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Record W1972583249 · doi:10.1111/jeb.12428

Misregulation of spermatogenesis genes in <i><scp>D</scp>rosophila</i> hybrids is lineage‐specific and driven by the combined effects of sterility and fast male regulatory divergence

2014· article· en· W1972583249 on OpenAlexafffund
Suzanne Gomes, Alberto Civetta

Bibliographic record

VenueJournal of Evolutionary Biology · 2014
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Behavior and Reproduction
Canadian institutionsUniversity of Winnipeg
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of California, San Diego
KeywordsBiologySterilityGeneGeneticsLineage (genetic)SpermatogenesisSpermEndocrinology

Abstract

fetched live from OpenAlex

Hybrid male sterility is a common outcome of crosses between different species. Gene expression studies have found that a number of spermatogenesis genes are differentially expressed in sterile hybrid males, compared with parental species. Late-stage sperm development genes are particularly likely to be misexpressed, with fewer early-stage genes affected. Thus, a link has been posited between misexpression and sterility. A more recent alternative explanation for hybrid gene misexpression has been that it is independent of sterility and driven by divergent evolution of male-specific regulatory elements between species (faster male hypothesis). The faster male hypothesis predicts that misregulation of spermatogenesis genes should be independent of sterility and approximately the same in both hybrids, whereas sterility should only affect gene expression in sterile hybrids. To test the faster male hypothesis vs. the effect of sterility on gene misexpression, we analyse spermatogenesis gene expression in different species pairs of the Drosophila phylogeny, where hybrid male sterility occurs in only one direction of the interspecies cross (i.e. unidirectional sterility). We find significant differences among genes in misexpression with effects that are lineage-specific and caused by sterility or fast male regulatory divergence.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.530
Threshold uncertainty score0.143

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 head0.010
GPT teacher head0.203
Teacher spread0.193 · 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

Citations22
Published2014
Admission routes2
Has abstractyes

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