Genome-wide analysis of the NAAT, DMAS, TOM, and ENA gene families in maize suggests their roles in mediating iron homeostasis
Bibliographic record
Abstract
BACKGROUND: Nicotianamine (NA), 2'-deoxymugineic acid (DMA), and mugineic acid (MA) are chelators required for iron uptake and transport in plants. Nicotianamine aminotransferase (NAAT), 2'-deoxymugineic acid synthase (DMAS), transporter of MAs (TOM), and efflux transporter of NA (ENA) are involved in iron uptake and transport in rice (Oryza sativa), wheat (Triticum aestivum), and barley (Hordeum vulgare); however, these families have not been fully identified and comprehensively analyzed in maize (Zea mays L.). RESULTS: Here, we identified 5 ZmNAAT, 9 ZmDMAS, 11 ZmTOM, and 2 ZmENA genes by genome mining. RNA-sequencing and quantitative real-time PCR analysis revealed that these genes are expressed in various tissues and respond differently to high and low iron conditions. In particular, iron deficiency stimulated the expression of ZmDMAS1, ZmTOM1, ZmTOM3, and ZmENA1. Furthermore, we determined protein subcellular localization by transient expression of green fluorescent protein fusions in maize mesophyll protoplasts. ZmNAAT1, ZmNAAT-L4, ZmDMAS1, and ZmDMAS-L1 localized in the cytoplasm, whereas ZmTOMs and ZmENAs targeted to plasma and tonoplast membranes, endomembranes, and vesicles. CONCLUSIONS: Our results suggest that the different gene expression profiles and subcellular localizations of ZmNAAT, ZmDMAS, ZmTOM, and ZmENA family members may enable specific regulation of phytosiderophore metabolism in different tissues and under different external conditions, shedding light on iron homeostasis in maize and providing candidate genes for breeding iron-rich maize varieties.
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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 | 0.001 | 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".