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Neutrophil extracellular traps-related genes contribute to sepsis-associated acute kidney injury

BMC Nephrology volume 26, Article number: 235 (2025) Cite this article

Abstract

Background

Neutrophil extracellular traps (NETs) and oxidative stress (OS) may be involved in sepsis-associated acute kidney injury (SA-AKI). The aim of this study was to identify potential regulators which modulate NETs and OS in SA-AKI, and to find potential therapeutic agents.

Methods and Materials

SA-AKI-related datasets GSE255281 and GSE225192 were downloaded from Gene Expression Omnibus. Molecular subtypes associated with NETs were identified by unsupervised clustering. The OS-related genes were obtained by weighted gene co-expression network analysis. Differentially expressed genes were screened by “limma” package in R. Least absolute shrinkage and selection operator algorithm was applied to identify the hub genes. Additionally, the biological functions of the hub genes were analyzed with single sample gene set enrichment analysis. NetworkAnalyst database was searched to screen the drugs targeting the hub targets. qRT-PCR was used to analyze the expression of key genes in the peripheral blood mononuclear cells (PBMCs) of the patients with SA-AKI and healthy controls. HK-2 cells and human umbilical vein endothelial cells (HUVECs) were induced by lipopolysaccharide (LPS) to construct a SA-AKI model, and the effects of estradiol and (+)-JQ1 on HK-2 cells and HUVECs were evaluated by CCK-8 assays, flow cytometry and OS indices.

Results

Based on NETs-related genes, SA-AKI samples could be divide into two subgroups, and the differentially expressed genes between two subgroups were associated with OS. In silico analyses identified 13 hub targets. The expression of ECT2 and CHRDL1 in PBMCs of SA-AKI patients was significantly lower than that in control group, and the expressions of PTAFR, CSF3 and FOS were significantly higher. Estradiol and (+)-JQ1, which targeted more of the hub targets with good binding affinity, could increase the viability of HK-2 cells and HUVECs induced by LPS and inhibit apoptosis and OS.

Conclusion

Formation of NETs, contributes to OS and pathogenesis of SA-AKI. Estradiol and (+)-JQ1, targeting multiple regulators in the formation of NETs, may be potential therapeutic agents for the treatment of SA-AKI.

Clinical trial number

Not applicable.

Peer Review reports

Introduction

Sepsis is a life-threatening clinical syndrome characterized by organ dysfunction due to the patient’s dysregulated response to infection [1, 2]. Sepsis is associated with high mortality and morbidity, resulting in a heavy social burden worldwide [3]. Acute kidney injury (AKI) caused by sepsis is a common complication in hospitalized and critically ill patients, and up to 60% of sepsis patients suffer from AKI [4]. Sepsis-associated acute kidney injury (SA-AKI) can increase the risk of death and longer hospital stay [5, 6]. Therefore, exploring the biological mechanism of SA-AKI and identifying biomarkers of SA-AKI are crucial for the early diagnosis of patients and the development of targeted therapeutic drugs.

Neutrophils belong to the first line of innate immunity against pathogens. In the early stages of sepsis, neutrophils recruit to the site of infection, and release reactive oxygen species (ROS), reactive nitrogen species (RNS), and neutrophil extracellular traps (NETs) to kill pathogens [7]. NETs are the “net” of DNA, histone proteins and antimicrobial peptides produced by active neutrophils, which can trap and kill pathogens such as bacteria, fungi and viruses and play an important role in innate immunity [8, 9]. However, NETs are a “double-edged sword”, and excessive NETs lead to severe inflammation [10], apoptosis [11], thrombosis [12], and organ dysfunction [13, 14]. The severity of infant sepsis is positively correlated with the level of NETs [15]. In the SA-AKI mouse model, NETs formation is significantly increased, and blocking NETs formation can significantly reduce mortality and ameliorate AKI in mice [16, 17]. Therefore, elucidating the molecular mechanisms by which NETs formation guide host defense against SA-AKI is helpful in developing potential strategy for SA-AKI treatment.

Oxidative stress (OS) is considered to be the main factor to accelerate SA-AKI progression. ROS and RNS mediate abnormal renal microcirculation, local tissue hypoxia and mitochondrial dysfunction [18]. In addition, enhanced OS is a key mediator of innate immune response and can cause a “cytokine storm” that aggravates kidney injury [19]. Increasing studies support that antioxidants may be potentially effective drugs for the treatment of SA-AKI [20, 21].

Increasing studies have supported that NETs formation contributes to the activation of inflammatory cascade, leading to increased OS and aggravated organ injury [22, 23]. However, there were few studies focusing on the relationship between NETs formation and OS in the pathogenesis of SA-AKI. In the present work, based on bioinformatics analysis, we report that formation of NETs, probably contributes to OS in the pathogenesis of SA-AKI, and potential drugs for treating SA-AKI are preliminarily screened.

