Abstract
The identification of microorganisms in vinegar production remains a challenge mainly, due to the difficulty of isolating and characterizing the microbiota involved. This implies that despite progress in understanding the composition and role of these microbial communities, there are still virtually unknown microorganisms in these environments. Metaproteomics reveals microbial diversity and behavior by analysis of proteins, offering precise insights into key moments and conditions in biological processes like acetification. This study consists of a thorough metaproteomic analysis during the elaboration of an innovative vinegar, Verdejo wine vinegar. Acetic acid fermentation occurred by submerged culture in an 8L automated Frings reactor operated in semi-continuous mode. LC-MS/MS identified 1626 proteins from 149 taxonomic groups, mainly Acetobacteraceae (1529), followed by yeast (47), lactic acid bacteria (29), and Archaea (21). Komagataeibacter (73.7%), Acetobacter (10.7%), Gluconacetobacter (1.7%), Novacetimonas (1.6%), and Gluconobacter (1.3%) were prevalent genera, with K. europaeus being the dominant species (56.7%). To our knowledge, this is the first metaproteomic approach to comprehensively address the study of so diverse microbial populations. GO Term analysis emphasized "heterocyclic compound binding" and "organic substance metabolic process", while analysis of protein-protein interactions identified key metabolic processes, such as amino acid biosynthesis, especially BCAAs, and crucial energetic pathways such as the TCA cycle. Stress response mechanisms, such as the dnaK-dnaJ-grpE and cl2p system, have been also highlighted. These analyses revealed the molecular and functional complexity of the acetification process, highlighting the critical importance of diverse metabolic routes in the adaptation of the microbiota members to the medium conditions. Overall, this study aims to characterize the microbiota driving Verdejo wine acetification and explore its composition, functions, and key metabolic processes.
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