Evidence linking Faecalibacterium prausnitzii abundance to nonalcoholic fatty liver disease (NAFLD) is accumulating; however, the causal relationship remains obscure. In this study, 12 F. prausnitzii strains were orally administered to high fat diet fed C57BL/6J mice for 12 weeks to evaluate the protective effects of F. prausnitzii on NAFLD. We found that five F. prausnitzii strains, A2-165, LB8, ZF21, PL45, and LC49, significantly restored serum lipid profiles and ameliorated glucose intolerance, adipose tissue dysfunction, hepatic steatosis, inflammation, and oxidative stress in a mouse model of NAFLD. Moreover, two strains, LC49 and LB8, significantly enhanced short-chain fatty acid (SCFA) production and modulated the gut microbiota. Based on the combined analysis of linear discriminant analysis effect size and microbial communities, the core microbiome related to NAFLD comprised Odoribacter, Roseburia, Erysipelatoclostridium, Tyzzerella, Faecalibaculum, Blautia, and Acetatifactor, and the last five genera can be reversed by treatment with the LC49 and LB8 strains. Additionally, the LC49 and LB8 strains enriched Lactobacillus, Ileibacterium, Faecalibacterium, Dubosiella, and Bifidobacterium and downregulated pathways involving carbohydrate metabolism, amino acid metabolism, and fatty acid biosynthesis. Interestingly, LC49 supplementation also upregulated tryptophan metabolism, glutathione metabolism, and valine, leucine, and isoleucine degradation, which might be related to NAFLD prevention. Collectively, F. prausnitzii LC49 and LB8 exerted considerable anti-NAFLD and microbiota-regulating effects, indicating their potential as probiotic agents for NAFLD treatment.

Bifidobacterium bifidum strains, an important component of probiotic foods, can form biofilms on abiotic surfaces, leading to increased self-resistance. However, little is known about the molecular mechanism of B. bifidum biofilm formation. A time series transcriptome sequencing and untargeted metabolomics analysis of both B. bifidum biofilm and planktonic cells was performed to identify key genes and metabolites involved in biofilm formation. Two hundred thirty-five nonredundant differentially expressed genes (DEGs) (including vanY, pstS, degP, groS, infC, groL, yajC, tadB and sigA) and 219 nonredundant differentially expressed metabolites (including L-threonine, L-cystine, L-tyrosine, ascorbic acid, niacinamide, butyric acid and sphinganine) were identified. Thirteen pathways were identified during the integration of both transcriptomics and metabolomics data, including ABC transporters; quorum sensing; two-component system; oxidative phosphorylation; cysteine and methionine metabolism; glutathione metabolism; glycine, serine and threonine metabolism; and valine, leucine and isoleucine biosynthesis. The DEGs that relate to the integration pathways included asd, atpB, degP, folC, ilvE, metC, pheA, pstS, pyrE, serB, ulaE, yajC and zwf. The differentially accumulated metabolites included L-cystine, L-serine, L-threonine, L-tyrosine, methylmalonate, monodehydroascorbate, nicotinamide, orthophosphate, spermine and tocopherol. These results indicate that quorum sensing, two-component system and amino acid metabolism are essential during B. bifidum biofilm formation.

Biofilm formation has evolved as an adaptive strategy for bacteria to cope with harsh environmental conditions. Currently, little is known about the molecular mechanisms of biofilm formation in bifidobacteria. A time series transcriptome sequencing analysis of both biofilm and planktonic cells of Bifidobacterium longum FGSZY16M3 was performed to identify candidate genes involved in biofilm formation. Protein–protein interaction network analysis of 1296 differentially expressed genes during biofilm formation yielded 15 clusters of highly interconnected nodes, indicating that genes related to the SOS response (dnaK, groS, guaB, ruvA, recA, radA, recN, recF, pstA, and sufD) associated with the early stage of biofilm formation. Genes involved in extracellular polymeric substances were upregulated (epsH, epsK, efp, frr, pheT, rfbA, rfbJ, rfbP, rpmF, secY and yidC) in the stage of biofilm maturation. To further investigate the genes related to biofilm formation, weighted gene co-expression network analysis (WGCNA) was performed with 2032 transcript genes, leading to the identification of nine WGCNA modules and 133 genes associated with response to stress, regulation of gene expression, quorum sensing, and two-component system. These results indicate that biofilm formation in B. longum is a multifactorial process, involving stress response, structural development, and regulatory processes.

The export trade of film industry has become a crucial part of international trade competition nowadays. This paper first discusses the problems of Chinese film industry and film export trade on this basis, and finds that the export trade of Chinese film is facing great difficulties in the scale and operation of the industry, the creativity and production of the film content, and the management and support of the government policy, so as to make a positioning for Chinese film trade in the Context of global value chain. Finally, based on the four aspects of film content, operation mode, cultural foundation and policy environment, this paper puts forward the way to promote the rapid development of Chinese film industry and export trade.

Currently, no generalizable tools exist for predicting the impact of interventions on aging using gene expression changes. Here, we build a model for predicting potential geroprotectors by deconstructing the aging transcriptome into coexpression modules and identifying those modulated by life span–extending interventions in Caenorhabditis elegans ( C. elegans ). Many of these modules exhibited adaptive changes during aging that are linked to longevity. Using these longevity-associated modules, we predicted and validated 10 compounds (7 previously unreported) that significantly extended life span in C. elegans . RNA sequencing analysis of two top candidates also showed targeted modulation of our longevity-associated modules. We identified conservation of several modules in mice that were also correlated with frailty. Together, our findings demonstrate that beneficial transcriptomic adaptations occur during aging and that targeting longevity-associated modules offers a framework for geroprotector discovery.