Identification of the tubulointerstitial infiltrating immune cell landscape and immune marker related molecular patterns in lupus nephritis using bioinformatics analysis

The function and mechanism of LAPTM5 in diseases

The Lysosomal Protein Transmembrane 5 (LAPTM5) is a lysosomal transmembrane protein preferentially expressed in hematopoietic cells. The human LAPTM5 gene is located at position 1p34 and extends approximately 25 kb. Its protein includes five transmembrane domains, three PY motifs, and one UIM. The PY and UIM motifs can interact with various substrates, mediating sorting of proteins from Golgi to lysosome and subsequently participating in intracellular substrate transport and lysosomal stability regulation. Overexpression of LAPTM5 can induce lysosomal cell death (LCD), although the integrity of LAPTM5 protein is necessary for maintaining lysosome stability. Furthermore, LAPTM5 plays a role in autophagy activation during disease processes and has been confirmed to be closely associated with the regulation of immunity and inflammation. Therefore, LAPTM5 regulates a wide range of physiological processes and is involved in various diseases. This article summarizes the characteristics of the LAPTM5 gene and protein structure and provides a comprehensive review of the mechanisms involved in cell death, autophagy, immunity, and inflammation regulation. It emphasizes the significance of LAPTM5 in the clinical prevention and treatment of cardiovascular diseases, immune system disorders, viral infections, cancer, and other diseases, which could provide new therapeutic ideas and targets for human diseases.

Recognition of differently expressed genes and DNA methylation markers in patients with Lupus nephritis

Background and Objectives

Systemic lupus erythematosus (SLE) is distinguished by dysregulated immune system activity, resulting in a spectrum of clinical manifestations, with lupus nephritis being particularly prominent. This study endeavors to discern novel targets as potential therapeutic markers for this condition.

Methods

Weighted correlation network analysis (WGCNA) was used to construct the network and select the key hub genes in the co-expression module based on the gene expression dataset GSE81622. Subsequently, functional enrichment and pathway analysis were performed for SLE and lupus nephritis. In addition, also identify genes and differences in SLE with lupus nephritis and methylation site. Finally, qRT-PCR and western blot were used to verify the up-regulated expression levels of the selected key genes.

Results

Within the co-expression modules constructed by WGCNA, the MElightcyan module exhibited the strongest positive correlation with lupus nephritis (0.4, P = 0.003), while showing a weaker correlation with the control group SLE (0.058) and a negative correlation with the control group (-0.41, P = 0.002). Additionally, the MEgreenyellow module displayed the highest positive correlation with SLE (0.25), but its P value was 0.06, which did not reach statistical significance(P > 0.05). Furthermore, it had a negative correlation with the control group was (-0.38, P = 0.004). The module associated with lupus nephritis was characterized by processes such as neutrophil activation (neutrophil_activation), neutrophil degranulation (neutrophil_degranulation), neutrophil activation involved in immune response (neutrophil_activation_involved_in_immune_response), neutrophils mediated immune (neutrophil_mediated_immunity) and white blood cells degranulation (leukocyte_degranulation) and so on the adjustment of the process. Secondly, in the analysis of SLE samples, the identification of differentially expressed genes revealed 125 genes, with 49 being up-regulated and 76 down-regulated. In the case of lupus nephritis samples, 156 differentially expressed genes were discerned, include in 70 up-regulated and 86 down-regulated genes. When examining differential methylation sites, we observed 12432 such sites in the SLE sample analysis, encompassing 2260 hypermethylation sites and 10172 hypomethylation sites. In the lupus nephritis samples analysis, 9613 differential methylation sites were identified, comprising 4542 hypermethylation sites and 5071 hypomethylation sites. Substantiating our findings, experimental validation of the up-regulated genes in lupus nephritis confirmed increased levels of gene expression and protein expression for CEACAM1 and SLC2A5.

Conclusions

We have identified several genes, notably CEACAM1 and SLC2A5, as potential markers for lupus nephritis. Their elevated expression levels and reduced DNA methylation in lupus nephritis contribute to a more comprehensive understanding of the aberrant epigenetic regulation of expression in this condition. These findings hold significant implications for the diagnosis and therapeutic strategies of lupus nephritis.