Materials and methods

Overall design of the present work

In this study, SA-AKI samples of mice were classified into two subtypes using unsupervised clustering based on NETs-related genes. The two subtypes also showed different OS status. Based on OS-related genes, OS-related gene modules were obtained by weighted gene co-expression network analysis (WGCNA). Subsequently, differentially expressed gene (DEG) analysis was performed on SA-AKI samples and control samples. Least absolute shrinkage and selection operator (LASSO) was used to further screen the hub genes from the genes in the intersection. The predictive value of hub genes for SA-AKI was analyzed by using the nomogram and receiver operator characteristic (ROC) curve. Next, bioinformatics analysis, molecular docking and in vitro assays were performed to explore the role of hub genes in SA-AKI pathogenesis, and evaluate their potential as drug targets. The work flow of the present work is shown in Fig. 1.

Fig. 1
figure 1

Work flow of the present work

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Data collection and data processing

In Gene Expression Omnibus (GEO) database (http://www.ncbi.nlm.nih.gov/geo), “SA-AKI” was used as the key work to search the datasets, and GSE255281 and GSE225192 datasets were downloaded after filtering. A total of 65 SA-AKI samples of C57/BL6 mouse constructed by cecal ligation and puncture (CLP) or lipopolysaccharide (LPS) induction, and 60 samples of sham group were obtained. “comBat” function in the R package “sva“ [24] was used to merge two datasets and eliminate batch effects, which was validated by principal component analysis (PCA). The basic information and the links of the datasets are shown in Table 1.

Table 1 The information of the datasets used in the present study
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Analysis of NETs-related subtypes

57 homologous NETs-related genes were obtained from a previously published study [25], and R package “ConsensusClusterPlus“ [26] was used to classify all SA-AKI samples based on these genes based on k-means algorithm and euclidean distance. Through the analysis of consensus cumulative distribution function (CDF) and Delta area plots, finally, the clustering number k = 2 was determined as the ideal value.

DEG analysis

R package “limma“ [27] was used for DEG analysis. The thresholds of DEGs were fold change (FC) ≥ 1.5 and P value < 0.05. The volcano map and heat map were drawn using R packages “ggplot” and “pheatmap” [28, 29].

WGCNA

All SA-AKI samples were analyzed using the R package “WGCNA“ [30], and the soft threshold was set as 4. Topological overlap matrix (TOM) was used to infer the network connection level. Next, hierarchical clustering was used to build a tree diagram to calculate the correlation between module characteristic genes and disease phenotypes. OS-related genes were obtained from GeneCards database (https://www.genecards.org/) [31], and single sample gene set enrichment analysis (ssGSEA) [32] was performed to evaluate the OS status of each sample, and the scores were used as the traits. The genes in the key modules (r > 0.3, P < 0.05) were considered to be the crucial genes associated with OS in SA-AKI.

Functional enrichment analysis

Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analyses were performed using R package “ClusterProfiler“ [33] (p.adjust < 0.05). GO included biological processes (BP), cellular components (CC), and molecular functions (MF). Gene set variation analysis (GSVA) was performed with R package “GSVA“ [32], based on GO-BP gene set and Reactome gene set, which were downloaded from MSigDB database [34], to assess potential biological functional changes in different samples. Gene set enrichment analysis (GSEA) was performed using the R package “clusterProfiler” to observe the differential pathways between subtypes. Mouse HALLMARK gene set was downloaded from MSigDB database [34], and then HALLMARK gene set score was calculated for all samples using ssGSEA algorithm [32], and a heat map was drawn to show the difference of HALLMARK scores among different groups. Spearman method was used to calculate the correlation between hub genes and HALLMARK pathways.

Screening of hub genes

LASSO algorithm [35] was used to conduct cross-validation based on the expression data of genes in the intersection, and lambda.1se was selected as the final lambda coefficient to obtain 13 key genes of the model. Subsequently, the 13 genes were analyzed with Spearman correlation analysis and differential expression analysis.

Construction and verification of nomogram

Based on the hub genes, the R package “rms“ [36] was applied to construct a nomogram to predict the possibility of SA-AKI, and a calibration curve was drawn to test the reliability of the nomogram. The area under the curve (AUC) was then calculated to further evaluate the predictive value of the nomogram for diagnosing SA-AKI [37, 38]. AUC > 0.7 was considered as the ideal predictive value.

