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Record W3136845941 · doi:10.1186/s12915-021-00996-4

Reorganization of the 3D chromatin architecture of rice genomes during heat stress

2021· article· en· W3136845941 on OpenAlexaff
Zhe Liang, Qian Zhang, Changmian Ji, Guihua Hu, Pingxian Zhang, Yifan Wang, Liwen Yang, Xiaofeng Gu

Bibliographic record

VenueBMC Biology · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenomics and Chromatin Dynamics
Canadian institutionsBiotechnology Research Institute
FundersChinese Academy of Agricultural Sciences
KeywordsChromatinBiologyGenomeGeneGeneticsGene expressionGenomic organizationRegulation of gene expressionCell biologyComputational biology

Abstract

fetched live from OpenAlex

BACKGROUND: The three-dimensional spatial organization of the genome plays important roles in chromatin accessibility and gene expression in multiple biological processes and has been reported to be altered in response to environmental stress. However, the functional changes in spatial genome organization during environmental changes in crop plants are poorly understood. RESULTS: Here we perform Hi-C, ATAC-seq, and RNA-seq in two agronomically important rice cultivars, Nipponbare (Nip; Japonica) and 93-11 (Indica), to report a comprehensive profile of nuclear dynamics during heat stress (HS). We show that heat stress affects different levels of chromosome organization, including A/B compartment transition, increase in the size of topologically associated domains, and loss of short-range interactions. The chromatin architectural changes were associated with chromatin accessibility and gene expression changes. Comparative analysis revealed that 93-11 exhibited more dynamic gene expression and chromatin accessibility changes, including HS-related genes, consistent with observed higher HS tolerance in this cultivar. CONCLUSIONS: Our data uncovered higher-order chromatin architecture as a new layer in understanding transcriptional regulation in response to heat stress 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 score0.046
Threshold uncertainty score0.262

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 head0.004
GPT teacher head0.200
Teacher spread0.196 · 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

Citations98
Published2021
Admission routes1
Has abstractyes

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