Immune cell infiltration characteristics and related core genes in lupus nephritis: results from bioinformatic analysis

LncRNA XIST/miR-381-3P/STAT1 axis as a potential biomarker for lupus nephritis

OBJECTIVE

We aim to investigate the potential roles of key genes in the development of lupus nephritis (LN), screen key biomarkers, and construct the lncRNA XIST/miR-381-3P/STAT1 axis by using bioinformatic prediction combined with clinical validation, thereby providing new targets and insights for clinical research.

METHODS

Gene expression microarrays GSE157293 and GSE112943 were downloaded from the GEO database to obtain differentially expressed genes (DEGs), followed by enrichment analyses on these DEGs, which were enriched and analyzed to construct a protein-protein interaction (PPI) network to screen core genes. The lncRNA-miRNA-mRNA regulatory network was predicted and constructed based on the miRNA database. 37 female patients with systemic lupus erythematosus (SLE) were recruited to validate the bioinformatics results by exploring the diagnostic value of the target ceRNA axis in LN by dual luciferase and real-time fluorescence quantitative PCR (RT-qPCR) and receiver operating characteristic (ROC).

RESULTS

The data represented that a total of 133 differential genes were screened in the GSE157293 dataset and 2869 differential genes in the GSE112943 dataset, yielding a total of 26 differentially co-expressed genes. Six core genes (STAT1, OAS2, OAS3, IFI44, DDX60, and IFI44L) were screened. Biological functional analysis identified key relevant pathways in LN. ROC curve analysis suggested that lncRNA XIST, miR-381-3P, and STAT1 could be used as potential molecular markers to assist in the diagnosis of LN.

CONCLUSION

STAT1 is a key gene in the development of LN. In conclusion, lncRNA XIST, miR-381-3P, and STAT1 can be used as new molecular markers to assist in the diagnosis of LN, and the lncRNA XIST/miR-381-3P/STAT1 axis may be a potential therapeutic target for LN.

scSemiProfiler: Advancing large-scale single-cell studies through semi-profiling with deep generative models and active learning

Single-cell sequencing is a crucial tool for dissecting the cellular intricacies of complex diseases. Its prohibitive cost, however, hampers its application in expansive biomedical studies. Traditional cellular deconvolution approaches can infer cell type proportions from more affordable bulk sequencing data, yet they fall short in providing the detailed resolution required for single-cell-level analyses. To overcome this challenge, we introduce "scSemiProfiler", an innovative computational framework that marries deep generative models with active learning strategies. This method adeptly infers single-cell profiles across large cohorts by fusing bulk sequencing data with targeted single-cell sequencing from a few rigorously chosen representatives. Extensive validation across heterogeneous datasets verifies the precision of our semi-profiling approach, aligning closely with true single-cell profiling data and empowering refined cellular analyses. Originally developed for extensive disease cohorts, "scSemiProfiler" is adaptable for broad applications. It provides a scalable, cost-effective solution for single-cell profiling, facilitating in-depth cellular investigation in various biological domains.

Combined application of mesenchymal stem cells and different glucocorticoid dosing alleviates osteoporosis in SLE murine models

OBJECTIVE

Bone mesenchymal stem cells (BMSCs) have been tentatively applied in the treatment of glucocorticoid-induced osteoporosis (GIOP) and systemic lupus erythematosus (SLE). However, the effects of BMSCs on osteoporosis within the context of glucocorticoid (GC) application in SLE remain unclear. Our aim was to explore the roles of BMSCs and different doses of GC interventions on osteoporosis in SLE murine models.

METHODS

MRL/MpJ-Faslpr mice were divided into eight groups with BMSC treatment and different dose of GC intervention. Three-dimensional imaging analysis and hematoxylin and eosin (H&E) staining were performed to observe morphological changes. The concentrations of osteoprotegerin (OPG) and receptor activator of nuclear factor κB ligand (RANKL) in serum were measured by enzyme-linked immunosorbent assay (ELISA). The subpopulation of B cells and T cells in bone marrows and spleens were analyzed by flow cytometry. Serum cytokines and chemokines were assessed using Luminex magnetic bead technology.

RESULTS

BMSCs ameliorated osteoporosis in murine SLE models by enhancing bone mass, improving bone structure, and promoting bone formation through increased bone mineral content and optimization of trabecular morphology. BMSC and GC treatments reduced the number of B cells in bone marrows, but the effect was not significant in spleens. BMSCs significantly promoted the expression of IL-10 while reducing IL-18. Moreover, BMSCs exert immunomodulatory effects by reducing Th17 expression and rectifying the Th17/Treg imbalance.

CONCLUSION

BMSCs effectively alleviate osteoporosis induced by SLE itself, as well as osteoporosis resulting from SLE combined with various doses of GC therapy. The therapeutic effects of BMSCs appear to be mediated by their influence on bone marrow B cells, T cell subsets, and associated cytokines. High-dose GC treatment exerts a potent anti-inflammatory effect but may hinder the immunotherapeutic potential of BMSCs. Our research may offer valuable guidance to clinicians regarding the use of BMSC treatment in SLE and provide insights into the judicious use of GCs in clinical practice.

Potential applications of dual haptoglobin expression in the reclassification and treatment of hepatocellular carcinoma

HCC is a malignancy characterized by high incidence and mortality rates. Traditional classifications of HCC primarily rely on tumor morphology, phenotype, and multicellular molecular levels, which may not accurately capture the cellular heterogeneity within the tumor. This study integrates scRNA-seq and bulk RNA-seq to spotlight HP as a critical gene within a subgroup of HCC malignant cells. HP is highly expressed in HCC malignant cells and lowly expressed in T cells. Within malignant cells, elevated HP expression interacts with C3, promoting Th1-type responses via the C3/C3AR1 axis. In T cells, down-regulating HP expression favors the expression of Th1 cell-associated marker genes, potentially enhancing Th1-type responses. Consequently, we developed a "HP-promoted Th1 response reclassification" gene set, correlating higher activity scores with improved survival rates in HCC patients. Additionally, four predictive models for neoadjuvant treatment based on HP and C3 expression were established: 1) Low HP and C3 expression with high Th2 cell infiltration; 2) High HP and low C3 expression with high Th2 cell infiltration; 3) High HP and C3 expression with high Th1 cell infiltration; 4) Low HP and high C3 expression with high Th1 cell infiltration. In conclusion, the HP gene selected from the HCC malignant cell subgroup (Malignant_Sub 6) might serve as a potential ally against the tumor by promoting Th1-type immune responses. The establishment of the "HP-promoted Th1 response reclassification" gene set offers predictive insights for HCC patient survival prognosis and neoadjuvant treatment efficacy, providing directions for clinical treatments.

Exploring T-cell exhaustion features in Acute myocardial infarction for a Novel Diagnostic model and new therapeutic targets by bio-informatics and machine learning

BACKGROUND

T-cell exhaustion (TEX), a condition characterized by impaired T-cell function, has been implicated in numerous pathological conditions, but its role in acute myocardial Infarction (AMI) remains largely unexplored. This research aims to identify and characterize all TEX-related genes for AMI diagnosis.

METHODS

By integrating gene expression profiles, differential expression analysis, gene set enrichment analysis, protein-protein interaction networks, and machine learning algorithms, we were able to decipher the molecular mechanisms underlying TEX and its significant association with AMI. In addition, we investigated the diagnostic validity of the leading TEX-related genes and their interactions with immune cell profiles. Different types of candidate small molecule compounds were ultimately matched with TEX-featured genes in the "DrugBank" database to serve as potential therapeutic medications for future TEX-AMI basic research.

RESULTS

We screened 1725 differentially expressed genes (DEGs) from 80 AMI samples and 71 control samples, identifying 39 differential TEX-related transcripts in total. Functional enrichment analysis identified potential biological functions and signaling pathways associated with the aforementioned genes. We constructed a TEX signature containing five hub genes with favorable prognostic performance using machine learning algorithms. In addition, the prognostic performance of the nomogram of these five hub genes was adequate (AUC between 0.815 and 0.995). Several dysregulated immune cells were also observed. Finally, six small molecule compounds which could be the future therapeutic for TEX in AMI were discovered.

CONCLUSION

Five TEX diagnostic feature genes, CD48, CD247, FCER1G, TNFAIP3, and FCGRA, were screened in AMI. Combining these genes may aid in the early diagnosis and risk prediction of AMI, as well as the evaluation of immune cell infiltration and the discovery of new therapeutics.

Identification of disease-specific genes related to immune infiltration in nonalcoholic steatohepatitis using machine learning algorithms

To identify disease signature genes associated with immune infiltration in nonalcoholic steatohepatitis (NASH), we downloaded 2 publicly available gene expression profiles, GSE164760 and GSE37031, from the gene expression omnibus database. These profiles represent human NASH and control samples and were used for differential genes (DEGs) expression screening. Two machine learning methods, the Least Absolute Shrinkage and Selection Operator regression model and Support Vector Machine Recursive Feature Elimination, were used to identify candidate disease signature genes. The CIBERSORT deconvolution algorithm was employed to analyze the infiltration of 22 immune cell types in NASH. Additionally, we constructed a NASH cell model using HepG2 cells treated with oleic acid and free fatty acids. The construction of the cell model was verified using oil red O staining, and Western blotting was used to detect the protein expression of the disease signature genes in both control and model groups. As a result, a total of 262 DEGs were identified. These DEGs were primarily associated with metal ion transmembrane transporter activity, sodium ion transmembrane transporter protein activity, calcium ion, and neuroactive ligand-receptor interactions. FOS, IGFBP2, dual-specificity phosphatase 1 (DUSP1), and IKZF3 were identified as disease signature genes of NASH by the least absolute shrinkage and selection operator and Support Vector Machine Recursive Feature Elimination algorithms for DEGs analysis. The receiver operating characteristic curves showed that FOS, IGFBP2, DUSP1, and IKZF3 had good diagnostic value (area under receiver operating characteristic curve > 0.8). These findings were validated in the GSE89632 dataset and through cellular assays. Immunocyte infiltration analysis revealed that NASH was associated with CD8 T cells, CD4 T cells, follicular helper T cells, resting NK cells, eosinophils, regulatory T cells, and γδ T cells. The FOS, IGFBP2, DUSP1, and IKZF3 genes were specifically associated with follicular helper T cells. Lipid droplet aggregation significantly increased in HepG2 cells treated with oleic acid and free fatty acids, indicating successful construction of the cell model. In this model, the expression of FOS, IGFBP2, and DUSP1 was significantly decreased, while that of IKZF3 was significantly elevated (P < .01, P < .001) compared with the control group. Therefore, FOS, IGFBP2, DUSP1, and IKZF3 can be considered as disease signature genes associated with immune infiltration in NASH.

