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Abstract Protein synthesis is frequently deregulated during tumorigenesis. However, the precise contexts of selective translational control and the regulators of such mechanisms in cancer is poorly understood. Here, we uncovered CNOT3, a subunit of the CCR4-NOT complex, as an essential modulator of translation in myeloid leukemia. Elevated CNOT3 expression correlates with unfavorable outcomes in patients with acute myeloid leukemia (AML). CNOT3 depletion induces differentiation and apoptosis and delayed leukemogenesis. Transcriptomic and proteomic profiling uncovers c-MYC as a critical downstream target which is translationally regulated by CNOT3. Global analysis of mRNA features demonstrates that CNOT3 selectively influences expression of target genes in a codon usage dependent manner. Furthermore, CNOT3 associates with the protein network largely consisting of ribosomal proteins and translation elongation factors in leukemia cells. Overall, our work elicits the direct requirement for translation efficiency in tumorigenesis and propose targeting the post-transcriptional circuitry via CNOT3 as a therapeutic vulnerability in AML.

Bacterial biofilm is an emerging form of life that involves cell populations living embedded in a self-produced matrix of extracellular polymeric substances (EPS). Currently, little is known about the molecular mechanisms of Bifidobacterium biofilm formation. We used the Bifidobacterium biofilm fermentation system to preparation of biofilms on wheat fibers, and multi-omics analysis of both B. pseudocatenulatum biofilms and planktonic cells were performed to identify genes and metabolites involved in biofilm formation. The average diameter of wheat fibers was around 50 μm, while the diameter of particle in wheat fibers culture of B. pseudocatenulatum was over 260 μm at 22 h with 78.96% biofilm formation rate (BR), and the field emission scanning electron microscopy (FESEM) results showed that biofilm cells on the surface of wheat fibers secreted EPS. Transcriptomic analysis indicated that genes associated with stress response (groS, mntH, nth, pdtaR, pstA, pstC, radA, rbpA, whiB, ybjG), quorum sensing (dppC, livM, luxS, sapF), polysaccharide metabolic process (rfbX, galE, zwf, opcA, glgC, glgP, gtfA) may be involved in biofilm formation. In addition, 17 weighted gene co-expression network analysis (WGCNA) modules were identified and two of them positively correlated to BR. Metabolomic analysis indicated that amino acids and amides; organic acids, alcohols and esters; and sugar (trehalose-6-phosphate, uridine diphosphategalactose, uridine diphosphate-N-acetylglucosamine) were main metabolites during biofilm formation. These results indicate that stress response, quorum sensing (QS), and EPS production are essential during B. pseudocatenulatum biofilm formation.

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.

This study investigated the effects of Lactobacillus (L.) reuteri strains on airway inflammation, immune responses, and gut microbiota in a house dust mite (HDM)-induced allergic asthma murine model. The L. reuteri strains CCFM1072 and CCFM1040, which increased Treg populations in the spleen, lowered airway inflammation as compared to other strains, downregulated histamine, IL-17A, IL-5, and IL-13 levels in the lungs, and reduced Th2-related immunoglobulin concentrations in serum. Gut microbiota and metabolism analysis revealed that CCFM1072 maintained the diversity of the gut microbiota, promoted the growth of L. reuteri and Bacteroides thetaiotaomicron, and increased propionate levels, whereas CCFM1040 remodeled the structure of the gut microbiota, particularly promoting the growth of Bifidobacterium, and these promoted bacteria were linked to the improvement of airway inflammation. In summary, the two L. reuteri strains have effects on alleviating airway inflammation partly depending on modulating gut microbiota, albeit differently, and promoting Tregs proliferation.

Faecalibacterium prausnitzii is prevalent in the human gut and is a potential candidate for next-generation probiotics (NGPs) or biotherapeutics. However, the biodiversity and physiological characteristics of Faecalibacterium prausnitzii remain unclear. This study isolated 26 novel F. prausnitzii strains from human feces using a combination of negative screening and prime-specific PCR amplification (NSPA). Based on a 16S rRNA gene analysis, F. prausnitzii strains can be classified into two main phylogroups (phylogroups I and II), which were further clustered into five subgroups (I-A, II-B, II-C, II-D, and II-E). The ultrastructure, colony morphology, growth performance, and short-chain fatty acids (SCFAs)-producing ability were found to be variable among these F. prausnitzii isolates. The optimal pH for the isolates growth ranged between 6.0 and 7.0, while most isolates were inhibited by 0.1% of bile salts. Antimicrobial resistance profiles showed that all F. prausnitzii isolates were susceptible to vancomycin, whereas >80% were kanamycin and gentamicin resistant. Additionally, all strains can utilize maltose, cellulose, and fructose but not xylose, sorbose, and 2′-FL. Overall, our work provides new insights into the biodiversity and physiological characteristics of F. prausnitzii, as well as the choices of strains suitable for NGPs.

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.