A genome-wide analysis of SWEET gene family in cotton and their expressions under different stresses
Bibliographic record
Abstract
The SWEET (Sugars will eventually be exported transporters) gene family plays multiple roles in plant physiological activities and development process. It participates in reproductive development and in the process of sugar transport and absorption, plant senescence and stress responses and plant-pathogen interaction. However, thecomprehensive analysis of SWEET genes has not been reported in cotton. In this study, we identified 22, 31, 55 and 60 SWEET genes from the sequenced genomes of Gossypium arboreum , G. raimondii, G. hirsutum and G. barbadense , respectively. Phylogenetic tree analysis showed that the SWEET genes could be divided into four groups, which were further classified into 14 sub-clades. Further analysis of chromosomal location, synteny analysis and gene duplication suggested that the orthologs showed a good collinearity and segmental duplication events played a crucial role in the expansion of the family in cotton. Specific MtN3_slv domains were highly conserved between Arabidopsis and cotton by exon-intron organization and motif analysis. In addition, the expression pattern in different tissues indicated that the duplicated genes in cotton might have acquired new functions as a result of sub-functionalization or neo-functionalization. The expression pattern of SWEET genes showed that the different genes were induced by diverse stresses. The identification and functional analysis of SWEET genes in cotton may provide more candidate genes for genetic modification. SWEET genes were classified into four clades in cotton. The expression patterns suggested that the duplicated genes might have experienced a functional divergence. This work provides insights into the evolution of SWEET genes and more candidates for specific genetic modification, which will be useful in future research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".