FABP4 deficiency ameliorates alcoholic steatohepatitis in mice via inhibition of p53 signaling pathway

FABP4 deficiency ameliorates alcoholic steatohepatitis in mice via inhibition of p53 signaling pathway | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article FABP4 deficiency ameliorates alcoholic steatohepatitis in mice via inhibition of p53 signaling pathway Hao Xing, Zhan Wu, Keqing Jiang, Guandou Yuan, Zhenya Guo, Shuiping Yu, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4292137/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 10 Sep, 2024 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Fatty acid-binding protein 4 (FABP4) plays an essential role in metabolism and inflammatory. However, the role of FABP4 in alcoholic steatohepatitis (ASH) remains unclear. This study aimed to investigate the function of FABP4 and the underlying mechanisms in the progression of ASH. Alcoholic hepatitis (AH) datasets were obtained from NCBI Gene Expression Omnibus (GEO). Bioinformatics analysis was performed to screen key genes in FABPs family. Wild-type (WT) and FABP4-deficient (FABP4 −/− ) mice were subjected to ASH models and the role of FABP4 was investigated. Transcriptional profiling of mouse liver tissue was performed and analyzed by integrative bioinformatics. The Fabp4 associated signaling pathway was further verified. FABP4 was up-regulated in two AH datasets and identified as a critical biomarker. Compared to control, FABP4 is higher expressed in liver tissues of ALD patients and ASH mice. FABP4 deficiency reduced hepatic lipid deposition and inflammation in ASH mice. Mechanistically, FABP4 was involved in regulating the p53 signaling pathway and Sirt1 signaling pathway, subsequently affecting the lipid metabolism and the polarization of macrophages in the liver of ASH mice. FABP4 is involved in the progression of ASH. FABP4 deficiency ameliorates mouse ASH, suggesting that FABP4 may be a potential therapeutic target for ASH. Biological sciences/Immunology Biological sciences/Molecular biology Health sciences/Medical research FABP4 Alcoholic steatohepatitis p53 Bioinformatics analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction According to the epidemiological investigation of alcoholic liver disease (ALD), the incidence and prevalence of ALD are increasing rapidly worldwide [ 1 ] . The initial symptom of ALD is mild alcoholic fatty liver (AFL), which is characterized by a slightly abnormal liver at biochemical and pathological levels. Of the patients with AFL, 10%-20% will progress to more severe liver diseases, such ASH, which is characterized by hepatic steatosis and inflammation, with or without liver fibrosis [ 2 ] . Chronic and excessive consumption of alcohol further aggravates the liver damage, which can develop into alcoholic liver fibrosis, alcoholic cirrhosis, and even hepatocellular carcinoma [ 3 ] . Compulsory abstinence from alcohol is the best treatment for ALD patients, however, most ALD patients are unable to effectively quit alcohol [ 4 ] . Currently, there are no effective drugs for treating ALD in clinical practice, and the pathogenesis of ALD has not been fully elucidated. As such, there is an urgent need to find new therapeutic targets and drugs for ALD. The pathogenesis of ALD has been extensively studied. Pro-inflammatory pathway, apoptosis and immune associated factors were involved in ethanol-induced liver damage. FABPs, including nine homologous proteins with similar tertiary structures and specific tissue distribution patterns, are a family of intracellular lipid-binding proteins that could bind hydrophobic ligands to regulate lipid trafficking and metabolism [ 5 , 6 ] . FABP4 is mainly expressed in adipocytes and macrophages and plays an essential regulatory role in energy metabolism and inflammatory response [ 7 ] . Several studies showed that ethanol metabolism promotes the expression of FABP4 in the liver [ 8 – 10 ] , implying that FABP4 is associated with the progression of ALD. However, the function and underlying mechanism of FABP4 in the progression of ASH remains unclear. In this study, we hypothesized that FABP4 may have an essential role in hepatic lipid metabolism and inflammation in ASH. Combined with bioinformatics analysis of GEO data, we analyzed the role of FABP4 in ethanol-induced liver steatosis and inflammation and further explored the underlying molecular mechanism in ASH mice. 2. Materials and methods 2.1. Dataset collection The original microarray datasets of GEO series GSE142530, GSE167308, and GSE73173 were downloaded from National Center of Biotechnology Information-GEO. GSE142530 [ 11 ] dataset includes 21 patients with AH (10 samples) and healthy livers (11 samples), based on GPL11154 platform. GSE167308 [ 12 ] dataset contains 12 patients with AH (7 samples) and healthy livers (5 samples), based on GPL20321 platform. In addition, GSE73173 [ 13 ] is a gene expression profile of RAW264.7 macrophages with the treatment of FABP4. Quality control analysis and microarray data pre-processing, including background correction and normalization, were performed in R using the Bioconductor package before formal analysis. 2.2. Identification of DEGs Differentially expressed genes (DEGs) were identified by R Bioconductor package limma (V3.54.2). P -values were adjusted by Benjamini-Hochberg’s false discovery rate (FDR), and genes with the P -value < 0.05 and |Log2 fold-change (log2FC)|≥0.585 were defined as differentially expressed. Volcano plots were generated by R software ggplot2 V3.4.1 package, and heatmaps for DEGs from each dataset were plotted by R software Pheatmap V1.0.12 package, and the Venn Diagram was drawn with R software VennDiagram V1.7.3 package. 2.3. Functional enrichment analysis of DEGs The functional enrichment analysis of DEGs was conducted using the R software clusterProfiler V4.6.2 package and the GO plot V1.0.2 package (significant as a P < 0.05). For all DEGs, gene set enrichment analysis (GSEA), gene ontology (GO) terms (BP, biological process; CC, cellular component; and MF, molecular function) and kyoto encyclopedia of genes and genomes (KEGG) pathways enrichment analysis were conducted. In addition, GO enrichment analysis of the up-regulated and down-regulated DEGs was performed. 2.4. Screening and validation of critical gene signatures Weighted gene co-expression network analysis (WGCNA) was used to confirm the relationship between FABP4 and AH using R software WGCNA V1.72-1 package. To evaluate the diagnostic value of FABP4 for AH, ROC curves and area under the curve (AUC) were calculated using R software pROC V1.18.0 package. A two-sided P < 0.05 defined statistical significance. Furthermore, three algorithms, which were random forests (RF), support vector machine (SVM), and eXtreme Gradient Boosting (XGBoost) were used to calculate AUC for AH using the R software mlr3 V0.15.0 package. 2.5. Evaluation and correlation analysis of infiltrated immune cells CIBERSORT was used to analyze the infiltration of 22 kinds of the immune cells in human and 25 kinds of the immune cells in mice. We obtained the relative abundance of infiltrated immune cell according to P < 0.05, and then drew the correlation heatmap for visualizing the correlation of infiltrated immune cells through R software corrplot V0.92 package, and next explored the differential infiltration of immune cells between AH and control groups using Wilcoxon rank sum test, and subsequently analyzed the Spearman relationship between biomarkers and infiltrating immune cells. The results were visualized via R software ggcorrplot V0.1.4 package. 2.6. 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 (Nanjing, Jiangsu, China). All mice were maintained in a specific pathogen-free facility at Guangxi Medical University (Nanning, Guangxi, China). We used the Lieber-Decarli ethanol liquid diet to establish a standardized murine ASH model based on the Gao-Binge model [ 14 ] . The feeding protocol can be extended to long-term feeding, up to 8 weeks, plus single ethanol binge based on body weight. The Lieber-Decarli diet was purchased from Trophic Animal Feed High-tech Co. Ltd (Nantong, Jiangsu, China). 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 ASH model. The mice were anesthetized 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, Guangxi, China. 2.7. Patient samples Liver tissue samples of clinically confirmed ALD patients and healthy individuals at Guangxi Medical University were used in this study. The healthy liver tissue was obtained from donated liver or pathological specimens of benign liver diseases (such as liver hemangioma). The use of these tissue samples was approved by the Ethics Committee of the First Affiliated Hospital of Guangxi Medical University. All samples were analyzed in accordance with the statutes of the Declaration of Helsinki. 2.8. Biochemical analyses Serum levels of alanine aminotransferase (ALT), aspartate aminotransferase (AST), triglyceride (TG), and total cholesterol (TC) were measured with an autoanalyzer (Catalyst one, IDEXX, USA). 2.9. Measurement of TG, TC, TNF-α, and IL-1β levels in liver homogenates TG (Catalog# A110-1-1) and TC (Catalog# A111-1-1) assay kits were purchased from Jiancheng Institution PeproTech (Nanjing, Jiangsu, China). According to the instructions, TG and TC levels in liver tissue homogenates were detected using TG and TC assay kits, respectively. Hepatic TNF-α and IL-1β levels were measured using commercially available ELISA kits (Thermo Scientific, Waltham, MA, USA), following the manufacturer’s instructions. 2.10. Histopathologic and immunohistochemical analyses Fresh liver tissues were frozen and sliced into 8-µm-thick sections. Then, the sections were stained with an Oil Red O staining kit. The resected liver tissue was fixed overnight in 10% neutral-buffered formalin solution, embedded in paraffin, and sectioned prior to routine histological staining. The paraffin-embedded liver tissue sections (5-µm-thick) were dewaxed with xylene, dehydrated with alcohol, and then microwaved in sodium citrate buffer (pH 6) for 5 min to obtain the antigen. The sections were stained with hematoxylin and eosin (H&E). Histopathological alterations of the liver biopsies were observed in high power fields (100x) per sample using a NanoZoomer S60 (Hamamatsu, Japan). The sections were incubated with 3% hydrogen peroxide for 10 min to eliminate endogenous peroxidase activity, then incubated with a goat anti-F4/80 antibody (CST, 1:500 dilution) for 2h at room temperature, followed by an anti-goat ImmPRESS kit (Vector Laboratories), according to the manufacturer’s instructions. The images were collected using NanoZoomer S60 (Hamamatsu, Japan). 