, regulatory T cells and dendritic cells were lower in DLE samples than in healthy control samples. Correlation analysis showed that ISG15, TRIM22, XAF1, IFIT2, OAS2, OAS3, OAS1, IFI44, IFI6, BST2, IFIT1 and MX2 were negatively correlated with the abundances of plasma cells, T-cell regulatory cells and resting dendritic cells and positively correlated with activated memory CD4 T cells and M1 macrophages. Our study shows that these hub genes may regulate DLE via immune-related pathways mediated by the infiltration of these immune cells.

Comprehensive analysis of lactate-related gene profiles and immune characteristics in lupus nephritis

Objectives

The most frequent cause of kidney damage in systemic lupus erythematosus (SLE) is lupus nephritis (LN), which is also a significant risk factor for morbidity and mortality. Lactate metabolism and protein lactylation might be related to the development of LN. However, there is still a lack of relative research to prove the hypothesis. Hence, this study was conducted to screen the lactate-related biomarkers for LN and analyze the underlying mechanism.

Methods

To identify differentially expressed genes (DEGs) in the training set (GSE32591, GSE127797), we conducted a differential expression analysis (LN samples versus normal samples). Then, module genes were mined using WGCNA concerning LN. The overlapping of DEGs, critical module genes, and lactate-related genes (LRGs) was used to create the lactate-related differentially expressed genes (LR-DEGs). By using a machine-learning algorithm, ROC, and expression levels, biomarkers were discovered. We also carried out an immune infiltration study based on biomarkers and GSEA.

Results

A sum of 1259 DEGs was obtained between LN and normal groups. Then, 3800 module genes in reference to LN were procured. 19 LR-DEGs were screened out by the intersection of DEGs, key module genes, and LRGs. Moreover, 8 pivotal genes were acquired via two machine-learning algorithms. Subsequently, 3 biomarkers related to lactate metabolism were obtained, including COQ2, COQ4, and NDUFV1. And these three biomarkers were enriched in pathways 'antigen processing and presentation' and 'NOD-like receptor signaling pathway'. We found that Macrophages M0 and T cells regulatory (Tregs) were associated with these three biomarkers as well.

Conclusion

Overall, the results indicated that lactate-related biomarkers COQ2, COQ4, and NDUFV1 were associated with LN, which laid a theoretical foundation for the diagnosis and treatment of LN.

Spatial characterization of interface dermatitis in cutaneous lupus reveals novel chemokine ligand-receptor pairs that drive disease

Chemokines play critical roles in the recruitment and activation of immune cells in both homeostatic and pathologic conditions. Here, we examined chemokine ligand-receptor pairs to better understand the immunopathogenesis of cutaneous lupus erythematosus (CLE), a complex autoimmune connective tissue disorder. We used suction blister biopsies to measure cellular infiltrates with spectral flow cytometry in the interface dermatitis reaction, as well as 184 protein analytes in interstitial skin fluid using Olink targeted proteomics. Flow and Olink data concordantly demonstrated significant increases in T cells and antigen presenting cells (APCs). We also performed spatial transcriptomics and spatial proteomics of punch biopsies using digital spatial profiling (DSP) technology on CLE skin and healthy margin controls to examine discreet locations within the tissue. Spatial and Olink data confirmed elevation of interferon (IFN) and IFN-inducible CXCR3 chemokine ligands. Comparing involved versus uninvolved keratinocytes in CLE samples revealed upregulation of essential inflammatory response genes in areas near interface dermatitis, including AIM2. Our Olink data confirmed upregulation of Caspase 8, IL-18 which is the final product of AIM2 activation, and induced chemokines including CCL8 and CXCL6 in CLE lesional samples. Chemotaxis assays using PBMCs from healthy and CLE donors revealed that T cells are equally poised to respond to CXCR3 ligands, whereas CD14+CD16+ APC populations are more sensitive to CXCL6 via CXCR1 and CD14+ are more sensitive to CCL8 via CCR2. Taken together, our data map a pathway from keratinocyte injury to lymphocyte recruitment in CLE via AIM2-Casp8-IL-18-CXCL6/CXCR1 and CCL8/CCR2, and IFNG/IFNL1-CXCL9/CXCL11-CXCR3.