Drug screening

NetworkAnalyst database [39] was applied to predict the drugs targeting the hub targets. Candidate drugs that targeted 5 or more hub targets were collected. Toxic drugs were further excluded based on literature reviewing, and finally 3 candidate drugs were obtained. From the CTD database (http://ctdbase.org/) [40] and the PubChem database (https://pubchem.ncbi.nlm.nih.gov/), the secondary structures of the drugs (SDF file) were downloaded. The structures of hub targets (mol2 file) were downloaded from the Uniprot database (https://uniprot.org/) [41]. The structure files were then preprocessed with the AutoDock software, and AutoDock Vina software (version: 1.5.7) [42] was used for molecular docking, and the PyMol software (version: 2.4.0) [43] was used to visualize the results. Binding affinity <-5 kcal/mol suggested that the compound could bind with the target.

Clinical sample collection

This study collected 22 patients with septic kidney injury from the Intensive Care Unit of Renmin Hospital of Wuhan University from January 2022 to December 2023. All patients met the following inclusion criteria: (1) aged between 18 and 90 years; (2) diagnosed with sepsis [44] and AKI in stage 2 of KDIGO classification [45]. Patients with a history of chronic kidney disease (chronic renal failure due to glomerulonephritis, diabetic nephropathy, hypertensive nephropathy, hereditary nephritis, and a variety of other conditions), multiple hospitalizations, and ICU stays of less than 24 h (suggesting the patient’s condition was speedily improving or declining, and the samples may not reflect the true condition of SA-AKI) were excluded. Blood samples of these patients, and 15 healthy controls from the Physical Examination Center were collected, and peripheral blood mononuclear cells (PBMCs) were obtained according to density differences in accordance with previous methods [46, 47]. This study was performed in accordance with the Declaration of Helsinki. Considering the information of the patients were not released during the study, and the study didn’t change the procedures of the diagnosis and treatment, and this study was exempted by the Ethics Committee of Renmin Hospital of Wuhan University, and the formal vote was exempted.

Cell culture

Human renal proximal tubular epithelial cell line HK-2, and human umbilical vein endothelial cells (HUVECs) were obtained from the Cell Bank of the Committee for the Preservation of Collections and Cultures of Chinese Academy of Sciences (Shanghai, China). The cells were cultured in RPMI-1640 medium (Invitrogen, Shanghai, China) containing 10% fetal bovine serum (FBS, Invitrogen, Shanghai, China), 100U/mL penicillin and 0.1 mg/mL streptomycin (Invitrogen, Shanghai, China), in an incubator (37℃, 5% CO2). LPS (1 µg/ml) (Beyotime, Shanghai, China) was used to stimulate HK-2 cells and HUVECs to construct the in vitro model of SA-AKI. After treatment with 100 nM estradiol or (+)-JQ1 compound (Beyotime, Shanghai, China), HK-2 cells and HUVECs were then treated with LPS for 22 h [48].

Quantitative reverse transcription-polymerase chain reaction (qPCR)

Total RNA, extracted with TRIzol reagent (Invitrogen, Shanghai, China), was reverse-transcribed into cDNA using a PrimeScript™ RT kit (Takara, Dalian, China). qPCR was performed using TB Green™ Premix Ex Taq™ kit (Takara, Dalian, China), primers and cDNA on the ABI 7300 Sequence Detection System (Applied Biosystems, Foster City, CA, USA). The condition of qPCR: denaturation at 95 °C for 3 min, 40 cycles of 95 °C for 12s, 62 °C for 12s, and then extended at 62 °C for 40 s. GAPDH served as an internal control for calculating mRNA expression levels. The primer information is shown in Table 2.

Table 2 Primer sequences used in the present study
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Cell viability assay

The Cell Counting Kit-8 (CCK-8) test Kit (Beyotime, Shanghai, China) was used to detect cell viability. In short, HK-2 cells and HUVECs were added to 96-well plates with a density of 6 × 103 cells / well and cultured for 24 h. After that, 10 µL CCK-8 solution was added to each well and the cells were incubated for 2 h. Finally, the absorbance of the samples was measured with a microplate reader (Invitrogen, Shanghai, China) at 450 nm wavelength.

Flow cytometry

An Annexin V-FITC/propidiumiodide (PI) Apoptosis Detection Kit (Yeasen Biotech, Shanghai, China) was used to detect the apoptosis of HK-2 cells and HUVECs. HK-2 cells and HUVECs were pre-cooled and washed with phosphate buffer saline twice, and then resuspended with 1×binding buffer. The cell suspension was added with 5 µL AnnexinV-FITC and 5 µL PI, thoroughly mixed and incubated at room temperature for 15 min. After washing with binding buffer, the apoptosis rate of the cells was detected by a flow cytometer (Attune NxT, Thermo Fisher, USA).

Detection of ROS and malondialdehyde (MDA)

The ROS and MDA contents in HK-2 cells and HUVECs were detected according to the manufacturer’s instructions of the corresponding ROS and MDA detection kits (Beyotime, Shanghai, China).

