Project description:Mixed fermentation improves the flavor quality of food. Untargeted metabolomics were used to evaluate the impact of mixed fermentation and single-strain fermentation on the volatile and non-volatile compound profiles of Kazak cheese. Lacticaseibacillus paracasei SMN-LBK and Kluyveromyces marxianus SMN-S7-LBK were used to make mixed-fermentation cheese (M), while L. paracasei SMN-LBK was applied in single-strain-fermentation cheese (S). A higher abundances of acids, alcohols, and esters were produced via mixed fermentation. Furthermore, 397 differentially expressed non-volatile metabolites were identified between S and M during ripening. The flavor compounds in mixed-fermentation cheese mainly resulted from ester production (ethyl butanoate, ethyl acetate, ethyl octanoate, and ethyl hexanoate) and amino acid biosynthesis (Asp, Glu, Gln, and Phe). The metabolites were differentially expressed in nitrogen metabolism, D-glutamine and D-glutamate metabolism, phenylalanine metabolism, D-alanine metabolism, and other metabolic pathways. The amount of flavor compounds was increased in M, indicating that L. paracasei SMN- LBK and K. marxianus SMN-S7-LBK had synergistic effects in the formation of flavor compounds. This study comprehensively demonstrated the difference in metabolites between mixed-fermentation and single-strain-fermentation cheese and provided a basis for the production of Kazak cheese with diverse flavor characteristics.
Project description:There is a substantial rise in the global incidence of obesity. Brown rice contains metabolic substances that can help minimize the prevalence of obesity. This study evaluated nine brown rice varieties using probiotic fermentation using Pediococcus acidilacti MNL5 to enhance bioactive metabolites and their efficacy. Among the nine varieties, FBR-1741 had the highest pancreatic lipase inhibitory efficacy (87.6 ± 1.51%), DPPH assay (358.5 ± 2.80 mg Trolox equiv./100 g, DW), and ABTS assay (362.5 ± 2.32 mg Trolox equiv./100 g, DW). Compared to other fermented brown rice and FBR-1741 varieties, UHPLC-Q-TOF-MS/MS demonstrated significant untargeted metabolite alterations. The 17 most abundant polyphenolic metabolites in the FBR-1741 variety and 132 putative targets were assessed for obesity-related target proteins, and protein interaction networks were constructed using the Cystoscope software. Network pharmacology analysis validated FBR-1741 with active metabolites in the C. elegans obesity-induced model. Administration of FBR-1741 with ferulic acid improved lifespan decreased triglycerides, and suppressed the expression of fat-related genes. The enhanced anti-obesity properties of FBR-1741 suggest its implementation in obesity-functional food.
Project description:Large-scale metabolite annotation is a challenge in liquid chromatogram-mass spectrometry (LC-MS)-based untargeted metabolomics. Here, we develop a metabolic reaction network (MRN)-based recursive algorithm (MetDNA) that expands metabolite annotations without the need for a comprehensive standard spectral library. MetDNA is based on the rationale that seed metabolites and their reaction-paired neighbors tend to share structural similarities resulting in similar MS2 spectra. MetDNA characterizes initial seed metabolites using a small library of MS2 spectra, and utilizes their experimental MS2 spectra as surrogate spectra to annotate their reaction-paired neighbor metabolites, which subsequently serve as the basis for recursive analysis. Using different LC-MS platforms, data acquisition methods, and biological samples, we showcase the utility and versatility of MetDNA and demonstrate that about 2000 metabolites can cumulatively be annotated from one experiment. Our results demonstrate that MetDNA substantially expands metabolite annotation, enabling quantitative assessment of metabolic pathways and facilitating integrative multi-omics analysis.
Project description:Oncogene-associated metabolic signatures in prostate cancer, identified by an integrative analysis of cultured cells and murine and human tumors, suggest that AKT activation results in a glycolytic phenotype whereas MYC induces aberrant lipid metabolism. Heterogeneity in human tumors makes this simplistic interpretation obtained from experimental models more challenging. Metabolic reprogramming as a function of distinct molecular aberrations has major diagnostic and therapeutic implications.
Project description:BackgroundColostrum, abundant in immunoglobulins and growth factors, plays a vital role in supporting immunity. Both yak and buffalo milk are characterized by their high protein and fat content. However, the metabolomic profiles of yak colostrum (YC), buffalo colostrum (BC), and bovine colostrum (CC) remain largely unexplored. The objective of this study is to identify unique metabolites that may impact the nutritional value of colostrum.MethodsThis study employed ultra-high performance liquid chromatography-electrospray ionization tandem mass spectrometry (UHPLC-ESI-MS/MS) for untargeted metabolomics analysis of YC, BC, and CC.ResultsThe analysis revealed 97, 70, and 75 differentially expressed metabolites in the YC-CC, BC-CC, and YC-BC comparisons, respectively. In comparison to CC, both YC and BC shared common features, including reduced choline levels and elevated O-acetylcarnitine. Moreover, metabolites such as 2-hydroxy-6-pentadecylbenzoic acid, DL-glycerol-1-phosphate, thiamine, L-carnitine, methyl β-D-galactoside, and uridine diphosphate (UDP) were identified as potential biomarkers for YC, while 21-deoxycortisol, D-synephrine, uridine, mannitol-1-phosphate, nonadecanoic acid, and perillic acid were specific to BC.ConclusionsYC has greater advantages in energy supply, antioxidant activity, immune regulation, and cell homeostasis, and BC holds unique significance in physical development and energy balance regulation. These findings provide valuable insights, enabling the selection of unique bioactive metabolites to develop targeted functional foods from colostrum, catering to diverse nutritional needs.