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.

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.

The Role of the Oxidative State and Innate Immunity Mediated by TLR7 and TLR9 in Lupus Nephritis

Lupus nephritis (LN) is a severe complication of systemic lupus erythematosus (SLE) and is considered one of the leading causes of mortality. Multiple immunological pathways are involved in the pathogenesis of SLE, which makes it imperative to deepen our knowledge about this disease's immune-pathological complexity and explore new therapeutic targets. Since an altered redox state contributes to immune system dysregulation, this document briefly addresses the roles of oxidative stress (OS), oxidative DNA damage, antioxidant enzymes, mitochondrial function, and mitophagy in SLE and LN. Although adaptive immunity's participation in the development of autoimmunity is undeniable, increasing data emphasize the importance of innate immunity elements, particularly the Toll-like receptors (TLRs) that recognize nucleic acid ligands, in inflammatory and autoimmune diseases. Here, we discuss the intriguing roles of TLR7 and TLR9 in developing SLE and LN. Also included are the essential characteristics of conventional treatments and some other novel and little-explored alternatives that offer options to improve renal function in LN.

Title: Bioinformatic Identification of Genes Involved in Diabetic Nephropathy Fibrosis and their Clinical Relevance

Tubulointerstitial fibrosis is an important pathological feature of diabetic nephropathy that is associated with impaired renal function. However, the mechanism by which fibrosis occurs in diabetic nephropathy is unclear. Differentially expressed genes were identified from transcriptome profiles of renal tissue from diabetic patients and unilateral ureteral obstruction mice and intersected to obtain genes that may be involved in diabetic fibrosis. Biological function analysis and protein-protein interaction network analysis were performed. ROC curve and Pearson correlation analysis between hub genes were performed and glomerular filtration rate estimated. Finally, the RNA levels of hub genes were measured using real-time PCR. A total of 283 genes were identified as potentially involved in diabetic nephropathy fibrosis. TYROBP, CTSS, LCP2, LUM and TLR7 were identified as aberrantly expressed hub genes. Immune cell infiltration analysis demonstrated higher numbers of cytotoxic lymphocytes, B lineage cells, monocyte lineage cells, myeloid dendritic cells, neutrophils, and fibroblasts in the diabetic nephropathy group. The areas under ROC curves for TYROBP, CTSS, LCP2, LUM and TLR7 were 0.9167, 0.9583, 0.9917, 0.93333, and 0.9583, respectively (P < 0.001), and their correlation coefficients with estimated glomerular filtration rate were - 0.8332, - 0.752, - 0.7875, - 0.7567, and - 0.7136, respectively (P < 0.001). The RNA levels of TYROBP, CTSS, LUM and TLR7 were upregulated in high-glucose-treated human renal tubular epithelial cells (P < 0.005). Our study identified TYROBP, CTSS, LCP2, LUM and TLR7 as potentially involved in diabetic nephropathy fibrosis. Furthermore, TYROBP, CTSS, LUM and TLR7 may be associated with epithelial-mesenchymal transition of tubular epithelial cells.

