OnPLS integration of transcriptomic, proteomic and metabolomic data shows multi-level oxidative stress responses in the cambium of transgenic hipI- superoxide dismutase Populus plants

Abstract

Background: Reactive oxygen species (ROS) are involved in the regulation of diverse physiological processes in plants, including various biotic and abiotic stress responses. Thus, oxidative stress tolerance mechanisms in plants are complex, and diverse responses at multiple levels need to be characterized in order to understand them. Here we present system responses to oxidative stress in Populus by integrating data from analyses of the cambial region of wild-type controls and plants expressing high-isoelectric-point superoxide dismutase (hipI-SOD) transcripts in antisense orientation showing a higher production of superoxide. The cambium, a thin cell layer, generates cells that differentiate to form either phloem or xylem and is hypothesized to be a major reason for phenotypic perturbations in the transgenic plants. Data from multiple platforms including transcriptomics (microarray analysis), proteomics (UPLC/QTOF-MS), and metabolomics (GC-TOF/MS, UPLC/MS, and UHPLC-LTQ/MS) were integrated using the most recent development of orthogonal projections to latent structures called OnPLS. OnPLS is a symmetrical multi-block method that does not depend on the order of analysis when more than two blocks are analysed. Significantly affected genes, proteins and metabolites were then visualized in painted pathway diagrams.

Results: The main categories that appear to be significantly influenced in the transgenic plants were pathways related to redox regulation, carbon metabolism and protein degradation, e.g. the glycolysis and pentose phosphate pathways (PPP). The results provide system-level information on ROS metabolism and responses to oxidative stress, and indicate that some initial responses to oxidative stress may share common pathways.

Conclusion: The proposed data evaluation strategy shows an efficient way of compiling complex, multi-platform datasets to obtain significant biological information.

Figure 1

Schematic flowchart of the integrated profiling strategy applied in this study. In the first step, transcriptomic, proteomic and metabolomic data were collected individually from the cambial region of Populus WT and transgenic plants. In the second step, the three omic datasets acquired were integrated by OnPLS to identify the joint variation in them (initially applying OnPLS modeling to all variables, and subsequently to targeted variables). Finally, the OnPLS model from the second step was visualized by Mapman and KEGG to explore the pathways (genes-proteins-metabolites) affected in the transgenics, and deepen the interpretation of their oxidative stress responses.

Figure 2

An illustration of what OnPLS does for three blocksX₁X₂and X_3. It separates each block into the parts that it has in common with the other blocks. The parts are globally joint (shared between all blocks), locally joint (shared between some, but not all, blocks) and unique, shared with no other block.

Figure 3

The genotype effect. (A) Joint genotype effect scores from the targeted variable model, and corresponding loadings for: (B) transcripts, (C) proteins and (D) metabolites. Many significant compounds are highlighted in their respective plots and discussed in the text. The directions of the “red colored” arrow heads and tails represent increasingly high levels of transcripts/proteins/metabolites in the transgenic hipI-SOD plants relative to WT plants, and vice versa, respectively. The variation in the WT is too small to be visible on this score plot.

Figure 4

KEGG Glycolysis/Gluconeogenesis pathway and Pentose Phosphate Pathways ‘painted’ with transcriptomic, proteomic and metabolomic data from the targeted OnPLS model. Black-bordered entry boxes indicate significant differences between the transgenic and WT plants at both transcript and protein levels. The first three sections of each gene box (left to right) indicate WT, AS-SOD9 and AS-SOD24 transcript levels, respectively, and the last two protein levels in AS-SOD9 and AS-SOD24 lines, respectively. The first sections in the metabolite entry boxes represent WT and the colored boxes levels in the AS-SOD9 and AS-SOD24 lines. Reduced levels in the transgenics are colored blue and increased levels red.