IL-17 neutralization attenuates PolyI:C induced acute hepatitis.

<p>(<b>A</b>) Serum ALT levels in mice neutralized of IL-17 or not at indicated times post PolyI:C stimulation. (<b>B</b>) Expression of cytokine mRNA level in liver MNC at 6 h, 18 h and 24 h post PolyI:C stimulation with or without IL-17 neutralization. αIL17: anti-IL-17. (<b>C</b>) Histological analysis of liver tissue from mice neutralized of IL-17 or not 8 h post PolyI:C stimulation. (<b>D</b>) Quantitative analysis of histological outcomes in (C): number of necrotic lesions in each microscopic field at a magnification of 200× (with 5 random microscopic fields per section). Data shown are Means ± SD (A-B,D) or representative (C) from 3 independent experiments.</p

Involvement of Kupffer cells in the induction of IL-17 by PolyI:C.

<p>(<b>A</b>) ELISA assay of IL-17 in the supernatant (SN) of liver mononuclear cells (MNC), Kupffer cells (KC), and non-adherent cells (MNC-KC). SN: supernatant. (<b>B,C</b>) Serum IL-17 levels (<b>B</b>) and ALT (<b>C</b>) in PolyI:C treated mice depleted of Kupffer cells by pretreatment by Gdcl3. (<b>D</b>) RT-PCR of Il1b and Il23p19 mRNA expression in MNC isolated ex vivo from PolyI:C treated mice. (<b>E</b>) RT-PCR assay of Il1b and Il23p19 mRNA expression in MNC stimulated in vitro by PolyI:C. (<b>F</b>) ELISA of IL-17 in the supernatant of MNC stimulated with PolyI:C. IL-23R was blockaded by anti-IL-23R mAb (10 μg/ml) in some experiments before PolyI:C simulation. Data shown are Means ± SD from 3 independent experiments. *P<0.05 ** P<0.01.</p

Pathological Role of Interleukin-17 in Poly I:C-Induced Hepatitis

<div><p>Immune-mediated responses were the main causes of liver damage during viral hepatitis, and recently viral RNA mimetic Poly I:C was used to induce a NK cell-dominated acute hepatitis. Interleukin-17A (IL-17A), the cytokine tightly associated with various autoimmune diseases, was known to play protective or pathological roles in LPS and ConA-induced hepatitis. However, its role in NK cell-mediated acute hepatitis remains unknown. Here we demonstrated that Poly I:C treatment triggered IL-17A production from hepatic γδT cells. Neutralizing IL-17A by monoclonal antibodies reduced Poly I:C-induced intrahepatic inflammatory responses and the liver injury through decreased accumulation, activation and cytolytic activity of NK cells in the liver. Furthermore, Poly I:C didn't trigger IL-17A secretion from γδT cells directly, and Kuppfer cells were demonstrated to be the accessory cell that can secrete IL-23. Finally, our findings demonstrated a pathological role of IL-17A and γδT cells in Poly I:C-induced acute hepatitis, which provides novel insights into viral infection-induced hepatitis and may serve as potential target in clinic immunotherapy against these disease.</p></div

IL-17 promotes NK cell recruitment and activation in the liver.

<p>(<b>A</b>) Serum ALT levels in NK cell depleted mice at indicated times post PolyI:C stimulation. IL-17 was neutralized or not at the time of PolyI:C injection. (<b>B,C</b>) Absolute number of NK cells (B) and CD69 positive NK cells (C) at indicated time points in the liver after PolyI:C treatment, neutralized of IL-17 or not. (<b>D</b>) Flow cytometry staining of killing associated molecules (NKG2D, IFN-γ, Gramzyme B, perforin) expressed in NK cells 8 hours post PolyI:C treatment. Plots were gated on NKp1.1+CD3- cells. (<b>E</b>) Percentage of NK cell cytotoxity isolated from mice liver 8 hours after PolyI:C stimulation against YAC-1 cells. IL-17 was neutralized with monoclonal antibodies or not during the hepatitis. Data shown are Means ± SD (A-C,E) or representative (D) from 3 independent experiments. *P<0.05 **P<0.01.</p

PolyIC induce IL17 production from liver γδT cells.

<p>(<b>A</b>) Serum IL-17 levels in male C57BL/6 mice at indicated time post PolyI:C stimulation. (<b>B</b>) Expression of Il17a mRNA in the spleen, MLN, pLN (peripheral lymph node) and liver after PolyI:C stimulation at 0 h, 6 h and 18 h. (<b>C</b>) Expression of Il17a mRNA in the mononuclear cell (MNC) isolated from the liver at indicated time after PolyI:C stimulation. (<b>D</b>) Flow cytometry intracellular staining of IL-17 in liver CD4 T cells, NKT cells and γδT cells. Plots were gated on CD4 T (CD3+CD4+) cells, NKT (NK1.1+CD3+) cells and γδT cells (γδTCR+) as indicated. The number shown was percentage of IL-17 positive cells in CD4, NKT or γδT cells. (<b>E</b>) Absolute number of IL-17 producing NKT cells, CD4 T cells and γδT cells in the liver. Data shown are representative of 3 independent experiments with similar results. Data shown are Means ± SD (A-C) or representative (D, E) from 3 independent experiments.</p

γδT cells contribute to PolyI:C induced hepatitis.

