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Record W2971491630 · doi:10.3389/fgene.2019.00925

Convergent Evolution of Mitochondrial Genes in Deep-Sea Fishes

2019· article· en· W2971491630 on OpenAlexaff
Xuejuan Shen, Zhiqing Pu, Xiao Chen, Robert W. Murphy, Yongyi Shen

Bibliographic record

VenueFrontiers in Genetics · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicPhysiological and biochemical adaptations
Canadian institutionsRoyal Ontario Museum
FundersChinese Academy of SciencesChina Agricultural UniversitySouth China Agricultural UniversityNational Natural Science Foundation of China
KeywordsDeep seaBiologyAdaptation (eye)Hydrostatic pressureEcologyMitochondrial DNACellular respirationConvergent evolutionEcological nicheGeneMitochondrionEvolutionary biologyGeneticsPhylogeneticsFisheryHabitat

Abstract

fetched live from OpenAlex

Deep seas have extremely harsh conditions including high hydrostatic pressure, total darkness, cold, and little food and oxygen. The adaptations of fishes to deep-sea environment apparently have occurred independently many times. The genetic basis of adaptation for obtaining their energy remains unknown. Mitochondria play a central role in aerobic respiration. Analyses of the available 2,161 complete mitochondrial genomes of 1,042 fishes, including 115 deep sea species, detect signals of positive selection in mitochondrial genes in nine branches of deep-sea fishes. Aerobic metabolism yields much more energy per unit of source material than anaerobic metabolism. The adaptive evolution of the mtDNA may reflect that aerobic metabolism plays a more important role than anaerobic metabolism in deep-sea fishes, whose energy sources (food) are extremely limited. This strategy maximizes the usage of energy sources. Eleven mitochondrial genes have convergent/parallel amino acid changes between branches of deep-sea fishes. Thus, these amino acid sites may be functionally important in the acquisition of energy, and reflect convergent evolution during their independent invasion of the harsh deep-sea ecological niche.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.244
Threshold uncertainty score0.321

Codex and Gemma teacher scores by category

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

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 head0.007
GPT teacher head0.191
Teacher spread0.184 · 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 teacher head, 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

Citations40
Published2019
Admission routes1
Has abstractyes

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