Identification of hub fatty acid metabolism-related genes and immune infiltration in IgA nephropathy

doi:10.21203/rs.3.rs-3460092/v1

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Identification of hub fatty acid metabolism-related genes and immune infiltration in IgA nephropathy

https://doi.org/10.21203/rs.3.rs-3460092/v1

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Aims: To identify the potential mechanism of fatty acid metabolism (FAM)-related genes in IgA nephropathy (IgAN) and to explore its immune cell infiltration feature.

Methods: IgAN datasets and FAM-related genes were respectively downloaded from GEO and MSigDB database. Differential expression analysis and WGCNA were used to identify overlapping genes. GO and KEGG analysis were conducted to explore the differences between IgAN and control. Furthermore, we utilized LASSO logistic regression to select a FAM-related gene predictive model. ROC was utilized to assess the efficacy of prognostic model. Immune cells and immune-related functions were analysed using CIBERSORT tool. Finally, the screened key genes were confirmed in blood derived IgAN and control patient, as well as in human mesangial cells (HMCs) after Gd-IgA stimulation by Real-time PCR.

Results: A total of 12 hub genes associated with FAM were obtained in IgAN. A 4 gene predictive model was conducted via LASSO regression analysis and the AUC values showed that the model had a relatively good diagnostic performance. The immune infiltration results revealed that several immune cells are significantly associated with IgAN. Real-time PCR assay further confirmed that the expression of hub genes were significantly lower in IgAN patients and Gd-IgA treated HMCsthan those in control.

Conclusion:

This study utilized bioinformatics tools to unveiled immune cell infiltration that occurred in IgAN and investigate the potential genetic link between FAM and IgAN. It may predict the risk of IgAN and improve the diagnosis and prognosis of this condition.

IgA nephropathy

fatty acid metabolism

hub gene

immune response

IgA nephropathy (IgAN) is the most prevalent type of primary glomerulonephritis (GN) worldwide, which is a major cause of end-stage kidney disease (ESRD)[1]. In South East Asia, IgAN accounts for 30–50% of renal biopsy diagnoses, making it particular prevalent in this region[2]. Among Asian countries, the incidence rates in China and Japan were relative higher[3, 4].To realize quick diagnosis and determine the progression risk using a less invasive methodology, such as blood-based biomarkers, rather than renal biopsy could be more preference in the clinical practice.

Fatty acid (FA) metabolism, which divided into catabolic process and anabolic process, is essential for energy production and storage, cell membrane proliferation, and the generation of signaling molecules[5]. FA metabolism has received increasing attention for tumorigenesis, disease progression and obesity-associated resistance [6]. Kidneys have the highest basal energy consumption of any organs, largely driven by the metabolic demands which are dependent on β-oxidation of FAs in mitochondria. FAs metabolism also contributes to adaptive immune response, particularly in T cells and macrophages. In IgAN, macrophages and T cells infiltrate the glomerulus and/or interstitial compartments[7, 8], with the intensity of cellular infiltration correlating with the severity of renal histologic lesions and the degree of proteinuria at the time of biopsy[8–10]. Thus, it remains necessary to further investigate the association of FA-metabolism-related genes in IgAN.

Bioinformatics analysis is a powerful tool for predicting molecular pathways and gene connections[11–13]. In recent years, microarray and bioinformatic analyses have been extensively employed to candidate biomarker genes and provide a deeper understanding of the molecular pathogenesis in various diseases but not many in IgAN[14–17]. In 2022, Wu et al. identified a five-gene set (DUSP1, FOS, JUN, EGR1, and FOSB) that was selected using support vector machine (SVM), least absolute shrinkage and selection operator (LASSO), and partial least square discriminant analysis (PLS-DA)[14]. Additionally, through weighed gene co-expression network analysis (WGCNA) and differential expression gene (DEG) analysis, DUSP1 and FOSB were identified and validated as potential targets for intervention and treatment of IgAN [17].

