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Record W4285386128 · doi:10.1038/s41598-022-14850-2

Transcriptome analysis and phenotyping of walnut seedling roots under nitrogen stresses

2022· article· en· W4285386128 on OpenAlexfundno aff
Yan Song, Rui Zhang, Shan Gao, Zhiyong Pan, Zhongzhong Guo, Shangqi Yu, Yu Wang, Qiang Jin, Xiaofei Chen, Lei Zhang

Bibliographic record

VenueScientific Reports · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPhotosynthetic Processes and Mechanisms
Canadian institutionsnot available
FundersXinjiang Production and Construction CorpsMinistère de la Santé et des Services sociaux
KeywordsSeedlingTranscriptomeBiologyNitrogenBotanyComputational biologyGeneticsGeneChemistryGene expression

Abstract

fetched live from OpenAlex

Abstract Nitrogen is an essential core element in walnut seedling growth and development. However, nitrogen starvation and excessive nitrogen stress can cause stunted growth and development of walnut seedlings, and environmental pollution is also of concern. Therefore, it is necessary to study the mechanism of walnut seedling resistance to nitrogen stress. In this study, morphological and physiological observations and transcriptome sequencing of walnut seedlings under nitrogen starvation and excess nitrogen stress were performed. The results showed that walnut seedlings under nitrogen starvation and excess stress could adapt to the changes in the nitrogen environment by changing the coordination of their root morphology and physiological indexes. Based on an analysis of transcriptome data, 4911 differential genes (DEGs) were obtained (2180 were upregulated and 2731 were downregulated) in a comparison of nitrogen starvation and control groups. A total of 9497 DEGs (5091 upregulated and 4406 downregulated) were obtained in the comparison between the nitrogen overdose and control groups. When these DEGs were analysed, the differential genes in both groups were found to be significantly enriched in the plant’s circadian pathway. Therefore, we selected the circadian rhythm as the focus for further analysis. We made some discoveries by analysing the gene co-expression network of nitrogen metabolism, circadian rhythm, and hormone signal transduction. (a) Nitrite nitrogen (NO2−) or Glu may act as a nitrogen signal to the circadian clock. (b) Nitrogen signalling may be input into the circadian clock by regulating changes in the abundance of the CRY1 gene. (c) After the nitrogen signal enters the circadian clock, the expression of the LHY gene is upregulated, which causes a phase shift in the circadian clock. (d) The RVE protein may send information about the circadian clock’s response to nitrogen stress back to the nitrogen metabolic pathway via the hormone transduction pathway. In conclusion, various metabolic pathways in the roots of walnut seedlings coordinated with one another to resist the ill effects of nitrogen stress on the root cells, and these coordination relationships were regulated by the circadian clock. This study is expected to provide valuable information on the circadian clock regulation of plant resistance to nitrogen stress.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
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)0.0000.000

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 head0.008
GPT teacher head0.231
Teacher spread0.223 · 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 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

Citations15
Published2022
Admission routes1
Has abstractyes

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