2.11. RNA extraction and quantitative reverse transcription-polymerase chain reaction (qRT-PCR) TRIzol (15596026, Invitrogen, Carlsbad, CA, USA) was used to extract the total RNA from 0.1 g of frozen liver tissues according to the manufacturer’s instructions. Next, cDNA was synthesized using a RevertAid First Strand cDNA Synthesis Kit (No. K1622, Thermo Scientific), followed by qPCR using a SYBR Green PCR master mix (No.1725125, Bio-Rad) on a real-time PCR system (CFX 96 Touch, Bio-Rad). The qRT-PCR primers used in this study were shown in supplementary Table S1 . GAPDH was used to normalize the gene expression. 2.12. Western blot analysis The proteins of hepatic samples were extracted by RIPA buffer solution (Beyotime, P0013B), and then the protein concentration was calculated using a BCA protein assay kit (Beyotime, Shanghai, China). The protein were analyzed by SDS-PAGE and transferred to a 0.45-µm PVDF membrane (Merck KGaA, Darmstadt, Germany). The PVDF membranes were blocked with 5% milk and then washed with TBS containing 0.1% Tween-20 (TBST) three times, for 10 min each time. After incubation with the special primary antibodies at 4°C overnight, the PVDF membranes were washed with TBST three times, for 10 min each time. The membranes were then incubated with HRP-labeled goat antibodies against rabbit or mouse IgG (1:1000, Proteintech, Wuhan, China). The ECL-chemiluminescent kit (Epizyme, Shanghai, China) was used for detection. The densities of the protein immune response bands were analyzed with Image J software. The primary antibodies used in this study are listed as follows: FABP4 (12802-1-AP), p53 (10442-1-AP), CASP3 (25128-1-AP), BCL-2 (26593-1-AP), BAX (50599-2-Ig), NF-κB (10745-1-AP), PPARα (66826-1-Ig), AMPK (10929-2-AP), CPT-1 (15184-1-AP), SREBP1 (14088-1-AP), SCD-1 (28678-1-AP) (Proteintech, Wuhan, China), F4/80 (70076T), NLRP3 (15101S), CASP1 (24232S), IL-1β (12242T), SIRT1 (2028S), IKK (61294S), ACC (3676T), FASN (3180T), and GAPDH (2118T) (Cell Signaling Technology, Danvers, Massachusetts, USA). GAPDH was used to normalize the signals. 2.13. Transcriptional profiling Total RNA was extracted from the flash-frozen liver tissues of FABP4 −/− and their littermate ASH mice using TRIzol reagent. The quality of the RNA samples was evaluated with a NanoDrop 2000c spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA) and Agilent’s bioanalyzer. Sequencing libraries were generated by reverse transcription-polymerase chain reaction (RT-PCR) amplification and sequenced on a HiSeq 2500 sequencing system (RIBOBIO, Guangzhou, China). 2.14. Statistical analysis Normally distributed continuous variables were expressed as the mean ± standard deviation (SD) and two groups were compared by a two-tailed Student’s t test. All statistical analyses were performed using SPSS software for windows (version 20.0; SPSS, Chicago, IL, USA), and a two-sided P value < 0.05 was considered statistically significant. 3. Results 3.1. DEGs in AH datasets and single-gene GSEA of FABP4 The results of differential expression analysis showed that 1872 down-regulated and 1877 up-regulated genes were identified as DEGs in GSE142530 (Fig. 1 A and C). Meanwhile, 3202 down-regulated and 2836 up-regulated genes were obtained from GSE167308 (Fig. 1 B and D). Venn diagrams displayed 2201 overlapping genes related to AH between the two datasets, including 1094 down-regulated and 1107 up-regulated genes (Fig. 1 E). We further analyzed the FABPs family and found 3 up-regulated genes in GSE142530, and 1 up-regulated and 2 down-regulated genes in GSE167308 (Fig. 1 F and G). Among the whole FABPs family, only FABP4 was up-regulated in both AH datasets (Fig. 1 F and G). GSEA showed that FABP4 was mainly 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. 1 H-M). 3.2. Verification of FABP4 in AH datasets and expression of FABP4 in ALD patients We combined the two AH datasets and removed batch effects by R software sva V3.46.0 package [ 15 ] . Moreover, using WGCNA analysis with the default-recommended parameters (Fig. 2 A and 2 B), 13 remarkable co-expression modules were identified (Fig. 2 C). As indicated from the investigations of module-trait correlations, turquoise module and salmon module were related to AH (Fig. 2 D). Given that the association of turquoise module and AH was the most significant, genes in the turquoise module were screened, and FABP4 was successfully identified (Fig. 2 E). In order to assess the predictive value of FABP4 in AH, we generated ROC curves. The AUC for FABP4 was 0.82 (Fig. 2 F). We carried out RF algorithm, SVM algorithm, and XGBoost algorithm to verify the predictive value of FABP4 in AH. The results showed that the AUCs for FABP4 with RF algorithm, SVM algorithm, and XGBoost algorithm were 0.83, 0.80, and 0.77 (Fig. 2 G), suggesting that FABP4 had the high accuracy of predictive value. Next, we verified the expression of FABP4 in AH datasets. The results showed that FABP4 expression was up-regulated in AH compared with controls in the GSE142530 and GSE167308 datasets (all P < 0.05, Fig. 2 H and I). To further verify the expression of FABP4 in ALD patients, the results of qRT-PCR and WB showed that the mRNA and protein levels of FABP4 in the liver tissues were increased compared with healthy controls (Fig. 2 J and K). These results indicated that FABP4 is significantly elevated in the liver tissues of patients with ALD. 3.3. The expression of Fabp4 in the liver tissues of ASH model mice We further examined the expression of FABP4 in ASH mice, which were fed the Lieber-DeCarli ethanol (5% v/v) liquid diet for 8 weeks plus single binges. The results of H&E and Oil Red O staining showed that the degree of liver injury and lipid deposition was worse in the EtOH-fed mice than that in the CD-fed mice (Fig. 3 A). Serum levels of ALT, AST, TG, and TC increased in the EtOH-fed compared to the CD-fed (Fig. 3 B-E). Moreover, the hepatic levels of TNF-α, IL-6, TG, and TC showed an ascending trend after alcohol intake compared with the CD-fed groups (Fig. 3 F-I). In addition, the mRNA and protein levels of Fabp4 in the liver tissues of ASH mice were increased (Fig. 3 J and K). To sum up, the expression of Fabp4 was elevated in the liver tissues of ASH mice. 3.4. Fabp4 deficiency reduced hepatic lipid deposition and liver injury in ASH mice To further explore the role of Fabp4 on the progression in the ASH mice, WT and Fabp4 knockout (Fabp4 −/− ) mice were fed the Lieber-DeCarli ethanol (5% v/v) liquid diet for 8 weeks plus single binges. The expression of Fabp4 was detected by qRT-PCR and WB. There is almost no expression of Fabp4 in the liver tissues of Fabp4 −/− mice (Fig. 4 A and B). The results of H&E and Oil Red O staining showed that compared with the WT mice, hepatic lipid droplet accumulation and liver injury was obviously reduced in Fabp4 −/− mice (Fig. 4 C). We also observed a reduction of serum ALT and AST in Fabp4 −/− mice compared to WT mice (Fig. 4 D and E). Moreover, the hepatic levels of TG and TC were decreased in Fabp4 −/− mice (Fig. 4 F and G). In addition, the hepatic levels of TNF-α and IL-6 were reduced in FABP4 −/− mice (Fig. 4 H and I). The above results indicated that Fabp4 deficiency ameliorated hepatic lipid deposition and liver injury in ASH mice. 3.5. 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. The results of differential expression analysis showed that a total of 964 genes, including 359 down-regulated genes and 605 up-regulated genes, were identified as DEGs in the liver tissues of WT and Fabp4 −/− mice (Fig. 5 A and B). The top 50 DEGs, including 30 up-regulated genes and 20 down-regulated genes were exhibited in the heatmaps (Fig. 5 C). We conducted gene set enrichment analysis of the DEGs. Cell cycle, DNA replication, cell adhesion molecules, B cell receptor signaling pathway, focal adhesion, and phagosome were considered to be the most highly enriched pathways (Fig. 5 D). We conducted GO and KEGG pathway enrichment analysis of the DEGs. The results showed that the significantly enriched BP included cell chemotaxis, regulation of cell cycle phase transition, leukocyte chemotaxis, negative regulation of cell cycle process, and negative regulation of cell cycle phase transition (Fig. 5 E). In the CC category, spindle pole, microtubule, collagen-containing extracellular matrix, myelin sheath, and replication fork were the top 5 enriched items (Fig. 5 F). As for MF, the most enriched terms were tau protein binding, phosphatase binding, protein serine/threonine kinase activity, extracellular matrix structural constituent, and transmembrane-ephrin receptor activity (Fig. 5 G). We also performed the GO functional enrichment analysis in down-regulated and up-regulated DEGs, respectively. The results showed that acute inflammatory response, acute-phase response, reactive oxygen species biosynthetic process, and fat cell differentiation were significantly enriched in down-regulated DEGs (Fig. 5 H). However, negative regulation of cell adhesion, negative regulation of cell cycle, negative regulation of immune system process, and negative regulation of leukocyte activation were the most enriched items in up-regulated DEGs (Fig. 5 I). Subsequently, the DEGs were subjected to KEGG pathway enrichment analysis. Cell adhesion molecules, p53 signaling pathway, cell cycle, insulin resistance, PI3K-Akt signaling pathway, IL-17 signaling pathway, and alcoholic liver disease were considered to be the most highly enriched pathways (Fig. 5 J). 3.6. FABP4 deficiency attenuated the progression of ASH in mice via the p53 signaling pathway Transcriptional profiling and integrative bioinformatics analysis showed that Fabp4 was related to p53 signaling pathway, insulin resistance, and PI3K-Akt signaling pathway (Fig. 5 J). Thus, we measured the critical factors in these pathways. The hepatic protein of p53 was greatly reduced in Fabp4 −/− ASH mice compared with WT mice (Fig. 6 A). The p53 signaling pathway associated molecules such as Casp3, Bax and Bcl2 were further evaluated. The results showed that the protein levels of Casp3 and Bax, both related to apoptosis, were decreased, while Bcl-2, which is related to anti-apoptosis, was increased in Fabp4 −/− mice (Fig. 6 A). The mRNA and protein levels of IRS-1, Pi3k, and Akt associated with alleviating insulin resistance were increased (Fig. 6 B and C). Taken together, these results demonstrate that FABP4 regulates the p53 signaling pathway and insulin/PI3K/AKT signaling pathway in ASH mice. Previous studies have shown that inhibition of p53 could induce hepatic SIRT1 upregulation [ 16 – 18 ] . Therefore, qRT-PCR and WB were used to detect the expression of SIRT1 in ASH mice. The results showed that the mRNA and protein levels of SIRT1 in liver tissues of FABP4 −/− mice were significantly increased (Fig. 6 D and E). The expression levels of lipid catabolic-related genes PPARα, AMPK, and CPT-1 and lipid anabolic-related genes ACC, SREBP1, SCD1, and FASN were detected. The results showed that the mRNA and protein levels of PPARα, AMPK, and CPT-1 were increased (Fig. 6 F and G); in contrast, the expression of ACC, SREBP1, SCD1, and FASN were decreased (Fig. 6 H and I). These results demonstrate that FABP4 deficiency inhibits fatty acid synthesis and promotes fatty acid oxidation in ASH mice through the p53 signaling pathway and SIRT1 signaling pathway. It has been 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 pathway [ 19 ] . Thus, we detected the hepatic expression of related factors in the NF-κB signaling pathway by qRT-PCR and WB. As expected, the results showed that the mRNA and protein expression of IKK and NF-κB decreased in FABP4 −/− mice (Fig. 6 J and K). In conclusion, FABP4 deficiency reduces hepatic inflammation in ASH mice by mediating the SIRT1 signaling pathway. 