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

α-methyltryptophan-mediated protection against diabetic nephropathy in db/db mice as studied with a metabolomics approach

Introduction

Diabetic nephropathy (DN), a major complication of diabetes, presents with poor clinical outcomes and affects patients throughout their lifetime. α-Methyltryptophan (α-MT) is a blocker of the amino acid transporter. SLC6A14 and also an inhibitor of indoleamine 2,3-dioxygenase-1 (IDO1).

Methods

In this study, we employed a nuclear magnetic resonance-based metabolomic approach to investigate the therapeutic effects of α-MT in a db/db mouse model of DN and explore the underlying molecular mechanisms.

Results

The results of the study demonstrated that α-MT significantly reduced the urinary excretion of albumin and creatinine, improved kidney function, and decreased renal fibrosis in db/db mice. Metabolomic analyses of kidney tissues and urine samples indicated that db/db mice displayed increased activity of the enzyme IDO1, and alongside pronounced metabolic disturbances. These disturbances are chiefly characterized by alterations in amino acid metabolism, energy production pathways, membrane biochemical features, and nicotinamide metabolism, all of which have been implicated in mTOR signaling and apoptotic pathways.

Discussion

Administration of α-MT to db/db mice showed evidence of IDO1 inhibition and rectification of metabolic dysfunctions with concurrent suppression of mTOR signaling and apoptosis. These findings highlight the potential of α-MT as a promising therapeutic agent for diabetic nephropathy.

Immunoprognostic analysis of indoleamine 2,3-dioxygenase 1 in patients with cervical cancer

The incidence of cervical cancer is increasing. Immunotherapies show better patient outcomes than monotherapies; however, the mainstay treatment for cervical cancer remains surgery and chemotherapy. Indoleamine 2,3-dioxygenase 1 (IDO1) acts on multiple tryptophan substrates, exhibiting antitumor, immunomodulatory, and antioxidant activities. Despite the association of elevated IDO1 expression with unfavorable outcomes in various cancers, its precise function in cervical cancer remains ambiguous. Here, we explored the prognostic significance of IDO1 in cervical carcinoma. Gene expression datasets were obtained from The Cancer Genome Atlas. Gene Expression Omnibus datasets were used for differential expression and functional correlation analyses. Using Human Protein Atlas alongside Tumor-Immune System Interaction Database, we assessed the association of IDO1 with survival rates. Given the link between cervical cancer prognosis and immune invasion, CIBERSORT was used to assess the connection between immune cells and IDO1, while the percentage of tumor-penetrating immune cells based on IDO1 expression in cervical cancer patients was analyzed using Tumor-Immune System Interaction Database. Incorporating a clinicopathological characteristic-based risk score model with IDO1 risk score, we devised a nomogram to predict cervical cancer patient survival. The effects of IDO1 in immune regulation and its prognostic significance were validated using data from patients with cervical cancer obtained from The Cancer Imaging Archive database. Compared with that in normal cervical tissues, IDO1 expression was significantly upregulated in cervical cancer tissues and significantly correlated with cervical cancer progression and prognosis. IDO1 expression showed a positive association with monocyte and macrophage abundance, while exhibiting a negative correlation with that of endothelial cells and eosinophils. Cox regression analyses highlighted IDO1 as the core immune gene implicated in cervical cancer. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses revealed an association of IDO1 with the metabolic pathways of tryptophan, phenylalanine, and tyrosine. Univariate and multivariate analyses revealed that elevated IDO1 expression correlates markedly with cervical cancer outcomes, suggesting it as a promising therapeutic target. The Cancer Imaging Archive data analysis revealed that the impact of anti-PD1 and CTLA4 therapy is more pronounced in cervical cancer patients exhibiting elevated IDO1 expression. IDO1 is a potential target for immunotherapy for cervical cancer.

VCAM1: an effective diagnostic marker related to immune cell infiltration in diabetic nephropathy

Introduction

The role of immune cells in the pathogenesis and advancement of diabetic nephropathy (DN) is crucial. The objective of this study was to identify immune-cell-related biomarkers that could potentially aid in the diagnosis and management of DN.

Methods

The GSE96804 dataset was obtained from the Gene Expression Omnibus (GEO) database. Then, screen for intersections between differentially expressed genes (DEGs) and immune-related genes (IRGs). Identify core genes through protein-protein interaction (PPI) networks and the Cytoscape plugin. Subsequently, functional enrichment analysis was conducted. In addition, ROC analysis is performed to accurately identify diagnostic biomarkers. Apply the CIBERSORT algorithm to evaluate the proportion of immune cell infiltration. Finally, the mRNA, protein, and immunofluorescence expression of the biomarker was validated in the DN rat model.

