Molecular Programming of Drought-Challenged Trichoderma harzianum-Bioprimed Rice (Oryza sativa L.)

Abstract

Trichoderma biopriming enhances rice growth in drought-stressed soils by triggering various plant metabolic pathways related to antioxidative defense, secondary metabolites, and hormonal upregulation. In the present study, transcriptomic analysis of rice cultivar IR64 bioprimed with Trichoderma harzianum under drought stress was carried out in comparison with drought-stressed samples using next-generation sequencing techniques. Out of the 2,506 significant (p < 0.05) differentially expressed genes (DEGs), 337 (15%) were exclusively expressed in drought-stressed plants, 382 (15%) were expressed in T. harzianum-treated drought-stressed plants, and 1,787 (70%) were commonly expressed. Furthermore, comparative analysis of upregulated and downregulated genes under stressed conditions showed that 1,053 genes (42%) were upregulated and 733 genes (29%) were downregulated in T. harzianum-treated drought-stressed rice plants. The genes exclusively expressed in T. harzianum-treated drought-stressed plants were mostly photosynthetic and antioxidative such as plastocyanin, small chain of Rubisco, PSI subunit Q, PSII subunit PSBY, osmoproteins, proline-rich protein, aquaporins, stress-enhanced proteins, and chaperonins. The Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis states that the most enriched pathways were metabolic (38%) followed by pathways involved in the synthesis of secondary metabolites (25%), carbon metabolism (6%), phenyl propanoid (7%), and glutathione metabolism (3%). Some of the genes were selected for validation using real-time PCR which showed consistent expression as RNA-Seq data. Furthermore, to establish host-T. harzianum interaction, transcriptome analysis of Trichoderma was also carried out. The Gene Ontology (GO) analysis of T. harzianum transcriptome suggested that the annotated genes are functionally related to carbohydrate binding module, glycoside hydrolase, GMC oxidoreductase, and trehalase and were mainly upregulated, playing an important role in establishing the mycelia colonization of rice roots and its growth. Overall, it can be concluded that T. harzianum biopriming delays drought stress in rice cultivars by a multitude of molecular programming.

Keywords: DEGs; Trichoderma harzianum; drought; rice; transcriptome (RNA-seq).

FIGURE 1

(A) Venn diagram showing the unique (non-overlapping region) and common expressed genes (overlapping region) obtained when drought-stressed rice is compared with T. harzianum-treated + drought-stressed rice. (B) Volcano plot representing significant and non-significant differentially expressed gene (DEG)-based p values. The green dot represents the significant DEGs.

FIGURE 2

Commonly expressed functional pathway categories in the T. harzianum-treated drought-stressed rice genome vs drought-stressed rice.

FIGURE 3

Heat map with cluster categorization representing the top 50 significant DEGs at two different comparisons of treatments (T. harzianum-treated + drought-stressed, drought-stressed). Each column represents the DEGs in different samples with two replicates. The red color shows upregulated genes and the green color represents downregulated genes based on highest FPKM values. Each row represents an individual transcript.

FIGURE 4

Gene Ontology (GO)-based functional annotation of genes present in the T. harzianum-treated drought-stressed rice genome vs. drought-stressed rice. (A) Biological process domains, (B) molecular function domain, and (C) cellular process domains.

FIGURE 5

KEGG pathway distribution of upregulated and downregulated genes.

FIGURE 6

KEGG enrichment for DEGs from the three pathways: (A) carbon fixation, (B) glutathione metabolism, and (C) phenyl propanoid biosynthesis. The red highlights represent the enriched enzymes of the pathways.

FIGURE 7

qRT-PCR validation of selected genes showed significant difference in their expression in T. harzianum-treated and drought-stressed when compared with drought-stressed at three different time intervals (4, 7, and 10 days). Error bars show ± SD among the biological replicates.