Characterisation of two wheat enolase <scp>cDNA</scp> showing distinct patterns of expression in leaf and crown tissues of plants exposed to low temperature
Bibliographic record
Abstract
Abstract Seasonal low temperature (LT) adversely affects growth of plants. The onset of LT in temperate zones also entails the process of cold acclimation, preparing the plants to withstand freezing temperatures. During this process of cold acclimation a number of physiological, biochemical and molecular changes occur. A differentially expressed enolase gene in wheat plants exposed to LT was previously identified by cDNA‐amplified fragment length polymorphism. In this study, two wheat enolase cDNA, TaENO‐a and TaENO‐b amplified by 5′,3′ rapid amplification of cDNA end (RACE)‐PCR (polymerase chain reaction), were isolated and characterised. Quantitative real‐time PCR (QPCR) was done to assess their expression patterns in leaf and crown tissues of wheat plants exposed to LT. BLAST searches and bioinformatic analyses were done to determine the structure, domains and phylogeny of the cloned sequences. The two cDNA sequences differed mostly in the 5′ and 3′ untranslated regions. Deduced amino acid sequence showed high identity to bacteria, yeast, fungi, human and plant enolases with conserved putative DNA‐binding and repressor domains. A genomic clone containing 17 exons distributed over 4.5 kb structurally shared a high degree of similarity to rice enolase. QPCR revealed combined effects of LT and ageing on expression of TaENO‐a and TaENO‐b. Down‐regulation of TaENO‐a was observed with age in the crown tissues upon exposure to LT, but in leaf initial up‐regulation was followed by down‐regulation. Expression of TaENO‐b was similar to expression patterns previously reported for cold‐regulated (COR) genes in wheat, wherein the recessive vrnA‐1 allele influenced its expression in the leaf and genetic background determines its expression in the crown.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".