3.7. FABP4 affected the proportion of macrophage M1/M2 and the expression of pro-inflammatory factors in ASH. Previous studies have suggested that activation of immune can accelerate the progression of AH [ 20 , 21 ] . We thus aimed to explore the relationship between FABP4 and the immune cells infiltration in AH. We performed CIBERSORT algorithm to analyze the immune cell phenotypes in GSE142530. These results demonstrate that AH samples had a lower proportion of macrophages M2 compared to control samples, and FABP4 showed a negative correlation with macrophages M2 (supplementary Fig. S1 A-D). To further explore the effects of FABP4 in macrophages, we conducted GSEA of the DEGs in GSE73173. These results suggest that the effects of exogenous FABP4 in RAW264.7 macrophages are mainly focused on immunity, inflammation and lipid metabolism (supplementary Fig. S1 E-I). FABP4 is highly expressed in macrophages, especially during the inflammatory activated station [ 22 , 23 ] . We performed CIBERSORT algorithm to analyze the 25 immune cell phenotypes including macrophages in the liver tissues of WT and FABP4 −/− mice. The proportions of macrophages M1 in FABP4 −/− mice was significantly lower than that in WT mice (P < 0.01). However, in comparison with WT mice, FABP4 −/− mice had a higher proportion of macrophages M2. Interestingly, the proportion of macrophages M2 in WT mice was zero, and the proportion of macrophages M2 in FABP4 −/− mice was 6% (Fig. 7 A and B). As indicated from the correlation heatmap of the 23 types of immune cells, macrophages are significantly correlated with some immune cells (Fig. 7 C). We then sought to explore the relationships between key genes and infiltrated immune cells in ASH. Based on the results of correlation analysis, p53, NLRP3, IL-1β, and CXCL-1 are positively correlated with macrophages M1 (r = 0.66, 0.6, 0.6, and 0.83, both P < 0.05) (Fig. 7 D). FABP4 showed a negative correlation with macrophages M2 (r = − 0.52) and SIRT1 displayed a negative correlation with mast cells (r=-0.65, P < 0.001) (Fig. 7 D).Thus, we investigated the relationship between the FABP4 and macrophage in ASH mouse. Immunohistochemistry analysis showed that F4/80 deposition was lower in FABP4 −/− mice as compared to WT mice (Fig. 7 E). Similarly, the mRNA and protein levels of F4/80 were also decreased in livers of FABP4 −/− mice (Fig. 7 F and G). Furthermore, the qRT-PCR results showed that inflammatory-related genes, such as Tnf-α , Il-6 , Il-1β , Il-8 , Trailr1 , Iy6g , Mcp-1 , and Cxcl-1 , were downregulated in the FABP4 −/− mice (Fig. 7 H). The protein levels of NLRP3, CASP1, pro-IL-1β, and IL-1β were decreased in the FABP4 −/− mice (Fig. 7 I). These results suggest that FABP4 deficiency prevents liver inflammation in ASH mice by reducing the proportion of Macrophages M1. 4. Discussion ALD markedly contributes to the global burden of disease and mortality [ 24 ] . ASH can further develop into more severe liver diseases, causing severe harm to the health of the liver and other organs [ 3 ] . In this study, we explored the role of FABP4 in the progression of ASH and the underlying mechanisms. We have come to some conclusions. Firstly, with integrative bioinformatics analysis, DEGs of the two AH datasets were obtained and FABP4 was up-regulated among the whole FABPs family. Furthermore, the expression of FABP4 has been confirmed in AH patients and ASH mice. FABP4 deficiency alleviated hepatic steatosis and inflammation in ASH mice. Mechanistically, FABP4 deficiency suppressed the p53 signaling pathway and then activated Sirt1 signaling pathway, subsequently inhibiting the lipogenesis, promoting fatty acid oxidation, and facilitating the M2 polarization of macrophage in the liver of ASH mice. These findings suggest that FABP4 may be a potential target for ASH drug development. In this study, we conducted integrative bioinformatics analysis and found FABP4 is correlated with the progression of AH. Functional enrichment analysis of DEGs revealed that the enrichment of terms is related to lipid metabolism, immunity, and inflammation. Our studies demonstrated that FABP4 may play a dominant role in liver lipid metabolism and inflammation in AH. Additionally, ROC analysis demonstrated that the elevated FABP4 could accurately distinguish AH from healthy. The results of three algorithms analysis have verified that FABP4 has the high prediction accuracy for AH. These results indicated that FABP4 may be a potential biomarker for clinical applications in AH prediction. FABP4 −/− mice were used to further explore the effects of Fabp4 on the progression of ASH. We found that FABP4 deletion reduced hepatic lipid deposition and inflammation in ASH mice. Then, multiple enrichment analyses of transcriptional profiling revealed that some signaling pathways, such as p53 signaling pathway, insulin resistance, and PI3K-Akt signaling pathway, were enriched between FABP4 −/− and WT ASH mice. Several studies have shown that p53 plays an important role in the regulation of lipid metabolism [ 25 , 26 ] . Inhibition of the transcriptional activity of p53 in the livers of high fat diet ( HFD ) mice diminished the diet-induced weight gain, hepatic steatosis, oxidative stress and apoptosis [ 16 ] . Derdak et al. [ 27 ] have shown that activation of p53 not only orchestrates various forms of cell death, but also regulates cellular energy metabolism and suppresses the insulin/PI3K/AKT axis, contributing to the metabolic abnormalities in ALD rat. Our results showed that FABP4 deficiecy downregulated the expression of p53 in the liver of ASH mice. It has been reported that inhibition of p53 could induce hepatic SIRT1 upregulation, which promoted the β-oxidation of liver fatty acids by up-regulating the expression of PPARα, AMPK, and CPT1 [ 16 – 18 ] . Moreover, a p53 inhibitor increased SIRT1 expression, therefore inhibiting the de novo synthesis of hepatic fatty acids by down-regulating the expression of SREBP1, FASN, SCD-1 and ACC [ 16 , 28 ] . Our results indicated that Fabp4 deficiency upregulated the expression of Sirt1, subsequently reducing the lipid synthesis and promoting lipolysis. In addition, SIRT1 can regulate downstream inflammatory pathways, such as IKK/NF-κB, and directly affect the expression of inflammatory factors [ 19 , 29 ] . Our studies showed that the expression of the IKK/NF-κB signaling pathway associated factors was decreased in FABP4 −/− mice. Taken together, these data demonstrate that FABP4 deficiency alleviates hepatic steatosis and inflammation via the p53 /PI3K/AKT/ SIRT1 signaling pathway. Given the important role of immune in AH, we sought to explore the relationship between FABP4 and immune cells by using AH datasets. FABP4 displayed varying degrees of correlation with immune cells, such as macrophages and T follicular helper cells. The results were further verified in RAW264.7 macrophages with exogenous FABP4 in GSE73173. Furthemore, the CIBERSORT showed that FABP4 is negatively correlated with macrophages M2. Consistent with this result, we found that the proportion of macrophage M2 in liver of FABP4 −/− mice was significantly higher than that in WT mice. Moreover, the inflammation associated factors in the FABP4 −/− mice were also reduced. In short, our results provide novel insights into immune mechanism of FABP4 in the progression of ASH. However, how FABP4 affects macrophage polarization is still unclear and further exploration is needed in future work. In summary, the present study demonstrated that FABP4 deficiency alleviates hepatic lipid accumulation and inflammation in ASH mice. Inhibition of FABP4 may be a potential therapeutic strategy for ASH. Declarations Author contributions FDZ and SQH designed and supervised the experiment and revised the manuscript. HX performed some experiments and wrote the manuscript; ZW, KQJ, GDY, ZYG and SPY participated in some experiments and analyzed some data. Funding National Natural Science Foundation of China (82160120); Natural Science Foundation of Guangxi Province (2023GXNSFAA026062, 2021GXNSFDA075002); the “111” Project (D17011); National Key Research and Development Program (2022YFE0131600)；Advanced Innovation Teams and Xinghu Scholars Program of Guanxi Medical University; Innovation Team of the First Affiliated Hospital of Guangxi Medical University (YYZS2022002). Competing interests The authors declare that there are no competing interests. 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Supplementary Files 20240419FABP4supplementarydata.pdf Cite Share Download PDF Status: Published Journal Publication published 10 Sep, 2024 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 18 Jun, 2024 Reviews received at journal 11 Jun, 2024 Reviewers agreed at journal 29 May, 2024 Reviews received at journal 17 May, 2024 Reviewers agreed at journal 06 May, 2024 Reviewers invited by journal 03 May, 2024 Editor assigned by journal 03 May, 2024 Editor invited by journal 25 Apr, 2024 Submission checks completed at journal 25 Apr, 2024 First submitted to journal 19 Apr, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4292137","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":296949534,"identity":"d66569ce-160a-402d-b3c4-4495cb3d0014","order_by":0,"name":"Hao Xing","email":"","orcid":"","institution":"the First Affiliated Hospital of Guangxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Hao","middleName":"","lastName":"Xing","suffix":""},{"id":296949538,"identity":"175e1a35-8d8a-4628-b6ae-4ddc67ee80e9","order_by":1,"name":"Zhan Wu","email":"","orcid":"","institution":"the First Affiliated Hospital of Guangxi Medical 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Zhong","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwUlEQVRIiWNgGAWjYHACA4aECgk5NvbmAyRo+XDGxpiP51gC8VoYZ7alJc6TyFEgTj3/7OZt0jxsh9PbGHIYGH5UbCOsReLOsTJpHp7DuW0MZw8w9py5TYQ1N3LMpHkkgFoY+xKYGduI0CIP1mJwOJ2NmceAOC0GQC2SMxLSEtjYiNVieCOt2OLDARvDNh62hINE+UXuRvLGG4n/JOTl5z8++OBHBTHeZ2BgkYCxDhClHgiYPxCrchSMglEwCkYoAAC91jtb3/2AHAAAAABJRU5ErkJggg==","orcid":"","institution":"the First Affiliated Hospital of Guangxi Medical University","correspondingAuthor":true,"prefix":"","firstName":"Fudi","middleName":"","lastName":"Zhong","suffix":""}],"badges":[],"createdAt":"2024-04-19 09:14:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4292137/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4292137/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-024-71311-8","type":"published","date":"2024-09-10T15:57:12+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":55639785,"identity":"dc3d0279-2095-4944-b67e-9a7db5988a6f","added_by":"auto","created_at":"2024-04-30 22:18:56","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":490199,"visible":true,"origin":"","legend":"\u003cp\u003eDEGs in AH and single-geneGSEA of Fabp4 analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e, \u003cstrong\u003eB\u003c/strong\u003eVolcano plots of DEGs distribution in GSE142530 (\u003cstrong\u003eA\u003c/strong\u003e) and GSE167308 (\u003cstrong\u003eB\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eC\u003c/strong\u003e, \u003cstrong\u003eD\u003c/strong\u003e Number of DEGs in GSE142530 (\u003cstrong\u003eC\u003c/strong\u003e) and GSE167308 (\u003cstrong\u003eD\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eE\u003c/strong\u003e Venn diagram of DEGs from the two AH datasets.