Exploring the Pathogenesis of NAFLD: A Study of the Correlation of Gut Microbes and Metabolites in Humanized Mouse Gut Microbiota

Exploring the Pathogenesis of NAFLD: A Study of the Correlation of Gut Microbes and Metabolites in Humanized Mouse Gut Microbiota | 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 Research Article Exploring the Pathogenesis of NAFLD: A Study of the Correlation of Gut Microbes and Metabolites in Humanized Mouse Gut Microbiota Yuanzhi Cheng, Yun Yang, Yaxi Guo, Yang Shi, Jianghao Feng, Xiaoliang Jiang, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4728601/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract There is mounting scientific evidence indicating a robust association between gut microbiota and nonalcoholic fatty liver disease (NAFLD). Exposure to commensal microbiota in germ-free mice has a significant impact on the regulatory mechanisms of gut genes, in contrast to those conventionally raised. In this study, we have successfully inoculated the gut microbiota from healthy individuals and NAFLD patients into germ-free mice, with the objective of developing a humanized mouse model that accurately replicates the gut microenvironment of NAFLD patients. Changes in blood composition and liver pathology in these mice were systematically measured. Furthermore, we have conducted a det ailed analysis of the variations in fecal microbiota and differential metabolites in the blood composition. Our findings indicate a high degree of similarity in disease characteristics between mice colonized with microbiota and humans suffering from NAFLD. Notably, we have observed a strong correlation between alterations in serum differential metabolites and gut microbiota in these mice. NAFLD Germ-free mice Gut microbes Metabolomics Microbiome Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Nonalcoholic fatty liver disease (NAFLD) has emerged as the most common chronic liver disease in clinic (with a global prevalence in adults at about 25%)[ 1 , 2 ]. Nevertheless, due to the diversity of risk factors and the complexity of the pathogenesis for NAFLD, approved therapeutic strategies targeting the characteristics of NAFLD are limited[ 3 – 5 ]. Therefore, it is urgent to actively explore the pathogenesis of NAFLD. In recent years, an increasing number of studies have shown that metabolic diseases such as NAFLD, diabetes and obesity are closely related to the intestinal microbiota[ 6 , 7 ]. Gut microbiota, a complex and highly diverse ecosystem, plays a crucial role in the pathogenesis of NAFLD[ 8 – 10 ]. These studies validated the close link between gut microbiota and NAFLD in mouse models. However, there exists a discrepancy in the composition of the intestinal microbiota between humans and mice, both in healthy conditions and during disease states[ 11 ]. Despite the apparent similarities, mice and humans exhibit profound disparities in the development, activation of both innate and adaptive immune systems, as well as their response to external stress factors[ 12 ]. Therefore, it is imperative to establish a precise and representative animal model that accurately mirrors the human intestinal ecosystem. Peter J. Turnbaugh et al[ 13 ] transplanted an adult human fecal microbiota into germ-free C57BL/6J mice, successfully establishing a stable human gut microbiota community in the recipient mice, resembling that of the donor. Also, they demonstrated how these humanized animals can be effectively utilized to conduct controlled proof-of-principle “clinical” metagenomic studies of host-microbiome interrelationships using diet-induced obesity as a model. The outcomes of these studies provide that humanized mouse models of gut microbiota are invaluable for advancing our understanding and exploration of the underlying mechanisms of various diseases. In this study, we inoculated the fecal microbiota of healthy individuals and NAFLD patients into germ-free C57BL/6J mice, aiming to establish a humanized gut microbiota-based mouse model of NAFLD. Subsequently, we examined the changes in gut microbes and blood metabolites in mice inoculated with different microbiota. Overall, we aim to identify differential microbes and metabolites in the mouse models of humanized gut microbiota, thereby enhancing our understanding of the role and underlying mechanisms of gut microbiota in NAFLD. 2. Materials and methods 2.1 Intestinal microbiota samples Stool samples were obtained from 10 healthy volunteers and 8 NAFLD patients, and informed consent was obtained from all personnel. The use of all human samples involved was supported by Medical Ethics Number: YZW23002. 2.2 Animals and animal experiments Germ-free C57BL/6J mice (8–10 weeks old) were obtained from the Institute of Laboratory Animal Science, Chinese Academy of Medical Sciences. All the animals were housed in sterilized isolators at the temperature of 22 ± 1°C, 55–65% relative humidity, in a 12-h light/12-h dark cycle. All animal experiments were conducted according to the ethical policies and procedures approved by the Animal Care and Use Committee at the Institute of Laboratory Animal Science, Chinese Academy of Medical Sciences and Peking Union Medical College (approval No. ZH23003). Fecal bacterial fluid: The night before the transplantation of fecal bacteria, the fecal samples of healthy volunteers and patients were transferred from − 80 ℃ to 4 ℃ to thaw. The next day, 1 g of feces was weighed in a biosafety cabinet and 100 mL of 0.1 mol/L PBS (pH 7.2) buffer was added, shaken and mixed to make a stool bacterial suspension for later use. The germ-free mice were divided into control group and NAFLD group according to body weight, with 8 mice in each group. After one week of acclimatization in the isolator, each group of germ-free mice was orally fed with fecal bacterial fluid (0.4µL) from healthy volunteers and non-alcoholic fatty liver patients for three consecutive days to obtain microbiota transplanted mice, and the transplantation cycle was 10 weeks. Ultrasonography of the mouse liver was performed 10 weeks after microbiota inoculation, followed by euthanasia of the mice and collection of feces, liver, and blood. 2.3 Histopathological Evaluation Livers were fixed in 10% neutral buffered formalin for 1 day, dehydrated, embedded in paraffin, cut into 4-µm slices, and stained with hematoxylin and eosin (H&E), Masson and Sirius red for histological examination. 2.4 Oil Red O Staining (ORO) Livers were fixed in 10% neutral buffered formalin for 1 day, dehydrated with 30% sucrose solution, embedded with OCT embedding medium, cut into 4-µm slices and processed for examination of fat accumulation by Oil Red O staining. 2.5 Live r triglyceride (TG) and total cholesterol (TCHO) Liver TG and TCHO were detected by Triglycerides assay kit and Total cholesterol assay kit (Nanjing Jiancheng Bioengineering Institute, China). 2.6 Quantitative real-time PCR (RT-qPCR) Liver tissue was harvested and total RNA were extracted with the Trizol reagent (Accurate Biology, AG21102), and transcription-PCR was performed by real-time PCR (AB Stepone). The gene expression was calculated via the 2 −ΔΔCT method, and GAPDH was used as a reference gene. Primers used for RT-qPCR are listed below. Genes Forward Primers (5’-3’) Reverse Primers (5’-3’) TNF-α GCCTCTTCTCATTCCTGCTT TGGGAACTTCTCATCCCTTTG IL-1β GCAACTGTTCCTGAACTCAACT ATCTTTTGGGGTCCGTCAACT IL-6 TAGTCCTTCCTACCCCAATTTCC TTGGTCCTTAGCCACTCCTTC GAPDH CTGGGCTACACTGAGCACC AAGTGGTCGTTGAGGGCAATG 2.7 Routine blood tests and blood biochemistry tests Blood and serum samples from the mice were collected and sent to Shanghai Model Organisms Center for testing. 2.8 Microbiome The α diversity indexes (Sobs, Ace and Chao) were used to evaluate the richness and diversity of species in the environmental community. Then, the between-group difference test was used to detect whether there was a significant difference in the alpha diversity index between two or more groups. Specifically, the Wilcoxon rank-sum test was used to study the significant differences in α diversity index between different groups after being corrected by false discovery rate (FDR) multiple tests. Principal co-ordinates analysis (PCoA) based on the mapping of the selected distance matrix was used to study the similarities or difference in sample community composition and identify the potential principal components that affect the differences in sample community composition. According to the relative abundance of microflora at the genus level, the Jensen-Shannon Distance (JSD) equidistance was calculated, and PAM (Partitioning Around Medoids) clustering was performed, and the optimal clustering K value was calculated by Calinski-Harabasz (CH) index, and then Between-class analysis (BCA, K ≥ 3) or PCoA (K ≥ 2). At the genus level, the Wilcoxon rank-sum test was used to evaluate the difference in the average relative abundance of the same species between different groups after FDR multiple test correction and two-tailed test. (Analysis software: R-3.3.1) 2.9 Untargeted metabolomics Principal Component Analysis (PCA) (Analytical methods: mix merge; Data conversion: Unit Variance Conversion; Confidence: 0.95) and Partial Least Squares Discriminant Analysis (PLS-DA) ((Analytical methods: mix merge; PLSDA data conversion: Pareto conversion; Confidence: 0.95; Number of substitutions:200) analysis were used to evaluate the similarity of samples within the group and the differences between the samples. OPLS-DA and differential statistical volcano plots were used to screen and visualize differential metabolites between different groups and then used. The model of OPLS-DA was evaluated using the R2 and Q2 values of the permutation test. Variable importance (VIP) value analysis (Metabolite clustering method: hierarchical clustering; Metabolite distance algorithm: euclidean; Metabolite hierarchical clustering method: complete; VIP value source: OPLS-DA; VIP value > 1) was used to show the expression patterns of metabolites in each sample in each differential group. Kyoto Encyclopedia of Genes and Genomes Enrichment (KEGG) enrichment analysis was performed using BH multiplex test correction and Relative-betweeness Centrality topology method. (Database version: kegg_v20230830) Correlations between changes in differential metabolites and changes in genus relative abundance were calculated by Spearman’s rank test. 2.10 Statistical Analysis All results were reported as means ± standard deviation (SD) or mean ± standard error of mean (SEM). Differences between two comparative groups were assessed using the Student’s t -test, and the significance among multiple groups was examined by the one-way analysis of variance (ANOVA). Statistical significance was considered when P < 0.05 using GraphPad Prism software 9.0 (GraphPad Software). 