S100A8/A9 Inhibition Reduces Neutrophil Extracellular Trap Formation and Mitigates Sepsis-Related Liver Injury

Abstract

Purpose

Sepsis is a leading cause of death in patients admitted to the intensive care unit. Sepsis-related liver injury (SRLI) is a fatal complication of sepsis with limited early diagnostic and therapeutic options. We investigated the role of the damage-associated molecular patterns S100A8/A9 in SRLI pathogenesis, with particular focus on their interaction with neutrophil extracellular traps (NETs).

Patients and Methods

Bioinformatics analysis of sepsis datasets from the Gene Expression Omnibus database was integrated with clinical validation using an enzyme-linked immunosorbent assay with patient plasma. The functional role of S100A8/A9 was further explored in a murine cecal ligation and puncture (CLP) sepsis model. Mice were pretreated with the S100A8/A9 inhibitor paquinimod to assess its effects on NET formation (via immunofluorescence and Western blotting), liver injury (serum alanine aminotransferase and aspartate aminotransferase levels and histopathology), inflammatory response (cytokine levels), and survival rates.

Results

Bioinformatics analysis identified S100A8/A9 as a key hub gene in sepsis. Clinically, plasma S100A8/A9 levels were significantly elevated in patients with sepsis, particularly in those with liver injury. In CLP mice, hepatic S100A8/A9 was upregulated, and paquinimod-induced inhibition of S100A8/A9 markedly reduced NET formation, lowered interleukin (IL)-1β, IL-6, and tumor necrosis factor-alpha levels, alleviated liver damage, and improved survival rates. Transcriptome sequencing revealed alterations in immune and metabolic pathways.

Conclusion

This study suggests that elevated S100A8/A9 and concurrent NETosis are closely associated with sepsis-related liver injury (SRLI). S100A8/A9 can be used as a potential predictive biomarker for early liver dysfunction, and prophylactic administration of paquinimod can reduce intrahepatic inflammation, reduce NET formation, and improve the survival rate of mice with sepsis.

Plain Language Summary

Sepsis, a life-threatening condition caused by infection, often leads to liver damage, which can worsen the patient’s chances of survival. In this study, we explored how certain proteins, called S100A8 and S100A9, contribute to liver injury in sepsis. These proteins, which are released by immune cells, play a major role in inflammation. When sepsis occurs, they induce immune cells to secrete large amounts of proinflammatory cytokines, exacerbating damage to organs such as the liver.

We observed that patients with sepsis and liver injury had much higher levels of S100A8 and S100A9. In a mouse model of sepsis, blocking these proteins helped reduce liver damage and inflammation. The results also showed that blocking S100A8/A9 was associated with improved survival in mice with sepsis. These findings suggest that S100A8/A9 may serve as a potential biomarker for identifying liver injury in patients with sepsis. In addition, targeting S100A8/A9 may help to reduce inflammation and liver damage during sepsis. Further studies are required to better understand the relationship between S100A8/A9 and NET formation and to evaluate its potential clinical application.

Introduction

Sepsis, characterized by dysfunction of host organs owing to infection, is a leading cause of high mortality rates among patients in intensive care units.¹ In China, the rate of sepsis among individuals aged 65 and older can be as high as 57.5%.^2,3 The liver is highly susceptible to damage during sepsis.⁴ Sepsis-related liver injury (SRLI) refers to liver damage that occurs directly or indirectly because of sepsis, leading to abnormal biochemical markers, liver dysfunction, or even complete liver failure.⁵ Hepatic inflammation, oxidative stress, coagulopathy, and bacterial translocation contribute to the onset and progression of SRLI.⁶ SRLI occurs in approximately one-third of patients with sepsis,⁷ and liver dysfunction or failure is an independent predictor of death in patients with sepsis.⁸ However, current diagnosis of SRLI mainly relies on conventional biochemical indicators such as alanine aminotransferase (ALT), aspartate aminotransferase (AST), bilirubin levels, and coagulation parameters, which often lack sensitivity and specificity for early liver dysfunction in sepsis. In addition, treatment strategies for SRLI remain largely supportive, including infection control, hemodynamic stabilization, and organ support, while effective targeted therapies are lacking. Therefore, identifying reliable biomarkers that may assist in the early recognition and risk stratification of SRLI is required. Studies have explored several inflammatory and liver-associated biomarkers in sepsis; however, their clinical utility is limited by insufficient specificity, heterogeneity among patients, and the complex pathophysiology of sepsis-related organ dysfunction. In this context, S100A8/A9, as inflammation-associated proteins released predominantly by activated neutrophils and monocytes, may provide additional insight into the inflammatory processes involved in SRLI.

Neutrophils are the predominant cell type in the immune system and play a crucial role in the initial defense against infections and tissue injury by migrating to the affected areas. The cytoplasmic content of neutrophils is rich in calcium-binding proteins S100A8 and S100A9, which make up approximately 45% of their cytoplasm.⁹ Extracellular S100A8/A9 functions as damage-associated molecular patterns (DAMPs) that bind to Toll-like receptor 4 (TLR4) and the receptor for advanced glycation end products (RAGE) to promote cell activation and recruitment.¹⁰ The liver accumulates DAMPs in the hepatic sinus, thereby exacerbating damage.⁸ Neutrophils and monocytes typically release S100A8/A9 during the intravascular phase of leukocyte recruitment in response to inflammatory stimuli; therefore, plasma S100A8/A9 levels can serve as biomarkers for various inflammatory diseases.^9,11 Recent studies have further highlighted the clinical relevance of S100 family proteins in severe inflammatory and infectious diseases. Circulating calprotectin (S100A8/A9) has been associated with disease severity, long-term mortality, and unfavorable functional outcomes in critically ill patients with coronavirus disease (COVID-19). In addition, S100A12 has been reported to increase in patients with moderate and severe COVID-19 and to be further elevated in patients with vancomycin-resistant Enterococcus bloodstream infection, suggesting that different S100 family members may reflect distinct inflammatory and infection-related processes.^12,13

