Analyzing the defense response mechanism of Atractylodes macrocephala to Fusarium oxysporum through small RNA and degradome sequencing

Abstract

Introduction: Fusarium oxysporum is a significant soil-borne fungal pathogen that affects over 100 plant species, including crucial crops like tomatoes, bananas, cotton, cucumbers, and watermelons, leading to wilting, yellowing, growth inhibition, and ultimately plant death. The root rot disease of A. macrocephala, caused by F. oxysporum, is one of the most serious diseases in continuous cropping, which seriously affects its sustainable development.

Methods: In this study, we explored the interaction between A. macrocephala and F. oxysporum through integrated small RNA (sRNA) and degradome sequencing to uncover the microRNA (miRNA)-mediated defense mechanisms.

Results: We identified colonization of F. oxysporum in A. macrocephala roots on day 6. Nine sRNA samples were sequenced to examine the dynamic changes in miRNA expression in A. macrocephala infected by F. oxysporum at 0, 6, and 12 days after inoculation. Furthermore, we using degradome sequencing and quantitative real-time PCR (qRT-PCR), validated four miRNA/target regulatory units involved in A. macrocephala-F. oxysporum interactions.

Discussion: This study provides new insights into the molecular mechanisms underlying A. macrocephala's early defense against F. oxysporum infection, suggesting directions for enhancing resistance against this pathogen.

Keywords: Atractylodes macrocephala; Fusarium oxysporum; high-throughput sequencing; miRNA; target.

Figure 1

The double standard curve method was established to detect the amount of F. oxysporum. (A) Standard curve of the relationship between DNA content and CT value of A. macrocephala. (B) Standard curve of the relationship between DNA content and CT value of F. oxysporum. (C) qPCR analysis of the relative DNA content of F. oxysporum and A. macrocephala. Significant is the difference between “a”, “b” and “c” (p < 0.05).

Figure 2

Profiling of sRNA sequencing in A. macrocephala plants infected by F. oxysporum. (A) Total clean reads distribution and radioactivity (% of total) in libraries at each time point between each experimental group and the control group. (B) Rfam classification table for sRNA.

Figure 3

Total abundance of sRNA sequences in each size class and miRNA nucleotide bias analysis results. (A) Length distribution of sequencing results (total). (B) Results of total miRNA nucleotide bias. (C) Results of miRNA first nucleotide bias at each position.

Figure 4

The statistics of miRNAs in the disparate sample. (A) Number of identified known and novel miRNAs. (B) Summary of the number of miRNA family members.

Figure 5

Venn-diagram analysis of the number and overlap of miRNAs in A. macrocephala plants infected by F. oxysporum at 0, 6, and 12 dpi. (A) Venn-diagram analysis of known miRNA (B) Venn-diagram analysis of novel miRNA.

Figure 6

Statistics of differentially expressed miRNAs in different comparison groups. (A) Bar graph of differential miRNA in different comparison groups. (B) Venn-diagram of differential miRNAs in different comparison groups.

Figure 7

Prediction of miRNA target genes by degradation group sequencing. (A) miR156 regulatory network. (B) hy-miR156a cut DN6217 gene. (C) miR396 regulatory network. (D) csi-miR396-5p cut DN8746 gene. (E) miR414 regulatory network. (F) ath-miR414 cut DN4613 gene. Yellow square: miRNA, blue circle: mRNA, red dot and arrow: nucleotide cleavage site on target gene.

Figure 8

GO term enrichment at 6 (A) and 12 (B) dpi.

Figure 9

qRT-PCR analysis of the expression of miRNAs and their corresponding targets during A. macrocephala–F. oxysporum interaction. (A) Heatmap of expression levels for 4 pairs of mRNA–miRNA. (B) qRT-PCR detection of miRNA–mRNA target pair expression after A. macrocephala has been infected. Significant is the difference between “a” and “b” (p < 0.05).