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Record W4386030257 · doi:10.3390/f14081686

Effects of Myo-Inositol on NaCl Stress in Tamarix ramosissima: Insights from Transcriptomics and Metabolomics

2023· article· en· W4386030257 on OpenAlexaff
Haijia Li, Yunlong Fan, Huanchao Zhang, Yahui Chen

Bibliographic record

VenueForests · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Stress Responses and Tolerance
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsInositolTranscriptomeBiologyReactive oxygen speciesOxidative stressBiochemistryHalophyteChemistryCell biologyGeneBotanyGene expressionSalinityEcologyReceptor

Abstract

fetched live from OpenAlex

NaCl stress adversely affects plant growth. Tamarix ramosissima Ledeb (T. ramosissima), a halophyte, thrives in saline-alkali areas. Myo-inositol, a lipid-soluble compound, is crucial for stress response, but its role in mitigating NaCl damage remains underexplored. We analyzed transcriptome sequencing and metabolites in T. ramosissima roots under NaCl stress at various intervals (0 h, 48 h, and 168 h). We identified ten Myo-inositol oxygenase-related genes. Nine of these genes, linked to metabolic pathways involving Myo-inositol, showed differential expression. Myo-inositol accumulation increased over time, suggesting its role as an osmotic regulator and reactive oxygen species (ROS) scavenger. This accumulation likely shields T. ramosissima from NaCl-induced osmotic and oxidative damage. Notably, Unigene0002140 and Unigene0095980, associated with Myo-inositol oxygenase, appear to regulate Myo-inositol accumulation and correlate significantly with its levels. We hypothesize they are key genes in controlling Myo-inositol levels, warranting further study. This research illuminates the role of Myo-inositol oxygenase-related genes in T. ramosissima roots combating NaCl stress, offering insights for selecting salt-tolerant tree species.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.704
Threshold uncertainty score0.304

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 head0.008
GPT teacher head0.200
Teacher spread0.192 · 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 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

Citations10
Published2023
Admission routes1
Has abstractyes

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