Application of LC-NMR to analysis of carotenoids in foods

Carotenoids in Health as Studied by Omics-Related Endpoints

Carotenoids have been associated with risk reduction for several chronic diseases, including the association of their dietary intake/circulating levels with reduced incidence of obesity, type 2 diabetes, certain types of cancer, and even lower total mortality. In addition to some carotenoids constituting vitamin A precursors, they are implicated in potential antioxidant effects and pathways related to inflammation and oxidative stress, including transcription factors such as nuclear factor κB and nuclear factor erythroid 2-related factor 2. Carotenoids and metabolites may also interact with nuclear receptors, mainly retinoic acid receptor/retinoid X receptor and peroxisome proliferator-activated receptors, which play a role in the immune system and cellular differentiation. Therefore, a large number of downstream targets are likely influenced by carotenoids, including but not limited to genes and proteins implicated in oxidative stress and inflammation, antioxidation, and cellular differentiation processes. Furthermore, recent studies also propose an association between carotenoid intake and gut microbiota. While all these endpoints could be individually assessed, a more complete/integrative way to determine a multitude of health-related aspects of carotenoids includes (multi)omics-related techniques, especially transcriptomics, proteomics, lipidomics, and metabolomics, as well as metagenomics, measured in a variety of biospecimens including plasma, urine, stool, white blood cells, or other tissue cellular extracts. In this review, we highlight the use of omics technologies to assess health-related effects of carotenoids in mammalian organisms and models.

Identification of Apocarotenoids as Chemical Markers of In Vitro Anti-Inflammatory Activity for Spirulina Supplements

Identification of chemical markers in food additives and dietary supplements is crucial for quantitative assessment and standardization of their quality and efficacy. Arthrospira platensis, formerly Spirulina platensis and known colloquially as spirulina, has been widely investigated for its various biological effects, including anti-inflammation, antihypertension, antioxidant, and antiatherosclerosis. In this study, we utilized an approach involving a combination of bioassay-guided fractionation, synthesis, mass spectral molecular networking, principal component analysis (PCA), and correlation analysis to identify measurable chemical markers in spirulina products that can be used to evaluate the efficacy of commercial products in downregulating the expression level of the proinflammatory cytokines, interleukin-1β (IL-1β), interleukin-6 (IL-6), and tumor necrosis factor α (TNFα). Consequently, we found that the apocarotenoids 3-hydroxy-β-ionone (1) and apo-13-zeaxanthinones (2a/2b) significantly repressed expression of IL-1β (9.5 ± 1.5 and 28.7 ± 0.6%, respectively) and IL-6 (10.1 ± 0.7 and 6.1 ± 0.4%, respectively) at 10 μg/mL (p < 0.05) using RAW 264.7 mouse macrophages. Notably, this is the first report of the isolation of these apocarotenoids from spirulina and their in vitro anti-inflammatory properties. Finally, we propose the use of our approach as a convenient way to establish markers in other dietary supplements.

A multifactorial approach in characterizing geographical origin of Sicilian cherry tomatoes using 1H-NMR profiling

In this study, metabolomic analysis of chloroform extracts was performed to characterize cherry tomatoes (cv Naomi and Shiren) grown in different Sicilian areas, using ¹H NMR spectroscopy coupled with multivariate statistical analysis. Principal components analysis showed clear discrimination between extracts of cherry tomatoes cultivated in two different seasons (winter and summer) and grown in three areas of Sicily (Gela, Licata, Pachino). In particular, carotenoids and phospholipids mainly were found to be more discriminating metabolites for both cultivars in summer and winter. In the present study, the simple separation only based on production area was found to be inadequate to distinguish the three groups of tomatoes. A clear separation among the different samples groups was obtained using a multifactorial approach not only based on the geographical origin classification, but considering also cultivar, year and seasonality.