Multi-organ single-cell analysis reveals an on/off switch system with potential for personalized treatment of immunological diseases

Prioritization of disease mechanisms, biomarkers, and drug targets in immune-mediated inflammatory diseases (IMIDs) is complicated by altered interactions between thousands of genes. Our multi-organ single-cell RNA sequencing of a mouse IMID model, namely collagen-induced arthritis, shows highly complex and heterogeneous expression changes in all analyzed organs, even though only joints showed signs of inflammation. We organized those into a multi-organ multicellular disease model, which shows predicted molecular interactions within and between organs. That model supports that inflammation is switched on or off by altered balance between pro- and anti-inflammatory upstream regulators (URs) and downstream pathways. Meta-analyses of human IMIDs show a similar, but graded, on/off switch system. This system has the potential to prioritize, diagnose, and treat optimal combinations of URs on the levels of IMIDs, subgroups, and individual patients. That potential is supported by UR analyses in more than 600 sera from patients with systemic lupus erythematosus.

Incorporating knowledge of disease-defining hub genes and regulatory network into a machine learning-based model for predicting treatment response in lupus nephritis after the first renal flare

BACKGROUND

Identifying candidates responsive to treatment is important in lupus nephritis (LN) at the renal flare (RF) because an effective treatment can lower the risk of progression to end-stage kidney disease. However, machine learning (ML)-based models that address this issue are lacking.

METHODS

Transcriptomic profiles based on DNA microarray data were extracted from the GSE32591 and GSE112943 datasets. Comprehensive bioinformatics analyses were performed to identify disease-defining genes (DDGs). Peripheral blood samples (GSE81622, GSE99967, and GSE72326) were used to evaluate the effect of DDGs. Single-sample gene set enrichment analysis (ssGSEA) scores of the DDGs were calculated and correlated with specific immunology genes listed in the nCounter panel. GSE60681 and GSE69438 were used to examine the ability of the DDGs to discriminate LN from other renal diseases. K-means clustering was used to obtain the separate gene sets. The clustering results were extended to data derived using the nCounter technique. The least absolute shrinkage and selection operator (LASSO) algorithm was used to identify genes with high predictive value for treatment response after the first RF in each cluster. LASSO models with tenfold validation were built in GSE200306 and assessed by receiver operating characteristic (ROC) analysis with area under curve (AUC). The models were validated by using an independent dataset (GSE113342).

RESULTS

Forty-five hub genes specific to LN were identified. Eight optimal disease-defining clusters (DDCs) were identified in this study. Th1 and Th2 cell differentiation pathway was significantly enriched in DDC-6. LCK in DDC-6, whose expression positively correlated with various subsets of T cell infiltrations, was found to be differentially expressed between responders and non-responders and was ranked high in regulatory network analysis. Based on DDC-6, the prediction model had the best performance (AUC: 0.75; 95% confidence interval: 0.44-1 in the testing set) and high precision (0.83), recall (0.71), and F1 score (0.77) in the validation dataset.

CONCLUSIONS

Our study demonstrates that incorporating knowledge of biological phenotypes into the ML model is feasible for evaluating treatment response after the first RF in LN. This knowledge-based incorporation improves the model's transparency and performance. In addition, LCK may serve as a biomarker for T-cell infiltration and a therapeutic target in LN.

Identification of hub ferroptosis-related genes and immune infiltration in lupus nephritis using bioinformatics