Results

The study yielded 74 shared genes associated with DN. Enrichment analysis indicated significant enrichment of these genes in focal adhesion, the humoral immune response, activation of the immune response, Cytokine-cytokine receptor interaction, and IL-17 signaling pathway. The optimal candidate gene VCAM1 was identified. The presence of VCAM1 in DN was further validated using the ROC curve. Analysis of immune cell infiltration matrices revealed a high abundance of monocytes, naïve B cells, memory B cells, and Macrophages M1/M2 in DN tissues. Correlation analysis identified one hub biomarker associated with immune-infiltrated cells in DN. Furthermore, our findings were validated through in vivo RT qPCR, WB, and IF techniques.

Conclusions

Our research indicates that VCAM1 is a signature gene associated with DN and is linked to the progression, treatment, and prognosis of DN. A comprehensive examination of immune infiltration signature genes may offer new perspectives on the clinical diagnosis and management of DN.

Identification of key immune-related genes and potential therapeutic drugs in diabetic nephropathy based on machine learning algorithms

BACKGROUND

Diabetic nephropathy (DN) is a major contributor to chronic kidney disease. This study aims to identify immune biomarkers and potential therapeutic drugs in DN.

METHODS

We analyzed two DN microarray datasets (GSE96804 and GSE30528) for differentially expressed genes (DEGs) using the Limma package, overlapping them with immune-related genes from ImmPort and InnateDB. LASSO regression, SVM-RFE, and random forest analysis identified four hub genes (EGF, PLTP, RGS2, PTGDS) as proficient predictors of DN. The model achieved an AUC of 0.995 and was validated on GSE142025. Single-cell RNA data (GSE183276) revealed increased hub gene expression in epithelial cells. CIBERSORT analysis showed differences in immune cell proportions between DN patients and controls, with the hub genes correlating positively with neutrophil infiltration. Molecular docking identified potential drugs: cysteamine, eltrombopag, and DMSO. And qPCR and western blot assays were used to confirm the expressions of the four hub genes.

RESULTS

Analysis found 95 and 88 distinctively expressed immune genes in the two DN datasets, with 14 consistently differentially expressed immune-related genes. After machine learning algorithms, EGF, PLTP, RGS2, PTGDS were identified as the immune-related hub genes associated with DN. In addition, the mRNA and protein levels of them were obviously elevated in HK-2 cells treated with glucose for 24 h, as well as their mRNA expressions in kidney tissues of mice with DN.

CONCLUSION

This study identified 4 hub immune-related genes (EGF, PLTP, RGS2, PTGDS), as well as their expression profiles and the correlation with immune cell infiltration in DN.

Interstitial Foci Expression of Indoleamine 2,3-Dioxygenase 1: A Potential Biomarker for Kidney Transplant Rejection

(1) Background: Kidney transplantation is the best therapy for patients with end-stage renal disease, but the risk of rejection complicates it. Indoleamine 2,3-dioxygenase 1 (IDO1), an enzyme involved in immune response modulation, has been suggested to play a role in transplant immunological injury. The aim of the study was to explore the expression of IDO1 in the interstitial foci of transplanted kidneys and its potential association with rejection episodes. (2) Methods: This retrospective study analysed kidney transplant biopsies from 121 patients, focusing on IDO1 expression in interstitial foci. Immunohistochemistry was used to detect IDO1, and patients were categorised based on IDO1 presence (IDO1-IF positive or negative). The incidence of rejection was compared between these groups. (3) Results: Patients with IDO1 expression in interstitial foci (IDO1-IF(+)) exhibited higher incidences of rejection 46/80 (57.5%) vs. 10/41 (24.34%) patients compared to IDO1-IF(-) patients, which was statistically significant with p = 0.0005. The analysis of antibody-mediated rejection showed that IDO1-IF(+) patients developed AMR at 12/80 (15%), while only 1 IDO1-IF(-) negative patient did (2,44%), with p = 0.035. T-cell-mediated rejection was also more common in IDO1-IF(+) patients 43/80 (53.75%) than in IDO1-IF(-) patients 7/41 (17.07%), with p = 0.0001. (4) Conclusions: IDO1 expression in interstitial foci of renal transplant biopsies is associated with a higher incidence of rejection, suggesting that IDO1 could serve as a potential biomarker for transplant rejection. These findings highlight the importance of IDO1 in immune regulation and its potential utility in improving the management of kidney transplant recipients.