Statistical analysis

Data processing and analysis were performed in the R software (version: 4.3.3) and GraphPad Prism software (version: 8.2.1). The continuous variables with normal distribution were represented by “mean ± standard deviation (SD)” and compared by independent student t tests or one-way ANOVA. Non-parametric test was used for variables that did not conform to normal distribution. Spearman correlation analysis was used to analyze the correlation. A P value < 0.05 was considered statistically significant.

Results

Classification of NETs-related subtypes in SA-AKI

First of all, GSE255281 and GSE225192 datasets were merged, and the batch effect was removed (Supplementary Fig. 1A&B). The merged dataset contained a total of 65 SA-AKI samples and 60 sham control (Sham) samples. Based on 57 NETs-related genes, two subtypes of SA-AKI were obtained using k-means algorithm. Subtype 1 contained 52 samples, and subtype 2 contained 13 samples (Fig. 2A). Between the two subgroups, a total of 2122 DEGs were identified (Fig. 2B), and the expression profiles of the top 10 up-regulated and down-regulated genes in the two subgroups are shown in a heat map (Fig. 2C). GO analysis showed that DEGs were mainly associated with regulation of inflammatory response, regulation of cell-cell adhesion, positive regulation of response to external stimulus, protein serine/threonine kinase activity, etc. (Fig. 2D). KEGG enrichment analysis suggested that the DEGs were mainly associated with inflammatory signaling pathways, including MAPK signaling pathway, NF-κB signaling pathway and TNF signaling pathway (Fig. 2E). Differences between biological processes and signaling pathways between the two subtypes were further analyzed by GSEA and GSVA. GSEA showed smooth muscle contraction, DNA recombination, protein localization to cell junction, oxidative phosphorylation, respiratory electron transport, detoxification of reactive oxygen species, and mitochondrial biogenesis were differentially enriched in the two subtypes (Fig. 3A-B). GSVA showed that biological processes such as amino acid metabolism, lipid metabolism, heme degradation, glutathione synthesis and recovery, and peroxisomal lipid metabolism were differentially enriched in the two subtypes (Fig. 3C-D). These results suggest that NETs in SA-AKI are probably related to OS.

Fig. 2
figure 2

Classification of NETs-related subtypes of SA-AKI samples in GSE255281 and GSE225192. (A) Based on 57 NETs-related genes, two subtypes of SA-AKI were obtained using k-means algorithm. (B) The volcano map of the results of differentially expressed genes (DEGs) between two NETs-related subgroups. (C) The heat map of top 10 DEGs with up-regulated or down-regulated expression in the two subgroups. (D) The bar chart showing the top 10 items of biological process (BP), cellular component (CC) and molecular function (MF) associated with the DEGs in GO analysis. (E) The bubble map of the top 20 items of KEGG enrichment analysis based on the DEGs

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Fig. 3
figure 3

Biological process differences between NETs-related subtypes in SA-AKI. (A) Biological process (BP) differences between the two subtypes were analyzed by gene set enrichment analysis (GSEA) based on GO Biological Process gene set. (B) BP differences between the two subtypes were analyzed by GSEA based on the Reactome gene set. (C) BP differences between the two subtypes were analyzed using gene set variation analysis (GSVA) based on GO Biological Process gene set. (D) BP differences between the two subtypes were analyzed by GSVA based on the Reactome gene set

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Identification OS-related genes associated with NETs formation in SA-AKI

Next, we tried to screen the regulators of OS during SA-AKI. Between SA-AKI and sham samples, there were 1432 DEGs (Fig. 4A-B). WGCNA was also performed, with the OS status, which were obtained by ssGSEA, as the traits. Suitable soft threshold β = 4 was screened (Fig. 4C). 17 gene modules were obtained by WGCNA (Fig. 4D), and MEbrown and MEroyalblue modules were remarkably associated with OS (r > 0.3 and P < 0.05) (Fig. 4E). Subsequently, 20 genes in the intersection of the genes in MEbrown and MEroyalblue modules, DEGs between SA-AKI samples and Sham samples, and DEGs between the two subgroups of SA-AKI were obtained (Fig. 4F). GO analysis showed that these 20 genes were associated with cellular response to leptin stimulus, response to leptin and cellular response to reactive oxygen species (Fig. 4G). KEGG enrichment analysis showed that these genes were involved in regulating apoptosis, cAMP signaling pathway, etc. (Fig. 4H).

