Conserved and unique functions of NIN-like proteins in nitrate sensing and signaling
Bibliographic record
Abstract
Nitrogen is the most abundant element in the atmosphere and serves as the foundation block of life, including plants on earth. Unlike carbon fixation through photosynthesis, plants rely heavily on external supports to acquire nitrogen. To this end, plants have adapted various strategies such as forming mutualistic relationships with nitrogen-fixing bacteria and evolving a large regulatory network that includes multiple transporters, sensors, and transcription factors for fine-tuning nitrate sensing and signaling. Nodule Inception (NIN) and NIN-like protein (NLP) are central in this network by executing multiple functions such as initiating and regulating the nodule symbiosis for nitrogen fixation, acting as the intracellular sensor to monitor the nitrate fluctuations in the environment, and activating the transcription of nitrate-responsive genes for optimal nitrogen uptake, assimilation, and usage. The involvement of NLPs in intracellular nitrate binding and early nitrate responses highlight their pivotal role in the primary nitrate response (PNR). Genome-wide reprogramming in response to nitrate by NLP is highly transient and rapid, requiring regulation in a precise and dynamic manner. This review aims to summarize recent progress in the study of NIN/NLP for a better understanding of the molecular basis of their roles and regulations in nitrate sensing and signaling, with the hope of shedding light on increasing biological nitrogen fixation and improving nitrogen use efficiency (NUE) to minimize fertilizer input in agriculture.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".