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Scientific Reports
2045-2322
Nature Publishing Group UK London

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10.1038/s41598-024-71311-8
Article
FABP4 deficiency ameliorates alcoholic steatohepatitis in mice via inhibition of p53 signaling pathway
Xing Hao 12
Wu Zhan 12
Jiang Keqing 1234
Yuan Guandou 123
Guo Zhenya 123
Yu Shuiping 124
He Songqing dr_hesongqing@163.com

1234
Zhong Fudi andy020@126.com

1234
1 https://ror.org/030sc3x20 grid.412594.f Division of Hepatobiliary Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, 530021 China
2 Guangxi Key Laboratory of Immunology and Metabolism for Liver Diseases, Nanning, 530021 China
3 https://ror.org/03m01yf64 grid.454828.7 0000 0004 0638 8050 Key Laboratory of Early Prevention and Treatment for Regional High Frequency Tumor (Guangxi Medical University), Ministry of Education, Nanning, 530021 China
4 Guangxi Key Laboratory of Basic and Clinical Application Research for Hepatobiliary Diseases, Nanning, 530021 Guangxi China
10 9 2024
10 9 2024
2024
14 2113519 4 2024
27 8 2024
© The Author(s) 2024
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Fatty acid-binding protein 4 (FABP4) plays an essential role in metabolism and inflammation. However, the role of FABP4 in alcoholic steatohepatitis (ASH) remains unclear. This study aimed to investigate the function and underlying mechanisms of FABP4 in the progression of ASH. We first obtained alcoholic hepatitis (AH) datasets from the National Center for Biotechnology Information–Gene Expression Omnibus database and conducted bioinformatics analysis to identify critical genes in the FABP family. We then established ASH models of the wild-type (WT) and Fabp4-deficient (Fabp4−/−) mice to investigate the role of FABP4 in ASH. Additionally, we performed transcriptional profiling of mouse liver tissue and analyzed the results using integrative bioinformatics. The FABP4-associated signaling pathway was further verified. FABP4 was upregulated in two AH datasets and was thus identified as a critical biomarker for AH. FABP4 expression was higher in the liver tissues of patients with alcoholic liver disease and ASH mice than in the corresponding control samples. Furthermore, the Fabp4−/− ASH mice showed reduced hepatic lipid deposition and inflammation compared with the WT ASH mice. Mechanistically, Fabp4 may be involved in regulating the p53 and sirtuin-1 signaling pathways, subsequently affecting lipid metabolism and macrophage polarization in the liver of ASH mice. Our results demonstrate that Fabp4 is involved in the progression of ASH and that Fabp4 deficiency may ameliorate ASH. Therefore, FABP4 may be a potential therapeutic target for ASH treatment.

Keywords

FABP4
Alcoholic steatohepatitis (ASH)
p53
Bioinformatics analysis
Subject terms

Immunology
Molecular biology
Medical research
National Natural Science Foundation of China82160120 Zhong Fudi issue-copyright-statement© Springer Nature Limited 2024
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pmcIntroduction

Epidemiological studies revealed that the incidence and prevalence of alcoholic liver disease (ALD) are rapidly increasing worldwide1. One of the initial symptoms of ALD is mild alcoholic fatty liver (AFL), which is characterized by slightly abnormal levels of hepatic biochemical and pathological factors. Among the ALD patients with AFL, 10–20% progress to more severe liver diseases, such as alcoholic steatohepatitis (ASH). ASH is characterized by hepatic steatosis and inflammation, with or without liver fibrosis2. Although ALD has been extensively studied, its pathogenesis has not yet been fully elucidated. Previous studies found that pro-inflammatory pathways, apoptosis, and immune-related factors are involved in ethanol-induced liver damage. Chronic and excessive consumption of alcohol further aggravates liver damage, leading to alcoholic liver fibrosis, alcoholic cirrhosis, and even hepatocellular carcinoma3. Compulsory abstinence from alcohol is the best treatment for ALD patients; however, most ALD patients are unable to effectively quit alcohol4. Currently, there are no effective drugs for the clinical treatment of ALD. Therefore, there is an urgent need to identify novel therapeutic targets and develop therapeutic interventions for ALD.

The fatty acid-binding proteins (FABPs) are a family of nine homologous intracellular lipid-binding proteins that regulate lipid trafficking and metabolism and have specific tissue distribution patterns5,6. FABP4 is predominantly expressed in adipocytes and macrophages and plays an essential regulatory role in energy metabolism and inflammatory response7. Several studies demonstrated that ethanol metabolism promotes the expression of FABP4 in the liver8–10, indicating that FABP4 is associated with ALD progression. However, the function and underlying mechanism of FABP4 in the progression of ASH remain unclear.

In this study, we hypothesized that FABP4 may have an essential role in hepatic lipid metabolism and inflammation in ASH. Therefore, we conducted a bioinformatics analysis of Gene Expression Omnibus (GEO) data to analyze the role of FABP4 in ethanol-induced ASH. Furthermore, we explored the underlying molecular mechanism of FABP4 in ASH mice.