3. Results 3.1 Establishment of germ-free mouse and gut microbiota transplant mouse models Firstly, the mice were delivered by caesarean section to a sterile isolator, and under aseptic feeding conditions, they were artificially fed until they could feed independently to establish germ-free mice. As shown in Fig. 1 A, we collected feces from healthy volunteers and NAFLD patients, then homogenized them and inoculated them by gavage into the intestines of germ-free mice. Mice were euthanized after 10 weeks of rearing in a sterile environment, and we collected liver tissue and blood samples from them for testing. 3.2 Pathological changes in the liver of mice after microbiota transplantation Conventional ultrasound (i.e., grayscale abdominal ultrasound evaluation of the liver) is the most common imaging modality for evaluation of hepatic steatosis, with good sensitivity and specificity in detecting steatosis [ 14 , 15 ]. The results illustrated that the gray ratio of liver to kidney in mice inoculated with feces of patients with non-alcoholic fatty liver disease was significantly higher than that of the control group (Fig. 1 B). Additionally, we observed a significantly increased liver weight and darker appearance in NAFLD group compared with control group (Fig. 1 D, G). To determine the effect of gut microbiota transplantation on mice, we further investigated other pathological findings in the livers of two groups of mice, each transplanted with different microbiotas. ORO and HE staining demonstrated that transplanting the microbiota of NAFLD patients in mice increased hepatic lipid accumulation and hepatocellular ballooning in the liver compared with the control group (Fig. 1 E). Simultaneously, fibrosis and inflammation were observed in livers of mice inoculated with NAFLD gut microbiota (Fig. 1 F, H). Notably, there was no significant change in intrahepatic TCHO content of mice transplanted with NAFLD microbiota, but the level of intrahepatic TG was remarkably elevated (Figure I, J). These pathological features are similar to those of patients with NAFLD. 3.3 Routine blood and serological markers of mice after microbial transplantation As shown in Fig. 2 A-B, compared with the control group, the blood levels of white blood cell (WBC), lymphocyte (LYM) and red blood cell (RBC) in the NAFLD group were significantly reduced, while the mean corpuscular volume (MCV) was significantly increased. There was no significant change in the levels of platelet (PLT), hemoglobin (HGB), hematocrit (HCT) and neutrophil (NEU) in the blood of the two groups. On the other hand, the serum content of choline esterase (CHE) in mice inoculated with the intestinal microbiota of NAFLD patients was decreased, while the content of aspartate transaminase (AST), alanine transaminase (ALT), gamma-glutamyl transferase (GGT), triglyceride (TG), lactate dehydrogenase (LDH), direct bilirubin (D-BIL) and nonestesterified fatty acid (NEFA) was significantly increased. However, no significant difference was observed in serum levels of high-density lipoprotein (HDL), alkaline phosphatase (ALP), TCHO and low-density lipoprotein (LDL) between the two groups. 3.4 Effect of microbiota transplantation on gut microbiota Compared with the control group, the alpha diversity (Sobs, Ace and Chao indexes) of the gut microbiota of mice in the NAFLD group was significantly reduced (Fig. 3 A). The principal coordinate analysis (PCoA) score plots based on Bray-Curtis distance at ASV proved a significant compositional difference in the microbiota β-diversity between the two groups (Fig. 3 B). Meanwhile, we confirmed that the enterotypes also changed significantly between the two groups (Fig. 3 C-E). The genus level analysis detected a lower relative abundance of Escherichia, Eisenbergiella and unclassified_f_Ruminococcazeae, and a higher abundance of Pygmaiobacter, Monoglobus and UBA1819 in the NAFLD group compared to the control group (Fig. 3 F). 3.5 Effect of microbiota transplantation on blood metabolites Non-targeted metabolomics is commonly used to identify potential alterations in key metabolites and metabolic pathways in serum samples. Subsequently, PCA analysis and PLS-DA analysis were used to analyze the differences between the NAFLD and control groups. The results showed significant differences in metabolic profiles between the two groups (Fig. 4 A, B). To find out more information about the differences in metabolites in mice inoculated with different microbiotas, we performed differential metabolite analysis. The orthogonal partial least-squares discriminant analysis (OPLS-DA) score plots separated the metabolite profiles between control and NAFLD groups. The intercepts of goodness-of-fit (R2) and goodness-of-prediction (Q2) illustrate the OPLS-DA model is reliable and not overfitting (Fig. 4 C, D). Furthermore, as shown in Fig. 4 E, a total of 385 significantly upregulated metabolites and 424 significantly downregulated metabolites were identified by comparing the NAFLD group with the control group. In summary, serum metabolites in mice inoculated with the gut microbiota of NAFLD patients produced significant changes. Figure 4 F showed the detailed expression trends of the top 30 differential metabolites in both groups. In order to figure out the mechanism of the change in the expression of these metabolites, we performed KEGG pathway enrichment analysis. The results showed that intestinal microbiota inoculation significantly affected the amino acid metabolism and carbon metabolism of the mice (Fig. 4 G). The correlation heat map showed that the differential microbial communities other than blautia, lachnoclostridium and coprobacillus were strongly correlated with the differential metabolites (Fig. 5 ). 4. Discussion Recent studies have shown that gut microbes are increasingly closely related to NAFLD[ 16 – 18 ]. Regarding liver diseases, the comparison of germ free (GF) and conventional (CV) mice has been used to assess the role played by the microbiota. Gut microbiota transplantation in GF mice has also been used to assess the causality between microbiota composition and susceptibility to NAFLD[ 9 , 19 ]. Le Roy et al. demonstrated that gut microbiota composition determines NAFLD development in C57BL/6 strain mice for the first time. In fact, by transplanting the gut microbiota of mice with or without NAFLD into GF mice, it was found that the propensity to develop NAFLD traits could be transmitted via the gut microbiota[ 9 , 20 ]. However, there are differences between the intestinal microbiota of NAFLD model mice and NAFLD patients. Therefore, it is particularly important to realize the colonization of the intestinal microbiota from NAFLD patients in germ-free mice, while striving to mimic the intestinal environment of NAFLD patients to the fullest extent possible. Chien-Chao Chiu et al[ 21 ] demonstrated that colonization of fecal bacteria from patients with non-alcoholic steatohepatitis (NASH), as well as HFD feeding, exacerbated disease progression in NAFLD. Nevertheless, the research conducted on the influence of intestinal microbiota colonization on disease progression in mice models of NAFLD patients remains insufficient and incomplete. In this study, we colonized the gut microbiota from healthy volunteers and NAFLD patients into germ-free mice and found that the liver histology and blood composition of the mice in the NAFLD group were similar to those of NAFLD disease. This indicates that there is great potential to establish a mouse model that mimics human NAFLD-related microbiota transplantation by optimizing the microbiota transplantation method. Notably, we noticed a significant increase in serum 1-Methylxanthine in mice in the NAFLD group. Yan GAO et al [ 22 ] reported that 1-methylxanthine was significantly positively correlated with IL-6 and TNF-α when an inflammatory response occurs in the body. In patients with NAFLD, metabolic disorders including insulin resistance and obesity further promoted liver inflammation and tumorigenesis through IL-6 and TNF-α[ 23 ]. Zahra Safari et al[ 9 ] reported that the abundance of clostridium