Iron accumulation and partitioning in hydroponically grown wild and cultivated chickpea (Cicer arietinum L)
Bibliographic record
Abstract
Chickpea ( Cicer arietinum L.) is a staple food in many developing countries where iron (Fe) deficiency often occurs in their population. The crop is a good source of protein, vitamins, and micronutrients. Fe biofortification in chickpea can be part of long-term strategy to enhance Fe intake in human diet to help to alleviate Fe deficiency. To develop cultivars with high Fe concentration in seeds, understanding the mechanisms of absorption and translocation of Fe into the seeds is critical. An experiment was conducted using a hydroponic system to evaluate Fe accumulation in seeds and other organs at different growth stages of selected genotypes of cultivated and wild relatives of chickpea. Plants were grown in media with Fe zero and Fe added conditions. Six chickpea genotypes were grown and harvested at six different growth stages: V3, V10, R2, R5, R6, and RH for analysis of Fe concentration in roots, stems, leaves, and seeds. The relative expression of genes related to Fe-metabolism including FRO2 , IRT1 , NRAMP3 , V1T1 , YSL1 , FER3 , GCN2 , and WEE1 was analyzed. The results showed that the highest and lowest accumulation of Fe throughout the plant growth stages were found in the roots and stems, respectively. Results of gene expression analysis confirmed that the FRO2 and IRT1 were involved in Fe uptake in chickpeas and expressed more in roots under Fe added condition. All transporter genes: NRAMP3 , V1T1 , YSL1 along with storage gene FER3 showed higher expression in leaves. In contrast, candidate gene WEE1 for Fe metabolism expressed more in roots under Fe affluent condition; however, GCN2 showed over-expression in roots under Fe zero condition. Current finding will contribute to better understanding of Fe translocation and metabolism in chickpea. This knowledge can further be used to develop chickpea varieties with high Fe in seeds.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".