Integrated multi-omics analysis reveals divergent molecular responses in Palmer amaranth ( <i>Amaranthus palmeri</i> ) biotypes susceptible and resistant to glyphosate
Bibliographic record
Abstract
ABSTRACT Environmental stress triggers coordinated changes across genetic, transcriptomic, proteomic, and metabolomic levels in plants, yet the extent of synchronization across these omic layers remains underexplored. We captured transcriptomic, proteomic and metabolomic perturbation of glyphosate-resistant (GR) and glyphosate-susceptible (GS) Palmer amaranth ( Amaranthus palmeri ) biotypes 24 hours after herbicide treatment, quantifying 30,371 transcripts, 5,606 proteins, and 220 metabolites. Glyphosate perturbed threefold more transcripts and proteins in GS than in GR and caused the accumulation of shikimate intermediates in both biotypes. In GS, glyphosate severely disrupted primary metabolism, including photosynthesis and carbon fixation, leading to a collapse of energy production and impairment of phenylpropanoid and terpenoid biosynthesis, compromising defense and detoxification. In contrast, GR maintained cellular homeostasis, with minimal perturbation in carbon metabolism and upregulation of detoxifying pathways, indicating metabolic rerouting. Integrated multi-omics analysis captured stress responses hidden from single-omic analysis, including elevated glutathione metabolism, perturbation of the phenylpropanoid pathway and elevated raffinose family oligosaccharide metabolism in GR, and perturbation of taurine-hypotaurine metabolism in GS. Transcript and protein changes were broadly correlated, but GS exhibited signs of translational inhibition under glyphosate stress, indicating reduced protein synthesis. These findings reveal pervasive perturbation of glyphosate beyond the shikimate pathway within 24 hours after exposure, and underscore the importance of multi-omics integration to elucidate complex stress responses in plants.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".