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Enregistrement W4414622444 · doi:10.3389/fgene.2025.1694599

Editorial: Genetic dissection and improvement of crop quality and stress adaptation

2025· editorial· en· W4414622444 sur OpenAlexaboutno aff
Liwei Zheng, Songwen Zhang, Yingpeng Hua

Notice bibliographique

RevueFrontiers in Genetics · 2025
Typeeditorial
Langueen
DomaineAgricultural and Biological Sciences
ThématiquePeanut Plant Research Studies
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésPolyploidGenomicsDomesticationArabidopsisQuantitative trait locusGeneTraitHousekeeping genePlant breeding

Résumé

récupéré en direct d'OpenAlex

From Descriptive Genomics to Predictive Breeding Across the studies, a common theme emerges: the transition from descriptive catalogues of genes to predictive frameworks that accelerate breeding cycles. In peanut, pangenome analyses and high-throughput phenotyping platforms are being integrated to untangle the polygenic nature of drought tolerance (Pokhrel et al.,2024). Likewise, the dissection of the EPF/EPFL gene family in poplar demonstrates how comparative genomics—anchored by Arabidopsis orthologues—can rapidly identify regulators of stomatal density and, by extension, water-use efficiency (Liu et al.,2024). These efforts mirror the PAO-family characterization in pear (Ma et al.,2024), where evolutionary analyses, cis-element profiling, and expression atlases converge to nominate candidate genes for fruit hardening and drought resilience. Collectively, these papers underscore a critical inflection point: we are no longer constrained by the linear "gene-to-trait" paradigm. Instead, we can now leverage multidimensional data—genomic, transcriptomic, epigenomic, metabolomic, and phenomic—to build machine-learning models that predict phenotype from genotype and environment. The wheat study by Sun et al., (2025) exemplifies this synergy. By coupling morphological and ultrastructural phenotyping (root hairs, chloroplast integrity) with expression profiling of ROS-scavenging and stress-responsive genes, the authors identify diagnostic markers that distinguish salt-tolerant cultivars from sensitive ones. Such integrative approaches should become the norm, not the exception. Polyploid Complexity as Opportunity, Not Obstacle Polyploidy—once viewed as a barrier to genetic dissection—is now recognized as a reservoir of adaptive potential. The review by Sakthivel et al. (2025) on vegetatively propagated crops (potato, strawberry, banana, sugarcane) reframes polyploid complexity as an opportunity for "genomic stacking" of favorable alleles. Modern genome-editing platforms, particularly CRISPR/Cas systems, can now target multiple homologous loci simultaneously, enabling trait fine-tuning without the meiotic instability inherent in traditional breeding. Conventional introgression of late-blight resistance from wild potato species into tetraploid cultivars requires decades of backcrossing to restore tuber quality. In contrast, CRISPR-mediated multiplex editing of susceptibility genes such as StDMR6-1 (Kieu et al.,2021) or StNRL1 (Nourozi et al.,2023) achieves durable resistance in a single transformation cycle. Similarly, sugarcane—an aneuploid with 8–14 copies of each gene—has seen rapid progress through transgenic overexpression of EaDREB2 or ShGPCR1 (Ramasamy et al.,2021), as well as CRISPR editing of SoLIM to reduce lignin for bioethanol production (Laksana et al.,2024). These successes illustrate that high ploidy need not impede precision improvement; rather, it offers multiple genetic "entry points" for trait enhancement. From Single-Gene Fixes to Network Engineering Early transgenic crops relied on single-gene interventions—Bacillus thuringiensis (Bt) toxins for insect resistance or 5-enolpyruvylshikimate-3-phosphate synthase (EPSPS) for herbicide tolerance. The current wave of research, however, embraces network-level engineering. In banana, stacking RGA2 and Ced9 confers resistance to Fusarium wilt tropical race 4 (Dale et al.,2017), while simultaneous editing of endogenous banana streak virus (eBSV) loci eliminates endogenous viral sequences (Tripathi et al.,2021). In peanut, pyramiding transcription factors (DREB1A, NAC4, WRKY3) enhances drought tolerance through synergistic modulation of root