Materials and methods

Dataset collection

The microarray datasets GSE142530, GSE167308, and GSE73173 were downloaded from the National Center for Biotechnology Information–GEO database. The GSE14253011 dataset (based on the GPL11154 platform) consisted of 10 samples from alcoholic hepatitis (AH) patients and 11 samples from healthy individuals. The GSE16730812 dataset (based on the GPL20321 platform) comprised 7 samples from AH patients and 5 samples from healthy individuals. The GSE7317313 dataset consisted of the gene expression profile of RAW264.7 macrophages subjected to exogenous FABP4 treatment. Quality control analysis and microarray data pre-processing, including background correction and normalization, were performed in the R software (v4.3.2, https://www.r-project.org/) using the Bioconductor package before formal analysis.

Identification of differentially expressed genes (DEGs)

DEGs were identified using the R Bioconductor package limma (v3.54.2). The P-values were adjusted based on Benjamini–Hochberg’s false discovery rate (FDR), and genes with the P-value of < 0.05 and |Log2 fold change (log2FC)| ≥ 0.585 were defined as DEGs. Volcano plots were generated using the R ggplot2 package (v3.4.1), heatmaps for DEGs from each dataset were plotted using the R Pheatmap package (v1.0.12), and Venn diagrams were generated using the R VennDiagram package (v1.7.3).

Functional enrichment analysis of DEGs

Functional enrichment analysis of DEGs was conducted using the R clusterProfiler package (v4.6.2) and the GO plot package (v1.0.2) at a P-value of < 0.05. All DEGs were subjected to gene set enrichment analysis (GSEA), gene ontology (GO) analysis (biological process, BP; cellular component, CC; and molecular function, MF), and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Additionally, the up- and downregulated DEGs were subjected to GO enrichment analysis.

Screening and validation of critical gene signatures

Weighted gene co-expression network analysis (WGCNA) was conducted using the default recommended parameters to determine the relationship between FABP4 and AH using the R WGCNA package (v1.72-1). The diagnostic value of FABP4 for AH was determined by generating receiver operating characteristic (ROC) curves and calculating the area under the curve (AUC) values using the R pROC package (v1.18.0). A two-sided P-value of < 0.05 was considered statistically significant. Furthermore, the random forests (RF), support vector machine (SVM), and eXtreme Gradient Boosting (XGBoost) algorithms were used to calculate the AUC values for AH using the R mlr3 package (v0.15.0).

Evaluation and correlation analysis of infiltrated immune cells

CIBERSORT was used to analyze the infiltration of 22 and 25 types of immune cells in humans and mice, respectively. The relative abundance of infiltrated immune cells was determined at P < 0.05. The correlation heatmap of infiltrated immune cells was plotted using the R corrplot package (v0.92). Wilcoxon rank-sum test was used to determine the differential immune cell infiltration between the AH and control groups, and Spearman’s correlation test was used to analyze the relationship between the biomarkers and infiltrating immune cells. The results were visualized using the R ggcorrplot package (v0.1.4).

Animal models

The present study was conducted in compliance with the ARRIVE guidelines. Female wild-type (WT) and FBAP4−/− C57BL/6 mice were purchased from GemPharmatech (Jiangsu, China). The Fabp4−/− mice were generated using the Clustered Regularly Interspaced Short Palindromic Repeats/CRISPR-associated protein 9 technology14. All mice were maintained in a specific pathogen-free facility at Guangxi Medical University (Guangxi, China). The Lieber–Decarli liquid diet (LD; Trophic Animal Feed High-tech Co. Ltd., Jiangsu, China) containing 5% v/v ethanol was used to establish a standardized murine ASH model based on the Gao-Binge model15. The feeding protocol can be extended to long-term feeding, up to 8 weeks, plus a single ethanol binge based on body weight. WT mice (8–12 weeks old) were given the control Lieber–Decarli liquid diet (CD-fed) and the ethanol Lieber–Decarli liquid diet (EtOH-fed). In addition, WT and Fabp4−/− mice (8–12 weeks old) were used to generate the ASH model. The mice were anesthetized using isoflurane (RWD Life Science Co., Ltd., Shenzhen, China) via inhalation, 8 h after the last ethanol gavage, and their retro-orbital blood and liver tissues were collected for subsequent analysis. All animal experiments were approved by the Animal Care and Use Committee of Guangxi Medical University.

Patient samples

Liver tissue samples were obtained from healthy individuals and clinically confirmed ALD patients admitted to the Guangxi Medical University Hospital. The healthy liver tissue was obtained from donated liver or pathological specimens of benign liver diseases (such as liver hemangioma). The use of liver tissue samples was approved by the Ethics Committee of the First Affiliated Hospital of Guangxi Medical University (No.2024‑E456‑01). All the samples were analyzed in accordance with the Declaration of Helsinki, and all patients provided informed written consent for the purpose of the research.

Serum biochemical analysis

The serum levels of alanine aminotransferase (ALT), aspartate aminotransferase (AST), triglyceride (TG), and total cholesterol (TC) were measured using an autoanalyzer (Catalyst one, IDEXX, USA). Serum FABP4 levels were measured using commercially available ELISA kits (Meimian Industrial Co., Jiangsu, China) following the manufacturer’s instructions.

Tissue biochemical analysis

The TG and TC levels in liver tissue homogenates were detected using the TG (#A110-1-1) and TC (#A111-1-1) assay kits (Jiancheng Institution PeproTech, Jiangsu, China), respectively, according to the manufacturer’s instructions. Hepatic tumor necrosis factor (TNF)-α and interleukin (IL)-1β levels were measured using commercially available ELISA kits (Thermo Fisher Scientific, Waltham, MA, USA).

