Flavonoids are a class of specialized metabolites found in many different plant species. They protect against UV radiation, scavenge reactive oxygen species, are involved in plant defense responses, and are associated with plant‐microorganism interactions. They have also been reported to possess health‐promoting effects including anti‐inflammatory, antioxidant, anticancer activity, and antihypertensive effects. Flavonoids encompass >10,000 structures where the types and amounts depend on the plant species, developmental stage, organ, and growth conditions. The diversity of flavonoid structures represents a significant challenge in the analysis of plant flavonoids. Many analytical techniques have been developed to detect and quantify flavonoids, and the most productive of these techniques use liquid chromatography (LC) coupled to mass spectrometry (MS) to analyze flavonoids due to the excellent combination of selectivity and sensitivity of MS. In addition, mass spectral libraries have been constructed to further aid flavonoid identification. Here, the use of ultra‐high pressure liquid chromatography coupled to mass spectrometry (UHPLC‐MS) in plant flavonoid analyses, with an emphasis on sample extraction, flavonoid separation, and MS detection, is described. © 2018 by John Wiley & Sons, Inc.

A total of 1,622 samples representing 201 Medicago truncatula ecotypes were analyzed using ultrahigh pressure liquid chromatography coupled to mass spectrometry (UHPLC-MS) to ascertain saponin profiles in different M. truncatula ecotypes and to provide data for a genome-wide association study and subsequent line selection for saponin biosynthesis. These ecotypes originated from 14 different Mediterranean countries, i.e., Algeria, Cyprus, France, Greece, Israel, Italy, Jordan, Libya, Morocco, Portugal, Spain, Syria, Tunisia, and Turkey. The results revealed significant differences in the saponin content among the ecotypes. European ecotypes generally contained higher saponin content than African ecotypes (p < 0.0001). This suggests that M. truncatula ecotypes modulate their secondary metabolism to adapt to their environments. Significant differences in saponin accumulation were also observed between the aerial and the root tissues of the same ecotypes (p < 0.0001). While some saponins were found to be present in both the aerial and root tissues, zanhic acid glycosides were found predominantly in the aerial tissues. Bayogenin and hederagenin glycosides were found mostly in roots. The differential spatially resolved accumulation of saponins suggests that saponins in the aerial and root tissues play different roles in plant fitness. Aerial saponins such as zanhic glycosides may act as animal feeding deterrent and root saponins may protect against soil microbes.

Black walnut (Juglans nigra L.) is one of the most economically valuable hardwood species and a high value tree for edible nut production in the United States. Although consumption of black walnut has been linked to multiple health-promoting effects (e.g., antioxidant, antimicrobial, anti-inflammatory), the bioactive compounds have not been systematically characterized. In addition, the associations between different black walnut cultivars and their health-promoting compounds have not been well established. In this study, the kernels of twenty-two black walnut cultivars selected for nut production by the University of Missouri Center for Agroforestry (Columbia, MO, USA) were evaluated for their antibacterial activities using agar-well diffusion assay. Among the selected cultivars, four black walnut cultivars (i.e., Mystry, Surprise, D.34, and A.36) exhibited antibacterial activity against a Gram-positive bacterium (Staphylococcus aureus), whereas other cultivars showed no effect on the inhibition of this bacterium. The antibacterial compounds showing the strongest activity were isolated with bioassay-guided purification and identified using a metabolomics approach. Six antibacterial bioactive compounds responsible for antimicrobial activity were successfully identified. Glansreginin A, azelaic acid, quercetin, and eriodictyol-7-O-glucoside are novel antibacterial compounds identified in the kernels of black walnuts. The metabolomics approach provides a simple and cost-effective tool for bioactive compound identification.