Integrative analysis and risk model construction for super‑enhancer‑related immune genes in clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) is the most common type of kidney cancer associated with poor prognosis, and accounts for the majority of RCC-related deaths. The lack of comprehensive diagnostic and prognostic biomarkers has limited further understanding of the pathophysiology of ccRCC. Super-enhancers (SEs) are congregated enhancer clusters that have a key role in tumor processes such as epithelial-mesenchymal transition, metabolic reprogramming, immune escape and resistance to apoptosis. RCC may also be immunogenic and sensitive to immunotherapy. In the present study, an Arraystar human SE-long non-coding RNA (lncRNA) microarray was first employed to profile the differentially expressed SE-lncRNAs and mRNAs in 5 paired ccRCC and peritumoral tissues and to identify SE-related genes. The overlap of these genes with immune genes was then determined to identify SE-related immune genes. A model for predicting clinical prognosis and response to immunotherapy was built following the comprehensive analysis of a ccRCC gene expression dataset from The Cancer Genome Atlas (TCGA) database. The patients from TCGA were divided into high- and low-risk groups based on the median score derived from the risk model, and the Kaplan-Meier survival analysis showed that the low-risk group had a higher survival probability. In addition, according to the receiver operating characteristic curve analysis, the risk model had more advantages than other clinical factors in predicting the overall survival (OS) rate of patients with ccRCC. Using this model, it was demonstrated that the high-risk group had a more robust immune response. Furthermore, 61 potential drugs with half-maximal inhibitory concentration values that differed significantly between the two patient groups were screened to investigate potential drug treatment of ccRCC. In summary, the present study provided a novel index for predicting the survival probability of patients with ccRCC and may provide some insights into the mechanisms through which SE-related immune genes influence the diagnosis, prognosis and potential treatment drugs of ccRCC.

Monocytes and Macrophages in Kidney Disease and Homeostasis

The monocyte-macrophage lineage of inflammatory cells is characterized by significant morphologic and functional plasticity. Macrophages have broad M1 and M2 phenotype subgroups with distinctive functions and dual reno-toxic and reno-protective effects. Macrophages are a major contributor to injury in immune-complex-mediated, as well as pauci-immune, glomerulonephritis. Macrophages are also implicated in tubulointerstitial and vascular disease, though there have not been many human studies. Patrolling monocytes in the intravascular compartment have been reported in auto-immune injury in the renal parenchyma, manifesting as acute kidney injury. Insights into the pathogenetic roles of macrophages in renal disease suggest potentially novel therapeutic and prognostic biomarkers and targeted therapy. This review provides a concise overview of the macrophage-induced pathogenetic mechanism as a background for the latest findings about macrophages' roles in different renal compartments and common renal diseases.

Identification of potential cell death-related biomarkers for diagnosis and treatment of osteoporosis

BACKGROUND

This study aimed to identify potential biomarkers for the diagnosis and treatment of osteoporosis (OP).

METHODS

Data sets were downloaded from the Gene Expression Omnibus database, and differentially programmed cell death-related genes were screened. Functional analyses were performed to predict the biological processes associated with these genes. Least absolute shrinkage and selection operator (LASSO), support vector machine (SVM), and random forest (RF) machine learning algorithms were used to screen for characteristic genes, and receiver operating characteristics were used to evaluate the diagnosis of disease characteristic gene values. Gene set enrichment analysis (GSEA) and single-sample GSEA were conducted to analyze the correlation between characteristic genes and immune infiltrates. Cytoscape and the Drug Gene Interaction Database (DGIdb) were used to construct the mitochondrial RNA-mRNA-transcription factor network and explore small-molecule drugs. Reverse transcription real-time quantitative PCR (RT-qPCR) analysis was performed to evaluate the expression of biomarker genes in clinical samples.

RESULTS

In total, 25 differential cell death genes were identified. Among these, two genes were screened using the LASSO, SVM, and RF algorithms as characteristic genes, including BRSK2 and VPS35. In GSE56815, the area under the receiver operating characteristic curve of BRSK2 was 0.761 and that of VPS35 was 0.789. In addition, immune cell infiltration analysis showed that BRSK2 positively correlated with CD56dim natural killer cells and negatively correlated with central memory CD4 + T cells. Based on the data from DGIdb, hesperadin was associated with BRSK2, and melagatran was associated with VPS35. BRSK2 and VPS35 were expectably upregulated in OP group compared with controls (all p < 0.05).

CONCLUSIONS

BRSK2 and VPS35 may be important diagnostic biomarkers of OP.

Anoikis-related gene CDKN2A predicts prognosis and immune response and mediates proliferation and migration in thyroid carcinoma

Thyroid carcinoma (THCA) is a tumor commonly occurring in the endocrine system, and its incidence rate is increasing yearly. Anoikis is a type of cell death involved in the carcinogenesis process. This study aimed to investigate the prognosis and immune correlations of anoikis in THCA. Our study used several bioinformatics algorithms (co-expression analysis, univariate and multivariate Cox analysis) to screen anoikis-related genes (ARGs) to construct risk models. Through receiver operating characteristic (ROC) curve, nomogram, and independent prognostic analysis found that the constructed model had ideal predictive value for THCA. The consensus clustering method was used to divide ARG patterns into three subgroups, and there were significant differences in survival among the three subgroups. The CIBERSORT algorithm demonstrated strong correlations among immune infiltrating cells, prognostic genes, and risk scores. Univariate and multivariate Cox analysis showed that CDKN2A is an independent prognostic gene. Basic experiments (immunohistochemistry, qRT-PCR, etc.) showed that the expression levels of CDKN2A mRNA and protein were highly expressed in THCA, which was consistent with the results of bioinformatics analysis. In vitro, the knockdown of CDKN2A significantly inhibited the proliferation and migration of THCA cells. In summary, our study utilized eight ARGs to construct an accurate risk model. ARGs, especially CDKN2A, play a crucial role in the occurrence and development of THCA and can become potential targets for treating THCA patients.

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.

Identification of the Key Genes of Immune Infiltration in Dilated Cardiomyopathy

Dilated cardiomyopathy (DCM) is a common cause of heart failure. In this study, we screened the immune infiltration-related genes associated with DCM to explore the potential molecular mechanisms and provide a basis for the early diagnosis and development of new immunotherapeutic targets. A dataset related to DCM was downloaded from the Gene Expression Omnibus (GEO) database. R software was applied to the genetic differential analysis of patients with DCM and healthy individuals, and the obtained differential expressed genes (DEGs) were screened for differentially expressed immune-related genes (DEIRGs) after comparison with the immune microsatellite database. Gene functional analysis established a protein interaction network (PPI). The immune infiltration in patients with DCM versus normal controls was assessed using the CIBERSORT algorithm, the hub genes were screened using the MOCDE app, and the hubs were validated in multiple datasets. A total of 246 DEGs were screened (adj. P < 0.05 and |logFC| > 0.3), and a total of 170 DEIRGs were compared. Gene Ontology analysis showed significant (adj. P < 0.05) Biological Process entries of 591, Cellular Component of 10, and Molecular Function of 39; Kyoto Encyclopedia of Genes and Genomes showed 20 significant entries, mainly focused on cytokines involved in immune-related response, etc. A protein interaction network comprising 28 hub DEGs was constructed in combination with the PPI network interactions. DEIRG was mainly distributed in the T-cell receptor pathway by immune infiltration detection analysis, and significant changes in central memory T-cells were found by analyzing T-cell-related subpathways, where INSR, HLA-B, IFITM1, and HBEGF were significantly differentially expressed. We selected 632 hospitalized patients for validation and found that INSR and HLA-B expression were associated with DCM development by Nomogram. The expression of HLA-B in peripheral blood T-cells was higher in DCM patients than in the normal group, as verified by qRT-PCR. However, the detailed mechanism needs to be further explored.

Identification of FCER1G as a cyclosporin A plus corticosteroid sensitization gene in female patients with Vogt-Koyanagi-Harada disease

The resistance development of the combination regimen of corticosteroids (CS) with cyclosporin A (CsA) leads to therapeutic failure of some patients with autoimmune diseases. In the male patients with Vogt-Koyanagi-Harada (VKH) disease, we have identified RPS4Y1 as an important resistance gene of the regimen and a functional mediator of chlorambucil (CLB). However, it remains unclear what is responsible for the resistance in female patients. In the present study, we performed RNA sequencing, tandem mass tag (TMT) proteomics, gain- and loss-of-function assays and rescue assays to screen and validate potential resistant mediators. The results showed that only Fc epsilon receptor Ig (FCER1G) exhibited significantly differential expression in CD4+ T cells among female CsA & CS resistant, sensitive and CLB & CsA & CS treated patients at transcription and protein levels. Inhibition of FCER1G was demonstrated to modulate CD4+ T cell resistance to CsA & CS in female patients. Importantly, the inhibition was mediated by elevated DNA methylation in the promoter region of the FCER1G gene. Moreover, we found that the salvage effect of CLB on CsA & CS resistance was mediated by an increased FCER1G expression via DNA demethylation in female patients. Taken together, the downregulation of FCER1G due to DNA hypermethylation is responsible for the resistance to CsA & CS and CLB reverses this resistance by inducing FCER1G expression via DNA demethylation in female patients. Modulation of FCER1G would be a promising sensitization strategy in female patients with resistance to CsA & CS.