Histopathologic and immunohistochemical analyses

Fresh liver tissue samples were frozen, sliced into 8-μm-thick sections, and treated with oil red O stain. The resected liver tissue samples were fixed overnight in 10% neutral-buffered formalin solution, embedded in paraffin, and sectioned into 5-μm-thick sections. The sections were then dewaxed with xylene, dehydrated with alcohol, placed in sodium citrate buffer (pH 6), and heated in a microwave for 5 min for antigen retrieval. The sections were stained with hematoxylin and eosin (H&E), and the histopathological alterations in the liver biopsies were observed using a slide scanner (NanoZoomer S60; Hamamatsu Photonics, Japan) at 100×.

The tissue sections were incubated with 3% hydrogen peroxide for 10 min to eliminate endogenous peroxidase activity. Thereafter, the sections were incubated with goat anti-F4/80 antibodies (1:500; Cell Signaling Technology, Danvers, MA, USA) and anti-CD206 antibodies (1:300; Cell Signaling Technology) for 2 h at room temperature, followed by anti-goat ImmPRESS kit (Vector Laboratories), according to the manufacturer’s instructions. The images were collected using the NanoZoomer S60 scanner (Hamamatsu, Japan). The pathological severity of histopathologic and immunohistochemical sections were by using Image J (v1.46r, https://imagej.nih.gov/ij/) to quantitatively assess.

Quantitative reverse transcription-polymerase chain reaction (qRT-PCR)

Total RNA from frozen liver tissues (0.1 g) was extracted using TRIzol (15596026, Invitrogen, Carlsbad, CA, USA), according to the manufacturer’s instructions. The cDNA was synthesized using the RevertAid First Strand cDNA Synthesis Kit (K1622, Thermo Fisher Scientific). The qRT-PCR assay was conducted using a SYBR Green PCR master mix (1725125, Bio-Rad) on a real-time PCR system (CFX 96 Touch, Bio-Rad). The primers used for the qRT-PCR assay are provided in Table S1. Glyceraldehyde 3-phosphate dehydrogenase (GAPDH) was used to normalize the gene expression.

Western blot analysis

Total protein from the liver tissue samples was extracted using radio-immunoprecipitation assay lysis buffer (P0013B, Beyotime, Shanghai, China) and quantified using a bicinchoninic acid assay kit (Beyotime). The protein samples were separated using sodium dodecyl sulfate–polyacrylamide gel electrophoresis and transferred to a 0.45-µm polyvinylidene fluoride (PVDF) membrane (Merck KGaA, Darmstadt, Germany). The PVDF membranes were then blocked with 5% milk and washed thrice with Tris-buffered saline containing 0.1% Tween-20 (TBST) for 10 min each. The membranes were incubated overnight with primary antibodies at 4 °C and washed thrice with TBST for 10 min each. The membranes were then incubated with HRP-labeled goat anti-rabbit/mouse IgG (1:1000; Proteintech, Wuhan, China). The protein bands were detected using the ECL chemiluminescent kit (Epizyme, Shanghai, China), and the band densities were analyzed using the ImageJ software. The following primary antibodies were used in this study: anti-FABP4 (12802-1-AP), anti-p53 (10442-1-AP), anti-CASP3 (25128-1-AP), anti-BCL-2 (26593-1-AP), anti-BAX (50599-2-Ig), anti-PI3K (20584-1-AP), anti-NF-κB (10745-1-AP), anti-PPARα (66826-1-Ig), anti-AKT (60203-2-Ig), anti-P-AKT (80455-1-RR), anti-AMPK (10929-2-AP), anti-CPT-1 (15184-1-AP), anti-SREBP1 (14088-1-AP), and anti-SCD-1 (28678-1-AP), purchasing from Proteintech (Wuhan, China); anti-F4/80 (70076T), anti-NLRP3 (15101S), anti-CASP1 (24232S), anti-IL-1β (12242T), anti-SIRT1 (2028S), anti-IKK (61294S), anti-ACC (3676T), anti-FASN (3180T), anti-CD206 (#24595), anti-P-IRS-1 (636) (#2388), anti-P-IRS-1(307) (#2381), anti-IRS-1 (#2382), anti-P-PI3K (#17366), anti-P-AMPK (#50081), anti-P-ACC (#3661), anti-P-IKK (#2078), anti-P-NF-κB (#3033), and anti-GAPDH (2118T), purchasing from Cell Signaling Technology (Danvers, Massachusetts, USA). GAPDH was used to normalize the protein expression signals.

Transcriptional profiling

Total RNA was extracted from the flash-frozen liver tissues of the control and ASH Fabp4−/− mice using TRIzol reagent (Invitrogen). The quality of the RNA samples was evaluated using the NanoDrop 2000c spectrophotometer (Thermo Fisher Scientific) and a bioanalyzer (Agilent). Sequencing libraries were generated by reverse transcription-polymerase chain reaction (RT-PCR) amplification and sequenced on the HiSeq 2500 sequencing system (RIBOBIO, Guangzhou, China).

Statistical analysis

The statistical and data analyses were performed utilizing R software. Normally distributed continuous variables were expressed as the mean ± standard deviation (SD). Two groups were compared using a two-tailed Student’s t-test. A two-sided P-value of < 0.05 was considered statistically significant.