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eF\u003c/strong\u003e, \u003cstrong\u003eG\u003c/strong\u003e Heatmaps of the differentially expressed genes of FABPs family in GSE142530 (\u003cstrong\u003eF\u003c/strong\u003e) and GSE167308 (\u003cstrong\u003eG\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH\u003c/strong\u003e-\u003cstrong\u003eM \u003c/strong\u003eSingle-gene\u003cstrong\u003e \u003c/strong\u003eGSEA profiles depicting the 6 significant GSEA sets in AH. Results are presented as mean ± SD.\u003c/p\u003e","description":"","filename":"image1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4292137/v1/274793dc4bace734354f3a94.jpeg"},{"id":55639710,"identity":"48c816fe-b00c-42ee-90e6-a92a9d385937","added_by":"auto","created_at":"2024-04-30 22:10:56","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":394434,"visible":true,"origin":"","legend":"\u003cp\u003eThe correlation between FABP4 and AH and expression of FABP4 in ALD patients were verified.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e-\u003cstrong\u003eE\u003c/strong\u003e Process of weighted gene co-expression network analysis (WGCNA).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eF \u003c/strong\u003eThe diagnostic power of FABP4 in AH by ROC curve.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eG \u003c/strong\u003eThe AUCs for FABP4 in AH with RF algorithm, SVM algorithm, and XGBoost algorithm.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH\u003c/strong\u003e-\u003cstrong\u003eI \u003c/strong\u003eThe expressions of FABP4 in GSE142530 (\u003cstrong\u003eH\u003c/strong\u003e) and GSE167308 (\u003cstrong\u003eI\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eJ\u003c/strong\u003e-\u003cstrong\u003eK\u003c/strong\u003e The mRNA (\u003cstrong\u003eJ\u003c/strong\u003e) and protein (\u003cstrong\u003eK\u003c/strong\u003e) levels of FABP4 in the liver tissues of patients with ALD. Results are presented as mean ± SD. *\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05.\u003c/p\u003e","description":"","filename":"image2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4292137/v1/1c1f065e0a693ba44e6ae146.jpeg"},{"id":55639713,"identity":"666a268c-b665-457d-9057-aaf2c24613ff","added_by":"auto","created_at":"2024-04-30 22:10:57","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":382817,"visible":true,"origin":"","legend":"\u003cp\u003eLiver injury and Fabp4 levels are elevated in ASH mice.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA.\u003c/strong\u003e H\u0026amp;E and Oil Red O staining was used to assess liver necrosis and lipid deposition (scale bar, 100 mm).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB\u003c/strong\u003e-\u003cstrong\u003eE\u003c/strong\u003e. Serum levels of ALT (\u003cstrong\u003eB\u003c/strong\u003e), AST(\u003cstrong\u003eC\u003c/strong\u003e), TG (\u003cstrong\u003eD\u003c/strong\u003e), and TC (\u003cstrong\u003eE\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eF\u003c/strong\u003e-\u003cstrong\u003eI\u003c/strong\u003e. Hepatic levels of TNF-α (F), IL-6(G), TG (\u003cstrong\u003eH\u003c/strong\u003e) and TC (\u003cstrong\u003eI\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eJ\u003c/strong\u003e. qRT-PCR analyses of hepatic Fabp4 expression in mice.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eK\u003c/strong\u003e. Protein level of FABP4 in liver tissue was assessed by WB. Results are presented as mean ± SD. *\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003eP\u003c/em\u003e\u0026lt; 0.01, ***\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"image3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4292137/v1/2d323d39bd97ecd8a736ee09.jpeg"},{"id":55639712,"identity":"4d54d7a5-c07b-43c7-aba2-e1e74bbffb41","added_by":"auto","created_at":"2024-04-30 22:10:56","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":407454,"visible":true,"origin":"","legend":"\u003cp\u003eFabp4 deficiency reduced hepatic lipid deposition and liver injury in ASH mice.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e. Relative expression level of Fabp4 was examined by qRT-PCR.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB\u003c/strong\u003e. The protein level of FABP4 in the liver tissue.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eC\u003c/strong\u003e. H\u0026amp;E and Oil Red O staining (scale bar, 100 mm).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eD\u003c/strong\u003e-\u003cstrong\u003eE\u003c/strong\u003e. Serum levels of ALT(\u003cstrong\u003eD\u003c/strong\u003e) and AST(\u003cstrong\u003eE\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eF\u003c/strong\u003e-\u003cstrong\u003eG\u003c/strong\u003e. Hepatic levels of TG (\u003cstrong\u003eF\u003c/strong\u003e) and TC (\u003cstrong\u003eG\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eH-I. Hepatic levels of TNF-α (H) and IL-6 (I) were detected by ELISA. Results were presented as mean ± SD. \u003cem\u003en \u003c/em\u003e= 6/per group. *\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003eP\u003c/em\u003e\u0026lt; 0.01, ***\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"image4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4292137/v1/4341d28880c0b447ca7c775c.jpeg"},{"id":55639786,"identity":"9c47cfa0-a3bc-4fe3-9bcd-b53a19d25998","added_by":"auto","created_at":"2024-04-30 22:18:57","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":543512,"visible":true,"origin":"","legend":"\u003cp\u003eTranscriptional profiling of liver tissues from ASH mice and bioinformatics analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e Volcano plots of DEGs distribution in liver tissues from WT and Fabp4\u003csup\u003e-/- \u003c/sup\u003eASH mice.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB\u003c/strong\u003e Number of DEGs in liver tissues.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eC\u003c/strong\u003e Heatmaps of the top 50 DEGs in liver tissues.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eD \u003c/strong\u003eGSEA profiles depicting the 14 significant GSEA sets.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eE\u003c/strong\u003e-\u003cstrong\u003eG\u003c/strong\u003e Bubble charts show GO-enriched items of DEGs in three functional groups: biological processes (BP, \u003cstrong\u003eE\u003c/strong\u003e), cell composition (CC, \u003cstrong\u003eF\u003c/strong\u003e), and molecular function (MF, \u003cstrong\u003eG\u003c/strong\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH\u003c/strong\u003e, \u003cstrong\u003eI\u003c/strong\u003e Chord plots show GO-enriched items of down-regulated (\u003cstrong\u003eH\u003c/strong\u003e) and up-regulated (\u003cstrong\u003eI\u003c/strong\u003e) DEGs\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eJ\u003c/strong\u003e Circle plot shows KEGG-enriched items of DEGs.\u003c/p\u003e","description":"","filename":"image5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4292137/v1/55e5488c72664d32dbfb7aa5.jpeg"},{"id":55639714,"identity":"fb947460-6f89-4556-bbd5-647d58f2eadf","added_by":"auto","created_at":"2024-04-30 22:10:57","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":316011,"visible":true,"origin":"","legend":"\u003cp\u003eFABP4 deficiency attenuated ASH in mice through the p53 signaling pathway, insulin/PI3K/AKT signaling pathway, and SIRT1 signaling pathway.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e. Protein levels of p53, CASP3, BAX, and BCL-2 was assayed by WB.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB\u003c/strong\u003e-\u003cstrong\u003eC\u003c/strong\u003e. Relative expression levels of \u003cem\u003eIrs-1\u003c/em\u003e, \u003cem\u003ePi3k\u003c/em\u003e, and \u003cem\u003eAkt\u003c/em\u003e were examined by qRT-PCR (\u003cstrong\u003eB\u003c/strong\u003e). Protein levels of IRS-1, PI3K, and AKT were assayed by WB (\u003cstrong\u003eC\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eD\u003c/strong\u003e. Hepatic level of \u003cem\u003eSirt1\u003c/em\u003e was examined by qRT-PCR.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eE.\u003c/strong\u003e Protein level of SIRT1 was assayed by WB.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eF\u003c/strong\u003e. Relative mRNA levels of \u003cem\u003eCpt-1\u003c/em\u003e, \u003cem\u003eAmpk\u003c/em\u003e, and \u003cem\u003ePparα\u003c/em\u003e were examined by qRT-PCR.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eG.\u003c/strong\u003e Protein levels of CPT-1, AMPK, and PPARα were assessed by WB.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH\u003c/strong\u003e. Relative mRNA levels of \u003cem\u003eAcc\u003c/em\u003e, \u003cem\u003eFasn\u003c/em\u003e, \u003cem\u003eSrebf1\u003c/em\u003e, and \u003cem\u003eScd-1\u003c/em\u003e were examined by qRT-PCR.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eI.\u003c/strong\u003e Protein levels of ACC, FASN, SREBP1, and SCD-1 was assessed by WB.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eJ\u003c/strong\u003e. Relative expression levels of \u003cem\u003eIkk\u003c/em\u003eand \u003cem\u003eNf-kb\u003c/em\u003e were examined by qRT-PCR.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eK.\u003c/strong\u003e Protein levels of IKK and NF-kB were assayed by WB. Results were presented as mean ± SD. *\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01, ***\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"image6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4292137/v1/b92cc5afc7637bb78daecbcd.jpeg"},{"id":55639717,"identity":"3fde4bd4-6de8-49b6-8694-15221b56cd9c","added_by":"auto","created_at":"2024-04-30 22:10:57","extension":"jpeg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":513275,"visible":true,"origin":"","legend":"\u003cp\u003eThe relationship of FABP4 and macrophage in ASH mice.