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Record W1976915655 · doi:10.1139/g08-037

Two different farnesyl diphosphate synthase genes exist in the genome of the green peach aphid,<i>Myzus persicae</i>

2008· article· en· W1976915655 on OpenAlexvenueno aff
Yong-Lei Zhang, Zheng‐Xi Li

Bibliographic record

VenueGenome · 2008
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPlant biochemistry and biosynthesis
Canadian institutionsnot available
Fundersnot available
KeywordsMyzus persicaeBiologyAphidFarnesyl diphosphate synthaseGeneGenomeBotanyGeneticsATP synthase

Abstract

fetched live from OpenAlex

Farnesyl diphosphate synthase (FPS; EC 2.5.1.1, 2.5.1.10) catalyzes biosynthesis of farnesyl diphosphate, which is important to insects as the precursor of juvenile hormone and the substrate for (E)-beta-farnesene synthase. Here, two FPS cDNAs were isolated from the green peach aphid, Myzus persicae (EU334430 and EU334431). Their shared identity within the coding region is approximately 82%. The deduced amino acid sequences of the two M. persicae FPS cDNAs have the highly conserved motifs characteristic of most known FPSs. Phylogenetic analyses showed that they are closely related to other insect FPSs. Homology modeling of structures suggested a very good fit between the three-dimensional structures of the two putative M. persicae FPSs (designated as MpFPS1 and MpFPS2) and the avian FPS crystal structure. The corresponding genomic DNA sequences were subsequently determined (EU429295 and EU429296). Sequence comparisons revealed a different splicing pattern between the two MpFPS genes. Furthermore, the two MpFPS genes exhibited a seemingly very primitive gene-splicing pattern at 5' ends but a gene-splicing style similar to mammalian FPS genes at 3' ends. These data, combined with results of Southern blotting, suggest that M. persicae contains two different FPS genes. This is the first report that two different FPS genes exist in a hemipteran insect.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.123
Threshold uncertainty score0.625

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 science0.0010.000
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 head0.014
GPT teacher head0.211
Teacher spread0.197 · 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 designBench or experimental
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

Citations24
Published2008
Admission routes1
Has abstractyes

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