Ethics statement

The Ethics Committee of the First Affiliated Hospital of Guangxi Medical University approved the protocol (No. 2024‑E456‑01), which was conducted according to the Declaration of Helsinki. All included patients provided informed written consent for the purpose of the research.

Results

DEGs in AH datasets and single-gene GSEA of FABP4

A total of 3749 (1872 downregulated and 1877 upregulated) and 6038 (3202 downregulated and 2836 upregulated) DEGs were identified in the GSE142530 and GSE167308 datasets, respectively (Fig. 1A–D). Among these, 2201 overlapping DEGs (1094 downregulated and 1107 upregulated) related to AH were identified between the two datasets, as shown in the Venn diagram (Fig. 1E). Further analysis revealed that three upregulated DEGs in the GSE142530 dataset and one upregulated and two downregulated DEGs in the GSE167308 dataset belonged the FABP family (Fig. 1F,G). FABP4 was upregulated in both the AH datasets (Fig. 1F,G). Single-gene GSEA showed that FABP4 was primarily involved in lipid metabolism, immunity, and inflammation-associated pathways, including regulation of lipolysis in adipocytes, PI3K-AKT signaling pathway, Wnt signaling pathway, MAPK signaling pathway, chemokine signaling pathway, and inflammatory mediator regulation of TRP channels (Fig. 1H–M).Fig. 1 Differential gene expression analysis of the GSE142530 and GSE167308 datasets and single-gene GSEA of FABP4. (A,B) Volcano plots of the DEGs in the GSE142530 (A) and GSE167308 (B) datasets. (C,D) Number of DEGs in the GSE142530 (C) and GSE167308 (D) datasets. (E) Venn diagram of the DEGs in the GSE142530 and GSE167308 datasets. (F,G) Heatmaps of the FABP family-related DEGs in the GSE142530 (F) and GSE167308 (G) datasets. (H–M) Single-gene GSEA profiles depicting the six significant GSEA sets in AH. Results are presented as the mean ± SD.

Verification of FABP4 in AH datasets and expression of FABP4 in ALD patients

The two AH datasets were combined, and the batch effects were removed by the R sva package (v3.46.0)16. The results of WGCNA revealed the presence of 13 co-expression modules in the AH datasets (Fig. 2A–C). Module–trait correlation analysis showed that the turquoise and salmon modules were associated with AH and that the association between the turquoise module and AH was more significant (Fig. 2D). Further screening revealed the presence of FABP4 in the turquoise module (Fig. 2E). The AUC of the generated ROC curve for the FABP4 predictive values in AH was 0.82 (Fig. 2F). Furthermore, the AUCs for FABP4 with RF, SVM, and XGBoost algorithms were 0.83, 0.80, and 0.77, respectively (Fig. 2G), confirming that FABP4 had high predictive accuracy. Analysis of FABP4 expression in the AH datasets showed that FABP4 expression was upregulated in the AH samples compared with that in the control samples in both the GSE142530 and GSE167308 datasets (P < 0.05, Fig. 2H,I). Furthermore, qRT-PCR and WB assays showed that hepatic FABP4 mRNA and protein levels were increased in ALD patients compared with those in healthy individuals (Fig. 2J,K). These results indicate that FABP4 is significantly elevated in the liver tissues of ALD patients.Fig. 2 Correlation analysis between Fabp4 expression and AH and verification of Fabp4 expression in ALD patients. (A–E) WGCNA of the combined datasets (GSE142530 and GSE167308). (F) AUC of the ROC curve for the FABP4 predictive values in AH. (G) AUCs for FABP4 in AH with RF, SVM, and XGBoost algorithms. (H,I) Fabp4 expression in the GSE142530 (H) and GSE167308 (I) datasets. (J–K) Hepatic mRNA (J) and protein (K) levels of FABP4 in ALD patients. Results are presented as the mean ± SD. *P < 0.05.

Expression of Fabp4 in the liver tissues of ASH mice

We further conducted biochemical, histopathologic, and immunohistochemical analyses of the EtOH-fed and CD-fed WT mouse models. The results of H&E and oil red O staining showed that the EtOH-fed mice had a higher degree of liver injury and lipid deposition than the CD-fed mice (Fig. 3A). Additionally, the EtOH-fed mice had higher serum levels of ALT, AST, TG, and TC than the CD-fed mice (Fig. 3B–E). Moreover, the EtOH-fed mice showed higher hepatic levels of TNF-α, IL-6, TG, and TC than the CD-fed mice (Fig. 3F–I). Furthermore, the EtOH-fed mice had higher Fabp4 mRNA and protein levels in the liver tissues than the CD-fed mice (Fig. 3J,K). Interestingly, serum FABP4 levels showed no significant difference between the EtOH-fed and CD-fed mice (Fig. S1). These results indicate that Fabp4 expression was elevated in the liver tissues of ASH mice.Fig. 3 Biochemical, histopathologic, and immunohistochemical analyses of the CD-fed and EtOH-fed WT mouse models. (A) H&E and oil red O staining of the liver tissue sections of the CD-fed and EtOH-fed WT mice (scale bar, 100 μm). (B–E) Serum levels of ALT (B), AST (C), TG (D), and TC (E) in the CD-fed and EtOH-fed WT mice. (F–I) Hepatic levels of TNF-α (F), IL-6 (G), TG (H), and TC (I) in the CD-fed and EtOH-fed WT mice. (J) Hepatic mRNA levels of Fabp4 in the CD-fed and EtOH-fed WT mice. (K) Hepatic protein levels of FABP4 in the CD-fed and EtOH-fed WT mice. Results are presented as the mean ± SD. *P < 0.05, **P < 0.01, and ***P < 0.001.

