Different amplitudes of temperature fluctuation induce distinct transcriptomic and metabolomic responses in the dung beetle <i>Phanaeus vindex</i>
Bibliographic record
Abstract
Most studies exploring molecular and physiological responses to temperature have focused on constant temperature treatments. To gain a better understanding of the impacts of fluctuating temperatures, we investigated impacts of increased temperature variation on Phanaeus vindex dung beetles across levels of biological organization. Specifically, we hypothesized that increased temperature variation is energetically demanding. We predicted that thermal sensitivity of metabolic rate and energetic reserves would be reduced with increasing fluctuation. To test this, we examined responses of dung beetles exposed to constant (20°C), low fluctuation (20±5°C), or high fluctuation (20±12°C) temperature treatments using respirometry, energetic reserves, and HPLC-MS-based metabolomics. We found no significant differences in metabolic rates or energetic reserves, suggesting increased fluctuations were not energetically demanding. To understand why there was no effect of increased amplitude on energetics, we assembled and annotated a de novo transcriptome, finding non-overlapping transcriptomic and metabolomic responses of beetles exposed to different fluctuations. We found that 58 metabolites increased in abundance in both fluctuation treatments, but 15 only did so in response to high amplitude fluctuations. We found 120 transcripts were significantly upregulated following acclimation to any fluctuation, but 174 were upregulated only in beetles from the high amplitude fluctuation. Several differentially expressed transcripts were associated with post-translational modifications to histones that support a more open chromatin structure. Our results demonstrate that acclimation to different temperature fluctuations is distinct and may be supported by increasing transcriptional plasticity. Our results indicate for the first time that histone modifications may underlie rapid acclimation to temperature variation.
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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".