Yang Wang's research while affiliated with Peking University People's Hospital and other places

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Publications (14)


Escherichia coli NF73-1 disrupts the gut-vascular barrier and aggravates high-fat diet-induced fatty liver disease via inhibiting Wnt/β-catenin signalling pathway
  • Article

January 2024

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17 Reads

Liver international: official journal of the International Association for the Study of the Liver

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Yibo Huang

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Background & Aims Gut–vascular barrier (GVB) dysfunction has been shown to be a prerequisite for nonalcoholic fatty liver disease (NAFLD) development. However, the causes of GVB disruption and the underlying mechanisms are still elusive. Here, we explored whether and how Escherichia coli ( E. coli ) NF73‐1, a pathogenic E. coli strain isolated from nonalcoholic steatohepatitis patients, contributes to NAFLD by modulating the GVB. Methods C57BL/6J mice were fed with high‐fat diet (HFD) or normal diet in the presence or absence of E. coli NF73‐1 for the indicated time periods. Intestinal barrier function and infiltration of immune cells were evaluated in these mice. Endothelial cells were exposed to E. coli NF73‐1 for barrier integrity analysis. Results HFD‐induced GVB disruption preceded the damage of intestinal epithelial barrier (IEB) as well as intestinal and hepatic inflammatory changes and can be reversed by vascular endothelial growth factor A blockade. Antibiotic treatment prevented mice from HFD‐induced liver steatosis by restoration of the GVB. Notably, E. coli NF73‐1 caused a more conspicuous damage of GVB than that of the IEB and contributed to NAFLD development. Mechanistically, E. coli NF73‐1 dismantled the GVB by inhibiting the Wnt/β‐catenin signalling pathway. Activation of Wnt/β‐catenin improved the GVB and impeded the translocation of E. coli NF73‐1 into the liver in vitro and in vivo. Conclusions E. coli NF73‐1 disrupts GVB and aggravates NAFLD via inhibiting the Wnt/β‐catenin signalling pathway. Targeting E. coli NF73‐1 or selectively enhancing the GVB may act as potential avenues for the prevention and treatment of NAFLD.

