Abstract
BACKGROUND:
The molecular mechanism of sepsis-associated acute kidney injury (SA-AKI) is unclear. We analyzed co-differentially expressed genes (co-DEGs) to elucidate the underlying mechanism and intervention targets of SA-AKI.
METHODS:
The microarray datasets GSE65682, GSE30718, and GSE174220 were downloaded from the Gene Expression Omnibus (GEO) database. We identified the co-DEGs and constructed a gene co-expression network to screen the hub genes. We analyzed immune correlations and disease correlations and performed functional annotation of the hub genes. We also performed single-cell and microenvironment analyses and investigated the enrichment pathways and the main transcription factors. Finally, we conducted a correlation analysis to evaluate the role of the hub genes.
RESULTS:
Interleukin 32 (IL32) was identified as the hub gene in SA-AKI, and the main enriched signaling pathways were associated with hemopoiesis, cellular response to cytokine stimulus, inflammatory response, and regulation of kidney development. Additionally, IL32 was significantly associated with mortality in SA-AKI patients. Monocytes, macrophages, T cells, and NK cells were closely related to IL32 and were involved in the immune microenvironment in SA-AKI patients. IL32 expression increased significantly in the kidney of septic mouse. Toll-like receptor 2 (TLR2) was significantly and negatively correlated with IL32.
CONCLUSION:
IL32 is the key gene involved in SA-AKI and is significantly associated with prognosis. TLR2 and relevant immune cells are closely related to key genes.
Keywords: Sepsis, Acute kidney injury, Interleukin 32, Toll-like receptor 2, Bioinformatics analysis
INTRODUCTION
Sepsis is the most common cause of acute kidney injury (AKI) in patients with critical illnesses. The prognosis of patients with sepsis-associated AKI (SA-AKI) is worse than that of patients with non-infectious AKI, and their risk of organ failure is higher.[1] Up to 60% of patients with sepsis can develop AKI, and the mortality of these patients is significantly higher than that of sepsis patients without AKI.[2,3] AKI is also associated with a higher risk of sepsis. In a cohort study, nearly 40% of patients developed sepsis on the fifth day (median) after the occurrence of AKI.[4] AKI impairs hemostasis and induces dysfunction of the immune system, which in turn increases the risk of infection and the incidence of sepsis. SA-AKI is a major healthcare problem that increases the difficulty of post-hospitalization rehabilitation and substantially increases the medical burden. Thus, more studies need to be conducted to identify biomarkers and therapeutic targets of SA-AKI.
In addition to the direct effects of septic shock and ischemia-reperfusion on kidney injury, the pathogenesis of SA-AKI is strongly influenced by inflammatory and immunological mediators. However, the immunological pathogenetic mechanism of SA-AKI is still unclear.[5] Several studies have shown that various cytokines, such as tumor necrosis factor-alpha (TNFα), interleukin 6 (IL6), and interferon-gamma (IFNγ), and many inflammatory cells and phagocytes influence inflammatory injury, which might lead to the necrosis and apoptosis of renal cells, resulting in kidney injury.[6,7] With advancements in biotechnology, a lot of information on genes, transcription factors, and signaling pathways has become available, which has enabled the screening and identification of key biomarkers and potential targets of SA-AKI. However, studies on the key genes and proteins associated with SA-AKI in humans are limited. In this study, we aimed to identify the core genes and biomarkers of SA-AKI using bioinformatics technology.
METHODS
Datasets for differential gene expression analysis
The Series Matrix File GSE65682 was downloaded from the NCBI Gene Expression Omnibus (GEO) public database. The annotation platform used was GPL13667, which contains 802 samples that were included in the healthy control group (n=42) and the sepsis group (n=760). The Series Matrix File GSE30718 was downloaded from the NCBI GEO public database. The annotation platform used was GPL570, which contains 39 samples that were included in the healthy control group (n=11) and the AKI group (n=28). The scRNA-seq data of GSE174220 which included two AKI samples, were downloaded to assess gene expression. The differentially expressed genes in the control and disease groups in the GSE65682 and GSE30718 datasets were identified using the R package limma. The differential gene screening conditions were |logFC| > 0.585 and P < 0.05, and the data were visualized through volcano plots and heatmaps.
