Hearing Aid for Vertebrates via Multiple Episodic Adaptive Events on Prestin Genes
Bibliographic record
Abstract
Auditory detection is essential for survival and reproduction of vertebrates, yet the genetic changes underlying the evolution and diversity of hearing are poorly documented. Recent discoveries concerning prestin, which is responsible for cochlear amplification by electromotility, provide an opportunity to redress this situation. We identify prestin genes from the genomes of 14 vertebrates, including three fishes, one amphibian, one lizard, one bird, and eight mammals. An evolutionary analysis of these sequences and 34 previously known prestin genes reveals for the first time that this hearing gene was under positive selection in the most recent common ancestor (MRCA) of tetrapods. This discovery might document the genetic basis of enhanced high sound sensibility in tetrapods. An investigation of the adaptive gain and evolution of electromotility, an important evolutionary innovation for the highest hearing ability of mammals, detects evidence for positive selections on the MRCA of mammals, therians, and placentals, respectively. It is suggested that electromotility determined by prestin might initially appear in the MRCA of mammals, and its functional improvements might occur in the MRCA of therian and placental mammals. Our patch clamp experiments further support this hypothesis, revealing the functional divergence of voltage-dependent nonlinear capacitance of prestin from platypus, opossum, and gerbil. Moreover, structure-based cdocking analyses detect positively selected amino acids in the MRCA of placental mammals that are key residues in sulfate anion transport. This study provides new insights into the adaptation and functional diversity of hearing sensitivity in vertebrates by evolutionary and functional analysis of the hearing gene prestin.
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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".