Integrative analyses of biomarkers and pathways for diabetic nephropathy

Background: Diabetic nephropathy (DN) is a widespread diabetic complication and a major cause of terminal kidney disease. There is no doubt that DN is a chronic disease that imposes substantial health and economic burdens on the world's populations. By now, several important and exciting advances have been made in research on etiopathogenesis. Therefore, the genetic mechanisms underlying these effects remain unknown. Methods: The GSE30122, GSE30528, and GSE30529 microarray datasets were downloaded from the Gene Expression Omnibus database (GEO). Analyses of differentially expressed genes (DEGs), enrichment of gene ontology (GO), the Kyoto Encyclopedia of Genes and Genomes (KEGG), and gene set enrichment analysis (GSEA) were performed. Protein-protein interaction (PPI) network construction was completed by the STRING database. Hub genes were identified by Cytoscape software, and common hub genes were identified by taking intersection sets. The diagnostic value of common hub genes was then predicted in the GSE30529 and GSE30528 datasets. Further analysis was carried out on the modules to identify transcription factors and miRNA networks. As well, a comparative toxicogenomics database was used to assess interactions between potential key genes and diseases associated upstream of DN. Results: Samples from 19 DNs and 50 normal controls were identified in the GSE30122 dataset. 86 upregulated genes and 34 downregulated genes (a total of 120 DEGs). GO analysis showed significant enrichment in humoral immune response, protein activation cascade, complement activation, extracellular matrix, glycosaminoglycan binding, and antigen binding. KEGG analysis showed significant enrichment in complement and coagulation cascades, phagosomes, the Rap1 signaling pathway, the PI3K-Akt signaling pathway, and infection. GSEA was mainly enriched in the TYROBP causal network, the inflammatory response pathway, chemokine receptor binding, the interferon signaling pathway, ECM receptor interaction, and the integrin 1 pathway. Meanwhile, mRNA-miRNA and mRNA-TF networks were constructed for common hub genes. Nine pivotal genes were identified by taking the intersection. After validating the expression differences and diagnostic values of the GSE30528 and GSE30529 datasets, eight pivotal genes (TYROBP, ITGB2, CD53, IL10RA, LAPTM5, CD48, C1QA, and IRF8) were finally identified as having diagnostic values. Conclusion: Pathway enrichment analysis scores provide insight into the genetic phenotype and may propose molecular mechanisms of DN. The target genes TYROBP, ITGB2, CD53, IL10RA, LAPTM5, CD48, C1QA, and IRF8 are promising new targets for DN. SPI1, HIF1A, STAT1, KLF5, RUNX1, MBD1, SP1, and WT1 may be involved in the regulatory mechanisms of DN development. Our study may provide a potential biomarker or therapeutic locus for the study of DN.

NR4A2 may be a potential diagnostic biomarker for myocardial infarction: A comprehensive bioinformatics analysis and experimental validation

Background

Myocardial infarction is a well-established severe consequence of coronary artery disease. However, the lack of effective early biomarkers accounts for the lag time before clinical diagnosis of myocardial infarction. The present study aimed to predict critical genes for the diagnosis of MI by immune infiltration analysis and establish a nomogram.

Methods

Gene microarray data were downloaded from Gene Expression Omnibus (GEO). Differential expression analysis, single-cell sequencing, and disease ontology (DO) enrichment analysis were performed to determine the distribution of Differentially Expressed Genes (DEGs) in cell subpopulations and their correlation with MI. Next, the level of infiltration of 16 immune cells and immune functions and their hub genes were analyzed using a Single-sample Gene Set Enrichment Analysis (ssGSEA). In addition, the accuracy of critical markers for the diagnosis of MI was subsequently assessed using receiver operating characteristic curves (ROC). One datasets were used to test the accuracy of the model. Finally, the genes with the most diagnostic value for MI were screened and experimentally validated.

Results

335 DEGs were identified in GSE66360, including 280 upregulated and 55 downregulated genes. Single-cell sequencing results demonstrated that DEGs were mainly distributed in endothelial cells. DO enrichment analysis suggested that DEGs were highly correlated with MI. In the MI population, macrophages, neutrophils, CCR, and Parainflammation were significantly upregulated compared to the average population. NR4A2 was identified as the gene with the most significant diagnostic value in the immune scoring and diagnostic model. 191 possible drugs for the treatment of myocardial infarction were identified by drug prediction analysis. Finally, our results were validated by Real-time Quantitativepolymerase chain reaction and Western Blot of animal samples.

Conclusion

Our comprehensive in silico analysis revealed that NR4A2 has huge prospects for application in diagnosing patients with MI.

