Alteration of Soil Rhizosphere Communities following Genetic Transformation of White Spruce
Bibliographic record
Abstract
The application of plant genetic manipulations to agriculture and forestry with the aim of alleviating insect damage through Bacillus thuringiensis transformation could lead to a significant reduction in the release of pesticides into the environment. However, many groups have come forward with very valid and important questions related to potentially adverse effects, and it is crucial to assess and better understand the impact that this technology might have on ecosystems. In this study, we analyzed rhizosphere soil samples collected from the first B. thuringiensis-transformed trees [with insertion of the CryIA(b) toxin-encoding gene] grown in Canada (Val-Cartier, QC, Canada) as part of an ecological impact assessment project. Using a robust amplified rRNA gene restriction analysis approach coupled with 16S rRNA gene sequencing, the rhizosphere-inhabiting microbial communities of white spruce (Picea glauca) genetically modified by biolistic insertion of the cryIA(b), uidA (beta-glucuronidase), and nptII genes were compared with the microbial communities associated with non-genetically modified counterparts and with trees in which only the genetic marker genes uidA and nptII have been inserted. Analysis of 1,728 rhizosphere bacterial clones (576 clones per treatment) using a Cramér-von Mises statistic analysis combined with a Monte Carlo comparison clearly indicated that there was a statistically significant difference (P < 0.05) between the microbial communities inhabiting the rhizospheres of trees carrying the cryIA(b), uidA, and nptII transgenes, trees carrying only the uidA and nptII transgenes, and control trees. Clear rhizosphere microbial community alterations due to B. thuringiensis tree genetic modification have to our knowledge never been described previously and open the door to interesting questions related to B. thuringiensis genetic transformation and also to the impact of commonly used uidA and nptII genetic marker genes.
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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) | 0.000 | 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 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".