Ultra-HPLC-MS pseudo-targeted metabolomic profiling reveals metabolites and associated metabolic pathway alterations in Asian plum (Prunus salicina) fruits in response to gummosis disease

Comparative Analysis of Carotenoid Profiles and Biosynthetic Gene Expressions among Ten Plum Cultivars

Plums are good sources of various bioactive phytochemical compounds such as vitamins, anthocyanins, and carotenoids, whereby all of which are noted for multiple potential health benefits. However, knowledge regarding plum carotenoid profiles remains limited. Hence, the total and individual carotenoids in the edible parts (skin and flesh) of ten plum cultivars were determined using a spectrophotometer and high-performance liquid chromatography-diode array detection, respectively. Total and individual carotenoid contents in skin were significantly higher (p < 0.05) than those in flesh among all plum cultivars tested. The cultivars with the highest content of total carotenoids in skin were Naili (36.73 μg/g FW), followed by Yinhongli (21.81 μg/g FW) and Yuhuangli (19.70 μg/g FW), with the lowest in Angeleno (8.97 μg/g FW). Lutein, zeaxanthine, β-cryptoxanthin, α-carotene, and β-carotene were the major types of carotenoids detected, with lutein and β-carotene being the predominant constituents of the skin and flesh tissues, respectively. Lutein, zeaxanthine, and total carotenoid contents were positively correlated with the expressions of PSY, LCYB, and LCYE, and negatively correlated with the expressions of PDS and CRTISO. Characterizing the carotenoid profiles and investigating variations in carotenoid biosynthetic gene expressions among plum cultivars are crucial for advancing genetic improvements in plums.

Simultaneous Quantitation and Discovery (SQUAD) Analysis: Combining the Best of Targeted and Untargeted Mass Spectrometry-Based Metabolomics

Untargeted and targeted approaches are the traditional metabolomics workflows acquired for a wider understanding of the metabolome under focus. Both approaches have their strengths and weaknesses. The untargeted, for example, is maximizing the detection and accurate identification of thousands of metabolites, while the targeted is maximizing the linear dynamic range and quantification sensitivity. These workflows, however, are acquired separately, so researchers compromise either a low-accuracy overview of total molecular changes (i.e., untargeted analysis) or a detailed yet blinkered snapshot of a selected group of metabolites (i.e., targeted analysis) by selecting one of the workflows over the other. In this review, we present a novel single injection simultaneous quantitation and discovery (SQUAD) metabolomics that combines targeted and untargeted workflows. It is used to identify and accurately quantify a targeted set of metabolites. It also allows data retro-mining to look for global metabolic changes that were not part of the original focus. This offers a way to strike the balance between targeted and untargeted approaches in one single experiment and address the two approaches' limitations. This simultaneous acquisition of hypothesis-led and discovery-led datasets allows scientists to gain more knowledge about biological systems in a single experiment.

Detecting Grapevine Virus Infections in Red and White Winegrape Canopies Using Proximal Hyperspectral Sensing

Grapevine virus-associated disease such as grapevine leafroll disease (GLD) affects grapevine health worldwide. Current diagnostic methods are either highly costly (laboratory-based diagnostics) or can be unreliable (visual assessments). Hyperspectral sensing technology is capable of measuring leaf reflectance spectra that can be used for the non-destructive and rapid detection of plant diseases. The present study used proximal hyperspectral sensing to detect virus infection in Pinot Noir (red-berried winegrape cultivar) and Chardonnay (white-berried winegrape cultivar) grapevines. Spectral data were collected throughout the grape growing season at six timepoints per cultivar. Partial least squares-discriminant analysis (PLS-DA) was used to build a predictive model of the presence or absence of GLD. The temporal change of canopy spectral reflectance showed that the harvest timepoint had the best prediction result. Prediction accuracies of 96% and 76% were achieved for Pinot Noir and Chardonnay, respectively. Our results provide valuable information on the optimal time for GLD detection. This hyperspectral method can also be deployed on mobile platforms including ground-based vehicles and unmanned aerial vehicles (UAV) for large-scale disease surveillance in vineyards.