Transcriptomics Analysis of Wheat Tassel Response to Tilletia laevis Kühn, Which Causes Common Bunt of Wheat

Abstract

Tilletia laevis Kühn [synonym T. foetida (Wallr.) Liro] can lead to a wheat common bunt, which is one of the most serious diseases affecting kernels, a serious reduction in grain yield, and losses can reach up to 80% in favorable environments. To understand how wheat tassels respond to T. laevis, based on an RNA-Seq technology, we analyzed a host transcript accumulation on healthy wheat tassels and on tassels infected by the pathogen. Our results showed that 7,767 out of 15,658 genes were upregulated and 7,891 out of 15,658 genes were downregulated in wheat tassels. Subsequent gene ontology (GO) showed that differentially expressed genes (DEGs) are predominantly involved in biological processes, cellular components, and molecular functions. Additionally, Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis showed that 20 pathways were expressed significantly during the infection of wheat with T. laevis, while biosynthesis of amino acids, carbon metabolism, and starch and sucrose metabolism pathways were more highly expressed. Our findings also demonstrated that genes involved in defense mechanisms and myeloblastosis (MYB) transcription factor families were mostly upregulated, and the RNA-seq results were validated by quantitative real-time polymerase chain reaction (qRT-PCR). This is the first report on transcriptomics analysis of wheat tassels in response to T. laevis, which will contribute to understanding the interaction of T. laevis and wheat, and may provide higher efficiency control strategies, including developing new methods to increase the resistance of wheat crops to T. laevis-caused wheat common bunt.

Keywords: Tilletia foetida; defense response; transcriptomic; wheat common bunt; wheat tassel.

FIGURE 1

Summary of differentially expressed genes (DEGs). Numbers of DEGs between the control and T. laevis infection groups.

FIGURE 2

Sample-to-sample clustering analysis for checking batch effects and their similarity.

FIGURE 3

Principal component analysis (PCA) for gene expression patterns. The first and second PCAs explained 72.7 and 16.62% of the variance, respectively.

FIGURE 4

Significant differentially expressed genes (DEGs) in T. laevis-infected vs. control samples. Up- or downregulated DEGs in response to T. laevis infection.

FIGURE 5

Hierarchical clustering heatmap of DEGs according to changes in expression in response to T. laevis infection. Each column shows a library, and each row shows a DEG expression. The colors blue, white, and red indicate low, medium, and high expression patterns of genes, respectively.

FIGURE 6

Gene ontology (GO) enrichment analysis of significant DEGs of T. laevis-infected and control samples. Annotations are grouped by biological process, cellular component, and molecular function.

FIGURE 7

KEGG enrichment analysis scatter plot representing pathways of DEGs in response to T. laevis infection. The colors blue, white and red indicate low, medium, and high expression patterns of genes, respectively.

FIGURE 8

Validation of RNA-Seq data by quantitative real-time PCR (qRT-PCR).