Comparative Transcriptomic Analysis Revealed Potential Differential Mechanisms of Grass Carp Reovirus Pathogenicity

Grass carp reovirus (GCRV), one of the most serious pathogens threatening grass carp (Ctenopharyngodon idella), can lead to grass carp hemorrhagic disease (GCHD). Currently, GCRV can be divided into three genotypes, but the comparison of their pathogenic mechanisms and the host responses remain unclear. In this study, we utilized the Ctenopharyngodon idella kidney (CIK) model infected with GCRV to conduct comparative studies on the three genotypes. We observed a cytopathic effect (CPE) in the GCRV-I and GCRV-III groups, whereas the GCRV-II group did not show any CPE. Moreover, a consistent trend in the mRNA expression levels of antiviral-related genes across all experimental groups of CIK cells was detected via qPCR and further explored through RNA-seq analysis. Importantly, GO/KEGG enrichment analysis showed that GCRV-I, -II, and -III could all activate the immune response in CIK cells, but GCRV-II induced more intense immune responses. Intriguingly, transcriptomic analysis revealed a widespread down-regulation of metabolism processes such as steroid biosynthesis, butanoate metabolism, and N-Glycan biosynthesis in infected CIK cells. Overall, our results reveal the CIK cells showed unique responses in immunity and metabolism in the three genotypes of GCRV infection. These results provide a theoretical basis for understanding the pathogenesis and prevention and control methods of GCRV.

Deconvolution of whole blood transcriptomics identifies changes in immune cell composition in patients with systemic lupus erythematosus (SLE) treated with mycophenolate mofetil

BACKGROUND

Systemic lupus erythematosus (SLE) is a clinically and biologically heterogeneous autoimmune disease. We explored whether the deconvolution of whole blood transcriptomic data could identify differences in predicted immune cell frequency between active SLE patients, and whether these differences are associated with clinical features and/or medication use.

METHODS

Patients with active SLE (BILAG-2004 Index) enrolled in the BILAG-Biologics Registry (BILAG-BR), prior to change in therapy, were studied as part of the MASTERPLANS Stratified Medicine consortium. Whole blood RNA-sequencing (RNA-seq) was conducted at enrolment into the registry. Data were deconvoluted using CIBERSORTx. Predicted immune cell frequencies were compared between active and inactive disease in the nine BILAG-2004 domains and according to immunosuppressant use (current and past).

RESULTS

Predicted cell frequency varied between 109 patients. Patients currently, or previously, exposed to mycophenolate mofetil (MMF) had fewer inactivated macrophages (0.435% vs 1.391%, p = 0.001), naïve CD4 T cells (0.961% vs 2.251%, p = 0.002), and regulatory T cells (1.858% vs 3.574%, p = 0.007), as well as a higher proportion of memory activated CD4 T cells (1.826% vs 1.113%, p = 0.015), compared to patients never exposed to MMF. These differences remained statistically significant after adjusting for age, gender, ethnicity, disease duration, renal disease, and corticosteroid use. There were 2607 differentially expressed genes (DEGs) in patients exposed to MMF with over-representation of pathways relating to eosinophil function and erythrocyte development and function. Within CD4 + T cells, there were fewer predicted DEGs related to MMF exposure. No significant differences were observed for the other conventional immunosuppressants nor between patients according disease activity in any of the nine organ domains.

CONCLUSION

MMF has a significant and persisting effect on the whole blood transcriptomic signature in patients with SLE. This highlights the need to adequately adjust for background medication use in future studies using whole blood transcriptomics.

Comprehensive immune landscape of lung-resident memory CD8+ T cells after influenza infection and reinfection in a mouse model

Background

Resident phenotypic memory CD8⁺ T cells are crucial for immune defense against pathogens. However, little is known about the potential transitions and regulation mechanisms of their function after influenza virus infection and reinfection. In this study, we utilized integrated transcriptome data and in vivo experiments to investigate the key characteristics behind it.

Methods

Two single-cell RNA sequencing (scRNA-seq) datasets of lung CD8⁺ T cells and one RNA-seq dataset of lung tissue after infection or reinfection were included. After Seurat procedures classifying CD8⁺ T subsets, the scCODE algorithm was used to identify the differentially expressed genes for GSVA, GO, and KEGG pathway enrichment. Monocle 3 and CellChat were used to infer pseudotime cell trajectory and cell interactions. The ssGSEA method was used to estimate the relative proportions of immune cells. The findings were confirmed with a mouse model via flow cytometry and RT-PCR analysis.

Results

Our study refined the landscape of CD8⁺ T-cell subsets in the lung, showing that CD8⁺ Trm cells accumulated in the lung within 14 days after influenza infection. The classical CD8⁺ Trm cells co-expressed a high level of CD49a and even maintained 90 days after primary infection. The ratio of CD8⁺ Trm cells decreased 1 day after influenza reinfection, which may be parallel with their potential transition into effector types, as observed in trajectory inference analysis. KEGG analysis suggested that PD-L1 expression and PD-1 checkpoint pathway were upregulated in CD8⁺ Trm cells on day 14 after infection. GO and GSVA analyses revealed that PI3K-Akt-mTOR and type I interferon signaling pathways were enriched in CD8⁺ Tem and Trm cells after reinfection. Additionally, CCL signaling pathways were involved in cell interaction between CD8⁺ Trm cells and other cells, with Ccl4-Ccr5 and Ccl5-Ccr5 ligand/receptor pairs being important between CD8⁺ Trm and other memory subsets after infection and reinfection.

Conclusion

Our data suggest that resident memory CD8⁺ T cells with CD49a co-expression account for a large proportion after influenza infection, and they can be rapidly reactivated against reinfection. Function differences exist in CD8⁺ Trm and Tem cells after influenza infection and reinfection. Ccl5-Ccr5 ligand/receptor pair is important in cell interactions between CD8⁺ Trm and other subsets.

Machine learning and bioinformatics framework integration reveal potential characteristic genes related to immune cell infiltration in preeclampsia

Introduction: Preeclampsia is a disease that affects both the mother and child, with serious consequences. Screening the characteristic genes of preeclampsia and studying the placental immune microenvironment are expected to explore specific methods for the treatment of preeclampsia and gain an in-depth understanding of the pathological mechanism of preeclampsia. Methods: We screened for differential genes in preeclampsia by using limma package. Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, disease ontology enrichment, and gene set enrichment analyses were performed. Analysis and identification of preeclampsia biomarkers were performed by using the least absolute shrinkage and selection operator regression model, support vector machine recursive feature elimination, and random forest algorithm. The CIBERSORT algorithm was used to analyze immune cell infiltration. The characteristic genes were verified by RT-qPCR. Results: We identified 73 differential genes, which mainly involved in reproductive structure and system development, hormone transport, etc. KEGG analysis revealed emphasis on cytokine-cytokine receptor interactions and interleukin-17 signaling pathways. Differentially expressed genes were dominantly concentrated in endocrine system diseases and reproductive system diseases. Our findings suggest that LEP, SASH1, RAB6C, and FLT1 can be used as placental markers for preeclampsia and they are associated with various immune cells. Conclusion: The differentially expressed genes in preeclampsia are related to inflammatory response and other pathways. Characteristic genes, LEP, SASH1, RAB6C, and FLT1 can be used as diagnostic and therapeutic targets for preeclampsia, and they are associated with immune cell infiltration. Our findings contribute to the pathophysiological mechanism exploration of preeclampsia. In the future, the sample size needs to be expanded for data analysis and validation, and the immune cells need to be further validated.

Decipher the Immunopathological Mechanisms and Set Up Potential Therapeutic Strategies for Patients with Lupus Nephritis

Lupus nephritis (LN) is one of the most severe complications in patients with systemic lupus erythematosus (SLE). Traditionally, LN is regarded as an immune complex (IC) deposition disease led by dsDNA-anti-dsDNA-complement interactions in the subendothelial and/or subepithelial basement membrane of glomeruli to cause inflammation. The activated complements in the IC act as chemoattractants to chemically attract both innate and adaptive immune cells to the kidney tissues, causing inflammatory reactions. However, recent investigations have unveiled that not only the infiltrating immune-related cells, but resident kidney cells, including glomerular mesangial cells, podocytes, macrophage-like cells, tubular epithelial cells and endothelial cells, may also actively participate in the inflammatory and immunological reactions in the kidney. Furthermore, the adaptive immune cells that are infiltrated are genetically restricted to autoimmune predilection. The autoantibodies commonly found in SLE, including anti-dsDNA, are cross-reacting with not only a broad spectrum of chromatin substances, but also extracellular matrix components, including α-actinin, annexin II, laminin, collagen III and IV, and heparan sulfate proteoglycan. Besides, the glycosylation on the Fab portion of IgG anti-dsDNA antibodies can also affect the pathogenic properties of the autoantibodies in that α-2,6-sialylation alleviates, whereas fucosylation aggravates their nephritogenic activity. Some of the coexisting autoantibodies, including anti-cardiolipin, anti-C1q, anti-ribosomal P autoantibodies, may also enhance the pathogenic role of anti-dsDNA antibodies. In clinical practice, the identification of useful biomarkers for diagnosing, monitoring, and following up on LN is quite important for its treatments. The development of a more specific therapeutic strategy to target the pathogenic factors of LN is also critical. We will discuss these issues in detail in the present article.

Machine learning identifies ferroptosis-related genes as potential diagnostic biomarkers for osteoarthritis

Background

Osteoarthritis (OA) is one of the most common forms of degenerative arthritis and a major cause of pain and disability. Ferroptosis, a novel mode of cell death, has been verified to participate in the development of OA, but its mechanism is still unclear. This paper analyzed the ferroptosis-related genes (FRGs) in OA and explored their potential clinical value.

