Proteogenomic Analysis Greatly Expands the Identification of Proteins Related to Reproduction in the Apogamous Fern Dryopteris affinis ssp. affinis - Grossmann, Jonas; Fernández, Helena; Chaubey, Pururawa M.; Valdés, Ana E.; Gagliardini, Valeria; Cañal, María J.; Russo, Giancarlo; Grossniklaus, Ueli (Frontiers in Plant Science, 2017)
Performing proteomic studies on non-model organisms with little or no genomic information is still difficult. However, many specific processes and biochemical pathways occur only in species that are poorly characterized at the genomic level. For example, many plants can reproduce both sexually and asexually, the first one allowing the generation of new genotypes and the latter their fixation. Thus, both modes of reproduction are of great agronomic value. However, the molecular basis of asexual reproduction is not understood in any plant. In ferns, it combines the production of unreduced spores (diplospory) and the formation of sporophytes from somatic cells (apogamy). To set the basis to study these processes, we performed transcriptomics by next-generation sequencing (NGS) and shotgun proteomics by tandem mass spectrometry in the apogamous fern D. affinis ssp. affinis. For protein identification we used the public viridiplantae database (VPDB) to identify orthologous proteins from other plant species and new transcriptomics data to generate a “species-specific transcriptome database” (SSTDB). In total 1397 protein clusters with 5865 unique peptide sequences were identified (13 decoy proteins out of 1410, protFDR 0.93% on protein cluster level). We show that using a “species-specific transcriptome database” for protein identification increases the number of identified peptides almost four times compared to using only the publically available viridiplantae database. We identified homologs of proteins involved in reproduction of higher plants, including proteins with a potential role in apogamy. With the increasing availability of genomic data from non-model species, similar proteogenomics approaches will improve the sensitivity in protein identification for species only distantly related to models.