Study on Tomato Pollen Heat Tolerance Using Proteomics and Artificial Intelligence
Abstract
Tomato (Solanum lycopersicum) is a major vegetable species worldwide. The most suitable temperature for growing tomatoes is between 21/29ºC (night/day). Among all the physiological responses, the development of male gametophyte is the most sensitive process to temperatures. Elevated temperature beyond the optimal range, typically 10-15ºC higher, during heat stress conditions negatively affect tomato plants, leading to a decline in pollen quality, viability and low levels of fruit sets. This research focuses to find the HS-induced proteomes in pollen mother cell and microspore of two different varieties of tomatoes, Black Vernissage and Micro-Tom exposed to high heat of 35-39ºC/25-27ºC (day/night) for 15 days. The non-heat-treated tomato plants were kept at 22-25ºC. The flower bud containing pollen mother cell and micropore were embedded in OCT compound and cryo-sectioned into 20uM thickness. The tissues were collected into capture cap using the Laser capture microdissection (LCM). The extracted protein was TMT labeled and peptides were identified with LC-MS/MS. The proteomes revealed that in a high heat of 38ºC, family of HSP and peptidase activity were upregulated as a tolerance mechanism. Proteins related to ribosomal and translation activity were repressed. At a temperature of 32ºC, the viability of BV was significantly higher compared to MT. Additionally, BV produced fruits with seeds, whereas MT did not exhibit any seed development. These observations strongly indicate that BV has a greater heat tolerance than MT. However, it is important to note that at an even higher temperature of 38ºC, the viability of pollen decreased in both tomato varieties.
Subject Area
Plant sciences|Artificial intelligence|Environmental science|Agriculture
Recommended Citation
Priya Thapa,
"Study on Tomato Pollen Heat Tolerance Using Proteomics and Artificial Intelligence"
(2023).
ETD Collection for Tennessee State University.
Paper AAI30571371.
https://digitalscholarship.tnstate.edu/dissertations/AAI30571371