Gene Validation and Remodelling Using Proteogenomics of Phytophthora cinnamomi, the Causal Agent of Dieback

Abstract

Phytophthora cinnamomi is a pathogenic oomycete that causes plant dieback disease across a range of natural ecosystems and in many agriculturally important crops on a global scale. An annotated draught genome sequence is publicly available (JGI Mycocosm) and suggests 26,131 gene models. In this study, soluble mycelial, extracellular (secretome), and zoospore proteins of P. cinnamomi were exploited to refine the genome by correcting gene annotations and discovering novel genes. By implementing the diverse set of sub-proteomes into a generated proteogenomics pipeline, we were able to improve the P. cinnamomi genome annotation. Liquid chromatography mass spectrometry was used to obtain high confidence peptides with spectral matching to both the annotated genome and a generated 6-frame translation. Two thousand seven hundred sixty-four annotations from the draught genome were confirmed by spectral matching. Using a proteogenomic pipeline, mass spectra were used to edit the P. cinnamomi genome and allowed identification of 23 new gene models and 60 edited gene features using high confidence peptides obtained by mass spectrometry, suggesting a rate of incorrect annotations of 3% of the detectable proteome. The novel features were further validated by total peptide support, alongside functional analysis including the use of Gene Ontology and functional domain identification. We demonstrated the use of spectral data in combination with our proteogenomics pipeline can be used to improve the genome annotation of important plant diseases and identify missed genes. This study presents the first use of spectral data to edit and manually annotate an oomycete pathogen.

Comparative sub-cellular proteome analyses reveals metabolic differentiation and production of effector-like molecules in the dieback phytopathogen Phytophthora cinnamomi

“…Qualitative, quantitative and functional analysis Label-free quantification was performed using the Proteome Discoverer 2.3 using the label-free precursor quantification workflow template 14 . Spectra were matched against the P. cinnamomi MU94-48 genome 12 . Sub-proteomes were relatively quantified using the default precursor ion quantifier.…”

“…The total protein identifications for each subproteomes are shown in Supplementary material 1 and 2. This accounts for 17.7% of the predicted annotated genes in the P. cinnamomi genome 12 . Of these, 1070, 698 and 278 were unique to the mycelia, zoospores and secretome respectively (Figure 1).…”

“…Phytophthora cinnamomi (MU94-48) cultures were obtained from the Centre of Phytophthora Science and Management (Murdoch University, Australia) and maintained by passaging through Malus domestica cv. Granny Smith fruit 11,12 . Mycelia were grown on V8 juice (Campbells) agar with growth periods of 5-7 days in the dark at room temperature.…”

“…The liquid media was passed through a 0.22 µm filter to remove mycelial fragments and obtain the secretome. Zoospores were produced as previously described 12 . Purified mycelia, secretome and zoospores were snap-frozen in liquid nitrogen and freezedried.…”

Uncovering the Role of Metabolism in Oomycete–Host Interactions Using Genome-Scale Metabolic Models

“…In omics-based bioinformatics studies, it is common practice to search for overrepresentation (enrichment) of functional annotations in differentially abundant molecules such as mRNA or proteins ( Reed et al, 2006 ; Bordbar et al, 2014 ). Over the last decade, there has been a continuous flow of studies presenting oomycete comparative genomics, transcriptomics, proteomics, proteogenomics, and metabolomics data ( Andronis et al, 2020 ; McGowan and Fitzpatrick, 2020 ). These large-scale omics datasets are analyzed to understand how oomycetes evolve, reproduce, and interact with their hosts.…”