Condition-dependent co-regulation of genomic clusters of virulence factors in the grapevine trunk pathogen Neofusicoccum parvum

Abstract

The ascomycete Neofusicoccum parvum, one of the causal agents of Botryosphaeria dieback, is a destructive wood-infecting fungus and a serious threat to grape production worldwide. The capability to colonize woody tissue, combined with the secretion of phytotoxic compounds, is thought to underlie its pathogenicity and virulence. Here, we describe the repertoire of virulence factors and their transcriptional dynamics as the fungus feeds on different substrates and colonizes the woody stem. We assembled and annotated a highly contiguous genome using single-molecule real-time DNA sequencing. Transcriptome profiling by RNA sequencing determined the genome-wide patterns of expression of virulence factors both in vitro (potato dextrose agar or medium amended with grape wood as substrate) and in planta. Pairwise statistical testing of differential expression, followed by co-expression network analysis, revealed that physically clustered genes coding for putative virulence functions were induced depending on the substrate or stage of plant infection. Co-expressed gene clusters were significantly enriched not only in genes associated with secondary metabolism, but also in those associated with cell wall degradation, suggesting that dynamic co-regulation of transcriptional networks contributes to multiple aspects of N. parvum virulence. In most of the co-expressed clusters, all genes shared at least a common motif in their promoter region, indicative of co-regulation by the same transcription factor. Co-expression analysis also identified chromatin regulators with correlated expression with inducible clusters of virulence factors, suggesting a complex, multi-layered regulation of the virulence repertoire of N. parvum.

Keywords: Botryosphaeria dieback; CAZymes; RNA-seq; cell wall degradation; secondary metabolism; single-molecule real-time (SMRT) sequencing; weighted co-expression network analysis.

Figure 1

Culture of Neofusicoccum parvum isolate UCD646So on potato dextrose agar (PDA) (A) and symptoms of Botryosphaeria dieback on grapevine wood (B, C): wedge‐shaped canker (B) and dead spurs (arrows) (C).

Figure 2

Circular representation of the Neofusicoccum parvum genome. Different tracks denote (moving inwards): (i) mapping coverage of short Illumina reads; (ii) percentage GC content; (iii) repeat density; (iv) protein‐coding gene density; (v–vii) localization of genes encoding virulence factors: (v) induced in presence of wood, (vi) expressed exclusively in planta, and (vii) differentially regulated in planta. Figure was prepared using the OmicCircos Bioconductor package (Hu et al., 2014).

Figure 3

Repertoire of putative plant cell wall‐degrading enzymes (CWDEs) encoded in the Neofusicoccum parvum genome. (A) List of carbohydrate‐active enzymes (CAZymes), grouped by substrate, showing a schematic representation of their enzymatic activities. Adapted from van den Brink and de Vries (2011). (B) Total number of protein‐coding genes annotated as lignocellulolytic CAZymes and detected as expressed in the in vitro and in planta experiments.

Figure 4

Example of Neofusicoccum parvum gene clusters modulated as a function of in vitro substrate. Arrows represent genes coding for carbohydrate‐active enzymes (CAZymes) (red), P450s (blue) and transporters (yellow). Grey arrows correspond to genes predicted to be part of a secondary metabolism cluster, but with other annotations. Asterisks highlight genes with significant up‐regulation in the presence of wood in vitro. Line plots show the RNA‐sequencing mapping coverage in samples grown on minimal medium amended with finely ground grape wood of either ‘Cabernet Sauvignon’ or ‘Merlot’ or on potato dextrose agar (PDA) without wood.

Figure 5

Expression profiles of four groups of genes sharing a similar expression pattern during in planta colonization of grape stems. Groups were obtained by performing a K‐means clustering analysis using the 78 differentially expressed (DE) genes expressed in planta. Each single line represents the log₂‐transformed average of the normalized counts for an individual transcript. Histograms show the counts of genes belonging to each virulence factor category [i.e. carbohydrate‐active enzyme (CAZyme), P450, cellular transporter, FAD‐binding domain, polyketide synthase (PKS) and part of biosynthetic gene cluster (BGC)].

Figure 6

Examples of gene clusters associated with secondary metabolism among the co‐expressed gene Groups 1 (A) and 2 (B) shown in Fig. 5. Arrows represent genes coding for polyketide synthases (PKSs) (pink), P450s (blue), transporters (yellow), transcription factors (green) and FAD‐binding proteins (orange). Grey arrows correspond to genes predicted to be part of a secondary metabolism cluster, but with other annotations. White arrows represent genes outside of the biosynthetic gene clusters (BGCs). Asterisks highlight genes differentially regulated at 24 h post‐inoculation (hpi) (A) and 2 weeks post‐inoculation (wpi) (B). For each transcript, the gene expression profile is represented by the average of the normalized counts at each time point of woody stem colonization.

Figure 7

Graphical visualization of the Neofusicoccum parvum co‐expression network. (A) Dendrogram of all transcripts clustered based on a dissimilarity measure (1 – TOM; TOM, topological overlap mapping metric). Each line of the dendrogram corresponds to a gene. The multi‐colour bar below the dendrogram shows the 40 modules with each gene colour coded based on module assignment. (B) Boxplots showing the number of adjacent co‐expressed genes in each genomic cluster (≥4 adjacent genes per cluster) identified within the co‐expression modules. Numbers above the boxplots represent the total number of genomic clusters in each co‐expression module. (C) Functional category distribution of the genes organized in co‐expressed genomic clusters. Asterisks point to virulence categories significantly over represented (Fisher's exact test; adjusted P < 0.01) in the genomic clusters. BGC, biosynthetic gene cluster; CWDE, cell wall‐degrading enzyme. (D) Bar plot representing the percentage of genomic clusters that contain at least one common promoter motif in all genes that compose the cluster.