Fabp4 deficiency reduced hepatic lipid deposition and liver injury in ASH mice

To further explore the role of Fabp4 in ASH progression, we conducted biochemical, histopathologic, and immunohistochemical analyses of the EtOH-fed WT and Fabp4−/− mice. The genotype verification of the Fabp4−/− mice is shown in Fig. S2. The results of ELISA, qRT-PCR, and WB showed almost no expression of Fabp4 in the serum and liver tissues of the EtOH-fed Fabp4−/− mice (Fig. 4A–C). The results of H&E and oil red O staining showed that the EtOH-fed Fabp4−/− mice showed reduced liver injury levels and lipid deposition compared with the EtOH-fed WT mice (Fig. 4D). Additionally, the EtOH-fed Fabp4−/− mice showed decreased serum ALT and AST levels compared with the EtOH-fed WT mice (Fig. 4E,F). Moreover, the EtOH-fed Fabp4−/− mice showed decreased hepatic levels of TG, TC, TNF-α, and IL-6 compared with the EtOH-fed WT mice (Fig. 4G–J). These results indicate that Fabp4 deficiency may ameliorate hepatic lipid deposition and liver injury in ASH mice.Fig. 4 Biochemical, histopathologic, and immunohistochemical analyses of the EtOH-fed WT and Fabp4−/− mouse models. (A) Serum levels of FABP4 in the EtOH-fed WT and Fabp4−/− mice. (B) Hepatic mRNA levels of Fabp4 in the EtOH-fed WT and Fabp4−/− mice. (C) Hepatic protein levels of FABP4 in the EtOH-fed WT and Fabp4−/− mice. (D) H&E and oil red O staining of the liver tissue sections of the EtOH-fed WT and Fabp4−/− mice (scale bar, 100 μm). (E,F) Serum levels of ALT (E) and AST (F) in the EtOH-fed WT and Fabp4−/− mice. (G–J) Hepatic levels of TG (G), TC (H), TNF-α (I), and IL-6 (J) in the EtOH-fed WT and Fabp4−/− mice. Results are presented as the mean ± SD (n = 6/group). *P < 0.05, **P < 0.01, and ***P < 0.001.

Transcriptional profiling of the liver tissue from WT and Fabp4−/− ASH mice and bioinformatics analysis

To further investigate the mechanisms of Fabp4 in the development of ASH, we performed transcriptional profiling of the liver tissues of ASH mice. A total of 964 (359 downregulated and 605 upregulated) DEGs were identified in the liver tissues of the WT and Fabp4−/− mice (Fig. 5A,B). The top 50 (30 upregulated and 20 downregulated) DEGs are presented in heatmaps in Fig. 5C. GSEA showed that the DEGs were significantly enriched in cell cycle, DNA replication, cell adhesion molecules, B cell receptor signaling pathway, focal adhesion, and phagosome (Fig. 5D).Fig. 5 Transcriptional profiling and bioinformatics analysis of the liver tissues of the EtOH-fed WT and Fabp4−/− mice. (A) Volcano plots of the DEGs in the liver tissues of the EtOH-fed WT and Fabp4−/− mice. (B) Number of DEGs in the liver tissues of the EtOH-fed WT and Fabp4−/− mice. (C) Heatmaps of the top 50 DEGs in the liver tissues of the EtOH-fed WT and Fabp4−/− mice. (D) GSEA profiles depicting the 14 significant GSEA sets in the EtOH-fed WT and Fabp4−/− mice. (E–G) Bubble charts showing the GO-enriched MF (E), BP (F), and CC (G) terms of the DEGs in the EtOH-fed WT and Fabp4−/− mice. (H,I) Chord plots showing the GO-enriched items of the downregulated (H) and upregulated (I) DEGs in the EtOH-fed WT and Fabp4−/− mice. (J) Circle plot showing the KEGG-enriched terms of the DEGs in the EtOH-fed WT and Fabp4−/− mice.

The GO enrichment analysis showed that the DEGs were significantly enriched in cell chemotaxis, regulation of cell cycle phase transition, leukocyte chemotaxis, negative regulation of cell cycle process, and negative regulation of cell cycle phase transition in the MF category (Fig. 5E); spindle pole, microtubule, collagen-containing extracellular matrix, myelin sheath, and replication fork in the BP category (Fig. 5F); and tau protein binding, phosphatase binding, protein serine/threonine kinase activity, extracellular matrix structural constituent, and transmembrane-ephrin receptor activity in the CC category (Fig. 5G). The GO functional enrichment analysis showed that the downregulated DEGs were significantly enriched in acute inflammatory response, acute-phase response, reactive oxygen species biosynthetic process, and fat cell differentiation (Fig. 5H), while the upregulated DEGs were enriched in the negative regulation of cell adhesion, cell cycle, immune system process, and leukocyte activation (Fig. 5I). Lastly, the KEGG pathway enrichment analysis showed that the DEGs were significantly enriched in cell adhesion molecules, p53 signaling pathway, cell cycle, insulin resistance, PI3K-AKT signaling pathway, IL-17 signaling pathway, and ALD (Fig. 5J).

