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

Molecular profiling and therapeutic tailoring to address disease heterogeneity in systemic lupus erythematosus

Systemic lupus erythematosus (SLE) is a chronic, heterogeneous, systemic autoimmune disease characterized by autoantibody production, complement activation, and immune complex deposition. SLE predominantly affects young, middle-aged, and child-bearing women with episodes of flare-up and remission, although it affects males at a much lower frequency (female: male; 7:1 to 15:1). Technological and molecular advancements have helped in patient stratification and improved patient prognosis, morbidity, and treatment regimens overall, impacting quality of life. Despite several attempts to comprehend the pathogenesis of SLE, knowledge about the precise molecular mechanisms underlying this disease is still lacking. The current treatment options for SLE are pragmatic and aim to develop composite biomarkers for daily practice, which necessitates the robust development of novel treatment strategies and drugs targeting specific responsive pathways. In this communication, we review and aim to explore emerging therapeutic modalities, including multiomics-based approaches, rational drug design, and CAR-T-cell-based immunotherapy, for the management of SLE.

Precision medicine in lupus nephritis

Lupus nephritis (LN) is a prominent manifestation of systemic lupus erythematosus (SLE), characterized by diverse clinical and histopathological features, imposing a substantial burden on patients. Although the exact cause of SLE remain undetermined, several genetic, epigenetics, hormonal, and other factors are implicated in LN pathogenesis. The management of LN rely on invasive renal biopsies, while the standard therapy of the proliferative form of LN remains empirical and relies on indiscriminate immunosuppressants (IS). These treatments exhibit unsatisfactory remission rates, trigger recurrent renal flares, and entail grave adverse effects (ADEs). The advent of precision medicine into LN entails a concentrated effort to pinpoint essential biomarkers, reshaping the landscape of LN management. The primary objective of this review is to synthesize and summarize existing research findings by elucidating the most prevalent immunological, genetic, and epigenetic alterations and deliberate on management strategies that can pave the way for precision medicine in tackling LN. Novel clinical biomarker such as serum anti-complement component 1q (anti-C1q), with urinary markers including neutrophil gelatinase-associated lipocalin (NGAL), monocyte chemoattractant protein-1 (MCP1) and tumour necrosis-like weak inducers of apoptosis (TWEAK) are strongly correlated with LN. These biomarkers have good sensitivity and specificity and perform better than conventional biomarkers in assessing LN activity. Similarly, more renal-specific genetic and epigenetic alteration have been correlated with LN susceptibility and severity. This includes variants of hyaluronan synthase 2 (HAS2), and platelet-derived growth factor receptor alpha (PDGFRA). In the future, integrating clinical, genetic, epigenetic, and targeted therapies holds promise for guiding precision medicine and improving LN outcomes.

HERC6 regulates STING activity in a sex-biased manner through modulation of LATS2/VGLL3 Hippo signaling

Interferon (IFN) activity exhibits a gender bias in human skin, skewed toward females. We show that HERC6, an IFN-induced E3 ubiquitin ligase, is induced in human keratinocytes through the epidermal type I IFN; IFN-κ. HERC6 knockdown in human keratinocytes results in enhanced induction of interferon-stimulated genes (ISGs) upon treatment with a double-stranded (ds) DNA STING activator cGAMP but not in response to the RNA-sensing TLR3 agonist. Keratinocytes lacking HERC6 exhibit sustained STING-TBK1 signaling following cGAMP stimulation through modulation of LATS2 and TBK1 activity, unmasking more robust ISG responses in female keratinocytes. This enhanced female-biased immune response with loss of HERC6 depends on VGLL3, a regulator of type I IFN signature. These data identify HERC6 as a previously unrecognized negative regulator of ISG expression specific to dsDNA sensing and establish it as a regulator of female-biased immune responses through modulation of STING signaling.

Identification of driver genes in lupus nephritis based on comprehensive bioinformatics and machine learning

Background

Lupus nephritis (LN) is a common and severe glomerulonephritis that often occurs as an organ manifestation of systemic lupus erythematosus (SLE). However, the complex pathological mechanisms associated with LN have hindered the progress of targeted therapies.

Methods

We analyzed glomerular tissues from 133 patients with LN and 51 normal controls using data obtained from the GEO database. Differentially expressed genes (DEGs) were identified and subjected to enrichment analysis. Weighted gene co-expression network analysis (WGCNA) was utilized to identify key gene modules. The least absolute shrinkage and selection operator (LASSO) and random forest were used to identify hub genes. We also analyzed immune cell infiltration using CIBERSORT. Additionally, we investigated the relationships between hub genes and clinicopathological features, as well as examined the distribution and expression of hub genes in the kidney.

