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Autoimmune hepatitis is an interface hepatitis characterized by the progressive destruction of the liver parenchyma, the cause of which is still obscure. Interleukin (IL)-17A is a major driver of autoimmunity, which can be produced by innate immune cells against several intracellular pathogens. Here, we investigated the involvement of IL-17A in a mice model of immune-mediated hepatitis with the intestine exposed to Salmonella typhimurium. Our results showed more severe Concanavalin (Con) A-induced liver injury and gut microbiome dysbiosis when the mice were treated with a gavage of S. typhimurium. Then, the natural killer (NK) T cells were overactivated by the accumulated IL-17A in the liver in the Con A and S. typhimurium administration group. IL-17A could activate NKT cells by inducing CD178 expression via IL-4/STAT6 signaling. Furthermore, via the portal tract, the laminae propria mucosal-associated invariant T (MAIT)-cell-derived IL-17A could be the original driver of NKT cell overactivation in intragastric administration of S. typhimurium and Con A injection. In IL-17A-deficient mice, Con A-induced liver injury and NKT cell activation were alleviated. However, when AAV-sh-mIL-17a was used to specifically knock down IL-17A in liver, it seemed that hepatic IL-17a knock down did not significantly influence the liver injury. Our results suggested that, under Con A-induction, laminae propria MAIT-derived IL-17A activated hepatic NKT, and this axis could be a therapeutic target in autoimmune liver disease.

Background: Long non-coding RNAs (lncRNAs) are emerging as important regulators in the modulation of virus infection by targeting mRNA transcription. However, their roles in chronic hepatitis B (CHB) remain to be elucidated. Objective: The study aimed to explore the lncRNAs and mRNA expression profiles in CHB and asymptomatic HBsAg carriers (ASC) and construct mRNA-lncRNA co-expression profile and ceRNA networks to identify the potential targets of diagnosis and treatment in CHB. Methods: We determined the expression profiles of lncRNAs and mRNAs in CHB and ASC using microarray analysis. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed to explore their function. We also constructed coexpression, cis-regulatory, and competing endogenous RNA (ceRNA) networks with bioinformatics methods. Results: We identified 1634 mRNAs and 5550 lncRNAs that were differentially expressed between CHB and ASC. Significantly enriched GO terms and pathways were identified, many of which were linked to immune processes and inflammatory responses. Co-expression analysis showed 1196 relationships between the top 20 up/downregulated lncRNAs and mRNA, especially 213 lncRNAs interacted with ZFP57. The ZFP57-specific ceRNA network covered 3 lncRNAs, 5 miRNAs, and 17 edges. Cis-correlation analysis showed that lncRNA T039096 was paired with the most differentially expressed gene, ZFP57. Moreover, by expending the clinical samples size, the qRT-PCR results showed that the expression of ZFP57 and T039096 increased in CHB compared to ASC. Conclusion: Our study provides insights into the roles of mRNA and lncRNA networks in CHB, highlighting potential applications of lncRNA-T039096 and mRNA-ZFP57 for diagnosis and treatment.

Background Renal cell carcinoma (RCC) is the most common kidney cancer and includes several molecular and histological subtypes with different clinical characteristics. The combination of DNA methylation and gene expression data can improve the classification of tumor heterogeneity, by incorporating differences at the epigenetic level and clinical features. Methods In this study, we identified the prognostic methylation and constructed specific prognosis-subgroups based on the DNA methylation spectrum of RCC from the TCGA database. Results Significant differences in DNA methylation profiles among the seven subgroups were revealed by consistent clustering using 3389 CpGs that indicated that were significant differences in prognosis. The specific DNA methylation patterns reflected differentially in the clinical index, including TNM classification, pathological grade, clinical stage, and age. In addition, 437 CpGs corresponding to 477 genes of 151 samples were identified as specific hyper/hypomethylation sites for each specific subgroup. A total of 277 and 212 genes corresponding to DNA methylation at promoter sites were enriched in transcription factor of GKLF and RREB-1, respectively. Finally, Bayesian network classifier with specific methylation sites was constructed and was used to verify the test set of prognoses into DNA methylation subgroups, which was found to be consistent with the classification results of the train set. DNA methylation-based classification can be used to identify the distinct subtypes of renal cell carcinoma. Conclusions This study shows that DNA methylation-based classification is highly relevant for future diagnosis and treatment of renal cell carcinoma as it identifies the prognostic value of each epigenetic subtype. Electronic supplementary material The online version of this article (10.1186/s12935-019-0900-4) contains supplementary material, which is available to authorized users.