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e. Stacked bar chart of the immune cells.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB\u003c/strong\u003e. Box-plot of the proportion of 23 type immune cells.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eC\u003c/strong\u003e. Heatmap of correlation in 23 type immune cells.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eD\u003c/strong\u003e. Correlations between p53, SIRT1, NLRP3, IL-1β, FABP4, CXCL1, BCL-2, and infiltrating immune cells.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eE\u003c/strong\u003e. IHC staining of F4/80.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eF\u003c/strong\u003e. Hepatic mRNA level of F4/80.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eG\u003c/strong\u003e. Hepatic protein level of F4/80.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH\u003c/strong\u003e. Relative mRNA levels of \u003cem\u003eTnf-α\u003c/em\u003e, \u003cem\u003eIl-6\u003c/em\u003e, \u003cem\u003eIl-1β\u003c/em\u003e, \u003cem\u003eIl-8\u003c/em\u003e, \u003cem\u003eTrailr1\u003c/em\u003e, \u003cem\u003eIy6g\u003c/em\u003e, \u003cem\u003eMcp-1\u003c/em\u003e, and \u003cem\u003eCxcl-1\u003c/em\u003ein liver tissues were examined by qRT-PCR.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eI\u003c/strong\u003e. Protein levels of NLRP3, CASP1, pro-IL-1β, and IL-1β were assessed by WB. Results were presented as mean ± SD. *\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003eP\u003c/em\u003e\u0026lt; 0.01, ***\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"image7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4292137/v1/deef61b0b88dc4ecbc03a212.jpeg"},{"id":64619001,"identity":"d9aeed86-dec9-47d0-85de-af2dacfb7490","added_by":"auto","created_at":"2024-09-16 16:10:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3840147,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4292137/v1/c3aa5a15-1909-4fe9-a9da-cf5e72016e7a.pdf"},{"id":55640011,"identity":"dea89b90-c05c-4892-a61e-789d50e83b65","added_by":"auto","created_at":"2024-04-30 22:26:57","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":716402,"visible":true,"origin":"","legend":"","description":"","filename":"20240419FABP4supplementarydata.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4292137/v1/2cad4288acea4d9c957fef8c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"FABP4 deficiency ameliorates alcoholic steatohepatitis in mice via inhibition of p53 signaling pathway","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eAccording to the epidemiological investigation of alcoholic liver disease (ALD), the incidence and prevalence of ALD are increasing rapidly worldwide\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. The initial symptom of ALD is mild alcoholic fatty liver (AFL), which is characterized by a slightly abnormal liver at biochemical and pathological levels. Of the patients with AFL, 10%-20% will progress to more severe liver diseases, such ASH, which is characterized by hepatic steatosis and inflammation, with or without liver fibrosis\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. Chronic and excessive consumption of alcohol further aggravates the liver damage, which can develop into alcoholic liver fibrosis, alcoholic cirrhosis, and even hepatocellular carcinoma\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. Compulsory abstinence from alcohol is the best treatment for ALD patients, however, most ALD patients are unable to effectively quit alcohol \u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. Currently, there are no effective drugs for treating ALD in clinical practice, and the pathogenesis of ALD has not been fully elucidated. As such, there is an urgent need to find new therapeutic targets and drugs for ALD.\u003c/p\u003e \u003cp\u003eThe pathogenesis of ALD has been extensively studied. Pro-inflammatory pathway, apoptosis and immune associated factors were involved in ethanol-induced liver damage. FABPs, including nine homologous proteins with similar tertiary structures and specific tissue distribution patterns, are a family of intracellular lipid-binding proteins that could bind hydrophobic ligands to regulate lipid trafficking and metabolism \u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. FABP4 is mainly expressed in adipocytes and macrophages and plays an essential regulatory role in energy metabolism and inflammatory response\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. Several studies showed that ethanol metabolism promotes the expression of FABP4 in the liver \u003csup\u003e[\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e, implying that FABP4 is associated with the progression of ALD. However, the function and underlying mechanism of FABP4 in the progression of ASH remains unclear.\u003c/p\u003e \u003cp\u003eIn this study, we hypothesized that FABP4 may have an essential role in hepatic lipid metabolism and inflammation in ASH. Combined with bioinformatics analysis of GEO data, we analyzed the role of FABP4 in ethanol-induced liver steatosis and inflammation and further explored the underlying molecular mechanism in ASH mice.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Dataset collection\u003c/h2\u003e \u003cp\u003eThe original microarray datasets of GEO series GSE142530, GSE167308, and GSE73173 were downloaded from National Center of Biotechnology Information-GEO. GSE142530\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e dataset includes 21 patients with AH (10 samples) and healthy livers (11 samples), based on GPL11154 platform. GSE167308\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e dataset contains 12 patients with AH (7 samples) and healthy livers (5 samples), based on GPL20321 platform. In addition, GSE73173\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e is a gene expression profile of RAW264.7 macrophages with the treatment of FABP4. Quality control analysis and microarray data pre-processing, including background correction and normalization, were performed in R using the Bioconductor package before formal analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Identification of DEGs\u003c/h2\u003e \u003cp\u003eDifferentially expressed genes (DEGs) were identified by R Bioconductor package limma (V3.54.2). \u003cem\u003eP\u003c/em\u003e-values were adjusted by Benjamini-Hochberg\u0026rsquo;s false discovery rate (FDR), and genes with the \u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and |Log2 fold-change (log2FC)|\u0026ge;0.585 were defined as differentially expressed. Volcano plots were generated by R software ggplot2 V3.4.1 package, and heatmaps for DEGs from each dataset were plotted by R software Pheatmap V1.0.12 package, and the Venn Diagram was drawn with R software VennDiagram V1.7.3 package.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Functional enrichment analysis of DEGs\u003c/h2\u003e \u003cp\u003eThe functional enrichment analysis of DEGs was conducted using the R software clusterProfiler V4.6.2 package and the GO plot V1.0.2 package (significant as a \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). For all DEGs, gene set enrichment analysis (GSEA), gene ontology (GO) terms (BP, biological process; CC, cellular component; and MF, molecular function) and kyoto encyclopedia of genes and genomes (KEGG) pathways enrichment analysis were conducted. In addition, GO enrichment analysis of the up-regulated and down-regulated DEGs was performed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Screening and validation of critical gene signatures\u003c/h2\u003e \u003cp\u003eWeighted gene co-expression network analysis (WGCNA) was used to confirm the relationship between FABP4 and AH using R software WGCNA V1.72-1 package. To evaluate the diagnostic value of FABP4 for AH, ROC curves and area under the curve (AUC) were calculated using R software pROC V1.18.0 package. A two-sided \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 defined statistical significance. Furthermore, three algorithms, which were random forests (RF), support vector machine (SVM), and eXtreme Gradient Boosting (XGBoost) were used to calculate AUC for AH using the R software mlr3 V0.15.0 package.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Evaluation and correlation analysis of infiltrated immune cells\u003c/h2\u003e \u003cp\u003eCIBERSORT was used to analyze the infiltration of 22 kinds of the immune cells in human and 25 kinds of the immune cells in mice. We obtained the relative abundance of infiltrated immune cell according to \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, and then drew the correlation heatmap for visualizing the correlation of infiltrated immune cells through R software corrplot V0.92 package, and next explored the differential infiltration of immune cells between AH and control groups using Wilcoxon rank sum test, and subsequently analyzed the Spearman relationship between biomarkers and infiltrating immune cells. The results were visualized via R software ggcorrplot V0.1.4 package.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6. Animal models\u003c/h2\u003e \u003cp\u003e The present study was conducted in compliance with the ARRIVE guidelines. Female wild type (WT) and FBAP4\u003csup\u003e\u0026minus;/\u0026minus;\u003c/sup\u003e C57BL/6 mice were purchased from GemPharmatech (Nanjing, Jiangsu, China). All mice were maintained in a specific pathogen-free facility at Guangxi Medical University (Nanning, Guangxi, China). We used the Lieber-Decarli ethanol liquid diet to establish a standardized murine ASH model based on the Gao-Binge model \u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. The feeding protocol can be extended to long-term feeding, up to 8 weeks, plus single ethanol binge based on body weight. The Lieber-Decarli diet was purchased from Trophic Animal Feed High-tech Co. Ltd (Nantong, Jiangsu, China). WT mice (8\u0026ndash;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\u003csup\u003e\u0026minus;/\u0026minus;\u003c/sup\u003e mice (8\u0026ndash;12 weeks old) were used to generate ASH model. The mice were anesthetized 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, Guangxi, China.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7. Patient samples\u003c/h2\u003e \u003cp\u003eLiver tissue samples of clinically confirmed ALD patients and healthy individuals at Guangxi Medical University were used in this study. The healthy liver tissue was obtained from donated liver or pathological specimens of benign liver diseases (such as liver hemangioma). The use of these tissue samples was approved by the Ethics Committee of the First Affiliated Hospital of Guangxi Medical University. All samples were analyzed in accordance with the statutes of the Declaration of Helsinki.