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Bacterial profiles at different gastrointestinal tract niches. A Study design. Three types of samples from differed GI niches were collected in this study, including salivary, buccal, and fecal samples. B Alpha diversity indices of the microbiota, including the richness, Simpson’s, Shannon’s, and Chao1 indices. Horizontal bars within boxes represent medians. The tops and bottoms of the boxes represent the 75th and 25th percentiles, respectively. The upper and lower whiskers cover 1.5 × the interquartile range from the upper and lower edges of the box, respectively. P-values were obtained using the one-way ANOVA test (comparisons among four groups). C The constrained principal coordinate analysis based on the Bray–Curtis distance. The R software (v 4.0.1) with the vegan (v 2.5–7) package were used and P-values were obtained using permutational multivariate analysis of variance (PERMANOVA). D and E The upset plot shows the bacterial family (D) and genus (E) count in each GI niche. F and G Relative abundance of the top 20 bacterial families (F) and genera (G). Visualization was performed using Circos (http://circos.ca/). The right circle in the outer part shows the groups and relative proportions of bacterial species. The left outer ring and inner bands indicate the relative proportions (%) of bacterial genera in the different groups. The left inner circle represents the relative abundances of all bacteria. H The ternary plot shows the distribution of the specific bacterial genus in each GI niche. The point color represents the phylum classification of the bacterial genus. The point size presents the mean percentage of a specific genus
The bacterial profile in salivary samples of UC patients with or without oral ulcers. A Alpha diversity indices of the microbiota, including the richness, Simpson’s, Shannon’s, and Chao1 indices. Horizontal bars within boxes represent medians. The tops and bottoms of the boxes represent the 75th and 25th percentiles, respectively. The upper and lower whiskers cover 1.5 × the interquartile range from the upper and lower edges of the box, respectively. P-values were obtained using the one-way ANOVA test (comparisons among four groups). B The constrained principal coordinate analysis based on the Bray–Curtis distance. The R software (v 4.0.1) with the vegan (v 2.5–7) package were used, and P-values were obtained using permutational multivariate analysis of variance (PERMANOVA). C Relative abundance of the top 20 bacterial families (the left panel) and genera (the right panel). Visualization was performed using Circos (http://circos.ca/). The right circle in the outer part shows the groups and relative proportions of bacterial species. The left outer circle and inner bands show the relative proportions (%) of bacterial genera in the different groups. The left inner circle represents the relative abundances of all bacteria. D and E Comparative analysis of bacterial genus abundance between two groups (D UC_OU vs. OU; E UC_OU vs. UC). The EdgeR package was used for comparative analysis. The difference between the two groups is shown as a Manhattan diagram. Point shape indicates the genus enriched, depleted, or not significant in the former group compared with the latter. Point size indicates the counts of a specific genus. CPM, count per million. F and G Comparative analysis of bacterial function between two groups (F UC_OU vs. OU; G UC_OU vs. UC). Phylogenetic Investigation of Communities annotated the pathway information by Reconstruction of Unobserved States (PICRUSt2) software by referring to the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. The STAMP software was used for data visualization. CON, healthy controls; OU, patients with only oral ulcers; UC, UC patients without oral ulcers; UC_OU, UC patients with oral ulcers; ns, not significant; *P-value < 0.05; **P-value < 0.01
The bacterial profile in buccal samples of UC patients with or without oral ulcers. A Alpha diversity indices of the microbiota, including the richness, Simpson’s, Shannon’s, and Chao1 indices. Horizontal bars within boxes represent medians. The tops and bottoms of the boxes represent the 75th and 25th percentiles, respectively. The upper and lower whiskers cover 1.5 × the interquartile range from the upper and lower edges of the box, respectively. P-values were obtained using the one-way ANOVA test (comparisons among four groups). B The constrained principal coordinate analysis based on the Bray–Curtis distance. The R software (v 4.0.1) with the vegan (v 2.5–7) package were used, and P-values were obtained using permutational multivariate analysis of variance (PERMANOVA). C Relative abundance of the top 20 bacterial families (the left panel) and genera (the right panel). Visualization was performed using Circos (http://circos.ca/). The right circle in the outer part shows the groups and relative proportions of bacterial species. The left outer circle and inner bands show the relative proportions (%) of bacterial genera in the different groups. The left inner circle represents the relative abundances of all bacteria. D and E, Comparative analysis of bacterial genus abundance between two groups (D UC_OU vs. OU; E UC_OU vs. UC). The EdgeR package was used for comparative analysis. The difference between the two groups is shown as a Manhattan diagram. Point shape indicates the genus enriched, depleted, or not significant in the former group compared with the latter. Point size indicates the counts of a specific genus. CPM, count per million. F and G, Comparative analysis of bacterial function between two groups (F UC_OU vs. OU; G UC_OU vs. UC). Phylogenetic Investigation of Communities annotated the pathway information by Reconstruction of Unobserved States (PICRUSt2) software by referring to the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. The STAMP software was used for data visualization. CON, healthy controls; OU, patients with only oral ulcers; UC, UC patients without oral ulcers; UC_OU, UC patients with oral ulcers; ns, not significant; *P-value < 0.05; **P-value < 0.01
The bacterial profile in fecal samples of UC patients with or without oral ulcers. A Alpha diversity indices of the microbiota, including the richness, Simpson’s, Shannon’s, and Chao1 indices. Horizontal bars within boxes represent medians. The tops and bottoms of the boxes represent the 75th and 25th percentiles, respectively. The upper and lower whiskers cover 1.5 × the interquartile range from the upper and lower edges of the box, respectively. P-values were obtained using the one-way ANOVA test (comparisons among four groups). B The constrained principal coordinate analysis based on the Bray–Curtis distance. The R software (v 4.0.1) with the vegan (v 2.5–7) package were used, and P-values were obtained using permutational multivariate analysis of variance (PERMANOVA). C, Relative abundance of the top 20 bacterial families (the left panel) and genera (the right panel). Visualization was performed using Circos (http://circos.ca/). The right circle in the outer part shows the groups and relative proportions of bacterial species. The left outer circle and inner bands show the relative proportions (%) of bacterial genera in the different groups. The left inner circle represents the relative abundances of all bacteria. D and E, Comparative analysis of bacterial genus abundance between two groups (D UC_OU vs. OU; E UC_OU vs. UC). The EdgeR package was used for comparative analysis. The difference between the two groups is shown as a Manhattan diagram. Point shape indicates the genus enriched, depleted, or not significant in the former group compared with the latter. Point size indicates the counts of a specific genus. CPM, count per million. F and G, Comparative analysis of bacterial function between two groups [F UC_OU vs. OU; G UC_OU vs. UC]. Phylogenetic Investigation of Communities annotated the pathway information by Reconstruction of Unobserved States (PICRUSt2) software by referring to the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. The STAMP software was used for data visualization. CON, healthy controls; OU, patients with only oral ulcers; UC, UC patients without oral ulcers; UC_OU, UC patients with oral ulcers; ns, not significant; *P-value < 0.05; **P-value < 0.01
The correlation between clinical parameters (inflammatory and immunological) and bacterial diversity (alpha and beta). A and B The correlation between inflammatory (A) or immunological (B) indices and salivary bacterial diversity. The length of the black bar shows the R square value of the indices-beta diversity correlation. The results were acquired by the redundancy analysis (RDA) using the R software (v 4.0.1) with the vegan (v 2.5–7) package. The color box shows the rho value of indices-alpha diversity correlation. The results were analyzed from Spearman’s correlation using the R software (v 4.0.1) with the vegan (v 2.5–7) package. The star symbols behind the left words show the P-values acquired from the RDA. The box’s star symbols show the adjusted P-values (false discovery rate, FDR) received from the Spearman’s correlation. (same methodology as in C–F). C and D The correlation between inflammatory (C) or immunological (D) indices and buccal bacterial diversity. E and F The correlation between inflammatory (E) or immunological (F) indices and buccal bacterial diversity