innocuum in the gut microbiota of patients with NAFLD was significantly higher than that in healthy people. Our study has validated a marked positive correlation between the significant elevation of 1-Methylxanthine in the serum of mice inoculated with gut microbiota from NAFLD patients and the substantial increase in the abundance of clostridium in their guts (Fig. 5 ). This finding indicates that clostridium may hold a pivotal role in the onset and progression of NAFLD. Similarly, there are still many potential links between the closely related differential microorganisms and differential metabolites in Fig. 5 that need to be further explored. Although there are important discoveries revealed by the current work, there are certain limitations. First, the similarities and differences between the gut microbiota of NAFLD patients and the colonized mouse gut microbiota were not further revealed. This is necessary for us to further understand the relationship between the dominant bacterial genera that promote the progression of NAFLD in humans and mice. Second, the relationship between serum metabolites in mice and serum metabolites in patients with NAFLD is unclear. In future research work, we will explore these questions more deeply. The interactions between the gut and liver, called the gut–liver axis, play an essential role in NAFLD development and evolution. Portal blood flow connects the intestine to the liver[ 24 – 26 ]. A significant portion of liver blood originating from the intestine subjects the liver to various metabolic products generated by the gut microbiome, encompassing phenols, acetaldehyde, and ammonia, in addition to pro-inflammatory bacterial components like peptidoglycan and lipopolysaccharides (LPS)[ 27 ]. Therefore, it is necessary to explore the potential association between serum metabolites and gut microbiota, in order to uncover further insights into the pathogenesis of NAFLD or discover an effective treatment for NAFLD. We sincerely anticipate that this study may shed new light on the search for a treatment for NAFLD. Declarations Funding This work was supported by grants from National key research and development program (2022YFF0710600), and the CAMS Innovation Fund for Medical Sciences (CAMS, 2021-I2M-1-072). Competing interests The authors declare no competing interests. Author contributions Conception and design of the research: Zhiwei Yang, Hua Zhu and Xing Liu; acquisition of data: Yuanzhi Cheng, Yun Yang and Xing Liu; analysis and interpretation of the data: Yuanzhi Cheng, Yaxi Guo and Yang Shi; statistical analysis: Yuanzhi Cheng, Xiaoliang Jiang and Xing Liu; supervising the experiments: Hua Zhu and Xing Liu; drafting the manuscript: Yuanzhi Cheng, Jianghao Feng and Yang Shi. Corresponding author Correspondence to Xing Liu at [email protected] and Hua Zhu at [email protected] . Data Availability Statement All data in this study are available from the corresponding author upon reasonable request. Ethics approval and consent to participate All animal experiments were conducted according to the ethical policies and procedures approved by the Animal Care and Use Committee at the Institute of Laboratory Animal Science, Chinese Academy of Medical Sciences and Peking Union Medical College (approval No. ZH23003). Consent to participate Informed consent was obtained from all individual participants included in the study. References Zhang W, Lu J, Feng L, Xue H, Shen S, Lai S, Li P, Li P, Kuang J, Yang Z and Xu X (2024) Sonic hedgehog-heat shock protein 90beta axis promotes the development of nonalcoholic steatohepatitis in mice. Nat Commun 15:1280. doi: 10.1038/s41467-024-45520-8 Targher G, Tilg H and Byrne CD (2021) Non-alcoholic fatty liver disease: a multisystem disease requiring a multidisciplinary and holistic approach. Lancet Gastroenterol Hepatol 6:578-588. doi: 10.1016/S2468-1253(21)00020-0 Mao Z, Ma X, Jing Y, Shen M, Ma X, Zhu J, Liu H, Zhang G and Chen F (2023) Ufmylation on UFBP1 alleviates non-alcoholic fatty liver disease by modulating hepatic endoplasmic reticulum stress. Cell Death Dis 14:584. doi: 10.1038/s41419-023-06095-2 Lambert JE, Ramos-Roman MA, Browning JD and Parks EJ (2014) Increased de novo lipogenesis is a distinct characteristic of individuals with nonalcoholic fatty liver disease. Gastroenterology 146:726-35. doi: 10.1053/j.gastro.2013.11.049 Lebeaupin C, Vallee D, Hazari Y, Hetz C, Chevet E and Bailly-Maitre B (2018) Endoplasmic reticulum stress signalling and the pathogenesis of non-alcoholic fatty liver disease. J Hepatol 69:927-947. doi: 10.1016/j.jhep.2018.06.008 Chen J and Vitetta L (2020) Mitochondria could be a potential key mediator linking the intestinal microbiota to depression. J Cell Biochem 121:17-24. doi: 10.1002/jcb.29311 Chen J and Vitetta L (2019) Bile acids and butyrate in the effects of probiotics/synbiotics on nonalcoholic fatty liver disease. Eur J Gastroenterol Hepatol 31:1475-1476. doi: 10.1097/MEG.0000000000001506 Wei J, Luo J, Yang F, Feng X, Zeng M, Dai W, Pan X, Yang Y, Li Y, Duan Y, Xiao X, Ye P, Yao Z, Liu Y, Huang Z, Zhang J, Zhong Y, Xu N and Luo M (2024) Cultivated Enterococcus faecium B6 from children with obesity promotes nonalcoholic fatty liver disease by the bioactive metabolite tyramine. Gut Microbes 16:2351620. doi: 10.1080/19490976.2024.2351620 Safari Z and Gerard P (2019) The links between the gut microbiome and non-alcoholic fatty liver disease (NAFLD). Cell Mol Life Sci 76:1541-1558. doi: 10.1007/s00018-019-03011-w Leung C, Rivera L, Furness JB and Angus PW (2016) The role of the gut microbiota in NAFLD. Nat Rev Gastroenterol Hepatol 13:412-25. doi: 10.1038/nrgastro.2016.85 Baiden-Amissah REM and Tuyaerts S (2019) Contribution of Aging, Obesity, and Microbiota on Tumor Immunotherapy Efficacy and Toxicity. Int J Mol Sci 20. doi: 10.3390/ijms20143586 Zheng Y, Hao S, Hu L and Cheng T (2015) [Development of immunodeficient mice/humanized mouse models and their applications in hematology research]. Zhonghua Xue Ye Xue Za Zhi 36:966-71. doi: 10.3760/cma.j.issn.0253-2727.2015.11.018 Turnbaugh PJ, Ridaura VK, Faith JJ, Rey FE, Knight R and Gordon JI (2009) The effect of diet on the human gut microbiome: a metagenomic analysis in humanized gnotobiotic mice. Sci Transl Med 1:6ra14. doi: 10.1126/scitranslmed.3000322 Hernaez R, Lazo M, Bonekamp S, Kamel I, Brancati FL, Guallar E and Clark JM (2011) Diagnostic accuracy and reliability of ultrasonography for the detection of fatty liver: a meta-analysis. Hepatology 54:1082-1090. doi: 10.1002/hep.24452 Neuschwander-Tetri BA, Clark JM, Bass NM, Van Natta ML, Unalp-Arida A, Tonascia J, Zein CO, Brunt EM, Kleiner DE, McCullough AJ, Sanyal AJ, Diehl AM, Lavine JE, Chalasani N, Kowdley KV and Network NCR (2010) Clinical, laboratory and histological associations in adults with nonalcoholic fatty liver disease. Hepatology 52:913-24. doi: 10.1002/hep.23784 Henao-Mejia J, Elinav E, Jin C, Hao L, Mehal WZ, Strowig T, Thaiss CA, Kau AL, Eisenbarth SC, Jurczak MJ, Camporez JP, Shulman GI, Gordon JI, Hoffman HM and Flavell RA (2012) Inflammasome-mediated dysbiosis regulates progression of NAFLD and obesity. Nature 482:179-85. doi: 10.1038/nature10809 Matsui M, Fukunishi S, Nakano T, Ueno T, Higuchi K and Asai A (2021) Ileal Bile Acid Transporter Inhibitor Improves Hepatic Steatosis by Ameliorating Gut Microbiota Dysbiosis in NAFLD Model Mice. mBio 12:e0115521. doi: 10.1128/mBio.01155-21 Wang X, Jin Y, Di C, Zeng Y, Zhou Y, Chen Y, Pan Z, Li Z and Ling W (2024) Supplementation of Silymarin Alone or in Combination with Salvianolic Acids B and Puerarin Regulates Gut Microbiota and Its Metabolism to Improve High-Fat Diet-Induced NAFLD in Mice. Nutrients 16. doi: 10.3390/nu16081169 Mayneris-Perxachs J, Cardellini M, Hoyles L, Latorre J, Davato F, Moreno-Navarrete JM, Arnoriaga-Rodriguez M, Serino M, Abbott J, Barton RH, Puig J, Fernandez-Real X, Ricart W, Tomlinson C, Woodbridge M, Gentileschi P, Butcher SA, Holmes E, Nicholson JK, Perez-Brocal V, Moya A, Clain DM, Burcelin R, Dumas ME, Federici M and Fernandez-Real JM (2021) Iron status influences non-alcoholic fatty liver disease in obesity through the gut microbiome. Microbiome 9:104. doi: 10.1186/s40168-021-01052-7 Le Roy T, Llopis M, Lepage P, Bruneau A, Rabot S, Bevilacqua C, Martin P, Philippe C, Walker F, Bado A, Perlemuter G, Cassard-Doulcier AM and Gerard P (2013) Intestinal microbiota determines development of non-alcoholic fatty liver disease in mice. Gut 62:1787-94. doi: 10.1136/gutjnl-2012-303816 Chiu CC, Ching YH, Li YP, Liu JY, Huang YT, Huang YW, Yang SS, Huang WC and Chuang HL (2017) Nonalcoholic Fatty Liver Disease Is Exacerbated in High-Fat Diet-Fed Gnotobiotic Mice by Colonization with the Gut Microbiota from Patients with Nonalcoholic Steatohepatitis. Nutrients 9. doi: 10.3390/nu9111220 Gao Y, Liu L, Li C, Liang YT, Lv J, Yang LF and Zhao BN (2022) Study on the Antipyretic and Anti-inflammatory Mechanism of Shuanghuanglian Oral Liquid Based on Gut Microbiota-Host Metabolism. Front Pharmacol 13:843877. doi: 10.3389/fphar.2022.843877 Campbell C, Kandalgaonkar MR, Golonka RM, Yeoh BS, Vijay-Kumar M and Saha P (2023) Crosstalk between Gut Microbiota and Host Immunity: Impact on Inflammation and Immunotherapy. Biomedicines 11. doi: 10.3390/biomedicines11020294 Albillos A, de Gottardi A and Rescigno M (2020) The gut-liver axis in liver disease: Pathophysiological basis for therapy. J Hepatol 72:558-577. doi: 10.1016/j.jhep.2019.10.003 Tilg H, Adolph