Methods

We downloaded data through the GEO database and screened for DEGs. Subsequently, FRGs were obtained using two machine learning methods, LASSO regression and SVM-RFE. The accuracy of the FRGs as disease diagnosis was identified using ROC curves and externally validated. The CIBERSORT analyzed the immune microenvironment rug regulatory network constructed through the DGIdb. The competitive endogenous RNA (ceRNA) visualization network was constructed to search for possible therapeutic targets. The expression levels of FRGs were verified by qRT-PCR and immunohistochemistry.

Results

In this study, we found 4 FRGs. The ROC curve showed that the combined 4 FRGs had the highest diagnostic value. Functional enrichment analysis showed that the 4 FRGs in OA could influence the development of OA through biological oxidative stress, immune response, and other processes. qRT-PCR and immunohistochemistry verified the expression of these key genes, further confirming our findings. Monocytes and macrophages are heavily infiltrated in OA tissues, and the persistent state of immune activation may promote the progression of OA. ETHINYL ESTRADIOL was a possible targeted therapeutic agent for OA. Meanwhile, ceRNA network analysis identified some lncRNAs that could regulate the FRGs.

Conclusion

We identify 4 FRGs (AQP8, BRD7, IFNA4, and ARHGEF26-AS1) closely associated with bio-oxidative stress and immune response, which may become early diagnostic and therapeutic targets for OA.

Artificial intelligence and high-dimensional technologies in the theragnosis of systemic lupus erythematosus

Systemic lupus erythematosus is a complex, systemic autoimmune disease characterised by immune dysregulation. Pathogenesis is multifactorial, contributing to clinical heterogeneity and posing challenges for diagnosis and treatment. Although strides in treatment options have been made in the past 15 years, with the US Food and Drug Administration approval of belimumab in 2011, there are still many patients who have inadequate responses to therapy. A better understanding of underlying disease mechanisms with a holistic and multiparametric approach is required to improve clinical assessment and treatment. This Review discusses the evolution of genomics, epigenomics, transcriptomics, and proteomics in the study of systemic lupus erythematosus and ways to amalgamate these silos of data with a systems-based approach while also discussing ways to strengthen the overall process. These mechanistic insights will facilitate the discovery of functionally relevant biomarkers to guide rational therapeutic selection to improve patient outcomes.

Biomarkers Associated with Immune Checkpoint, N6-Methyladenosine, and Ferroptosis in Patients with Restenosis

Purpose

This study aimed to identify potential diagnostic markers of restenosis after stent implantation and to determine their association with immune checkpoint, ferroptosis, and N6-methyladenosine (m6A).

Patients and methods

Microarray data were downloaded from the National Center for Biotechnology Information (NCBI: GSE46560 and GSE48060 datasets) to identify differentially expressed genes (DEGs) between in-stent restenosis and no-restenosis samples. We then conducted systematic functional enrichment analyses of the DEGs based on Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG), and further predicted the interactions of different proteins using the Search Tool for the Retrieval of Interacting Genes (STRING). We used the MCC and MCODE algorithms in the cytoHubba plug-in to screen three key genes in the network, and employed receiver operating characteristic (ROC) curves to determine their diagnostic significance using a multiscale curvature classification algorithm. Next, we investigated the relationships between these target genes, immune checkpoint, ferroptosis, and m6A. Finally, quantitative real-time polymerase chain reaction (qRT-PCR) was used to verify the above results.

Results

We identified 62 upregulated genes and 243 downregulated genes. Based on GO, KEGG, and screening results, EEF1D, RPL36, and RPSA are promising genes for predicting restenosis. In addition, the methylation of YTHDF2, the ferroptosis-related gene GLS2, and the immune checkpoint-related gene CTLA4 were observed to be associated with restenosis. The qRT-PCR test confirmed that RPSA and RPL36 are useful diagnostic markers of the restenosis that can provide new insights for future studies on its occurrence and molecular mechanisms.

Conclusion

We found that RPSA and RPL36, as useful diagnostic markers of restenosis, can provide new insights for future studies on its occurrence and molecular mechanisms.

IRF8 and its related molecules as potential diagnostic biomarkers or therapeutic candidates and immune cell infiltration characteristics in steroid-induced osteonecrosis of the femoral head

PURPOSE

Steroid-induced osteonecrosis of the femoral head (SONFH) was a refractory orthopedic hip joint disease in the young and middle-aged people, but the pathogenesis of SONFH remained unclear. We aimed to identify the potential genes and screen potential therapeutic compounds for SONFH.

METHODS

The microarray was obtained for blood tissue from the GEO database, and then it identifies differentially expressed genes (DEGs). The DEGs were analyzed to obtain the differences in immune cell infiltration. The gene functional enrichment analysis of SONFH was analyzed. The PPI of DEGs was identified through the STRING database, and the cluster modules and hub genes were ascertained using MCODE and CytoHubba, and the ROC curve of hub genes was analyzed, and the tissues distribution of hub genes was understood by the HPA, Bgee and BioGPS databases. The hub genes and target miRNAs and corresponding upstream lncRNAs were predicted by TargetScan, miRDB and ENCORI database. Subsequently, we used CMap, DGIdb and L1000FWD databases to identify several potential therapeutic molecular compounds for SONFH. Finally, the AutoDockTools Vina, PyMOL and Discovery Studio were employed for molecular docking analyses between compounds and hub genes.

RESULTS

The microarray dataset GSE123568 was obtained related to SONFH. There were 372 DEGs including 197 upregulated genes and 175 downregulated genes by adjusted P value < 0.01 and |log₂FC|> 1. Several significant GSEA enrichment analysis and biological processes and KEGG pathway associated with SONFH were identified, which were significantly related to cytoskeleton organization, nucleobase-containing compound catabolic process, NOD-like receptor signaling pathway, MAPK signaling pathway, FoxO signaling pathway, neutrophil-mediated immunity, neutrophil degranulation and neutrophil activation involved in immune response. Activated T cells CD4 memory, B cells naïve, B cells memory, T cells CD8 and T cells gamma delta might be involved in the occurrence and development of SONFH. Three cluster modules were identified in the PPI network, and eleven hub genes including FPR2, LILRB2, MNDA, CCR1, IRF8, TYROBP, TLR1, HCK, TLR8, TLR2 and CCR2 were identified by Cytohubba, which were differed in bone marrow, adipose tissue and blood, and which had good diagnostic performance in SONFH. We identified IRF8 and 10 target miRNAs that was utilized including Targetsan, miRDB and ENCORI databases and 8 corresponding upstream lncRNAs that was revealed by ENCORI database. IRF8 was detected with consistent expression by qRT-PCR. Based on the CMap, DGIdb and L1000FWD databases, the 11 small molecular compounds that were most strongly therapeutic correlated with SONFH were estradiol, genistein, domperidone, lovastatin, myricetin, fenbufen, rosiglitazone, sirolimus, phenformin, vorinostat and vinblastine. All of 11 small molecules had good binding affinity with the IRF8 in molecular docking.

CONCLUSION

The occurrence of SONFH was associated with a "multi-target" and "multi-pathway" pattern, especially related to immunity, and IRF8 and its noncoding RNA were closely related to the development of SONFH. The CMap, DGIdb and L1000FWD databases could be effectively used in a systematic manner to predict potential drugs for the prevention and treatment of SONFH. However, additional clinical and experimental research is warranted.

Anoikis-related genes combined with single cell sequencing: Insights into model specification of lung adenocarcinoma and applicability for prognosis and therapy

Background: Anoikis has therapeutic potential against different malignancies including lung adenocarcinoma. This study used anoikis and bioinformatics to construct a prognostic model for lung adenocarcinoma and explore new therapeutic strategies. Methods: Several bioinformatic algorithms (co-expression analysis, univariate Cox analysis, multivariate Cox analysis, and cross-validation) were used to screen anoikis-related genes (ARGs) to construct a risk model. Lung adenocarcinoma patients were divided into training and testing groups at a ratio of 1:1. The prognostic model was validated by risk score comparison between high- and low-risk groups using receiver operating characteristic curve (ROC), nomograms, independent prognostic analysis and principal component analysis. In addition, two anoikis-related genes patterns were classified utilizing consensus clustering method and were compared with each other in survival time, immune microenvironment, and regulation in pathway. Single cell sequencing was applied to analyze anoikis-related genes constructed the model. Results: This study demonstrated the feasibility of the model based on seven anoikis-related genes, as well as identifying axitinib, nibtinib and sorafenib as potential therapeutic strategies for LUAD. Risk score based on this model had could be used as an independent prognostic factor for lung adenocarcinoma (HR > 1; p < 0.001) and had the highest accuracy to predict survival compared with the clinical characteristics. Single cell sequencing analysis discovered Keratin 14 (KRT14, one of the seven anoikis-related genes) was mainly expressed in malignant cells in various cancers. Conclusion: We identified seven anoikis-related genes and constructed an accurate risk model based on bioinformatics analysis that can be used for prognostic prediction and for the design of therapeutic strategies in clinical practice.

Macrophages in Lupus Nephritis: Exploring a potential new therapeutic avenue

Lupus nephritis (LN) is a serious complication of systemic lupus erythematosus (SLE) that occurs in about half of patients. LN is characterized by glomerular deposition of immune complexes, leading to subendothelial, mesangial and subepithelial electron dense deposits, triggering immune cell infiltration and glomerular as well as tubulointerstitial injury. Monocytes and macrophages are abundantly present in inflammatory lesions, both in glomeruli and the tubulointerstitium. Here we discuss how monocytes and macrophages are involved in this process and how monocytes and macrophages may represent specific therapeutic targets to control LN.

