Reconstruction of Ancestral 16S rRNA Reveals Mutation Bias in the Evolution of Optimal Growth Temperature in the Thermotogae Phylum
Bibliographic record
Abstract
Optimal growth temperature is a complex trait involving many cellular components, and its physiology is not yet fully understood. Evolution of continuous characters, such as optimal growth temperature, is often modeled as a one-dimensional random walk, but such a model may be an oversimplification given the complex processes underlying the evolution of continuous characters. Recent articles have used ancestral sequence reconstruction to infer the optimal growth temperature of ancient organisms from the guanine and cytosine content of the stem regions of ribosomal RNA, allowing inferences about the evolution of optimal growth temperature. Here, we investigate the optimal growth temperature of the bacterial phylum Thermotogae. Ancestral sequence reconstruction using a nonhomogeneous model was used to reconstruct the stem guanine and cytosine content of 16S rRNA sequences. We compare this sequence reconstruction method with other ancestral character reconstruction methods, and show that sequence reconstruction generates smaller confidence intervals and different ancestral values than other reconstruction methods. Unbiased random walk simulation indicates that the lower temperature members of the Thermotogales have been under directional selection; however, when a simulation is performed that takes possible mutations into account, it is the high temperature lineages that are, in fact, under directional selection. We find that the evolution of Thermotogales optimal growth temperatures is best fit by a biased random walk model. These findings suggest that it may be easier to evolve from a high optimal growth temperature to a lower one than vice versa.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".