TE and Trauner M (2022) Gut-liver axis: Pathophysiological concepts and clinical implications. Cell Metab 34:1700-1718. doi: 10.1016/j.cmet.2022.09.017 Pabst O, Hornef MW, Schaap FG, Cerovic V, Clavel T and Bruns T (2023) Gut-liver axis: barriers and functional circuits. Nat Rev Gastroenterol Hepatol 20:447-461. doi: 10.1038/s41575-023-00771-6 Compare D, Coccoli P, Rocco A, Nardone OM, De Maria S, Carteni M and Nardone G (2012) Gut--liver axis: the impact of gut microbiota on non alcoholic fatty liver disease. Nutr Metab Cardiovasc Dis 22:471-6. doi: 10.1016/j.numecd.2012.02.007 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-4728601","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":328264154,"identity":"b9a09d38-2d71-4592-9ba3-010e0c9d8c57","order_by":0,"name":"Yuanzhi Cheng","email":"","orcid":"","institution":"Chinese Academy of Medical Sciences (CAMS) \u0026 National Center of Technology Innovation for animal model, Peking Union Medical College (PUMC)","correspondingAuthor":false,"prefix":"","firstName":"Yuanzhi","middleName":"","lastName":"Cheng","suffix":""},{"id":328264155,"identity":"fa34d422-fcad-4486-a133-0df8faadd67b","order_by":1,"name":"Yun Yang","email":"","orcid":"","institution":"Taihe County People’s Hospital, The Taihe Hospital of Wannan Medical College","correspondingAuthor":false,"prefix":"","firstName":"Yun","middleName":"","lastName":"Yang","suffix":""},{"id":328264156,"identity":"6203df7c-30d7-449f-bc7c-ef45256f34fd","order_by":2,"name":"Yaxi Guo","email":"","orcid":"","institution":"Chinese Academy of Medical Sciences (CAMS) \u0026 National Center of Technology Innovation for animal model, Peking Union Medical College (PUMC)","correspondingAuthor":false,"prefix":"","firstName":"Yaxi","middleName":"","lastName":"Guo","suffix":""},{"id":328264157,"identity":"68f14a75-2aa7-496e-8351-43fd10a10cde","order_by":3,"name":"Yang Shi","email":"","orcid":"","institution":"Chinese Academy of Medical Sciences (CAMS) \u0026 National Center of Technology Innovation for animal model, Peking Union Medical College (PUMC)","correspondingAuthor":false,"prefix":"","firstName":"Yang","middleName":"","lastName":"Shi","suffix":""},{"id":328264158,"identity":"9117c836-730e-4ac9-91f5-d3470815ce15","order_by":4,"name":"Jianghao Feng","email":"","orcid":"","institution":"Chinese Academy of Medical Sciences (CAMS) \u0026 National Center of Technology Innovation for animal model, Peking Union Medical College (PUMC)","correspondingAuthor":false,"prefix":"","firstName":"Jianghao","middleName":"","lastName":"Feng","suffix":""},{"id":328264159,"identity":"170f81cd-13f2-4385-b54c-84ce8014a24f","order_by":5,"name":"Xiaoliang Jiang","email":"","orcid":"","institution":"Chinese Academy of Medical Sciences (CAMS) \u0026 National Center of Technology Innovation for animal model, Peking Union Medical College (PUMC)","correspondingAuthor":false,"prefix":"","firstName":"Xiaoliang","middleName":"","lastName":"Jiang","suffix":""},{"id":328264160,"identity":"a8d0030e-70ba-4d9f-8c88-c5f8c1466052","order_by":6,"name":"Zhiwei Yang","email":"","orcid":"","institution":"Chinese Academy of Medical Sciences (CAMS) \u0026 National Center of Technology Innovation for animal model, Peking Union Medical College (PUMC)","correspondingAuthor":false,"prefix":"","firstName":"Zhiwei","middleName":"","lastName":"Yang","suffix":""},{"id":328264161,"identity":"405205c0-612a-457a-8079-d03c7f4f5f11","order_by":7,"name":"Hua Zhu","email":"","orcid":"","institution":"Chinese Academy of Medical Sciences (CAMS) \u0026 National Center of Technology Innovation for animal model, Peking Union Medical College (PUMC)","correspondingAuthor":false,"prefix":"","firstName":"Hua","middleName":"","lastName":"Zhu","suffix":""},{"id":328264162,"identity":"ecbcf049-a163-4e55-a3a7-81498d7c9f78","order_by":8,"name":"Xing Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABCElEQVRIiWNgGAWjYBACxvbGhgMfDCR42OQfH3wAFTTAr6Xn8MGHMwos5PgZ0pINiNLCIJGWbMzzocJYsiHHTIIoLcxAldI8BhKJGw6cMasubNsmz8DevE2CoeYOboc1nDGTnAPScrCt7PbMttuGDTzHyiQYjj3DraWxx0ziDUjLYeZtt3nbbicwSABdyNhwGLeWZh4zCbDDjjGYFYO1yL8hoKWNLdkQqMVYsofFjBliCw8BLT3MwEA2kJDjl2BLlp5x7rZhG09asUXCMdxaDOc/BEblnzoeNgnmg58Lym7L87Mf3njjQw0eLQ1IHGYQwQYiEnBqYGCQR+Yw41E4CkbBKBgFIxgAAErPVjR4XtcIAAAAAElFTkSuQmCC","orcid":"","institution":"Chinese Academy of Medical Sciences (CAMS) \u0026 National Center of Technology Innovation for animal model, Peking Union Medical College (PUMC)","correspondingAuthor":true,"prefix":"","firstName":"Xing","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2024-07-12 07:18:53","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4728601/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4728601/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":62653066,"identity":"5c4bc5c3-c868-45be-80e5-8a15fa6e661d","added_by":"auto","created_at":"2024-08-17 01:05:56","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":19879355,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHumanization of intestinal microbiota in mice \u003c/strong\u003e(A) Schematic diagram of the operation of intestinal microbiota inoculation. (B) Representative ultrasound images of liver and kidney from two groups of mice. (C) Ratio of gray value of liver and kidney. n=8. (D) Representative macroscopic photographs of livers from two groups of mice. (E) Representative H\u0026amp;E-stained liver sections and ORO-stained frozen liver sections. Scale bar, 100 μm. (F) Representative liver sections stained with Masson or Sirius red. Scale bar, 100 μm. (G) Liver weight of two groups of mice. n=6. (H) qRT-PCR analysis of TNF-α，IL-1β and IL-6 in the livers of two groups of mice. n=6. (I-J) The contents of CHO and TG in the livers of two groups of mice. n=6. CHO, cholesterol; TG, triglyceride. ns = no significance;\u003cem\u003e \u003c/em\u003e\u003csup\u003e\u003cem\u003e*\u003c/em\u003e\u003c/sup\u003e\u003cem\u003eP \u003c/em\u003e\u0026lt; 0.05; \u003csup\u003e\u003cem\u003e**\u003c/em\u003e\u003c/sup\u003e\u003cem\u003eP \u003c/em\u003e\u0026lt; 0.01;\u003csup\u003e\u003cem\u003e ***\u003c/em\u003e\u003c/sup\u003e\u003cem\u003eP \u003c/em\u003e\u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-4728601/v1/13953a4bad441e436f4cb1dd.png"},{"id":62653063,"identity":"9dc57fb9-c3ad-475d-b5b2-4d5cbb9a7c30","added_by":"auto","created_at":"2024-08-17 01:05:56","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2191051,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRoutine blood and blood biochemical tests were carried out in the two groups of mice \u003c/strong\u003e(A-B) The contents of Multiple indicators in the blood of the two groups of mice. n=8. WBC, white blood cell; MCV, mean corpuscular volume; LYM, lymphocyte; RBC, red blood cell; PLT, platelet; HGB, hemoglobin; HCT, hematocrit; NEU, neutrophil. AST, Aspartate transaminase; ALT, alanine transaminase; GGT, gamma-glutamyltransferase; TG, triglyceride; LDH, Lactate dehydrogenase; D-BIL, direct bilirubin; NEFA, nonestesterified fatty acid; CHE, choline esterase; HDL, high-density lipoprotein; ALP, alkaline phosphatase; TCHO, total cholesterol; LDL, low-density lipoprotein. ns = no significance;\u003cem\u003e \u003c/em\u003e\u003csup\u003e\u003cem\u003e*\u003c/em\u003e\u003c/sup\u003e\u003cem\u003eP \u003c/em\u003e\u0026lt; 0.05; \u003csup\u003e\u003cem\u003e**\u003c/em\u003e\u003c/sup\u003e\u003cem\u003eP \u003c/em\u003e\u0026lt; 0.01;\u003csup\u003e\u003cem\u003e ***\u003c/em\u003e\u003c/sup\u003e\u003cem\u003eP \u003c/em\u003e\u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-4728601/v1/59aa4c9dd8b53cc441fa4941.png"},{"id":62653065,"identity":"fc580d10-0314-49b0-846f-76c2fb1bf530","added_by":"auto","created_at":"2024-08-17 01:05:56","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2379297,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eChanges of global gut microbiota after intervention in each group\u003c/strong\u003e (A) α-Diversity at ASV\u0026nbsp;level estimated by Sobs, Ace and Chao estimator. (B) Principal coordinate analysis (PCoA) score plots based on Bray-Curtis distance at ASV level. (C) A bar chart of clustering effects for different number of clusters. The x-axis shows the\u0026nbsp;cluster number, the y-axis shows the Calinski-Harabasz (CH) index. (D) Typing analysis on genus level. (E) A bar graph of the percentage of different subtypes in each group. (F) Bar plots and markers of the difference in the mean relative abundance of the same species between different groups. Abscissa: The percentage value of the abundance of a species in the sample; Ordinate: The name of the species at different taxonomic levels; Different colors indicate different groups. The far right is the P-value.\u003cem\u003e \u003c/em\u003e\u003csup\u003e\u003cem\u003e*\u003c/em\u003e\u003c/sup\u003e\u003cem\u003eP \u003c/em\u003e\u0026lt; 0.05; \u003csup\u003e\u003cem\u003e**\u003c/em\u003e\u003c/sup\u003e\u003cem\u003eP \u003c/em\u003e\u0026lt; 0.01.