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Record W4413908148 · doi:10.1186/s12864-025-11983-2

Transcriptome dynamics and allele-specific regulation underlie wheat heterosis at the anthesis and grain-filling stages

2025· article· en· W4413908148 on OpenAlexaff
Xiaojun Wu, Xiangdong Chen, Ren Wang, Xigui Hu, Yuquan Wang, Li Gan, Na Dong, Tiezhu Hu, Zhengang Ru

Bibliographic record

VenueBMC Genomics · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic Mapping and Diversity in Plants and Animals
Canadian institutionsMinistry of Agriculture
FundersHenan Provincial Science and Technology Research ProjectNational Natural Science Foundation of China
KeywordsHeterosisBiologyAnthesisOverdominanceAlleleGeneticsTranscriptomeGeneRegulation of gene expressionGene expressionHybridBotanyCultivar

Abstract

fetched live from OpenAlex

BACKGROUND: As wheat is a globally important staple crop, the molecular regulatory network underlying heterosis in wheat remains incompletely understood. The flag leaf is the primary source of photoassimilates during grain filling and plays a crucial role in yield formation. However, the genetic mechanisms linking flag leaf development to heterosis are still unclear. RESULTS: Transcriptomic analysis revealed dynamic transcriptional reprogramming during the anthesis to grain-filling transition, with a pronounced expression bias toward superior parental alleles in hybrids. Anthesis-stage non-additive dominance and grain-filling-stage additive enhancement synergistically orchestrated the temporal regulatory shift underlying heterosis. The dominant alleles from the superior parent accounted for more than 60% of the non-additive genes, and the superior parent bias in allele-specific expression ratios progressively increased during development. This highlighted the role of the superior parent as an allelic reservoir. Cis-regulatory variations primarily contributed to additive effects, whereas cis×trans interactions were the primary regulatory driver of positive overdominance. Notably, weighted co-expression network analysis identified HSP90.2-B as a putative heterosis-related gene, whose coordinated overexpression with AP2/ERF transcription factors provides valuable insights for elucidating the molecular basis of yield heterosis. CONCLUSIONS: This study establishes two complementary models to decode the molecular regulation of heterosis in wheat. The "dual-engine" model demonstrates stage-specific gene expression patterns: non-additive effects predominantly drive early growth vigor during the anthesis stage, whereas additive expression patterns stabilize grain development and yield-related traits at the grain-filling stage. The "two-phase regulatory shift" model captures the dynamic temporal progression of heterotic regulation, evolving from trans-regulation-driven plastic responses at the anthesis stage to cis-regulation-mediated homeostatic control at the grain-filling stage. Importantly, the preferential coupling between cis-regulation/additive and trans-regulation/non-additive expression provides molecular evidence supporting the complementary nature of the models. We further identified developmentally specific modules (the anthesis-stage Red module and grain-filling-stage Brown module) with their core regulatory networks through weighted gene co-expression network analysis. These findings preliminarily characterize the multi-layered cooperative networks regulating heterosis development, potentially offering valuable theoretical clues for deciphering the molecular mechanisms underlying wheat heterosis.

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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.018
GPT teacher head0.217
Teacher spread0.199 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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