Sea cucumber sulfated polysaccharides and Lactobacillus gasseri synergistically ameliorate the overweight induced by altered gut microbiota in mice.

Sulfated polysaccharides from sea cucumber Stichopus japonicus (SCSPsj) have been found to modulate the gut microbiota by promoting the growth of probiotics. However, the effects of the combination of SCSPsj and probiotics are still less known. Thus, the present study aimed to investigate the effects of SCSPsj and Lactobacillus gasseri on gut microbiota-altered mice through gut microbiota and metabolomics analysis. In the present study, supplementation with SCSPsj, L. gasseri or the combination of SCSPsj and L. gasseri could effectively ameliorate the body weight gain and fat accumulation in gut microbiota-altered mice treated with low-dose penicillin. The better effect of the combination of SCSPsj and L. gasseri is attributed to the synergistic effect of SCSPsj and L. gasseri. 16S rRNA sequencing revealed that the combination of SCSPsj and L. gasseri can synergistically improve gut microbiota dysbiosis by increasing Lactobacillus and reducing Coriobacteriaceae_UCG-002. Furthermore, metabolomics results revealed that the combination of SCSPsj and L. gasseri can alleviate metabolic disorders by reducing the levels of lipid and lipid-like molecules in the serum samples, such as trans-vaccenic acid and 3ß-hydroxy-5-cholestene. Our findings have proved that the combination of SCSPsj and L. gasseri can benefit host health attributed to the synergistic effect, which is conducive to further application in functional food.

Responses of the gut microbiota and metabolite profiles to sulfated polysaccharides from sea cucumber in humanized microbiota mice.

Sea cucumber Stichopus japonicus has been consumed as functional food traditionally in Asia, and its sulfated polysaccharide (SCSPsj) demonstrates health-promoting effects in rodents which are related to the regulation of the gut microbiota. However, little is known about the response of the human gut microbiota to SCSPsj. Therefore, the present study aimed to study the response of the donor microbiota to SCSPsj in vivo through a humanized microbiota mice model, which was constructed by antibiotic treatment combined with fecal microbiota transplant. The results revealed that the SCSPsj supplement could positively interact with the specific donor microbiota. It could significantly regulate the gut microbiota community, especially the abundance of Lactobacillus. In addition, SCSPsj could modulate the metabolites in serum and cecal contents of mice, including short-chain fatty acids (SCFAs) and lactic acid, and the changes of some bioactive metabolites were associated with the gut microbiota enriched by SCSPsj. Furthermore, in vitro experiments demonstrated that the Lactobacillus strains isolated could not be proliferated directly by SCSPsj, but SCSPsj significantly promoted biofilm formation and mucus binding of Lactobacillus spp., which contributed to the enrichment of Lactobacillus in vivo. The present study could provide insight into the application of SCSPsj as microbiota-directed food.

Development and assessment of machine learning models for predicting recurrence risk after endovascular treatment in patients with intracranial aneurysms.

Intracranial aneurysms (IAs) remain a major public health concern and endovascular treatment (EVT) has become a major tool for managing IAs. However, the recurrence rate of IAs after EVT is relatively high, which may lead to the risk for aneurysm re-rupture and re-bleed. Thus, we aimed to develop and assess prediction models based on machine learning (ML) algorithms to predict recurrence risk among patients with IAs after EVT in 6 months. Patient population included patients with IAs after EVT between January 2016 and August 2019 in Hunan Provincial People's Hospital, and an adaptive synthetic (ADASYN) sampling approach was applied for the entire imbalanced dataset. We developed five ML models and assessed the models. In addition, we used SHapley Additive exPlanations (SHAP) and local interpretable model-agnostic explanation (LIME) algorithms to determine the importance of the selected features and interpret the ML models. A total of 425 IAs were enrolled into this study, and 66 (15.5%) of which recurred in 6 months. Among the five ML models, gradient boosting decision tree (GBDT) model performed best. The area under curve (AUC) of the GBDT model on the testing set was 0.842 (sensitivity: 81.2%; specificity: 70.4%). Our study firstly demonstrated that ML-based models can serve as a reliable tool for predicting recurrence risk in patients with IAs after EVT in 6 months, and the GBDT model showed the optimal prediction performance.

Functional integration of multiple genes into the genome of the oleaginous yeast Rhodosporidium toruloides.

The basidiomycetous yeast Rhodosporidium toruloides represents an excellent producer for microbial lipids and carotenoids. However, further rational engineering of this unconventional yeast remains challenging partially because of the absence of efficient and reliable transformation method. In this study, we developed an Agrobacterium-mediated transformation (ATMT) protocol for effective gene integration into the R. toruloides genome. Both haploid and diploid strains were successfully modified, and the integration was confirmed by colony PCR, Western blot analysis and genome walking. We further demonstrated that multiple genes could be integrated by consecutive ATMT, leading to engineered strains simultaneously resistant to multiple antibiotics. Our results provided a practical method for functional integration and expression of exogenous genes in R. toruloides, which should facilitate the development of genetic tools and the construction of superior strains to produce biofuel molecules and biochemicals.