Genetically modified soybean lines exhibit less transcriptomic variation compared to natural varieties
Bibliographic record
Abstract
Genetically modified (GM) soybeans provide a huge amount of food for human consumption and animal feed. However, the possibility of unexpected effects of transgenesis has increased food safety concerns. High-throughput sequencing profiling provides a potential approach to directly evaluate unintended effects caused by foreign genes. In this study, we performed transcriptomic analyses to evaluate differentially expressed genes (DEGs) in individual soybean tissues, including cotyledon (C), germ (G), hypocotyl (H), and radicle (R), instead of using the whole seed, from four GM and three non-GM soybean lines. A total of 3,351 DEGs were identified among the three non-GM soybean lines. When the GM lines were compared with their non-GM parents, 1,836 to 4,551 DEGs were identified. Furthermore, Gene Ontology (GO) analysis of the DEGs showed more abundant categories of GO items (199) among non-GM lines than between GM lines and the non-GM natural varieties (166). Results of Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis showed that most KEGG pathways were the same for the two types of comparisons. The study successfully employed RNA sequencing to assess the differences in gene expression among four tissues of seven soybean varieties, and the results suggest that transgenes do not induce massive transcriptomic alterations in transgenic soybeans compared with those that exist among natural varieties. This work offers empirical evidence to investigate the genomic-level disparities induced by genetic modification in soybeans, specifically focusing on seed tissues.
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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) | 0.001 | 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".