Analysis of GO and KEGG functions
The Metascape database was used to annotate and visualize the differential genes at the intersection, and the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed on the differential genes at the intersection. A minimum overlap ≥ 3 and P ≤ 0.01 were considered to be the criteria for statistical significance.
Construction of the WGCNA network and GWAS analysis
We constructed the co-expression network of all samples in the GSE65682 dataset using the weighted gene coexpression network analysis (WGCNA)-R package and screened the genes with the top 5,000 variances with this algorithm for further analysis, where the soft threshold was set to 19. We transformed the weighted adjacency matrix into a topological overlap matrix (TOM) to estimate the degree of network connectivity and used the hierarchical clustering method to construct the clustering tree structure of the TOM matrix. The Gene Atlas database (http://geneatlas.roslin.ed.ac.uk/) was used for conducting the genome-wide association study (GWAS) analysis.
Single-cell analysis and immune gene correlation analysis
The scRNA-seq data of GSE 174220 were downloaded to examine gene expression. The data were processed using the Seurat package, and the positional relationship between each cluster was obtained using the tSNE algorithm. The cells were annotated by the function HumanPrimaryCellAtlasData of the celldex package. The analysis of the RNA-seq data from different patient subgroups was performed using the CIBERSORT algorithm. The “corrplot” package was used to analyze the interaction between immune cells and their effects. The “vioplot” package was used to visualize the relative content of immune cells. All results were considered to be statistically significant at P < 0.05.
GSEA and regulatory network analysis of core genes
Gene set enrichment analysis (GSEA) was performed with a predefined gene set (KEGG) to rank the genes, after which we tested whether the predefined gene set was at the top or bottom of the ranking list. The number of substitutions was set to 1,000, and the substitution type was set to phenotype. The R package “RcisTarget” was used to predict transcription factors. The normalized enrichment score (NES) for the motifs depended on the total number of motifs in the database. In the first step in estimating the overexpression of each motif in a gene set, we calculated the area under the curve (AUC) for each motif-motif set pair. Then, we calculated the NES for each motif based on the AUC distribution of all motifs in the gene set. Finally, we used the rcistarget.hg19.motifdb.cisbpont.500bp for the Gene-motif rankings database.
Validation experiment
We further established a SA-AKI mice model which was induced via cecal ligation and puncture (CLP)[8] Total RNA was extracted from the kidney tissues of these mice. The expression of the IL32 mRNA was analyzed via quantitative real-time polymerase chain reaction (qPCR) (Roche Diagnostics, Germany). The expression of glyceraldehyde-3-phosphate dehydrogenase (GAPDH) mRNA was used as the endogenous control for normalization. The specific primers used for IL32 (161 bp) were as follows: sense: 5’- CGAGGCAACAGATCCCC -3’; antisense: 5’- GCCACCACCTTCTCCTTCA -3’. Western blotting analysis was performed to evaluate the expression of the IL32 protein.
Statistical analysis
Statistical analysis was performed using the R language (version 4.0). The Shapiro-Wilk test was used to assess whether continuous variables followed a normal distribution, and the homogeneity of variance was assessed by Bartlett’s test. The independent-samples t-test or Wilcoxon signed rank test was performed based on the assessment of homogeneity of variance and normal distribution. Survival analysis was performed using the log-rank test. Pearson’s correlation coefficients were evaluated to determine the strength of the correlation between continuous variables. Survival curves were generated by the Kaplan-Meier method. All the statistical tests were two-sided, and all the results were considered to be statistically significant at P < 0.05.
RESULTS
Dataset collection and analysis workflow
The limma package was used to identify differential genes in the GSE65682 and GSE30718 datasets. We identified 2,924 differential genes in GSE65682, including 1,290 upregulated genes and 1,634 downregulated genes. We identified 438 differential genes in the GSE30718, including 189 upregulated genes and 249 downregulated genes. The co-DEGs were identified and used to find hub genes by comparison to the co-expression network of GSE65682. The immune associations, disease associations, and functional annotations of hub genes were evaluated. The workflow of the study is shown in Figure 1. Functional annotation of these 70 intersection differential genes was performed, and the results showed that these genes were enriched in signaling pathways, such as regulation of hemopoiesis, cellular response to cytokine stimulus, inflammatory response, and regulation of kidney development (Supplementary Figure 1).
