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Record W2161975983 · doi:10.2174/138920211796429736

The Genetics of Vitamin C Loss in Vertebrates

2011· article· en· W2161975983 on OpenAlexaff
Guy Drouin, J.R.P. Godin, Benoît Pagé

Bibliographic record

VenueCurrent Genomics · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicAmphibian and Reptile Biology
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsVertebratePseudogeneBiologyGeneAscorbic acidVitaminGeneticsOxidase testZoologyBiochemistryEnzymeGenome

Abstract

fetched live from OpenAlex

Vitamin C (ascorbic acid) plays important roles as an anti-oxidant and in collagen synthesis. These important roles, and the relatively large amounts of vitamin C required daily, likely explain why most vertebrate species are able to synthesize this compound. Surprisingly, many species, such as teleost fishes, anthropoid primates, guinea pigs, as well as some bat and Passeriformes bird species, have lost the capacity to synthesize it. Here, we review the genetic bases behind the repeated losses in the ability to synthesize vitamin C as well as their implications. In all cases so far studied, the inability to synthesize vitamin C is due to mutations in the L-gulono-γ-lactone oxidase (GLO) gene which codes for the enzyme responsible for catalyzing the last step of vitamin C biosynthesis. The bias for mutations in this particular gene is likely due to the fact that losing it only affects vitamin C production. Whereas the GLO gene mutations in fish, anthropoid primates and guinea pigs are irreversible, some of the GLO pseudogenes found in bat species have been shown to be reactivated during evolution. The same phenomenon is thought to have occurred in some Passeriformes bird species. Interestingly, these GLO gene losses and reactivations are unrelated to the diet of the species involved. This suggests that losing the ability to make vitamin C is a neutral trait.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.024
GPT teacher head0.229
Teacher spread0.205 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations357
Published2011
Admission routes1
Has abstractyes

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