<p>(<b>A, B</b>) Serum IL-17 (A) and ALT (B) levels from PolyI:C treated mice depleted of γδT cells or not. (<b>C</b>) Analysis of NK cell activation in the liver depleted of γδT cells or not. (<b>D</b>) Serum ALT levels in PolyI:C treated mice with γδT cell depletion and then given exogenous rIL-17. Data shown are Means ± SD from 3 independent experiments.</p

Overcoming Sample Matrix Effect in Quantitative Blood Metabolomics Using Chemical Isotope Labeling Liquid Chromatography Mass Spectrometry

Blood is widely used for discovery metabolomics to search for disease biomarkers. However, blood sample matrix can have a profound effect on metabolome analysis, which can impose an undesirable restriction on the type of blood collection tubes that can be used for blood metabolomics. We investigated the effect of blood sample matrix on metabolome analysis using a high-coverage and quantitative metabolome profiling technique based on differential chemical isotope labeling (CIL) LC-MS. We used ¹²C-/¹³C-dansylation LC-MS to perform relative quantification of the amine/phenol submetabolomes of four types of samples (i.e., serum, EDTA plasma, heparin plasma, and citrate plasma) collected from healthy individuals and compare their metabolomic results. From the analysis of 80 plasma and serum samples in experimental triplicate, we detected a total of 3651 metabolites with an average of 1818 metabolites per run (<i>n</i> = 240). The number of metabolites detected and the precision and accuracy of relative quantification were found to be independent of the sample type. Within each sample type, the metabolome data set could reveal biological variation (e.g., sex separation). Although the relative concentrations of some individual metabolites might be different in the four types of samples, for sex separation, all 66 significant metabolites with larger fold-changes (FC ≥ 2 and <i>p</i> < 0.05) found in at least one sample type could be found in the other types of samples with similar or somewhat reduced, but still significant, fold-changes. Our results indicate that CIL LC-MS could overcome the sample matrix effect, thereby greatly broadening the scope of blood metabolomics; any blood samples properly collected in routine clinical settings, including those in biobanks originally used for other purposes, can potentially be used for discovery metabolomics

Additional file 1 of SGOL2 is a novel prognostic marker and fosters disease progression via a MAD2-mediated pathway in hepatocellular carcinoma

Additional file 1. Supplementary methods

Table2_Identification of effective diagnostic biomarker and immune cell infiltration characteristics in acute liver failure by integrating bioinformatics analysis and machine-learning strategies.xlsx

Background: To determine effective biomarkers for the diagnosis of acute liver failure (ALF) and explore the characteristics of the immune cell infiltration of ALF.Methods: We analyzed the differentially expressed genes (DEGs) between ALF and control samples in GSE38941, GSE62029, GSE96851, GSE120652, and merged datasets. Co-expressed DEGs (co-DEGs) identified from the five datasets were analyzed for enrichment analysis. We further constructed a PPI network of co-DEGs using the STRING database. Then, we integrated the two kinds of machine-learning strategies to identify diagnostic biomarkers of top hub genes screened based on MCC and Degree methods. And the potential diagnostic performance of the biomarkers for ALF was estimated using the AUC values. Data from GSE14668, GSE74000, and GSE96851 databases was performed as external verification sets to validate the expression level of potential diagnostic biomarkers. Furthermore, we analyzed the difference in the protein level of diagnostic biomarkers between normal and ALF mice models. Finally, we used CIBERSORT to estimate relative infiltration levels of 22 immune cell subsets in ALF samples and further analyzed the relationships between the diagnostic biomarkers and infiltrated immune cells.Results: A total of 200 co-DEGs were screened. Enrichment analyses depicted that they are highly enriched in metabolism and matrix collagen production-associated processes. The top 28 hub genes were obtained by integrating MCC and Degree methods. Then, the collagen type IV alpha 2 chain (COL4A2) was regarded as the diagnostic biomarker and showed excellent specificity and sensitivity. COL4A2 also showed a statistically significant difference and excellent diagnostic effectiveness in the verification set. In addition, there was a significant upregulation in the COL4A2 protein level in ALF mice models compared with the normal group. CIBERSORT analysis showed that activated CD4 T cells, plasma cells, macrophages, and monocytes may be implicated in the progress of ALF. In addition, COL4A2 showed different degrees of correlation with immune cells.Conclusion: In conclusion, COL4A2 may be a diagnostic biomarker for ALF, and immune cell infiltration may have important implications for the occurrence and progression of ALF.</p

Additional file 2 of SGOL2 is a novel prognostic marker and fosters disease progression via a MAD2-mediated pathway in hepatocellular carcinoma

Additional file 2: Fig. S1. High expression of SGOL2 in HCC in public database. Fig. S2. The mRNA and protein levels of SGOL1 after the knockdown of SGOL2 in HCC cell lines. Fig. S3. Subgroup expression analysis of SGOL2 in HCC. Fig. S4. Elevated expression of SGOL2 indicated a poor prognosis in HCC patients. Fig. S5. SGOL2 mutations and the associations between SGOL2 and immune cells in HCC. Fig. S6. Genes related to SGOL2 or MAD2 in HCC. Fig. S7. Hub gene analysis. Fig. S8. The predicted transcription factors binding to SGOL2 or MAD2