Project description:Probiotic functional products have drawn wide attention because of their increasing popularity. However, few studies have analyzed probiotic-specific metabolism in the fermentation process. This study applied UPLC-QE-MS-based metabolomics to track changes in the milk metabolomes in the course of fermentation by two probiotic strains, Lacticaseibacillus paracasei PC-01 and Bifidobacterium adolescentis B8589. We observed substantial changes in the probiotic fermented milk metabolome between 0 and 36 h of fermentation, and the differences between the milk metabolomes at the interim period (36 h and 60 h) and the ripening stage (60 h and 72 h) were less obvious. A number of time point-specific differential metabolites were identified, mainly belonging to organic acids, amino acids, and fatty acids. Nine of the identified differential metabolites are linked to the tricarboxylic acid cycle, glutamate metabolism, and fatty acid metabolism. The contents of pyruvic acid, γ-aminobutyric acid, and capric acid increased at the end of fermentation, which can contribute to the nutritional quality and functional properties of the probiotic fermented milk. This time-course metabolomics study analyzed probiotic-specific fermentative changes in milk, providing detailed information of probiotic metabolism in a milk matrix and the potential beneficial mechanism of probiotic fermented milk.
Project description:Among the extracellular vesicles, apoptotic bodies (ABs) are only formed during the apoptosis and perform a relevant role in the pathogenesis of different diseases. Recently, it has been demonstrated that ABs from human renal proximal tubular HK-2 cells, either induced by cisplatin or by UV light, can lead to further apoptotic death in naïve HK-2 cells. Thus, the aim of this work was to carry out a non-targeted metabolomic approach to study if the apoptotic stimulus (cisplatin or UV light) affects in a different way the metabolites involved in the propagation of apoptosis. Both ABs and their extracellular fluid were analyzed using a reverse-phase liquid chromatography-mass spectrometry setup. Principal components analysis showed a tight clustering of each experimental group and partial least square discriminant analysis was used to assess the metabolic differences existing between these groups. Considering the variable importance in the projection values, molecular features were selected and some of them could be identified either unequivocally or tentatively. The resulting pathways indicated that there are significant, stimulus-specific differences in metabolites abundancies that may propagate apoptosis to healthy proximal tubular cells; thus, we hypothesize that the share in apoptosis of these metabolites might vary depending on the apoptotic stimulus.
Project description:BackgroundMetabolomics has become increasingly popular in the study of disease phenotypes and molecular pathophysiology. One branch of metabolomics that encompasses the high-throughput screening of cellular metabolism is metabolic profiling. In the present study, the metabolic profiles of different tumour cells from colorectal carcinoma and breast adenocarcinoma were exposed to hypoxic and normoxic conditions and these have been compared to reveal the potential metabolic effects of hypoxia on the biochemistry of the tumour cells; this may contribute to their survival in oxygen compromised environments. In an attempt to analyse the complex interactions between metabolites beyond routine univariate and multivariate data analysis methods, correlation analysis has been integrated with a human metabolic reconstruction to reveal connections between pathways that are associated with normoxic or hypoxic oxygen environments.ResultsCorrelation analysis has revealed statistically significant connections between metabolites, where differences in correlations between cells exposed to different oxygen levels have been highlighted as markers of hypoxic metabolism in cancer. Network mapping onto reconstructed human metabolic models is a novel addition to correlation analysis. Correlated metabolites have been mapped onto the Edinburgh human metabolic network (EHMN) with the aim of interlinking metabolites found to be regulated in a similar fashion in response to oxygen. This revealed novel pathways within the metabolic network that may be key to tumour cell survival at low oxygen. Results show that the metabolic responses to lowering oxygen availability can be conserved or specific to a particular cell line. Network-based correlation analysis identified conserved metabolites including malate, pyruvate, 2-oxoglutarate, glutamate and fructose-6-phosphate. In this way, this method has revealed metabolites not previously linked, or less well recognised, with respect to hypoxia before. Lactate fermentation is one of the key themes discussed in the field of hypoxia; however, malate, pyruvate, 2-oxoglutarate, glutamate and fructose-6-phosphate, which are connected by a single pathway, may provide a more significant marker of hypoxia in cancer.ConclusionsMetabolic networks generated for each cell line were compared to identify conserved metabolite pathway responses to low oxygen environments. Furthermore, we believe this methodology will have general application within metabolomics.