architecture, osmolyte accumulation, and antioxidant capacity (Pruthvi et al.,2014;Venkatesh et al.,2022). Such "trait stacking" must be guided by systems biology. The poplar study (Liu et al.,2024) illustrates how EPF/EPFL peptides integrate hormonal (ABA, auxin) and environmental (drought) cues to fine-tune stomatal patterning. Future work should map these regulatory circuits across species, identifying "hub genes" whose manipulation yields pleiotropic benefits. CRISPR base editors and prime editors—capable of precise nucleotide substitutions—will be instrumental in rewiring such networks without disrupting beneficial allelic diversity. Democratizing Technology Through Genotype-Independent Platforms A persistent bottleneck in clonally propagated crops is genotype-dependent transformation. Sakthivel et al. (2025) highlights how transient ribonucleoprotein (RNP) delivery of CRISPR/Cas complexes can bypass this limitation, yielding transgene-free edits that are more acceptable to regulators and consumers. Such "DNA-free" editing has already been demonstrated in potato (Gonzalez et al.,2019) and strawberry (Gao et al.,2020), and its extension to banana and sugarcane will accelerate cultivar improvement in the Global South, where these crops underpin food security and livelihoods. Equally important is the development of open-source guide-RNA libraries and multiplexed editing protocols. The wheat study (Sun et al.,2025) exemplifies how publicly available transcriptomic datasets can be mined to prioritize candidate genes (TaSOD1, TaDREB3, and TaWRKY19). Global consortia— modeled after the International Wheat Genome Sequencing Consortium—should curate analogous resources for under-studied polyploids such as yam, taro, and sweet potato. Navigating Regulatory and Societal Landscapes Technical breakthroughs must be coupled with transparent regulatory frameworks. The Sakthivel et al. (2025) review delineates the divergent global policies on genome-edited crops, contrasting product-based (USA, Canada) and process-based (EU, India) approaches. As SDN-1 and SDN-2 products (transgene-free) gain regulatory exemptions, breeders can deploy edited cultivars without the protracted timelines associated with GMO approval. However, consumer acceptance remains nuanced. Surveys cited in the review reveal that labeling transparency and tangible benefits (e.g., low acrylamide potatoes, provitamin A bananas) significantly sway public opinion. Meaningful stakeholder engagement— particularly with farmers and indigenous communities—will be essential for equitable technology deployment. Conclusion The convergence of genomics, phenomics, and genome editing has placed us on the cusp of a new agricultural era. The studies in this issue demonstrate that we can now dissect and re-engineer the genetic underpinnings of complex traits—drought tolerance, disease resistance, fruit quality—in species once deemed intractable. Yet technology alone will not suffice. Success will hinge on equitable access, transparent governance, and sustained investment in both basic science and on-the-ground implementation. If we rise to this challenge, the crops profiled here—peanut, pear, poplar, wheat, and their polyploid kin—will not merely survive climate change; they will thrive, nourishing a resilient and food-secure world.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,005
score de la tête « metaresearch » (Gemma)0,015
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Éditorial · Signal consensuel: Éditorial
Score de désaccord entre enseignants0,023
Score d'incertitude au seuil0,078

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0050,015
Méta-épidémiologie (sens strict)0,0040,001
Méta-épidémiologie (sens large)0,0030,003
Bibliométrie0,0030,001
Études des sciences et des technologies0,0020,003
Communication savante0,0050,005
Science ouverte0,0040,001
Intégrité de la recherche0,0120,016
Charge utile insuffisante (le modèle a refusé de juger)0,0230,015

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.

Tête enseignante Opus0,015
Tête enseignante GPT0,275
Écart entre enseignants0,259 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreÉditorial

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2025
Routes d'admission1
Résumé présentoui

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