Abstract
A lot of research has focused on investigating mechanisms of vegetative desiccation tolerance in resurrection plants. Various approaches have been used to undertake such research and these include high throuput approaches such as the 'omics' - transcriptomics and metabolomics. Proteomics has since become more prefarable than transcriptomics as it it provides a view of the end-point of gene expression. However, most proteomics investigations in literature publish differentially expresses protein lists and attempt to interpret such lists in isolation. This is despite the fact that proteins do not act in isolation. A comprehensive bioinformatics investigation can reveal more information on the desiccation tolerance mechanism of resurrection plants. In this work, a comprehensive bioinformatic analysis of the published proteomic results in Ingle et al. (2007) was carried out. GeneMania was used to carry out protein-protein interaction studies while ClueGo was used to identify GO biological process terms. A preliminary map of protein-protein interactions was built up and these led to the predicted of more proteins that are likely to to be connect to the ones identified by Ingle et al. (2007). Briefly, whereas 2DE proteomics identified 17 proteins as being differentially regulated (4 de novo, 6 up-regulated and 7 down-regulated), GeneMania managed to add 57 more proteins to the network (de novo - 20, up-regulated - 17 and down-regulated - 20). Each protein set has unique GO biological process terms overrepresented in it. This study explores the protein pathways affected by desiccation stress from an interactomic prospective highlighting the importance of advanced bioinformatic analysis.
Introduction
Resurrection plants can survive extreme water loss and survive long periods in an abiotic state and upon watering, rapidly restore their normal metabolism (reviewed inter alia in Farrant, 2007). Understanding the mechanisms of desiccation tolerance (DT) in resurrection plants is important as they are deemed to be an excellent model to study the mechanisms associated with DT. Proteomic profiling offers the opportunity to identify proteins that mediate the pathways involved in the DT mechanisms, when cells are subjected to desiccation stress. A number of proteomics studies have been reported for leaves of some angiosperm resurrection plants during desiccation (Röhrig et al., 2006; Ingle et al., 2007; Jiang et al., 2007; Abdalla et al., 2010; Wang et al., 2010; Oliver et al., 2011; Abdalla and Rafudeen, 2012 etc.).
Since DT involves the integrated actions of many proteins, a systems-level understanding of experimentally derived proteomics data is essential to gain deeper insights into the protection mechanisms employed by resurrection plants against desiccation. In recent years, an increasing emphasis has been put on integrated analysis of gene expression data via protein protein interactions (PPI), which are widely applied in interaction prediction, functional modules identification and protein function prediction.
In this work, PPI analysis is applied to study the proteomics data obtained by Ingle et al. (2007) during the desiccation of Xerophyta viscosa leaves. In their study, using 2DE, they identified 17 desiccation responsive proteins(4 de novo, 6 up-regulated and 7 down-regulated). The aim of the work is to establish if the proteins in each set interact and if they do, the second aim would be to establish if there are any statistically significant GO biological process terms that can be observed in each set.
Methods
Protein lists
The initial protein lists used in PPI analyses in this work were obtained from the 2DE data from Ingle et al. (2007) - (see Table 2 in Ingle et al. (2007)).
Protein-protein integration
The Cytoscape v3.8.1 (Shannon et al., 2003) app GeneMANIA (Warde-Farley et al., 2010), was used to derive the interactome of empirically determined and predicted PPIs of differentially regulated gene lists. Protein lists for 'up-regulated', 'down-regulated' and 'de novo' proteins were used as query lists for PPI studies. Arabidopsis thaliana analogs of the desiccation responsive protein sets were used as query genes, and the program was run with default settings.
GO biological process functional enrichment analysis
The Cytoscape app ClueGO v2.5.7 (Bindea et al., 2009) was used for enrichment of GO biological process terms. ClueGO extracts the non-redundant biological information for groups of genes/proteins using GO terms and can conduct cluster – cluster comparisons. In the present study, for input, TAIR identifiers from the extended list of desiccation responsive proteins obtained from GeneMania were used as protein cluster lists and ontology terms were derived from A. thaliana. The ClueGO ‘cluster comparison’ allowed the identification of biological process terms that were unique to each protein/gene list.