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Enregistrement W4412775058 · doi:10.3389/fgeed.2025.1632120

Editorial: Gene editing to achieve Zero Hunger

2025· editorial· en· W4412775058 sur OpenAlexaboutno aff
Shakeel Ahmad, Iqrar Ahmad Rana, Kevin M. Folta, Christian Damian Lorenzo, Sultan Habibullah Khan

Notice bibliographique

RevueFrontiers in Genome Editing · 2025
Typeeditorial
Langueen
DomaineBiochemistry, Genetics and Molecular Biology
ThématiqueCRISPR and Genetic Engineering
Établissements canadiensnon disponible
Organismes subventionnairesMinistry of Science and Technology, Pakistan
Mots-clésZero (linguistics)Computer scienceBiologyComputational biologyGeneticsLinguisticsPhilosophy

Résumé

récupéré en direct d'OpenAlex

Ensuring universal access to nutritious and healthy food remains a pressing global challenge. In 2015, the United Nations (UN) adopted 17 Sustainable Development Goals with Goal 2 (SDG 2): Zero Hunger, aiming to ensure global food security and to improve nutrition, sustainability in food production and resilience in agricultural practices, well-being and income of small-scale food producers, biodiversity conservation, and investment in agricultural research and gene banks by 2030 (United Nations, 2015). However, several persistent and emerging factors continue to threaten these targets, specifically food security and nutrition. These include a rapidly growing global human population, yield reductions imposed by climate change, and slow-paced plant breeding techniques. Furthermore, social and economic factors have further compromised the present scenario, along with the recent geopolitical conflicts (e.g., Russia-Ukraine, Palestine-Israel, civil war in Sudan, and other countries in East and Central Africa), the COVID-19 pandemic and even ravages derived from natural disasters (e.g., Hurricane Milton) (FAO, 2023). As of now, the global population has reached nearly eight billion and is projected to grow to 8.5 billion by 2030 and 10 billion by 2050. According to the Food and Agriculture Organization (FAO), current agricultural production falls short of meeting present demands and must double by 2030 to keep pace with this growth, highlighting a significant gap between food supply and demand (FAO, 2024). To meet this challenge, the rapid development of climate-resilient, high-yielding crop varieties is essential. This demands a transformative shift in breeding strategies, accelerating the process of developing cultivars that can withstand climate stress, deliver high productivity, and meet both regulatory standards and societal expectations.While conventional breeding methods have substantially contributed to food security, indicators from the SDGs show that the current pace of progress is insufficient to meet expectations. In 2019, the FAO forewarned that continuing at the current pace in crop improvement would not suffice to eradicate hunger, which was also reported by Ahmad et al. (2021), and the same trends have been seen in the current report of 2023 (FAO, 2023). This concern urges the world to move towards innovative and efficient biotechnology and breeding tools. Gene editing technologies (GETs), notably site-directed nucleases, have revolutionized crop improvement. Since its application in plants was demonstrated in 2013 (Shan et al., 2013), CRISPR/Cas has emerged as a robust and efficient tool for developing crops with enhanced yield, stress tolerance, and nutritional quality (Ahmad et al., 2025). The acceptance, deregulation, approval, and eventual commercialization of GETs-derived products can significantly accelerate progress towards Zero Hunger, aligning agricultural innovation with the 2030 SDGs. Hence, in light of the ongoing food security crisis, this special issue aims to present the current status, key advances, and future potential of GETs in achieving SDG 2. The collection includes five articles: two original research articles and three review articles, each highlighting the potential of gene editing in the field of agriculture.Recalcitrance of tropical maize lines to genetic transformation has always limited the application of advanced biotechnological tools for improving agronomically important tropical maize lines. In this regard, Jos Hernandes-Lopes and colleagues successfully enabled gene editing in transformation-recalcitrant agronomically important tropical maize lines using morphogenic regulator (MR)-assisted, agrobacterium-mediated transformation protocol. The VIRESCENT YELLOW-LIKE (VYL) gene, encoding a proteolytic subunit of the chloroplast Clp protease complex, was targeted using the CRISPR/Cas9 system and knockout mutants of three maize lines (CML360, CML444, and PCL1) were efficiently generated. The transformation efficiency remained up to 6.63% in these responsive lines, which was also confirmed by protoplast assays and inherited edits in subsequent generations. The findings of this research will potentially open the doors for gene editing in other recalcitrant tropical maize lines that could be important for food security and nutrition.The legislation and regulatory processes regarding GETs, also referred to as new plant breeding technologies or new genomic technologies, are slow-paced in various countries, which are affecting countries' agriculture and economies, thus lagging them behind the countries that are flexible in embracing the agricultural innovations. In this regard, Stuart J. Smyth and colleagues presented the findings of a survey conducted on Canada’s Plants with Novel Traits (PNTs) regulatory framework, established in the early 1990s. The plant breeders believe the PNTs framework has been outdated and is causing hindrance in developing new varieties using GETs. Thus, the authors have concluded that Canada’s PNTs regulations need to be updated and aligned with technological advancements to foster innovations in agriculture, pertinent to regional as well as global food security and nutrition.Similarly, Ritika Kumari et al., have also summarized regulatory frameworks, guidelines, and legislations of various countries for nano-technology-based products, including the products developed through CRISPR/Cas-based gene editing system, in agriculture.Aayushi Patel and colleagues extensively reviewed the applications and methods of gene editing in various plant species. They summarized the mechanism of action of gene editing systems and their applications in agriculture for developing desirable traits in plants. This review also provides a comprehensive discussion on the regulation of gene-edited crops. Finally, it is concluded that the GETs are a cost-effective, efficient, robust, and innovative plant breeding tool that can help to meet the UN’s sustainable development goals of “zero hunger” and “good human health and well-being”.Cotton is a major and economically important crop worldwide. It directly supports Target 2.3 of SDG 2, by contributing to the economic well-being and livelihood security of small-scale food producers, which is a key pillar of the Zero Hunger goal. In this regard, to cover the potential of CRISPR/Cas-based gene editing systems in cotton, Muhammad Sulyman Saleem and colleagues have extensively reviewed their applications in cotton. They have summarized the utilization of CRISPR/Cas9, CRISPR/nCas9, and CRISPR/Cas12a systems for targeting undesirable genes and improving the 4Fs – fiber, food, feed, and fuel – of cotton, highlighting how cotton improvement can contribute to achieving Zero Hunger by 2030.In conclusion, GETs, particularly CRISPR-based systems, are evolving rapidly and demonstrating high efficiency, reliability, robustness, and effectiveness in generating new, transgene-free, desirable lines that may also bypass strict regulatory processes. As highlighted in various published reports, i.e., those by Aayushi Patel and colleagues and Muhammad Sulyman Saleem and collaborators, these systems have significantly improved a wide range of crops by targeting key traits, thereby contributing to the UN’s mission to achieve sustainable food security and nutrition, as well as to improve well-being and livelihoods of small-scale food producers globally. We hope that the articles featured in this special issue will further underscore the utility of GETs in crop improvement and pave the way for the deregulation of their products, enabling faster outcomes in support of the Zero Hunger goal.

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,002
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict), Intégrité de la recherche
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 enseignants0,062
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,002
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0010,001
Intégrité de la recherche0,0020,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,003
Tête enseignante GPT0,258
Écart entre enseignants0,255 · 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 tête enseignante, pas un consensus.

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

Citations1
Publié2025
Routes d'admission1
Résumé présentoui

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