Fabp4 deficiency attenuated the progression of ASH in mice via the p53 signaling pathway

Transcriptional profiling and integrative bioinformatics analyses showed that Fabp4 was associated with the p53 signaling pathway, insulin resistance, and the PI3K-AKT signaling pathway (Fig. 5J). Therefore, we measured the critical factors in these pathways. The hepatic p53 level was significantly reduced in the EtOH-fed Fabp4−/− mice compared with that in the EtOH-fed WT mice (Fig. 6A). Further evaluation of the p53 pathway-associated molecules, such as CASP3, BAX, and BCL-2, showed that the protein levels of CASP3 and BAX (related to apoptosis) were decreased, while that of BCL-2 (related to anti-apoptosis) was increased in the EtOH-fed Fabp4−/− mice (Fig. 6A). Additionally, the mRNA levels of Irs-1, Pi3k, and Akt and the protein levels of P-IRS1 (IRS1-636 and IRS1-307), P-PI3K, and P-AKT (associated with alleviating insulin resistance) were increased in the EtOH-fed Fabp4−/− mice (Fig. 6B,C). Altogether, these results demonstrate that Fabp4 may regulate the p53 and insulin/PI3K/AKT signaling pathway in ASH mice.Fig. 6 Expression analysis of the p53 pathway, insulin/PI3K/AKT pathway, and SIRT1 pathway-related molecules in the EtOH-fed WT and Fabp4−/− mice. (A) Protein levels of p53, CASP3, BAX, and BCL-2 in the EtOH-fed WT and Fabp4−/− mice. (B) mRNA levels of Irs-1, Pi3k, and Akt in the EtOH-fed WT and Fabp4−/− mice. (C) Protein levels of P-IRS-1 (636), P-IRS-1 (307), P-PI3K, and P-AKT in the EtOH-fed WT and Fabp4−/− mice. (D) Hepatic mRNA levels of Sirt1 in the EtOH-fed WT and Fabp4−/− mice. (E) Hepatic protein levels of SIRT1 in the EtOH-fed WT and Fabp4−/− mice. (F) mRNA levels of Cpt-1, Ampk, and Pparα in the EtOH-fed WT and Fabp4−/− mice. (G) Protein levels of CPT-1, P-AMPK, and PPARα in the EtOH-fed WT and Fabp4−/− mice. (H) mRNA levels of Acc, Fasn, Srebf1, and Scd-1 in the EtOH-fed WT and Fabp4−/− mice. (I) Protein levels of P-ACC, FASN, SREBP1, and SCD-1 in the EtOH-fed WT and Fabp4−/− mice. (J) mRNA levels of Ikk and Nf-κb in the EtOH-fed WT and Fabp4−/− mice. (K) Protein levels of P-IKK and P-NF-κB in the EtOH-fed WT and Fabp4−/− mice. Results are presented as the mean ± SD. *P < 0.05, **P < 0.01, and ***P < 0.001.

Previous studies have shown that the inhibition of p53 can upregulate hepatic SIRT1 expression17–19. Therefore, qRT-PCR and WB were used to detect the expression of SIRT1 in ASH mice. The results showed that the hepatic SIRT1 mRNA and protein levels were significantly increased in the EtOH-fed Fabp4−/− mice (Fig. 6D,E). Furthermore, qRT-PCR analysis showed that in the EtOH-fed Fabp4−/− mice, the expression of lipid catabolism-related genes Pparα, Ampk, and Cpt-1 was increased, while the expression of lipid anabolism-related genes Acc, Srebf1, Scd1, and Fasn was decreased (Fig. 6F,H). Similarly, WB analysis showed that in the EtOH-fed Fabp4−/− mice, the expression of P-AMPK, CPT-1, and PPARα was increased, while the expression of P-ACC, SREBP1, SCD1, and FASN was decreased (Fig. 6G,I). These results indicate that Fabp4 deficiency may inhibit fatty acid synthesis and promote fatty acid oxidation in ASH mice through the p53 and SIRT1 signaling pathways.

Previous studies reported that SIRT1 inhibits the expression of inflammatory factors, such as TNF-α, IL-1β, and IL-6, by directly inhibiting the NF-κB signaling pathway20. Therefore, we detected the hepatic expression of NF-κB pathway-related factors using qRT-PCR and WB assays. The results showed that the mRNA and phosphorylated protein expression levels of IKK and NF-κB were decreased in the EtOH-fed Fabp4−/− mice (Fig. 6J,K). These results suggest that Fabp4 deficiency may reduce hepatic inflammation in ASH mice by regulating the SIRT1 signaling pathway.

Fabp4 affected the proportion of macrophage M1/M2 and the expression of pro-inflammatory factors in ASH

Previous studies have found that immune system activation can accelerate AH progression21,22. Therefore, we explored the association between FABP4 expression and immune cell infiltration in AH. CIBERSORT analysis of the immune cell phenotypes in the GSE142530 dataset revealed that compared with the M2 macrophage levels in the control samples, those in the AH samples were lower and negatively correlated with FABP4 expression (Fig. S3A–D). To further explore the effects of Fabp4 in macrophages, we conducted GSEA of the DEGs in GSE73173. The results showed that the effects of exogenous Fabp4 in RAW264.7 macrophages were primarily associated with immunity, inflammation, and lipid metabolism (Fig. S3E–I).