After stimulation with phorbol 12-myristate 13-acetate, interleukin (IL)-8, or lipopolysaccharide (LPS), neutrophils release a network of neutrophil extracellular traps (NETs) that encapsulate and degrade pathogens.¹⁴ NETs are composed of DNA and neutrophil granule proteins, such as histones, neutrophil elastase (NE) and myeloperoxidase (MPO). During sepsis, neutrophils exert anti-inflammatory effects by releasing NETs and NET components that possess intrinsic antibacterial activity.¹⁵ However, NETs released by excessive neutrophil activation will convert endothelial cells from anti-inflammatory and anticoagulant phenotypes to proinflammatory and procoagulant phenotypes, thereby causing inflammation and tissue damage.¹⁶

Advancements in high-throughput sequencing have enabled bioinformatics to identify biomarkers and their associated pathways. Additionally, enrichment analysis can reveal the pathways associated with the development of sepsis, providing valuable insights into its diagnosis, classification, and prognosis. The intricate pathophysiological mechanisms underlying sepsis necessitate the discovery of clinical biomarkers for early detection and targeted therapies. In septic lung damage caused by abdominal infection, S100A9 induces the formation of reactive oxygen species (ROS)-dependent NETs through TLR4 and RAGE receptors.¹⁷ This enhancement is reduced by S100A9. Additionally, elevated plasma NET biomarker levels (MPO, double-stranded DNA [dsDNA], and NE) in patients with acute myocardial infarction are associated with increased S100A8/A9 levels.¹⁸ However, the relationship between S100A8/A9 and NETs remains unclear, especially regarding sepsis-induced organ damage.

Therefore, we aimed to investigate the role of S100A8/A9 in SRLI pathogenesis, particularly its interaction with NETs, using bioinformatics and enrichment analyses. Our results indicate that NETs released during the immune response to sepsis may be associated with S100A8/A9 signaling and could contribute to SRLI.

Materials and Methods

Data Acquisition and Differential Expression Gene Analysis

Gene expression datasets related to sepsis, specifically GSE243217,¹⁸ GSE241238, GSE232753,^19,20 GSE252275, and GSE186054²¹ were obtained from the Gene Expression Omnibus (GEO) database (http://www.ncbi.nlm.nih.gov/geo/). The limma R package in R software was used to conduct differential expression analysis.²² Data preprocessing and normalization were performed according to the standard workflows recommended for GEO transcriptomic datasets prior to differential expression analysis. Using the criteria | log2 Fold Change | > 2 and an adjusted p value < 0.05, upregulated or downregulated differentially expressed genes (DEGs) were identified for each dataset. A volcano plot was created using the ggplot software package. To identify common DEGs, a Venn diagram was generated from the DEGs in each dataset, revealing overlapping genes. We created a data portal (https://gitee.com/yuyezhang/bio.git) to store raw data and analysis code.

Analysis of Protein–Protein Interaction Networks

DEGs related to protein–protein interactions (PPIs) were examined using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database (https://cn.string-db.org/). This platform aids in determining the relationships between target proteins, encompassing both direct binding interactions and shared pathways that govern upstream and downstream processes, thereby allowing the development of complex PPI networks with intricate regulatory mechanisms. Interactions with a confidence score greater than 0.7 were included in the PPI network analysis. Cytoscape (http://www.cytoscape.org) was used to visually represent the PPI network.²³

Enrichment Analysis

DEGs were analyzed using functional enrichment analysis to assess the biological significance of genes and their functions. The Gene Ontology (GO) database annotates gene functions, including molecular activities, biological processes, and cellular structures. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway database was used to explore gene functions and associated advanced genomic information.²⁴ To gain a more comprehensive understanding of the central genes, GO, and KEGG analyses were performed using the org.Hs.egdb software packages. A significance threshold of p < 0.05 was set, with GO analysis results displayed as a histogram and KEGG findings illustrated in a bubble chart.

Collection of Biological Samples and Enzyme-Linked Immunosorbent Assay (ELISA)

Peripheral venous blood samples were collected from patients at Nantong Third People’s Hospital. All blood samples were obtained as part of routine clinical procedures. The biological specimens were categorized into sepsis and control groups. Each participant provided 4 mL of peripheral venous blood in an ethylenediaminetetraacetic acid (EDTA) anticoagulant tube. Blood samples were centrifuged at 4000 rpm for 10 min and stored at −80 °C until ELISA analysis. Serum S100A8 and S100A9 levels in patients and controls were measured using ELISA kits (Jianglaibio, Shanghai, China) according to the manufacturer’s instructions.

Diagnostic Criteria for Sepsis and SRLI

The diagnosis of sepsis was based on the Third Edition of the International Consensus Definitions of Sepsis and Septic Shock (Sepsis 3.0).¹ The diagnostic basis for sepsis complicated by liver injury (including at least two of the following):²⁵ (1) Plasma total bilirubin (TBIL) ≥ 43 μmol/L (2.5 mg/dL); (2) Coagulation dysfunction with an International Normalized Ratio (INR) > 1.5; (3) Plasma alanine aminotransferase (ALT) levels exceeding two times the upper limit of normal. Furthermore, patients with chronic hepatitis, cirrhosis, liver malignant tumors, drug-induced liver injury, alcohol-induced liver injury, acute exacerbation of chronic liver dysfunction, obstructive jaundice, and previous liver surgery history need to be excluded.

