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
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».