The purpose of this study was to investigate whether the FAM pathway could be identified as a prognostic factor for IgAN patients. To achieve this, we utilized abundant public resources and bioinformatics methods to identified 12 hub genes associated with FAM and IgAN through differential analysis and WGCNA. We further investigated the biological processes and pathways using GO and GSEA analyses. Individualized FAM-derived profiles were established usinglogistic regression analyses to predict the overall prognostic value of IgAN patients. Additionally, we explored the immune infiltration of these samples and the correlation of different immune factors with these hub genes. Then, in vitro experiments were conducted using peripheral blood from IgAN patient and health controls, as well as HMCs treated with Gd-IgA to mimic IgAN in cells. This study unraveled the mechanism of immune regulation in disease development in IgAN and identified 4 hub gene, which may become prospective biomarkers for noninvasive diagnosis and the prognostic availability of the signature of IgAN patients subjected to various therapy strategies was validated.

Data collection and acquisition.

Gene expression data of IgAN patients were obtained from the NCBI Gene Expression Omnibus public database (GEO, www.ncbi.nlm.nih.gov/geo/). The GSE93798 dataset, containing 22 healthy control and 20 IgAN kidney tissue samples were selected. Probes were converted to gene symbols according to the GPL22945 platform (Affymetrix Human Genome U133 Plus 2.0 Array). GSE37460 annotated by GPL14663 (Affymetrix GeneChip Human Genome HG-U133A Custom CDF) and GPL11670 (Affymetrix Human Genome U133 Plus 2.0 Array) included 27 IgAN samples and 27 health control sample. Datasets GSE141295 and GSE104948 were used for verification sets. All data were freely available.

Data Processing and Differential Expression Analysis

Two datasets of GSE93798 and GSE37460 microarray normalized expression matrix and annotate the probes using the dataset’s annotation file. Using the ComBat algorithm of R software “SVA” package for batch correction of datasets GSE93798 and GSE37460. After ID conversion, Differential expression analysis of IgAN and health control samples was performed using the “limma” package in R software. Genes with p < 0.01 and |log₂FC| > 2.0 were considered as differential expression genes (DEGs). Heat maps and volcano plots of DEGs were created using the “pheatmap” and “ggplot2” packages.

Weighted Correlation Network Analysis (WGCNA)

WGCNA was carried out according to the method in the literature[18]. WGCNA was applied to analyze differential genes in GSE93798 and GSE37460. We used gene expression profiles, then calculated the MAD (Median Absolute Deviation) of each gene separately, eliminated the top 50% of genes with the smallest MAD. The correlation coefficient between each gene pair was calculated to construct a similarity matrix. To ensure the construction of a scale-free network, a suitable soft threshold was chosen to transform the similarity matrix into an adjacency matrix. Subsequently, the adjacency matrix was transformed into a topological overlap matrix (TOM). Based on the relevant parameters of the blockwiseModules function, genes with similar expression profiles were grouped into different modules using the dynamic tree cutting method. The module eigengene (ME) and the module membership (MM) were used to distinguish the vital modules. The module with the highest absolute value of the correlation coefficient was identified as the key module for further analysis. Module membership (MM) refers to the correlation coefficient between genes and module eigengenes. Gene significance (GS) was the correlation coefficient between the expression value of a gene and a phenotype, representing the association between genes and phenotypes[19].

Fatty acid Metabolism-Related Genes (FAM)

We extracted two fatty acid metabolism-related gene sets from the Molecular Signatures Database v7.5.1 (MSigDB; www.gsea-msigdb.org), including Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway database (Pathway: hsa00071) and Reactome database (R-HSA-8978868), and they are presented in Supplementary 1.

Identification of Hub Genes

We identified overlapping 12 hub genes associated with both fatty acid metabolism and IgAN (FAM-IgAN), the intersection of DEGs, genes obtained by WGCNA, and genes in the fatty acid metabolism gene sets were taken using the “VennDiagram” package in R software.

Functional enrichment, pathway analysis of hub genes
Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed using the “clusterProfiler” R package. Adjusted p < 0.05 was selected as the cut-off criterion. The respective functions of each hub genes were revealed by Gene Set Enrichment Analysis (GSEA). IgAN patients were differentiated into two groups (the low expression group and the high expression group) based on median values of hub gene expression levels. We computed the consistency p-value for each gene set, and p-values less than 0.05 were considered significantly enriched. All these analyses were conducted using the “clusterProfiler” package in R software.

