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Multidimensional patterns of metabolic response in abiotic stress-induced growth of Arabidopsis thaliana

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Contextualization of specific transcriptional responses of Arabidopsis within the stress–tissue–time perspective provides a simplified representation of the cellular transcriptional response pathways to abiotic stress, while reducing the dimensions in gene-oriented response description.

Abstract

Crops resistant to abiotic stresses are a long-term goal of many research programs, thus understanding the progression of stress responses is of great interest. We reanalyzed the AtGenExpress transcription dataset to go beyond gene-level characterization, and to contextualize the discrete information into (1) a process-level signature of stress-specific, time-specific, and tissue-specific responses and (2) identify patterns of response progression across a time axis. To gain a functional perspective, ∼1000 pathways associated with the differentially-expressed genes were characterized across all experiments. We find that the global response of pathways to stress is multi-dimensional and does not obviously cluster according to stress, time or tissue. The early response to abiotic stress typically involves induction of genes involved in transcription, hormone synthesis and signaling modules; a later response typically involves metabolism of amino acids and secondary metabolites. By linking specific primary and secondary response pathways, we outline possible stress-associated routes of response progression. The contextualization of specific processes within stress–tissue–time perspective provides a simplified representation of cellular response while reducing the dimensions in gene-oriented response description. Such simplified representation allows finding stress-specific markers based on process-combinations pointing whether a stress-specific response was invoked as well as provide a reference point for the conductance of comparative inter-plant study of stress response, bypassing the need in detailed orthologous mapping.

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Acknowledgments

We thank Profs. Georg Jander, Doron Holland and Gad Galili for helpful comments on the manuscript. We also thank Eyal Zinger and Dr. Efrat Oron for technical support related to R and MatLab languages. This research was supported by the I-CORE Program of the Planning and Budgeting Committee, The Israel Science Foundation (Grant No. 757/12) and the Chief Scientist of Israel Ministry of Agriculture and Rural Development (Grant No. 277059016). DAC is a member Edmond J. Safra Center for Bioinformatics at Tel-Aviv University. B.Y.S. was supported by a fellowship from the PBC (Planning and Budgeting Committee) Fellowship Program for Outstanding Post-Doctoral Researchers from India.

Author contributions

BSY carried out the analyses and wrote the manuscript; TL aided programming and MatLab analyses; ER aided in gene-enrichment analyses; DAC and SF conceived study, oversaw its execution and wrote the manuscript.

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Authors and Affiliations

  1. Department of Molecular Biology and Ecology of Plants, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv, Israel

    Brijesh S. Yadav & Daniel A. Chamovitz

  2. Newe-Ya’ar Research Center, Institute of Plant Sciences, Agricultural Research Organization, PO Box 1021, 30095, Ramat Yishay, Israel

    Tamar Lahav, Eli Reuveni & Shiri Freilich

Authors
  1. Brijesh S. Yadav
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  2. Tamar Lahav
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  4. Daniel A. Chamovitz
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  5. Shiri Freilich
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Corresponding authors

Correspondence to Daniel A. Chamovitz or Shiri Freilich.

Electronic supplementary material

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11103_2016_539_MOESM1_ESM.xls

Figure 1. Distribution of differentially up-regulated genes or significantly enriched pathways across experiments. A. Heat map illustrating the number of upregulated genes. B. Heat map illustrating the number of pathways significantly enriched with upregulated genes. C. A PCA analysis of the up-regulated gene profiles across the 96 experiments. The data were normalized and auto scaled prior to PCA. Numbers in parenthesis indicate the percentage of variance explained by each PC. (XLS 29 KB)

11103_2016_539_MOESM2_ESM.xls

Figure 2. Distribution of significantly enriched pathways from key categories across experiments. The heat map illustrates scaled distribution of the number of pathways significantly enriched with up-regulated genes across key MapMan categories. For each stress condition, the data point with the largest number of pathways was defined as 1 (dark red), to which all other points in the same conditions were scaled against (color bar on right). (XLS 4729 KB)

11103_2016_539_MOESM3_ESM.xls

Figure 3. Cross-stress response patterns of pathways. A. Heat maps illustrating scaled distribution of pathways across multiple stresses. Values in each cell represent the summing together of scaled values across all stress conditions (as in Figure 2). B. Diagram of pathways response progression during abiotic stress. (XLS 1061 KB)

11103_2016_539_MOESM4_ESM.xlsx

Figure 4. Enriched pathways from selected key categories in specific stresses within each panel, each color is corresponding to the top-category of the pathways indicated on the right side of the graph. A, B. Enriched pathways at 0.5 hours following stress classified to (A) the hormonal category, or (B) the RNA category. C, D. Enriched pathways at 6 hours following stress classified to the (C) amino acids category, or (D) secondary metabolites category. (XLSX 16 KB)

11103_2016_539_MOESM5_ESM.xls

Figure 5. Network association of primary and secondary categories at selected time points. Nodes represent pathways in the following categories: Primary response (0.5 h) - Hormones (green) and RNA transcriptome (red); Secondary response (6 h) - amino acid (blue) and secondary metabolite (yellow). The edges were drawn between nodes when their co-appearance score was higher than threshold (Methods). Nodes of hormonal and secondary metabolites categories are shown at the second-level of classification; Nodes of RNA and amino-acids categories are shown at the third and fourth level of classification, respectively. (A) The full network containing associations from both shoot and root. Subnetworks composed of the direct neighbors of the nodes (B) for methionine synthesis and degradation and (C) for asparagine synthesis and degradation. (XLS 29 KB)

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Yadav, B.S., Lahav, T., Reuveni, E. et al. Multidimensional patterns of metabolic response in abiotic stress-induced growth of Arabidopsis thaliana . Plant Mol Biol 92, 689–699 (2016). https://doi.org/10.1007/s11103-016-0539-7

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