Results

A total of 270 DEGs were identified in LN. Using weighted gene co-expression network analysis (WGCNA), we clustered these DEGs into 14 modules. Among them, the turquoise module displayed a significant correlation with LN (cor=0.88, p<0.0001). Machine learning techniques identified four hub genes, namely CD53 (AUC=0.995), TGFBI (AUC=0.997), MS4A6A (AUC=0.994), and HERC6 (AUC=0.999), which are involved in inflammation response and immune activation. CIBERSORT analysis suggested that these hub genes may contribute to immune cell infiltration. Furthermore, these hub genes exhibited strong correlations with the classification, renal function, and proteinuria of LN. Interestingly, the highest hub gene expression score was observed in macrophages.

Conclusion

CD53, TGFBI, MS4A6A, and HERC6 have emerged as promising candidate driver genes for LN. These hub genes hold the potential to offer valuable insights into the molecular diagnosis and treatment of LN.

Revealing common differential mRNAs, signaling pathways, and immune cells in blood, glomeruli, and tubulointerstitium of lupus nephritis patients based on transcriptomic data

Lupus nephritis (LN) is a potentially fatal autoimmune disease. The purpose of this study was to find potential key molecular markers of LN to aid in the early diagnosis and management of the disease. Datasets GSE99967_blood, GSE32591_glomeruli, and GSE32591_tubulointerstitium were included in this study. Differentially expressed mRNAs (DEmRNAs) were identified between the normal control and LN groups using the limma package in R. Common DEmRNAs in the three datasets were taken. Subsequently, functional enrichment analysis, immune correlation analysis, receiver operating characteristic (ROC) curve analysis and real-time polymerase chain reaction (RT-PCR) verification were performed. In this study, 11 common DEmRNAs were obtained and all of them were up-regulated. In protein-protein interaction (PPI) networks, we found that MX dynamin like GTPase 1 (MX1) and radical S-adenosyl methionine domain containing 2 (RSAD2) had the highest interaction score (0.997). Functional enrichment analysis revealed that MX1 and RSAD2 were enriched in influenza A and hepatitis C signaling pathways. The area under the curve (AUC) values of interferon-induced protein 44 (IFI44) and MX1 in GSE32591_glomeruli and GSE32591_tubulointerstitium datasets are 1, which is worthy of further study on their diagnostic value and molecular mechanism. The xCell analysis showed abnormal distribution of granulocyte-macrophage progenitor (GMP) cells in blood, glomeruli, and tubulointerstitium. Pearson's correlation analysis found that GMP cells were significantly correlated with lactotransferrin (LTF) and cell cycle. Identification of common DEmRNAs and key pathways in the blood, glomeruli, and tubulointerstitium of patients with LN provides potential research directions for exploring the molecular mechanisms of the disease.

B cell subsets-related biomarkers and molecular pathways for systemic lupus erythematosus by transcriptomics analyses

BACKGROUND

Systemic lupus erythematosus (SLE), an autoimmune disease, is characterised by B-cell abnormalities and a loss of tolerance that can produce autoantibody. However, the imperative genes and molecular pathways involved in the change of B cell populations remain unclear.

METHODS

The expression of B cell subsets between SLE and healthy controls (HCs) was detected based on micro-array transcriptome data. The Weighted Gene Co-Expression Network Analysis (WGCNA) further revealed the co-expression modules of naïve and memory B cells. Whereafter, we performed the functional enrichment analysis, Protein-protein interaction (PPI) networks construction and feature selection to screen hub genes. Ultimately, we recruited SLE patients and HCs from the Second Hospital of Shanxi Medical University and further verified these genes in transcriptome sequencing samples.

RESULTS

Total of 1087 SLE patients and 86 HCs constituted in the study. Compared to HCs, the levels of peripheral naïve B cells of SLE patients decreased, while memory B cells increased. WGCNA identified two modules with the highest correlation for the subsequent analysis. The purple module was primarily in connection with naïve B cells, and the GO analysis indicated that these genes were mainly abundant in B cell activation. The blue module relevant to memory B cells was most significantly enriched in the "defence response to virus" correlation pathway. Then we screened six hub genes by PPI and feature selection. Finally, four biomarkers (IFI27, IFITM1, MX2, IRF7) were identified by transcriptome sequencing verification.

CONCLUSION

Our study identified hub genes and key pathways associated with the naïve and memory B cells respectively, which may offer novel insights into the behaviours of B cells and the pathogenesis of SLE.