Construction of Animal Models

The animal study was approved by the Animal Experimental Center of Nantong University. Male C57BL/6 wild-type mice aged 6–8 weeks and weighing 20–25 g were used. The mice were housed in a specific pathogen-free environment maintained at 25–26 °C, with a 12-h light and dark cycle, and were provided with standard mouse diet and water. Overall, 24 male wild-type mice were randomly assigned to experimental groups using a random allocation method and acclimatized for 1 week before induction of a polymicrobial sepsis model via cecal ligation and puncture (CLP) as described previously.²⁶ Mice were anesthetized through intraperitoneal injection of 1% pentobarbital (0.02 mL/g), followed by a 1-cm incision along the midline of the abdomen. The cecum was exposed, tightly ligated using a 6–0 silk suture, and punctured once using a 19-gauge needle. The cecum was then repositioned into the abdominal cavity, and the incision was closed with sutures. Preheated saline (1 mL, 37 °C) was injected intraperitoneally to address fluid loss during the procedure. The sham control group underwent the same surgical procedure but without CLP. To investigate the effect of S100A8/A9 on SRLI, wild-type mice were intraperitoneally injected with the S100A8/A9 inhibitor paquinimod (MedChemExpress, Monmouth Junction, NJ, USA) at a dose of 10 mg/kg 24 h before CLP or sham surgery. Mice were anesthetized by intraperitoneal injection of 1% pentobarbital sodium (0.02 mL/g). At the end of the experiment, mice were euthanized by carbon dioxide inhalation in accordance with the American Veterinary Medical Association (AVMA) Guidelines for the Euthanasia of Animals. Animal experiments and reporting were conducted in accordance with the Animal Research: Reporting of In vivo Experiments (ARRIVE) 2.0 guidelines.

Quantitative Real-Time Polymerase Chain Reaction (qPCR)

Total RNA was extracted from the liver tissue using TRIzol reagent (368711; Ambion, Austin, TX, USA) at 24 h postinfection, following the guidelines provided by the RNA Reverse Transcription Kit (Takara, Shiga, Japan). Total RNA (1.0 μg) was used to synthesize complementary DNA (cDNA) using amplicon primers. A CFX96 PCR detection system (Bio-Rad, Hercules, CA, USA) was used. Relative mRNA expression levels were assessed by qPCR with specific primers. Relative gene expression levels were normalized to glyceraldehyde-3-phosphate dehydrogenase (GAPDH) and calculated using the 2^−ΔΔCt method. All reactions were performed in triplicate. The qPCR cycling conditions consisted of an initial denaturation step followed by 40 amplification cycles according to the manufacturer’s instructions. All the primers for the target genes were synthesized by Sangon Biotech Co., Ltd. (Shanghai, China). Primers were listed in Table 1.

Table 1.

Primer Sequences Used for Quantitative RT-PCR

Gene	Primer Direction	Primer Sequence (5′–3′)
GAPDH	Forward	AGGTCGGTGTGAACGGATTTG
GAPDH	Reverse	GGGGTCGTTGATGGCAACA
S100A8	Forward	GGAGTTCCTTGCGATGGTGA
S100A8	Reverse	GGCCAGAAGCTCTGCTACTC
S100A9	Forward	TCCATGATGTCATTATGAGGGC
S100A9	Reverse	ATACTCTAGGAAGGAAGGACACC
IL1-β	Forward	CACTACAGGCTCCGAGATGAACAAC
IL1-β	Reverse	TGTCGTTGCTTGGTTCTCCTTGTAC
IL-6	Forward	CTCCCAACAGACCTGTCTATAC
IL-6	Reverse	CCATTGCACAACTCTTTTCTCA
TNF-α	Forward	ATGTCTCAGCCTCTTCTCATTC
TNF-α	Reverse	GCTTGTCACTCGAATTTTGAGA

Biochemical Measurements

Serum alanine aminotransferase (ALT) and aspartate aminotransferase (AST) levels were evaluated as indicators of hepatic function. Plasma was extracted by collecting blood from the ocular region of mice and centrifuging it at 4000 rpm for 10 min. An automatic biochemical analyzer (AU7100) was used to measure plasma transaminase levels.

Hematoxylin–Eosin Staining

One day postinfection, liver samples were collected and stained with hematoxylin–eosin to assess histopathological alterations. Briefly, the liver tissues were sectioned, preserved in 4% paraformaldehyde, dehydrated, and embedded in paraffin. The samples were sliced into 3–5 micron sections using a rotary microtome, mounted on slides, and oven-dried at 45 °C. The stained sections were covered with neutral resin for examination under an optical microscope. Pathological scoring was independently performed according to the Suzuki liver injury scoring criteria in a blinded manner. Scoring content includes: spotty necrosis (grade 0–4), capsule inflammation (grade 0–3), portal vein inflammation (grade 0–3), ballooning degeneration (grade 0–3) and statosis (grade 0–3). The sum of the scores for each character constitutes the total liver injury pathology score, ranging from 0 to 16.²⁷

Western Blotting

Liver samples were disrupted using RIPA buffer (Sangon Biotech, Shanghai, China) containing a cocktail of proteinase inhibitors. Sodium dodecyl sulfate–polyacrylamide gel electrophoresis was performed to separate the proteins. The proteins were then transferred onto polyvinylidene fluoride membranes. The membranes were incubated overnight with primary antibodies, including antihistone H3 (1:1000, ab281584; Abcam, Cambridge, UK) and anti-NE (1:1000, ab131260; Abcam).

Immunofluorescence Assay

Liver tissue samples from mice were embedded in paraffin, washed using phosphate-buffered saline (pH 7.4), and fixed in a 4% paraformaldehyde solution. To enhance permeability, the samples were treated with 0.5% Triton X-100. They were then exposed to rabbit anti-MPO IgG and rabbit anticitrullinated histone H3 (CitH3) IgG, both at a concentration of 20 μg/mL (Abcam), and incubated with a CoraLite488-labeled goat antirabbit IgG secondary antibody (Abcam). The cell nuclei were stained with 1 μg/mL of DAPI. Fluorescence images were obtained using a microscope.