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8. Biochemical analyses\u003c/h2\u003e \u003cp\u003eSerum levels of alanine aminotransferase (ALT), aspartate aminotransferase (AST), triglyceride (TG), and total cholesterol (TC) were measured with an autoanalyzer (Catalyst one, IDEXX, USA).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.9. Measurement of TG, TC, TNF-α, and IL-1β levels in liver homogenates\u003c/h2\u003e \u003cp\u003eTG (Catalog# A110-1-1) and TC (Catalog# A111-1-1) assay kits were purchased from Jiancheng Institution PeproTech (Nanjing, Jiangsu, China). According to the instructions, TG and TC levels in liver tissue homogenates were detected using TG and TC assay kits, respectively. Hepatic TNF-α and IL-1β levels were measured using commercially available ELISA kits (Thermo Scientific, Waltham, MA, USA), following the manufacturer\u0026rsquo;s instructions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.10. Histopathologic and immunohistochemical analyses\u003c/h2\u003e \u003cp\u003eFresh liver tissues were frozen and sliced into 8-\u0026micro;m-thick sections. Then, the sections were stained with an Oil Red O staining kit. The resected liver tissue was fixed overnight in 10% neutral-buffered formalin solution, embedded in paraffin, and sectioned prior to routine histological staining. The paraffin-embedded liver tissue sections (5-\u0026micro;m-thick) were dewaxed with xylene, dehydrated with alcohol, and then microwaved in sodium citrate buffer (pH 6) for 5 min to obtain the antigen. The sections were stained with hematoxylin and eosin (H\u0026amp;E). Histopathological alterations of the liver biopsies were observed in high power fields (100x) per sample using a NanoZoomer S60 (Hamamatsu, Japan).\u003c/p\u003e \u003cp\u003e The sections were incubated with 3% hydrogen peroxide for 10 min to eliminate endogenous peroxidase activity, then incubated with a goat anti-F4/80 antibody (CST, 1:500 dilution) for 2h at room temperature, followed by an anti-goat ImmPRESS kit (Vector Laboratories), according to the manufacturer\u0026rsquo;s instructions. The images were collected using NanoZoomer S60 (Hamamatsu, Japan).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e2.11. \u003cb\u003eRNA extraction and\u003c/b\u003e quantitative reverse transcription-polymerase chain reaction (qRT-PCR)\u003c/h2\u003e \u003cp\u003eTRIzol (15596026, Invitrogen, Carlsbad, CA, USA) was used to extract the total RNA from 0.1 g of frozen liver tissues according to the manufacturer\u0026rsquo;s instructions. Next, cDNA was synthesized using a RevertAid First Strand cDNA Synthesis Kit (No. K1622, Thermo Scientific), followed by qPCR using a SYBR Green PCR master mix (No.1725125, Bio-Rad) on a real-time PCR system (CFX 96 Touch, Bio-Rad). The qRT-PCR primers used in this study were shown in supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. GAPDH was used to normalize the gene expression.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e2.12. Western blot analysis\u003c/h2\u003e \u003cp\u003eThe proteins of hepatic samples were extracted by RIPA buffer solution (Beyotime, P0013B), and then the protein concentration was calculated using a BCA protein assay kit (Beyotime, Shanghai, China). The protein were analyzed by SDS-PAGE and transferred to a 0.45-\u0026micro;m PVDF membrane (Merck KGaA, Darmstadt, Germany). The PVDF membranes were blocked with 5% milk and then washed with TBS containing 0.1% Tween-20 (TBST) three times, for 10 min each time. After incubation with the special primary antibodies at 4\u0026deg;C overnight, the PVDF membranes were washed with TBST three times, for 10 min each time. The membranes were then incubated with HRP-labeled goat antibodies against rabbit or mouse IgG (1:1000, Proteintech, Wuhan, China). The ECL-chemiluminescent kit (Epizyme, Shanghai, China) was used for detection. The densities of the protein immune response bands were analyzed with Image J software. The primary antibodies used in this study are listed as follows: FABP4 (12802-1-AP), p53 (10442-1-AP), CASP3 (25128-1-AP), BCL-2 (26593-1-AP), BAX (50599-2-Ig), NF-κB (10745-1-AP), PPARα (66826-1-Ig), AMPK (10929-2-AP), CPT-1 (15184-1-AP), SREBP1 (14088-1-AP), SCD-1 (28678-1-AP) (Proteintech, Wuhan, China), F4/80 (70076T), NLRP3 (15101S), CASP1 (24232S), IL-1β (12242T), SIRT1 (2028S), IKK (61294S), ACC (3676T), FASN (3180T), and GAPDH (2118T) (Cell Signaling Technology, Danvers, Massachusetts, USA). GAPDH was used to normalize the signals.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e2.13. Transcriptional profiling\u003c/h2\u003e \u003cp\u003eTotal RNA was extracted from the flash-frozen liver tissues of FABP4\u003csup\u003e\u0026minus;/\u0026minus;\u003c/sup\u003e and their littermate ASH mice using TRIzol reagent. The quality of the RNA samples was evaluated with a NanoDrop 2000c spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA) and Agilent\u0026rsquo;s bioanalyzer. Sequencing libraries were generated by reverse transcription-polymerase chain reaction (RT-PCR) amplification and sequenced on a HiSeq 2500 sequencing system (RIBOBIO, Guangzhou, China).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e2.14. Statistical analysis\u003c/h2\u003e \u003cp\u003eNormally distributed continuous variables were expressed as the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) and two groups were compared by a two-tailed Student\u0026rsquo;s \u003cem\u003et\u003c/em\u003e test. All statistical analyses were performed using SPSS software for windows (version 20.0; SPSS, Chicago, IL, USA), and a two-sided \u003cem\u003eP\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.1. DEGs in AH datasets and single-gene GSEA of FABP4\u003c/h2\u003e \u003cp\u003eThe results of differential expression analysis showed that 1872 down-regulated and 1877 up-regulated genes were identified as DEGs in GSE142530 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA and C). Meanwhile, 3202 down-regulated and 2836 up-regulated genes were obtained from GSE167308 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB and D). Venn diagrams displayed 2201 overlapping genes related to AH between the two datasets, including 1094 down-regulated and 1107 up-regulated genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE). We further analyzed the FABPs family and found 3 up-regulated genes in GSE142530, and 1 up-regulated and 2 down-regulated genes in GSE167308 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF and G). Among the whole FABPs family, only FABP4 was up-regulated in both AH datasets (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF and G). GSEA showed that FABP4 was mainly 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.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eH-M).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Verification of FABP4 in AH datasets and expression of FABP4 in ALD patients\u003c/h2\u003e \u003cp\u003eWe combined the two AH datasets and removed batch effects by R software sva V3.46.0 package\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. Moreover, using WGCNA analysis with the default-recommended parameters (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB), 13 remarkable co-expression modules were identified (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). As indicated from the investigations of module-trait correlations, turquoise module and salmon module were related to AH (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). Given that the association of turquoise module and AH was the most significant, genes in the turquoise module were screened, and FABP4 was successfully identified (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE). In order to assess the predictive value of FABP4 in AH, we generated ROC curves. The AUC for FABP4 was 0.82 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF). We carried out RF algorithm, SVM algorithm, and XGBoost algorithm to verify the predictive value of FABP4 in AH. The results showed that the AUCs for FABP4 with RF algorithm, SVM algorithm, and XGBoost algorithm were 0.83, 0.80, and 0.77 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eG), suggesting that FABP4 had the high accuracy of predictive value. Next, we verified the expression of FABP4 in AH datasets. The results showed that FABP4 expression was up-regulated in AH compared with controls in the GSE142530 and GSE167308 datasets (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eH and I). To further verify the expression of FABP4 in ALD patients, the results of qRT-PCR and WB showed that the mRNA and protein levels of FABP4 in the liver tissues were increased compared with healthy controls (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eJ and K). These results indicated that FABP4 is significantly elevated in the liver tissues of patients with ALD.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e3.3. The expression of Fabp4 in the liver tissues of ASH model mice\u003c/h2\u003e \u003cp\u003eWe further examined the expression of FABP4 in ASH mice, which were fed the Lieber-DeCarli ethanol (5% v/v) liquid diet for 8 weeks plus single binges. The results of H\u0026amp;E and Oil Red O staining showed that the degree of liver injury and lipid deposition was worse in the EtOH-fed mice than that in the CD-fed mice (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Serum levels of ALT, AST, TG, and TC increased in the EtOH-fed compared to the CD-fed (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB-E). Moreover, the hepatic levels of TNF-α, IL-6, TG, and TC showed an ascending trend after alcohol intake compared with the CD-fed groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF-I). In addition, the mRNA and protein levels of Fabp4 in the liver tissues of ASH mice were increased (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eJ and K). To sum up, the expression of Fabp4 was elevated in the liver tissues of ASH mice.