Figure 1.
Flowchart of the identification and bioinformatics analysis of the key genes related to sepsis-associated acute kidney injury (SA-AKI). The co-differentially expressed genes (co-DEGs) were identified and used to search for hub genes. Immune association and disease association were evaluated, and functional annotation of hub genes was performed. AKI: acute kidney injury; WGCNA: weighted gene coexpression network analysis; GWAS: genome-wide association study; GSEA: gene set enrichment analysis.
WGCNA of the sepsis-associated dataset GSE65682
To identify the key genes that influence sepsis, we used the information about whether the patients had sepsis. We also constructed the WGCNA network based on the expression profile data of GSE65682 to examine the sepsis-related co-expression regulatory network. The soft threshold β was determined by the function “sft$powerEstimate”, and the gene modules were subsequently detected based on the TOM matrix. Seven gene modules were detected, which included black (95), blue (1152), brown (305), grey (2,683), magenta (79), pink (80), and red (606); the red module had the highest absolute correlation [r= –0.54, P=(7e-62)] (Supplementary Figure 2).
IL32 identified as the hub gene
The 606 genes in the red module were mapped to the intersection of differential genes associated with sepsis and AKI. IL32 was identified as an intersection gene. We performed a Kaplan-Meier survival analysis of these genes, and the results showed that IL32 was associated with significant differences in the survival rate of septic patients (Figure 2).
Figure 2.
The Kaplan-Meier survival analysis of septic patients with differences in the expression of IL32. IL32 was identified as an intersection gene of sepsis and AKI. The results showed that IL32 had significant survival differences in septic patients. A higher expression of IL32 was associated with a better prognosis in survival probability.
The SNP pathogenic regions related to IL32
The GWAS data of sepsis patients were analyzed to identify the pathogenic regions of the core genes in sepsis. The Q-Q plot showed that the single nucleotide polymorphism (SNP) loci were significantly associated with sepsis, as determined by the GWAS data (Supplementary Figure 3). The key SNP loci distributed in the enriched region were described by the precise location of the GWAS data. The SNP pathogenic regions corresponding to IL32 were also determined. IL32 was located in the pathogenic region of chromosome 16 (Supplementary Figure 3).
Single-cell analysis and immune microenvironment analysis
The Seurat package was used for single-cell analysis with the scRNA-seq data of GSE174220, and all the clusters were annotated into five cell categories, including hepatocytes, epithelial cells, monocytes, endothelial cells, and tissue stem cells. The expression of the IL32 gene in the five kinds of cells is shown in Supplementary Figure 4. The immune microenvironment is mainly composed of immune cells, extracellular matrix, growth factors, inflammatory factors, which special physical and chemical characteristics, that significantly affect the diagnosis of diseases and the sensitivity of clinical treatment. By analyzing the relationship between key genes and immune infiltration in the sepsis dataset, we further investigated the molecular mechanisms by which the key genes affect the progression of sepsis. Compared to those in normal samples, the number of memory B cells, gamma-delta T cells, monocytes of sepsis samples increased markedly, the contents of the cluster of differentiation (CD) 8 T cells, naïve CD4 T cells, resting memory CD4 T cells, activated memory CD4 T cells, resting natural killer (NK) cells, and activated NK cells were significantly lower. IL32 was strongly correlated with the immune cell content (Supplementary Figure 5).
Enriched signaling pathways of IL32
KEGG analysis was used to study the specific signaling pathways associated with the two key genes through the GSEA and to determine the molecular mechanism by which the key genes affect the progression of sepsis. The results indicated that the main enriched pathways associated with high expression of IL32 were involved in antigen processing and presentation, the T-cell receptor signaling pathway, and the NK cell-mediated cytotoxicity signaling pathway (Supplementary Figure 6).