Molecular profiling of kidney compartments from serial biopsies differentiate treatment responders from non-responders in lupus nephritis

The immune pathways that define treatment response and non-response in lupus nephritis (LN) are unknown. To characterize these intra-kidney pathways, transcriptomic analysis was done on protocol kidney biopsies obtained at flare (initial biopsy (Bx1)) and after treatment (second biopsy (Bx2)) in 58 patients with LN. Glomeruli and tubulointerstitial compartments were isolated using laser microdissection. RNA was extracted and analyzed by nanostring technology with transcript expression from clinically complete responders, partial responders and non-responders compared at Bx1 and Bx2 and to the healthy controls. Top transcripts that differentiate clinically complete responders from non-responders were validated at the protein level by confocal microscopy and urine ELISA. At Bx1, cluster analysis determined that glomerular integrin, neutrophil, chemokines/cytokines and tubulointerstitial chemokines, T cell and leukocyte adhesion genes were able to differentiate non-responders from clinically complete responders. At Bx2, glomerular monocyte, extracellular matrix, and interferon, and tubulointerstitial interferon, complement, and T cell transcripts differentiated non-responders from clinically complete responders. Protein analysis identified several protein products of overexpressed glomerular and tubulointerstitial transcripts at LN flare, recapitulating top transcript findings. Urine complement component 5a and fibronectin-1 protein levels reflected complement and fibronectin expression at flare and after treatment. Thus, transcript analysis of serial LN kidney biopsies demonstrated how gene expression in the kidney changes with clinically successful and unsuccessful therapy. Hence, these insights into the molecular landscape of response and non-response may help align LN management with the pathogenesis of kidney injury.

Identification of Biomarkers in Intracranial Aneurysm and Their Immune Infiltration Characteristics

BACKGROUND

Intracranial aneurysm (IA), known as the intracranial "unscheduled bomb," is one of the most dangerous cerebrovascular diseases, with unclear pathogenesis. This study aimed to show the mechanisms and identify the new biological targets by applying bioinformatics analysis.

METHODS

Expression profiling for control superficial temporal artery and IA walls in GSE26969 and GSE75436 datasets were downloaded. By executing the LIMMA package in R software, the differentially expressed genes (DEGs) were filtered, and the functional enrichments were consequently performed. Further cross-linking with the 2483 immune-related genes (IRGs) from the ImmPort database, the differentially expressed IRGs were identified. Based on them, the least absolute shrinkage and selection operator logistic regression and support vector machine-recursive feature elimination algorithms were used to screen the biomarkers, which were validated in the GSE54083 datasets. The CIBERSORT algorithm was applied to evaluate the infiltration of immune cells in tissues.

RESULTS

A total of 668 DEGs were obtained, and the functional enrichment suggested that they were closely related to the immune process. After intersecting them with the IRGs, 90 differentially expressed IRGs emerged, and ADIPOQ and ESM1 were identified as the biomarkers. Besides, we found that the infiltrated immune cells, such as the mast cells resting, might be associated with them.

CONCLUSIONS

We explored the contributing factors involving IA, which may generate a better understanding of the complex interactions among them and inspire a promising strategy for clinical works.

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.

CCNB1 and CCNB2 involvement in the pathogenesis of psoriasis: a bioinformatics study

Objective

The cell cycle-related proteins cyclin B1 (CCNB1) and cyclin B2 (CCNB2) are potentially involved in the underlying mechanisms of psoriasis. The present study aimed to explore this possibility using bioinformatics approaches.

Methods

CCNB1 and CCNB2 protein levels were evaluated in 14 psoriasis patients and five healthy controls by enzyme-linked immunosorbent assays, and their mRNA levels were evaluated using data from four publicly available datasets (GSE53552, GSE41664, GSE14905, and GSE13355). Comparison of high- and low-expressing groups were performed to reveal CCNB1- and CCNB2-related differentially expressed genes, which were then assessed based on gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses. Correlation analyses between CCNB1 and CCNB2 levels and immune infiltration, as well as typical targets of psoriasis, were also performed.

Results

Overall, 12 CCNB1 and CCNB2 common immune-related targets potentially involved in psoriasis were identified. These could regulate the cell cycle of through multiple pathways. In addition, CCNB1 and CCNB2 were found to potentially support the release of key molecular targets of psoriasis through the regulation of mast cell activation and macrophage polarization.

Conclusions

These findings suggest that CCNB1 and CCNB2 may represent valuable molecular biomarkers of psoriasis, contributing to its onset and progression.

Prediction of Immune Infiltration Diagnostic Gene Biomarkers in Kawasaki Disease

Kawasaki disease (KD) is characterized by disorder of immune response with unknown etiology. Immune cells may be closely related to the onset of KD. The focus of this research was to evaluate the significance of the infiltration of immune cells for this disease and find possible diagnostic biomarkers for KD. The Gene Expression Omnibus database was utilized to retrieve two freely accessible gene expression patterns (GSE68004 and GSE18606 datasets) from human KD and control specimens. 114 KD, as well as 46 control specimens, were searched for obtaining differentially expressed genes (DEGs). Candidate biological markers were determined utilizing the support vector machine recursive feature elimination and the least absolute shrinkage and selection operator regression model analysis. To assess discriminating capacity, the area under the receiver operating characteristic curve (AUC) was computed. The GSE73461 dataset was utilized to observe the biomarkers' expression levels and diagnostic significance in KD (78 KD patients and 55 controls). CIBERSORT was employed to assess the composition profiles of the 22 subtypes of immune cell fraction in KD on the basis of combined cohorts. 37 genes were discovered. The DEGs identified were predominantly involved in arteriosclerotic cardiovascular disease, atherosclerosis, autoimmune disease of the urogenital tract, and bacterial infectious disease. Gene sets related to complement and coagulation cascades, Toll-like receptor signaling pathway, Fc gamma R-mediated phagocytosis, NOD-like receptor signaling pathway, and regulation of actin cytoskeleton underwent differential activation in KD as opposed to the controls. KD diagnostic biomarkers, including the alkaline phosphatase (ALPL), endoplasmic reticulum degradation-enhancing alpha-mannosidase-like protein 2 (EDEM2), and histone cluster 2 (HIST2H2BE), were discovered (AUC = 1.000) and verified utilizing the GSE73461 dataset (AUC = 1.000). Analyses of immune cell infiltration demonstrated that ALPL, EDEM2, and HIST2H2BE were linked to CD4 memory resting T cells, monocytes, M0 macrophages, CD8 T cells, neutrophils, and memory CD4 T cells. ALPL, EDEM2, and HIST2H2BE could be utilized as KD diagnostic indicators, and they can also deliver useful information for future research on the disease's incidence and molecular processes.

The Function, Role and Process of DDX58 in Heart Failure and Human Cancers

Background

Heart failure (HF) is the most common outcome of cardiovascular disease, and an increasing number of patients with heart failure die from noncardiac causes, such as cancer. Epidemiological data suggest that ischemic cardiomyopathy–induced HF (ischemic HF) may be associated with an increased incidence of cancer. This study aimed to investigate the possible mechanisms of the association between ischemic HF and cancer, as well as potential therapeutic targets.

Methods

Weighted gene co-expression network analysis was performed to analyze the correlations between phenotypes and gene modules using immune cells as phenotypes. Differential analysis was then performed to screen differentially expressed genes (DEGs) in ischemic HF and normal control samples. The macrophage-related Brown module was identified as the key module, and immune-related DEGs were obtained by taking the intersection of the Brown module, DEGs, and immune-related genes using a Venn diagram. DDX58 was identified as the key gene using a protein–protein interaction network and expression analyses and validated using immunohistochemistry. Kaplan–Meier survival analysis was performed to analyze the correlation between DDX58 expression and tumor prognosis. Spearman correlation analysis was performed to assess the correlation between DDX58 expression and immune cell infiltration.

Results

DDX58 was identified as a key immune-related gene associated with ischemic HF and was highly expressed in most cancer types. The survival analysis revealed a significant negative correlation between high DDX58 expression and prognosis in multiple tumor types. Moreover, DDX58 expression was significantly associated with immune cell infiltration and immune checkpoint gene expression in many cancer types.

Conclusion

DDX58 is a key immune-related gene in ischemic HF and may play a crucial role in the relationship between ischemic HF and cancer. Pan-cancer analysis suggests that DDX58 is a promising clinical prognostic marker for most cancers and may be a therapeutic target for cancer patients and ischemic HF patients at an increased risk of cancer.

Integrins are double-edged swords in pulmonary infectious diseases

Integrins are an important family of adhesion molecules that are widely distributed on immune cells in the lungs. Of note, accumulating evidences have shown that integrins are double-edged swords in pulmonary infectious diseases. On one hand, integrins promote the migration of immune cells to remove the invaded pathogens in the infected lungs. However, on the other hand, integrins also act as the targets for pathogens to escape from host immune system, which is a potential factor leading to further tissue damage. Thus, the innovative therapeutic strategies based on integrins has inspired well-founded hopes to treat pulmonary infectious diseases. In this review, we illustrate the involvement of integrins in pulmonary infectious diseases, and further discuss the innovative therapeutic targets based on integrins.

Glomerular Expression of S100A8 in Lupus Nephritis: An Integrated Bioinformatics Analysis

Introduction

Lupus nephritis (LN) is a major risk factor of morbidity and mortality. Glomerular injury is associated with different pathogeneses and clinical presentations in LN patients. However, the molecular mechanisms involved are not well understood. This study aimed to explore the molecular characteristics and mechanisms of this disease using bioinformatics analysis.

Methods

To characterize glomeruli in LN, microarray datasets GSE113342 and GSE32591 were downloaded from the Gene Expression Omnibus database and analyzed to determine the differentially expressed genes (DEGs) between LN glomeruli and normal glomeruli. Functional enrichment analyses and protein–protein interaction network analyses were then performed. Module analysis was performed using the Search Tool for the Retrieval of Interacting Genes/Proteins and Cytoscape software. Immunofluorescence staining was performed to identify the glomerular expression of S100A8 in various International Society of Nephrology/Renal Pathology Society (ISN/RPS) class LN patients. The image of each glomerulus was acquired using a digital imaging system, and the green fluorescence intensity was quantified using Image-Pro Plus software.

Results

A total of 13 DEGs, consisting of 12 downregulated genes and one upregulated gene (S100A8), were identified in the microarray datasets. The functions and pathways associated with the DEGs mainly include inflammatory response, innate immune response, neutrophil chemotaxis, leukocyte migration, cell adhesion, cell–cell signaling, and infection. We also found that monocytes and activated natural killer cells were upregulated in both GSE113342 and GSE32591. Glomerular S100A8 staining was significantly enhanced compared to that in the controls, especially in class IV.

