Food Funct. 2026 Mar 31. doi: 10.1039/d5fo04958j. Online ahead of print.

ABSTRACT

The probiotic yeast Saccharomyces boulardii is renowned for its clinical efficacy, which is intrinsically linked to its exceptional ability to survive the harsh gastrointestinal (GI) environment. However, a comprehensive understanding of the molecular mechanisms and regulatory pathways underlying the stress tolerance of S. boulardii remains limited. This study employed an integrated transcriptomic and proteomic approach to systematically map the dynamic responses of S. boulardii to simulated GI transit. Our analysis revealed that the intestinal phase posed a significantly greater challenge than the gastric phase, triggering extensive molecular reprogramming. A core adaptive strategy was the marked upregulation of the central carbon metabolism, particularly glycolysis, as evidenced by the concerted overexpression of key enzymes at both transcriptional and translational levels, indicating a heightened demand for energy to fuel stress defence mechanisms. Furthermore, significant enrichment was observed in the pathways related to nitrogen and fatty acid metabolism. Integration of the multi-omics datasets highlighted the complexity of the regulatory response, with frequent discordance between mRNA and protein abundance underscoring the importance of post-transcriptional regulation. This study provides a detailed molecular profile of the stress tolerance network in S. boulardii, elucidating the strategic metabolic rewiring and multi-layered regulation that underpin its probiotic resilience. The findings offer valuable insights and a foundational resource for the future development of enhanced probiotic therapies.

PMID:41914832 | DOI:10.1039/d5fo04958j

Food Funct. 2026 Mar 31. doi: 10.1039/d5fo04958j. Online ahead of print.

ABSTRACT

The probiotic yeast Saccharomyces boulardii is renowned for its clinical efficacy, which is intrinsically linked to its exceptional ability to survive the harsh gastrointestinal (GI) environment. However, a comprehensive understanding of the molecular mechanisms and regulatory pathways underlying the stress tolerance of S. boulardii remains limited. This study employed an integrated transcriptomic and proteomic approach to systematically map the dynamic responses of S. boulardii to simulated GI transit. Our analysis revealed that the intestinal phase posed a significantly greater challenge than the gastric phase, triggering extensive molecular reprogramming. A core adaptive strategy was the marked upregulation of the central carbon metabolism, particularly glycolysis, as evidenced by the concerted overexpression of key enzymes at both transcriptional and translational levels, indicating a heightened demand for energy to fuel stress defence mechanisms. Furthermore, significant enrichment was observed in the pathways related to nitrogen and fatty acid metabolism. Integration of the multi-omics datasets highlighted the complexity of the regulatory response, with frequent discordance between mRNA and protein abundance underscoring the importance of post-transcriptional regulation. This study provides a detailed molecular profile of the stress tolerance network in S. boulardii, elucidating the strategic metabolic rewiring and multi-layered regulation that underpin its probiotic resilience. The findings offer valuable insights and a foundational resource for the future development of enhanced probiotic therapies.

PMID:41914832 | DOI:10.1039/d5fo04958j

J Hepatocell Carcinoma. 2026 Mar 11;13:574690. doi: 10.2147/JHC.S574690. eCollection 2026.

ABSTRACT

BACKGROUND: Hepatocellular carcinoma (HCC) exhibits significant molecular heterogeneity and complex immune microenvironment, which to some extent limits the accuracy of prognosis assessment and the formulation of individualized treatment strategies. This study aims to identify immune-derived molecular signatures based on multi-omics data and machine learning methods for the prognosis prediction and risk stratification of HCC.

METHODS: Based on weighted gene co-expression network analysis(WGCNA) and differential gene analysis,immune-derived molecular signature (IDMS) were screened in both single-cell and bulk transcriptomes. Prognostic model was constructed by multi-machine learning approachs. Subsequently, we investigated the differences in mutations, biological functions, and immune cell infiltration within the tumor microenvironment between the high- and low-risk groups.In addition, we comprehensively analyzed the drug sensitivity of IDMS and predicted potential drugs.

RESULTS: We identified seven hub genes at the single-cell and bulk transcriptome levels. Based on multiple machine learning, we constructed a prognostic model that demonstrated excellent performance in predicting overall survival for patients with HCC. IDMS -integrated normograms provide a promising and quantitative tool for clinical risk management.Notably, a significant difference in microsatellite instability (MSI) was observed between the high- and low-risk groups. This indicates that patients in the high-risk group might have a better response to immunotherapy. Additionally, we predicted potential drugs targeting to these risk subgroups.

CONCLUSION: Our research developed an IDMS that could serve as an effective tool for patient stratification management and prognosis prediction. This signature could provide a reference for immunotherapy for patients with HCC and improve their prognosis.

PMID:41847219 | PMC:PMC12991065 | DOI:10.2147/JHC.S574690