Prognostic value of lung immune prognostic index in non-small cell lung cancer patients receiving immune checkpoint inhibitors: a meta-analysis

Background and Purpose

Until now, it has been difficult to accurately predict the efficacy of immunotherapy in patients with non-small cell lung cancer (NSCLC). A novel indicator, the lung immune prognostic index (LIPI), has shown relatively high prognostic value in patients with solid cancer. Therefore, this study aimed to further identify the association between LIPI and the survival of patients with NSCLC who receive immune checkpoint inhibitors (ICIs).

Methods

Several electronic databases were searched for available publications up to April 23, 2023. Immunotherapy outcomes included overall survival (OS), progression-free survival (PFS), and hazard ratios (HRs) with 95% confidence intervals (CIs). Subgroup analysis based on the study design and comparison of the LIPI was conducted.

Results

In this meta-analysis, 21 studies with 9,010 patients were included in this meta-analysis. The pooled results demonstrated that elevated LIPI was significantly associated with poor OS (HR = 2.50, 95% CI:2.09-2.99, p < 0.001) and PFS (HR = 1.77, 95% CI:1.64-1.91, p < 0.001). Subgroup analyses stratified by study design (retrospective vs. prospective) and comparison of LIPI (1 vs. 0, 2 vs. 0, 1-2 vs. 0, 2 vs. 1 vs. 0, 2 vs. 0-1 and 2 vs. 1) showed similar results.

Conclusion

LIPI could serve as a novel and reliable prognostic factor in NSCLC treated with ICIs, and elevated LIPI predicts worse prognosis.

Immunoglobulin heavy constant gamma 1 silencing decreases tonicity-responsive enhancer-binding protein expression to alleviate diabetic nephropathy

AIMS/INTRODUCTION

The molecular mechanisms of diabetic nephropathy (DN) are poorly identified. However, the advantage of an increasing amount on microarray data of diabetic nephropathy intrigued us to explore the mechanisms based on bioinformatics prediction for diabetic nephropathy.

MATERIALS AND METHODS

Bioinformatics analysis was conducted to screen the hub genes associated with diabetic nephropathy. The average human renal tubular epithelial cells were exposed to high glucose (HG) to generate an in vitro cell model. In addition, a mouse model of diabetic nephropathy was established using a high-fat diet and streptozotocin injection. Finally, the shRNA targeting immunoglobulin heavy constant gamma 1 (IGHG1) was introduced in vitro and in vivo to illustrate its effect on downstream factors and on the development diabetic nephropathy.

RESULTS

Bioinformatics analysis revealed that IGHG1, TRIM11 (tripartite motif protein 11), and TonEBP are highly expressed in diabetic nephropathy. In vitro cell experiments demonstrated that IGHG1 positively regulates the expression of TRIM11 and TonEBP (tonicity-responsive enhancer binding protein) in HK2 cells treated with high glucose. Furthermore, TRIM11 upregulates the expression of TonEBP through activation of the MEK/ERK (mitogen-activated protein kinase/extracellular signal-regulated kinase) signaling pathway in HK2 cells treated with high glucose. In vivo, animal experiments further confirmed that silencing IGHG1 could prevent the occurrence and development of diabetic nephropathy.

CONCLUSION

The silencing of IGHG1 alleviated diabetic nephropathy by inhibiting the TRIM11/MEK/ERK axis and by downregulating TonEBP.

YY1 Lactylation Aggravates Autoimmune Uveitis by Enhancing Microglial Functions via Inflammatory Genes

Activated microglia in the retina are essential for the development of autoimmune uveitis. Yin-Yang 1 (YY1) is an important transcription factor that participates in multiple inflammatory and immune-mediated diseases. Here, an increased YY1 lactylation in retinal microglia within in the experimental autoimmune uveitis (EAU) group is observed. YY1 lactylation contributed to boosting microglial activation and promoting their proliferation and migration abilities. Inhibition of lactylation suppressed microglial activation and attenuated inflammation in EAU. Mechanistically, cleavage under targets & tagmentation （CUT&Tag） analysis revealed that YY1 lactylation promoted microglial activation by regulating the transcription of a set of inflammatory genes, including STAT3, CCL5, IRF1, IDO1, and SEMA4D. In addition, p300 is identified as the writer of YY1 lactylation. Inhibition of p300 decreased YY1 lactylation and suppressed microglial inflammation in vivo and in vitro. Collectively, the results showed that YY1 lactylation promoted microglial dysfunction in autoimmune uveitis by upregulating inflammatory cytokine secretion and boosting cell migration and proliferation. Therapeutic effects can be achieved by targeting the lactate/p300/YY1 lactylation/inflammatory genes axis.