Roles of interferon induced protein with tetratricopeptide repeats (IFIT) family in autoimmune disease

Interferon-induced tetrapeptide repeat (IFIT) family proteins are an important component of the antiviral immune response. There are four known members of the human IFIT family, namely IFIT1, IFIT2, IFIT3 and IFIT5. More and more evidence shows that IFIT family members are involved in a variety of pathophysiological processes in vivo, regulate the homeostasis and differentiation of a variety of cells including immune cells, and are closely related to a variety of autoimmune diseases, which is expected to become a new therapeutic target. This review reviews the biological roles of different IFIT proteins in various autoimmune diseases, and highlights the potential use of these molecules as biomarkers and prognostic factors in autoimmune diseases, with a view to providing ideas for exploring the diagnosis and treatment of autoimmune diseases.

Single cell RNA-seq resolution revealed CCR1+/SELL+/XAF+ CD14 monocytes mediated vascular endothelial cell injuries in Kawasaki disease and COVID-19

INTRODUCTION

The COVID-19 pandemic provide the opportunities to explore the numerous similarities in clinical symptoms with Kawasaki disease (KD), including severe vasculitis. Despite this, the underlying mechanisms of vascular injury in both KD and COVID-19 remain elusive. To identify these mechanisms, this study employs single-cell RNA sequencing to explore the molecular mechanisms of immune responses in vasculitis, and validate the results through in vitro experiments.

METHOD

The single-cell RNA sequencing (scRNA-seq) analysis of peripheral blood mononuclear cells (PBMCs) was carried out to investigate the molecular mechanisms of immune responses in vasculitis in KD and COVID-19. The analysis was performed on PBMCs from six children diagnosed with complete KD, three age-matched KD healthy controls (KHC), six COVID-19 patients (COV), three influenza patients (FLU), and four healthy controls (CHC). The results from the scRNA-seq analysis were validated through flow cytometry and immunofluorescence experiments on additional human samples. Subsequently, monocyte adhesion assays, immunofluorescence, and quantitative polymerase chain reaction (qPCR) were used to analyze the damages to endothelial cells post-interaction with monocytes in HUVEC and THP1 cultures.

RESULTS

The scRNA-seq analysis revealed the potential cellular types involved and the alterations in genetic transcriptions in the inflammatory responses. The findings indicated that while the immune cell compositions had been altered in KD and COV patients, and the ratio of CD14+ monocytes were both elevated in KD and COV. While the CD14+ monocytes share a large scale of same differentiated expressed geens between KD and COV. The differential activation of CD14 and CD16 monocytes was found to respond to both endothelial and epithelial dysfunctions. Furthermore, SELL+/CCR1+/XAF1+ CD14 monocytes were seen to enhance the adhesion and damage to endothelial cells. The results also showed that different types of B cells were involved in both KD and COV, while only the activation of T cells was recorded in KD.

CONCLUSION

In conclusion, our study demonstrated the role of the innate immune response in the regulation of endothelial dysfunction in both KD and COVID-19. Additionally, our findings indicate that the adaptive immunity activation differs between KD and COVID-19. Our results demonstrate that monocytes in COVID-19 exhibit adhesion to both endothelial cells and alveolar epithelial cells, thus providing insight into the mechanisms and shared phenotypes between KD and COVID-19.

Establishment and analysis of a disease risk prediction model for the systemic lupus erythematosus with random forest

Systemic lupus erythematosus (SLE) is a latent, insidious autoimmune disease, and with the development of gene sequencing in recent years, our study aims to develop a gene-based predictive model to explore the identification of SLE at the genetic level. First, gene expression datasets of SLE whole blood samples were collected from the Gene Expression Omnibus (GEO) database. After the datasets were merged, they were divided into training and validation datasets in the ratio of 7:3, where the SLE samples and healthy samples of the training dataset were 334 and 71, respectively, and the SLE samples and healthy samples of the validation dataset were 143 and 30, respectively. The training dataset was used to build the disease risk prediction model, and the validation dataset was used to verify the model identification ability. We first analyzed differentially expressed genes (DEGs) and then used Lasso and random forest (RF) to screen out six key genes (OAS3, USP18, RTP4, SPATS2L, IFI27 and OAS1), which are essential to distinguish SLE from healthy samples. With six key genes incorporated and five iterations of 10-fold cross-validation performed into the RF model, we finally determined the RF model with optimal mtry. The mean values of area under the curve (AUC) and accuracy of the models were over 0.95. The validation dataset was then used to evaluate the AUC performance and our model had an AUC of 0.948. An external validation dataset (GSE99967) with an AUC of 0.810, an accuracy of 0.836, and a sensitivity of 0.921 was used to assess the model's performance. The external validation dataset (GSE185047) of all SLE patients yielded an SLE sensitivity of up to 0.954. The final high-throughput RF model had a mean value of AUC over 0.9, again showing good results. In conclusion, we identified key genetic biomarkers and successfully developed a novel disease risk prediction model for SLE that can be used as a new SLE disease risk prediction aid and contribute to the identification of SLE.

