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A multi-omics analysis of the grapevine pathogen Lasiodiplodia theobromae reveals that temperature affects the expression of virulence- and pathogenicity-related genes

ABSTRACT: A multi-omics analysis of the grapevine pathogen Lasiodiplodia theobromae reveals that temperature affects the expression of virulence- and pathogenicity-related genes

PROVIDER: PRJNA543298 | ENA |

REPOSITORIES: ENA

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Project description:Purpose: Lasiodiplodia theobromae is a pathogenic fungus causing Botryosphaeria dieback in grapevine. Heat and drought stress was suggested to promote the disease incidence in recent years, therefore we decided to evaluate the effect of heat on in vitro experiment, adding grapevine wood to simulate the environment in the trunk during in field infection. Methods: Fungal mRNA was extracted from mycellium collected after 49 hours of growth without stress in the presence of GW (GW_noHS) or in its absence (VS_noHS). Some mRNA were extracted form mycellium collected 10 min after stress (1 hour at 42 C) both in the presence of GW (GW_HS) or in its absence (VS_HS). The cDNA and sequenciation was done according with Illumina HiSeq 2500 recomendations by triplicate. Paired end reads (100bp) were used for de novo assembly through Trinity. Transdecoder was used to predict ORFs. The transcripts functional annotatio was done through BlastX and Blast2GO. Bowtie2 was used to map the reads for each sample to the previously assembled transcritome, a homemade Perl script was used to count the mapped reads and edgeR was employed for differential expression and statistical analysis. Results: Seventy million reads were asembled to produce 19,860 transcripts, from those 15,981 were predicted to codify for ORFs. Thirty-five % of transcripts did not have any homologue in NCBI NR database, most of those also did not were predicted to codify for ORFs. With the remainning 38 % of transcripts the functional annotation was obtained. Around 90% of reads for each sample were mapped to the de novo assembled transcriptome. Around 2339 genes were differentially expressed when comparing GW_HS vs GW_noHS or vs VS_noHS and GW_noHS vs VS_noHS and VS_HS vs VS_noHS. Hierarchical clustering with functional enrichment were used to predict metabolic pathways with differential regulation. In silico prediction were then verified with RT-qPCR showing linear correlation (r2=0.87) in in vitro experiment. Then trough in planta fungal gene expression helps to verify that phenolic metabolism is important for colonization in grapevine. Conclusion: A model for fungal response to HS in the presence of GW was proposed. This correspond to the first work to identify nucleotide sequences, measure gene expression and propose pathogenicity factors important for disease establishment in grapevine.

Dataset Information

A multi-omics analysis of the grapevine pathogen Lasiodiplodia theobromae reveals that temperature affects the expression of virulence- and pathogenicity-related genes

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