Fig. 4
figure 4

Identification and enrichment analysis of NETs-related oxidative stress (OS) genes. (A) The volcano map showing the differentially expressed genes (DEGs) between SA-AKI samples and sham samples. Orange represents the significantly up-regulated gene, green represents the significantly down-regulated gene, and gray represents a gene with insignificant. (B) The heat map showing the top 10 up-regulated and top 10 down-regulated DEGs between SA-AKI and Sham samplesexpression difference. (C) Scale independence and average connectivity plots are used to select the best soft threshold for weighted gene co-expression network analysis (WGCNA). (D) The clustering tree of gene clusters in WGCNA. (E) The heat map showing the correlation between gene modules and ssGSEA scores of OS. (F) A venn diagram was used to obtain the genes in the intersection of the genes in MEbrown and MEroyalblue modules (OSG_module), DEGs between SA-AKI samples and Sham samples (Groups_Degs), and DEGs between the two subgroups of SA-AKI (Cluster_Dges). (G) String diagram of the top 20 items of GO analysis based on the genes in the intersection. (H) String diagram of the top 10 items of KEGG enrichment analysis based on the genes in the intersection

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Identification of hub genes involved in regulating NETs and OS during SA-AKI

In order to further identify hub genes from the 20 genes mentioned above, LASSO algorithm was used (Fig. 5A-B), and 13 hub genes were obtained. They were Ccdc159, Abtb3, Ect2, Chrdl1, Gm11434, 6030443J06Rik, Gm43860, Gm3953, Fos, Inhbb, Csf3, Fhad1, and Ptafr. Among them, in the SA-AKI samples, Ccdc159, Abtb3, Ect2, Chrdl1, Gm11434, 6030443J06Rik, Gm43860 and Gm3953 were lowly expressed; while Fos, Inhbb, Csf3, Fhad1, and Ptafr were significantly overexpressed (Fig. 5C). Correlation analysis showed that most of these 13 genes showed significant positive correlation, indicating that these 13 genes jointly play a role in the formation of NETs and promoting OS during SA-AKI (Fig. 5D). Next, a nomogram was constructed based on the 13 genes (Fig. 5E). Calibration curve showed the nomogram had good ability to predict SA-AKI (Fig. 5F). Then ROC curve was used to investigate the ability of hub genes to predict the occurrence of SA-AKI. The results showed that AUC of all 13 hub genes was greater than 0.7, indicating that these 13 genes had a good predictive ability for the occurrence of SA-AKI (Fig. 5G). Compared with single genes, the nomogram had better predictive value (AUC = 0.910) (Fig. 5H). These results suggest that these 13 hub genes, which are associated with the formation of NETs and OS, are involved in the pathogenesis of SA-AKI, and they are potential biomarkers and drug targets of SA-AKI.

Fig. 5
figure 5

Identification of hub genes related with formation of NETs and OS in SA-AKI. (A-B) The hub genes were obtained by least absolute shrinkage and selection operator (LASSO) and 10-fold cross-validation. (C) The box plot of hub gene expression in SA-AKI and sham samples. (D) Correlation analysis between hub genes in SA-AKI samples. (E) A nomogram based on the 13 hub genes predicting the occurrence of SA-AKI. (F) Calibration curves to assess the predictive efficacy of the nomogram. (G) Receiver operator characteristic (ROC) curves of hub genes for diagnosing SA-AKI. (H) ROC curve of the nomogram for diagnosing SA-AKI. *P < 0.05, **P < 0.01, ***P < 0.001

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In silico analysis of the biological functions of the 13 hub genes

Based on the mouse HALLMARK gene set, functional enrichment analysis showed that grouping of SA-AKI samples and the expression level of the 13 hub genes were associated with multiple inflammation-related pathways including TNF-α signaling, NF-κB signaling, IL6-JAK-STAT3 signaling, etc. (Fig. 6A,B). ssGSEA was then applied to perform immunoinfiltration analysis, and it showed that type 17 T helper cell, regulatory T cell, gamma delta T cell, central memory CD8 T cell, activated dendritic cell, macrophage, activated CD4 T cell, and myeloid derived suppressor cell showed significant differences between SA-AKI and sham samples (Fig. 6C). Correlation analysis showed that the infiltration of regulatory T cell was significantly negatively correlated with Ccdc159 and Abtb3; infiltration of macrophage was significantly negatively correlated with Ccdc159, Gm3953 and Abtb3; infiltration of myeloid derived suppressor cell was significantly positively correlated with Fos, Csf3 and Ptafr; and infiltration of activated dendritic cell was significantly positively correlated with Csf3 and Ptafr; infiltration of activated CD4 T cell was significantly positively correlated with Csf3 and Inhbb, and negatively correlated with Ccdc159 (Fig. 6D). Notably, neutrophil infiltration was significantly positively correlated with 9 hub genes (Fig. 6D).

