Fermentation dynamics of Lactobacillus helveticus H9 revealed by ultra‐performance liquid chromatography quadrupole time‐of‐flight mass spectrometry

Abstract

Summary Lactobacillus (L.) helveticus H9 is a probiotic strain that can produce antihypertensive peptides during milk fermentation. This study analysed the dynamics of skim milk fermentation by L. helveticus H9 by ultra‐performance liquid chromatography coupled to quadrupole time‐of‐flight mass spectrometry (UPLC/Q‐TOF MS). A total of 1992 metabolites were detected from all of the fermented samples in the LC‐MS analysis by multivariate statistical analysis. Metabolites with variable importance in projection (VIP) values ≥2 were considered differentially abundant among samples and were responsible for the unique taste and nutritional and functional qualities of fermented milk. Valine, threonine, l‐methionine, tyrosine, asparagine and leucine were the predominant amino acids produced during fermentation, and their quantities changed remarkably during the fermentation process. Citric acid and uric acid were the major, and only detectable, organic acids. Some intermediate metabolites, such as N‐acryloylglycine and nicotinamide‐N‐oxide, were also detected. Moreover, certain oligopeptides such as Val‐Leu, Lys‐Gly, Ala‐Glu, Asp‐Ser, Leu‐Pro and Val‐Phe‐Ala were not detected until the middle and late fermentation periods. This study demonstrated dynamic metabolic changes, providing a strong foundation for deciphering the physiological and biochemical mechanisms of the fermentation process.

Metabolic footprinting revealed key biochemical changes in a brown fermented milk product using Streptococcus thermophilus

“…For each sample, 1 mL of supernatant was pipetted into a fresh centrifuge tube and mixed vigorously with 7 mL of acetonitrile for 2 min. All samples were then centrifuged at 12,000 × g for 10 min at 4°C to remove macromolecular aggregates like proteins (Mi et al, 2018). The supernatants were collected and concentrated by rotary evaporation for 10 h before reconstituting them in 500 μL of acetonitrile solution (40%).…”

Chromatography hyphenated to high resolution mass spectrometry in untargeted metabolomics for investigation of food (bio)markers

“…PCA has also been used as exploratory tool previous to supervised analysis for gaining an in-depth understanding of the inherent differences among samples. In this line, PCA was able to suggest that metabolomic changes during milk fermentation by L. helveticus H9 were more obvious at the fermentation phases (0-8 h), as PCA scores of earlier time points scattered away from those of the later time points (beyond 10 h) and this information was useful for further supervised analysis [92].…”

“…Many software options, free and commercial, are available for univariate and/or multivariate statistical tests. Among commercial software, SIMCA P+ (Soft independent modelling by class analogy) (Umetrics, Sweden) [12,23,25,28,47,[89][90][91] is one of the most used, as well as its light version EzInfo, (U-Metrics, Sweden) [11,92]. Regarding the free software, MetaboAnalyst, which also provides a companion R package (MetaboAnalystR) to complement the web-based application, is a suitable option [57,86].…”

“…29 helveticus H9 strain [92] and the distinction between the biofilm and planktonic state B. bifidum strain [29].…”

Metabolomic profile of milk fermented with Streptococcus thermophilus cocultured with Bifidobacterium animalis ssp. lactis, Lactiplantibacillus plantarum, or both during storage

“…Untargeted techniques based on mass spectrometry and nuclear magnetic resonance spectroscopy are comprehensive approaches and are expected to lead to new discoveries, which have been applied to the study of milk fermentation (Mazzei and Piccolo, 2012;Le Boucher et al, 2015). Furthermore, the effectiveness and reliability of liquid chromatography-mass spectrometry to determine metabolites associated with microbial activity during fermentation and storage of fermented dairy products have been repeatedly ascertained (Kašparovská et al, 2017;Pan et al, 2018;Mi et al, 2018).…”