Metabolite Fingerprinting in Transgenic <i>Nicotiana tabacum</i> Altered by the <i>Escherichia coli</i> Glutamate Dehydrogenase Gene
Bibliographic record
Abstract
With about 200,000 phytochemicals in existence, identifying those of biomedical significance is a mammoth task. In the postgenomic era, relating metabolite fingerprints, abundances, and profiles to genotype is also a large task. Ion analysis using Fourier transformed ion cyclotron resonance mass spectrometry (FT-ICR-MS) may provide a high-throughput approach to measure genotype dependency of the inferred metabolome if reproducible techniques can be established. Ion profile inferred metabolite fingerprints are coproducts. We used FT-ICR-MS-derived ion analysis to examine gdhA (glutamate dehydrogenase (GDH; EC 1.4.1.1)) transgenic Nicotiana tabacum (tobacco) carrying out altered glutamate, amino acid, and carbon metabolisms, that fundamentally alter plant productivity. Cause and effect between gdhA expression, glutamate metabolism, and plant phenotypes was analyzed by (13) NH(4)(+) labeling of amino acid fractions, and by FT-ICR-MS analysis of metabolites. The gdhA transgenic plants increased (13)N labeling of glutamate and glutamine significantly. FT-ICR-MS detected 2,012 ions reproducible in 2 to 4 ionization protocols. There were 283 ions in roots and 98 ions in leaves that appeared to significantly change abundance due to the measured GDH activity. About 58% percent of ions could not be used to infer a corresponding metabolite. From the 42% of ions that inferred known metabolites we found that certain amino acids, organic acids, and sugars increased and some fatty acids decreased. The transgene caused increased ammonium assimilation and detectable ion variation. Thirty-two compounds with biomedical significance were altered in abundance by GDH including 9 known carcinogens and 14 potential drugs. Therefore, the GDH transgene may lead to new uses for crops like tobacco.
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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) | 0.000 | 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 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".