Conclusions

The DEGs identified in the present study help us understand the underlying molecular mechanisms of LN. Our results show that glomerular S100A8 expression varies in different pathological types; however, further research is required to confirm the role of S100A8 in LN.

Hematogenesis Adaptation to Long-Term Hypoxia Acclimation in Zebrafish (Danio rerio)

When fish live in the wild or are cultured artificially, they will inevitably suffer from hypoxia. At the same time, blood physiological indexes represent the physiological state of fish. In order to study the effect of long-term hypoxia acclimation on fish hematogenesis, we cultured zebrafish embryos into adulthood in a hypoxia incubator (1.5 ± 0.2 mg/L). Then we compared the hematological parameters of zebrafish cultured in normoxia and hypoxia conditions. Transcriptome sequencing analysis of the main hematopoietic tissue, the head kidney, was also compared between the two groups. Results showed that the number of erythrocytes increased significantly in the long-term hypoxia acclimated group, while the size of several cell types, such as red blood cells, eosinophils, basophils, small lymphocytes and thrombocytes, decreased significantly. The transcriptomic comparisons revealed that there were 6475 differentially expressed genes (DEGs) between the two groups. A Gene Ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis showed that hematopoiesis and cell proliferation signaling were the most significantly enriched pathways in the head kidney of hypoxia acclimated zebrafish. In addition, many genes involved in the hematopoietic process showed significantly higher levels of expression in the hypoxia acclimated zebrafish, when compared to the normoxia zebrafish. When considered together, these data allowed us to conclude that long-term hypoxia can promote the hematopoiesis process and cell proliferation signaling in the zebrafish head kidney, which resulted in higher red blood cell production. Higher numbers of red blood cells allow for better adaptation to the hypoxic environment. In conclusion, this study provides a basis for the in-depth understanding of the effects of hypoxia on hematogenesis in fish species.

Machine-Learning Algorithm-Based Prediction of Diagnostic Gene Biomarkers Related to Immune Infiltration in Patients With Chronic Obstructive Pulmonary Disease

Objective

This study aims to identify clinically relevant diagnostic biomarkers in chronic obstructive pulmonary disease (COPD) while exploring how immune cell infiltration contributes towards COPD pathogenesis.

Methods

The GEO database provided two human COPD gene expression datasets (GSE38974 and GSE76925; n=134) along with the relevant controls (n=49) for differentially expressed gene (DEG) analyses. Candidate biomarkers were identified using the support vector machine recursive feature elimination (SVM-RFE) analysis and the LASSO regression model. The discriminatory ability was determined using the area under the receiver operating characteristic curve (AUC) values. These candidate biomarkers were characterized in the GSE106986 dataset (14 COPD patients and 5 controls) in terms of their respective diagnostic values and expression levels. The CIBERSORT program was used to estimate patterns of tissue infiltration of 22 types of immune cells. Furthermore, the in vivo and in vitro model of COPD was established using cigarette smoke extract (CSE) to validated the bioinformatics results.

Results

80 genes were identified via DEG analysis that were primarily involved in cellular amino acid and metabolic processes, regulation of telomerase activity and phagocytosis, antigen processing and MHC class I-mediated peptide antigen presentation, and other biological processes. LASSO and SVM-RFE were used to further characterize the candidate diagnostic markers for COPD, SLC27A3, and STAU1. SLC27A3 and STAU1 were found to be diagnostic markers of COPD in the metadata cohort (AUC=0.734, AUC=0.745). Their relevance in COPD were validated in the GSE106986 dataset (AUC=0.900 AUC=0.971). Subsequent analysis of immune cell infiltration discovered an association between SLC27A3 and STAU1 with resting NK cells, plasma cells, eosinophils, activated mast cells, memory B cells, CD8+, CD4+, and helper follicular T-cells. The expressions of SLC27A3 and STAU1 were upregulated in COPD models both in vivo and in vitro. Immune infiltration activation was observed in COPD models, accompanied by the enhanced expression of SLC27A3 and STAU1. Whereas, the knockdown of SLC27A3 or STAU1 attenuated the effect of CSE on BEAS-2B cells.

Conclusion

STUA1 and SLC27A3 are valuable diagnostic biomarkers of COPD. COPD pathogenesis is heavily influenced by patterns of immune cell infiltration. This study provides a molecular biology insight into COPD occurrence and in exploring new therapeutic means useful in COPD.

Using a Machine Learning Approach to Identify Key Biomarkers for Renal Clear Cell Carcinoma

Background

The most common and deadly subtype of renal carcinoma is kidney renal clear cell carcinoma (KIRC), which accounts for approximately 75% of renal carcinoma. However, the main cause of death in KIRC patients is tumor metastasis. There are no obvious clinical features in the early stage of kidney cancer, and 25–30% of patients have already metastasized when they are first diagnosed. Moreover, KIRC patients whose local tumors have been removed by nephrectomy are still at high risk of metastasis and recurrence and are not sensitive to chemotherapy and radiotherapy, leading to poor prognosis. Therefore, early diagnosis and treatment of this disease are very important.

Methods

KIRC-related patient datasets were downloaded from the GEO database and TCGA database. DEG screening and GO, KEGG and GSEA enrichment analysis was firstly conducted and then the LASSO and support vector machine (SVM) RFE algorithms were adopted to identify KIRC-associated key genes in training sets and validate them in the test set. The clinical prognostic analysis including the association between the expression of key genes and the overall survival, stage, grade across KIRC, the immune infiltration difference between normal samples and cancer samples, the correlation between the key genes and immune cells, immunomodulator, immune subtypes of KIRC were investigated in this research.

Results

We finally screened out 4 key genes, including ACPP, ANGPTL4, SCNN1G, SLC22A7. The expression of key genes show difference among normal samples and tumor samples, SCNN1G and SLC22A7 could be predictor of prognosis of patients. The expression of key genes was related with the abundance of tumor infiltration immune cells and the gene expression of immune checkpoint.

Conclusion

This study screened the 4 key genes, which contributed to early diagnosis, prognosis assessment and immune target treatment of patients with KIRC.

Exploration of Prognostic Immune-Related Genes and lncRNAs Biomarkers in Kidney Renal Clear Cell Carcinoma and Its Crosstalk with Acute Kidney Injury

Kidney renal clear cell carcinoma (KIRC) has a poor prognosis and a high death rate globally. Cancer prognosis is strongly linked to immune-related genes (IRGs), according to numerous research. We utilized KIRC RNA-seq data from the TCGA database to build a prognostic model incorporating seven immune-related (IR) lncRNAs, and we constructed the model using LASSO regression. Additionally, we calculated a risk score for each patient using a prognostic model that divided patients into high-risk and low-risk groups. The ESTIMATE and CIBERSORT methodologies were then used to analyze the differences in the tumor microenvironment of the two groups of patients. Finally, we predicted three small molecule drugs that may have potential therapeutic effects for high-risk patients. We combined the acute kidney injury dataset to obtain differential genes that may serve standard biological functions with two risk groups. Our study shows that the model we constructed for IR-lncRNAs has reliable predictive efficacy for patients with KIRC.

Identification of Hub Genes and Biological Pathways in Inclusion Body Myositis Using Bioinformatics Analysis

Background

Inclusion body myositis (IBM) is a unique idiopathic inflammatory myopathy with unclear pathogenesis and poor prognosis. Although previous publications have identified some molecular biomarkers, the value of these biomarkers is unknown.

Objective

To identify hub genes and signaling pathways related to IBM for understanding the IBM-related mechanisms and providing guidance for therapy development.

Methods

Two microarray datasets (GSE3112 and GSE128470) were downloaded from the Gene Expression Omnibus (GEO) database. GEO2R was used to detect differentially expressed genes (DEGs) between IBM and normal muscle tissues. The hub genes were determined using protein–protein interaction (PPI) network in Cytoscape. The specific signaling pathways and biological functions of IBM were identified using GO, KEGG, and GSEA enrichment analyses. Moreover, CIBERSORT was applied to estimate the expression level of 22 immune cell types in IBM and normal muscle tissue. The relationship between the immune cell types and hub genes was then explored.

Results

A total of 219 DEGs and 10 hub genes were identified. Enrichment analyses revealed that the chemokine signaling pathway, cellular response to interferon-gamma, and P53 pathway have crucial roles in IBM. Immune infiltration analyses showed that IBM was associated with high level of CD8 T cells, Tregs, and macrophages. Finally, five potential drugs were predicted for IBM patients through CMap (connectivity map) database.

Conclusion

In this study, the underlying molecular mechanisms and immunological landscape of IBM were investigated, and thus may provide new directions for future research on IBM pathogenesis.

HSPB5 suppresses renal inflammation and protects lupus-prone NZB/W F1 mice from severe renal damage

BACKGROUND

Lupus nephritis (LN) is an inflammatory disease of the kidneys affecting patients with systemic lupus erythematosus. Current immunosuppressive and cytotoxic therapies are associated with serious side effects and fail to protect 20-40% of LN patients from end-stage renal disease. In this study, we investigated whether a small heat shock protein, HSPB5, can reduce kidney inflammation and the clinical manifestations of the disease in NZB/W F1 mice. Furthermore, we investigated whether HSPB5 can enhance the effects of methylprednisolone, a standard-of-care drug in LN, in an endotoxemia mouse model.

METHODS

NZB/W F1 mice were treated with HSPB5, methylprednisolone, or vehicle from 23 to 38 weeks of age. Disease progression was evaluated by weekly proteinuria scores. At the end of the study, the blood, urine, spleens, and kidneys were collected for the assessment of proteinuria, blood urea nitrogen, kidney histology, serum IL-6 and anti-dsDNA levels, immune cell populations, and their phenotypes, as well as the transcript levels of proinflammatory chemokine/cytokines in the kidneys. HSPB5 was also evaluated in combination with methylprednisolone in a lipopolysaccharide-induced endotoxemia mouse model; serum IL-6 levels were measured at 24 h post-endotoxemia induction.