The Role of Immune Cells in DKD: Mechanisms and Targeted Therapies

Diabetic kidney disease (DKD), is a common microvascular complication and a major cause of death in patients with diabetes. Disorders of immune cells and immune cytokines can accelerate DKD development of in a number of ways. As the kidney is composed of complex and highly differentiated cells, the interactions among different cell types and immune cells play important regulatory roles in disease development. Here, we summarize the latest research into the molecular mechanisms underlying the interactions among various immune and renal cells in DKD. In addition, we discuss the most recent studies related to single cell technology and bioinformatics analysis in the field of DKD. The aims of our review were to explore immune cells as potential therapeutic targets in DKD and provide some guidance for future clinical treatments.

Identification of necroptosis-related features in diabetic nephropathy and analysis of their immune microenvironent and inflammatory response

Background: Diabetic nephropathy (DN) was considered a severe microvascular complication of diabetes, which was recognized as the second leading cause of end-stage renal diseases. Therefore, identifying several effective biomarkers and models to diagnosis and subtype DN is imminent. Necroptosis, a distinct form of programmed cell death, has been established to play a critical role in various inflammatory diseases. Herein, we described the novel landscape of necroptosis in DN and exploit a powerful necroptosis-mediated model for the diagnosis of DN. Methods: We obtained three datasets (GSE96804, GSE30122, and GSE30528) from the Gene Expression Omnibus (GEO) database and necroptosis-related genes (NRGs) from the GeneCards website. Via differential expression analysis and machine learning, significant NRGs were identified. And different necroptosis-related DN subtypes were divided using consensus cluster analysis. The principal component analysis (PCA) algorithm was utilized to calculate the necroptosis score. Finally, the logistic multivariate analysis were performed to construct the necroptosis-mediated diagnostic model for DN. Results: According to several public transcriptomic datasets in GEO, we obtained eight significant necroptosis-related regulators in the occurrence and progress of DN, including CFLAR, FMR1, GSDMD, IKBKB, MAP3K7, NFKBIA, PTGES3, and SFTPA1 via diversified machine learning methods. Subsequently, employing consensus cluster analysis and PCA algorithm, the DN samples in our training set were stratified into two diverse necroptosis-related subtypes based on our eight regulators' expression levels. These subtypes exhibited varying necroptosis scores. Then, we used various functional enrichment analysis and immune infiltration analysis to explore the biological background, immune landscape and inflammatory status of the above subtypes. Finally, a necroptosis-mediated diagnostic model was exploited based on the two subtypes and validated in several external verification datasets. Moreover, the expression level of our eight regulators were verified in the singe-cell level and glomerulus samples. And we further explored the relationship between the expression of eight regulators and the kidney function of DN. Conclusion: In summary, our necroptosis scoring model and necroptosis-mediated diagnostic model fill in the blank of the relationship between necroptosis and DN in the field of bioinformatics, which may provide novel diagnostic insights and therapy strategies for DN.

Single-cell and transcriptome analysis reveals TAL cells in diabetic nephropathy

Diabetic nephropathy is a global public health concern with multifaceted pathogenesis, primarily involving hypertension. Excessive activation of AT1R has been strongly associated with hypertension onset and progression in diabetic nephropathy. This study aimed to conduct thick ascending limb cell single-cell and transcriptomic analysis in diabetic nephropathy, including screening for biological markers, cellular communication, and immune infiltration, to identify potential biomarkers and effective means for prevention and treatment. By using high-dimensional weighted gene co-expression network analysis, least absolute shrinkage and selection operator, machine learning, neural deconvolution, quasi-chronological analysis, non-negative matrix factorization clustering, and monocyte chemotactic protein-induced counter, we identified 7 potential thick ascending limb cell biomarkers for diabetic nephropathy and elucidated the bone morphogenetic protein pathway's regulation of thick ascending limb cells through podocyte epithelial cells and podocyte cells. The study also highlighted the role of COBL, PPARGC1A, and THSD7A in non-negative matrix factorization clustering and their relationship with thick ascending limb cell immunity in diabetic nephropathy. Our findings provide new insights and avenues for managing diabetic nephropathy, ultimately alleviating the burden on patients and society.