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The oral bacterial microbiota facilitates the stratification for ulcerative colitis patients with oral ulcers
  • Article
  • Full-text available

November 2023

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10 Reads

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1 Citation

Annals of Clinical Microbiology and Antimicrobials

Background Clinically, a large part of inflammatory bowel disease (IBD) patients is complicated by oral lesions. Although previous studies proved oral microbial dysbiosis in IBD patients, the bacterial community in the gastrointestinal (GI) tract of those IBD patients combined with oral ulcers has not been profiled yet. Methods In this study, we enrolled four groups of subjects, including healthy controls (CON), oral ulcer patients (OU), and ulcerative colitis patients with (UC_OU) and without (UC) oral ulcers. Bio-samples from three GI niches containing salivary, buccal, and fecal samples, were collected for 16S rRNA V3-V4 region sequencing. Bacterial abundance and related bio-functions were compared, and data showed that the fecal microbiota was more potent than salivary and buccal microbes in shaping the host immune system. ~ 22 UC and 10 UC_OU 5-aminosalicylate (5-ASA) routine treated patients were followed-up for six months; according to their treatment response (a decrease in the endoscopic Mayo score), they were further sub-grouped as responding and non-responding patients. Results We found those UC patients complicated with oral ulcers presented weaker treatment response, and three oral bacterial genera, i.e., Fusobacterium, Oribacterium, and Campylobacter, might be connected with treatment responding. Additionally, the salivary microbiome could be an indicator of treatment responding in 5-ASA routine treatment rather than buccal or fecal ones. Conclusions The fecal microbiota had a strong effect on the host’s immune indices, while the oral bacterial microbiota could help stratification for ulcerative colitis patients with oral ulcers. Additionally, the oral microbiota had the potential role in reflecting the treatment response of UC patients. Three oral bacteria genera (Fusobacterium, Oribacterium, and Campylobacter) might be involved in UC patients with oral ulcers lacking treatment responses, and monitoring oral microbiota may be meaningful in assessing the therapeutic response in UC patients.

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Signature of gene expression profile of liver sinusoidal endothelial cells in nonalcoholic steatohepatitis

September 2022

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293 Reads

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1 Citation

Frontiers in Cell and Developmental Biology

Frontiers in Cell and Developmental Biology

Background: There has been emerging evidence that liver sinusoidal endothelial cells (LSECs) play an important role in the pathogenesis of nonalcoholic steatohepatitis (NASH). This study aims to figure out the signature of the gene expression profile of LSECs in NASH and to explore potential biomarkers related to damaged LSECs in NASH. Methods and materials: Animal experiments were performed to demonstrate the significant structural damage of LSECs in the NASH model. To further understand the functional changes of these damaged LSECs in NASH, we used the public GEO database that contained microarray data for the gene expression of LSECs in NASH and normal mouse liver. Differentially expressed genes (DEGs) were analyzed, and further Gene Ontology (GO) enrichment analysis was performed to understand the functional changes. The hub genes were then identified and validated via external GEO databases. Results: There was significant structural damage to LSECs in the NASH model, accompanied by remarkable functional changes of LSECs with 174 DEGs (156 upregulated and 18 downregulated genes). The functions of these DEGs were mainly enriched in the inflammatory reactions and immune responses. Nine specifically expressed hub genes were identified. Among them, CCL4 and ITGAX showed the most significant correlation with NASH, with AUROC of 0.77 and 0.86, respectively. The protein–protein interaction network, mRNA–miRNA interaction network, and ceRNA network were further predicted. Conclusion: LSECs show significant structural damage and functional changes in NASH. The LSEC-related DEGs, such as CCL4 and ITGAX, might be promising biomarkers as well as potential treatment targets for NASH.