Fig. 6
figure 6

Analysis of functional enrichment and immune infiltration of hub genes. (A) Based on HALLMARK gene set, single sample gene set enrichment analysis (ssGSEA) was performed to analyze the differences of biological processes between SA-AKI and sham samples. (B) The heat map of the correlations between the 13 hub genes and HALLMARK gene set scores. (C) ssGSEA was used to evaluate the infiltration of 28 kinds of immune cells in SA-AKI and sham samples. (D) The heat map of the correlations between the 13 hub genes and the infiltration of 28 immune cells. *P < 0.05, **P < 0.01, and ***P < 0.001

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Results of drug prediction and molecular docking

Subsequently, 415 drugs targeting hub targets were obtained through NetworkAnalyst database (Fig. 7A), and 6 compounds targeted at least 5 hub targets, including acetaminophen, silicon dioxide, estradiol, aflatoxin B1, potassium chromate (VI) and (+)-JQ1 (Table 3). Acetaminophen, estradiol and (+)-JQ1 were chosen for further analysis. Molecular docking showed that estradiol and (+)-JQ1 have much better binding affinities with the hub targets, compared with those of acetaminophen (Fig. 7B). Molecular docking showed that, estradiol could form two hydrogen bonds with TYR-77 and ARG-14 amino acid residue of PTAFR, two hydrogen bonds with ARG-155 amino acid residue of CSF3, and one hydrogen bond with GLN-144 amino acid residue of CHRDL1; (+)-JQ1 could form two hydrogen bonds with TYR-1030 and ALA-1001 amino acid residues of PTAFR, one hydrogen bond with TYR-206 amino acid residue of CSF3, and two hydrogen bonds with SER-739 and GLU-652 amino acid residues of ECT2 (Fig. 7C). These results suggest that estradiol and (+)-JQ1 are candidate drugs to treat SA-AKI.

Fig. 7
figure 7

The potential drugs targeting the hub targets. (A) Network diagram of the drugs targeting the hub targets in NetworkAnalyst database. The orange nodes represent the hub gene and the blue nodes represent the drug. (B) Heat map of molecular docking results. (C) 3D binding mode diagrams of estradiol and (+)-JQ1, and their targets. Orange represents the drug, green represents the amino acid residue, yellow dashed line represents the hydrogen bond, and gray represents the 3D structure of the protein

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Table 3 The top 6 components targeting the hub targets in networkanalyst database
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The expression characteristics of hub genes in PBMCs of patients with SA-AKI and the regulatory effects of candidate drugs on renal tubular epithelial cells and vascular endothelial cells

To further verify the correlation between hub genes and SA-AKI, qRT-PCR was used to detect the expression of some hub genes in PBMCs of patients with SA-AKI and healthy controls. The results showed that the expression levels of ECT2 and CHRDL1 in SA-AKI patients were significantly lower than that in control group; the expression levels of PTAFR, CSF3 and FOS in SA-AKI patients were significantly higher than those in the control group (Fig. 8A), which was consistent with the in silico analysis. HK2 cells and HUVECs were stimulated by LPS, and CCK-8 assay showed that cell viability in the LPS treatment group was significantly lower than that in the control group, while estradiol and (+)-JQ1 compound treatment significantly increased the viability of HK-2 cells and HUVECs (Fig. 8B). Flow cytometry confirmed that estradiol and (+)-JQ1 significantly alleviated LPS-induced apoptosis of HK-2 cells and HUVECs (Fig. 8C). In addition, compared with LPS treatment group, treatment with estradiol and (+)-JQ1 significantly inhibited LPS-induced intracellular ROS and MDA accumulation (Fig. 8D&E).

Fig. 8
figure 8

The expression characteristics of hub genes in PBMCs of patients with SA-AKI and the regulatory effects of candidate drugs on renal tubular cells. (A) mRNA expressions of CCDC159, ECT2, CHRDL1, FOS, INHBB, CSF3, FHAD1 and PTAFR in peripheral blood mononuclear cells of 15 healthy controls and 22 SA-AKI patients were detected by qRT-PCR. (B) The viability of HK-2 cells and HUVECs in control group, lipopolysaccharide (LPS) treatment group, estradiol treatment group and (+)-JQ1 treatment group was detected by CCK-8 assay. (C) The apoptosis level of cells in different groups was detected by flow cytometry. (D-E) Reactive oxygen species (ROS) (D) and malonaldehyde (MDA) (E) levels in HK-2 cells and HUVECs of different treatment groups were assessed to evaluate the oxidative stress. *P < 0.05 and **P < 0.01 vs. Control group; #P < 0.05, ##P < 0.01, and ###P < 0.001 vs. LPS group