Identification of immune-related hub genes and miRNA-mRNA pairs involved in immune infiltration in human septic cardiomyopathy by bioinformatics analysis

Abstract

Septic cardiomyopathy (SCM) is a serious complication caused by sepsis that will further exacerbate the patient's prognosis. However, immune-related genes (IRGs) and their molecular mechanism during septic cardiomyopathy are largely unknown. Therefore, our study aims to explore the immune-related hub genes (IRHGs) and immune-related miRNA-mRNA pairs with potential biological regulation in SCM by means of bioinformatics analysis and experimental validation.

Method

Firstly, screen differentially expressed mRNAs (DE-mRNAs) from the dataset GSE79962, and construct a PPI network of DE-mRNAs. Secondly, the hub genes of SCM were identified from the PPI network and the hub genes were overlapped with immune cell marker genes (ICMGs) to further obtain IRHGs in SCM. In addition, receiver operating characteristic (ROC) curve analysis was also performed in this process to determine the disease diagnostic capability of IRHGs. Finally, the crucial miRNA-IRHG regulatory network of IRHGs was predicted and constructed by bioinformatic methods. Real-time quantitative reverse transcription-PCR (qRT-PCR) and dataset GSE72380 were used to validate the expression of the key miRNA-IRHG axis.

Result

The results of immune infiltration showed that neutrophils, Th17 cells, Tfh cells, and central memory cells in SCM had more infiltration than the control group; A total of 2 IRHGs were obtained by crossing the hub gene with the ICMGs, and the IRHGs were validated by dataset and qRT-PCR. Ultimately, we obtained the IRHG in SCM: THBS1. The ROC curve results of THBS1 showed that the area under the curve (AUC) was 0.909. Finally, the miR-222-3p/THBS1 axis regulatory network was constructed.

Conclusion

In summary, we propose that THBS1 may be a key IRHG, and can serve as a biomarker for the diagnosis of SCM; in addition, the immune-related regulatory network miR-222-3p/THBS1 may be involved in the regulation of the pathogenesis of SCM and may serve as a promising candidate for SCM therapy.

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.

MX2: Identification and systematic mechanistic analysis of a novel immune-related biomarker for systemic lupus erythematosus

Background

Systemic lupus erythematosus (SLE) is an autoimmune disease that involves multiple organs. However, the current SLE-related biomarkers still lack sufficient sensitivity, specificity and predictive power for clinical application. Thus, it is significant to explore new immune-related biomarkers for SLE diagnosis and development.

Methods

We obtained seven SLE gene expression profile microarrays (GSE121239/11907/81622/65391/100163/45291/49454) from the GEO database. First, differentially expressed genes (DEGs) were screened using GEO2R, and SLE biomarkers were screened by performing WGCNA, Random Forest, SVM-REF, correlation with SLEDAI and differential gene analysis. Receiver operating characteristic curves (ROCs) and AUC values were used to determine the clinical value. The expression level of the biomarker was verified by RT‒qPCR. Subsequently, functional enrichment analysis was utilized to identify biomarker-associated pathways. ssGSEA, CIBERSORT, xCell and ImmuCellAI algorithms were applied to calculate the sample immune cell infiltration abundance. Single-cell data were analyzed for gene expression specificity in immune cells. Finally, the transcriptional regulatory network of the biomarker was constructed, and the corresponding therapeutic drugs were predicted.

Results

Multiple algorithms were screened together for a unique marker gene, MX2, and expression analysis of multiple datasets revealed that MX2 was highly expressed in SLE compared to the normal group (all P < 0.05), with the same trend validated by RT‒qPCR (P = 0.026). Functional enrichment analysis identified the main pathway of MX2 promotion in SLE as the NOD-like receptor signaling pathway (NES=2.492, P < 0.001, etc.). Immuno-infiltration analysis showed that MX2 was closely associated with neutrophils, and single-cell and transcriptomic data revealed that MX2 was specifically expressed in neutrophils. The NOD-like receptor signaling pathway was also remarkably correlated with neutrophils (r >0.3, P < 0.001, etc.). Most of the MX2-related interacting proteins were associated with SLE, and potential transcription factors of MX2 and its related genes were also significantly associated with the immune response.

Conclusion

Our study found that MX2 can serve as an immune-related biomarker for predicting the diagnosis and disease activity of SLE. It activates the NOD-like receptor signaling pathway and promotes neutrophil infiltration to aggravate SLE.