Assessing the between-background stability of metabolic effects arising from lignin-related transgenic modifications, in two Populus hybrids using non-targeted metabolomics
Bibliographic record
Abstract
The advances in 'high-throughput' biology have significantly expanded our fundamental understanding of complex biological processes inherent to tree growth and development. Relative to the significant achievements attained with whole genome re-sequencing and transcriptomics efforts, the development and power of post-transcriptional tools such as proteomics and metabolomics continue to lag behind in tree biology. However, the inclusion of these powerful functional genomics platforms should substantially enable systems biology assessments of tree development, physiology and response(s) to biotic and abiotic stresses. Herein, we employ a non-targeted metabolomics platform to elucidate the metabolic plasticity of xylem lignification in distinct hybrid poplar genetic backgrounds, as well as in transgenic trees in these backgrounds expressing two common constructs: the first construct (C4H::F5H) augments monolignol content (syringyl:guaiacyl (S:G) ratio), while the second construct (C3'H-RNAi) reduces cell wall lignification. The results clearly show that genotype-specific metabolism exists, and provide an appropriate foundation for properly comparing the influence of background on the relationships between metabolic and specific phenotypic traits. Moreover, it was apparent that transgene-induced phenotypic gradients in cell wall chemical wood can be associated with global metabolism of secondary xylem biosynthesis, however in a genotype-specific manner. This result implies that the same may be true for phenotypic gradients arising through natural genetic variation, intensive breeding or environmental factors. It is also apparent that while distinct, at a global level the wood-forming metabolisms of different poplar hybrids can, to some extent, respond similarly to the influences of genetic manipulation of lignin-related genes. This further implies that with the correct approach, it may be possible to associate the emergence of specific wood traits from different genetic backgrounds-be they transgene-induced or otherwise-with stable metabolic signatures.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".