Hepatocellular carcinoma (HCC), a primary liver cancer, is closely associated with the gut microbiota. However, the role of gut fungi in the development of HCC remains unclear. The aim of this study was to explore the influence of intestinalCandida albicanson HCC. Here, We found that patients with HCC showed significantly decreased diversity of the gut mycobiome and increased abundance ofC. albicans, compared to the patients with liver cirrhosis. The gavage ofC. albicansin the WT models increased the tumor size and weight and influenced the plasma metabolome, which was indicated by alterations in 117 metabolites, such as L-carnitine and L-acetylcarnitine, and several KEGG enriched pathways, such as phenylalanine metabolism and citrate cycle. Moreover, the expression of nucleotide oligomerization domain-like receptor family pyrin domain containing 6 (NLRP6) in the intestinal tissues and primary intestinal epithelial cells of the WT mice interacted withC. albicansincreased. Notably, the colonization ofC. albicanshad no effect on tumor growth inNlrp6^–/–mice. In conclusion, the abnormal colonization ofC. albicansreprogrammed HCC metabolism and contributed to the progression of HCC dependent on NLRP6, which provided new targets for the treatment of HCC.

Background: The gut mycobiome of patients with lung adenocarcinoma (LUAD) remains unexplored. This study aimed to characterize the gut mycobiome in patients with LUAD and evaluate the potential of gut fungi as non-invasive biomarkers for early diagnosis. Methods: In total, 299 fecal samples from Beijing, Suzhou, and Hainan were collected prospectively. Using internal transcribed spacer 2 sequencing, we profiled the gut mycobiome. Five supervised machine learning algorithms were trained on fungal signatures to build an optimized prediction model for LUAD in a discovery cohort comprising 105 patients with LUAD and 61 healthy controls (HCs) from Beijing. Validation cohorts from Beijing, Suzhou, and Hainan comprising 44, 17, and 15 patients with LUAD and 26, 19, and 12 HCs, respectively, were used to evaluate efficacy. Results: Fungal biodiversity and richness increased in patients with LUAD. At the phylum level, the abundance of Ascomycota decreased, while that of Basidiomycota increased in patients with LUAD. Candida and Saccharomyces were the dominant genera, with a reduction in Candida and an increase in Saccharomyces, Aspergillus, and Apiotrichum in patients with LUAD. Nineteen operational taxonomic unit markers were selected, and excellent performance in predicting LUAD was achieved (area under the curve (AUC) = 0.9350) using a random forest model with outcomes superior to those of four other algorithms. The AUCs of the Beijing, Suzhou, and Hainan validation cohorts were 0.9538, 0.9628, and 0.8833, respectively. Conclusions: For the first time, the gut fungal profiles of patients with LUAD were shown to represent potential non-invasive biomarkers for early-stage diagnosis.

This study aimed to identify key genes related to copper metabolism in Parkinson's disease (PD), providing insight into their roles in disease progression. Using bioinformatic analyses, the study identified hub genes related to copper metabolism in PD patients. Differentially expressed genes (DEGs) were identified using the limma package, and copper-metabolism-related genes (CMRGs) were sourced from the Genecard database. Immune cell-related genes were derived through immune infiltration and Weighted Gene Co-expression Network Analysis (WGCNA). Hub genes were pinpointed by integrating DEGs, CMRGs, and immune cell-related genes. Functional analyses included Receiver Operating Characteristic (ROC) analysis, Ingenuity Pathway Analysis (IPA), and networks for miRNA-mRNA-transcription factor (TF), Competitive Endogenous RNA (ceRNA), and hub gene-drug interactions. Validation was performed in cerebrospinal fluid (CSF) samples from PD patients, while in vitro experiments utilized GBE1- overexpressing SH-SY5Y cells to examine cell proliferation, migration, and viability. Nine hub genes (HPRT1, GLS, SNCA, MDH1, GBE1, DDC, STXBP1, ACHE, and AGTR1) were identified from 753 CMRGs, 416 DEGs, and 951 immune cell-related genes. ROC analysis showed high predictive accuracy for PD, and principal component analysis (PCA) effectively distinguished PD patients from controls. IPA identified 20 significant pathways, and various networks highlighted miRNA, TF, and drug interactions with the hub genes. Hub gene expression was validated in PD CSF samples. GBE1-overexpressing cells displayed enhanced proliferation, migration, and viability. The study identified nine copper metabolism-related genes as potential therapeutic targets in PD, highlighting their relevance in PD pathology and possible treatment pathways.

The internalization of Aspergillus fumigatus into alveolar epithelial cells (AECs) is tightly controlled by host cellular actin dynamics, which require close modulation of the ADF (actin depolymerizing factor)/cofilin family. However, the role of cofilin in A. fumigatus internalization into AECs remains unclear. Here, we demonstrated that germinated A. fumigatus conidia were able to induce phosphorylation of cofilin in A549 cells during the early stage of internalization. The modulation of cofilin activity by overexpression, knockdown, or mutation of the cofilin gene in A549 cells decreased the efficacy of A. fumigatus internalization. Reducing the phosphorylation status of cofilin with BMS-5 (LIM kinase inhibitor) or overexpression of the slingshot phosphatases also impeded A. fumigatus internalization. Both the C. botulimun C3 transferase (a specific RhoA inhibitor) and Y27632 (a specific ROCK inhibitor) reduced the internalization of A. fumigatus and the level of phosphorylated cofilin. β-1,3-glucan (the major component of the conidial cell wall) and its host cell receptor dectin-1 did not seem to be associated with cofilin phosphorylation during A. fumigatus infection. These results indicated that cofilin might be involved in the modulation of A. fumigatus internalization into type II alveolar epithelial cells through the RhoA-ROCK-LIM kinase pathway.