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Fabp4 deficiency reduced hepatic lipid deposition and liver injury in ASH mice\u003c/h2\u003e \u003cp\u003eTo further explore the role of Fabp4 on the progression in the ASH mice, WT and Fabp4 knockout (Fabp4\u003csup\u003e\u0026minus;/\u0026minus;\u003c/sup\u003e) mice were fed the Lieber-DeCarli ethanol (5% v/v) liquid diet for 8 weeks plus single binges. The expression of Fabp4 was detected by qRT-PCR and WB. There is almost no expression of Fabp4 in the liver tissues of Fabp4\u003csup\u003e\u0026minus;/\u0026minus;\u003c/sup\u003e mice (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA and B). The results of H\u0026amp;E and Oil Red O staining showed that compared with the WT mice, hepatic lipid droplet accumulation and liver injury was obviously reduced in Fabp4\u003csup\u003e\u0026minus;/\u0026minus;\u003c/sup\u003e mice (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). We also observed a reduction of serum ALT and AST in Fabp4\u003csup\u003e\u0026minus;/\u0026minus;\u003c/sup\u003e mice compared to WT mice (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD and E). Moreover, the hepatic levels of TG and TC were decreased in Fabp4\u003csup\u003e\u0026minus;/\u0026minus;\u003c/sup\u003e mice (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF and G). In addition, the hepatic levels of TNF-α and IL-6 were reduced in FABP4\u003csup\u003e\u0026minus;/\u0026minus;\u003c/sup\u003e mice (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eH and I). The above results indicated that Fabp4 deficiency ameliorated hepatic lipid deposition and liver injury in ASH mice.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e3.5. Transcriptional profiling of the liver tissue from WT and Fabp4\u003csup\u003e-/-\u003c/sup\u003e ASH mice and bioinformatics analysis\u003c/h2\u003e \u003cp\u003eTo further investigate the mechanisms of Fabp4 in the development of ASH, we performed transcriptional profiling of the liver tissues of ASH mice. The results of differential expression analysis showed that a total of 964 genes, including 359 down-regulated genes and 605 up-regulated genes, were identified as DEGs in the liver tissues of WT and Fabp4\u003csup\u003e\u0026minus;/\u0026minus;\u003c/sup\u003e mice (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA and B). The top 50 DEGs, including 30 up-regulated genes and 20 down-regulated genes were exhibited in the heatmaps (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). We conducted gene set enrichment analysis of the DEGs. Cell cycle, DNA replication, cell adhesion molecules, B cell receptor signaling pathway, focal adhesion, and phagosome were considered to be the most highly enriched pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe conducted GO and KEGG pathway enrichment analysis of the DEGs. The results showed that the significantly enriched BP included cell chemotaxis, regulation of cell cycle phase transition, leukocyte chemotaxis, negative regulation of cell cycle process, and negative regulation of cell cycle phase transition (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE). In the CC category, spindle pole, microtubule, collagen-containing extracellular matrix, myelin sheath, and replication fork were the top 5 enriched items (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eF). As for MF, the most enriched terms were tau protein binding, phosphatase binding, protein serine/threonine kinase activity, extracellular matrix structural constituent, and transmembrane-ephrin receptor activity (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eG). We also performed the GO functional enrichment analysis in down-regulated and up-regulated DEGs, respectively. The results showed that acute inflammatory response, acute-phase response, reactive oxygen species biosynthetic process, and fat cell differentiation were significantly enriched in down-regulated DEGs (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eH). However, negative regulation of cell adhesion, negative regulation of cell cycle, negative regulation of immune system process, and negative regulation of leukocyte activation were the most enriched items in up-regulated DEGs (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eI). Subsequently, the DEGs were subjected to KEGG pathway enrichment analysis. Cell adhesion molecules, p53 signaling pathway, cell cycle, insulin resistance, PI3K-Akt signaling pathway, IL-17 signaling pathway, and alcoholic liver disease were considered to be the most highly enriched pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eJ).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e3.6. FABP4 deficiency attenuated the progression of ASH in mice via the p53 signaling pathway\u003c/h2\u003e \u003cp\u003eTranscriptional profiling and integrative bioinformatics analysis showed that Fabp4 was related to p53 signaling pathway, insulin resistance, and PI3K-Akt signaling pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eJ). Thus, we measured the critical factors in these pathways. The hepatic protein of p53 was greatly reduced in Fabp4\u003csup\u003e\u0026minus;/\u0026minus;\u003c/sup\u003e ASH mice compared with WT mice (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). The p53 signaling pathway associated molecules such as Casp3, Bax and Bcl2 were further evaluated. The results showed that the protein levels of Casp3 and Bax, both related to apoptosis, were decreased, while Bcl-2, which is related to anti-apoptosis, was increased in Fabp4\u003csup\u003e\u0026minus;/\u0026minus;\u003c/sup\u003e mice (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). The mRNA and protein levels of IRS-1, Pi3k, and Akt associated with alleviating insulin resistance were increased (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB and C). Taken together, these results demonstrate that FABP4 regulates the p53 signaling pathway and insulin/PI3K/AKT signaling pathway in ASH mice.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003ePrevious studies have shown that inhibition of p53 could induce hepatic SIRT1 upregulation \u003csup\u003e[\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. Therefore, qRT-PCR and WB were used to detect the expression of SIRT1 in ASH mice. The results showed that the mRNA and protein levels of SIRT1 in liver tissues of FABP4\u003csup\u003e\u0026minus;/\u0026minus;\u003c/sup\u003e mice were significantly increased (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD and E). The expression levels of lipid catabolic-related genes PPARα, AMPK, and CPT-1 and lipid anabolic-related genes ACC, SREBP1, SCD1, and FASN were detected. The results showed that the mRNA and protein levels of PPARα, AMPK, and CPT-1 were increased (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eF and G); in contrast, the expression of ACC, SREBP1, SCD1, and FASN were decreased (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eH and I). These results demonstrate that FABP4 deficiency inhibits fatty acid synthesis and promotes fatty acid oxidation in ASH mice through the p53 signaling pathway and SIRT1 signaling pathway.\u003c/p\u003e \u003cp\u003eIt has been 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 pathway\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. Thus, we detected the hepatic expression of related factors in the NF-κB signaling pathway by qRT-PCR and WB. As expected, the results showed that the mRNA and protein expression of IKK and NF-κB decreased in FABP4\u003csup\u003e\u0026minus;/\u0026minus;\u003c/sup\u003e mice (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eJ and K). In conclusion, FABP4 deficiency reduces hepatic inflammation in ASH mice by mediating the SIRT1 signaling pathway.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e3.7. FABP4 affected the proportion of macrophage M1/M2 and the expression of pro-inflammatory factors in ASH.\u003c/h2\u003e \u003cp\u003ePrevious studies have suggested that activation of immune can accelerate the progression of AH\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. We thus aimed to explore the relationship between FABP4 and the immune cells infiltration in AH. We performed CIBERSORT algorithm to analyze the immune cell phenotypes in GSE142530. These results demonstrate that AH samples had a lower proportion of macrophages M2 compared to control samples, and FABP4 showed a negative correlation with macrophages M2 (supplementary Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eA-D). To further explore the effects of FABP4 in macrophages, we conducted GSEA of the DEGs in GSE73173. These results suggest that the effects of exogenous FABP4 in RAW264.7 macrophages are mainly focused on immunity, inflammation and lipid metabolism (supplementary Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eE-I).\u003c/p\u003e \u003cp\u003eFABP4 is highly expressed in macrophages, especially during the inflammatory activated station \u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. We performed CIBERSORT algorithm to analyze the 25 immune cell phenotypes including macrophages in the liver tissues of WT and FABP4\u003csup\u003e\u0026minus;/\u0026minus;\u003c/sup\u003e mice. The proportions of macrophages M1 in FABP4\u003csup\u003e\u0026minus;/\u0026minus;\u003c/sup\u003e mice was significantly lower than that in WT mice (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01). However, in comparison with WT mice, FABP4\u003csup\u003e\u0026minus;/\u0026minus;\u003c/sup\u003e mice had a higher proportion of macrophages M2. Interestingly, the proportion of macrophages M2 in WT mice was zero, and the proportion of macrophages M2 in FABP4\u003csup\u003e\u0026minus;/\u0026minus;\u003c/sup\u003e mice was 6% (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA and B). As indicated from the correlation heatmap of the 23 types of immune cells, macrophages are significantly correlated with some immune cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC). We then sought to explore the relationships between key genes and infiltrated immune cells in ASH. Based on the results of correlation analysis, p53, NLRP3, IL-1β, and CXCL-1 are positively correlated with macrophages M1 (r\u0026thinsp;=\u0026thinsp;0.66, 0.6, 0.6, and 0.83, both P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eD). FABP4 showed a negative correlation with macrophages M2 (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.52) and SIRT1 displayed a negative correlation with mast cells (r=-0.65, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eD).Thus, we investigated the relationship between the FABP4 and macrophage in ASH mouse. Immunohistochemistry analysis showed that F4/80 deposition was lower in FABP4\u003csup\u003e\u0026minus;/\u0026minus;\u003c/sup\u003e mice as compared to WT mice (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eE). Similarly, the mRNA and protein levels of F4/80 were also decreased in livers of FABP4\u003csup\u003e\u0026minus;/\u0026minus;\u003c/sup\u003e mice (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eF and G). Furthermore, the qRT-PCR results showed that inflammatory-related genes, such as \u003cem\u003eTnf-α\u003c/em\u003e, \u003cem\u003eIl-6\u003c/em\u003e, \u003cem\u003eIl-1β\u003c/em\u003e, \u003cem\u003eIl-8\u003c/em\u003e, \u003cem\u003eTrailr1\u003c/em\u003e, \u003cem\u003eIy6g\u003c/em\u003e, \u003cem\u003eMcp-1\u003c/em\u003e, and \u003cem\u003eCxcl-1\u003c/em\u003e, were downregulated in the FABP4\u003csup\u003e\u0026minus;/\u0026minus;\u003c/sup\u003e mice (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eH). The protein levels of NLRP3, CASP1, pro-IL-1β, and IL-1β were decreased in the FABP4\u003csup\u003e\u0026minus;/\u0026minus;\u003c/sup\u003e mice (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eI). These results suggest that FABP4 deficiency prevents liver inflammation in ASH mice by reducing the proportion of Macrophages M1.