Trichoderma spp.-mediated mitigation of heat, drought, and their combination on the Arabidopsis thaliana holobiont: a metabolomics and metabarcoding approach

Abstract

Introduction: The use of substances to increase productivity and resource use efficiency is now essential to face the challenge of feeding the rising global population with the less environmental impact on the ecosystems. Trichoderma-based products have been used as biopesticides, to inhibit pathogenic microorganisms, and as biostimulants for crop growth, nutrient uptake promotion, and resistance to abiotic stresses.

Methods: In this work, plant metabolomics combined with roots and rhizosphere bacterial metabarcoding were exploited to inspect the performance of Trichoderma spp. biostimulants on Arabidopsis thaliana under drought, heat and their combination and its impact on plant holobiont.

Results and discussion: An overall modulation of N-containing compounds, phenylpropanoids, terpenes and hormones could be pointed out by metabolomics. Moreover, metabarcoding outlined an impact on alpha and beta-diversity with an abundance of Proteobacteria, Pseudomonadales, Burkholderiales, Enterobacteriales and Azospirillales. A holobiont approach was applied as an integrated analytical strategy to resolve the coordinated and complex dynamic interactions between the plant and its rhizosphere bacteria using Arabidopsis thaliana as a model host species.

Keywords: abiotic stress; biostimulants; climate change; combined stress; meta-barcoding; multi-omics; rhizosphere microbiome.

Figure 1

Effects of single and combined stress on adult plants of Arabidopsis thaliana in control conditions or treated with Trichoderma before stress application. Fresh weight (FW), dry weight (DW), their ratio (FW/DW), leaf temperature (leaf T), SPAD index (SPAD) and photosystem II efficiency (Fv/Fm) are provided from panel (A–F) for the following treatments: C (untreated control); CT (control treated with Trichoderma); D (untreated control + Drought stress), CT+D (CT + drought stress); H (untreated control + Heat stress), C+H (untreated control + heat stress), CT+H (CT + heat stress); H+D (untreated control + stress combination), CT +H+D (stress combination). Data were analyzed through two-way ANOVA using the LSD’s test as post-hoc. Different letters along the bars indicate statistical differences with p≤0.05 (N=4).

Figure 2

Score plot of orthogonal projection to latent structures discriminant analysis (OPLS-DA) supervised modeling carried out on untargeted metabolomics profiles of Arabidopsis roots exposed to heat, drought, and combined stresses (R2Y = 0.98, Q2Y = 0.93).

Figure 3

Metabolic processes (A) and specialised metabolite biosynthesis (B) modulated Arabidopsis thaliana roots treated with Trichoderma spp. and exposed to heat, drought and the combined stress (heat+ drought). The metabolomics dataset produced through UHPLC-ESI/QTOF-MS was subjected to ANOVA and FC analysis (p < 0.05, FC ≥ 2), and differential metabolites were loaded into the PlantCyc Pathway Tool (https://www.plantcyc.org/).

Figure 4

Hormones biosynthesis modulated in Arabidopsis thaliana roots treated with Trichoderma spp. and exposed to heat, drought, and the combined stress (heat+ drought). The metabolomics dataset produced through UHPLC-ESI/QTOF-MS was subjected to ANOVA and FC analysis (p < 0.05, FC ≥ 2), and differential metabolites were loaded into the PlantCyc Pathway Tool (https://www.plantcyc.org/)The x axis represents each set of metabolic subcategories, while the y axis corresponds to the accumulative 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.

Figure 5

(A) Taxonomic Barplot of relative abundances for the top 14 most abundant Phyla in root and rhizosphere soil samples, grouped by treatments (C = Control watered plants, CT = Control + Trichoderma, CT+D = Trichoderma + Drought, CT+H = Trichoderma + Heat, CT+H+D = Trichoderma + Heat + Drought). (B) Taxonomic Barplot shows the 14 most abundant Orders within the dominating Phylum Proteobacteria.

Figure 6

(A, B) show alpha diversities (as a number of Observed ASV and Inverse Simpson index) of root and soil samples. (C, D) show beta diversity, the filtered read counts were transformed by Hellinger transformation, PCoA was used for the ordination plot based on Bray-Curtis distances of samples.

Figure 7

Arrow Plot from multiblock sPLS-DA (DIABLO): Samples of the data blocks metabarcoding and metabolomics are plotted into the space spanned by the first two components of the model. The length of the arrows indicates the distance of each sample from the centroids of both datasets. Short distances indicate high levels of agreement between metabolomics and metabarcoding blocks.