Cholecystectomy-induced secondary bile acids accumulation ameliorates colitis through inhibiting monocyte/macrophage recruitment

September 2022

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101 Reads

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14 Citations

Although post-cholecystectomy (PC) patients usually have gastrointestinal complications and a higher risk of colorectal cancer, previous studies undetected a heightened risk of inflammatory bowel disease. Thus, we tried to investigate cholecystectomy’s impact and pathophysiological mechanism on murine colitis models and clarify the association among fecal bile acids (BAs), mucosal bacterial microbiota, and immune cells in the PC patients. One month or three months after cholecystectomy, mice have induced colitis and tested BAs and fecal microbiota analysis. Next, mice were treated with various cholecystectomy-accumulated bile acids in drinking water for three months before inducing colitis. All 14 paired PC patients and healthy subjects were enrolled for BAs and mucosal microbiota analysis. Cholecystectomy ameliorated DSS-induced murine colitis, accelerated mucosal repair, and induced a significant shifting of fecal microbiota and BAs profiles under colitis status, which featured a higher relative abundance of species involved in BAs metabolism and increased secondary BAs concentrations. Cholecystectomy-associated secondary BAs (LCA, DCA, and HDCA) also ameliorated DSS-induced colitis and accelerated mucosal repair in mice. Cholecystectomy and specific secondary BAs treatments inhibited monocytes/macrophages recruitment in colitis mice. In vitro, cholecystectomy-associated secondary BAs also downregulated monocytes chemokines in the THP-1 derived macrophages through activation of the LXRα-linked signaling pathway. The alterations of mucosal microbiota and fecal BAs profiles were found in the PC patients, characterized as increased species with potential immuno-modulating effects and secondary BAs, which were negatively associated with peripheral monocytes levels. Cholecystectomy-induced secondary bile acids accumulation ameliorated colitis through inhibiting monocyte/macrophage recruitment, which might be mediated by the LXRα-related signaling pathway. Cholecystectomy, after 3 months follow-up, has an immune-regulatory role in murine colitis, preliminarily explaining that no increased risk of IBD had been reported in the PC patients, which still warrants further studies.


NAFLD aggravates acute pancreatitis through bacterial translocation and cholesterol metabolic dysregulation in the liver and pancreas in mice

July 2022

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15 Reads

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6 Citations

Hepatobiliary & Pancreatic Diseases International

Background : Nonalcoholic fatty liver disease (NAFLD) is an independent risk factor for severe acute pancreatitis (AP). The underlying mechanism remains unclear. We sought to determine how bacterial translocation and cholesterol metabolism in the liver and pancreas affect the severity of AP in NAFLD mice. Methods : C57BL/6N mice were fed on a high-fat diet (HFD) to generate the NAFLD model, and mice in the control group were provided with a normal diet (ND). After being anesthetized with ketamine/xylazine, mice got a retrograde infusion of taurocholic acid sodium into the pancreatic duct to induce AP, and sham operation (SO) was used as control. Serum amylase and Schmidt's pathological score system were used to evaluate AP severity. Bacterial loads, total cholesterol level, and cholesterol metabolic-associated molecules [low-density lipoprotein receptor (LDLR) and ATP-binding cassette transporter A1 (ABCA1)] were analyzed in the liver and pancreas. Results : Compared with the ND-AP group, mice in the HFD-AP group had severer pancreatitis, manifested with higher serum amylase levels and higher AP pathologic scores, especially the inflammation and hemorrhage scores. Compared with the HFD-SO group and ND-AP group, bacterial loads in the liver and pancreas were significantly higher in the HFD-AP group. Mice in the HFD-AP group showed a decreased LDLR expression and an increased ABCA1 expression in the pancreas, although there was no significant difference in pancreas total cholesterol between the HFD-AP group and the ND-AP group. Conclusions : NAFLD aggravates AP via increasing bacterial translocation in the liver and pancreas and affecting pancreas cholesterol metabolism in mice.


Flow chart showing the flow of study selection
Forest plots showing pooled odds ratio of gastrointestinal symptoms associated with the mortality of COVID-19
Forest plots showing pooled odds ratio of (A) diarrhea, (B) nausea/vomiting and (C) abdominal pain associated with the mortality of COVID-19
Funnel plots for evaluation of publication bias. A shows the funnel plots of gastrointestinal symptoms, diarrhea, nausea/vomiting and abdominal pain. B shows the funnel plots after trim-and-fill method
Are gastrointestinal symptoms associated with higher risk of Mortality in COVID-19 patients? A systematic review and meta-analysis