Indoleamine 2,3-Dioxygenase (IDO) Activity: A Perspective Biomarker for Laboratory Determination in Tumor Immunotherapy

Indoleamine 2,3-dioxygenase 1 (IDO1) is a heme enzyme involved in catalyzing the conversion of tryptophan (Trp) into kynurenine (Kyn) at the first rate-limiting step in the kynurenine pathway of L-tryptophan metabolism. It has been found to be involved in several biological functions such as aging, immune microorganism, neurodegenerative and infectious diseases, and cancer. IDO1 plays an important role in immune tolerance by depleting tryptophan in the tumor microenvironment and inhibiting the proliferation of effector T cells, which makes it an important emerging biomarker for cancer immunotherapy. Therefore, the research and development of IDO1 inhibitors are of great importance for tumor therapy. Of interest, IDO activity assays are of great value in the screening and evaluation of inhibitors. Herein, we mainly review the biological functions of IDO1, immune regulation, key signaling molecules in the response pathway, and the development of IDO1 inhibitors in clinical trials. Furthermore, this review provides a comprehensive overview and, in particular, a discussion of currently available IDO activity assays for use in the evaluation of IDO inhibitors in human blood. We believe that the IDO activity is a promising biomarker for the immune escape and laboratory evaluation of tumor immunotherapy.

Integrated Analysis of Single-Cell RNA-Seq and Bulk RNA-Seq Combined with Multiple Machine Learning Identified a Novel Immune Signature in Diabetic Nephropathy

Background

Increasing evidence suggests that immune modulation contributes to the pathogenesis and progression of diabetic nephropathy (DN). However, the role of immune modulation in DN has not been elucidated. The purpose of this study was to search for potential immune-related therapeutic targets and molecular mechanisms of DN.

Methods

Gene expression datasets were obtained from the Gene Expression Omnibus (GEO) database. A total of 1793 immune-related genes were acquired from the Immunology Database and Analysis Portal (ImmPort). Weighted gene co-expression network analysis (WGCNA) was performed for GSE142025, and the red and turquoise co-expression modules were found to be key for DN progression. We utilized four machine learning algorithms, namely, random forest (RF), support vector machine (SVM), adaptive boosting (AdaBoost), and k-nearest neighbor (KNN), to evaluate the diagnostic value of hub genes. Immune infiltration patterns were analyzed using the CIBERSORT algorithm, and the correlation between immune cell type abundance and hub gene expression was also investigated.

Results

A total of 77 immune-related genes of advanced DN were selected for subsequent analyzes. Functional enrichment analysis showed that the regulation of cytokine-cytokine receptor interactions and immune cell function play a corresponding role in the progression of DN. The final 10 hub genes were identified through multiple datasets. In addition, the expression levels of the identified hub genes were corroborated through a rat model. The RF model exhibited the highest AUC. CIBERSORT analysis and single-cell sequencing analysis revealed changes in immune infiltration patterns between control subjects and DN patients. Several potential drugs to reverse the altered hub genes were identified through the Drug-Gene Interaction database (DGIdb).

Conclusion

This pioneering work provided a novel immunological perspective on the progression of DN, identifying key immune-related genes and potential drug targets, thus stimulating future mechanistic research and therapeutic target identification for DN.

APOC1 exacerbates renal fibrosis through the activation of the NF-κB signaling pathway in IgAN

Introduction: IgA nephropathy (IgAN) is the most common disease leading to end-stage renal disease, and tubular fibrosis represents an important risk factor for disease progression. However, research on early molecular diagnostic indicators of tubular fibrosis and the mechanisms underlying disease progression is still lacking. Methods: The GSE93798 dataset was downloaded from the GEO database. DEGs were screened and analyzed for GO and KEGG enrichment in IgAN. The least absolute shrinkage and selection operator (LASSO) and support vector machine recursive feature elimination (SVM-RFE) algorithms were applied to screen for hub secretory genes. The expression and diagnostic efficacy of hub genes were confirmed by the GSE35487 dataset. ELISA was applied to detect the expression of APOC1 in serum. The expression and localization of hub genes in IgAN were verified by the expression of IHC and IF in human kidney tissues, and the correlation of expression with clinical data was verified in the Nephroseq database. Finally, cellular experiments clarified the role of hub genes in the signaling pathway. Results: A total of 339 DEGs were identified in IgAN, of which 237 were upregulated and 102 downregulated. The KEGG signaling pathway is enriched in the ECM-receptor interaction and AGE-RAGE signaling pathway. APOC1, ALB, CCL8, CXCL2, SRPX2, and TGFBI identified six hub secretory genes using the LASSO and SVM-RFE algorithms. In vivo and in vitro experiments demonstrated that APOC1 expression was elevated in IgAN. The serum concentration of APOC1 was 1.232 ± 0.1812 μg/ml in IgAN patients, whereas it was 0.3956 ± 0.1233 μg/ml in healthy individuals. APOC1 exhibited high diagnostic efficacy for IgAN (AUC of 99.091%, specificity of 95.455%, and sensitivity of 99.141%) in the GSE93798 dataset. APOC1 expression negatively correlated with eGFR (R ² = 0.2285, p = 0.0385) and positively correlated with serum creatinine (R ² = 0.41, p = 0.000567) in IgAN. APOC1 exacerbated renal fibrosis, possibly in part by activating the NF-κB pathway in IgAN. Conclusion: APOC1 was identified as the core secretory gene of IgAN, which was closely associated with blood creatinine and eGFR and had significant efficacy in the diagnosis of IgAN. Mechanistic studies revealed that the knockdown of APOC1 could improve IgAN renal fibrosis by inhibiting the NF pathway, which may be a potential therapeutic target for improving renal fibrosis in IgAN.

