A novel strategy to uncover specific GO terms/phosphorylation pathways in phosphoproteomic data in Arabidopsis thaliana

Abstract

Background: Proteins are the workforce of the cell and their phosphorylation status tailors specific responses efficiently. One of the main challenges of phosphoproteomic approaches is to deconvolute biological processes that specifically respond to an experimental query from a list of phosphoproteins. Comparison of the frequency distribution of GO (Gene Ontology) terms in a given phosphoproteome set with that observed in the genome reference set (GenRS) is the most widely used tool to infer biological significance. Yet, this comparison assumes that GO term distribution between the phosphoproteome and the genome are identical. However, this hypothesis has not been tested due to the lack of a comprehensive phosphoproteome database.

Results: In this study, we test this hypothesis by constructing three phosphoproteome databases in Arabidopsis thaliana: one based in experimental data (ExpRS), another based in in silico phosphorylation protein prediction (PredRS) and a third that is the union of both (UnRS). Our results show that the three phosphoproteome reference sets show default enrichment of several GO terms compared to GenRS, indicating that GO term distribution in the phosphoproteomes does not match that of the genome. Moreover, these differences overshadow the identification of GO terms that are specifically enriched in a particular condition. To overcome this limitation, we present an additional comparison of the sample of interest with UnRS to uncover GO terms specifically enriched in a particular phosphoproteome experiment. Using this strategy, we found that mRNA splicing and cytoplasmic microtubule compounds are important processes specifically enriched in the phosphoproteome of dark-grown Arabidopsis seedlings.

Conclusions: This study provides a novel strategy to uncover GO specific terms in phosphoproteome data of Arabidopsis that could be applied to any other organism. We also highlight the importance of specific phosphorylation pathways that take place during dark-grown Arabidopsis development.

Keywords: Arabidopsis thaliana; Etiolation; Gene ontology; Phosphoproteome; Reference datasets.

Fig. 1

Scheme representing the relations among the three phosphoproteome reference sets ExpRS, PredRS, UnRS and the GenRS. The number of genes for each dataset is indicated below the name. The number of genes shared between pairs of phosphoproteome datasets is indicated beside each arrow. Percentages among relations are indicated

Fig. 2

Heatmap representing significantly overrepresented GO terms in the phosphoproteome reference sets compared to the GenRS. Heatmaps represent the number of GO terms that change significantly in one, two or the three comparisons among the ExpRS, PredRS and UnRS phosphoproteome reference sets against the GenRS. Results are categorized in 7 groups (G1-G7). Gray: significant Parent-Child Fisher_FDR < 0.01; white: not significant. BP: Biological Process, MF: Molecular Function, CC: Cellular Component

Fig. 3

Heatmap representing significantly overrepresented GO terms in the Et phosphoproteome sample. Heatmap represents the number of GO terms that change significantly in one, two or the three comparisons: Et vs GenRS; Et vs Un11RS and Un11RS vs GenRS. Results are categorized in 6 groups (CG1-CG6; CG: for Contrast Group). Gray: significant Parent-Child Fisher_FDR < 0.01; white: not significant. BP: Biological Process, MF: Molecular Function, CC: Cellular Component

Fig. 4

Semantic similarity for CG2 and CG3 for BP. The terms were represented graphically in two dimensions such that the distance between points on the plot approximates their multivariate dissimilarity as closely as possible. Each circle represents a GO term. The GenRatio is directly proportional to the diameter of the symbol and is indicated between brackets in red