\u003c/p\u003e","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-4728601/v1/dc4ec91bb902e27fe3818ff7.png"},{"id":62653067,"identity":"e67e64d3-582c-44ad-9c45-aa2fcad79027","added_by":"auto","created_at":"2024-08-17 01:05:56","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":5063304,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMetabolomic analysis of the metabolite profiles in mice\u003c/strong\u003e (A-C) Principal Component Analysis (PCA), Partial Least Squares Discriminant Analysis (PLS-DA) and Orthogonal\u0026nbsp;Partial Least Squares Discriminant Analysis (OPLS-DA) plots of serum from different groups of mice. (D) Permutation test plot of the OPLS-DA model. The abscissa represents the displacement retention of the displacement test (the proportion consistent with the order of the Y variables of the original model, the point with the displacement retention of 1 is the R2 and Q2 values of the original model), the ordinate represents the values of R2 (red dot) and Q2 (blue triangle) displacement test, and the two dashed lines represent the regression lines of R2 and Q2 respectively. (E) Volcano plot illustrating the altered metabolites. The abscissa is the fold change value of the difference in the expression of metabolites between the two groups, i.e., log2FC, and the ordinate is the statistical test value of the difference in the expression of metabolites, i.e., the -log10(p) value Each dot represented a metabolite.\u0026nbsp;(F) The Variable importance in the projection (VIP) values of metabolites in each sample were analyzed in each differential group. Each column represents a sample, with the sample name below; Each row represents a metabolite. (G) Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of differential metabolites in the two groups. Abscissa: enrichment significance p-value; Ordinate: KEGG pathway.\u003csup\u003e\u003cem\u003e *\u003c/em\u003e\u003c/sup\u003e\u003cem\u003eP \u003c/em\u003e\u0026lt; 0.05; \u003csup\u003e\u003cem\u003e**\u003c/em\u003e\u003c/sup\u003e\u003cem\u003eP \u003c/em\u003e\u0026lt; 0.01;\u003csup\u003e\u003cem\u003e ***\u003c/em\u003e\u003c/sup\u003e\u003cem\u003eP \u003c/em\u003e\u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"Fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-4728601/v1/e45c24cce8282bfdb289ff61.png"},{"id":62653064,"identity":"6e9d82cb-7dc8-43fe-800c-3df4b84e1fd2","added_by":"auto","created_at":"2024-08-17 01:05:56","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":2325279,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation between changes in serum metabolites and changes in genus abundance \u003c/strong\u003eCorrelation heat map of differential metabolites and fecal differential microorganisms. Abscissa: metabolite name; Ordinate: The name of the genus. The R value is shown in different colors in the figure, if the P value is less than 0.05, it is marked with an * sign, and the legend on the right is the color range of different R values.\u003cem\u003e \u003c/em\u003e\u003csup\u003e\u003cem\u003e*\u003c/em\u003e\u003c/sup\u003e\u003cem\u003eP \u003c/em\u003e\u0026lt; 0.05; \u003csup\u003e\u003cem\u003e**\u003c/em\u003e\u003c/sup\u003e\u003cem\u003eP \u003c/em\u003e\u0026lt; 0.01;\u003csup\u003e\u003cem\u003e ***\u003c/em\u003e\u003c/sup\u003e\u003cem\u003eP \u003c/em\u003e\u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"Fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-4728601/v1/6e07e8b39118b69cf0c4ded9.png"},{"id":62654239,"identity":"d936bb62-ff27-4a2b-b7a7-f93f67790aa9","added_by":"auto","created_at":"2024-08-17 01:22:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":45652855,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4728601/v1/99b7187a-b54f-423d-91b9-95fb1378ba9a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Exploring the Pathogenesis of NAFLD: A Study of the Correlation of Gut Microbes and Metabolites in Humanized Mouse Gut Microbiota","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eNonalcoholic fatty liver disease (NAFLD) has emerged as the most common chronic liver disease in clinic (with a global prevalence in adults at about 25%)[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Nevertheless, due to the diversity of risk factors and the complexity of the pathogenesis for NAFLD, approved therapeutic strategies targeting the characteristics of NAFLD are limited[\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Therefore, it is urgent to actively explore the pathogenesis of NAFLD.\u003c/p\u003e \u003cp\u003eIn recent years, an increasing number of studies have shown that metabolic diseases such as NAFLD, diabetes and obesity are closely related to the intestinal microbiota[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Gut microbiota, a complex and highly diverse ecosystem, plays a crucial role in the pathogenesis of NAFLD[\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. These studies validated the close link between gut microbiota and NAFLD in mouse models. However, there exists a discrepancy in the composition of the intestinal microbiota between humans and mice, both in healthy conditions and during disease states[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDespite the apparent similarities, mice and humans exhibit profound disparities in the development, activation of both innate and adaptive immune systems, as well as their response to external stress factors[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Therefore, it is imperative to establish a precise and representative animal model that accurately mirrors the human intestinal ecosystem. Peter J. Turnbaugh et al[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] transplanted an adult human fecal microbiota into germ-free C57BL/6J mice, successfully establishing a stable human gut microbiota community in the recipient mice, resembling that of the donor. Also, they demonstrated how these humanized animals can be effectively utilized to conduct controlled proof-of-principle \u0026ldquo;clinical\u0026rdquo; metagenomic studies of host-microbiome interrelationships using diet-induced obesity as a model. The outcomes of these studies provide that humanized mouse models of gut microbiota are invaluable for advancing our understanding and exploration of the underlying mechanisms of various diseases.\u003c/p\u003e \u003cp\u003eIn this study, we inoculated the fecal microbiota of healthy individuals and NAFLD patients into germ-free C57BL/6J mice, aiming to establish a humanized gut microbiota-based mouse model of NAFLD. Subsequently, we examined the changes in gut microbes and blood metabolites in mice inoculated with different microbiota. Overall, we aim to identify differential microbes and metabolites in the mouse models of humanized gut microbiota, thereby enhancing our understanding of the role and underlying mechanisms of gut microbiota in NAFLD.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Intestinal microbiota samples\u003c/h2\u003e \u003cp\u003eStool samples were obtained from 10 healthy volunteers and 8 NAFLD patients, and informed consent was obtained from all personnel. The use of all human samples involved was supported by Medical Ethics Number: YZW23002.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Animals and animal experiments\u003c/h2\u003e \u003cp\u003eGerm-free C57BL/6J mice (8\u0026ndash;10 weeks old) were obtained from the Institute of Laboratory Animal Science, Chinese Academy of Medical Sciences. All the animals were housed in sterilized isolators at the temperature of 22\u0026thinsp;\u0026plusmn;\u0026thinsp;1\u0026deg;C, 55\u0026ndash;65% relative humidity, in a 12-h light/12-h dark cycle. All animal experiments were conducted according to the ethical policies and procedures approved by the Animal Care and Use Committee at the Institute of Laboratory Animal Science, Chinese Academy of Medical Sciences and Peking Union Medical College (approval No. ZH23003).\u003c/p\u003e \u003cp\u003eFecal bacterial fluid: The night before the transplantation of fecal bacteria, the fecal samples of healthy volunteers and patients were transferred from \u0026minus;\u0026thinsp;80 ℃ to 4 ℃ to thaw. The next day, 1 g of feces was weighed in a biosafety cabinet and 100 mL of 0.1 mol/L PBS (pH 7.2) buffer was added, shaken and mixed to make a stool bacterial suspension for later use.\u003c/p\u003e \u003cp\u003eThe germ-free mice were divided into control group and NAFLD group according to body weight, with 8 mice in each group. After one week of acclimatization in the isolator, each group of germ-free mice was orally fed with fecal bacterial fluid (0.4\u0026micro;L) from healthy volunteers and non-alcoholic fatty liver patients for three consecutive days to obtain microbiota transplanted mice, and the transplantation cycle was 10 weeks.\u003c/p\u003e \u003cp\u003eUltrasonography of the mouse liver was performed 10 weeks after microbiota inoculation, followed by euthanasia of the mice and collection of feces, liver, and blood.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Histopathological Evaluation\u003c/h2\u003e \u003cp\u003eLivers were fixed in 10% neutral buffered formalin for 1 day, dehydrated, embedded in paraffin, cut into 4-\u0026micro;m slices, and stained with hematoxylin and eosin (H\u0026amp;E), Masson and Sirius red for histological examination.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Oil Red O Staining (ORO)\u003c/h2\u003e \u003cp\u003eLivers were fixed in 10% neutral buffered formalin for 1 day, dehydrated with 30% sucrose solution, embedded with OCT embedding medium, cut into 4-\u0026micro;m slices and processed for examination of fat accumulation by Oil Red O staining.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003e2.5 Live\u003c/b\u003er \u003cb\u003etriglyceride (TG) and total cholesterol (TCHO)\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eLiver TG and TCHO were detected by Triglycerides assay kit and Total cholesterol assay kit (Nanjing Jiancheng Bioengineering Institute, China).