Analysis of the Formation of Characteristic Aroma Compounds by Amino Acid Metabolic Pathways during Fermentation with Saccharomyces cerevisiae

Dynamic changes of microbiota and metabolite of traditional Hainan dregs vinegar during fermentation based on metagenomics and metabolomics

Hainan dregs vinegar (HNDV) is a traditional fermented food in China that is renowned for its unique flavor. HNDV is one of the most popular vinegars in Southeast Asia. However, research on the microorganisms and characteristic metabolites specific to HNDV is lacking. This study investigated the changes in microbial succession, volatile flavor compounds and characteristic non-volatile flavor compounds during HNDV fermentation based on metagenomics and metabolomics. The predominant microbial genera were Lactococcus, Limosilactobacillus, Lactiplantibacillus, and Saccharomyces. Unlike traditional vinegar, l-lactic acid was identified as the primary organic acid in HNDV. Noteworthy flavor compounds specific to HNDV included 3-methylthiopropanol and dl-phenylalanine. Significant associations were observed between six predominant microorganisms and six characteristic volatile flavor compounds, as well as seven characteristic non-volatile flavor compounds. The present results contribute to the development of starter cultures and the enhancement of HNDV quality.

An integrative multi-omic analysis defines gut microbiota, mycobiota, and metabolic fingerprints in ulcerative colitis patients

Background

Ulcerative colitis (UC) is a multifactorial chronic inflammatory bowel disease (IBD) that affects the large intestine with superficial mucosal inflammation. A dysbiotic gut microbial profile has been associated with UC. Our study aimed to characterize the UC gut bacterial, fungal, and metabolic fingerprints by omic approaches.

Methods

The 16S rRNA- and ITS2-based metataxonomics and gas chromatography-mass spectrometry/solid phase microextraction (GC-MS/SPME) metabolomic analysis were performed on stool samples of 53 UC patients and 37 healthy subjects (CTRL). Univariate and multivariate approaches were applied to separated and integrated omic data, to define microbiota, mycobiota, and metabolic signatures in UC. The interaction between gut bacteria and fungi was investigated by network analysis.

Results

In the UC cohort, we reported the increase of Streptococcus, Bifidobacterium, Enterobacteriaceae, TM7-3, Granulicatella, Peptostreptococcus, Lactobacillus, Veillonella, Enterococcus, Peptoniphilus, Gemellaceae, and phenylethyl alcohol; and we also reported the decrease of Akkermansia; Ruminococcaceae; Ruminococcus; Gemmiger; Methanobrevibacter; Oscillospira; Coprococus; Christensenellaceae; Clavispora; Vishniacozyma; Quambalaria; hexadecane; cyclopentadecane; 5-hepten-2-ol, 6 methyl; 3-carene; caryophyllene; p-Cresol; 2-butenal; indole, 3-methyl-; 6-methyl-3,5-heptadiene-2-one; 5-octadecene; and 5-hepten-2-one, 6 methyl. The integration of the multi-omic data confirmed the presence of a distinctive bacterial, fungal, and metabolic fingerprint in UC gut microbiota. Moreover, the network analysis highlighted bacterial and fungal synergistic and/or divergent interkingdom interactions.

Conclusion

In this study, we identified intestinal bacterial, fungal, and metabolic UC-associated biomarkers. Furthermore, evidence on the relationships between bacterial and fungal ecosystems provides a comprehensive perspective on intestinal dysbiosis and ecological interactions between microorganisms in the framework of UC.