Correlation analysis of IL32
We obtained disease-regulating genes related to sepsis from the GeneCards database, and the top 15 genes in the relevant rankings were selected for the differential analysis. The results showed that the expression of many genes, such as ELANE, F5, GALT, IL10, and IL18, significantly differed between the patient groups. To determine the relationship between the key genes and the regulation of sepsis, we conducted a correlation analysis between the key genes and 15 disease-related genes. We found that Toll-like receptor 2 (TLR2) was significantly and negatively correlated (Pearson’s r= –0.43) with IL32 (Supplementary Figure 7).
Validation of the identified genes in the kidneys of the SA-AKI mouse model
To validate the role of IL32 in SA-AKI, a CLP-induced SA-AKI mouse model was constructed. Kidney samples were collected 6 h, 12 h, and 24 h after the onset of CLP-induced sepsis. The relative expression of the IL32 gene increased significantly 6 h after sepsis (1.52 ±0.34, P < 0.05), peaked at 12 h (3.63±0.98, P < 0.01), and remained at a high level at 24 h (2.53±0.75, P < 0.01) (Figure 3A). The results of the Western blotting assay showed a similar trend (Figure 3B and 3C). The IL32 protein level increased 6 h (0.80±0.23, P < 0.01) after the onset of sepsis and decreased 12 h (0.45±0.13, P < 0.01) and 24 h (0.41±0.11, P < 0.01) after the onset.
Figure 3.
Validation of the expression of IL32 in the kidneys of SA-AKI mice. A: The expression of the IL32 gene at different time intervals. B and C: The expression of IL32 protein level at different time intervals with western blotting analysis. * P<0.05; **P<0.01.
DISCUSSION
AKI is a common and life-threatening complication of sepsis, the global incidence of SA-AKI is approximately 11 million every year.[9-11] Many studies have investigated the biomarkers and prognosis of AKI. However, fewer studies have investigated SA-AKI.[12] Many genes and proteins are involved in the pathogenesis of SA-AKI, which makes the research challenging. Bioinformatics analysis provides an efficient way to identify biomarkers of interest.[13] Our results showed that IL32 is a key gene that is highly active in SA-AKI and is significantly associated with the mortality of sepsis. Additionally, we determined the exact location of this gene on the chromosome. Hepatocytes, epithelial cells, monocytes, endothelial cells, and tissue stem cells were found to be the major cell types in gene analysis based on the co-DEGs, according to the single-cell analysis. The results of the immune microenvironment analysis showed that the infiltration of certain immune cells (e.g. lymphocytes, dendric cells, macrophages, monocytes, NK cells) increased significantly in patients with sepsis. IL32 was significantly associated with the content of these immune cells. We also found significant interactions between IL32 and TLR2. TLR2 plays a crucial role in sepsis. IL32 was identified as the key gene in SA-AKI. We comprehensively examined the signaling pathways, relevant inflammatory cells, immune cells, and interactions with other important molecules, to elucidate the underlying mechanism of the development and progression of SA-AKI and provide new insight into this disease.
IL32 is a key cytokine involved in many diseases. Its effects are extensive and include the stimulation of IL8, IL1, IL6, TNFα, and macrophage inflammatory protein 2 (MIP-2) and the activation of classical proinflammatory pathways, such as nuclear factor (NF)-κB and p38 mitogen-activated protein kinase (MAPK).[14] IL32 has nine isoforms, in which IL32γ regulates inflammation and the immune response. In this study, IL32 was identified as a key gene and was highly expressed in patients with SA-AKI. The results of the Kaplan-Meier survival analysis showed that IL32 was significantly associated with mortality due to sepsis. Septic patients with higher expression of IL32 were more likely to survive. The results of the animal experiment showed that the expression of the IL32 gene and protein increased 6 h after CLP-induced sepsis in the kidneys of mice. A previous study reported that genetically modified mice in which IL32γ was highly expressed were more resistant to lipopolysaccharide-induced sepsis. Also, the local immune response was enhanced, and the release of systemic cytokines was inhibited.[15] A clinical cohort study reported that the serum IL32 levels increased in patients with acute or chronic kidney injury. The increase in serum IL32 levels was positively associated with the level of serum creatinine (SCr) and 24-h urine protein concentration.[16] Thus, an increase in the level of IL32 partly reflects the severity of SA-AKI in the early stage. These findings were similar to those reported in our study.