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Quantitative real-time PCR (RT-qPCR)\u003c/h2\u003e \u003cp\u003eLiver tissue was harvested and total RNA were extracted with the Trizol reagent (Accurate Biology, AG21102), and transcription-PCR was performed by real-time PCR (AB Stepone). The gene expression was calculated via the 2\u003csup\u003e\u0026minus;ΔΔCT\u003c/sup\u003e method, and \u003cem\u003eGAPDH\u003c/em\u003e was used as a reference gene. Primers used for RT-qPCR are listed below.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGenes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForward Primers (5\u0026rsquo;-3\u0026rsquo;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReverse Primers (5\u0026rsquo;-3\u0026rsquo;)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTNF-α\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGCCTCTTCTCATTCCTGCTT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTGGGAACTTCTCATCCCTTTG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIL-1β\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGCAACTGTTCCTGAACTCAACT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eATCTTTTGGGGTCCGTCAACT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIL-6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTAGTCCTTCCTACCCCAATTTCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTTGGTCCTTAGCCACTCCTTC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGAPDH\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCTGGGCTACACTGAGCACC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAAGTGGTCGTTGAGGGCAATG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Routine blood tests and blood biochemistry tests\u003c/h2\u003e \u003cp\u003eBlood and serum samples from the mice were collected and sent to Shanghai Model Organisms Center for testing.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8 Microbiome\u003c/h2\u003e \u003cp\u003eThe α diversity indexes (Sobs, Ace and Chao) were used to evaluate the richness and diversity of species in the environmental community. Then, the between-group difference test was used to detect whether there was a significant difference in the alpha diversity index between two or more groups. Specifically, the Wilcoxon rank-sum test was used to study the significant differences in α diversity index between different groups after being corrected by false discovery rate (FDR) multiple tests. Principal co-ordinates analysis (PCoA) based on the mapping of the selected distance matrix was used to study the similarities or difference in sample community composition and identify the potential principal components that affect the differences in sample community composition. According to the relative abundance of microflora at the genus level, the Jensen-Shannon Distance (JSD) equidistance was calculated, and PAM (Partitioning Around Medoids) clustering was performed, and the optimal clustering K value was calculated by Calinski-Harabasz (CH) index, and then Between-class analysis (BCA, K\u0026thinsp;\u0026ge;\u0026thinsp;3) or PCoA (K\u0026thinsp;\u0026ge;\u0026thinsp;2). At the genus level, the Wilcoxon rank-sum test was used to evaluate the difference in the average relative abundance of the same species between different groups after FDR multiple test correction and two-tailed test. (Analysis software: R-3.3.1)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.9 Untargeted metabolomics\u003c/h2\u003e \u003cp\u003ePrincipal Component Analysis (PCA) (Analytical methods: mix merge; Data conversion: Unit Variance Conversion; Confidence: 0.95) and Partial Least Squares Discriminant Analysis (PLS-DA) ((Analytical methods: mix merge; PLSDA data conversion: Pareto conversion; Confidence: 0.95; Number of substitutions:200) analysis were used to evaluate the similarity of samples within the group and the differences between the samples. OPLS-DA and differential statistical volcano plots were used to screen and visualize differential metabolites between different groups and then used. The model of OPLS-DA was evaluated using the R2 and Q2 values of the permutation test. Variable importance (VIP) value analysis (Metabolite clustering method: hierarchical clustering; Metabolite distance algorithm: euclidean; Metabolite hierarchical clustering method: complete; VIP value source: OPLS-DA; VIP value\u0026thinsp;\u0026gt;\u0026thinsp;1) was used to show the expression patterns of metabolites in each sample in each differential group. Kyoto Encyclopedia of Genes and Genomes Enrichment (KEGG) enrichment analysis was performed using BH multiplex test correction and Relative-betweeness Centrality topology method. (Database version: kegg_v20230830) Correlations between changes in differential metabolites and changes in genus relative abundance were calculated by Spearman\u0026rsquo;s rank test.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.10 Statistical Analysis\u003c/h2\u003e \u003cp\u003eAll results were reported as means\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) or mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard error of mean (SEM). Differences between two comparative groups were assessed using the Student\u0026rsquo;s \u003cem\u003et\u003c/em\u003e-test, and the significance among multiple groups was examined by the one-way analysis of variance (ANOVA). Statistical significance was considered when \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 using GraphPad Prism software 9.0 (GraphPad Software).\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Establishment of germ-free mouse and gut microbiota transplant mouse models\u003c/h2\u003e \u003cp\u003eFirstly, the mice were delivered by caesarean section to a sterile isolator, and under aseptic feeding conditions, they were artificially fed until they could feed independently to establish germ-free mice.\u003c/p\u003e \u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA, we collected feces from healthy volunteers and NAFLD patients, then homogenized them and inoculated them by gavage into the intestines of germ-free mice. Mice were euthanized after 10 weeks of rearing in a sterile environment, and we collected liver tissue and blood samples from them for testing.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Pathological changes in the liver of mice after microbiota transplantation\u003c/h2\u003e \u003cp\u003eConventional ultrasound (i.e., grayscale abdominal ultrasound evaluation of the liver) is the most common imaging modality for evaluation of hepatic steatosis, with good sensitivity and specificity in detecting steatosis [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. The results illustrated that the gray ratio of liver to kidney in mice inoculated with feces of patients with non-alcoholic fatty liver disease was significantly higher than that of the control group (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). Additionally, we observed a significantly increased liver weight and darker appearance in NAFLD group compared with control group (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD, G). To determine the effect of gut microbiota transplantation on mice, we further investigated other pathological findings in the livers of two groups of mice, each transplanted with different microbiotas. ORO and HE staining demonstrated that transplanting the microbiota of NAFLD patients in mice increased hepatic lipid accumulation and hepatocellular ballooning in the liver compared with the control group (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE). Simultaneously, fibrosis and inflammation were observed in livers of mice inoculated with NAFLD gut microbiota (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF, H). Notably, there was no significant change in intrahepatic TCHO content of mice transplanted with NAFLD microbiota, but the level of intrahepatic TG was remarkably elevated (Figure I, J). These pathological features are similar to those of patients with NAFLD.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Routine blood and serological markers of mice after microbial transplantation\u003c/h2\u003e \u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA-B, compared with the control group, the blood levels of white blood cell (WBC), lymphocyte (LYM) and red blood cell (RBC) in the NAFLD group were significantly reduced, while the mean corpuscular volume (MCV) was significantly increased. There was no significant change in the levels of platelet (PLT), hemoglobin (HGB), hematocrit (HCT) and neutrophil (NEU) in the blood of the two groups. On the other hand, the serum content of choline esterase (CHE) in mice inoculated with the intestinal microbiota of NAFLD patients was decreased, while the content of aspartate transaminase (AST), alanine transaminase (ALT), gamma-glutamyl transferase (GGT), triglyceride (TG), lactate dehydrogenase (LDH), direct bilirubin (D-BIL) and nonestesterified fatty acid (NEFA) was significantly increased. However, no significant difference was observed in serum levels of high-density lipoprotein (HDL), alkaline phosphatase (ALP), TCHO and low-density lipoprotein (LDL) between the two groups.