Identification of key immune-related genes and immune infiltration in diabetic nephropathy based on machine learning algorithms

BACKGROUND

Diabetic nephropathy (DN) is a complication of diabetes. This study aimed to identify potential diagnostic markers of DN and explore the significance of immune cell infiltration in this pathology.

METHODS

The GSE30528, GSE96804, and GSE1009 datasets were downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were identified by merging the GSE30528 and GSE96804 datasets. Enrichment analyses of the DEGs were performed. A LASSO regression model, support vector machine recursive feature elimination analysis and random forest analysis methods were performed to identify candidate biomarkers. The CIBERSORT algorithm was utilised to compare immune infiltration between DN and normal controls.

RESULTS

In total, 115 DEGs were obtained. The enrichment analysis showed that the DEGs were prominent in immune and inflammatory responses. The DEGs were closely related to kidney disease, urinary system disease, kidney cancer etc. CXCR2, DUSP1, and LPL were recognised as diagnostic markers of DN. The immune cell infiltration analysis indicated that DN patients contained a higher ratio of memory B cells, gamma delta T cells, M1 macrophages, M2 macrophages etc. cells than normal people.

CONCLUSION

Immune cell infiltration is important for the occurrence of DN. CXCR2, DUSP1, and LPL may become novel diagnostic markers of DN.

APOC1 as a novel diagnostic biomarker for DN based on machine learning algorithms and experiment

INTRODUCTION

Diabetic nephropathy is the leading cause of end-stage renal disease, which imposes a huge economic burden on individuals and society, but effective and reliable diagnostic markers are still not available.

METHODS

Differentially expressed genes (DEGs) were characterized and functional enrichment analysis was performed in DN patients. Meanwhile, a weighted gene co-expression network (WGCNA) was also constructed. For further, algorithms Lasso and SVM-RFE were applied to screening the DN core secreted genes. Lastly, WB, IHC, IF, and Elias experiments were applied to demonstrate the hub gene expression in DN, and the research results were confirmed in mouse models and clinical specimens.

RESULTS

17 hub secretion genes were identified in this research by analyzing the DEGs, the important module genes in WGCNA, and the secretion genes. 6 hub secretory genes (APOC1, CCL21, INHBA, RNASE6, TGFBI, VEGFC) were obtained by Lasso and SVM-RFE algorithms. APOC1 was discovered to exhibit elevated expression in renal tissue of a DN mouse model, and APOC1 is probably a core secretory gene in DN. Clinical data demonstrate that APOC1 expression is associated significantly with proteinuria and GFR in DN patients. APOC1 expression in the serum of DN patients was 1.358±0.1292μg/ml, compared to 0.3683±0.08119μg/ml in the healthy population. APOC1 was significantly elevated in the sera of DN patients and the difference was statistical significant (P > 0.001). The ROC curve of APOC1 in DN gave an AUC = 92.5%, sensitivity = 95%, and specificity = 97% (P < 0.001).

CONCLUSIONS

Our research indicates that APOC1 might be a novel diagnostic biomarker for diabetic nephropathy for the first time and suggest that APOC1 may be available as a candidate intervention target for DN.

Two distinct resident macrophage populations coexist in the ovary

Introduction

Tissue-resident macrophages (TRMs) are highly heterogeneous and have a complex and important role in tissue support, homeostasis, and function. The heterogeneity, maintenance, and function of TRMs, as one of the major immune cells in the ovary, are not well understood.

Methods

Application of flow cytometry, Parabiosis, Fate mapping, Macrophage depletion, etc.

