A comparative gene co-expression networks analysis of two congener filmy ferns identifies specific desiccation tolerance mechanisms associated to their microhabitat preference.
Bibliographic record
Abstract
Abstract Background Filmy-ferns (Hymenophyllaceae) are poikilohydric, homoiochlorophyllous desiccation-tolerant (DT) epiphytes. They can colonize lower and upper canopy environments of humid forest. Filmy-ferns desiccate rapidly (hours), contrasting with DT angiosperms (days/weeks). We hypothesized that desiccation tolerance in filmy-ferns would be associated mainly with constitutive features rather than induced responses during dehydration. Hence, the inter-specific differences in vertical distribution would be associated with different dynamics of gene expression within the dehydration or rehydration phases. A comparative transcriptomic analysis with an artificial neural network was done on Hymenophyllum caudiculatum (restricted to lower canopy) and Hymenophyllum dentatum (reach upper canopy) during a desiccation/rehydration cycle.Results Raw reads were assembled into 69,599 transcripts for H. dentatum and 34,726 transcripts for H. caudiculatum . Few transcripts showed significant changes in differential expression (DE). H. caudiculatum had ca. twice DE genes than H. dentatum and higher proportion of increased-and-decreased abundance of genes occurs during dehydration. In contrast, the abundance of genes in H. dentatum decreased significantly when transitioning from dehydration to rehydration. According to the artificial neural network results, H. caudiculatum enhanced osmotic responses and phenylpropanoid related pathways, whilst H. dentatum enhanced its immune system responses and protection against high light stress.Conclusions Our findings provide a deeper understanding of the mechanisms underlying the desiccation tolerance responses of two filmy ferns and the relationship between the species-specific response and the microhabitats these ferns occupy in nature.
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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 | 0.001 | 0.001 |
| 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".