Solid-phase microextraction (SPME) was coupled to gas chromatography mass spectrometry (GC-MS) and a method optimized to quantitatively and qualitatively measure a large array of volatile metabolites in alfalfa glandular trichomes isolated from stems, trichome-free stems, and leaves as part of a non-targeted metabolomics approach. Major SPME extraction parameters optimized included SPME fiber composition, extraction temperature, and extraction time. The optimized SPME method provided the most chemically diverse coverage of alfalfa volatile and semi-volatile metabolites using a DVB/CAR/PDMS fiber, extraction temperature of 60 °C, and an extraction time of 20 min. Alfalfa SPME-GC-MS profiles were processed using automated peak deconvolution and identification (AMDIS) and quantitative data extraction software (MET-IDEA). A total of 87 trichome, 59 stem, and 99 leaf volatile metabolites were detected after background subtraction which removed contaminants present in ambient air and associated with the fibers and NaOH/EDTA buffer solution containing CaCl₂. Thirty-seven volatile metabolites were detected in all samples, while 15 volatile metabolites were uniquely detected only in glandular trichomes, 9 only in stems, and 33 specifically in leaves as tissue specific volatile metabolites. Hierarchical cluster analysis (HCA) and principal component analysis (PCA) of glandular trichomes, stems, and leaves showed that the volatile metabolic profiles obtained from the optimized SPME-GC-MS method clearly differentiated the three tissues (glandular trichomes, stems, and leaves), and the biochemical basis for this differentiation is discussed. Although optimized using plant tissues, the method can be applied to other types of samples including fruits and other foods.

The methanol extracts of nine popular cultivated Vietnamese rice cultivars (Oryza sativa L.cv. OM 2395, 5451, 6976, 380, 5930, 4498, 3536, N406, and 7347) were used to explore their allelopathic potential on barnyardgrass (Echinochola crus-galli L.). At 0.1 g mL⁻¹, OM 5930, OM 4498, and OM 6976 correlatively possessed greatest phytotoxicity on barnyardgrass shoot (98.77%, 90.75%, and 87.17%) and root (99.39%, 92.83%, and 86.56%) growth. The following study aimed to detect previously-known allelochemicals in those rice using XCMS online cloud-based metabolomics platform. Twenty allelochemicals were semi-quantified and seven of them were detected predominantly and five was putatively confirmed in OM 5930 (mg/ 100g fresh rice) as salicylic acid (5.0076), vanillic acid (0.1246), p-coumaric acid (0.1590), 2,4-dimethoxybenzoic acid (0.1045), and cinnamic acid (3.3230). These compounds were active at concentrations greater than 0.5 mM and the average EC₅₀ were 1.24 mM. The results indicated that OM 5930 may use as promising candidates in weed biological control for rice production.

Recent SARS-CoV-2 wastewater-based epidemiology (WBE) surveillance have documented a positive correlation between the number of COVID-19 patients in a sewershed and the level of viral genetic material in the wastewater. Efforts have been made to use the wastewater SARS-CoV-2 viral load to predict the infected population within each sewershed using a multivariable regression approach. However, reported clear and sustained variability in SARS-CoV-2 viral load among treatment facilities receiving industrial wastewater have made clinical prediction challenging. Several classes of molecules released by regional industries and manufacturing facilities, particularly the food processing industry, can significantly suppress the SARS-CoV-2 signals in wastewater by breaking down the lipid-bilayer of the membranes. Therefore, a systematic ranking process in conjugation with metabolomic analysis was developed to identify the wastewater treatment facilities exhibiting SARS-CoV-2 suppression and identify and quantify the chemicals suppressing the SARS-COV-2 signals. By ranking the viral load per diagnosed case among the sewersheds, we successfully identified the wastewater treatment facilities in Missouri, USA that exhibit SARS-CoV-2 suppression (significantly lower than 5 × 10¹¹ gene copies/reported case) and determined their suppression rates. Through both untargeted global chemical profiling and targeted analysis of wastewater samples, 40 compounds were identified as candidates of SARS-CoV-2 signal suppressors. Among these compounds, 14 had higher concentrations in wastewater treatment facilities that exhibited SARS-CoV-2 signal suppression compared to the unsuppressed control facilities. Stepwise regression analyses indicated that 4-nonylphenol, palmitelaidic acid, sodium oleate, and polyethylene glycol dioleate are positively correlated with SARS-CoV-2 signal suppression rates. Suppression activities were further confirmed by incubation studies, and the suppression kinetics for each bioactive compound were determined. According to the results of these experiments, bioactive molecules in wastewater can significantly reduce the stability of SARS-CoV-2 genetic marker signals. Based on the concentrations of these chemical suppressors, a correction factor could be developed to achieve more reliable and unbiased surveillance results for wastewater treatment facilities that receive wastewater from similar industries.