Construction of the Fatty acid Metabolism-Related risk signature

The least absolute shrinkage and selection operator (LASSO) logistic regression was used to classify the diagnostic markers of IgAN using the “glmnet” package, with a regression penalty score of 0.03679984165. Then the risk score of the predictive model was calculated. The diagnostic accuracy was measured using receiver operating characteristic curves (ROCs) and the area under the ROC curve (R package: survivalROC).

Evaluation of subtype distribution among immune‑infiltrated cells.

CIBERSORT was shown to transform a normalized gene expression matrix into the composition of 22 immune cell types based on a deconvolution algorithm. Here, CIBERSORT was employed to calculate the composition of immune cells in IgAN and healthy samples.

Correlation and differential analysis of immune‑infiltrated cells.

The correlations between hub genes expression and immune cell infiltration were determined using Pearson correlation coefficients (r) > 0.4 and P < 0.05. Pearson correlation coefficient was obtained from the sample data screened by CIBERSORT, P < 0.05.

HMC culture and treatment

HMCs were obtained from Pricella (CP-H067, Shanghai, China) and cultured in RPMI-1640 supplemented with 10% FBS, 100 U/mL penicillin, and 100mg/ml streptomycin. HMCs treated with extracted Gd-IgA1 to established IgAN model in vitro reported previously[15]. When HMCs were cultured om serum-free medium and treated with 1mg/ml Gd-IgA1 for 24 h after cell confluence reached 70-80%.

Human subjects

A total of 25 patients who had biopsy-proven IgAN and enrolled in the Xinhua Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China, between July 2021 and July 2023. Health controls (n=25) were selected from age-match normal individuals from the medical examination center.

RNA extraction and real-time PCR

The total RNA from peripheral blood and HMCs were extracted by Trizol reagent (Tiangen Biotech, Beijing, China). We used a cDNA synthesis kit (HiScript II Q RT SuperMix for qPCR, Vazyme, China) to compound cDNA according to the standard instructions. The cDNA was analyzed in an ABI ViiA 7 real-time polymerase chain reaction system to detect mRNA expression for RT-PCR. All the primers were synthesized by Sangon Biotech (Sangon, Shanghai, China). The relative expression level of mRNA was calculated by 2^-^∆∆^Ct method and three replicate experiments were involved. The internal control GAPDH was used for correction. All primers were used as summarized in Supplementary 2.

Statistical Analysis

All statistical analyses were performed in R software. The t-test and Mann–Whitney U-test were selected according to whether the data conformed to a normal distribution. Univariate and multivariate logistic regression analysis were utilized to assess the diagnostic value of the predictive mode. Significance was usually defined as p < 0.05.

Identification of Hub Genes Associated with IgAN and Fatty Acid Metabolism

The combined datasets from GSE93798 and GSE37460 included 40 healthy control and 47 IgAN samples. We normalized the original expression data from two datasets and then compared the gene expression levels between samples from patients with IgAN than healthy control groups (Figure 1A). A total of 561 DEGs were detected, out of which 259 were upregulated and 302 were downregulated genes with 1.5-fold difference (p<0.05). The volcano map in Figure 1B show the expression pattern of all DEGs in the two datasets. The heatmap demonstrated the top 30 DEGs (Figure 1C).

Then the weighed gene co-expression network (WGCNA) involved using the expression profiles of 11629 genes and 96 samples from GSE93798 and GSE37460 was applied to establish a co-expression network. A soft threshold power of 5 was set, resulting in a scale independence reached 0.93 and an average connection value was 24.28 (Figure 2A and 2B). We further visualized the gene network with a heatmap and metamodules, with the grey module containing genes that could not cluster into any module (Figure 2C and 2D). Figure 2E shows the correlations between the modules and clinical trait, with the turquoise module displaying the highest positive correlation with IgAN (r=-0.65, p=1.0e-12). A total of 203 genes with high connectivity in the clinically significant module were identified as hub genes based on the cut-off criteria (|MM| > 0.8) (Figure 2F). In addition, venn analysis was applied to obtain the overlapping genes from the intersections of 561 DEGs, 203 turquoise module genes, and 193FAM-related genes from Reactome and KEGG fatty acid metabolism gene sets. As a result, 12 hub genes were extracted for further analysis (Figure 3A).