RESULTS

HSPB5 significantly reduced terminal proteinuria and BUN and substantially improved kidney pathology. Similar trends, although to a lower extent, were observed with methylprednisolone treatment. Serum IL-6 levels and kidney expression of BAFF, IL-6, IFNγ, MCP-1 (CCL2), and KIM-1 were reduced, whereas nephrin expression was significantly preserved compared to vehicle-treated mice. Lastly, splenic Tregs and Bregs were significantly induced with HSPB5 treatment. HSPB5 in combination with methylprednisolone also significantly reduced serum IL-6 levels in endotoxemia mice.

CONCLUSIONS

HSPB5 treatment reduces kidney inflammation and injury, providing therapeutic benefits in NZB/W F1 mice. Given that HSPB5 enhances the anti-inflammatory effects of methylprednisolone, there is a strong interest to develop HSBP5 as a therapeutic for the treatment of LN.

Use of Glomerular CD68+ Cells as a Surrogate Marker for Endocapillary Hypercellularity in Lupus Nephritis

Introduction

Lupus nephritis (LN) class III or IV is strongly related to patient mortality and morbidity. The interobserver agreement of endocapillary hypercellularity by routine light microscopy, one of the most important lesions determining whether class III or IV is present, is moderate. In IgA nephropathy (IgAN), the presence of glomerular CD68+ cells was found to be a good surrogate marker for endocapillary hypercellularity. We investigated whether the presence of glomerular CD68+ cells could serve as a surrogate marker for endocapillary hypercellularity as well in LN.

Methods

A total of 92 LN biopsies were scored for the number of glomerular CD68+ cells using CD68 staining, including endocapillary hypercellularity and the activity index (AI). A new AI was calculated in which CD68+ cells replaced endocapillary hypercellularity. Clinical parameters were obtained from time of biopsy, 1 year after, and 2 years after.

Results

The number of glomerular CD68+ cells significantly correlated with endocapillary hypercellularity. A cutoff value of 7 for the maximum number of CD68+ cells within 1 glomerulus in a biopsy yielded a sensitivity of 88% and a specificity of 67% for the presence of endocapillary hypercellularity. Both endocapillary hypercellularity and CD68+ cells correlated with renal function during follow-up. The current and the new AI correlated equally well with the clinical outcome.

Conclusion

In LN, CD68+ cells can be used as a surrogate marker for endocapillary hypercellularity.

Fatty acid metabolism-related genes in bronchoalveolar lavage fluid unveil prognostic and immune infiltration in idiopathic pulmonary fibrosis

BACKGROUND

Idiopathic pulmonary fibrosis (IPF) is a chronic and progressive condition with an unfavorable prognosis. A recent study has demonstrated that IPF patients exhibit characteristic alterations in the fatty acid metabolism in their lungs, suggesting an association with IPF pathogenesis. Therefore, in this study, we have explored whether the gene signature associated with fatty acid metabolism could be used as a reliable biological marker for predicting the survival of IPF patients.

METHODS

Data on the fatty acid metabolism-related genes (FAMRGs) were extracted from databases like Kyoto Encyclopedia of Genes and Genomes (KEGG), Hallmark, and Reactome pathway. The GSE70866 dataset with information on IPF patients was retrieved from the Gene Expression Omnibus (GEO). Next, the consensus clustering method was used to identify novel molecular subgroups. Gene Set Enrichment Analysis (GSEA) was performed to understand the mechanisms involved. The Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) algorithm was used to evaluate the level of immune cell infiltration in the identified subgroups based on gene expression signatures of immune cells. Finally, the Least Absolute Shrinkage and Selection Operator (LASSO) regression and multivariate Cox regression analysis were performed to develop a prognostic risk model.

RESULTS

The gene expression signature associated with fatty acid metabolism was used to create two subgroups with significantly different prognoses. GSEA reveals that immune-related pathways were significantly altered between the two subgroups, and the two subgroups had different metabolic characteristics. High infiltration of immune cells, mainly activated NK cells, monocytes, and activated mast cells, was observed in the subgroup with a poor prognosis. A risk model based on FAMRGs had an excellent ability to predict the prognosis of IPF. The nomogram constructed using the clinical features and the risk model could accurately predict the prognosis of IPF patients.

CONCLUSION

The fatty acid metabolism-related gene expression signature could be used as a potential biological marker for predicting clinical outcomes and the level of infiltration of immune cells. This could eventually enhance the accuracy of the treatment of IPF patients.

Identification of CALU and PALLD as Potential Biomarkers Associated With Immune Infiltration in Heart Failure

Background: Inflammatory activation and immune infiltration play important roles in the pathologic process of heart failure (HF). The current study is designed to investigate the immune infiltration and identify related biomarkers in heart failure patients due to ischemic cardiomyopathy. Methods: Expression data of HF due to ischemic cardiomyopathy (CM) samples and non-heart failure (NF) samples were downloaded from gene expression omnibus (GEO) database. Differentially expressed genes (DEGs) between CM and NF samples were identified. Single sample gene set enrichment analysis (ssGSEA) was performed to explore the landscape of immune infiltration. Weighted gene co-expression network analysis (WGCNA) was applied to screen the most relevant module associated with immune infiltration. The diagnostic values of candidate genes were evaluated by receiver operating curves (ROC) curves. The mRNA levels of potential biomarkers in the peripheral blood mononuclear cells (PBMCs) isolated from 10 CM patients and 10 NF patients were analyzed to further assess their diagnostic values. Results: A total of 224 DEGs were identified between CM and NF samples in GSE5406, which are mainly enriched in the protein processing and extracellular matrix related biological processes and pathways. The result of ssGSEA showed that the abundance of dendritic cells (DC), mast cells, natural killer (NK) CD56dim cells, T cells, T follicular helper cells (Tfh), gammadelta T cells (Tgd) and T helper 2 (Th2) cells were significantly higher, while the infiltration of eosinophils and central memory T cells (Tcm) were lower in CM samples compared to NF ones. Correlation analysis revealed that Calumenin (CALU) and palladin (PALLD) were negatively correlated with the abundance of DC, NK CD56dim cells, T cells, Tfh, Tgd and Th2 cells, but positively correlated with the level of Tcm. More importantly, CALU and PALLD were significantly lower in PBMCs from CM patients compared to NF ones. Conclusion: Our study revealed that CALU and PALLD are potential biomarkers associated with immune infiltration in heart failure due to ischemic cardiomyopathy.

Machine learning gene expression predicting model for ustekinumab response in patients with Crohn's disease

Background

Recent studies reported the responses of ustekinumab (UST) for the treatment of Crohn's disease (CD) differ among patients, while the cause was unrevealed. The study aimed to develop a prediction model based on the gene transcription profiling of patients with CD in response to UST.

Methods

The GSE112366 dataset, which contains 86 CD and 26 normal samples, was downloaded for analysis. Differentially expressed genes (DEGs) were identified first. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were administered. Least absolute shrinkage and selection operator regression analysis was performed to build a model for UST response prediction.

Results

A total of 122 DEGs were identified. GO and KEGG analyses revealed that immune response pathways are significantly enriched in patients with CD. A multivariate logistic regression equation that comprises four genes (HSD3B1, MUC4, CF1, and CCL11) for UST response prediction was built. The area under the receiver operator characteristic curve for patients in training set and testing set were 0.746 and 0.734, respectively.

Conclusions

This study is the first to build a gene expression prediction model for UST response in patients with CD and provides valuable data sources for further studies.

Tumor purity as a prognosis and immunotherapy relevant feature in cervical cancer

Background: Tumor purity plays a vital role in the biological process of solid tumors, but its function in gynecologic cancers remains unclear. This study explored the correlation between tumor purity and immune function of gynecological cancers and its reliability as a prognostic indicator of immunotherapy.

Methods: Gynecological cancer-related datasets were downloaded from The Cancer Genome Atlas (TCGA). Tumor purity was calculated by the ESTIMATE algorithm. A LASSO Cox regression analysis was performed to construct the risk score model. A Kaplan–Meier Plotter was used to explore the relationships between tumor purity and cancer prognosis. We performed the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Set Enrichment Analysis (GSEA) to explore the pathways in the subgroups. A nomogram was used to quantitatively assess the cancer prognosis.

Results: Tumor purity was negatively correlated with B cell infiltration in cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC). Approximately 420 genes were positively associated with B cell infiltration and CESC prognosis and were enriched in immune-related signaling pathways. There were 11 key genes used to construct a risk score model. The low-risk group had a higher immune score and better prognosis than the high-risk group. A nomogram based on risk score, T stage, and clinical-stage had good predictive value in quantitatively evaluating CESC prognosis.

Conclusions: This study is the first to reveal the correlation between tumor purity and immunity in CESC and suggests that low-risk patients may be more sensitive to immunotherapy. This provides a theoretical basis for the clinical treatment of CESC.

Screening of key biomarkers of tendinopathy based on bioinformatics and machine learning algorithms

Tendinopathy is a complex multifaceted tendinopathy often associated with overuse and with its high prevalence resulting in significant health care costs. At present, the pathogenesis and effective treatment of tendinopathy are still not sufficiently elucidated. The purpose of this research is to intensely explore the genes, functional pathways, and immune infiltration characteristics of the occurrence and development of tendinopathy. The gene expression profile of GSE106292, GSE26051 and GSE167226 are downloaded from GEO (NCBI comprehensive gene expression database) and analyzed by WGCNA software bag using R software, GSE26051, GSE167226 data set is combined to screen the differential gene analysis. We subsequently performed gene enrichment analysis of Gene Ontology (GO) and "Kyoto Encyclopedia of Genes and Genomes" (KEGG), and immune cell infiltration analysis. By constructing the LASSO regression model, Support vector machine (SVM-REF) and Gaussian mixture model (GMMs) algorithms are used to screen, to identify early diagnostic genes. We have obtained a total of 171 DEGs through WGCNA analysis and differentially expressed genes (DEGs) screening. By GO and KEGG enrichment analysis, it is found that these dysregulated genes were related to mTOR, HIF-1, MAPK, NF-κB and VEGF signaling pathways. Immune infiltration analysis showed that M1 macrophages, activated mast cells and activated NK cells had infiltration significance. After analysis of THE LASSO SVM-REF and GMMs algorithms, we found that the gene MACROD1 may be a gene for early diagnosis. We identified the potential of tendon disease early diagnosis way and immune gene regulation MACROD1 key infiltration characteristics based on comprehensive bioinformatics analysis. These hub genes and functional pathways may as early biomarkers of tendon injuries and molecular therapy level target is used to guide drug and basic research.