Lupus nephritis (LN) is one of the most severe and more common organ manifestations of the autoimmune disease, systemic lupus erythematosus. Ferroptosis, a novel type of programmed cell death, so far its role in LN remains uncertain. In the present study, we explored the role of ferroptosis in LN and its relationship with the immune response. The GSE112943 LN dataset was downloaded from the Gene Expression Omnibus database. Ferroptosis-Related Genes (FRGs) that drive, suppress or mark ferroptosis were retrieved from the public FerrDb database. The gene expression matrix of the GSE112943 dataset was analyzed with the "limma" package in R to obtain differentially expressed genes (DEGs) between LN and healthy samples. Subsequently, the crossover genes between DEGs and FRGs were identified as differentially expressed ferroptosis-related genes (DE-FRGs). Protein-protein interaction (PPI) network analysis, visualization, and identification of hub lupus nephritis ferroptosis-related genes (LN-FRGs) were performed with STRING and Cytoscape, while their Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were determined with the clusterProfiler package. Immune cell infiltration was calculated with CIBERSORT. The relationship between hub LN-FRGs and immune-infiltrated cells in LN was determined by Pearson correlation. A total of 96 DE-FRGs and 8 hub LN-FRGs (KRAS, PIK3CA, EGFR, MAPK14, SRC, MAPK3, VEGFA, and ATM) were identified. GO and KEGG functional classification indicated these genes enrichment in apoptotic process, programmed cell death, autophagy-animal, FoxO signaling pathway, relaxin signaling pathway, and VEGF signaling pathway. Infiltration matrix analysis of immune cells showed abundant Monocytes and M0/M1/M2 macrophages in LN kidney tissues. Correlation analysis revealed 8 hub LN-FRGs associated with immune-infiltrated cells in LN. In summary, overproduction of ROS and abnormal infiltration of immune cells would be implicated in the LN caused by ferroptosis. 8 hub lupus nephritis ferroptosis-related genes (LN-FRGs) which might be good biomarkers of ferroptosis in LN were identified in this study. These findings point to the immune response playing an important role in LN caused by ferroptosis via mutual regulation between hub LN-FRGs and immune-infiltrated cells.

Identification of Significant Genes and Pathways for the Chronic and Subacute Cutaneous Lupus Erythematosus via Bioinformatics Analysis

Background

Chronic cutaneous lupus erythematosus (CCLE) and subacute cutaneous lupus erythematosus (SCLE) are both common variants of cutaneous lupus erythematosus (CLE) that mainly involve the skin and mucous membrane. Oral mucosal involvement is frequently observed in patients of CLE. Despite that they have different clinicopathological features, whether there is a significant difference in pathogenesis between them remains unclear. Herein, we investigated specific genes and pathways of SCLE and CCLE via bioinformatics analysis.

Methods

Microarray expression datasets of GSE109248 and GSE112943 were both retrieved from the GEO database. Differentially expressed genes (DEGs) between CCLE or SCLE skin tissues and health controls were selected by GEO2R. Common DEGs were picked out via the Venn diagram software. Then, functional enrichment and PPI network analysis were conducted, and the top 10 key genes were identified via Cytohubba.

Results

Totally, 176 DEGs of SCLE and 287 DEGs of CCLE were identified. The GO enrichment and KEGG analysis of DEGs of SCLE is significantly enriched in the response to virus, defense response to virus, response to IFN-gamma, cellular response to IFN-γ, type I IFN signaling pathway, chemokine activity, chemokine receptor binding, NOD-like receptor signaling pathway, etc. The GO enrichment and KEGG analysis of DEGs of CCLE is significantly enriched in the response to virus, regulation of multiorganism process, negative regulation of viral process, regulation of lymphocyte activation, chemokine receptor binding, CCR chemokine receptor binding, NOD-like receptor signaling pathway, etc. The top 10 hub genes of SCLE and CCLE, respectively, include STAT1, CXCL10, IRF7, ISG15, and RSAD2 and CXCL10, IRF7, IFIT3, CTLA4, and ISG15.

Conclusion

Our finding suggests that SCLE and CCLE have the similar potential key genes and pathways and majority of them belong to IFN signatures and IFN signaling pathway. Besides, the NOD-like receptor signaling pathway might also have an essential role in the pathogenesis of SCLE and CCLE. Together, the identified genes and signaling pathways have enhanced our understanding of the mechanism underlying the occurrence and development of both SCLE and CCLE.