Results

Here, we described two distinct macrophage subsets, F4/80^hiCD11b^int and F4/80^intCD11b^hi, with different phenotypic characteristics in the ovary of mice. The F4/80^hiCD11b^int population contained a distinct CD206⁺ subgroup and highly expressed CD81, while the F4/80^intCD11b^hi subset showed higher expression of CCR2 and TLR2. Notably, Ly6c⁺ macrophages were present almost exclusively in the F4/80^intCD11b^hi subpopulation. Combining in vivo fate mapping and parabiotic mouse models, we characterized the longevity and replenishment of the two macrophage populations. We found that both the F4/80^hiCD11b^int and F4/80^intCD11b^hi subsets were ovary-resident. Importantly, the F4/80^hiCD11b^int macrophages acted as a self-maintaining and long-lived population with a modest monocyte contribution at a steady state, and the F4/80^intCD11b^hi subpopulation had a relatively short lifespan with a greater contribution from monocytes. After macrophage ablation, disturbance of estradiol secretion and ovarian hemorrhage due to damaged vascular integrity was observed in mice.

Discussion

Our data provide critical insights into ovarian macrophage heterogeneity and highlight the strategic role of TRMs in ovarian homeostasis and physiology.

Identification and validation of P4HB as a novel autophagy-related biomarker in diabetic nephropathy

Diabetic nephropathy (DN), a frequent microvascular complication of diabetes, has been recognized as a primary cause of chronic kidney disease (CKD) and end-stage renal disease (ESRD). Previous studies found that autophagy of renal tubular epithelial cells plays an important role in DN pathogenesis. Our research aimed to investigate the differentially expressed autophagy-related genes (DEARGs) between DN and healthy renal tubule samples and identify a novel autophagy-related biomarker associated with tubulointerstitial injury in DN. In this study, gene expression profiles of renal tubules from 10 DN patients and 24 healthy controls in the GSE30122 dataset were analyzed, and 43 DEARGs were identified by bioinformatics analysis. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis and correlation analysis were performed on DEARGs, and the hub gene prolyl 4-hydroxylase subunit beta (P4HB) was screened by protein–protein interaction and verified by utilizing other datasets and stimulating HK-2 cells under high glucose concentration. We found that the expression of P4HB in renal tubules was correlated with renal function. In summary, our research provided novel insights for comprehension of DN molecular mechanisms and identified P4HB as a novel autophagy-related biomarker of DN.

Bioinformatics analysis identifies immune-related gene signatures and subtypes in diabetic nephropathy

BACKGROUND

Systemic inflammation and immune response are involved in the pathogenesis of diabetic nephropathy (DN). However, the specific immune-associated signature during DN development is unclear. Our study aimed to reveal the roles of immune-related genes during DN progression.

METHODS

The GSE30529 and GSE30528 datasets were acquired from the Gene Expression Omnibus (GEO) database. Then, the intersection between differentially expressed genes (DEGs) and immune score-related genes (ISRGs) was screened. Subsequently, functional enrichment analyses were performed. The different immune phenotype-related subgroups were finally divided using unsupervised clustering. The core genes were identified by WGCNA and the protein-protein interaction (PPI) network. xCell algorithm was applied to assess the proportion of immune cell infiltration.

RESULTS

92 immune score-related DEGs (ISRDEGs) were identified, and these genes were enriched in inflammation- and immune-associated pathways. Furthermore, two distinct immune-associated subgroups (C1 and C2) were identified, and the C1 subgroup exhibited activated immune pathways and a higher percentage of immune cells compared to the C2 subgroup. Two core genes (LCK and HCK) were identified and all up-regulated in DN, and the expressions were verified using GSE30122, GSE142025, and GSE104954 datasets. GSEA indicated the core genes were mainly enriched in immune-related pathways. Correlation analysis indicated LCK and HCK expressions were positively correlated with aDC, CD4+ Tem, CD8+T cells, CD8+ Tem, and mast cells.

CONCLUSIONS

We identified two immune-related genes and two immune-associated subgroups, which might help to design more precise tailored immunotherapy for DN patients.

The Kynurenine Pathway in Acute Kidney Injury and Chronic Kidney Disease

BACKGROUND

The kynurenine pathway (KP) is the major catabolic pathway for tryptophan degradation. The KP plays an important role as the sole de novo nicotinamide adenine dinucleotide (NAD+) biosynthetic pathway in normal human physiology and functions as a counter-regulatory mechanism to mitigate immune responses during inflammation. Although the KP has been implicated in a variety of disorders including Huntington's disease, seizures, cardiovascular disease, and osteoporosis, its role in renal diseases is seldom discussed.

