Comparative root transcriptome analysis of two soybean cultivars with different cadmium sensitivities reveals the underlying tolerance mechanisms
Bibliographic record
Abstract
Soybean can provide rich protein and fat and has great economic value worldwide. Cadmium (Cd) is a toxic heavy metal to organisms. It can accumulate in plants and be transmitted to the human body via the food chain. Cd is a serious threat to soybean development, particularly root growth. Some soybean cultivars present tolerant symptoms under Cd stress; however, the potential mechanisms are not fully understood. Here, we optimized RNA-seq to identify the differentially expressed genes (DEGs) in Cd-sensitive (KUAI) and Cd-tolerant (KAIYU) soybean roots and compared the DEGs between KAIYU and KUAI. A total of 1506 and 1870 DEGs were identified in the roots of KUAI and KAIYU, respectively. Through Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, and gene function analyses, we found that genes related to antioxidants and sequestration were responsible for Cd tolerance in KAIYU. In addition, overexpression of Glyma11g02661, which encodes a heavy metal-transporting ATPase, significantly improved Cd tolerance in transgenic hairy roots. These results provide a preliminary understanding of the tolerance mechanisms in response to Cd stress in soybean root development and are of great importance in developing Cd-resistant soybean cultivars by using the identified DEGs through genetic modification.
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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".