Background: Curcumin is a kind of plant polyphenol that is extracted from the rhizome of Curcuma longa. Studies about the effect of curcumin on the activity of ...

Early intervention in rheumatoid arthritis (RA) is critical for optimal treatment, but initiation of pharmacotherapy to prevent damage remains unsatisfactory currently. Manipulation of the gut microbiome and microbial metabolites can be effective in protecting against RA. Thus, probiotics can be utilized to explore new strategies for preventing joint damage. The aim of this study was to explore the metabolites and mechanisms by which Bifidobacterium pseudocatenulatum affects RA. Based on 16S rRNA sequencing and UPLC-MS/MS assays, we focused on bile acid (BA) metabolism. In a collagen-induced arthritis (CIA) mouse model, B. pseudocatenulatum prevented joint damage by protecting the intestinal barrier and reshaped gut microbial composition, thereby elevating bile salt hydrolase (BSH) enzyme activity and increasing the levels of unconjugated secondary BAs to suppress aberrant T-helper 1/17-type immune responses; however, these benefits were eliminated by the Takeda G protein-coupled receptor 5 (TGR5) antagonist SBI-115. The results suggested that a single bacterium, B. pseudocatenulatum, can prevent RA, indicating that prophylactic administration of probiotics may be an effective therapy.

Esophageal squamous cell carcinoma (ESCC) is one of the most common aggressive malignancies worldwide, particularly in northern China. The absence of specific early symptoms and biomarkers leads to late-stage diagnosis, while early diagnosis and risk stratification are crucial for improving overall prognosis. We performed UPLC-MS/MS on 450 ESCC patients and 588 controls consisting of a discovery group and two validation groups to identify biomarkers for early detection and prognosis. Bioinformatics and clinical statistical methods were used for profiling metabolites and evaluating potential biomarkers. A total of 105 differential metabolites were identified as reliable biomarker candidates for ESCC with the same tendency in three cohorts, mainly including amino acids and fatty acyls. A predictive model of 15 metabolites [all-trans-13,14-dihydroretinol, (±)-myristylcarnitine, (2S,3S)-3-methylphenylalanine, 3-(pyrazol-1-yl)-L-alanine, carnitine C10:1, carnitine C10:1 isomer1, carnitine C14-OH, carnitine C16:2-OH, carnitine C9:1, formononetin, hyodeoxycholic acid, indole-3-carboxylic acid, PysoPE 20:3, PysoPE 20:3(2n isomer1), and resolvin E1] was developed by logistic regression after LASSO and random forest analysis. This model held high predictive accuracies on distinguishing ESCC from controls in the discovery and validation groups (accuracies > 89%). In addition, the levels of four downregulated metabolites [hyodeoxycholic acid, (2S,3S)-3-methylphenylalanine, carnitine C9:1, and indole-3-carboxylic acid] were significantly higher in early cancer than advanced cancer. Furthermore, three independent prognostic markers were identified by multivariate Cox regression analyses with and without clinical indicators: a high level of MG(20:4)isomer and low levels of 9,12-octadecadienoic acid and L-isoleucine correlated with an unfavorable prognosis; the risk score based on these three metabolites was able to stratify patients into low or high risk. Moreover, pathway analysis indicated that retinol metabolism and linoleic acid metabolism were prominent perturbed pathways in ESCC. In conclusion, metabolic profiling revealed that perturbed amino acids and lipid metabolism were crucial metabolic signatures of ESCC. Both panels of diagnostic and prognostic markers showed excellent predictive performances. Targeting retinol and linoleic acid metabolism pathways may be new promising mechanism-based therapeutic approaches. Thus, this study would provide novel insights for the early detection and risk stratification for the clinical management of ESCC and potentially improve the outcomes of ESCC.