MétaCan
Menu
Retour à la cohorte
Enregistrement W4412957747 · doi:10.3389/fpls.2025.1642175

Editorial: Omics applications in agriculture systems: unveiling functionality and practicality

2025· editorial· en· W4412957747 sur OpenAlexaff
Dinesh Adhikary, Joann Mudge, Thiruvarangan Ramaraj

Notice bibliographique

RevueFrontiers in Plant Science · 2025
Typeeditorial
Langueen
DomaineAgricultural and Biological Sciences
ThématiqueGenetic and Environmental Crop Studies
Établissements canadiensUniversity of Alberta
Organismes subventionnairesnon disponible
Mots-clésAgricultureOmicsBiologyComputational biologyData scienceBiotechnologyComputer scienceBioinformaticsEcology

Résumé

récupéré en direct d'OpenAlex

approaches and the utility of harnessing the power of data integration is highlighted in the review article (Sen et al.).One of the key areas of focus is biotic stress, particularly the threat posed by Fusarium head blight (FHB), a devastating fungal disease that affects a majority of cereal crops and leads to significant yield losses and mycotoxin contamination (Shin et al., 2014;Hay et al., 2022;Moonjely et al., 2023). To understand the molecular basis of FHB resistance, Walker et al. performed a comparative transcriptomic analysis of three wheat genotypes: FHB-resistant AC Emerson, FHB-moderately resistant AC Morley, and FHB-susceptible CDC Falcon in response to F. graminearum, a dominant causative agent of the disease. The study applied an RNAsequencing approach and identified key defense mechanisms such as lignin biosynthesis and DON detoxification via UDP-glycosyltransferases, providing insights into the FHB resistance in wheat. Additionally, differential expression of pathogenicity factors in F. graminearum was assessed, which offered potential targets for developing resistant wheat varieties.Moving from biotic stress to hormone-mediated development, another study explored strigolactones (SLs) in rice, a class of carotenoid-derived hormones that regulate plant architecture, response to nutrient availability, and developmental processes, including root and shoot development (López-Ráez et al., 2008;Koltai, 2011;Xu et al., 2019;Li et al., 2023). Li et al. integrated a yeast one-hybridization screening assay and metabolome approach to identify OsSPL3 as a transcriptional repressor of OsDWARF10 (OsD10), a key gene in SL biosynthesis.The repression of OsD10 altered the metabolomic profile of polished rice, leading to an increase in amino acids and vitamins. The discovery provided new opportunities to manipulate SL pathways for improving nutritional quality in rice, potentially addressing global food security concerns (Li et al.).In a related theme of agronomic trait enhancement, pigment accumulation of anthocyanins in radish taproots presents both economic and nutritional value (Khoo et al., 2017).An integrative study using a genome-wide association study and yeast two-hybrid assay has identified a novel genetic locus on the R2 chromosome near RsMYB1.1 as a key genetic factor regulating skin colour variation in radish, with a large AT-rich insertion in the promoter region of non-red radishes inhibiting anthocyanin biosynthesis (Kim et al.). This presence/absence variation mechanism represents a novel genetic regulation model that could be leveraged for crop improvement through targeted gene editing.While these studies focus on traits shaped by environmental interactions and selective pressures, evolutionary aspects of plant morphology are also crucial. Since the transition of plants from water to land, terrestrial plants have undergone numerous morphological changes over time. Ginkgo biloba, a living fossil, retains ancient characteristics that can provide insights into plant adaptation (Beerling et al., 2001). The unique flabellate (fan-shaped) leaves of Ginkgo biloba exhibit distinct anatomical and physiological characteristics compared to other plant leaves. An integrative study involving transcriptomic and metabolomic analyses suggests that endogenous hormones, such as gibberellin (GA), auxin, and jasmonic acid, contribute to leaf shape formation (Li et al.). Additionally, differences in flavonoid and phenolic acid accumulation indicate potential adaptive advantages. Understanding the genetic basis of leaf morphology in relict plant species like Ginkgo offers insights into the organ development and the evolution of plants in the terrestrial ecosystem.Extending the theme of plant resilience, Quintans et al. investigated how beneficial plantmicrobe interactions can enhance disease resistance in flax (Linum usitatissimum L.). The crop has been challenged by several pests and pathogens (Moyse et al., 2023). Fusarium wilt in flax, caused by F. oxysporum f. sp. lini, is a major agricultural concern. However, research has shown that inoculation with the mutualistic arbuscular mycorrhizal fungus (AMF) Rhizoglomus irregulare can mitigate the negative effects of the pathogen (Quintans et al.). This study integrated phenotypic and transcriptomic analysis and investigated the response of flax seedlings to F. oxysporum in the presence of AMF Rhizoglomus irregulare. The findings revealed that flax prioritizes the expression of mutualism-related genes over conventional defence responses, thereby reducing pathogen-induced growth inhibition. This study highlights the potential of AMF inoculation as a biological control strategy to enhance crop resilience in flax.Complementing the focus on pathogen resistance, herbicide resistance in weeds presents a significant challenge in modern agriculture (Baucom, 2019). While several studies have explored functional genomics, transcriptomics, proteomics, and metabolomics separately, an integrated approach is needed to fully understand resistance mechanisms, particularly non-target site resistance (Adhikary et al., 2022a;Adhikary et al., 2022b;Peng et al., 2023;Sen et al., Dong et al., 2024). High-throughput sequencing and molecular profiling can help dissect the complex, multi-pathway responses that enable weeds to survive multiple herbicide modes of action.Especially, multi-omics provides a holistic picture of gene function within complex biological systems. This systems biology approach involving different layers of omics from molecular and cellular levels enhances our ability to precisely identify biomarkers that are related to various agronomic traits, including herbicide resistance, and develop more effective weed management strategies. Through this approach, we can link genes to phenotypes, capture regulatory mechanisms, identify post-translational or post-transcriptional modifications, which potentially affect gene functions, most importantly, it improves the accuracy of gene annotation and discovery. By continuing to explore these molecular pathways, we can accelerate the development of crops with enhanced resistance, improved nutrition, and greater adaptability to environmental challenges, ultimately paving the way for a more sustainable and food-secure future.These studies collectively emphasize the transformative potential of multi-omics approaches in revealing the genetic and molecular mechanisms underlying key plant traits, evolutionary adaptations, and resistance strategies. Future research should prioritize the integration of various omics technologies, such as genomics, transcriptomics, proteomics, and metabolomics, to develop a holistic model of plant stress response. In parallel, functional validation strategies such as genome editing, gene overexpression, and gene silencing are essential to move beyond descriptive omics data and rigorously confirm the roles of candidate genes. This approach not only enhances the scientific value of research outputs but also addresses a critical gap in basic science, where functional characterization remains limited. Furthermore, validating gene function strengthens the biological relevance of findings and lays the groundwork for translational applications in crop improvement. Additionally, leveraging beneficial plant-microbe interactions can contribute to the development of sustainable and resilient agricultural systems.

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)0,006
score de la tête « metaresearch » (Gemma)0,020
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 enseignants0,017
Score d'incertitude au seuil0,055

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0060,020
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 savante0,0070,006
Science ouverte0,0040,002
Intégrité de la recherche0,0120,014
Charge utile insuffisante (le modèle a refusé de juger)0,0170,011

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 Opus0,007
Tête enseignante GPT0,213
Écart entre enseignants0,206 · 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

Explorer davantage

Même revueFrontiers in Plant ScienceMême sujetGenetic and Environmental Crop StudiesTravaux en français237 207