Gene purging and the evolution of Neoave metabolism and longevity
Bibliographic record
Abstract
Maintenance of the proteasome requires oxidative phosphorylation (ATP) and mitigation of oxidative damage, in an increasingly dysfunctional relationship with aging. SLC3A2 plays a role on both sides of this dichotomy as an adaptor to SLC7A5, a transporter of branched-chain amino acids (BCAA: Leu, Ile, Val), and to SLC7A11, a cystine importer supplying cysteine to the synthesis of the antioxidant glutathione. Endurance in mammalian muscle depends in part on oxidation of BCAA; however, elevated serum levels are associated with insulin resistance and shortened lifespans. Intriguingly, the evolution of modern birds (Neoaves) has entailed the purging of genes including SLC3A2 , SLC7A5, -7, -8, -10 , and SLC1A4, -5 , largely removing BCAA exchangers and their interacting Na + /Gln symporters in pursuit of improved energetics. Additional gene purging included mitochondrial BCAA aminotransferase ( BCAT2 ), pointing to reduced oxidation of BCAA and increased hepatic conversion to triglycerides and glucose. Fat deposits are anhydrous and highly reduced, maximizing the fuel/weight ratio for prolonged flight, but fat accumulation in muscle cells of aging humans contributes to inflammation and senescence. Duplications of the bidirectional α-ketoacid transporters SLC16A3 , SLC16A7 , the cystine transporters SLC7A9 , SLC7A11 , and N-glycan branching enzymes MGAT4B , MGAT4C in Neoaves suggests a shift to the transport of deaminated essential amino acid, and stronger mitigation of oxidative stress supported by the galectin lattice. We suggest that Alfred Lotka's theory of natural selection as a maximum power organizer (PNAS 8:151,1922) made an unusually large contribution to Neoave evolution. Further molecular analysis of Neoaves may reveal novel rewiring with applications for human health and longevity.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".