Transcriptome Sequencing of Mouse Liver Tissue

Transcriptome sequencing was performed by Shanghai Paisano Biotechnology Co., Ltd. For differential expression analysis, DESeq software (v1.38.3) was used. The criteria for identifying DEGs included an expression fold-change of | logFC | > 1 and a significance threshold of p < 0.05. Genes that satisfied these criteria were subjected to functional enrichment analysis. For enrichment analysis, GO enrichment was performed using the top GO (v2.50.0), with p-values determined using a hypergeometric distribution test (p < 0.05). Additionally, KEGG pathway annotation for the DEGs was performed using cluster Profiler software, focusing on pathways with significant enrichment indicated by p < 0.05.

Statistical Analyses

Bioinformatics analysis was conducted using R software (version 4.4.1; https://www.r-project.org), with the Wilcoxon test used for group comparisons. The ELISA results were analyzed using SPSS version 26.0. Normally distributed measurement data, as determined by normality testing, were reported as the mean ± standard deviation, and the independent-samples t test was used to compare groups. Nonnormally distributed measurement data were reported as median (interquartile range), and the Mann–Whitney U-test was used to compare the two groups. Count data are expressed as the number of cases (percentage), and comparisons between groups were performed using the x² test. GraphPad Prism 9.0 was used for creating visual representations. Kaplan–Meier survival analysis was performed, and differences between groups were assessed using the Log rank test. A p value of < 0.05 was deemed significant for all analyses.

Ethics Approval and Consent to Participate

The study involving human participants was approved by the Ethics Committee of Nantong Third People’s Hospital (Approval No. EK2020106). All procedures involving human participants were performed in accordance with the Declaration of Helsinki. Written informed consent was obtained from all participants prior to enrollment.

Animal experiments were approved by the Laboratory Animal Center of Nantong University (Approval No. P20250808-012) and were conducted in accordance with institutional guidelines for the care and use of laboratory animals.

Informed Consent Statement

All participants provided written informed consent prior to study participation, acknowledging their understanding of the study’s objectives, procedures, potential risks, and the confidentiality of their personal information.

Results

Identification of DEGs in Sepsis

The GSE243217, GSE241238, GSE232753, GSE252275, and GSE186054 datasets revealed 2742, 14,715, 2556, 4661, and 1140 DEGs, respectively. To display the results more clearly, only the three most upregulated and downregulated genes are highlighted in the volcano plot (Figure 1A). To refine the selection of con-DEGs across the five datasets, DEGs from each dataset were compared, yielding 40 common differentially expressed genes (con-DEGs) (Figure 1B). The corresponding protein names for these con-DEGs were entered into the STRING database to create a PPI network. Hub genes were prioritized using the betweenness algorithm via the CytoCNA plug-in and visualized using Cytoscape. In this network, the proteins are referred to as “nodes,” which includes 29 nodes and 152 edges (Figure 1B). The proteins with the most nodes were S100A8, S100A9, S100A12, matrix metalloproteinase 9 (MMP9), lactoferrin (LTF), matrix metalloproteinase 8 (MMP8), and arginase 1 (ARG1) (Figure 1C). Furthermore, to depict the expression levels of S100A8 and S100A9 across the sepsis datasets, S100A8 and S100A9 were analyzed in both healthy control (HC) and sepsis groups across the five datasets. S100A8 and S100A9 expression were significantly elevated in the sepsis group compared with the HC group (p < 0.05) (Figure 1D and E).

Figure 1.

(A) Volcano plot of differentially expressed genes (DEGs) of the sepsis and healthy groups in the five datasets. The more the point deviates from the center, the greater the difference. (B) Venn analysis was performed on five datasets to identify con-DEGs, and the results are displayed in a petal diagram; (C) Protein–protein interaction (PPI) network; the smaller the circle, the lower the degree of connectivity within the network; (D and E): S100A8 and S100A9 expression levels in the sepsis and the healthy groups in five transcriptome datasets (Mann–Whitney U-test). (*p < 0.05, **p < 0.01, ****p < 0.0001).

GO and KEGG Analyses

GO analysis revealed that the key biological processes enriched in the 40 con-DEGs included enhancement of inflammatory responses, suppression of cytokine production, defense mechanisms against bacterial and fungal infections, and neutrophil migration. The significant cellular components identified were tertiary granules, secretory granules, specialized granules, and synaptic vesicle membranes. In terms of molecular functions, the prominent activities included calcium-dependent protein interactions, binding to RAGE receptors, long-chain fatty acid interactions, engagement with Toll-like receptors, serine-type endopeptidase activity, and monocarboxylic acid binding (Figure 2A). The pathways highlighted by the con-DEGs were primarily enriched in transcriptional misregulation, hematopoietic cell lineage, the IL-17 signaling pathway, atherosclerosis, and NET formation in cancer (Figure 2B).

Figure 2.

(A) Gene Ontology (GO) enrichment analysis of con-DEGs. The GO enrichment analysis of all con-DEGs is shown in a histogram. The degree of enrichment was measured using the enrichment score. The ordinate represents the enrichment score. The larger the value, the greater the degree of enrichment. (B) Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of con-DEGs. The KEGG enrichment analysis of all con-DEGs is shown as bubble plots. The abscissa is the enrichment factor; the larger the value, the greater the degree of enrichment. The p value is represented by color, and a smaller p value is indicated by a redder color, indicating higher enrichment. The size of the point represents the number of DEGs under the pathway, and the larger the point, the more genes.

