Visualizing Glutamine Accumulation in Root Systems Involved in the Legume–Rhizobia Symbiosis by Placement on Agar Embedded with Companion Biosensor Cells
Bibliographic record
Abstract
Microbial symbiotic nitrogen fixation (SNF) occurs inside root nodules, where fixed-N (NH4+) from rhizobia is first assimilated into the amino acid glutamine (Gln). Visualization of Gln dynamics in nodulated root systems of different plant species would require re-engineering transgenic Gln reporters specific for each rhizobia/host genotype. Here we demonstrate the use of companion biosensor cells called GlnLux (Escherichia coli auxotrophic for Gln and constitutively expressing lux) to image Gln accumulation in nodulated root systems across a diversity of legume/rhizobia species. Companion GlnLux cells are embedded into agar (GlnLux agar) upon which legume root systems are placed following freeze-thawing to cause Gln leakage. Photons released from nearby activated biosensor cells are captured using a photon capture camera. Using split root systems, we demonstrate that in diverse amide-exporting legumes (alfalfa, lentil, and green pea) and a ureide-exporting legume (soybean) that GlnLux agar imaging is sufficiently sensitive to detect Gln release from individual nodules and can differentiate root systems with active nif+ from inactive nif− nodules. The assay permits visualization of both source and sink dynamics of nodule Gln, specifically, Gln import into nodules from roots (for nodule growth and/or amino acid cycling), Gln assimilated from fixed nitrogen that accumulates inside nodules, and Gln export from nodules into roots from this assimilatory-N. GlnLux agar-based imaging is thus a new research tool to localize the accumulation and transfer of a critical amino acid required for rhizobia symbionts within legume phytobiomes. We discuss the ability of this technology to open new frontiers in basic research and its limitations.
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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 | 0.000 | 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) | 0.001 | 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".