Yield maintenance under drought is orchestrated by the <i>qDTY12.1</i> -encoded <i>DECUSSATE</i> gene of rice through a network with other flowering-associated genes across the genetic background
Bibliographic record
Abstract
Abstract Introgression of major-effect QTLs is an important component of rice breeding for yield-retention under drought. While largely effective, the maximum potentials of such QTLs have not been consistent across genetic backgrounds. We hypothesized that synergism or antagonism with additive-effect peripheral genes across the background could either enhance or undermine the QTL effects. To elucidate the molecular underpinnings of such interaction, we dissected qDTY12.1 synergy with numerous peripheral genes in context of network rewiring effects. By integrative transcriptome profiling and network modeling, we identified the DECUSSATE ( OsDEC ) within qDTY12.1 as the core of the synergy and shared by two sibling introgression lines in IR64 genetic background, i.e., LPB (low-yield penalty) and HPB (high-yield penalty). OsDEC is expressed in flag leaves and induced by progressive drought at booting stage in LPB but not in HPB. The unique OsDEC signature in LPB is coordinated with 35 upstream and downstream peripheral genes involved in floral development through the cytokinin signaling pathway, which are lacking in HPB. Results further support the differential network rewiring effects through genetic coupling-uncoupling between qDTY12.1 and other upstream and downstream peripheral genes across the distinct genetic backgrounds of LPB and HPB. We propose that the functional DEC -network in LPB defines a mechanism for early flowering as a means for avoiding the depletion of photosyntate needed for reproductive growth due to drought. Its impact on yield-retention is likely through the timely establishment of stronger source-sink dynamics that sustains a robust reproductive transition under drought. Author summary While the Green Revolution of the 1960’s significantly increased rice grain yields through the creation of high-yielding varieties for high input systems, current marginal climates pose a significant challenge for providing consistent yield. In rice growing regions of the world, drought affects the livelihood of small-scale and subsistence farmers by inflicting significant yield penalties to their production systems. Breeding of next-generation rice varieties with optimal balance of survivability and productivity traits will be key to providing consistent yields year to year. Within this paradigm, the use of large effect QTLs such as qDTY12.1 to improve yield retention under drought have been largely successful. By integrating the use of high resolution transcriptome datasets with a focused biological interrogation of agronomic results from this and previous studies, we uncovered a putative functional genetic network, anchored by the DECUSSATE gene ( OsDEC ) within qDTY12.1 , that effectively minimizes drought penalties to yield by driving cellular processes that culminate in timely flowering that maximizes the use of photosynthetic sources for efficient reproduductive transition and ultimately seed development. Our study further illuminates the qDTY12.1 function and speaks to the misconception that qDTY introgression alone is sufficient for providing consistently large positive effects to yield retention under reproductive stage drought.
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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".