Transcriptome Analysis Revealed the Molecular Response Mechanism of Non-heading Chinese Cabbage to Iron Deficiency Stress
Bibliographic record
Abstract
Iron is a trace metal that is found in animals, plants, and the human body. Human iron absorption is hampered by plant iron shortage, which leads to anemia. Leafy vegetables are one of the most direct and efficient sources of iron for humans. Despite the fact that ferrotrophic disorder is common in calcareous soil, however, non-heading Chinese cabbage performs a series of reactions in response to iron deficiency stress that help to preserve iron homeostasis in vivo. In this study, we discovered that iron deficiency stress caused leaf yellowing and impeded plant development in both iron-deficient and control treatments by viewing or measuring phenotypic, chlorophyll content, and Fe2+ content in both iron-deficient and control treatments. We found a total of 9213 differentially expressed genes (DEGs) in non-heading Chinese cabbage by comparing root and leaf transcriptome data with iron deficiency and control treatments. For instance, 1927 DEGs co-expressed in root and leaf, including 897 up-regulated and 1030 down-regulated genes, respectively. We selected some key antioxidant genes, hormone signal transduction, iron absorption and transport, chlorophyll metabolism, and transcription factors involved in the regulation of iron deficiency stress utilizing GO enrichment, KEGG enrichment, multiple types of functional annotation, and Weighted Gene Co-expression Network Analysis (WGCNA). This study identifies prospective genes for maintaining iron homeostasis under iron-deficient stress, offering a theoretical foundation for further research into the molecular mechanisms of greater adaptation to iron-deficient stress, and perhaps guiding the development of iron-tolerant varieties.
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 | 0.001 |
| Bibliometrics | 0.000 | 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".