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Record W4205816913 · doi:10.3389/fpls.2021.814928

Identification and Pleiotropic Effect Analysis of GSE5 on Rice Chalkiness and Grain Shape

2022· article· en· W4205816913 on OpenAlexfundno aff
Liangrong Jiang, Hui Zhong, Jiang XianBin, Jiaoping Zhang, Rongyu Huang, Furong Liao, Yaqin Deng, Qingqing Liu, Yumin Huang, Houcong Wang, Yi Tao, Jingsheng Zheng

Bibliographic record

VenueFrontiers in Plant Science · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic Mapping and Diversity in Plants and Animals
Canadian institutionsnot available
FundersInstitute of Genetics and Developmental Biology, Chinese Academy of SciencesChinese Academy of SciencesInstitute of GeneticsNational Natural Science Foundation of China
KeywordsBiologyQuantitative trait locusGeneCandidate geneLocus (genetics)Genetic analysisSugarOryza sativaGeneticsFood science

Abstract

fetched live from OpenAlex

Chalkiness is one of several major restricting factors for the improvement of rice quality. Although many chalkiness-related quantitative trait loci have been mapped, only a small number of genes have been cloned to date. In this study, the candidate gene GSE5 of a major quantitative trait locus (QTL) for rice chalkiness, qDEC5, was identified by map-based cloning. Phenotyping and haplotype analysis of proActin:GSE5 transgenic line, gse5-cr mutant, and 69 rice varieties further confirmed that GSE5 had the pleiotropic effects and regulated both chalkiness and grain shape. Genetic analysis showed GSE5 was a dominant gene for grain length and a semi-dominant gene for grain width and chalkiness. The DNA interval closely linked to GSE5 was introgressed to Zhenshan 97B (ZB) based on molecular marker-assisted selection, and the improved ZB showed lower chalkiness and longer but smaller grains, which showed that GSE5 played an important role in breeding rice varieties with high yield and good quality. Transcriptomics, proteomics, and qRT-PCR analyses showed that thirty-nine genes associated with carbon and protein metabolism are regulated by GSE5 to affect the formation of chalkiness, including some newly discovered genes, such as OsCESA9, OsHSP70, OsTPS8, OsPFK04, OsSTA1, OsERdj3A, etc. The low-chalkiness lines showed higher amino sugar and nucleotide sugar metabolism at 10 days after pollination (DAP), lower carbohydrate metabolism at 15 DAP, and lower protein metabolism at 10 and 15 DAP. With heat shock at 34/30°C, rice chalkiness increased significantly; OsDjC10 and OsSUS3 were upregulated at 6 and 12 DAP, respectively, and OsGSTL2 was downregulated at 12 DAP. Our results identified the function and pleiotropic effects of qDEC5 dissected its genetic characteristics and the expression profiles of the genes affecting the chalkiness formation, and provided a theoretical basis and application value to harmoniously pursue high yield and good quality in rice production.

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 score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.006
GPT teacher head0.214
Teacher spread0.208 · 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

Citations18
Published2022
Admission routes1
Has abstractyes

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