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eALD markedly contributes to the global burden of disease and mortality\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. ASH can further develop into more severe liver diseases, causing severe harm to the health of the liver and other organs \u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. In this study, we explored the role of FABP4 in the progression of ASH and the underlying mechanisms. We have come to some conclusions. Firstly, with integrative bioinformatics analysis, DEGs of the two AH datasets were obtained and FABP4 was up-regulated among the whole FABPs family. Furthermore, the expression of FABP4 has been confirmed in AH patients and ASH mice. FABP4 deficiency alleviated hepatic steatosis and inflammation in ASH mice. Mechanistically, FABP4 deficiency suppressed the p53 signaling pathway and then activated Sirt1 signaling pathway, subsequently inhibiting the lipogenesis, promoting fatty acid oxidation, and facilitating the M2 polarization of macrophage in the liver of ASH mice. These findings suggest that FABP4 may be a potential target for ASH drug development.\u003c/p\u003e \u003cp\u003eIn this study, we conducted integrative bioinformatics analysis and found FABP4 is correlated with the progression of AH. Functional enrichment analysis of DEGs revealed that the enrichment of terms is related to lipid metabolism, immunity, and inflammation. Our studies demonstrated that FABP4 may play a dominant role in liver lipid metabolism and inflammation in AH. Additionally, ROC analysis demonstrated that the elevated FABP4 could accurately distinguish AH from healthy. The results of three algorithms analysis have verified that FABP4 has the high prediction accuracy for AH. These results indicated that FABP4 may be a potential biomarker for clinical applications in AH prediction.\u003c/p\u003e \u003cp\u003eFABP4\u003csup\u003e\u0026minus;/\u0026minus;\u003c/sup\u003e mice were used to further explore the effects of Fabp4 on the progression of ASH. We found that FABP4 deletion reduced hepatic lipid deposition and inflammation in ASH mice. Then, multiple enrichment analyses of transcriptional profiling revealed that some signaling pathways, such as p53 signaling pathway, insulin resistance, and PI3K-Akt signaling pathway, were enriched between FABP4\u003csup\u003e\u0026minus;/\u0026minus;\u003c/sup\u003e and WT ASH mice. Several studies have shown that p53 plays an important role in the regulation of lipid metabolism\u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. Inhibition of the transcriptional activity of p53 in the livers of high fat diet ( HFD ) mice diminished the diet-induced weight gain, hepatic steatosis, oxidative stress and apoptosis\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. Derdak et al.\u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e have shown that activation of p53 not only orchestrates various forms of cell death, but also regulates cellular energy metabolism and suppresses the insulin/PI3K/AKT axis, contributing to the metabolic abnormalities in ALD rat. Our results showed that FABP4 deficiecy downregulated the expression of p53 in the liver of ASH mice. It has been reported that inhibition of p53 could induce hepatic SIRT1 upregulation, which promoted the β-oxidation of liver fatty acids by up-regulating the expression of PPARα, AMPK, and CPT1\u003csup\u003e[\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. Moreover, a p53 inhibitor increased SIRT1 expression, therefore inhibiting the de novo synthesis of hepatic fatty acids by down-regulating the expression of SREBP1, FASN, SCD-1 and ACC\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e. Our results indicated that Fabp4 deficiency upregulated the expression of Sirt1, subsequently reducing the lipid synthesis and promoting lipolysis. In addition, SIRT1 can regulate downstream inflammatory pathways, such as IKK/NF-κB, and directly affect the expression of inflammatory factors \u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e. Our studies showed that the expression of the IKK/NF-κB signaling pathway associated factors was decreased in FABP4\u003csup\u003e\u0026minus;/\u0026minus;\u003c/sup\u003e mice. Taken together, these data demonstrate that FABP4 deficiency alleviates hepatic steatosis and inflammation via the p53 /PI3K/AKT/ SIRT1 signaling pathway.\u003c/p\u003e \u003cp\u003eGiven the important role of immune in AH, we sought to explore the relationship between FABP4 and immune cells by using AH datasets. FABP4 displayed varying degrees of correlation with immune cells, such as macrophages and T follicular helper cells. The results were further verified in RAW264.7 macrophages with exogenous FABP4 in GSE73173. Furthemore, the CIBERSORT showed that FABP4 is negatively correlated with macrophages M2. Consistent with this result, we found that the proportion of macrophage M2 in liver of FABP4\u003csup\u003e\u0026minus;/\u0026minus;\u003c/sup\u003e mice was significantly higher than that in WT mice. Moreover, the inflammation associated factors in the FABP4\u003csup\u003e\u0026minus;/\u0026minus;\u003c/sup\u003e mice were also reduced. In short, our results provide novel insights into immune mechanism of FABP4 in the progression of ASH. However, how FABP4 affects macrophage polarization is still unclear and further exploration is needed in future work.\u003c/p\u003e \u003cp\u003eIn summary, the present study demonstrated that FABP4 deficiency alleviates hepatic lipid accumulation and inflammation in ASH mice. Inhibition of FABP4 may be a potential therapeutic strategy for ASH.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFDZ and SQH designed and supervised the experiment and revised the manuscript. HX performed some experiments and wrote the manuscript; ZW, KQJ, GDY, ZYG and SPY participated in some experiments and analyzed some data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNational Natural Science Foundation of China (82160120); Natural Science Foundation of Guangxi Province (2023GXNSFAA026062, 2021GXNSFDA075002);\u0026nbsp;the \u0026ldquo;111\u0026rdquo; Project (D17011); National Key Research and Development Program (2022YFE0131600)；Advanced Innovation Teams and Xinghu Scholars Program of Guanxi Medical University; Innovation Team of the First Affiliated Hospital of Guangxi Medical University (YYZS2022002).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that there are no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSequence 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.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAsrani S, Mellinger J, Arab J, et al. 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J Hepatol, 2013, 58(1): 119\u0026ndash;125.DOI: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jhep.2012.08.008\u003c/span\u003e\u003cspan address=\"10.1016/j.jhep.2012.08.008\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"FABP4, Alcoholic steatohepatitis, p53, Bioinformatics analysis","lastPublishedDoi":"10.21203/rs.3.rs-4292137/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4292137/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eFatty acid-binding protein 4 (FABP4) plays an essential role in metabolism and inflammatory. However, the role of FABP4 in alcoholic steatohepatitis (ASH) remains unclear. This study aimed to investigate the function of FABP4 and the underlying mechanisms in the progression of ASH. Alcoholic hepatitis (AH) datasets were obtained from NCBI Gene Expression Omnibus (GEO). Bioinformatics analysis was performed to screen key genes in FABPs family. Wild-type (WT) and FABP4-deficient (FABP4\u003csup\u003e\u0026minus;/\u0026minus;\u003c/sup\u003e) mice were subjected to ASH models and the role of FABP4 was investigated. Transcriptional profiling of mouse liver tissue was performed and analyzed by integrative bioinformatics. The Fabp4 associated signaling pathway was further verified. FABP4 was up-regulated in two AH datasets and identified as a critical biomarker. Compared to control, FABP4 is higher expressed in liver tissues of ALD patients and ASH mice. FABP4 deficiency reduced hepatic lipid deposition and inflammation in ASH mice. Mechanistically, FABP4 was involved in regulating the p53 signaling pathway and Sirt1 signaling pathway, subsequently affecting the lipid metabolism and the polarization of macrophages in the liver of ASH mice. FABP4 is involved in the progression of ASH. FABP4 deficiency ameliorates mouse ASH, suggesting that FABP4 may be a potential therapeutic target for ASH.\u003c/p\u003e","manuscriptTitle":"FABP4 deficiency ameliorates alcoholic steatohepatitis in mice via inhibition of p53 signaling pathway","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-30 22:10:52","doi":"10.21203/rs.3.rs-4292137/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-06-18T07:20:51+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-12T02:26:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"165814448179048130731089662943482577313","date":"2024-05-29T18:42:11+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-05-17T15:36:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"3157bd05-2c8a-4b76-9038-5243e5387505","date":"2024-05-06T12:16:08+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-05-03T20:43:20+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-05-03T20:38:47+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-04-25T09:36:18+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-04-25T09:34:43+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-04-19T09:13:07+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e9e4b2ee-f5b3-4c99-acf3-359f1d101415","owner":[],"postedDate":"April 30th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":31322315,"name":"Biological sciences/Immunology"},{"id":31322316,"name":"Biological sciences/Molecular biology"},{"id":31322317,"name":"Health sciences/Medical research"}],"tags":[],"updatedAt":"2024-09-16T16:00:17+00:00","versionOfRecord":{"articleIdentity":"rs-4292137","link":"https://doi.org/10.1038/s41598-024-71311-8","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2024-09-10 15:57:12","publishedOnDateReadable":"September 10th, 2024"},"versionCreatedAt":"2024-04-30 22:10:52","video":"","vorDoi":"10.1038/s41598-024-71311-8","vorDoiUrl":"https://doi.org/10.1038/s41598-024-71311-8","workflowStages":[]},"version":"v1","identity":"rs-4292137","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4292137","identity":"rs-4292137","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}