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Record W4385778890 · doi:10.1111/pbi.14156

Directed evolution rice genes with randomly multiplexed sgRNAs assembly of base editors

2023· article· en· W4385778890 on OpenAlexaff
Ao Zhang, Tiaofeng Shan, Yan V. Sun, Zhipeng Chen, Jianjian Hu, Zhichao Hu, Ziheng Ming, Zhitao Zhu, Xue Li, Jun He, Shijia Liu, Ling Jiang, Oliver Xiaoou Dong, Yufeng Wu, Yanpeng Wang, Yuqiang Liu, Chao Li, Jianmin Wan

Bibliographic record

VenuePlant Biotechnology Journal · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCRISPR and Genetic Engineering
Canadian institutionsInstitute of Genetics
FundersChinese Academy of Agricultural SciencesNatural Science Foundation of Jiangsu ProvinceNanjing Agricultural UniversityGovernment of Jiangsu ProvinceJiangsu Agricultural Science and Technology Innovation Fund
KeywordsBiologyGeneComputational biologyGeneticsBase (topology)BiotechnologyEvolutionary biology

Abstract

fetched live from OpenAlex

CRISPR-based directed evolution is an effective breeding biotechnology to improve agronomic traits in plants. However, its gene diversification is still limited using individual single guide RNA. We described here a multiplexed orthogonal base editor (MoBE), and a randomly multiplexed sgRNAs assembly strategy to maximize gene diversification. MoBE could induce efficiently orthogonal ABE (<36.6%), CBE (<36.0%), and A&CBE (<37.6%) on different targets, while the sgRNA assembling strategy randomized base editing events on various targets. With respective 130 and 84 targets from each strand of the 34th exon of rice acetyl-coenzyme A carboxylase (OsACC), we observed the target-scaffold combination types up to 27 294 in randomly dual and randomly triple sgRNA libraries. We further performed directed evolution of OsACC using MoBE and randomly dual sgRNA libraries in rice, and obtained single or linked mutations of stronger herbicide resistance. These strategies are useful for in situ directed evolution of functional genes and may accelerate trait improvement in rice.

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 score0.014
Threshold uncertainty score0.496

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 head0.006
GPT teacher head0.239
Teacher spread0.233 · 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

Citations30
Published2023
Admission routes1
Has abstractyes

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