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Effect of microbiota transplantation on gut microbiota\u003c/h2\u003e \u003cp\u003eCompared with the control group, the alpha diversity (Sobs, Ace and Chao indexes) of the gut microbiota of mice in the NAFLD group was significantly reduced (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). The principal coordinate analysis (PCoA) score plots based on Bray-Curtis distance at ASV proved a significant compositional difference in the microbiota β-diversity between the two groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). Meanwhile, we confirmed that the enterotypes also changed significantly between the two groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC-E). The genus level analysis detected a lower relative abundance of Escherichia, Eisenbergiella and unclassified_f_Ruminococcazeae, and a higher abundance of Pygmaiobacter, Monoglobus and UBA1819 in the NAFLD group compared to the control group (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Effect of microbiota transplantation on blood metabolites\u003c/h2\u003e \u003cp\u003eNon-targeted metabolomics is commonly used to identify potential alterations in key metabolites and metabolic pathways in serum samples. Subsequently, PCA analysis and PLS-DA analysis were used to analyze the differences between the NAFLD and control groups. The results showed significant differences in metabolic profiles between the two groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA, B). To find out more information about the differences in metabolites in mice inoculated with different microbiotas, we performed differential metabolite analysis. The orthogonal partial least-squares discriminant analysis (OPLS-DA) score plots separated the metabolite profiles between control and NAFLD groups. The intercepts of goodness-of-fit (R2) and goodness-of-prediction (Q2) illustrate the OPLS-DA model is reliable and not overfitting (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC, D). Furthermore, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE, a total of 385 significantly upregulated metabolites and 424 significantly downregulated metabolites were identified by comparing the NAFLD group with the control group. In summary, serum metabolites in mice inoculated with the gut microbiota of NAFLD patients produced significant changes. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF showed the detailed expression trends of the top 30 differential metabolites in both groups. In order to figure out the mechanism of the change in the expression of these metabolites, we performed KEGG pathway enrichment analysis. The results showed that intestinal microbiota inoculation significantly affected the amino acid metabolism and carbon metabolism of the mice (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eG). The correlation heat map showed that the differential microbial communities other than blautia, lachnoclostridium and coprobacillus were strongly correlated with the differential metabolites (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eRecent studies have shown that gut microbes are increasingly closely related to NAFLD[\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Regarding liver diseases, the comparison of germ free (GF) and conventional (CV) mice has been used to assess the role played by the microbiota. Gut microbiota transplantation in GF mice has also been used to assess the causality between microbiota composition and susceptibility to NAFLD[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Le Roy et al. demonstrated that gut microbiota composition determines NAFLD development in C57BL/6 strain mice for the first time. In fact, by transplanting the gut microbiota of mice with or without NAFLD into GF mice, it was found that the propensity to develop NAFLD traits could be transmitted via the gut microbiota[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. However, there are differences between the intestinal microbiota of NAFLD model mice and NAFLD patients. Therefore, it is particularly important to realize the colonization of the intestinal microbiota from NAFLD patients in germ-free mice, while striving to mimic the intestinal environment of NAFLD patients to the fullest extent possible. Chien-Chao Chiu et al[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] demonstrated that colonization of fecal bacteria from patients with non-alcoholic steatohepatitis (NASH), as well as HFD feeding, exacerbated disease progression in NAFLD. Nevertheless, the research conducted on the influence of intestinal microbiota colonization on disease progression in mice models of NAFLD patients remains insufficient and incomplete. In this study, we colonized the gut microbiota from healthy volunteers and NAFLD patients into germ-free mice and found that the liver histology and blood composition of the mice in the NAFLD group were similar to those of NAFLD disease. This indicates that there is great potential to establish a mouse model that mimics human NAFLD-related microbiota transplantation by optimizing the microbiota transplantation method.\u003c/p\u003e \u003cp\u003eNotably, we noticed a significant increase in serum 1-Methylxanthine in mice in the NAFLD group. Yan GAO et al [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] reported that 1-methylxanthine was significantly positively correlated with IL-6 and TNF-α when an inflammatory response occurs in the body. In patients with NAFLD, metabolic disorders including insulin resistance and obesity further promoted liver inflammation and tumorigenesis through IL-6 and TNF-α[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Zahra Safari et al[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] reported that the abundance of clostridium innocuum in the gut microbiota of patients with NAFLD was significantly higher than that in healthy people. Our study has validated a marked positive correlation between the significant elevation of 1-Methylxanthine in the serum of mice inoculated with gut microbiota from NAFLD patients and the substantial increase in the abundance of clostridium in their guts (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). This finding indicates that clostridium may hold a pivotal role in the onset and progression of NAFLD. Similarly, there are still many potential links between the closely related differential microorganisms and differential metabolites in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e that need to be further explored.\u003c/p\u003e \u003cp\u003eAlthough there are important discoveries revealed by the current work, there are certain limitations. First, the similarities and differences between the gut microbiota of NAFLD patients and the colonized mouse gut microbiota were not further revealed. This is necessary for us to further understand the relationship between the dominant bacterial genera that promote the progression of NAFLD in humans and mice. Second, the relationship between serum metabolites in mice and serum metabolites in patients with NAFLD is unclear. In future research work, we will explore these questions more deeply.\u003c/p\u003e \u003cp\u003eThe interactions between the gut and liver, called the gut\u0026ndash;liver axis, play an essential role in NAFLD development and evolution. Portal blood flow connects the intestine to the liver[\u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. A significant portion of liver blood originating from the intestine subjects the liver to various metabolic products generated by the gut microbiome, encompassing phenols, acetaldehyde, and ammonia, in addition to pro-inflammatory bacterial components like peptidoglycan and lipopolysaccharides (LPS)[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Therefore, it is necessary to explore the potential association between serum metabolites and gut microbiota, in order to uncover further insights into the pathogenesis of NAFLD or discover an effective treatment for NAFLD. We sincerely anticipate that this study may shed new light on the search for a treatment for NAFLD.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by grants from National key research and development program (2022YFF0710600), and the CAMS Innovation Fund for Medical Sciences (CAMS, 2021-I2M-1-072).\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests. \u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConception and design of the research: Zhiwei Yang, Hua Zhu and Xing Liu; acquisition of data: Yuanzhi Cheng, Yun Yang and Xing Liu; analysis and interpretation of the data: Yuanzhi Cheng, Yaxi Guo and Yang Shi; statistical analysis: Yuanzhi Cheng, Xiaoliang Jiang and Xing Liu; supervising the experiments: Hua Zhu and Xing Liu; drafting the manuscript: Yuanzhi Cheng, Jianghao Feng and Yang Shi. \u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorresponding author\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCorrespondence to Xing Liu at [email protected] and Hua Zhu at [email protected].\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data in this study are available from the corresponding author upon reasonable request.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll animal experiments were conducted according to the ethical policies and procedures approved by the Animal Care and Use Committee at the Institute of Laboratory Animal Science, Chinese Academy of Medical Sciences and Peking Union Medical College (approval No. ZH23003).