Metabolomics and Physiological Insights into the Ability of Exogenously Applied Chlorogenic Acid and Hesperidin to Modulate Salt Stress in Lettuce Distinctively

Abstract

Recent studies in the agronomic field indicate that the exogenous application of polyphenols can provide tolerance against various stresses in plants. However, the molecular processes underlying stress mitigation remain unclear, and little is known about the impact of exogenously applied phenolics, especially in combination with salinity. In this work, the impacts of exogenously applied chlorogenic acid (CA), hesperidin (HES), and their combination (HES + CA) have been investigated in lettuce (Lactuca sativa L.) through untargeted metabolomics to evaluate mitigation effects against salinity. Growth parameters, physiological measurements, leaf relative water content, and osmotic potential as well as gas exchange parameters were also measured. As expected, salinity produced a significant decline in the physiological and biochemical parameters of lettuce. However, the treatments with exogenous phenolics, particularly HES and HES + CA, allowed lettuce to cope with salt stress condition. Interestingly, the treatments triggered a broad metabolic reprogramming that involved secondary metabolism and small molecules such as electron carriers, enzyme cofactors, and vitamins. Under salinity conditions, CA and HES + CA distinctively elicited secondary metabolism, nitrogen-containing compounds, osmoprotectants, and polyamines.

Biostimulant Effects of an Aqueous Extract of Duckweed (Lemna minor L.) on Physiological and Biochemical Traits in the Olive Tree

“…However, the extracts seemed to be more than just a plant regulator due to the presence of molecules such as phenolic compounds. The addition of exogenous phenolics has been previously reported to enhance plant performance [75]. In particular, Zhang et al [75] showed that the addition of chlorogenic acid and hesperidin alleviates the impact of salt stress by improving photosynthetic performance.…”

“…The addition of exogenous phenolics has been previously reported to enhance plant performance [75]. In particular, Zhang et al [75] showed that the addition of chlorogenic acid and hesperidin alleviates the impact of salt stress by improving photosynthetic performance. Moreover, Zhang et al [76] pointed out that the addition of phenolics, including hesperidin, can modulate functional traits in lettuces, also modifying the endogenous phenolic content.…”

“…In this sense, hesperidin has been found to be the most abundant flavone in duckweed extracts [26]. Exogenous phenolics have been reported to trigger the accumulation of electron carriers, increase stomatal conductance and elicit secondary metabolism in lettuce, both under normal and abiotic stress conditions [75].…”

Untargeted Phenolic Profiling and Functional Insights of the Aerial Parts and Bulbs of Drimia maritima (L.) Stearn

“…The phenolic profiles of different D. maritima extracts were analyzed using a UHPLC-QTOF-MS (Agilent Technologies, Stevens Creek Blvd, Santa Clara, CA, USA). The instrument parameters and methods adopted for the analysis were optimized in previous works [ 66 , 67 ]. Regarding chromatographic separation, it was achieved by using an Agilent InfinityLab Poroshell 120 pentafluorophenyl (PFP) column (2.1 × 100 mm, 1.9 μm) (Agilent Technologies, Stevens Creek Blvd, Santa Clara, CA, USA) and a binary mixture of water and acetonitrile acidified with 0.1% ( v / v ) formic acid as mobile phase (LC-MS grade, VWR, Milan, Italy).…”

“…The raw metabolomic data set was transformed and normalized in Mass Profiler Professional B.15.1 (Agilent Technologies, Stevens Creek Blvd, Santa Clara, CA, USA). Therefore, chemometric interpretation such as Principal component analysis (PCA) was performed by using MetaboAnalyst 5.0 ( accessed on 10 December 2022), while the orthogonal projection to latent structures discriminant analysis (OPLS-DA) was created using SIMCA 16 software (Umetrics, Malmo, Sweden), as previously described [ 67 ]. To validate and to investigate outliers of the OPLS-DA model, the model fitness parameters (goodness of fit: R 2 Y; goodness of prediction: Q 2 Y; cross-validation: CV-ANOVA, p < 0.01) were determined, and permutation test ( n = 100) and Hotelling’s T2 (95% and 99% confidence limit for the suspect and strong outliers, respectively) performed [ 71 ].…”