Seasonal temperature, the lunar cycle and diurnal rhythms interact in a combinatorial manner to modulate genomic responses to the environment in a reef‐building coral
Bibliographic record
Abstract
Rhythms of various periodicities drive cyclical processes in organisms ranging from single cells to the largest mammals on earth, and on scales from cellular physiology to global migrations. The molecular mechanisms that generate circadian behaviours in model organisms have been well studied, but longer phase cycles and interactions between cycles with different periodicities remain poorly understood. Broadcast spawning corals are one of the best examples of an organism integrating inputs from multiple environmental parameters, including seasonal temperature, the lunar phase and hour of the day, to calibrate their annual reproductive event. We present a deep RNA-sequencing experiment utilizing multiple analyses to differentiate transcriptomic responses modulated by the interactions between the three aforementioned environmental parameters. Acropora millepora was sampled over multiple 24-hr periods throughout a full lunar month and at two seasonal temperatures. Temperature, lunar and diurnal cycles produce distinct transcriptomic responses, with interactions between all three variables identifying a core set of genes. These core genes include mef2, a developmental master regulator, and two heterogeneous nuclear ribonucleoproteins, one of which is known to post-transcriptionally interact with mef2 and with biological clock-regulating mRNAs. Interactions between diurnal and temperature differences impacted a range of core processes ranging from biological clocks to stress responses. Genes involved with developmental processes and transcriptional regulation were impacted by the lunar phase and seasonal temperature differences. Lastly, there was a diurnal and lunar phase interaction in which genes involved with RNA-processing and translational regulation were differentially regulated. These data illustrate the extraordinary levels of transcriptional variation across time in a simple radial cnidarian in response to the environment under normal conditions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".