Biological Processes and Pathways Enriched for the Hub Genes

GO and KEGG enrichment analyses were performed on the 12 hub genes to predict their biological function. The KEGG pathway analysis showed that the hub genes were significantly enriched in metabolic pathways, fatty acid degradation, PPAR signaling pathway, fatty acid metabolism pathway and peroxisome (Figure 3B). The GO analysis revealed that the shared genes might be related to fatty acid catabolic process, monocarboxy lic acid catabolic process, organic acid catabolic process, fatty acid metabolic process and small molecule catabolic process (Figure 3C). To further explore the interaction of these hub genes, we conducted a PPI analysis using STRING platform, and the results are shown in Figure 3D. Boxplots showed that these 12 hub genes were expressed at lower levels in IgAN than in the ND group (Figure 3E).

Construction of FAM-IgAN-related gene predictive model

To construct a gene prognostic signature based on 12 overlapping genes, univariate logistic regression analysis was used. All 12 genes met the significance of p<0.05 and retained for further analysis. Subsequently, LASSO regression analysis demonstrated that 4 out of 12 genes were tested and selected for the prediction model according to the optimum λ value (Figure 4A and 4B). The 4 FAM-related genes are DPEP1, ACADM, CYP4A11 and SLC22A5. Based on these genes, a risk-score model was developed using the follow algorithm: RiskScore= -0.0180*DPEP1-0.773*ACADM-0.211*CYP4A11-0.510*SLC22A5. Based on the median risk score, total IgAN patients were divided into high risk and low risk groups (Figure 4C). AS shown in Figure 4C, patients in the high risk group trended to expressed the four candidate genes higher than those in the low risk group. Kaplan-Meier survival analysis exhibited a notable difference between the two risk group. Patients in the low risk group had a better overall survival than those in the high risk group (Figure 4D). To compared the risk score between IgAN and healthy controls, we found that the riskscore of IgAN group is significantly lower than that of the control group (Figure 4E). ROC analysis indicated that the predictive model constructed from these four genes had good diagnostic performance, with the AUC values of all four genes were greater than 0.85 (Figure 5A). The model was then further validated in other two datasets. The AUCs of the models in GSE141295 and GSE104948 were relatively high (Figure 5B and 5C). To identify the potential biology functions related to the risk score in IgAN, Gene Set Enrichment Analysis (GSEA) of IgAN-FAM-related genes revealed their association with PPAR signaling pathway, peroxisome, oxidative phosphorylation, and fatty acid metabolism (Figure 6A-6D).

The Association of IgAN Risk Score with Immune Infiltration in IgAN

To estimate the proportion of 22 immune cells in 47 IgAN samples and 49 healthy control samples, we used the CIBERSORT algorithm. According to the immune cell infiltration results, significant difference in the proportion of immune cells between IgAN and healthy control groups (Figure 7A). Box plot also shows that B cells native, T cells CD4 memory, NK cells resting, and neutrophils were significantly decreased in IgAN samples, while plasma cells, T cells CD8, NK cells activated, monocytes and macrophages M1 were significantly increased (p < 0.01) (Figure 7B). To determine whether feature genes were involved in mediating immune homeostasis, we utilized heat map to illustrate the correlation between feature genes and immune cells (Figure 8A). We further evaluate the relationship between IgAN risk score and immune-infiltrated cells in IgAN using Pearson correlation (Figure 8B). The results indicated that patients with an elevated risk score showed a negative correlation with B cells (p=3.7e-6, r=0.45), T CD4 memory resting cells (p=7.0e-9, r=0.55) and neutrophils (p=1.8e-3, r=0.31). On the contrary, patients with an elevated risk score showed a positive correlation with T cells CD8 (p=1.8e-3, r=0.31) and NK cells activated (p=3.1e-4, r=0.36). These findings suggest that feature genes may play a role in immune regulation during IgAN.