SUMMARY

This review summarizes the roles of the KP and its metabolites in acute kidney injury (AKI) and chronic kidney disease (CKD) based on current literature evidence. Metabolomics studies demonstrated that the KP metabolites were significantly altered in patients and animal models with AKI or CKD. The diagnostic and prognostic values of the KP metabolites in AKI and CKD were highlighted in cross-sectional and longitudinal human observational studies. The biological impact of the KP on the pathophysiology of AKI and CKD has been studied in experimental models of different etiologies. In particular, the activation of the KP was found to confer protection in animal models of glomerulonephritis, and its immunomodulatory mechanism may involve the regulation of T cell subsets such as Th17 and regulatory T cells. Manipulation of the KP to increase NAD+ production or diversion toward specific KP metabolites was also found to be beneficial in animal models of AKI. Key Messages: KP metabolites are reported to be dysregulated in human observational and animal experimental studies of AKI and CKD. In AKI, the magnitude and direction of changes in the KP depend on the etiology of the damage. In CKD, KP metabolites are altered with the onset and progression of CKD all the way to advanced stages of the disease, including uremia and its related vascular complications. The activation of the KP and diversion to specific sub-branches are currently being explored as therapeutic strategies in these diseases, especially with regards to the immunomodulatory effects of certain KP metabolites. Further elucidation of the KP may hold promise for the development of biomarkers and targeted therapies for these kidney diseases.

Identification and verification of vascular cell adhesion protein 1 as an immune-related hub gene associated with the tubulointerstitial injury in diabetic kidney disease

Diabetic kidney disease (DKD) is the leading cause of chronic kidney disease (CKD) and end-stage renal disease (ESRD), but the pathogenesis is not completely understood. Tubulointerstitial injury plays critical roles in the development and progression of DKD. The present study aimed to investigate the profile of tubulointerstitial immune cell infiltration and reveal the underlying mechanisms between tubular cell injury and interstitial inflammation in DKD using bioinformatics strategies. First, xCell analysis identified immune cells displaying significant changes in the DKD tubulointerstitium, including upregulated CD4⁺ T cells, Th2 cells, CD8⁺ T cells, M1 macrophages, activated dendritic cells (DCs) and conventional DCs, as well as downregulated Tregs. Second, pyroptosis was identified as the main form of cell death compared with other forms of programmed cell death. Vascular cell adhesion protein 1 (VCAM1) was identified as the top ranked hub gene. The correlation analysis showed that VCAM1 was significantly positively correlated with pyroptosis and infiltrated immune cells in the tubulointerstitium. Upregulation of VCAM1 in the DKD tubulointerstitium was further verified in European Renal cDNA Bank cohort and was observed to negatively correlate with the glomerular filtration rate (GFR). Our in vitro study validated increased VCAM1 expression in HK-2 cells under diabetic conditions, and pyroptosis inhibition by disulfiram decreased VCAM1 expression, inflammatory cytokine release and fibrosis. In conclusion, our study identified upregulated VCAM1 expression in renal tubular cells, which might interact with infiltrated immune cells, thus promoting fibrosis. The FDA-approved drug disulfiram might improve fibrosis in DKD by targeting tubular pyroptosis and VCAM1 expression.

SMOX expression predicts the prognosis of non-small cell lung cancer

Background

The development of non-small cell lung cancer (NSCLC) is very rapid, and the effect of its treatment is often closely related to the diagnosis time of the disease. Therefore, simple and convenient tumor biomarkers are helpful for the timely diagnosis and prevention of NSCLC.

Methods

Through univariate and multivariate Cox regression analyses, SMOX was determined as an independent prognostic factor of GSE42127, GSE41271, GSE68465, and TCGA datasets. Furthermore, western blot, reverse transcription-polymerase chain reaction (RT-PCR), and immunohistochemical analysis were performed to confirm the predictive efficiency of SMOX expression in NSCLC.

Results

Patients were divided into high and low expression groups according to the median value of SMOX expression, and Kaplan-Meier curves of multiple datasets indicated that patients with low SMOX expression had a better survival rate. According to the analysis of immune infiltration, the immune microenvironment, and immune checkpoints, SMOX expression of the high and low groups showed differences in immunity in NSCLC. By comparing cancer and adjacent tissues using western blot analysis, RT-PCR and immunohistochemical analysis, we found that SMOX was highly expressed in tumor tissues and had low expression in adjacent tissues. Simultaneously, the Kaplan-Meier curve suggested that among the 155 NSCLC patients, those with low SMOX expression had better survival.

Conclusions

SMOX can be used as an effective predictive target for NSCLC.