S100A8/A9 Expression is Upregulated in Patients with SRLI

To determine the relationship between S100A8/A9 expression and SRLI in patients with sepsis, serum S100A8/A9 concentrations were measured in the HC (n = 28), non-SRLI (n = 65), and SRLI groups (n = 48). Serum S100A8/A9 levels were higher in patients with sepsis than in HCs. Serum S100A8/A9 levels were markedly higher in patients with SRLI than in those without SRLI (Table 2). Additionally, binary logistic regression analysis revealed that S100A8/A9 was a risk factor for SRLI and was incorporated into the SRLI prediction model. The area under the curve of S100A8/A9 for predicting the occurrence of SRLI was 0.758 (p < 0.001) (Table 3), indicating moderate diagnostic performance. These results suggest that elevated S100A8/A9 levels may be associated with SRLI, although its clinical utility requires further validation and comparison with established liver injury indicators.

Table 2.

Baseline Characteristics of Healthy Controls (HCs), Patients Without Sepsis-Related Liver Injury (SRLI), or Patients Without Sepsis-Related Liver Injury (Non-SRLI)

Group		HC (n=28)	SRLI (n=48)	Non-SRLI (n=65)	Z/χ2	p
Sex	Male	NA	32(66.70%)	48(73.80%)	χ2=0.688	0.407
	Female	NA	16(33.30%)	17(26.20%)
Years		NA	72(65,79)	79(69,85)	Z=−2.955	0.003
ALT (U/L)		NA	171.5(83.25,603.5)	23(14.00,39.50)	Z=−7.379	<0.001
AST (U/L)		NA	297.5(96.50,967.00)	33(22.00,71.50)	Z=−7.114	<0.001
TBIL (µmol/L)		NA	42.7(22.60,76.45)	12.9(8.10,21.15)	Z=−6.529	<0.001
INR		NA	1.75(1.55,2.26)	1.33(1.16,1.48)	Z=−6.355	<0.001
CRP (mg/L)		NA	206.0(169.50,324.75)	167.79(90.45,235.00)	Z=−2.730	0.006
PCT (ng/mL)		NA	24.00(2.91,172.50)	3.40(0.37,12.00)	Z=−3.827	<0.001
S100A8/A9 (ng/mL)		8.76(2.64,13.18)	161.38(81.09,248.49)	86.30(40.47,90.63)	Z=−4.670	<0.001
SOFA SCORE		NA	12(11,14)	10(8,11)	Z=−4.822	<0.001
APACHE-II SCORE		NA	23(20,28)	22(18,27)	Z=−0.963	0.336
28-day survival rate		NA	26(54.20%)	40(61.50%)	χ2=0.618	0.432

Abbreviations: ALT, alanine aminotransferase; AST, aspartate aminotransferase; TBIL, total bilirubin; INR, international normalized ratio; CRP, C-reactive protein; PCT, procalcitonin; SOFA, Sequential Organ Failure Assessment; APACHE-II, Acute Physiology and Chronic Health Evaluation II.

Table 3.

Receiver Operating Characteristic (ROC) Curve Analysis Results of S100A8/A9 on Patients Without Sepsis-Related Liver Injury (SRLI)

Variable	AUC	ROC	SE	p	95% CI
S100A8/A9	0.758	87.199	0.046	0.000	(0.667–0.848)

Abbreviations: AUC, area under the curve; CI, confidence interval; SE, standard error.

Inhibiting S100A8/A9 Attenuates Intrahepatic Inflammation and Liver Injury in CLP Mice

S100A8 and S100A9 levels in the liver of the CLP group were markedly elevated, compared with the control group (p < 0.001, p < 0.0001). Paquinimod significantly reduced the expression level of S100A8 and S100A9 (p < 0.001, p < 0.0001) (Figure 3A and B). Inhibiting S100A8/A9 decreased ALT and AST levels in the CLP group (p < 0.01, p < 0.0001), whereas no significant changes were observed in the sham-operated group (p > 0.05) (Figure 3C and D). Furthermore, survival curve assessments indicated an improvement in the survival rate of CLP mice after blocking S100A8/A9 (p < 0.05) (Figure 3E). Additionally, the mRNA expression of IL-1β, IL-6, and tumor necrosis factor-alpha (TNF-α) in the livers of CLP mice was significantly reduced after inhibiting S100A8/A9, compared with the CLP group (p < 0.001) (Figure 3F–H). Pathological examination of liver tissues from the four groups revealed that the CLP group exhibited extensive inflammatory infiltration and significant hepatocyte necrosis. In contrast, these damages were mitigated after blocking S100A8/A9 (p < 0.001) (Figure 3I and J).

Figure 3.

(A) The S100A8 expression level in liver tissue of cecal ligation and puncture (CLP) mice was significantly increased; (B) The S100A9 expression level in liver tissue of CLP mice was significantly increased. (C and D) Alanine aminotransferase (ALT) and aspartate aminotransferase (AST) levels in mice; (E) survival curve of mice; (F–H) mRNA expression levels of inflammatory factors (interleukin [IL]-1β, IL-6, and tumor necrosis factor-alpha [TNF-ɑ]) in liver tissue of mice; (I and J): Hematoxylin–eosin staining of mouse liver tissue. All data were analyzed using the Mann–Whitney U-test. (* p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001, NS indicates not significant).

