De novo transcriptome sequencing and analysis for Venturia inaequalis, the devastating apple scab pathogen

Abstract

Venturia inaequalis is the causal agent of apple scab, one of the most devastating diseases of apple. Due to several distinct features, it has emerged as a model fungal pathogen to study various aspects of hemibiotrophic plant pathogen interactions. The present study reports de novo assembling, annotation and characterization of the transcriptome of V. inaequalis. Venturia transcripts expressed during its growth on laboratory medium and that expressed during its biotrophic stage of infection on apple were sequenced using Illumina RNAseq technology. A total of 94,350,055 reads (50 bp read length) specific to Venturia were obtained after filtering. The reads were assembled into 62,061 contigs representing 24,571 unique genes. GO analysis suggested prevalence of genes associated with biological process categories like metabolism, transport and response to stimulus. Genes associated with molecular function like binding, catalytic activities and transferase activities were found in majority. EC and KEGG pathway analyses suggested prevalence of genes encoding kinases, proteases, glycoside hydrolases, cutinases, cytochrome P450 and transcription factors. The study has identified several putative pathogenicity determinants and candidate effectors in V. inaequalis. A large number of transcripts encoding membrane transporters were identified and comparative analysis revealed that the number of transporters encoded by Venturia is significantly more as compared to that encoded by several other important plant fungal pathogens. Phylogenomics analysis indicated that V. inaequalis is closely related to Pyrenophora tritici-repentis (the causal organism of tan spot of wheat). In conclusion, the findings from this study provide a better understanding of the biology of the apple scab pathogen and have identified candidate genes/functions required for its pathogenesis. This work lays the foundation for facilitating further research towards understanding this host-pathogen interaction.

Figure 1. Workflow of RxLR effector prediction.

A flow chart describing the strategies for prediction of RxLR effectors and open reading frame of the transcripts.

Figure 2. Quality score for each lane of Illumina reads.

The line diagram of the quality score of the reads obtained from different lanes.

Figure 3. General annotation of V. inaequalis transcripts.

Gene Ontology (GO) term assignments to V. inaequalis unigenes, based on significant GO slims, summarized into two main GO categories: biological process (A), and molecular function (B). Functional characterization and abundance of V. inaequalis transcriptome for enzyme classes, and KEGG pathways are represented in C and D respectively. Only top 25 most abundant EC and KEGG pathways are represented. Area under each pie represents the value in percent.

Figure 4. Bar Graph illustrating the Conserved Domains in Set B transcripts.

Sequences showing no homology with nr database (Set B transcripts) were subjected to Conserved Domain Database (CDD) to predict the conserved domain. Top 15 highly represented functional conserved domains are shown by bars.

Figure 5. Secretome analysis of V. inaequalis.

Comparative analysis of secretome sizes of various filamentous fungi (A), showing that the secretome size of V. inaequalis is comparable to other fungi. Gene Ontology (GO) term assignments to V. inaequalis secretome, based on significant GO slims, summarized into two main GO categories: biological process (B), and molecular function (C). Area under each pie represents the value in percent. Nearly 50% of genes are involved in metabolic processes as shown in B, while peptidase activity and catalytic activity are predominant molecular functions (C).

Figure 6. Gene Ontology classification of V. inaequalis orthologs of PHI genes.

GO term assignments to V. inaequalis unigenes, with homology to PHI genes. A: represents biological process and B: represents the molecular function. PHI analysis revealed that the metabolic processes are overrepresented in PHI genes ortholog of V. inaequalis, thus highlighting the potential role of these processes in pathogenicity.

Figure 7. Gene Ontology classification of V. inaequalis orthologs of PHI genes that are associated with loss of pathogenicity and reduced virulence.

GO term assignments to V. inaequalis unigenes with homology to PHI genes associated with loss of pathogenicity (A, C) and reduced virulence (B, D). A, B and C, D represents the biological process and molecular function, respectively. This analysis revealed that the majority of PHI genes associated with loss of pathogenicity and reduced virulence are involved in metabolic processes, thus highlighting the potential role of these processes in pathogenicity.

Figure 8. Comparative analysis of protein families of V. inaequalis with different plant fungal-pathogens.

The histogram reflects the number of selected protein families across different phytopathogens namely F. graminearum, M. oryzae, B. cinerea and S. sclerotiorum.

Figure 9. Histogram showing the abundance of Peptidase families in different plant fungal pathogens.

V. inaequalis transcripts encoding peptidase families were predicted using MEROPS database and the respective percent sizes were compared with that of F. graminearum, M. oryzae, B. cinerea and S. sclerotiorum.

Figure 10. Phylogenomic analysis of V. inaequalis.

Maximum Likelihood based phylogenetic analysis using RAxML tool, on three different super-alignments of V. inaequalis and that of other nine selected fungal pathogens, were used to establish phylogenomic relationship. A, B, C represents phylogenetic tree from super-alignment constructed by Gblocks-con, Gblocks-lib and remgaps, respectively using Hal pipeline.