Levels of DPEP1, ACADM, CYP4A11 and SLC22A5 were decreased expression in IgAN patients and HMCs

To further validate the findings of bioinformatics analysis, we firstly used peripheral blood from IgAN patient and health controls. As shown in Figure 9A, the expression levels of DPEP1, ACADM, CYP4A11 and SLC22A5 were decreased in IgAN sample (n=25), compared with those in health control (n=25). Then, we stimulated HMCs with Gd-IgA1 to established an in vitro IgAN model. We found that levels of DPEP1, ACADM, CYP4A11 and SLC22A5were significantly decreased relative to the control group after treatment of HMCs with Gd-IgA1 (Figure 9B). Taken together, these results indicate that DPEP1, ACADM, CYP4A11 and SLC22A5 may be key players in the pathogenesis of IgAN.

The pathogenesis of IgAN is a form of glomerulonephritis that results from the interaction of multiple factors, including environmental and genetic factors. Although the mechanism of IgAN has not been fully elucidated, abnormal fatty acid metabolism (e.g. fatty acid oxidation) acts as an important role in its pathogenesis[15]. In the present study, we merged and analyzed gene expression profiling of 47 IgAN and 49 normal samples from 2 datasets by bioinformatics methods. 12 genes linking IgAN and fatty acid metabolism (FAM) were screen. Functional enrichment analysis of GO terms showed that they were involved in small molecule metabolism-related and fatty acid metabolism-related biological processes. KEGG pathway analysis indicated hub genes were significantly enriched in metabolic pathways, fatty acid metabolism-related biological processes, PPAR signaling pathway and peroxisome. Several key proteins involved in the regulation of PPAR signaling pathway and peroxisome are closely associated with the progression of IgAN [15, 20]. Jing et. at suggested that PPAR agonist attenuated inflammatory response in activated tubular epithelial cells in IgAN[21]. Both these GO terms and the results of the KEGG pathway suggest that the DE-FAMs or hub IgAN-FAMs identified in this study may be involved in the progression of IgAN. Both these GO terms and the results of the KEGG pathway suggest that the DE-FAMs or hub IgAN-FAMs identified in this study may be involved in the progression of IgAN.

Then, we constructed a logistic regression model for these genes and drew a ROC curve. Four of the twelve genes, including DPEP1, CYP4A11, SLC22A5 and ACADM, were screened to construct a diagnostic model, which may be involved in the occurrence and progression of IgAN.. Dipeptidase-1 (DPEP1) is a major neutrophil adhesion receptor highly expressed on proximal tubular cells and peritubular capillaries of the kidney[22]. It hydrolyzes a wide range of dipeptides and is also implicated in the renal metabolism of glutathione and leukotrienes [22]. Arthur Lau et.al reported that DPEP1 as major leukocyte adhesion receptor in the kidney and contributes to the severity of AKI. In addition, DPEP1 was reported that could serve as a novel marker associated with FSGS[23]. Medium-chain acyl-CoA dehydrogenase (ACADM) catalyzes the first step of β-oxidation and is responsible for the breakdown of medium-chain fatty acids in the mitochondria[24], which mutation caused one of the most common mitochondrial fatty acid β-oxidation disorders. Alteration of ACADM expression is found in subjects with cardiovascular, metabolic, and nonalcoholic fatty liver diseases[25, 26]. Although the molecular pathological mechanism of low expression of ACADM in kidney has not been well decoded yet, many studies reported that the inhibition of fatty acid oxidation was essential in organs fibrosis and cancer progression. The SLC22 transporter family consists of many members, which are expressed in the kidney, liver, heart and other tissues. SLC22A5 also known as OCTN2 is the high affinity carnitine transport responsible for tissue carnitine uptake[27], which is highly expressed in the kidney, with some expression in the intestine and placenta. SLC22A5 is closely associated with different diseases, such as primary carnitine deficiency, IBD, diabetes and asthma[28].

CYP4A is a member of the CYP4 enzyme family and is involved in the metabolism of medium- and long-chain fatty acids, such as arachidonic acid, palmitate, laurate and compounds with highly regionally selective hydroxylated terminal ω-carbon[29]. CYP4A11 primarily metabolizes endobiotics and catalyzes the ω-hydroxylation of fatty acids in human liver and kidney tissues[30]. However, it is worth noting that there is few research about the roles of DPEP1, ACADM, CYP4A11 and SLC22A5 on IgAN progression and those potential role in the pathogenesis of IgAN needs further study. These four distinct genes with associated with β-oxidation, immunity and energy metabolism, which may be provide new strategies for preventing IgAN following progression to chronic kidney disease.