GBP2 acts as a member of the interferon signalling pathway in lupus nephritis

Lupus nephritis (LN) is a common and serious clinical manifestation of systemic lupus erythematosus. However, the pathogenesis of LN is not fully understood. The currently available treatments do not cure the disease and appear to have a variety of side effects in the long term. The purpose of this study was to search for key molecules involved in the LN immune response through bioinformatics techniques to provide a reference for LN-specific targeted therapy. The GSE112943 dataset was downloaded from the Gene Expression Omnibus database, and 20 of the samples were selected for analysis. In total, 2330 differentially expressed genes were screened. These genes were intersected with a list of immune genes obtained from the IMMPORT immune database to obtain 128 differentially expressed immune-related genes. Enrichment analysis showed that most of these genes were enriched in the interferon signalling pathway. Gene set enrichment analysis revealed that the sample was significantly enriched for expression of the interferon signalling pathway. Further analysis of the core gene cluster showed that nine genes, GBP2, VCAM1, ADAR, IFITM1, BST2, MX2, IRF5, OAS1 and TRIM22, were involved in the interferon signalling pathway. According to our analysis, the guanylate binding protein 2 (GBP2), interferon regulatory factor 5 and 2′-5′-oligoadenylate synthetase 1 (OAS1) genes are involved in three interferon signalling pathways. At present, we do not know whether GBP2 is associated with LN. Therefore, this study focused on the relationship between GBP2 and LN pathogenesis. We speculate that GBP2 may play a role in the pathogenesis of LN as a member of the interferon signalling pathway. Further immunohistochemical results showed that the expression of GBP2 was increased in the renal tissues of LN patients compared with the control group, confirming this conjecture. In conclusion, GBP2 is a member of the interferon signalling pathway that may have implications for the pathogenesis of LN and serves as a potential biomarker for LN.

The Genetic Landscape of Cutaneous Lupus Erythematosus

Cutaneous lupus erythematosus (CLE) is an autoimmune connective tissue disease that can exist as a disease entity or within the context of systemic lupus erythematosus (SLE). Over the years, efforts to elucidate the genetic underpinnings of CLE and SLE have yielded a wealth of information. This review examines prior studies investigating the genetics of CLE at the DNA and RNA level and identifies future research areas. In this literature review, we examined the English language literature captured within the MEDLINE and Embase databases using pre-defined search terms. First, we surveyed studies investigating various DNA studies of CLE. We identified three predominant areas of focus in HLA profiling, complement deficiencies, and genetic polymorphisms. An increased frequency of HLA-B8 has been strongly linked to CLE. In addition, multiple genes responsible for mediating innate immune response, cell growth, apoptosis, and interferon response confer a higher risk of developing CLE, specifically TREX1 and SAMHD1. There was a strong association between C2 complement deficiency and CLE. Second, we reviewed literature studying aberrations in the transcriptomes of patients with CLE. We reviewed genetic aberrations initiated by environmental insults, and we examined the interplay of dysregulated inflammatory, apoptotic, and fibrotic pathways in the context of the pathomechanism of CLE. These current learnings will serve as the foundation for further advances in integrating personalized medicine into the care of patients with CLE.

Prominent B cell signature differentiates discoid from subacute cutaneous lupus erythematosus

While B cells account for a significant proportion of the lymphocytic infiltrate in discoid lupus erythematosus (DLE), their contribution to pathogenesis is unknown. In this study, we compare the immune landscape of 17 subjects with DLE to 21 subjects with subacute cutaneous lupus erythematosus (SCLE) using transcriptomic and histologic analyses of lesional skin. A few of the subjects (3/17 DLE, 5/21 SCLE) had concomitant systemic lupus erythematosus (SLE). Using a modified Autoimmune Profiling Panel (NanoString), we demonstrate that B cell-specific genes, including canonical pan-B cell markers CD19 (p=0.0060), CD20 (p=0.0047), and CD79a (p=0.0201), are among the most upregulated genes in DLE. Numerous other genes encoding B cell-associated proteins, including immunoglobulins, B cell activating factor (BAFF) receptors, and Fc-receptor like (FCRL) family members, are similarly enriched. Relative cell type scoring reveals that among various inflammatory cell types, only B cells are more prevalent in DLE. Digital whole-image slide analysis of immunohistochemistry for B cells (CD20) and T cells (CD3) supports our gene expression findings of a disproportionately greater B cell infiltrate in DLE lesions. Overall, this study identifies a B cell-predominant signature unique to DLE and highlights the importance of studying the role of cutaneous B cells in DLE pathogenesis.