Inhibiting S100A8/A9 Reduces NET Formation in the Liver Tissues of CLP Mice

To investigate the effect of S100A8/A9 blockade on NET formation in the liver of CLP mice, immunofluorescence colocalization analysis was conducted using the NET markers MPO and CitH3 across the four mouse groups. The CLP group exhibited a marked increase in NETs in liver tissue, compared with the sham operation group. However, after S100A8/A9 inhibition, NET formation in the CLP group was markedly reduced (p < 0.001) (Figure 4A–C). Additionally, Western blotting revealed that the NE and CitH3 levels were considerably elevated in the CLP group than in the other groups, confirming extensive NET formation in the CLP group, which was notably reduced after S100A8/A9 inhibition (p < 0.01, p < 0.0001) (Figure 4D–F).The uncropped and unedited Western blot images corresponding to Figure 4D are provided in Supplementary Figure S1.

Figure 4.

(A–C) Immunofluorescence staining of myeloperoxidase (MPO) and citrullinated histone H3 (CitH3) in liver tissues from the Sham, Sham + Paq, CLP, and CLP + Paq groups. Representative images and quantitative analysis of MPO-positive and CitH3-positive areas are shown. (D–F) Western blot analysis and quantitative densitometric analysis of CitH3 and neutrophil elastase (NE) protein expression in liver tissues from the four groups. β-actin was used as the loading control. Data were analyzed using the Mann–Whitney U-test. **p < 0.01, ***p < 0.001, ****p < 0.0001, and ns indicates not significant. Uncropped and unedited Western blot images for (D) are provided as Supplementary Figure S1.

Transcriptome Analysis of Mouse Liver Tissue After Inhibiting S100A8/A9

Transcriptome sequencing revealed that, compared with the CLP group, inhibition of S100A8/A9 resulted in the identification of 703 DEGs, including 311 upregulated and 392 downregulated genes. Notably, Srebf1, Akrlc6, and Mcm10 were significantly upregulated, whereas Col4al, Sdsl, and Gbp9 were markedly downregulated (Figure 5A). GO enrichment revealed that the DEGs were predominantly associated with biological processes, including immune responses, bacterial or LPS defense mechanisms, stress responses, and interactions between cytokines and their receptors (Figure 5B). KEGG enrichment analysis revealed notable alterations in the IL-17 signaling pathway, interactions among cytokines, retinol metabolism, and lipid metabolism after the inhibition of S100A8/A9 (Figure 5C).

Figure 5.

(A) Volcano map of DEGs in the CLP and CLP + paquinimod groups. The horizontal axis represents logFC; the farther a point is from the center, the greater the difference. (B) GO functional enrichment analysis of DEGs. The GO enrichment analysis is displayed as a histogram, and enrichment is measured using the enrichment score. The larger the value, the greater the degree of enrichment. KEGG functional enrichment analysis of DEGs. (C) The KEGG enrichment analysis of all DEGs is displayed as a bubble diagram, and the abscissa indicates the enrichment factor. The larger the value, the greater the enrichment degree. The p value is represented by color, and a smaller p value is indicated by a redder color, indicating higher enrichment. The size of the point represents the number of DEGs in the pathway, and the larger the point, the more genes.

Discussion

This study revealed that S100A8/A9 levels were elevated in the liver tissues of CLP mice, correlating with NET formation. Inhibiting S100A8/A9 with paquinimod decreased NET formation, reduced liver damage, and enhanced survival.

Transcriptomic screening and DEG-based network analysis have been widely used to identify disease-associated hub genes and potential regulatory pathways in complex diseases. Similar multiomics and DEG-identification strategies have also been applied to discover key molecular targets and pathway alterations in other disease models.²⁸ This study initially examined five sepsis datasets from the GEO database and identified seven critical con-DEGs, including S100A8, S100A9, S100A12, MMP9, LTF, MMP8, and ARG1, that play significant roles in sepsis pathology. These are severely dysregulated during sepsis development, with S100A8, S100A9, S100A12, MMP9, and MMP8 expression upregulated, and LTF and ARG1 expression downregulated. Subsequently, S100A8 and S100A9 protein expression levels were significantly upregulated in patients with sepsis.

S100A8, S100A9, and S100A12 are members of the S100 protein family and have been identified as significant differential markers of sepsis through various bioinformatic analyses.²⁹ The functions of S100A8 and S100A9 are primarily associated with their formation of heterodimer complexes, whereas S100A12 typically exists as a homodimer or tetramer.³⁰ S100A12 contributes to antibacterial activity by enhancing superoxide production and facilitating macrophage antigen clearance. Additionally, it plays a role in immune defense and inflammation regulation by inducing proinflammatory cytokine expression via RAGE-dependent NF-κB activation.³¹ S100A8/A9 can aid in assessing severity and predicting mortality in patients with septic shock.³² Elevated plasma levels of S100A12 and S100A8/A9 upon admission correlate with a higher mortality risk in patients with septic shock.³³ Furthermore, a clinical investigation revealed a significant increase in S100A12 and S100A9 expression in the peripheral blood of patients with sepsis compared with healthy individuals.³⁴ Similarly, this study confirmed a notable increase in S100A8 and S100A9 levels in patients with sepsis, suggesting their involvement in the onset and progression of sepsis, aligning with the bioinformatics findings.

