Longitudinal gut fungal alterations and potential fungal biomarkers for the progression of primary liver disease

Liver disease,a major health concern worldwide,is a serious and progressive disorder.Herein,we not only established a mouse model of DEN+CCl4-induced primary liver disease but also collected clinical human samples to investigate longitudinal alterations in the gut mycobiome.As liver disease advanced,gut integrity was disrupted,and the mycobiota was disturbed in the mouse models.The metabolites associated with hepatocellular carcinoma(HCC)differed from those associated with the cirrhotic phase as follows:levels of stercobilin and aflatoxin B1 dialcohol were reduced,while levels of triterpenoids,bafilomycin A1,and DHEA were increased in the HCC group.The abundance of the phylum Chytridiomycota increased as the chronic liver disease progressed and was then replaced by the phylum Ascomycota in HCC.Based on the results from clinical human samples,the genus Candida(Ascomycota)(in humans)and the genus Kazachstania(Ascomycota)(in mice)occupied a dominant position in the HCC group,while other fungi were depleted.The increased abundance of C.albicans and depletion of S.cerevisiae may be hallmarks of the progression of liver cirrhosis to early HCC.Moreover,the administration of C.albicans and S.cerevisiae in the LC-HCC progression could accelerate or retard the progression of HCC.Therefore,gut fungi have the potential to serve as a noninvasive clinical biomarker and even a treatment method.

Integrated analysis of human influenza A(H1N1)virus infectionrelated genes to construct a suitable diagnostic model

The genome characteristics and structural functions of coding proteins correlate with the genetic diversity of the H1N1 virus,which aids in the understanding of its underlying pathogenic mechanism.In this study,analyses of the characteristic of the H1N1 virus infection-related genes,their biological functions,and infection-related reversal drugs were performed.Additionally,we used multi-dimensional bioinformatics analysis to identify the key genes and then used these to construct a diagnostic model for the H1N1 virus infection.There was a total of 169 differently expressed genes in the samples between 21 h before infection and 77 h after infection.They were used during the protein-protein interaction(PPI)analysis,and we obtained a total of 1725 interacting genes.Then,we performed a weighted gene co-expression network analysis(WGCNA)on these genes,and we identified three modules that showed significant potential for the diagnosis of the H1N1 virus infection.These modules contained 60 genes,and they were used to construct this diagnostic model,which showed an effective prediction value.Besides,these 60 genes were involved in the biological functions of this infectious virus,like the cellular response to type I interferon and in the negative regulation of the viral life cycle.However,20 genes showed an upregulated expression as the infection progressed.Other 36 upregulated genes were used to examine the relationship between genes,human influenza A virus,and infection-related reversal drugs.This study revealed numerous important reversal drug molecules on the H1N1 virus.They included rimantadine,interferons,and shikimic acid.Our study provided a novel method to analyze the characteristic of different genes and explore their corresponding biological function during the infection caused by the H1N1 virus.This diagnostic model,which comprises 60 genes,shows that a significant predictive value can be the potential biomarker for the diagnosis of the H1N1 virus infection.