Tissue-Specific Expression Profiling of Seedling Stage in Early-Maturity Mutant Induced by Carbon Ion Beam in Sweet Sorghum
Bibliographic record
Abstract
An early-maturity mutant KFJT-1 has been screened out after carbon ion irradiation in sweet sorghum (Sorghum bicolor (L.) Moench). In this study, tissue specific digital gene expression analysis was performed between the KFJT-1 mutant and the wild type KFJT-CK at seedling stage. The results showed that a total of 717, 2160 and 2,331 tags-mapped genes were differently expressed in roots, stems and leaves of young seedling, respectively. In KFJT-1, 557 (77.7%) genes were up-regulated and 160 (22.3%) genes were down-regulated in young root; 1,232 (57.0%) genes were up-regulated and 928 (43.0%) were down-regulated in young stem; and 1,577 (67.7%) genes were up-regulated and 754 (32.3%) genes were down-regulated in young leaf. Functional annotation revealed that most induced genes functioned as “binding”, “synthase activity”, “transferase” and “transporter activity” which involved in the biological processes of metabolic and response to stimulus. Surprisingly, the up-regulated genes in KFJT-1 were classified into four KEGG pathways: “alpha-Linolenic acid metabolism”, “flavonoid biosynthesis”, “inositol phosphate metabolism” and “fatty acid biosynthesis”, which related to the stress resistance and supported the outstanding agronomic traits of KFJT-1 in the process of plant growth and development. Among the DEGs, a critical photoreceptor from photoperiod pathway PHYA gene was significantly up-regulated in leaf and root of KFJT-1, suggesting the mutation could occur on the genomic upstream of PHYA. This work may provide helpful insights to further understand the mutation mechanism in sweet sorghum.
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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 | 0.000 | 0.000 |
| 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".