FABP4 is highly expressed in macrophages, especially during the activation of inflammatory response23,24. CIBERSORT analysis of 25 immune cell phenotypes in the liver tissues of the WT and Fabp4−/− mice revealed that the Fabp4−/− mice had a significantly lower proportion of M1 macrophages (P < 0.01) and a higher proportion of M2 macrophages than the WT mice. Interestingly, the WT mice showed no M2 macrophage infiltration, while the Fabp4−/− mice showed 6% M2 macrophage infiltration (Fig. 7A,B). The correlation heatmap of 23 immune cell types showed that the macrophage levels were significantly correlated with some immune cells (Fig. 7C). We then conducted a correlation analysis between essential genes and infiltrated immune cells in ASH. The results showed that p53, Nlrp3, Il-1β, and Cxcl-1 were positively correlated with M1 macrophages (r = 0.66, 0.6, 0.6, and 0.83, respectively; P < 0.05) (Fig. 7D), while Fabp4 and Sirt1 were negatively correlated with M2 macrophages (r = − 0.52) and mast cells (r = − 0.65, P < 0.001), respectively (Fig. 7D). Therefore, we investigated the relationship between Fabp4 expression and macrophages in the ASH mouse model. The results of immunohistochemistry and WB analyses showed that the Fabp4−/− mice had lower F4/80 levels but higher CD206 levels than the WT mice (Fig. 7E,F). Furthermore, the results of the qRT-PCR assay showed that the expression of inflammation-related genes Tnf-α, Il-6, Il-1β, Il-8, Trailr1, Iy6g, Mcp-1, and Cxcl-1 was downregulated in the Fabp4−/− mice compared with that in the WT mice (Fig. 7G). Meanwhile, the results of WB analysis showed that the expression of NLRP3, CASP1, pro-IL-1β, and IL-1β proteins was decreased in the Fabp4−/− mice compared with that in the WT mice (Fig. 7H). These results suggest that Fabp4 deficiency may prevent liver inflammation in ASH mice by increasing the proportion of M2 macrophages.Fig. 7 Correlation analysis between FABP4 expression and macrophage levels in the EtOH-fed WT and Fabp4−/− mice. (A) Stacked bar chart showing the immune cells in the EtOH-fed WT and Fabp4−/− mice. (B) Box-plot of the proportion of 23 types of immune cells in the EtOH-fed WT and Fabp4−/− mice. (C) Heatmap of the correlation between the 23 immune cell types in the EtOH-fed WT and Fabp4−/− mice. (D) Heatmap of the correlation between infiltrating immune cells and p53, Sirt1, Nlrp3, Il1b, Fabp4, Cxcl1, and Bcl-2 in the EtOH-fed WT and Fabp4−/− mice. (E) Immunohistochemistry staining of F4/80 and CD206 proteins in the EtOH-fed WT and Fabp4−/− mice. (F) Hepatic protein levels of F4/80 and CD206 in the EtOH-fed WT and Fabp4−/− mice. (G) Hepatic mRNA levels of Tnf-α, Il-6, Il-1β, Il-8, Trailr1, Iy6g, Mcp-1, and Cxcl-1 in the EtOH-fed WT and Fabp4−/− mice. (H) Protein levels of NLRP3, CASP1, pro-IL-1β, and IL-1β in the EtOH-fed WT and Fabp4−/− mice. Results are presented as the mean ± SD. *P < 0.05, **P < 0.01, and ***P < 0.001.

Discussion

ALD significantly contributes to the global burden of disease and mortality25. Moreover, ASH can further develop into more severe liver diseases, severely harming the liver and other organs3. In this study, we explored the role and underlying mechanisms of FABP4 in ASH progression. Integrative bioinformatics analysis showed that FABP4 was upregulated in the AH samples of the GSE142530 and GSE167308 datasets. Furthermore, the expression of FABP4 was found to be upregulated in the liver tissues of AH patients and ASH mice. Interestingly, the serum levels of FABP4 were not significantly different between the EtOH-fed and CD-fed WT mice. Moreover, Fabp4 deficiency alleviated hepatic steatosis and inflammation in the EtOH-fed Fabp4−/− mice. These results suggest that hepatic Fabp4 expression may play a crucial role in ASH progression. Additionally, our results revealed that Fabp4 deficiency may inhibit lipogenesis, promote fatty acid oxidation, and facilitate M2 macrophage polarization by suppressing the p53 signaling pathway and activating the SIRT1 signaling pathway in the liver of ASH mice. Therefore, FABP4 may be a potential drug development target for ASH.

Our study found that FABP4 expression was correlated with AH progression and that the DEGs in the GSE142530 and GSE167308 datasets were significantly enriched in lipid metabolism, immunity, and inflammation. Additionally, our results demonstrated that FABP4 expression may play a crucial role in hepatic lipid metabolism and inflammation in AH. In addition, ROC analysis and machine learning algorithms (RF, SVM, and XGBoost) revealed that high FABP4 expression could serve as an effective predictive biomarker for AH, indicating its potential clinical application in AH prediction.

