Identification of Differential, Health-Related Compounds in Chardonnay Marc through Network-Based Meta-Analysis

Abstract

Objectives Food/residue waste streams may be a significant source of bioactive compounds that benefit human health. Dietary intervention trials demonstrate the health benefits of such residues, but they are resource and time intensive. Bioinformatics meta-analyses can elucidate putative pathways, genes and chemicals that are relevant to human health, hence guiding further experimentation and intervention trials. To this end, we integrated publicly available phytochemical datasets related to general grape marc from different varieties (GM) and Chardonnay grape marc (CM) to investigate their differences and potential implications to human health through a network-based meta-analysis. Methods To characterize the phytochemical profile of grape marc, compositional data was aggregated from publicly available literature. To identify potential health effects based on this chemical information, associations between disease states and the chemical profiles of GM/CM were extracted from the Comparative Toxicogenomics Database (CTD). Disease associative networks were constructed for a) marc products, b) all marc-related phenolics, c) compounds that are differentially abundant in CM. Results The union of available marc composition datasets from 14 articles contained 66 phenolic compounds; 29 of these were associated with at least 1 disease state in the CTD. There were 5 differentially over-abundant compounds in CM versus other grape marcs (red varietals n = 75, white varietals n = 57). These were flavan-3-ols catechin, epicatechin, epigallocatechin, gallocatechin, and proanthocyanidin C1 (P < 0.001); with gallocatechin unique to CM. Studies investigating marc products indicated associations to 15 diseases. CTD evidence from 934 studies associated the phenolic profile of GM to 358 diseases of 34 disease classes. Network-based meta-analysis suggested associations between GM and CM phenolics and several disease targets. This includes confirmatory associations between flavan-3-ols and cardiovascular disease outcomes. Conclusions Chardonnay marc is not widely studied; however, the developed framework of network-based meta-analysis utilizing composition information provides a holistic view of the knowledge space for grape marc, and highlights suggested health effects that can guide future research programs. Funding Sources Sonomaceuticals, LLC.

Chardonnay Marc as a New Model for Upcycled Co-products in the Food Industry: Concentration of Diverse Natural Products Chemistry for Consumer Health and Sensory Benefits

“…63 Finally, considerable interindividual differences in microbial epicatechin metabolism have been reported, with the influence of phylotype on epicatechin bioactivity yet to be explored. 142 Simmons et al 143 offered a glimpse into the potential of this approach for accelerating research regarding PBNPs present in Chardonnay marc. These investigators integrated publicly available natural products chemistry data sets related to wine grape marc from different varieties, including Chardonnay marc specifically, to investigate their differences and potential implications to human health through a network-based metaanalysis.…”

“…Subsequent analysis with these compounds of 934 studies of 358 disease states and 34 disease classes was able to confirm that these compounds were positively associated with cardiovascular disease outcomes. 143 Although Chardonnay marc is not widely studied at present, the general framework of networkbased meta-analysis utilizing natural products composition information provided a holistic view of the knowledge space for wine grape marc and suggested potential areas of focus for future research programs.…”

Volatile Organic Compound-Based Predictive Modeling of Smoke Taint in Wine

“…Data-driven approaches, especially machine learning, can accelerate the discovery of the VOCs related to smoke taint and the predictive modeling of the smoke taint index. In recent years, machine learning algorithms as a part of the Artificial Intelligence (AI) have increasingly been applied in food science and agriculture for a sustainable food system, including predicting micronutrients, − creating food ontologies and knowledge bases, precision agriculture, and crop and animal management . Although VOCs in smoke-affected grapes and wine have been reported, the levels contributing to the smoke taint effect of VOCs have been evaluated, and a few studies that model the smoke flavor based on chemical composition have been published recently, the number of studies focusing on data-driven approaches, especially predictive modeling of smoke taint based on VOC concentrations, are still limited.…”