The histological features of IgAN are diverse and span the gamut seen in most forms of immune complex-mediated proliferative glomerulonephritis[31]. The human immune system is a complex network comprised of both innate and adaptive immunity that can influence IgAN. In this study, the possible immune relationship in IgAN was preliminarily explored using CIBERSORT algorithm to evaluate the immune cell infiltration in IgAN kidney glomerular tissue. We identified several immune cells potentially associated with prognosis in IgAN, mainly involving B cells, CD4 memory T cells, NK cells, dendritic cells, and neutrophils. This may reveal that CD8 T cells and NK cells activated in the kidneys of IgAN patients may promote disease progression. Recent studies have suggested that T cells and their cytokines are involved in posttranslational modification of IgA1 hinge region [32, 33]. Peng et al. demonstrated that Th cell disorder is correlated with proteinuria in IgAN[34]. Those may reveal that T cells in the kidneys of IgAN patients may promote disease progression. In addition, chronic fibrosis induced by chronic inflammation is a common pathological change in IgAN and is closely correlated with prognosis[35]. Chronic inflammation is characterized by recruitment of inflammatory cells, neutrophils, and macrophages. Moreover, almost all immune cell types including Th cell, regulatory T cells, and B lymphocytes are involved in this process[35, 36]. Those have been shown that immune cell infiltration in IgAN may be result in increased glomerular permeability and tubulointerstitial inflammation and renal failure interactions. Nonetheless, this study has some limitations to be addressed. First, the sample size of the dataset was relatively small. Our findings should be further validated in a larger dataset. Second, data on age, sex, medication, and comorbidities of patients in the samples were not considered in this study. Lastly, the detailed functions of the four novel hub genes in IgAN have not been clarified, and further experimental and clinical studies are needed.

Through bioinformatics approaches, we obtained twelve hub genes linking fatty acid metabolism and IgAN. The biological processes and pathways in which they are involved were explored, which will help in understanding the development of IgAN. Based on the logistic regression analysis, we constructed a diagnostic model that can diagnose patients with IgAN by detecting the expression of several genes in the kidney tissue. In addition, these hub genes have been found to be associated with different immune factors, suggesting that they may also have an important role in the immune microenvironment. However, further studies are needed to explore their specific roles.

Ethics approval and consent to participate

Written informed consent was obtained from all patients, and the study was approved by the Xin Hua Hospital Committee on Human Research. The research was performed in accordance with the ethical guidelines of the Declaration of Helsinki 1975.

Consent for publication

Not applicable

Availability of data and materials

All data needed to evaluate the conclusions in the paper are present in the paper and Supplementary Materials.

Competing Interests

The authors declare no conflict of interest.

Funding

This work was supported by National Natural Science Foundation of China (82070697).

Authors’ Contributions

Xiaoqian Qian performed the experiment and drafted the manuscript. Shuyang Bian and Qin Guo performed the experiment. Dongdong Zhu and Fan Bian reviewed and edited manuscript. Gengru Jiang and Jingyang Li designed and supervised the study. All authors contributed to the final version of the manuscript and approved the final manuscript.

Acknowledgements

We appreciated GEO and Arrayexpress databases for providing the original study data.

Author details

^aRenal Division, Department of Internal Medicine, Xin Hua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Yangpu district, 1665 Kongjiang Road, Shanghai, 200092, China. ^bCentre for Rare Disease, Yangpu district, 1665 Kongjiang Road, Shanghai 200092, China. ^cDepartment of Pediatrics, Xin Hua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Yangpu district, 1665 Kongjiang Road, Shanghai 200092, China. ^dStudent/Intern, Emory University, Atlanta, GA, 30322, USA

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No competing interests reported.

Supplementary1.xlsx
Additional file 1: Supplementary 1. Fatty acid metabolism-related gene sets from the Molecular Signatures Database v7.5.1 (MSigDB; www.gsea-msigdb.org)
supplementary2.docx
Additional file 2: Supplementary 2. All primers were used in this manuscript.

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Identification of hub fatty acid metabolism-related genes and immune infiltration in IgA nephropathy

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