March 2022

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52 Reads

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24 Citations

BMC Gastroenterology

Background Gastrointestinal symptoms have been reported in patients with COVID-19. Several clinical investigations suggested that gastrointestinal symptoms were associated with disease severity of COVID-19. However, the relevance of gastrointestinal symptoms and mortality of COVID-19 remains largely unknown. We aim to investigate the relationship between gastrointestinal symptoms and COVID-19 mortality. Methods We searched the PubMed, Embase, Web of science and Cochrane for studies published between Dec 1, 2019 and May 1, 2021, that had data on gastrointestinal symptoms in COVID-19 patients. Additional literatures were obtained by screening the citations of included studies and recent reviews. Only studies that reported the mortality of COVID-19 patients with/without gastrointestinal symptoms were included. Raw data were pooled to calculate OR (Odds Ratio). The mortality was compared between patients with and without gastrointestinal symptoms, as well as between patients with and without individual symptoms (diarrhea, nausea/vomiting, abdominal pain). Results Fifty-three literatures with 55,245 COVID-19 patients (4955 non-survivors and 50,290 survivors) were included. The presence of GI symptoms was not associated with the mortality of COVID-19 patients (OR=0.88; 95% CI 0.71–1.09; P=0.23). As for individual symptoms, diarrhea (OR=1.01; 95% CI 0.72–1.41; P=0.96), nausea/vomiting (OR=1.16; 95% CI 0.78–1.71; P=0.46) and abdominal pain (OR=1.55; 95% CI 0.68–3.54; P=0.3) also showed non-relevance with the death of COVID-19 patients. Conclusions Gastrointestinal symptoms are not associated with higher mortality of COVID-19 patients. The prognostic value of gastrointestinal symptoms in COVID-19 requires further investigation.


Patients With IBD Receiving Methotrexate Are at Higher Risk of Liver Injury Compared With Patients With Non-IBD Diseases: A Meta-Analysis and Systematic Review

November 2021

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41 Reads

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7 Citations

Background: Methotrexate is well-known in treating inflammatory bowel disease (IBD), rheumatoid arthritis (RA), psoriasis (Ps), and psoriatic arthritis (PsA). Several reports have indicated a higher incidence of methotrexate (MTX)-related liver adverse events in patients with IBD. We aim to investigate the risk of liver injury in patients with IBD and those with non-IBD diseases treated with MTX. Methods: We searched PubMed, Embase, and the Cochrane Library for articles that reported liver adverse events in patients with IBD, RA, and Ps/PsA, receiving MTX therapy. Additional articles were obtained by screening the references of recent meta-analysis and reviews. Raw data from included articles were pooled to calculate the cumulative incidence of total liver injury (TLI), MTX discontinuation (MTX-D), and liver fibrosis (LF). RR (relative risk) was calculated to compare the difference between patients with IBD and those with non-IBD diseases. Results: A total of 326 articles with 128,876 patients were included. The patients with IBD had higher incidence of TLI [11.2 vs. 9.2%; relative risk (RR) = 1.22; P = 0.224] and MTX-D (2.6 vs. 1.8%; RR, 1.48; P = 0.089) than patients with non-IBD diseases. Due to the publication bias, trim-and-fill was performed. Afterwards, the patients with IBD showed significantly higher risk of TLI (11.2 vs. 3%; RR = 3.76; p < 0.001), MTX-D (3.3 vs. 0.7%; RR = 5; p < 0.001) and LF (3.1 vs. 0.1%; RR = 38.62; P = 0.001) compared with patients with non-IBD diseases. Conclusion: IBD is associated with a higher risk of MTX-related liver injury. The mechanism of MTX-induced hepatotoxicity might be different in IBD and non-IBD diseases, and needs to be verified in future research.


Membrane assembly of NOX2. NOX2 (NADPH oxidase 2) is an electron-transfer complex with multiple components including gp91phox and p22. When neutrophil is activated, phosphorylation reactions recruit cytoplasmic regulatory subunits. Enzymatically active NOX2 induces the electron reduction of NADPH, as well as the transfer of electron to oxygen. H2O2 is further formed using superoxide anion and H⁺ released from the proton channels. Via pentose phosphate pathway (PPP), NADP⁺ can be recycled to NADPH.
Structure of neutrophil extracellular trap. Neutrophil extracellular trap (NET) formation can be triggered by diverse extrinsic stimuli including bacterial products, immune complexes, and damage-associated molecular patterns. The sticky web-like structure of NET is mainly composed of extracellular DNA. These web-like structures are decorated with histones and neutrophil granule proteins such as myeloperoxidase (MPO), elastase, and cathepsin G.
Mechanisms of neutrophil-induced liver injury. Neutrophils are injurious to hepatocytes via at least 4 mechanisms: (1) oxidative damage to cellular components of hepatocytes by neutrophil-derived reactive oxygen species (ROS), (2) neutrophil extracellular trap (NET)-induced hepatotoxicity, (3) neutrophil-released cytotoxic proteinases during degranulation, and (4) further enhanced inflammation by early accumulated neutrophils.
Interactions between liver sinusoidal endothelial cells and neutrophils during liver injury. During the course of liver diseases, liver sinusoidal endothelial cells (LSECs) are capable of producing various chemokines, thus contributing to the recruitment of neutrophils. Neutrophils in the liver sinusoids can adhere to the sinusoidal endothelium by the engagement of CD44 and hyaluronic acid. Neutrophils and LSECs are further interacted via adhesion molecules and integrins to induce neutrophil transmigration. Extravasated neutrophils thus are capable of inducing hepatotoxicity. The activation of neutrophils might also be associated with LSECs-generated cytokines.
Neutrophil-Induced Liver Injury and Interactions Between Neutrophils and Liver Sinusoidal Endothelial Cells