Potential Small Molecules for Therapy of Lupus Nephritis Based on Genetic Effect and Immune Infiltration

Lupus nephritis (LN) is the most common and significant complication of systemic lupus erythematosus (SLE) due to its poor prognosis and mortality rates in SLE patients. There is a critical need for new drugs as the pathogenesis of LN remains to be elucidated and immunosuppressive therapy comes with many deficiencies. In this study, 23 hub genes (IFI6, PLSCR1, XAF1, IFI16, IFI44, MX1, IFI44L, IFIT3, IFIT2, IFI27, DDX58, EIF2AK2, IFITM1, RTP4, IFITM3, TRIM22, PARP12, IFIH1, OAS1, HERC6, RSAD2, DDX60, and MX2) were identified through bioinformatics and network analysis and are closely related to interferon production and function. Interestingly, immune cell infiltration analysis and correlation analysis demonstrate a positive correlation between the expression of 23 hub genes and monocyte infiltration in glomeruli and M2 macrophage infiltration in the tubulointerstitium of LN patients. Additionally, the CTD database, DsigDB database, and DREIMT database were used to explore the bridging role of genes in chemicals and LN as well as the potential influence of these chemicals on immune cells. After comparison and discussion, six small molecules (Acetohexamide, Suloctidil, Terfenadine, Prochlorperazine, Mefloquine, and Triprolidine) were selected for their potential ability in treating lupus nephritis.

Using Gene Expression Analysis to Understand Complex Autoimmune Skin Disease Patients: A Series of Four Canine Cutaneous Lupus Erythematosus Cases

Cutaneous Lupus Erythematosus (CLE) is an autoimmune skin disease that occurs in almost two-thirds of people with Systemic Lupus Erythematosus (SLE) and can exist as its own entity. Despite its negative impact on the quality of life of patients, lupus pathogenesis is not fully understood. In recent years, the role of gene expression analysis has become important in understanding cellular functions and disease causation within and across species. Interestingly, dogs also develop CLE, providing a spontaneous animal model of disease. Here, we present a targeted transcriptomic analysis of skin biopsies from a case series of four dogs with complex autoimmunity with suspected CLE. We identified 92 differentially expressed genes (DEGs), including type 1 interferon, B cell, and T cell-related genes, in the four cases compared to healthy skin margin controls. Additionally, we compared our results with existing CLE datasets from humans and mice and found that humans and canines share 49 DEGs, whereas humans and mice shared only 25 DEGs in our gene set. Immunohistochemistry of IFNG and CXCL10, two of the most highly upregulated inflammatory mediators, confirmed protein-level expression and revealed immune cells as the primary source of CXCL10 in dogs with SLE, whereas keratinocytes stained strongly for CXCL10 in dogs without SLE. We propose that gene expression analysis may aid the diagnosis of complex autoimmune skin diseases and that dogs may provide important insights into CLE and SLE pathogeneses, or more broadly, skin manifestations during systemic autoimmunity.

B Cell Signatures Distinguish Cutaneous Lupus Erythematosus Subtypes and the Presence of Systemic Disease Activity

Cutaneous lupus erythematosus (CLE) is a chronic inflammatory skin disease characterized by a diverse cadre of clinical presentations. CLE commonly occurs in patients with systemic lupus erythematosus (SLE), and CLE can also develop in the absence of systemic disease. Although CLE is a complex and heterogeneous disease, several studies have identified common signaling pathways, including those of type I interferons (IFNs), that play a key role in driving cutaneous inflammation across all CLE subsets. However, discriminating factors that drive different phenotypes of skin lesions remain to be determined. Thus, we sought to understand the skin-associated cellular and transcriptional differences in CLE subsets and how the different types of cutaneous inflammation relate to the presence of systemic lupus disease. In this study, we utilized two distinct cohorts comprising a total of 150 CLE lesional biopsies to compare discoid lupus erythematosus (DLE), subacute cutaneous lupus erythematosus (SCLE), and acute cutaneous lupus erythematosus (ACLE) in patients with and without associated SLE. Using an unbiased approach, we demonstrated a CLE subtype-dependent gradient of B cell enrichment in the skin, with DLE lesions harboring a more dominant skin B cell transcriptional signature and enrichment of B cells on immunostaining compared to ACLE and SCLE. Additionally, we observed a significant increase in B cell signatures in the lesional skin from patients with isolated CLE compared with similar lesions from patients with systemic lupus. This trend was driven primarily by differences in the DLE subgroup. Our work thus shows that skin-associated B cell responses distinguish CLE subtypes in patients with and without associated SLE, suggesting that B cell function in skin may be an important link between cutaneous lupus and systemic disease activity.