A comparative gene co-expression analysis using self-organizing maps on two congener filmy ferns identifies specific desiccation tolerance mechanisms associated to their microhabitat preference
Bibliographic record
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). It has been proposed that desiccation tolerance in filmy-ferns would be associated mainly with constitutive features rather than induced responses during dehydration. However, we hypothesize that 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 defense 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 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".