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Holm oak (Quercus ilex) is the most important and representative species of the Mediterranean forest and of the Spanish agrosilvo-pastoral “dehesa” ecosystem. Despite its environmental and economic interest, Holm oak is an orphan species whose biology is very little known, especially at the molecular level. In order to increase the knowledge on the chemical composition and metabolism of this tree species, the employment of a holistic and multi-omics approach, in the Systems Biology direction would be necessary. However, for orphan and recalcitrant plant species, specific analytical and bioinformatics tools have to be developed in order to obtain adequate quality and data-density before to coping with the study of its biology. By using a plant sample consisting of a pool generated by mixing equal amounts of homogenized tissue from acorn embryo, leaves, and roots, protocols for transcriptome (NGS-Illumina), proteome (shotgun LC-MS/MS), and metabolome (GC-MS) studies have been optimized. These analyses resulted in the identification of around 62629 transcripts, 2380 protein species, and 62 metabolites. Data are compared with those reported for model plant species, whose genome has been sequenced and is well annotated, including Arabidopsis, japonica rice, poplar, and eucalyptus. RNA and protein sequencing favored each other, increasing the number and confidence of the proteins identified and correcting erroneous RNA sequences. The integration of the large amount of data reported using bioinformatics tools allows the Holm oak metabolic network to be partially reconstructed: from the 127 metabolic pathways reported in KEGG pathway database, 123 metabolic pathways can be visualized when using the described methodology. They included: carbohydrate and energy metabolism, amino acid metabolism, lipid metabolism, nucleotide metabolism, and biosynthesis of secondary metabolites. The TCA cycle was the pathway most represented with 5 out of 10 metabolites, 6 out of 8 protein enzymes, and 8 out of 8 enzyme transcripts. On the other hand, gaps, missed pathways, included metabolism of terpenoids and polyketides and lipid metabolism. The multi-omics resource generated in this work will set the basis for ongoing and future studies, bringing the Holm oak closer to model species, to obtain a better understanding of the molecular mechanisms underlying phenotypes of interest (productive, tolerant to environmental cues, nutraceutical value) and to select elite genotypes to be used in restoration and reforestation programs, especially in a future climate change scenario. [ABSTRACT FROM AUTHOR]

The proteomics of orphan, unsequenced, and recalcitrant organisms is highly challenging. This is the case of the typical Mediterranean forest tree Holm oak (Quercus ilex). Proteomics has moved on quite fast from the classical 2DE-MS to shotgun or gel-free/label-free approaches, with the latter possessing a series of advantages over the gel-based ones. Before translating proteomics data into biological knowledge, a few questions as to the analysis technique itself have to be answered including its confidence in protein identification and quantification. It is important to clearly differentiate a hit from an ortholog and gene product identification, with the difference depending on the database and the confidence parameters (score, number of peptides, and coverage). With respect to quantification and for comparative purposes it is important to make sure that we are within the linear dynamic range. For that, a calibration curve based on mass spectrometry analysis of a serial dilution of the extracts should be performed. Thus, just by validating our data with the aim of improving the quality of the analysis enables us to give a correct interpretation of our results. We show a method that aims to improve the confidence in protein identification and quantification in the orphan species Q. ilex using a shotgun proteomics approach.

Proteomics encompasses efforts to identify all the proteins of a proteome, with most of studies about plant proteomics based on a bottom-up mass spectrometry (MS) strategy, in which the proteins are subjected to digestion by trypsin and the tryptic fragments are subjected to MS analysis. The identification of proteins from MS/MS spectra has been performed using different algorithms (Mascot, Sequest) against plant protein sequence databases such as UniProtKB or NCBI_Viridiplantae. But these databases are not the best choice for nonmodel species where they are underrepresented, resulting in poor identification rates. A high identification rate requires a sequenced and well-annotated genome of the species under investigation. For nonmodel organisms, the identification of proteins is challenging since, in the best of the cases, only hits or orthologs instead of gene products are identified. However, in the absence of a sequenced genome, this situation can be improved using transcriptome data to generate a specific species database to compare proteins. In this chapter, we report the protein database construction from RNA-Seq data in a nonmodel species, in this particular case Holm oak (Q. ilex).

It is impossible to capture in just one experiment all or most of the total set of protein species that constitute the cell's proteome. Thus, according to our results, and even considering that they depend on the experimental system carried out (plant, yeast, fungi, or bacteria), the best protein extraction protocol yielded less than 20 % of the total amount of proteins, as determined by the Kjeldahl method. For this reason, protein cataloguing and the whole proteome characterization require the use of firstly, fractionation techniques at the cellular, subcellular, protein, or peptide level, and secondly, the use of complementary approaches.Within our current research on Holm oak (Quercus ilex subsp. ballota), we aim to characterize its seed proteome. For that we have optimized an experimental workflow in which the Osborne sequential protein extraction (Osborne, Science 28:417-427, 1908) is combined with downstream electrophoretic protein separation or shotgun MS analysis. In general, it can be used to study any plant seed, as well as to investigate on seed maturation and germination, genotype characterization, allergens identification, food traceability, and substantial equivalence, among others.