FABP4 plays an essential regulatory role in energy metabolism and inflammatory response7. It is associated with several diseases and is a reliable biomarker for aging. FABP4 knockdown promotes cholesterol and fatty acid degradation and reduces inflammation and metabolic disorders in the liver26. Additionally, FABP4 promotes M1 macrophage polarization by activating the NF-κB/p65 signaling pathway in nonalcoholic fatty liver disease27. Moreover, studies have shown that FABP4 could help to predict poor outcomes in patients with nonalcoholic fatty liver28. However, another study found that serum FABP4 levels do not increase in patients with NASH or fibrosis and that the increased hepatic expression of FABP4 may not contribute significantly to the circulating FABP4 levels29. In the present study, we found that the serum FABP4 levels did not vary significantly between the EtOH-fed and CD-fed mice; however, the hepatic mRNA and protein levels of FABP4 were increased in the EtOH-fed mice compared with those in the CD-fed mice. Furthermore, our study found that Fabp4 deletion reduced hepatic lipid deposition and inflammation in the EtOH-fed Fabp4−/− mice. Therefore, our results indicate that the hepatic FABP4 levels, but not the serum FABP4 levels, may play a dominant role in ASH progression.

We further explored the underlying mechanisms of FABP4-mediated progression of ASH. Multiple enrichment analyses of the transcription profiling data revealed that the p53 signaling pathway, insulin resistance, and the PI3K-AKT signaling pathway were enriched in the EtOH-fed Fabp4−/− and WT mice. Several studies have shown that p53 plays an vital role in regulating lipid metabolism30,31. A study found that the inhibition of p53 transcriptional activity in the livers of high-fat diet mice reduces diet-induced weight gain, hepatic steatosis, oxidative stress, and apoptosis17. Derdak et al.32 found that p53 activation induces various forms of cell death, regulates cellular energy metabolism, and suppresses the insulin/PI3K/AKT axis, contributing to metabolic abnormalities in ALD rats. Our results showed that Fabp4 deficiency downregulated the expression of p53 in the liver of ASH mice. A study reported that p53 inhibition upregulates hepatic SIRT1 expression, which further upregulates the expression of PPARα, AMPK, and CPT1 and promotes the β-oxidation of hepatic fatty acids17–19. Other studies reported that p53 inhibition increases SIRT1 expression, which downregulates the expression of SREBP1, FASN, SCD-1, and ACC, thereby inhibiting the de novo synthesis of hepatic fatty acids17,33. Our results showed that Fabp4 deficiency upregulated the expression of SIRT1, subsequently reducing lipid synthesis and promoting lipolysis. SIRT1 regulates downstream inflammatory pathways, such as the IKK/NF-κB pathway, and directly affects the expression of inflammatory factors20,34. Our study showed that the expression of the IKK/NF-κB pathway-associated factors was decreased in the Fabp4−/− mice. These results demonstrate that Fabp4 deficiency may alleviate hepatic steatosis and inflammation via the p53/PI3K/AKT/SIRT1 signaling pathway; however, further exploration is required to determine its underlying mechanisms.

Considering the essential role of the immune system in AH, we further analyzed the association between FABP4 expression and immune cells in the AH datasets. The results showed that FABP4 exhibited varying degrees of correlation with immune cells, such as macrophages and T follicular helper cells. The results were further verified by the GSEA of DEGs obtained from the GSE73173 dataset. Furthermore, the CIBERSORT analysis showed that Fabp4 was negatively correlated with M2 macrophages. Consistently, the proportion of hepatic M2 macrophages was significantly higher in the Fabp4−/− mice than in the WT mice. Moreover, the proportion of inflammation-associated factors was reduced in the Fabp4−/− mice. These results provide novel insights into the immune mechanism of FABP4-mediated progression of ASH; however, further research is required to determine the effects of FABP4 expression on macrophage polarization in ASH.

In summary, the present study demonstrates that Fabp4 deficiency alleviated hepatic lipid accumulation and inflammation in ASH mice. Additionally, our study suggests that the inhibition of FABP4 may be a potential therapeutic strategy for ASH.

Supplementary Information

Supplementary Information 1.

Supplementary Information 2.

Supplementary Information

The online version contains supplementary material available at 10.1038/s41598-024-71311-8.

Author contributions

Fudi Zhong and Keqing Jiang designed and supervised the experiment and revised the manuscript. Hao Xing performed some experiments and wrote the manuscript; Zhan Wu, Shuiping Yu, Guandou Yuan, Zhenya Guo and Songqing He participated in some experiments and analyzed some data.

Funding

National Key Research and Development Program (2022YFE0131600); Joint Project on Regional High-Incidence Diseases Research of Guangxi Natural Science Foundation (2023GXNSFAA026062); National Natural Science Foundation of China (82160120); Natural Science Foundation of Guangxi Province (2021GXNSFDA075002); the “111” Project (D17011); Advanced Innovation Teams and Xinghu Scholars Program of Guanxi Medical University; Innovation Team of the First Affiliated Hospital of Guangxi Medical University (YYZS2022002).

Data availability

Sequence data related to this study have been deposited in the NCBI GEO database (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE265758), with the GEO series GSE265758.

Competing interests

The authors declare no competing interests.

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

These authors contributed equally: Hao Xing and Zhan Wu.
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