Bibliographic record
Abstract
There's more than one way to crack an egg, shear a sheep or do pretty well anything else, but what about the evolution of physiological traits? If the trait in question is a protein's function, then shouldn't biophysical constraints on protein structure demand that the independent evolution of the same function arise from the same amino acid substitution? Chandrasekhar Natarajan, from the University of Nebraska, USA, and his team were sufficiently intrigued by this question to devise a truly impressive comparative study to get to the bottom of it, and used haemoglobin, and its affinity for oxygen, as their representative protein.The team started by clustering 56 avian species into 28 closely related species pairs, each pair comprising a low- and high-altitude native relative. After isolating each bird's haemoglobin, the team measured their affinities for oxygen and found that the high-altitude inhabitants consistently had higher affinity haemoglobins than their low-altitude counterparts, the evolutionary result of adaptation to a consistently more hypoxic environment. The question then became whether the evolved increases in haemoglobin–oxygen affinities were caused by amino acid substitutions at the same sites.They set about answering this by sequencing each species’ haemoglobins and searching for common amino acid substitutions that might be responsible for the higher affinities that evolved independently in multiple high-altitude species. When assessing their results across the entire family tree of birds, they found no consistency in the amino acid substitutions that they identified in the high-affinity haemoglobins, suggesting that there are multiple ways to make a high-affinity haemoglobin. However, when they zoomed in on closely related branches within the broad family tree – focusing in particular on the hummingbirds – they noticed a glycine-to-serine substitution that consistently appeared in the high-altitude hummingbirds at a site on the protein (amino acid 83 in the β chain) that could potentially enhance its affinity for oxygen. Intriguingly, a substitution at the same site also appeared in the high-altitude flowerpiercers.This parallelism hinted at a common mechanism for enhancement of oxygen affinity within closely related species, but one that was not effective in more distantly related species. The team tested this hypothesis by reconstructing the ancestral haemoglobin sequences for hummingbirds, flowerpiercers, Neoaves (which are the common ancestor of all extant birds excluding the ducks, chickens and kin) and Neornithes (which are the common ancestor of all currently surviving birds). They used site-directed mutagenesis to introduce the key amino acid substitution in each haemoglobin sequence, and then tasked Escherichia coli with physically manufacturing these ‘new’ haemoglobins. After isolating the proteins, the team measured their oxygen affinities and found that the mutations significantly increased the affinities of ancestral hummingbird and flowerpiercer haemoglobins, but had no effect on the more distantly related haemoglobins. These results plainly showed that despite environmental consistency – all of the species were adapted to high-altitude hypoxia and so the proteins were adapted to increased oxygen affinity – the causal genetic mechanism was only similar among closely related species. When the team zoomed out to look at more distantly related species, they found that other differences dispersed throughout the haemoglobin chains precluded the mutation at β83 from having the same effect on oxygen affinity as it did within the closely related hummingbirds and flowerpiercers. Enhanced affinities therefore evolved in these more distantly related species through a different set of mutations.So, yes, there's more than one way to shine a penny, shoe a horse and catch a rabbit. Now we know that there's also more than one way to evolve a high-affinity haemoglobin. As idioms go, it's a good one, but it should probably be reserved for use among physiologists.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".