Traditional pear cultivars are increasingly in demand by consumers because of their excellent taste, the possibility of use in sustainable food production systems, convenience as raw materials for obtaining products of high nutritional quality, and perceived health benefits. In this study, individual sugars, organic acids, and polyphenols in the fruits of nine traditional and one commercial pear cultivar during two growing seasons were determined by HPLC. A significant influence of cultivars, growing years, and their interaction on the content of analyzed primary and secondary metabolites was determined. The commercial pear cultivar \'Président Drouard\' and traditional cultivars \'Dolokrahan\', \'Budaljača\', and \'Krakača\' had a lower content of all analyzed sugars. Overall, traditional pear cultivars had higher total polyphenols in the peel and pulp than \'Président Drouard\', with the exception \'Takiša\' and \'Ahmetova\'. High polyphenol content detected in \'Budaljača\', \'Dolokrahan\', and \'Krakača\' shows the utilization value of traditional pear germplasm. The obtained data can serve as practical supporting data for the use of traditional pears in the neutraceutical, pharmaceutical, and food industries.

Plants\' primary metabolites are of great importance from the survival and nutritional perspectives. However, the genetic bases underlying the profiles of primary metabolites in oilseed crops remain largely unclear. As one of the main oilseed crops, sesame (Sesamum indicum L.) is a potential model plant for investigating oil metabolism in plants. Therefore, the objective of this study is to disclose the genetic variants associated with variation in the content of primary metabolites in sesame. We performed a comprehensive metabolomics analysis of primary metabolites in 412 diverse sesame accessions using gas chromatography-mass spectrometry and identified a total of 45 metabolites, including fatty acids, monoacylglycerols (MAGs), and amino acids. Genome-wide association study unveiled 433 significant single-nucleotide polymorphism loci associated with variation in primary metabolite contents in sesame. By integrating diverse genomic analyses, we identified 10 key candidate causative genes of variation in MAG, fatty acid, asparagine, and sucrose contents. Among them, SiDSEL was significantly associated with multiple traits. SiCAC3 and SiKASI were strongly associated with variation in oleic acid and linoleic acid contents. Overexpression of SiCAC3, SiKASI, SiLTPI.25, and SiLTPI.26 in transgenic Arabidopsis and Saccharomyces cerevisiae revealed that SiCAC3 is a potential target gene for improvement of unsaturated fatty acid levels in crops. Furthermore, we found that it may be possible to breed several quality traits in sesame simultaneously. Our results provide valuable genetic resources for improving sesame seed quality and our understanding of oilseed crops\' primary metabolism.

The Chinese bayberry (Morella rubra Sieb. et Zucc.) is grown commercially in China and other Asian countries for its flavorful and appealing fruit. Here, two bayberry varieties differing in both color and flavor, namely, BDK (\'Baidongkui\') and DK (\'Dongkui\'), in China were compared. A total of 18 anthocyanins, three proanthocyanidins, and 229 primary metabolites were identified in the pulp of the two varieties; these were analyzed and compared using ultra-performance liquid chromatography-tandem mass spectrometry. The DK pulp showed higher concentrations of all 18 anthocyanins compared with BDK, apart from peonidin-3,5-O-diglucoside which was not detected in BDK and which was responsible for the formation of pink pulp in BDK. Principal component analysis and cluster analysis of the primary metabolites indicated that the two bayberry varieties had distinct metabolite profiles with approximately 37% (85/229) of the primary metabolome being significantly different. Of these, 62 metabolites were down-regulated and 23 metabolites were up-regulated in BDK relative to DK. Our results suggested that the flavor of the BDK fruit was different from DK, which could be explained by the reduced saccharide, organic acid, amino acid, and proanthocyanidin contents. These findings enhance our understanding of the metabolites responsible for color and taste differences in the Chinese bayberry.

Plants defend themselves from most microbial attacks via mechanisms including cell wall fortification, production of antimicrobial compounds, and generation of reactive oxygen species. Successful pathogens overcome these host defenses, as well as obtain nutrients from the host. Perturbations of plant metabolism play a central role in determining the outcome of attempted infections. Metabolomic analyses, for example between healthy, newly infected and diseased or resistant plants, have the potential to reveal perturbations to signaling or output pathways with key roles in determining the outcome of a plant-microbe interaction. However, application of this -omic and its tools in plant pathology studies is lagging relative to genomic and transcriptomic methods. Thus, it is imperative to bring the power of metabolomics to bear on the study of plant resistance/susceptibility. This review discusses metabolomics studies that link changes in primary or specialized metabolism to the defense responses of plants against bacterial, fungal, nematode, and viral pathogens. Also examined are cases where metabolomics unveils virulence mechanisms used by pathogens. Finally, how integrating metabolomics with other -omics can advance plant pathology research is discussed.

This chapter introduces the emerging field of metabolomics and its application in the context of cancer biomarker research. Taking advantage of modern high-throughput technologies, and enhanced computational power, metabolomics has a high potential for cancer biomarker identification and the development of diagnostic tools. This chapter describes current metabolomics technologies used in cancer research, starting with metabolomics sample preparation, elaborating on current analytical methodologies for metabolomics measurement and introducing existing software for data analysis. The last part of this chapter deals with the statistical analysis of very large metabolomics datasets and their relevance for cancer biomarker identification.