RESUMO
Low temperature is a common biological abiotic stress in major cotton-growing areas. Cold stress significantly affects the growth, yield, and yield quality of cotton. Therefore, it is important to develop more robust and cold stress-resilient cotton germplasms. In response to climate change and erratic weather conditions, plants have evolved various survival mechanisms, one of which involves the induction of various stress responsive transcript factors, of which the C-repeat-binding factors (CBFs) have a positive effect in enhancing plants response to cold stress. In this study, genomewide identification and functional characterization of the cotton CBFs were carried out. A total of 29, 28, 25, 21, 30, 26, and 15 proteins encoded by the CBF genes were identified in seven Gossypium species. A phylogenetic evaluation revealed seven clades, with Clades 1 and 6 being the largest. Moreover, the majority of the proteins encoded by the genes were predicted to be located within the nucleus, while some were distributed in other parts of the cell. Based on the transcriptome and RT-qPCR analysis, Gthu17439 (GthCBF4) was highly upregulated and was further validated through forward genetics. The Gthu17439 (GthCBF4) overexpressed plants exhibited significantly higher tolerance to cold stress, as evidenced by the higher germination rate, increased root growth, and high-induction levels of stress-responsive genes. Furthermore, the overexpressed plants under cold stress had significantly reduced oxidative damage due to a reduction in hydrogen peroxide (H2O2) production. Moreover, the overexpressed plants under cold stress had minimal cell damage compared to the wild types, as evidenced by the Trypan and 3,3'-Diaminobenzidine (DAB) staining effect. The results showed that the Gthu17439 (GthCBF4) could be playing a significant role in enhancing cold stress tolerance in cotton and can be further exploited in developing cotton germplasm with improved cold-stress tolerance.
RESUMO
BACKGROUND: Monitoring vegetation dynamics and their responses to climate change has been the subject of considerable research. This paper aims to detect change trends in grassland activity on the Tibetan Plateau between 1982 and 2006 and relate these to changes in climate. METHODOLOGY/PRINCIPAL FINDINGS: Grassland activity was analyzed by evaluating remotely sensed Normalized Difference Vegetation Index (NDVI) data collected at 15-day intervals between 1982 and 2006. The timings of vegetation stages (start of green-up, beginning of the growing season, plant maturity, start of senescence and end of the growing season) were assessed using the NDVI ratio method. Mean NDVI values were determined for major vegetation stages (green-up, fast growth, maturity and senescence). All vegetation variables were linked with datasets of monthly temperature and precipitation, and correlations between variables were established using Partial Least Squares regression. Most parts of the Tibetan Plateau showed significantly increasing temperatures, as well as clear advances in late season phenological stages by several weeks. Rainfall trends and significant long-term changes in early season phenology occurred on small parts of the plateau. Vegetation activity increased significantly for all vegetation stages. Most of these changes were related to increasing temperatures during the growing season and in some cases during the previous winter. Precipitation effects appeared less pronounced. Warming thus appears to have shortened the growing season, while increasing vegetation activity. CONCLUSIONS/SIGNIFICANCE: Shortening of the growing season despite a longer thermally favorable period implies that vegetation on the Tibetan Plateau is unable to exploit additional thermal resources availed by climate change. Ecosystem composition may no longer be well attuned to the local temperature regime, which has changed rapidly over the past three decades. This apparent lag of the vegetation assemblage behind changes in climate should be taken into account when projecting the impacts of climate change on ecosystem processes.