MMP8 and MMP9 belong to the matrix metalloproteinase family and are primarily produced by neutrophils.³⁵ They play a pivotal role in the development of several inflammatory conditions, including rheumatoid arthritis and osteoarthritis.³⁶ MMP8 and MMP9 also regulate inflammatory mediators, including IL-1, IL-6, IL-8, and prostaglandins, during the initial phases of inflammation.³⁷ Increased MMP8 and MMP9 levels correlate with higher mortality rates in individuals with sepsis or septic shock.^38,39 LTF, a member of the transferrin family, exhibits antibacterial properties and plays a crucial role in the innate immune system.⁴⁰ This protein has diverse functions, including regulating iron balance, immune response, cell growth and differentiation, and preventing cancer progression and spread.^40,41 LTF has a dual regulatory function; it can inhibit excessive inflammation, reduce proinflammatory factor expression, and promote regulatory T cell activation to counteract immunosuppression.⁴² In sepsis, the immune response is compromised by immunosuppressive processes, leading to high ARG1 expression in both regulatory T and myeloid-derived suppressor cells.⁴³ Blocking ARG1 in mice with sepsis enhances T cell function and reduces mortality.⁴⁴ Furthermore, patients with sepsis exhibit higher plasma ARG1 levels than healthy individuals.⁴⁵

S100A8/A9-mediated activation of the TLR4/RAGE signaling axis may amplify downstream inflammatory responses, including NF-κB-related cytokine production and tissue injury.⁴⁶ The paquinimod used in this study is a selective inhibitor of S100A8, S100A9 proteins. By blocking its binding to the receptor TLR4, it inhibits the activation of downstream pathways, ultimately inhibiting inflammatory pathways and regulating immune cells.⁴⁷ The role of S100A8/A9 in sepsis-related organ dysfunction has been studied in the lungs,⁴⁸ myocardium,^49,50 brain,⁵¹ and kidneys.⁵² In this study, significant reductions in serum ALT and AST levels were observed in CLP mice following S100A8/A9 blockade. This effect may stem from two mechanisms. First, the inhibition was associated with reduced NET infiltration, which may help mitigate endothelial damage and microcirculatory disturbances. Second, the levels of proinflammatory cytokines IL-1β, IL-6, and TNF-α were lowered, potentially alleviating direct hepatocyte damage. Notably, although S100A8/A9 inhibition improved survival rates, they remained lower than those in the sham group, indicating the complexity of sepsis pathology and presence of multiple regulatory mechanisms. Controlling this process using a single S100A8/A9 inhibitor is challenging; therefore, future studies should consider combined application of these immunomodulators.

A study recently reported biomarkers for predicting sepsis-related liver dysfunction, for example, plasminogen activator inhibitor-1 (PAI-1) is a principal inhibitor of fibrinolysis, which has an AUC of 0.671 for predicting the occurrence of sepsis-related liver dysfunction (SRLD).⁵³ Similarly, another study showed that tissue plasminogen activator-inhibitor complex (T-PAIC) levels were significantly higher in patients with sepsis-induced liver injury (SILI), which could also predict the development of SILI (AUC = 0.86).⁵⁴ In this study, plasma S100A8/A9 levels in patients with SRLI were higher than those in patients without SRLI, indicating that S100A8/A9 may be related to liver damage in the setting of sepsis; it was further found that S100A8/A9 has certain diagnostic value for the occurrence of SRLI, with an AUC of 0.758. Although plasma S100A8/A9 levels were significantly higher in patients with SRLI than in those without liver injury, we also observed significant elevations in a small proportion of patients without liver injury; this may reflect severe systemic inflammation that has not yet led to detectable liver injury, or the time lag between the onset of inflammation and liver injury, resulting in the moderate diagnostic power of SRLI in this analysis. Judging from the results, S100A8/A9 cannot predict the occurrence of SRLI well, but its AUC is significantly improved when combined with other indicators, and can be used as a potential auxiliary biomarker for SRLI risk assessment and early warning. Its predictive role or prognostic value in combination with other markers could be further studied in the future.

NETs are associated with sepsis-related tissue injury.⁵⁵ NET components are markedly elevated in patients with sepsis and NET infiltration occurs in various organs in sepsis models.⁵⁶ The results of this study corroborate these findings; a significant quantity of NET markers (CitH3, MPO, and NE) was present in the liver tissues of CLP mice. S100A8/A9 can stimulate nicotinamide adenine dinucleotide phosphate (NADPH) oxidase, increase ROS production, and accelerate NET formation.⁵⁷ In the CLP model, S100A8/A9 triggered gasdermin D (GSDMD)-dependent platelet pyroptosis via TLR4, and oxidized mitochondrial DNA (mtDNA) from platelet-enhanced NET formation, creating a positive feedback loop.⁵⁸ This study shows that preblocking S100A8/A9 can significantly reduce the formation of NETs in the liver of CLP mice. In addition, the KEGG enrichment analysis results of sepsis common differential genes such as S100A8 and S100A9 show that the NET formation pathway is one of its important pathways, which suggests that some important connection exists between S100A8/A9 and NET formation in SRLI. In addition, one study pointed out that inhibiting S100A8/A9 improves neuroinflammation by blocking NET formation after traumatic brain injury (TBI), and proposed that the S100A8/A9 inhibitor Paquinimod can be used as a potential treatment strategy for TBI.⁵⁷ However, the relationship between S100A8/A9 and NETs remains poorly understood because our study only showed that preblocking S100A8/A9 reduces NET formation.

Calprotectin (S100A8/A9) and S100A12 are host defense proteins, and different bacterial species may have different effects on the expression of S100 proteins.⁵⁹ For example, patients with severe COVID-19 and vancomycin-resistant enterococci (VRE) bloodstream infections had significantly elevated S100A12 levels, compared with patients without VRE coinfection.¹³ In our study, we did not perform a microbiological classification analysis of the causative pathogens in septic patients. Whether the S100A8/A9-NETosis axis is differentially activated by different bacterial species (eg, gram-negative vs gram-positive pathogens, or antibiotic-resistant strains vs susceptible strains) remains unclear. Future studies are required to combine detailed microbial taxonomy and strain characterization to determine whether S100A8/A9 can serve not only as a general marker of sepsis-related liver injury, but also as a species-specific or resistance-related biomarker, and to elucidate whether different pathogens trigger different S100A8/A9-NETosis activation profiles in SRLI.