August 2021

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6,347 Reads

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34 Citations

Inflammation

Neutrophils are the most abundant type of leukocytes with diverse functions in immune defense including production of reactive oxygen species, bacteriocidal proteins, neutrophil extracellular traps, and pro-inflammatory mediators. However, aberrant accumulation of neutrophils in host tissues and excessive release of bacteriocidal compounds can lead to unexpected injury to host organs. Neutrophil-mediated liver injury has been reported in various types of liver diseases including liver ischemia/reperfusion injury, nonalcoholic fatty liver disease, endotoxin-induced liver injury, alcoholic liver disease, and drug-induced liver injury. Yet the mechanisms of neutrophil-induced hepatotoxicity in different liver diseases are complicated. Current knowledge of these mechanisms are summarized in this review. In addition, a substantial body of evidence has emerged showing that liver sinusoidal endothelial cells (LSECs) participate in several key steps of neutrophil-mediated liver injury including neutrophil recruitment, adhesion, transmigration, and activation. This review also highlights the current understanding of the interactions between LSECs and neutrophils in liver injury. The future challenge is to explore new targets for selectively interfering neutrophil-induced liver injury without impairing host defense function against microbial infection. Further understanding the role of LSECs in neutrophil-induced hepatotoxicity would aid in developing more selective therapeutic approaches for liver disease.


FIGURE 1 | Colitis aggravates liver injury when LSECs are damaged. (A) H&E staining was used to investigate the pathological findings of colon and liver in normal control (NC), acute dextran sulfate sodium (DSS) colitis (aDSS) group, and chronic DSS colitis (cDSS) group. Disease Activity Index (DAI) score was used to evaluate the severity of colitis during the course of disease. Alanine aminotransferase (ALT) and aspartate aminotransferase (AST) were tested to evaluate liver injury. Scanning electron microscopy (SEM) and transmission electron microscopy (TEM) showed the ultrastructural features of normal liver sinusoidal endothelial cells (LSECs). The effect of DSS colitis on liver injury and the damage of LSECs were also evaluated in (B) the sinusoidal obstruction syndrome (SOS) model; (C) the liver fibrosis (LF) model; (D) the non-alcoholic steatohepatitis (NASH) model; (E) the drug-induced liver injury (DILI) model; and (F) the high-dose ConA hepatitis (H-ConA) model. ns, non-significant; *p < 0.05; **p < 0.01; ***p < 0.001 by t-test; n = 6 at each time point per group; figures are representative of three experiments.
FIGURE 4 | Infiltration and accumulation of neutrophils are related with the expression of CXCL1 in various tissues. The number of neutrophils and expression of CXCL1 were evaluated at day 3 in the following seven groups: normal control (NC), acute dextran sulfate sodium colitis (aDSS), sinusoidal obstruction syndrome (SOS), lipopolysaccharide-induced pneumonia (LPSPn), SOS + LPSPn, DSS + SOS, and DSS + SOS + LPSPn. (A) In the peripheral blood, a whole blood cell test was performed and CXCL1 was tested by enzyme-linked immunosorbent assay. (B) In the liver, the number of neutrophils was evaluated by flow cytometry (anti-neutrophil + SSA high ), and mRNA level of CXCL1 was tested. (C) In the lung, immunohistochemistry for myeloperoxidase (MPO) and flow cytometry were performed and mRNA level of CXCL1 was tested. (D) In the colon, immunohistochemistry for MPO and mRNA level of CXCL1 were tested. Asterisk indicates that the level is significantly different from normal control. *p < 0.05; **p < 0.01; ***p < 0.001 by t-test; n = 5-6 per group; figures are representative of three experiments.
FIGURE 6 | Pro-inflammatory phenotype of damaged LSECs. After LSECs were treated with different stimuli [vehicle, lipopolysaccharide (LPS), monocrotaline (MCT), and MCT + LPS], the following parameters were examined: (A) the supernatant CXCL1 and TNF-α, tested by ELISA; (B) mRNA levels of CXCL1 and TNF-α; (C) phosphorylation of LSEC p65. (D) The influence of CXCL1 on neutrophil chemotaxis was evaluated by the number of trans-membrane migrated neutrophils in the Transwell experiment. (E) LSEC-derived CXCL1 was also examined by immunocytochemistry of CXCL1 (red) and RECA-1 (green, a marker of rat endothelial cells). (F) The co-localization of CXCL1 (red) and RECA-1 (green) was detected by immunohistochemistry in colitis-induced liver injury. Asterisk indicates that the level is significantly different from normal control. *p < 0.05; **p < 0.01; ***p < 0.001 by one-way ANOVA; n = 6 in each group; panels (A-C) are representative of three experiments; panel (C) is representative of one experiment, while other panels are representative of three experiments.
Gut–Liver Axis: Liver Sinusoidal Endothelial Cells Function as the Hepatic Barrier in Colitis-Induced Liver Injury

