| Literature DB >> 27148291 |
Deepti B Gupta1, Yogita Rai1, Saurabh Gayali2, Subhra Chakraborty2, Niranjan Chakraborty2.
Abstract
Stress adaptation or tolerance in plants is a complex phenomenon involving changes in physiological and metabolic processes. Plants must develop elaborate networks of defense mechanisms, and adapt to and survive for sustainable agriculture. Water-deficit or dehydration is the most critical environmental factor that plants are exposed to during their life cycle, which influences geographical distribution and productivity of many crop species. The cellular responses to dehydration are orchestrated by a series of multidirectional relays of biochemical events at organelle level. The new challenge is to dissect the underlying mechanisms controlling the perception of stress signals and their transmission to cellular machinery for activation of adaptive responses. The completeness of current descriptions of spatial distribution of proteins, the relevance of subcellular locations in diverse functional processes, and the changes of protein abundance in response to dehydration hold the key to understanding how plants cope with such stress conditions. During past decades, organellar proteomics has proved to be useful not only for deciphering reprograming of plant responses to dehydration, but also to dissect stress-responsive pathways. This review summarizes a range of organellar proteomics investigations under dehydration to gain a holistic view of plant responses to water-deficit conditions, which may facilitate future efforts to develop genetically engineered crops for better adaptation.Entities:
Keywords: adaptive responses; crop yield; dehydration; spatiotemporal regulation; stress signals; subcellular proteome
Year: 2016 PMID: 27148291 PMCID: PMC4829595 DOI: 10.3389/fpls.2016.00460
Source DB: PubMed Journal: Front Plant Sci ISSN: 1664-462X Impact factor: 5.753
Large-scale subcellular proteome studies under water-deficit conditions.
| Cell fraction | Plant | Number of proteins | Tissue | Proteomic method | Reference |
|---|---|---|---|---|---|
| Cell wall/Apoplast/ECM | Chickpea | 134 | Leaf | 2-DE, LC-ESI-MS/MS | |
| Chickpea | 81 | Leaf | 2-DE, LC-ESI-MS/MS | ||
| Rice | 94 | Leaf | 2-DE, LC-ESI-MS/MS | ||
| Maize | 152 | Root elongation zone | 2-DE, ESI, Q-TOF-MS/MS | ||
| Poplar | 279 | Leaf, Steam | 2-DE, LC-MS/MS | ||
| Nuclear | Chickpea | 147 | Leaf | 2-DE, LC/MS/TOF | |
| Rice | 109 | Leaf | 2-DE, LC/MS/TOF | ||
| Resurrection plant | 18 | Leaf | 2-D, MS/MS | ||
| Resurrection plant | 28 | Leaf | i-TRAQ together with 2 DLC & ESI-MS/MS | ||
| Membrane/PM | Soybean | 85 | Seedling | 2-DE, nano-LC-MS/MS | |
| Chickpea | 91 | 2-DE, LC-ESI-MS/MS | |||
| Mitochondria | 62 | Leaf | 2-DE, Q-TOF-MS/MS | ||
| 417 | Leaf | 15N labeling | |||
| Chloroplast | 81 | Leaf | Q-TOF -MS/MS | ||
| Wild watermelon | 60 | Leaf | LC-MS/MS |
Number of phosphoproteins identified in different plants under water-deficit conditions.
| Plant | Number of proteins | Proteomic method | Reference |
|---|---|---|---|
| 468 | Label-free LAXIC | ||
| 22 | 2-DE, MS/MS | ||
| Chickpea | 91 | 2-DE, MS/MS | |
| Wheat | 31 (seedling leaves) | LC-MS/MS | |
| Wheat | 61 (developed seeds) | LC-MS/MS | |
| 29 | 15N metabolic labeling, MS/MS |