Bud maturation is a physiological process which implies a set of morphophysiological changes which lead to the transition of growth patterns from young to mature. This transition defines tree growth and architecture, and in consequence traits such as biomass production and wood quality. In Pinus pinaster, a conifer of great timber value, bud maturation is closely related to polycyclism (multiple growth periods per year). This process causes a lack of apical dominance, and consequently increased branching that reduces its timber quality and value. However, despite its importance, little is known about bud maturation. In this work, proteomics and metabolomics were employed to study apical and basal sections of young and mature buds in P. pinaster. Proteins and metabolites in samples were described and quantified using (n)UPLC-LTQ-Orbitrap. The datasets were analyzed employing an integrative statistical approach, which allowed the determination of the interactions between proteins and metabolites and the different bud sections and ages. Specific dynamics of proteins and metabolites such as HISTONE H3 and H4, RIBOSOMAL PROTEINS L15 and L12, CHAPERONIN TCP1, 14–3-3 protein gamma, gibberellins A1, A3, A8, strigolactones and ABA, involved in epigenetic regulation, proteome remodeling, hormonal signaling and abiotic stress pathways showed their potential role during bud maturation. Candidates and pathways were validated employing interaction databases and targeted transcriptomics. These results increase our understanding of the molecular processes behind bud maturation a key step towards improving timber production and natural pine forests management in a future scenario of climate change. However, further studies are necessary by using different P. pinaster populations that show contrasting wood quality and stress tolerance in order to generalize the results.
The increasing availability of massive omics data requires improving the quality of reference databases and their annotations. The combination of full-length isoform sequencing (Iso-Seq) with short-read transcriptomics and proteomics has been successfully used for increasing proteoform characterization, which is a main ongoing goal in biology. However, the potential of including Oxford Nanopore Technologies Direct RNA Sequencing (ONT-DRS) data has not been explored. In this paper, we analyzed the impact of combining Iso-Seq- and ONT-DRS-derived data on the identification of proteoforms in Arabidopsis MS proteomics data. To this end, we selected a proteomics dataset corresponding to senescent leaves and we performed protein searches using three different protein databases: AtRTD2 and AtRTD3, built from the homonymous transcriptomes, regarded as the most complete and up-to-date available for the species; and a custom hybrid database combining AtRTD3 with publicly available ONT-DRS transcriptomics data generated from Arabidopsis leaves. Our results show that the inclusion and combination of long-read sequencing data from Iso-Seq and ONT-DRS into a proteogenomic workflow enhances proteoform characterization and discovery in bottom-up proteomics studies. This represents a great opportunity to further investigate biological systems at an unprecedented scale, although it brings challenges to current protein searching algorithms.
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