July 2021

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134 Reads

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16 Citations

Frontiers in Cell and Developmental Biology

Frontiers in Cell and Developmental Biology

Background Based on the gut–liver axis theory, a leaky gut can aggravate liver injury. However, clinical studies suggest that although gut mucosa damage is commonly observed in inflammatory bowel disease (IBD), it seldom leads to severe liver injury. We hypothesize that there is a hepatic barrier in the gut–liver axis, which protects the liver against gut-derived invasive factors. Methods Colitis was induced by dextran sulfate sodium (DSS) in eight different liver injury models in Sprague–Dawley rats. Liver sinusoidal endothelial cell (LSEC) injury was evaluated by a scanning and transmission electron microscope. Neutrophils were depleted by injection of anti-rat polymorphonuclear serum. Two pneumonia models were also induced to investigate the mechanism of neutrophil recruitment and activation. LSECs isolated from rat liver were used to investigate the effect on neutrophil recruitment and activation. Results Among eight liver injury models, DSS colitis had no effect on liver injury in three models with normal LSECs. In the other five models with LSEC rupture, liver injury was significantly exacerbated by colitis, and increased hepatic neutrophil accumulation was observed. When neutrophils were depleted, colitis-induced liver injury was significantly attenuated. In pneumonia, liver injury, and colitis models, the level of CXCL1 correlated with the recruitment of neutrophils in different tissues, while DSS colitis and LSEC injury synergistically contributed to increased CXCL1 expression in the liver. In colitis-induced liver injury, neutrophils were activated in the liver. Injured LSECs showed both structural and functional changes, with significantly increased expression of CXCL1 and TNF-α under the stimulation of lipopolysaccharide (LPS). The combination of gut-derived LPS and LSEC-derived TNF-α led to the activation of neutrophils, characterized by enhanced production of reactive oxygen species, pro-inflammatory cytokines, and the formation of neutrophil extracellular traps. Conclusion LSECs constitute a vitally important barrier in the gut–liver axis, defending the liver against colitis-induced injury. When LSECs are damaged, they can turn into a pro-inflammatory pattern under the stimulation of LPS. LSEC injury and colitis-derived LPS synergistically contribute to the recruitment and activation of hepatic neutrophils. Neutrophils play a pivotal role as a downstream effector in colitis-induced liver injury.


Gut‐liver‐axis: barrier function of liver sinusoidal endothelial cell (LSEC)

April 2021

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31 Reads

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20 Citations

Journal of Gastroenterology and Hepatology

Liver diseases are associated with the leaky gut via the gut‐liver‐axis. Previous studies have paid much attention to the effect of gut barrier damage. Notably, clinical observations and basic research reveal that the gut barrier damage seldom leads to liver injury independently, but aggravates pre‐existing liver diseases such as nonalcoholic fatty liver disease and drug‐induced liver injury. These evidences suggest that there is a hepatic barrier in the gut‐liver‐axis, protecting the liver against gut‐derived pathogenic factors. However, it has never been investigated which type of liver cell plays the role of hepatic barrier. Under physiological conditions, liver sinusoidal endothelial cell (LSEC) can take up and eliminate virus, bacteriophage, microbial products and metabolic wastes. LSEC also keeps the homeostasis of liver immune environment via tolerance‐inducing and anti‐inflammatory functions. In contrast, under pathological conditions, the clearance function of LSEC is impaired, and LSEC turns into a pro‐inflammatory pattern. Given its anatomical position and physiological functions, LSEC is proposed as the hepatic barrier in the gut‐liver‐axis. In this review, we aim to further understand the role of LSEC as the hepatic barrier. Future studies are warranted to seek effective treatments to improve LSEC health, which appears to be a promising approach to prevent gut‐derived liver injury.