Role of vitamin D/VDR nuclear translocation in down-regulation of NF-κB/NLRP3/caspase-1 axis in lupus nephritis

BACKGROUND

Vitamin D receptor (VDR) and NLRP3 inflammasome play critical roles in lupus nephritis (LN) pathogenesis.

AIM OF THE STUDY

This study explored the therapeutic effect of VDR agonist on LN and its molecular mechanism to inhibit NLRP3 signalling.

METHODS

C57BL/6 mice, lupus-prone MRL/lpr mice, and VDR agonist paricacitol-treated MRL/lpr mice (300 ng/kg/mouse per dose, 5 times/week for 8 weeks from 8 weeks old) were used to assess kidney histopathology and measure proteinuria, serum anti-ds-DNA antibody and expression of NF-κB/NLRP3/caspase-1/IL-1β/IL-18 axis. We used mouse renal tubular epithelial cells (mRTECs) to identify protein-protein interactions and examine the effects of paricalcitol.

RESULTS AND CONCLUSION

LN pathogenesis decreased after paricalcitol treatment. We observed a marked improvement in renal pathology and a time-dependent decrease urine protein and serum anti-dsDNA antibody levels. In 16-week-old MRL/lpr LN mice, the upregulated expression of NLRP3/caspase-1/IL-1β/IL-18 axis was significantly downregulated after paricalcitol treatment. Paricalcitol can reverse the apoptosis induced by anti-dsDNA antibody via the NF-κB/NLRP3/caspase-1/IL-1β/IL-18 axis in mRTECs. Furthermore, paricalcitol suppressed NF-κB nuclear translocation by competitively binding to importin-4. In summary, the VDR agonist can alleviate LN by modulating the NF-κB/NLRP3/caspase-1/IL-1β/IL-18 axis and suppressing the NF-κB nuclear translocation.

Significant Difference of Immune Cell Fractions and Their Correlations With Differential Expression Genes in Parkinson's Disease

Parkinson’s disease (PD) is the second most neurodegenerative disease in the world. T cell infiltration in the central nervous system (CNS) has provided insights that the peripheral immune cells participate in the pathogenesis of PD. However, the association between the peripheral immune system and CNS remains to be elucidated. In this study, we analyzed incorporative substantia nigra (SN) expression data and blood expression data using the CIBERSORT to obtain the 22 immune cell fractions and then explored the molecular function to identify the potential key immune cell types and genes of PD. We observed that the proportions of naïve CD4 T cells, gamma delta T cells, resting natural killer (NK) cells, neutrophils in the blood, and regulatory T cells (Tregs) in the SN were significantly different between patients with PD and healthy controls (HCs). We identified p53-induced death domain protein 1 (PIDD1) as the hub gene of a PD-related module. The enrichment score of the neuron-specific gene set was significantly different between PD and HC, and genes in the neuron-related module were enriched in the biological process about mitochondria and synapses. These results suggested that the fractions of naïve CD4 T cells, gamma delta T cells, resting NK cells, and neutrophils may be used as a combined diagnostic marker in the blood, and Tregs in SN may be a potential therapeutic design target for PD.

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.

IDO1 as a new immune biomarker for diabetic nephropathy and its correlation with immune cell infiltration

INTRODUCTION

Indoleamine 2,3-dioxygenase 1(IDO1) has complicated roles in immune-inflammatory response regulation, but its correlation with immune cell infiltration in diabetic nephropathy (DN) remains unknown.

METHODS

Gene expression data were extracted from the GEO database. Differentially expressed genes (DEGs) were identified and functional correlation analysis was performed. The immune hub gene was screened using Maximal Clique Centrality, and verified in DN model mice via western blotting, immunohistochemistry, and immunofluorescence analysis. CIBERSORTx was used to assign values to immune cell infiltration in DN and determine a correlation with the hub gene. The prognostic significance of the hub gene was then validated.

RESULTS

The 330 screened DEGs from the GEO dataset were most enriched in GO functions and KEGG pathways associated with immune inflammation. IDO1 was identified as a hub immune gene, with upregulated expression in DN model mice. IDO1 expression was positively correlated with M1 macrophages (R = 0.58, P < 0.001) and monocytes (R = 0.44, P = 0.049), and was negatively correlated with resting memory CD4 T cells (R = -0.51, P = 0.019). IDO1 expression was upregulated in peritoneal macrophages after high glucose stimulation, and inflammatory factor production was reversed by IDO1 inhibition. Higher IDO1 expression was associated with worse prognosis in DN patients via multivariate survival analysis (P < 0.001).

CONCLUSIONS

IDO1 was identified as a diagnostic and prognostic biomarker for DN and shown to play a vital role in immune cell infiltration in DN, ascertained using microarray data and CIBERSORTx for the first time.

Weighted Gene Co-expression Network Analysis Reveals Different Immunity but Shared Renal Pathology Between IgA Nephropathy and Lupus Nephritis

Both IgA nephropathy (IgAN) and lupus nephritis (LN) are immunity-related diseases with a complex, polygenic, and pleiotropic genetic architecture. However, the mechanism by which the genetic variants impart immunity or renal dysfunction remains to be clarified. In this study, using gene expression datasets as a quantitative readout of peripheral blood mononuclear cell (PBMC)- and kidney-based molecular phenotypes, we analyzed the similarities and differences in the patterns of gene expression perturbations associated with the systematic and kidney immunity in IgAN and LN. Original gene expression datasets for PBMC, glomerulus, and tubule from IgAN and systemic lupus erythematosus (SLE) patients as well as corresponding controls were obtained from the Gene Expression Omnibus (GEO) database. The similarities and differences in the expression patterns were detected according to gene differential expression. Weighted gene co-expression network analysis (WGCNA) was used to cluster and screen the co-expressed gene modules. The disease correlations were then identified by cell-specific and functional enrichment analyses. By combining these results with the genotype data, we identified the differentially expressed genes causatively associated with the disease. There was a significant positive correlation with the kidney expression profile, but no significant correlation with PBMC. Three co-expression gene modules were screened by WGCNA and enrichment analysis. Among them, blue module was enriched for glomerulus and podocyte (P < 0.05) and positively correlated with both diseases (P < 0.05), mainly via immune regulatory pathways. Pink module and purple module were enriched for tubular epithelium and correlated with both diseases (P < 0.05) through predominant cell death and extracellular vesicle pathways, respectively. In genome-wide association study (GWAS) enrichment analysis, blue module was identified as the high-risk gene module that distinguishes LN from SLE and contains PSMB8 and PSMB9, the susceptibility genes for IgAN. In conclusion, IgAN and LN showed different systematic immunity but similarly abnormal immunity in kidney. Immunological pathways may be involved in the glomerulopathy and cell death together with the extracellular vesicle pathway, which may be involved in the tubular injury in both diseases. Blue module may cover the causal susceptibility gene for IgAN and LN.

Elucidating the immune infiltration in acne and its comparison with rosacea by integrated bioinformatics analysis

BACKGROUND

Acne vulgaris and rosacea are common inflammatory complications of the skin, both characterized by abnormal infiltration of immune cells. The two diseases can be differentiated based on characteristic profile of the immune cell infiltrates at the periphery of disease lesions. In addition, dysregulated infiltration of immune cells not only occur in the acne lesions but also in non-lesional areas of patients with the disease, thus characterizing the immune infiltration in these sites can further enhance our understanding on the pathogenesis of acne.

METHODS

Five microarray data-sets (GSE108110, GSE53795, GSE65914, GSE14905 and GSE78097) were downloaded from Gene Expression Omnibus. After removing the batch effects and normalizing the data, we applied the CIBERSORT algorithm combined with signature matrix LM22, to describe 22 types of immune cells' infiltration in acne less than 48 hour (H) old, in comparation with non-lesional skin of acne patients, healthy skin and rosacea (including erythematotelangiectatic rosacea, papulopustular rosacea and phymatous rosacea) and we compared gene expression of Th1 and Th17-related molecules in acne, rosacea and healthy control.

RESULTS

Compared with the non-lesional skin of acne patients, healthy individuals and rosacea patients, there is a significant increase in infiltration of neutrophils, monocytes and activated mast cells around the acne lesions, less than 48 H after their development. Contrarily, few naive CD4+ T cells, plasma cells, memory B cells and resting mast cells infiltrate acne sites compared to the aforementioned groups of individuals. Moreover, the infiltration of Regulatory T cells (Tregs) in acne lesions is substantially lower, relative to non-lesional sites of acne patients and skin of healthy individuals. In addition, non-lesional sites of acne patients exhibit lower infiltration of activated memory CD4+ T cells, plasma cells, memory B cells, M0 macrophages, neutrophils, resting mast cells but higher infiltration of Tregs and resting dendritic cells relative to skin of healthy individuals. Intriguingly, we found that among the 3 rosacea subtypes, the immune infiltration profile of papulopustular rosacea is the closest to that of acne lesions. In addition, through gene expression analysis of acne, rosacea and skin tissues of healthy individuals, we found a higher infiltration of Th1 and Th17 cells in acne lesions, relative to non-lesional skin areas of acne patients.

CONCLUSIONS

Our study provides new insights into the inflammatory pathogenesis of acne, and the difference between acne and rosacea, which helps in differentiating the two diseases. Our findings also guide on appropriate target therapy of the immune cell infiltrates in the two disease conditions.