A transcriptomic analysis for identifying the unintended effects of introducing a heterologous glyphosate-tolerant EPSP synthase into <i>Escherichia coli</i>
Bibliographic record
Abstract
Glyphosate is one of the most commonly used broad-spectrum herbicides with little to no hazard to animals, human beings, or the environment. Some microbial 5-enolpyruvylshikimate-3-phosphate (EPSP) synthase variants are not inhibited by glyphosate, and they provide a powerful tool to engineer glyphosate-tolerant plants. However, the unintended effects of EPSP synthase expression patterns on microbes are not yet clear. Here, we use an Affymetrix GeneChip analysis to study how introduction of a heterologous glyphosate-tolerant EPSP synthase into a model microorganism Escherichia coli (E. coli) affects the global gene expression profile. The profile showed that 161 of 4071 genes were differentially expressed after the introduction of the synthase: 19 (0.47%) were up-regulated and 143 (3.49%) were down-regulated. The microarray results, in combination with BiOLOG substrate utilization and amino acid composition assays, suggested that heterologous EPSP synthase expression had very minor effects on E. coli. Although a small number of genes and metabolites were affected by EPSP synthase expression, no functional correlations were identified among the dataset. This study may shed light on the effect of EPSP synthase expression on microbes, which should help in the assessment of environmental safety.
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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 | 0.001 |
| 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".