Citations (8)


... 78 Furthermore, cholecystectomy-induced accumulation of secondary bile acids regulates chemokines like CCL2, CCL7, and CCL8 to suppress monocytes recruitment. 79 In summary, alterations in intestinal structure, microbiota, diet, and metabolic byproducts influence monocytes recruitment to the colon. Although the intestinal microbiota appears to be a dominant factor in this process, further research is needed to validate these findings. ...

Reference:

Origin and Function of Monocytes in Inflammatory Bowel Disease
Cholecystectomy-induced secondary bile acids accumulation ameliorates colitis through inhibiting monocyte/macrophage recruitment
Gut Microbes

Gut Microbes

... Furthermore, in another study, they found lower alpha-1-antitrypsin levels in both human and rat AP models. 35 Lastly, in the most recent study by Lin et al., 36 authors found increased bacterial translocation in the liver and pancreas in the FLD rat model. ...

NAFLD aggravates acute pancreatitis through bacterial translocation and cholesterol metabolic dysregulation in the liver and pancreas in mice
  • Citing Article
  • July 2022

Hepatobiliary & Pancreatic Diseases International

... Наявність шлунково-кишкових симптомів не була пов'язана зі смертністю пацієнтів з COVID-19 (OR=0,88; 95% ДІ 0,71-1,09; P=0,23). Що стосується індивідуальних симптомів, то діарея (OR=1,01; 95% ДІ 0,72-1,41; P=0,96), нудота/блювання (OR=1,16; 95% ДІ 0,78-1,71; P=0,46) і біль у животі (OR=1,55; 95% ДІ 0,68-3,54; P=0,3) також показали невідповідність зі смертю пацієнтів з COVID-19 [12]. ...

Are gastrointestinal symptoms associated with higher risk of Mortality in COVID-19 patients? A systematic review and meta-analysis

BMC Gastroenterology

... Even though we observed signi cant increases to pro-in ammatory, pro-brotic, and Cho/fatty acid transport biomarkers in the heart (+ sitagliptin), they are far outweighed by those in the liver. This could be due to the liver's increased exposure to compounds in the blood, since it primarily functions to metabolize, transport, and lter compounds that are absorbed and placed into circulation [72]. In either case of the liver or heart, sitagliptin was shown to enhance the adverse biochemical responses seen in HChol. ...

Gut‐liver‐axis: barrier function of liver sinusoidal endothelial cell (LSEC)
  • Citing Article
  • April 2021

Journal of Gastroenterology and Hepatology

... Previous studies have shown that inhibiting glycolysis, specifically in endothelial cells (ECs), can lower CXCL1 production and is closely linked to the infiltration of neutrophils in liver fibrosis (31). Therefore, further investigations are required to determine if QE can alleviate liver fibrosis by reducing CXCL1 production through inhibiting EC glycolysis and limiting neutrophil infiltration. ...

Neutrophil-Induced Liver Injury and Interactions Between Neutrophils and Liver Sinusoidal Endothelial Cells

Inflammation

... Teknologi MEMS berbasis pada teknologi silikon atau fabrikasi semikonduktor [5]. Selain silikon, alternatif substrat seperti logam, kaca / kuarsa, keramik, plastik, dan karet silikon yang lebih populer [6]. Perubahan substrat ini betujuan untuk melakukan pengembangan atau inovasi terhadap pembuatan alat -alat yang menggunaan bio sensor yang kompatibel dengan bahan substrat yang lebih terjangkau, selain itu dapat juga mempermudah dalam proses fabrikasinya. ...

A Flexible PI/Si/SiO2 Piezoresistive Microcantilever for Trace-Level Detection of Aflatoxin B1

Sensors

... Animal studies have shown that Escherichia coli exacerbates HFD-induced fibrosis in mice via its flagellin, 38 however, treatment with Escherichia coli alone does not induce liver fibrosis. 39 The underlying mechanism may involve the bacterial translocation of Escherichia coli facilitated by the disrupted gut barrier. Interestingly, the presence of specific microbiome alterations in patients with advanced liver fibrosis was further linked to distinct functional changes, specifically related to Escherichia coli-mediated L-tryptophan (Fig. 5h), which is consistent with previous reports. ...

E. coli NF73-1 Isolated From NASH Patients Aggravates NAFLD in Mice by Translocating Into the Liver and Stimulating M1 Polarization

... For titanium, the resistance change is mainly caused by dimension change; thus, the deflection sensitivity is usually lower than singlecrystal silicon as the piezoresistive material. Compared to the conference paper [33], a new microcantilever biosensor was fabricated with different wheatstone bridge configurations and photolithography masks to improve both mechanical and electrical performance. This paper expands on biosensor design, fabrication process, biochemical molecules detection, and specific detection. ...

Ultrasensitive Detection of Aflatoxin B1 Using a Polyimide-Based Piezoresistive Microcantilever Biosensor
  • Citing Conference Paper
  • June 2019