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformed consent was obtained from all individual participants included in the study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eZhang W, Lu J, Feng L, Xue H, Shen S, Lai S, Li P, Li P, Kuang J, Yang Z and Xu X (2024) Sonic hedgehog-heat shock protein 90beta axis promotes the development of nonalcoholic steatohepatitis in mice. Nat Commun 15:1280. doi: 10.1038/s41467-024-45520-8\u003c/li\u003e\n\u003cli\u003eTargher G, Tilg H and Byrne CD (2021) Non-alcoholic fatty liver disease: a multisystem disease requiring a multidisciplinary and holistic approach. Lancet Gastroenterol Hepatol 6:578-588. doi: 10.1016/S2468-1253(21)00020-0\u003c/li\u003e\n\u003cli\u003eMao Z, Ma X, Jing Y, Shen M, Ma X, Zhu J, Liu H, Zhang G and Chen F (2023) Ufmylation on UFBP1 alleviates non-alcoholic fatty liver disease by modulating hepatic endoplasmic reticulum stress. Cell Death Dis 14:584. doi: 10.1038/s41419-023-06095-2\u003c/li\u003e\n\u003cli\u003eLambert JE, Ramos-Roman MA, Browning JD and Parks EJ (2014) Increased de novo lipogenesis is a distinct characteristic of individuals with nonalcoholic fatty liver disease. Gastroenterology 146:726-35. doi: 10.1053/j.gastro.2013.11.049\u003c/li\u003e\n\u003cli\u003eLebeaupin C, Vallee D, Hazari Y, Hetz C, Chevet E and Bailly-Maitre B (2018) Endoplasmic reticulum stress signalling and the pathogenesis of non-alcoholic fatty liver disease. J Hepatol 69:927-947. doi: 10.1016/j.jhep.2018.06.008\u003c/li\u003e\n\u003cli\u003eChen J and Vitetta L (2020) Mitochondria could be a potential key mediator linking the intestinal microbiota to depression. J Cell Biochem 121:17-24. doi: 10.1002/jcb.29311\u003c/li\u003e\n\u003cli\u003eChen J and Vitetta L (2019) Bile acids and butyrate in the effects of probiotics/synbiotics on nonalcoholic fatty liver disease. Eur J Gastroenterol Hepatol 31:1475-1476. doi: 10.1097/MEG.0000000000001506\u003c/li\u003e\n\u003cli\u003eWei J, Luo J, Yang F, Feng X, Zeng M, Dai W, Pan X, Yang Y, Li Y, Duan Y, Xiao X, Ye P, Yao Z, Liu Y, Huang Z, Zhang J, Zhong Y, Xu N and Luo M (2024) Cultivated Enterococcus faecium B6 from children with obesity promotes nonalcoholic fatty liver disease by the bioactive metabolite tyramine. Gut Microbes 16:2351620. doi: 10.1080/19490976.2024.2351620\u003c/li\u003e\n\u003cli\u003eSafari Z and Gerard P (2019) The links between the gut microbiome and non-alcoholic fatty liver disease (NAFLD). Cell Mol Life Sci 76:1541-1558. doi: 10.1007/s00018-019-03011-w\u003c/li\u003e\n\u003cli\u003eLeung C, Rivera L, Furness JB and Angus PW (2016) The role of the gut microbiota in NAFLD. Nat Rev Gastroenterol Hepatol 13:412-25. doi: 10.1038/nrgastro.2016.85\u003c/li\u003e\n\u003cli\u003eBaiden-Amissah REM and Tuyaerts S (2019) Contribution of Aging, Obesity, and Microbiota on Tumor Immunotherapy Efficacy and Toxicity. Int J Mol Sci 20. doi: 10.3390/ijms20143586\u003c/li\u003e\n\u003cli\u003eZheng Y, Hao S, Hu L and Cheng T (2015) [Development of immunodeficient mice/humanized mouse models and their applications in hematology research]. Zhonghua Xue Ye Xue Za Zhi 36:966-71. doi: 10.3760/cma.j.issn.0253-2727.2015.11.018\u003c/li\u003e\n\u003cli\u003eTurnbaugh PJ, Ridaura VK, Faith JJ, Rey FE, Knight R and Gordon JI (2009) The effect of diet on the human gut microbiome: a metagenomic analysis in humanized gnotobiotic mice. Sci Transl Med 1:6ra14. doi: 10.1126/scitranslmed.3000322\u003c/li\u003e\n\u003cli\u003eHernaez R, Lazo M, Bonekamp S, Kamel I, Brancati FL, Guallar E and Clark JM (2011) Diagnostic accuracy and reliability of ultrasonography for the detection of fatty liver: a meta-analysis. Hepatology 54:1082-1090. doi: 10.1002/hep.24452\u003c/li\u003e\n\u003cli\u003eNeuschwander-Tetri BA, Clark JM, Bass NM, Van Natta ML, Unalp-Arida A, Tonascia J, Zein CO, Brunt EM, Kleiner DE, McCullough AJ, Sanyal AJ, Diehl AM, Lavine JE, Chalasani N, Kowdley KV and Network NCR (2010) Clinical, laboratory and histological associations in adults with nonalcoholic fatty liver disease. Hepatology 52:913-24. doi: 10.1002/hep.23784\u003c/li\u003e\n\u003cli\u003eHenao-Mejia J, Elinav E, Jin C, Hao L, Mehal WZ, Strowig T, Thaiss CA, Kau AL, Eisenbarth SC, Jurczak MJ, Camporez JP, Shulman GI, Gordon JI, Hoffman HM and Flavell RA (2012) Inflammasome-mediated dysbiosis regulates progression of NAFLD and obesity. Nature 482:179-85. doi: 10.1038/nature10809\u003c/li\u003e\n\u003cli\u003eMatsui M, Fukunishi S, Nakano T, Ueno T, Higuchi K and Asai A (2021) Ileal Bile Acid Transporter Inhibitor Improves Hepatic Steatosis by Ameliorating Gut Microbiota Dysbiosis in NAFLD Model Mice. mBio 12:e0115521. doi: 10.1128/mBio.01155-21\u003c/li\u003e\n\u003cli\u003eWang X, Jin Y, Di C, Zeng Y, Zhou Y, Chen Y, Pan Z, Li Z and Ling W (2024) Supplementation of Silymarin Alone or in Combination with Salvianolic Acids B and Puerarin Regulates Gut Microbiota and Its Metabolism to Improve High-Fat Diet-Induced NAFLD in Mice. Nutrients 16. doi: 10.3390/nu16081169\u003c/li\u003e\n\u003cli\u003eMayneris-Perxachs J, Cardellini M, Hoyles L, Latorre J, Davato F, Moreno-Navarrete JM, Arnoriaga-Rodriguez M, Serino M, Abbott J, Barton RH, Puig J, Fernandez-Real X, Ricart W, Tomlinson C, Woodbridge M, Gentileschi P, Butcher SA, Holmes E, Nicholson JK, Perez-Brocal V, Moya A, Clain DM, Burcelin R, Dumas ME, Federici M and Fernandez-Real JM (2021) Iron status influences non-alcoholic fatty liver disease in obesity through the gut microbiome. Microbiome 9:104. doi: 10.1186/s40168-021-01052-7\u003c/li\u003e\n\u003cli\u003eLe Roy T, Llopis M, Lepage P, Bruneau A, Rabot S, Bevilacqua C, Martin P, Philippe C, Walker F, Bado A, Perlemuter G, Cassard-Doulcier AM and Gerard P (2013) Intestinal microbiota determines development of non-alcoholic fatty liver disease in mice. Gut 62:1787-94. doi: 10.1136/gutjnl-2012-303816\u003c/li\u003e\n\u003cli\u003eChiu CC, Ching YH, Li YP, Liu JY, Huang YT, Huang YW, Yang SS, Huang WC and Chuang HL (2017) Nonalcoholic Fatty Liver Disease Is Exacerbated in High-Fat Diet-Fed Gnotobiotic Mice by Colonization with the Gut Microbiota from Patients with Nonalcoholic Steatohepatitis. Nutrients 9. doi: 10.3390/nu9111220\u003c/li\u003e\n\u003cli\u003eGao Y, Liu L, Li C, Liang YT, Lv J, Yang LF and Zhao BN (2022) Study on the Antipyretic and Anti-inflammatory Mechanism of Shuanghuanglian Oral Liquid Based on Gut Microbiota-Host Metabolism. Front Pharmacol 13:843877. doi: 10.3389/fphar.2022.843877\u003c/li\u003e\n\u003cli\u003eCampbell C, Kandalgaonkar MR, Golonka RM, Yeoh BS, Vijay-Kumar M and Saha P (2023) Crosstalk between Gut Microbiota and Host Immunity: Impact on Inflammation and Immunotherapy. Biomedicines 11. doi: 10.3390/biomedicines11020294\u003c/li\u003e\n\u003cli\u003eAlbillos A, de Gottardi A and Rescigno M (2020) The gut-liver axis in liver disease: Pathophysiological basis for therapy. J Hepatol 72:558-577. doi: 10.1016/j.jhep.2019.10.003\u003c/li\u003e\n\u003cli\u003eTilg H, Adolph TE and Trauner M (2022) Gut-liver axis: Pathophysiological concepts and clinical implications. Cell Metab 34:1700-1718. doi: 10.1016/j.cmet.2022.09.017\u003c/li\u003e\n\u003cli\u003ePabst O, Hornef MW, Schaap FG, Cerovic V, Clavel T and Bruns T (2023) Gut-liver axis: barriers and functional circuits. Nat Rev Gastroenterol Hepatol 20:447-461. doi: 10.1038/s41575-023-00771-6\u003c/li\u003e\n\u003cli\u003eCompare D, Coccoli P, Rocco A, Nardone OM, De Maria S, Carteni M and Nardone G (2012) Gut--liver axis: the impact of gut microbiota on non alcoholic fatty liver disease. Nutr Metab Cardiovasc Dis 22:471-6. doi: 10.1016/j.numecd.2012.02.007\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"NAFLD, Germ-free mice, Gut microbes, Metabolomics, Microbiome","lastPublishedDoi":"10.21203/rs.3.rs-4728601/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4728601/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThere is mounting scientific evidence indicating a robust association between gut microbiota and nonalcoholic fatty liver disease (NAFLD). Exposure to commensal microbiota in germ-free mice has a significant impact on the regulatory mechanisms of gut genes, in contrast to those conventionally raised. In this study, we have successfully inoculated the gut microbiota from healthy individuals and NAFLD patients into germ-free mice, with the objective of developing a humanized mouse model that accurately replicates the gut microenvironment of NAFLD patients. Changes in blood composition and liver pathology in these mice were systematically measured. Furthermore, we have conducted a det ailed analysis of the variations in fecal microbiota and differential metabolites in the blood composition. Our findings indicate a high degree of similarity in disease characteristics between mice colonized with microbiota and humans suffering from NAFLD. Notably, we have observed a strong correlation between alterations in serum differential metabolites and gut microbiota in these mice.\u003c/p\u003e","manuscriptTitle":"Exploring the Pathogenesis of NAFLD: A Study of the Correlation of Gut Microbes and Metabolites in Humanized Mouse Gut Microbiota","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-17 01:05:49","doi":"10.21203/rs.3.rs-4728601/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e74bd418-7e4f-46d5-9dcd-e024bf32df6d","owner":[],"postedDate":"August 17th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-08-17T01:05:51+00:00","versionOfRecord":[],"versionCreatedAt":"2024-08-17 01:05:49","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4728601","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4728601","identity":"rs-4728601","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}