Application of weighted co-expression network analysis and machine learning to identify the pathological mechanism of Alzheimer's disease

Aberrant deposits of neurofibrillary tangles (NFT), the main characteristic of Alzheimer's disease (AD), are highly related to cognitive impairment. However, the pathological mechanism of NFT formation is still unclear. This study explored differences in gene expression patterns in multiple brain regions [entorhinal, temporal, and frontal cortex (EC, TC, FC)] with distinct Braak stages (0- VI), and identified the hub genes via weighted gene co-expression network analysis (WGCNA) and machine learning. For WGCNA, consensus modules were detected and correlated with the single sample gene set enrichment analysis (ssGSEA) scores. Overlapping the differentially expressed genes (DEGs, Braak stages 0 vs. I-VI) with that in the interest module, metascape analysis, and Random Forest were conducted to explore the function of overlapping genes and obtain the most significant genes. We found that the three brain regions have high similarities in the gene expression pattern and that oxidative damage plays a vital role in NFT formation via machine learning. Through further filtering of genes from interested modules by Random Forest, we screened out key genes, such as LYN, LAPTM5, and IFI30. These key genes, including LYN, LAPTM5, and ARHGDIB, may play an important role in the development of AD through the inflammatory response pathway mediated by microglia.

Mechanisms and modeling of wound repair in the intestinal epithelium

The intestinal epithelial barrier is susceptible to injury from insults, such as ischemia or infectious disease. The epithelium's ability to repair wounded regions is critical to maintaining barrier integrity. Mechanisms of intestinal epithelial repair can be studied with models that recapitulate the in vivo environment. This review focuses on in vitro injury models and intestinal cell lines utilized in such systems. The formation of artificial wounds in a controlled environment allows for the exploration of reparative physiology in cell lines modeling diverse aspects of intestinal physiology. Specifically, the use of intestinal cell lines, IPEC-J2, Caco-2, T-84, HT-29, and IEC-6, to model intestinal epithelium is discussed. Understanding the unique systems available for creating intestinal injury and the differences in monolayers used for in vitro work is essential for designing studies that properly capture relevant physiology for the study of intestinal wound repair.

Spine‑specific downregulation of LAPTM5 expression promotes the progression and spinal metastasis of estrogen receptor‑positive breast cancer by activating glutamine‑dependent mTOR signaling

Estrogen receptor-positive (ER⁺) breast cancer (BC) is a malignancy that is prone to metastasis to the spine, which is difficult to treat and often results in poor prognosis. However, the mechanism underlying the tumorigenesis and spinal metastasis of ER⁺ BC remains unclear. Lysosomal protein transmembrane 5 (LAPTM5) has been reported as a tumor suppressor in several types of cancer, but its role in ER⁺ BC has not been described. Here, by analyzing a gene sequencing dataset and ER⁺ BC tissues, tumor-adjacent normal tissues and spinal metastatic tissues from patients and mouse models, we found that LAPTM5 expression is negatively related to the progression and spinal metastasis of ER⁺ BC. Subsequently, in vitro experiments demonstrated that downregulation of LAPTM5 expression promoted the proliferation, migration, and chemoresistance of ER⁺ BC cells by activating glutamine-dependent mTOR signaling. A high level of CX3CL1 could inhibit LAPTM5 expression, explaining how ER⁺ BC metastasized to the spine. Thus, we found that LAPTM5 functions as a tumor suppressor in ER⁺ BC and that the CX3CL/CX3CR1/LAPTM5/glutamine axis mediates the spinal metastasis of ER⁺ BC. This axis may be a promising therapeutic target for ER⁺ BC.

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.

Comprehensive analysis of epigenetic modifications and immune-cell infiltration in tissues from patients with systemic lupus erythematosus

Aim: To explore potential abnormal epigenetic modifications and immune-cell infiltration in tissues from systemic lupus erythematosus (SLE) patients. Materials & methods: To utilize bioinformatics analysis and 'wet lab' methods to identify and verify differentially expressed genes in multiple targeted organs in SLE. Results: Seven key genes, IFI44, IFI44L, IFIT1, IFIT3, PLSCR1, RSAD2 and OAS2, which are regulated by epigenetics and may be involved in the pathogenesis of SLE, are identified by combined long noncoding RNA-miRNA-mRNA network analysis and DNA methylation analysis. The results of quantitative reverse transcription PCR, immunohistochemistry and DNA methylation analysis confirmed the potential of these genes as biomarkers. Conclusion: This study reveals the potential mechanisms in SLE from epigenetic modifications and immune-cell infiltration, providing diagnostic biomarkers and therapeutic targets for SLE.