Current and Future Transgenic Whole-Cell Biosensors for Plant Macro- and Micronutrients
Bibliographic record
Abstract
From the soil, plants take up macronutrients (calcium, magnesium, nitrogen, phosphorus, potassium, sulfur) and micronutrients (boron, chloride, cobalt, copper, iron, manganese, molybdenum, nickel, selenium, and zinc). In acidic soils, aluminum can interfere with nutrient uptake. There is a need for improved diagnostic tests for these soil-derived minerals that are inexpensive and sensitive, provide spatial and temporal information in plants and soil, and report bioavailable nutrient pools. A transgenic whole-cell biosensor detects a stimulus inside or outside a cell and causes a change in expression of a visible reporter such as green fluorescent protein, and thus can convert an invisible plant nutrient into a visible signal. Common transgenic whole-cell biosensors consist of promoter-reporter fusions, auxotrophs for target analytes that are transformed with constitutively expressed reporters, riboswitches and reporters based on Forster Resonance Energy Transfer (FRET). Here, we review transgenic plant biosensors that have been used to detect macronutrients and micronutrients. As plant-based biosensors are limited by the requirement to introduce and optimize a transgene in every genotype of interest, we also review microbial biosensor cells that have been used to measure plant or soil nutrients by co-inoculation with their respective extracts. Additionally, we review published transgenic whole-cell biosensors from other disciplines that have the potential to measure plant nutrients, with the goal of stimulating the development of these diagnostic technologies. We discuss current limitations and future improvements needed, and the long-term potential of transgenic whole-cell biosensors to inform plant physiology, improve soil nutrient management, and assist in breeding crops with improved nutrient use efficiency.
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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 | 0.000 | 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) | 0.000 | 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".