Comparative Transcriptomic Approaches Exploring Contamination Stress Tolerance in <i>Salix</i> sp. Reveal the Importance for a Metaorganismal de Novo Assembly Approach for Nonmodel Plants
Bibliographic record
Abstract
Metatranscriptomic study of nonmodel organisms requires strategies that retain the highly resolved genetic information generated from model organisms while allowing for identification of the unexpected. A real-world biological application of phytoremediation, the field growth of 10 Salix cultivars on polluted soils, was used as an exemplar nonmodel and multifaceted crop response well-disposed to the study of gene expression. Sequence reads were assembled de novo to create 10 independent transcriptomes, a global transcriptome, and were mapped against the Salix purpurea 94006 reference genome. Annotation of assembled contigs was performed without a priori assumption of the originating organism. Global transcriptome construction from 3.03 billion paired-end reads revealed 606,880 unique contigs annotated from 1588 species, often common in all 10 cultivars. Comparisons between transcriptomic and metatranscriptomic methodologies provide clear evidence that nonnative RNA can mistakenly map to reference genomes, especially to conserved regions of common housekeeping genes, such as actin, α/β-tubulin, and elongation factor 1-α. In Salix, Rubisco activase transcripts were down-regulated in contaminated trees across all 10 cultivars, whereas thiamine thizole synthase and CP12, a Calvin Cycle master regulator, were uniformly up-regulated. De novo assembly approaches, with unconstrained annotation, can improve data quality; care should be taken when exploring such plant genetics to reduce de facto data exclusion by mapping to a single reference genome alone. Salix gene expression patterns strongly suggest cultivar-wide alteration of specific photosynthetic apparatus and protection of the antenna complexes from oxidation damage in contaminated trees, providing an insight into common stress tolerance strategies in a real-world phytoremediation system.
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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) | 0.001 | 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 | 0.001 |
| 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".