This study has some limitations. First, the expression and functional roles of other con-DEGs (S100A12, MMP9, LTF, MMP8, ARG1) in sepsis require further validation. Second, the single-center study design may restrict the applicability of the findings, and the influence of preexisting conditions was not accounted for. Third, our pretreatment protocol was designed to fully block S100A8/A9 before target activation to verify its causal role in sepsis pathogenesis. However, in clinical practice, patients are almost always treated after onset; therefore, this regimen does not fully reflect real-world clinical practice. Future studies will adopt a more clinically relevant treatment plan, such as administering inhibitors at different time points (1, 6, and 12 h) after CLP to assess the window for delayed treatment. Additionally, although our findings demonstrate a robust correlation between S100A8/A9 levels and NET formation, transcriptome sequencing identified key pathways, and pharmacological inhibition of S100A8/A9 reduced both, we acknowledge that our experimental design cannot distinguish between direct and indirect effects. The reduction in NETs observed after paquinimod treatment may be secondary to the global attenuation of inflammation rather than a direct blockade of NETosis. Thus, the mechanistic relationship between S100A8/A9 and NET formation requires further investigation using methods such as cell-specific knockout models or in vitro systems that isolate S100A8/A9 signaling from confounding factors of systemic inflammation. Finally, in the microbiological data, the number of cases of VRE infection is small. Although studies have suggested that such infections can lead to an increase in calprotectin and S100A12, subgroup verification of this effect has not been possible in this cohort.

Conclusion

This study highlights the significant association between S100A8/A9, NET formation, and SRLI pathogenesis. By integrating multiomics bioinformatics, clinical validation, and functional animal models, we observed that S100A8/A9 may further promote SRLI by affecting the formation of NETs. Clinically, S100A8/A9 may serve as a potential predictive biomarker for early liver dysfunction in patients with sepsis. Mechanistically, paquinimod pretreatment significantly attenuated intrahepatic inflammation, reduced NET formation, and improved overall survival in mice. However, this study mainly provides preliminary experimental and bioinformatic evidence. The therapeutic window, optimal dose, and safety of paquinimod in the treatment of SRLI remain unclear. Further mechanism studies and large-scale clinical validation are still required in the future to clarify the exact role of S100A8/A9 in SRLI.

Funding Statement

This work was supported by the Nantong Science and Technology Bureau (Grant No. MS2025058 and Grant No. JC2024074) and the Nantong Municipal Health Commission (Grant No. MS2024061), and the Jiangsu Province Engineering Research Center of Development and Translation of Key Technologies for Chronic Disease Prevention and Control (Grant No.CDSGK2202615).

Abbreviations

ALT, alanine aminotransferase; ARG1, arginase 1; ARRIVE, Animal Research: Reporting of In Vivo Experiments; AST, aspartate aminotransferase; AUC, area under the curve; AVMA, American Veterinary Medical Association; cDNA, complementary DNA; CitH3, citrullinated histone H3; CLP, cecal ligation and puncture; COVID-19, coronavirus disease; CRP, C-reactive protein; DAMP, damage-associated molecular pattern; DEG, differentially expressed gene; dsDNA, double-stranded DNA; EDTA, ethylenediaminetetraacetic acid; ELISA, enzyme-linked immunosorbent assay; GAPDH, glyceraldehyde-3-phosphate dehydrogenase; GEO, Gene Expression Omnibus; GO, Gene Ontology; GSDMD, gasdermin D; HC, healthy control; IL, interleukin; INR, international normalized ratio; KEGG, Kyoto Encyclopedia of Genes and Genomes; LPS, lipopolysaccharide; LTF, lactoferrin; MMP8, matrix metalloproteinase 8; MMP9, matrix metalloproteinase 9; MPO, myeloperoxidase; mtDNA, mitochondrial DNA; dsDNA, double-stranded DNA; NADPH, nicotinamide adenine dinucleotidee phosphate; NE, neutrophil elastase; NET, neutrophil extracellular trap; ns, not significant; NF-κВ, Nuclear factor–κВ; PAI-1, plasminogen activator inhibitor-1; PCT, procalcitonin; PPI, protein–protein interaction; RAGE, receptor for advanced glycation end products; ROS, reactive oxygen species; SOFA, Sequential Organ Failure Assessment; SRLI, sepsis-related liver injury; SILI, sepsis-induced liver injury; SRLD, sepsis-related liver dysfunction; STRING, Search Tool for the Retrieval of Interacting Genes/Proteins; T-PAIC, tissue plasminogen activator-inhibitor complex; TBIL, total bilirubin; TLR4, Toll-like receptor 4; TNF-α, tumor necrosis factor-alpha; TBI, Traumatic brain injury; VRE, vancomycin-resistant enterococci.

Data Sharing Statement

The data supporting the results reported in this study are available from the corresponding author upon reasonable request. The gene expression datasets used in this study (GSE243217, GSE241238, GSE232753, GSE252275, and GSE186054) are publicly accessible through the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/). Additionally, raw data generated from the animal experiments, including histopathological analysis and quantitative measurements, can be accessed by contacting the corresponding author. All data are shared in compliance with ethical guidelines and regulations.

Author Contributions

Yuye Zhang: Conceptualization, Methodology, Investigation, Data Curation, Formal Analysis, Writing – Original Draft.

Shiqi Yan: Conceptualization, Methodology, Investigation, Formal Analysis, Writing – Original Draft.

Jiayi Feng: Investigation, Data Curation.

Qiuyan Zhang: Investigation, Data Curation.

Junxian Xu: Investigation, Data Curation, Writing – Review & Editing.

Xudong Han: Conceptualization, Supervision, Funding Acquisition, Writing – Review & Editing.

Lijun Tian: Conceptualization, Supervision, Funding Acquisition, Writing – Review & Editing.

All authors made a significant contribution to the work reported, whether in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas; took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.

Disclosure

The authors report no conflicts of interest in this work.