Noncoding elements in wheat defence response to fusarium head blight

Abstract

Wheat (Triticum aestivum L.) is a major source of global food security while various stressors, including biotic and abiotic factors, directly affect its production. Among these stressors, Fusarium infection poses a significant risk, leading to severe yield losses, and compromising the overall quality of the crop. To understand the regulatory mechanisms modulating wheat's response against Fusarium Head Blight (FHB) stress, a comprehensive analysis of the noncoding RNA profiles of two wheat varieties, Vida and Hank, was conducted. A dataset has been generated utilizing high throughput RNA sequencing (RNAseq) and small RNA sequencing (sRNAseq) technologies for identifying and characterizing microRNA (miRNA) and long noncoding RNA (lncRNA) profiles of these cultivars and the changes upon Fusarium infection. Our analysis revealed not only common but also cultivar- and condition-specific miRNAs and lncRNA transcripts, showing the unique regulatory responses exhibited by these wheat varieties under Fusarium stress. Furthermore, the functional properties of the identified miRNAs were investigated by identifying their putative coding sequence (CDS) targets. Additionally, the regulatory relationships between the putative miRNAs and lncRNAs were explored, providing a view of the complex molecular networks coordinating wheat's response against Fusarium infection. The proposed regulatory network includes the dynamic interplay between miRNAs, CDS targets, and lncRNAs, offering insights into potential key players in the adaptive responses of wheat to biotic stressors.

Keywords: Fusarium; Long noncoding RNA; MicroRNA; Noncoding; Wheat.

Fig. 1

(A) X-axis represents the total number of transcripts obtained for each sample after mapping RNA-seq data to the reference genome. Dark bars represent the number of eliminated transcripts at each tier of the stepwise filtering process whereas the light bars indicate the remaining number of transcripts, which were identified as putative lncRNA transcripts. (B) The number of shared clusters of lncRNA transcripts across four samples. Each cluster represents a group of lncRNA transcripts that exhibit a 90% sequence similarity. Yellow squares correspond to the Vida variety, and orange squares represent the Hank variety, highlighting the presence of shared lncRNA clusters among these two varieties.

Fig. 2

Known miRNA families identified in each sample. Yellow squares represent the presence of miRNA family in a Vida variety sample, orange squares represent the presence of the identified miRNA family in a Hank variety sample.

Fig. 3

Number of total miRNA sequences, including novel and known miRNA sequences, shared among each sample.

Fig. 4

(A) The distribution of identified putative miRNA sequences across chromosomes. (B) The number of putative lncRNA sequences targeted by identified miRNA sequences from each chromosome.

Fig. 5

(A) The average number of miRNA families with multiple predicted coding sequence (CDS) targets. The number of targets was categorized into 7 bins of specific sizes for simplicity. Most miRNA families demonstrated a tendency to target between 2 and 50 CDS, with only a few instances of miRNA families either focusing on a single target or having more than 50 predicted targets. (B) The average number of predicted CDS targets, classified as being targeted by either one or multiple miRNA families. In contrast to miRNAs with multiple targets, each individual target predominantly appeared to be targeted by a single miRNA family. (C) The average number of miRNA families with multiple putative lncRNA targets. Similar to the CDS targets, number of lncRNA targets was distributed into 7 bins of specific sizes. The majority of the miRNA families were identified to target a single lncRNA transcript. (D) The average number of predicted lncRNA targets, categorized based on whether they were targeted by only one or multiple miRNA families. Individual lncRNA targets predominantly targeted by a single miRNA family, consistent with our observations for coding sequences.