A 3-year application of different mycorrhiza-based plant biostimulants distinctively modulates photosynthetic performance, leaf metabolism, and fruit quality in grapes (Vitis vinifera L.)

Abstract

The use of microbial biostimulants in agriculture is recognized as a sustainable approach to promoting crop productivity and quality due to improved nutrient uptake, enhanced stress tolerance, and improved ability to cope with non-optimal environments. The present paper aimed to comparatively investigate the effect of seven different commercial mycorrhizal-based treatments in terms of yield, phytochemical components, and technological traits of Malvasia di Candia Aromatica grape (Vitis vinifera L.) plants. Metabolomic analysis and photosynthetic performance were first investigated in leaves to point out biochemical differences related to plant growth. Higher photosynthetic efficiency and better PSII functioning were found in biostimulant-treated vines, reflecting an overall decrease in photoinhibition compared to untreated plants. Untargeted metabolomics followed by multivariate statistics highlighted a robust reprogramming of primary (lipids) and secondary (alkaloids and terpenoids) metabolites in treated plants. The analysis of berry yield and chemical components exhibited significant differences depending on the biostimulant product. Generally, berries obtained from treated plants displayed improved contents of polyphenols and sugars, while yield remained unchanged. These results elucidated the significant role of microbial biostimulants in determining the quality of grape berries and eliciting biochemical changes in vines.

Keywords: arbuscular mycorrhizal fungi; fruit quality; metabolomics; multivariate analysis; photosynthetic parameters; plant symbiont.

Figure 1

Graphical representation of climate trend and grapevine chlorophyll fluorescence parameters during the trial, between June 30 and August 27, 2022. The maximum and average daily temperature (°C) are represented by the red and gray lines respectively, while the daily precipitation (mm) is illustrated by the inverted blue histograms. Each bar represents mean values (n = 8) of energy partition for each treatment for each day of measurement. The bars represent the three complementary PSII quantum yield contributions: orange = quantum yield of photochemical energy conversion in PSII (Phi2), blue = quantum yield of non-regulated non-photochemical energy loss in PSII (PhiNO), and light blue = quantum yield of regulated non-photochemical energy loss in PSII (PhiNPQ). Letters indicate homogenous sub-classes resulting from ANOVA (p-value ≤ 0.05, Tukey’s post hoc test). *, p-value < 0,05; **, p-value < 0,01; ***, p-value < 0,001; n.s., not significant.

Figure 2

(A) Unsupervised hierarchical cluster analysis of grape leaves’ metabolomic profiles, obtained by UHPLC/QTOF-MS untargeted analysis, as a function of the treatment with mycorrhizal-based biostimulants. A fold-change-based heatmap was built, and samples were clustered according to Ward’s algorithm and based on Euclidean distances. (B) Orthogonal projections to latent structures discriminant analysis (OPLS-DA) score plot for grape leaves metabolomic following treatment with mycorrhizal-based biostimulants.

Figure 3

Plant leaves’ metabolic processes (A) and the relative details of secondary metabolism (B) as affected by arbuscular mycorrhizal fungi (AMF)-based biostimulants compared to Control. The 246 compounds selected by ANOVA (p-value ≤ 0.05, Benjamini correction) were subjected to fold change analysis (FC ≥ 1), and the resulting values were loaded into the PlantCyc pathway tool¹. The x-axis represents each set of metabolic subcategories, while the y-axis corresponds to the cumulative log fold change (FC). The large dots represent the average (mean) of all FCs for the different metabolites in the class, while the small dots represent the individual log FC. AA syn, amino acid biosynthesis; Nucleo syn, nucleoside and nucleotide biosynthesis; FA/Lip syn, fatty acid and lipid biosynthesis; Sec metab, secondary metabolite biosynthesis; Cofactor syn, cofactor, carrier, and vitamin biosynthesis; Cell-struct syn, cell structure biosynthesis; N-containing, nitrogen-containing secondary compound biosynthesis; Phenylprop derivs, phenylpropanoid derivative biosynthesis.

Figure 4

Canonical discriminant analysis (CDA) showing differences in the qualitative and quantitative profiles of grape berries following seven different treatments with arbuscular mycorrhizal fungi (AMF)-based biostimulants (T1, T2, T3, T4, T5, T6, and T7) and Control. The 95% confidence interval of each treatment is shown as a colored circle around the treatment mean. The vectors indicate the contributions of fruit qualitative and quantitative traits to discriminating among treatments; the longer the vector, the stronger its influence in the direction shown.