Novel and Transgressive Salinity Tolerance in Recombinant Inbred Lines of Rice Created by Physiological Coupling-Uncoupling and Network Rewiring Effects
Bibliographic record
Abstract
The phenomenon of transgressive segregation, where a small minority of recombinants are outliers relative to the range of parental phenotypes, is commonly observed in plant breeding populations. While this phenomenon has been attributed to complementation and epistatic effects, the physiological and developmental synergism involved have not been fully illuminated by the QTL mapping approach alone, especially for stress-adaptive traits involving highly complex interactions. By systems-level profiling of the IR29 × Pokkali recombinant inbred population of rice, we addressed the hypothesis that novel salinity tolerance phenotypes are created by reconfigured physiological networks due to positive or negative coupling-uncoupling of developmental and physiological attributes of each parent. Real-time growth and hyperspectral profiling distinguished the transgressive individuals in terms of stress penalty to growth. Non-parental network signatures that led to either optimal or non-optimal integration of developmental with stress-related mechanisms were evident at the macro-physiological, biochemical, metabolic, and transcriptomic levels. Large positive net gain in super-tolerant progeny was due to ideal complementation of beneficial traits while shedding antagonistic traits. Super-sensitivity was explained by the stacking of multiple antagonistic traits and loss of major beneficial traits. The synergism uncovered by the phenomics approach in this study supports the modern views of the Omnigenic Theory, emphasizing the synergy or lack thereof between core and peripheral components. This study also supports a breeding paradigm rooted on genomic modeling from multi-dimensional genetic, physiological, and phenotypic profiles to create novel adaptive traits for new crop varieties of the 21st century.
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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) | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".