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Discussion

The incidence of SA-AKI varies between 25 and 75% depending on the the differences of cohort, severity of sepsis, and diagnostic criteria; in any case the occurrence of SA-AKI is associated with higher mortality and longer ICU stay [49, 50], and clarifying the mechanism of SA-AKI pathogenesis is of great significance to develop new treatment strategies and improve the patients’ prognosis. The pathogenesis of SA-AKI is extremely complex and is influenced by multiple factors, including microvascular dysfunction, ROS formation, cell death, inflammation and metabolic reprogramming [51]. Exploring the pathogenesis of SA-AKI is of great significance to identify novel diagnostic biomarkers and therapeutic targets. After the innate immune system is activated, mast cells will rapidly release a large number of pro-inflammatory factors, which can not only induce microvascular changes [52, 53], but also promote large recruitment of neutrophils, leading to AKI [54,55,56]. Activated neutrophils release NETs that stimulate macrophage recruitment and antigen presentation, thereby triggering cytotoxic T cell responses and enhancing renal tubular necrosis in AKI [57,58,59]. The studies mentioned above imply the importance of NETs in the pathogenesis of AKI, however few studies report the relationship between NETs and SA-AKI. In the present work, based on NETs-related genes, via unsupervised clustering, SA-AKI samples of mouse could be divided into two subgroups with distinct expression profiles of NETs-related genes. This suggests that NETs are involved in the pathogenesis of SA-AKI.

Interestingly, in the present work, bioinformatics analysis suggested that, OS were markedly associated with NETs in SA-AKI. Subsequently, 13 hub genes were screened, which were considered to be the crucial regulators of NETs formation and OS during SA-AKI, including Ccdc159, Abtb3, Ect2, Chrdl1, Gm11434, 6030443J06Rik, Gm43860, Gm3953, Fos, Inhbb, Csf3, Fhad1, and Ptafr. Epithelial cell transforming 2 (ECT2) is involved in regulating vascular permeability during neutrophil exosmosis during acute inflammation [60]. In addition, ECT2 contributes to the tight junction function of renal epithelial cells and the maintenance of cell polarity, and the dysfunction of ECT2 leads to renal tubulointerstitial lesion [61]. In this study, ECT2 was found to be down-regulated in SA-AKI. Chordin like 1 (CHRDL1) is a specific inhibitor of bone morphogenetic proteins (BMPs), especially bone morphogenetic protein 4 (BMP4); BMPs play an important role in renal interstitial fibrosis and renal tubular injury in acute renal injury [62, 63]. Fos proto-oncogene, AP-1 transcription factor subunit (FOS) is a subunit of activating protein 1 (AP-1), belonging to the leucine zipper transcription factor family. The expression of FOS is up-regulated in kidney tissues of rats with AKI [64], which is consistent with the result of this study. FOS can directly modulate the transcription of inflammatory cytokines (such as TNF-α, IL-6 and IL-1β), and promote the pathogenesis of AKI [65]. Notably, a recent study reports that, FOS may be involved in the formation of NETs in myocardial infarction [66]. Inhibin subunit beta B (INHBB) encodes a member of the transforming growth factor β protein superfamily that plays a key role in the activation of NF-κB pathway [67]. Up-regulation of INHBB in renal tubular epithelial cells can activate interstitial fibroblasts and trigger fibrosis [68]. Colony stimulating factor 3 (CSF3) encodes a member of the IL-6 superfamily, which participates in the recruitment, adhesion and activation of neutrophils at the inflammatory site [69]. Previous studies have reported that inhibition of CSF3 pathway to reduce neutrophil accumulation may be a potentially effective way to improve the prognosis of SA-AKI [70, 71]. Platelet activating factor receptor (PTAFR) stimulates the infiltration of neutrophils, which impairs renal function [72, 73]. Our work suggested that these hub genes are pivotal in regulating the formation of NETs and OS in SA-AKI, and their biological functions and related molecular mechanism deserves to be further explored in the subsequent studies with biochemical assays. LASSO algorithm can compress the coefficients in the model so that some of the unimportant feature coefficients are reduced to zero. This advantage helps to simplify the model, reduce unnecessary features, and improve the interpretability of the model; in addition, LASSO also has the advantage of controlling the model to reduce the risk of overfitting and improve the model generalization ability [35]. In the present work, LASSO was used to identify the hub genes. It is worth noting that, other machine learning approaches may help find more potential regulators in SA-AKI, which may be tried in the following work.

