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Sifan Zhang

Publications and source records attributed to Sifan Zhang.

2 recordsLinked to original sources

Isolation, genomic characterization, and safety assessment of an O-desmethylangolensin-producing Clostridium beijerinckii strain from Chinese Stinky Tofu.

The health benefits of dietary soy isoflavones are largely mediated by specific microbial metabolites, such as O-desmethylangolensin (O-DMA). However, the diversity and application potential of O-DMA-producing strains remain poorly explored, primarily due to the limited availability of isolated strains, narrow ecological sources, and a lack of practical applications. In this study, an O-DMA-producing bacterium, designated strain FRJF5, was isolated from Chinese stinky tofu under anaerobic conditions and was identified as Clostridium beijerinckii. The biosynthesized O-DMA exhibited an enantiomeric excess (e.e.) of 78.6%. Based on phylogenetic and average nucleotide identity analyses against 235 public C. beijerinckii genomes, the clustering of FRJF5 with strains from diverse habitats-including industrial fermentation settings, animal feces, and soil-highlights the broad ecological diversity within this species. Functional gene mining and intra-species comparative genomics revealed a unique flavonoid metabolism gene cluster in FRJF5. Using apigenin as a representative flavonoid, we confirmed the successful conversion to 3-(4-hydroxyphenyl)-propionic acid. Moreover, the strain was predicted and verified to possess a substantial butyrate-producing capacity. Genomic screening for virulence or antibiotic resistance genes, combined with phenotypic tests (hemolysis, antibiotic susceptibility, and mouse gavage), revealed a favorable safety profile for strain FRJF5. Finally, intervention experiments in a mouse model of colitis supported its potential in alleviating the disease. Collectively, this study identifies C. beijerinckii FRJF5 as a strain capable of simultaneously producing O-DMA and butyrate, highlighting its potential for future applications in functional foods.IMPORTANCESoy isoflavones require gut bacterial conversion into bioactive metabolites-such as the anti-inflammatory compound O-desmethylangolensin (O-DMA)-to exert health benefits. Yet O-DMA-producing strains remain scarce, largely confined to fecal sources, and poorly characterized. Here, we isolated Clostridium beijerinckii FRJF5 from Chinese stinky tofu, an unexplored ecological niche. This strain not only produces enantiomerically enriched O-DMA but also co-produces butyrate, a metabolite known to strengthen gut barrier function. Genomic mining uncovered a unique flavonoid metabolism gene cluster responsible for this dual activity. Combined with favorable safety profiles, FRJF5 emerges as a strong candidate for functional food applications. This work expands the known diversity of O-DMA producers and bridges traditional fermented foods with next-generation probiotic development.

O-desmethylangolensin

Transcriptome-wide analysis reveals potential roles of CFD and ANGPTL4 in fibroblasts regulating B cell lineage for extracellular matrix-driven clustering and novel avenues for immunotherapy in breast cancer.

BACKGROUND: The remodeling of the extracellular matrix (ECM) plays a pivotal role in tumor progression and drug resistance. However, the compositional patterns of ECM in breast cancer and their underlying biological functions remain elusive. METHODS: Transcriptome and genome data of breast cancer patients from TCGA database was downloaded. Patients were classified into different clusters by using non-negative matrix factorization (NMF) based on signatures of ECM components and regulators. Weighted Gene Co-expression Network Analysis (WGCNA) was used to identify core genes related to ECM clusters. Additional 10 independent public cohorts including Metabric, SCAN_B, GSE12276, GSE16446, GSE19615, GSE20685, GSE21653, GSE58644, GSE58812, and GSE88770 were collected to construct Training or Testing cohort, following machine learning calculating ECM correlated index (ECI) for survival analysis. Pathway enrichment and correlation analysis were used to explore the relationship among ECM clusters, ECI and TME. Single-cell transcriptome data from GSE161529 was processed for uncovering the differences among ECM clusters. RESULTS: Using NMF, we identified three ECM clusters in the TCGA database: C1 (Neuron), C2 (ECM), and C3 (Immune). Subsequently, WGCNA was employed to pinpoint cluster-specific genes and develop a prognostic model. This model demonstrated robust predictive power for breast cancer patient survival in both the Training cohort (n = 5,392, AUC = 0.861) and the Testing cohort (n = 1,344, AUC = 0.711). Upon analyzing the tumor microenvironment (TME), we discovered that fibroblasts and B cell lineage were the core cell types associated with the ECM cluster phenotypes. Single-cell RNA sequencing data further revealed that angiopoietin like 4 (ANGPTL4)+ fibroblasts were specifically linked to the C2 phenotype, while complement factor D (CFD)+ fibroblasts characterized the other ECM clusters. CellChat analysis indicated that ANGPTL4+ and CFD+ fibroblasts regulate B cell lineage via distinct signaling pathways. Additionally, analysis using the Kaplan-Meier Plotter website showed that CFD was favorable for immunotherapy response, whereas ANGPTL4 negatively impacted the outcomes of cancer patients receiving immunotherapy. CONCLUSION: We identified distinct ECM clusters in breast cancer patients, irrespective of molecular subtypes. Additionally, we constructed an effective prognostic model based on these ECM clusters and recognized ANGPTL4+ and CFD+ fibroblasts as potential biomarkers for immunotherapy in breast cancer.

Humans