In the present study, the results of the immune infiltration analysis of patients with sepsis showed that lymphocytes, monocytes, macrophages, and NK cells were significantly involved. IL32 was also closely associated with these immune cells. The immune microenvironment is complex and strongly affects the pathogenesis of AKI.[17] It consists of immune cells, extracellular matrix, growth factors, inflammatory factors, and specific physicochemical characteristics.[18] Immune response-mediated kidney injury is an important factor in SA-AKI, which needs further investigation. Proximal tubule cells (PTCs) interact closely with infiltrating immune cells, such as T cells, monocytes, and macrophages.[19-21] Our findings showed that the numbers of gamma-delta T cells, monocytes, and M0/M1 macrophages increased significantly during sepsis. In contrast, the numbers of CD8 T cells, CD4 T cells, naïve memory CD4 T cells, and NK cells decreased significantly. We also found that gamma-delta T cells, monocytes, and macrophages are important components of native immunity. T cells and B cells also participate in adaptive immunity. The gamma-delta T cells are unconventional T cells and play an important role in immune monitoring and autoimmunity related to kidney injury or transplantation.[22] The results of the gene set enrichment analysis showed that IL32 was closely related to immunological signaling pathways, including T cell receptor signaling, antigen processing and presentation, and NK cell-mediated cytotoxicity. Another study reported that IL32 promoted the development of Foxp3+ T-regulatory cells (Tregs) and the expression of interferon-γ in CD8+ T cells.[23] Tregs can secrete the anti-inflammatory cytokine IL10 and transforming growth factor-β (TGF-β). Tregs can suppress inflammation and contribute to immune tolerance, which can protect against AKI .[24] Inhibition of Tregs by the CD25 antibody resulted in a decrease in resistance to inflammatory injury and an increase in mortality in mice with AKI.[25] TLR2 is an important receptor of Tregs, which can facilitate Tregs to show a Th17-like phenotype and decrease their suppressive function.[26] Correlation analysis from the GeneCards database showed that TLR2 was significantly and negatively correlated with IL32. These results indicated that IL32 plays an important role in SA-AKI through TLR2 and Tregs. These findings elucidated the molecular mechanism and helped in identifying new biomarkers for SA-AKI.
Our study has several limitations. First, this study was based on bioinformatics analysis. Experiments were also conducted to measure the gene and protein levels to validate the significant correlation between IL32 and SA-AKI. However, more animal experiments and clinical studies are needed to confirm these results. Second, although significant biomarkers and signaling pathways were identified, further details were absent. For example, whether they have beneficial or deleterious effects remain unclear. It was not possible for us to ascertain whether this cytokine takes role in SA-AKI at the initiation, intermediate, or recovery phase after renal damage. These limitations might affect the generalizability of the results. Well-designed experiments need to be conducted to validate the biological roles of these key genes and biomarkers.
CONCLUSION
The IL32 gene plays a key role in SA-AKI. It is closely associated with the mortality of septic patients. Monocytes, macrophages, T cells, and NK cells are involved in the immune microenvironment and are closely associated with IL32. TLR2 is an important receptor of Tregs and is also closely associated with IL32. These findings elucidate the underlying molecular mechanism of the development and progression of SA-AKI and provide new strategies for treating this disease.
ACKNOWLEDGMENTS
We also thank to Qinglin Li for his help in the statistical work.
Footnotes
Funding: This study was supported by Beijing Natural Science Foundation (No. 7222162 to Dr. Hui Liu).
Ethical approval: This study has been approved by the Ethics Committee of the Chinese PLA General Hospital (number: S2017–054–01). The requirement for written informed consent was waived by the ethics committee of the designated hospital for clinical data from a big database.
Conflicts of interest: The authors declare that they have no competing interests.
Author contributions: QZ and JFM contributed equally to this work. QZ and JFM took part in the study design, collected the data, performed bioinformatics analyses and drafted the manuscript. JGX and ZF participated in the design, and did the statistical work. HL conceived of the study and revised the manuscript critically for important intellectual content. All authors read and approved the final manuscript.
All the supplementary files in this paper are available at http://wjem.com.cn.
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