Six compounds including acetaminophen, silicon dioxide, estradiol, aflatoxin B1, potassium chromate (VI) and (+)-JQ1 compound, targeted at least five hub targets. Silicon dioxide is inorganic compound and can not be absorbed by the body. Aflatoxin B1 and potassium chromate (VI) are toxic and can induce OS-related injury in kidney cells [74, 75]. Acetaminophen is one of the common analgesic and antipyretic drugs. Studies have shown that acetaminophen can reduce OS and inhibit excessive innate immune response in patients with sepsis [76,77,78]. However, excessive acetaminophen can lead to severe acute kidney injury [79], so it is necessary to be cautious in choosing Acetaminophen as treatment for SA-AKI patients. Estradiol can inhibit the accumulation of neutrophils and alleviate inflammatory response [80]. In addition, Estradiol can promote the regeneration of injured renal tubular cells, reduce apoptosis and fibrosis, and thus ameliorate the progression of AKI [81, 82]. (+)-JQ1 compound is an inhibitor of bromodomain and extraterminal domain (BET), which can reduce the expression of pro-inflammatory factors IL-1, IL-18β and IL-1 in patients with sepsis, and effectively attenuate the inflammatory damage induced by sepsis [83, 84]. In addition, (+)-JQ1 compound has shown promise in preclinical models of AKI and chronic kidney disease [85]. In this study, it was found that estradiol and (+)-JQ1, respectively, had good binding affinities with FOS, CSF3, ECT2, INHBB, PTAFR and CHRDL1. In vitro assays further showed that estradiol and (+)-JQ1 compound could significantly inhibit the OS in LPS-induced HK-2 cells and HUVECs, and increase cell viability. These data suggests that estradiol and (+)-JQ1 are promising drugs to treat SA-AKI. Of course, their safety and efficacy should be validated with clinical trials in the following studies.

There are some limitations to this study. First, the patients with SA-AKI rarely receive biopsy, so the present work only collected datasets of mouse model with SA-AKI. However in the patients’ samples, the crucial genes, identified by in silico analysis, were indeed dysregulated, which validated the findings based on mice samples. Anyway, the roles of the hub genes in regulating NETs formation and OS, and in modulating the pathogenesis of SA-AKI requires further validation with animal models and clinical data. Secondly, even though in the present work we preliminarily proved the protective effects of estradiol and (+)-JQ1 on renal tubular epithelial cells, the current data are insufficient to clarify the effects of the two drugs on the formation of NETs, which should be explored in the following work. Additionally, injury of glomerular cells and renal interstitial cells are also involved in SA-AKI, the regulatory effects of estradiol and (+)-JQ1 on these cells should be further investigated in the future. Last but not least, increasing studies have shown that metabolic alterations are associated with organ injury in septic shock [86, 87], and there is a reciprocal regulation mechanism between NETs formation and metabolic alteration [88]. In the following studies, the potential roles of hub genes and (+)-JQ1 in regulating metabolic alterations in SA-AKI deserves further investigation.

Conclusion

Formation of NETs contributes to the pathogenesis of SA-AKI, which may be associated with OS. Multiple hub genes, including ECT2, CHRDL1, PTAFR, CSF3 and FOS, are potential regulators of the formation of NETs and OS in SA-AKI. Additionally, estradiol and (+)-JQ1 are promising drugs to ameliorate SA-AKI by targeting these hub targets.

Data availability

Related codes in the present work are available from https://github.com/tangshaoqun-R/SAKI. Data used to support the findings of this study are also available from the corresponding author upon request.

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Acknowledgement

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Funding

This study was supported by grants from the National Natural Science Foundation of China (82372188, 82202408) and the Hubei Province Natural Science Foundation (No. 2023AFB821).

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  1. Tang Shaoqun, Yu Xi and Wang Wei shared as co-first authors.

Authors and Affiliations

  1. Department of Anesthesiology, Renmin Hospital of Wuhan University, Wuhan, Hubei, 430060, China

    Tang Shaoqun, Yu Xi, Wang Wei, Luo Yaru, Lei Shaoqing, Qiu Zhen, Yang Yanlin, Sun Qian & Xia Zhongyuan

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Contributions

Conceived and designed the experiments: Xia Zhongyuan & Sun Qian; Performed the in silico analysis: Tang Shaoqun & Yu Xi; Performed the experiments: Tang Shaoqun, Wang Wei, Luo Yaru, Lei Shaoqing, Qiu Zhen & Yang Yanlin; Analyzed the data: Tang Shaoqun, Yu Xi & Wang Wei; Drafted the paper: Tang Shaoqun. Reviewed the manuscript: Xia Zhongyuan & Sun Qian. All authors read and approved the final manuscript.

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Correspondence to Sun Qian or Xia Zhongyuan.

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This study is approved by the Research Ethics Committee of Renmin Hospital of Wuhan University.

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12882_2025_4126_MOESM1_ESM.jpg

Supplementary Material 1: Merging GSE33118 and GSE26440 datasets. (A-B) sva algorithm was used to remove batch effect of GSE33118 and GSE26440 datasets, and principal component analysis (PCA) was used to show the original data distribution pattern (A) and mixed data distribution pattern (B)

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Shaoqun, T., Xi, Y., Wei, W. et al. Neutrophil extracellular traps-related genes contribute to sepsis-associated acute kidney injury. BMC Nephrol 26, 235 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12882-025-04126-y

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BMC Nephrology

ISSN: 1471-2369

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