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Enregistrement W6940220148 · doi:10.7488/era/4123

Regulation, traceability and stakeholder engagement for genome editing technology

2024· other· en· W6940220148 sur OpenAlexaboutno aff

Notice bibliographique

RevueERA · 2024
Typeother
Langueen
Domaine
Thématique
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésTraceabilitySelection (genetic algorithm)ProductivityAgricultureStakeholderGenome editingProduction (economics)Agricultural biotechnology

Résumé

récupéré en direct d'OpenAlex

Over the course of time, improvements based on quantifiable traits have been made to both crops and animals to either increase their productivity or make them more resilient to diseases or pests. For generations, farmers have employed a method of seed selection based on performance, where they carefully chose seeds each planting season to cultivate crops with desirable traits. These traits might encompass improved taste, higher yields, distinct appearances, extended storage life, or resistance to diseases and pests. Starting in the mid-20th century, the practice of deliberate and precise plant breeding has expedited this natural selection process, leading to the enhancement of various characteristics, notably drought tolerance, among others. In farmed animals, selection has emphasised production of milk, meat, eggs and fibre and efficiency of production, as well as appearance. During the 1980s, scientists initiated the use of biotechnology techniques, which involved the direct transfer of genes responsible for specific traits into plants or animals. This process, commonly referred to as genetic modification, allowed for the precise introduction of desired traits into organisms. With the adoption of biotechnology, countries began putting in place regulatory frameworks that governed research using genetically modified (GM) crops and animals, their movement and commercialisation or placing on the market. Studies done on the adoption of GM technology have shown that the regulatory framework adopted by a country played a huge role in whether GM crops and animals were adopted and subsequently contributed towards the agricultural output. For this PhD, the focus was on genome-edited (GEd) organisms, particularly animals, with the overall research question being ‘How will genome editing and the products of the technology be regulated?’. Genome editing encompasses a set of technologies that empower scientists to modify an organism's DNA by adding, removing, or altering genetic material at specific locations within the genome. This precision allows for the targeted and controlled manipulation of an organism's genetic information. An interdisciplinary approach is adopted based on three aspects involved in the future development of genetic technologies - establishment of regulatory frameworks, traceability and public engagement. The first objective aimed at investigating countries’ experience so far in regulating GEd organisms. Ten countries were selected to give a range of geographical locations, approaches to regulation and degree of adoption. They comprised Argentina, Australia, Canada, China, Brazil, EU, Japan, Kenya, United States and Zambia. The results show that the countries that are among the world’s top producers and exporters of agricultural commodities, for example Argentina, Canada, Brazil and the United States, have regulatory frameworks which create an enabling environment for uptake of innovations and are benefiting greatly from the introduction of GEd-based products. GEd applications that have been submitted to regulators in the selected countries have traits that can contribute to sustainable agriculture and the factors listed as crucial for realization of GEd potential for sustainable agricultural development were regulatory predictability/legal certainty, end-user empowerment and global co-operation. The second objective looked at traceability of organisms whose genome was edited using CRISPR-Cas9. A DNA footprint was introduced in the exon 10 region of the prolactin receptor gene in sheep where mutations for the SLICK phenotype are known to occur in cattle. The SLICK phenotype is caused by a mutation that results in shorter hair and lower follicle density in the coat in comparison to wild type animals, which contributes to heat tolerance adaptation in Senepol cattle. The DNA footprint comprised a restriction site for the EcoRI enzyme incorporated in an oligonucleotide intended as a template for homology directed repair. Transfection was done in sheep embryonic fibroblast cells using plasmid and ribonucleoprotein transfection techniques. Single cells were then isolated and screened for the edits of interest. Three techniques were used to screen for the introduced edits: polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) and Sanger sequencing. EcoRI PCR and PCR-RFLP can be applied in low infrastructure settings such as universities and public institutions in low and middle-income countries, where sequencing infrastructure is not well established, while Sanger sequencing can be used in areas with established sequencing infrastructures. It was demonstrated that, for GEd organisms, traceability is possible using a DNA footprint to identify where the edit was introduced and to distinguish them from products made using genetic modification or conventional breeding. The third objective sought to identify through surveys the knowledge levels of various genetic technologies; views towards use of GEd technology for various uses; where people obtain science information; who they trust as reliable sources of science and technology information; attitudes towards labelling of GEd products; concerns regarding GEd products and the kind of information that should be included during public engagement forums with various stakeholders. The targeted stakeholders were people in academia and research environments. The aim of this was to establish baseline information for this particular cohort of stakeholders through which regulators, developers and scientists can draw specific insights on how to design public engagement programs. The results obtained showed that for this select group of stakeholders: initially self-analysed knowledge levels for various technologies were low but improved when brief descriptions were given; and they were positive towards use of GEd to reduce crop losses, make crops and livestock more resilient to environmental stress and climate change, and improve productivity. However, their views were that improvement of animals should not encourage farmers to keep them in poor welfare conditions. Further, respondents in both UK and Kenya emphasised the importance of labelling GEd products to facilitate informed decision-making by consumers. Additionally, the results showed that this particular cohort of stakeholders (in universities and research environments) highly trust scientists and teachers rather than journalists, social media influencers or religious leaders as sources of science information. Some of the aspects they had concerns on with respect to GEd comprised: effects of GEd crops and animals on the environment; long-term effects of consumption of GEd products; unexpected effects of GEd; and new allergens from GEd crop and animal products. We argue that although this was a particular sub set of stakeholders, these findings can inform public engagement on products of GEd technology and help provide knowledge on some areas of concern in terms of risk decision-making. Overall, the thesis chapters show that for governance of GEd products and other genetic technologies, aspects of regulation, traceability and stakeholder engagement should be integrated. This is because during drafting of guidelines or regulations, policy makers have to consider how the rules will be enforced as well as how they will communicate to the public and various stakeholders involved. For future governance of GEd plants and animals, having these three components in mind will lead to regulatory frameworks that are fit for purpose and create enabling environments for technology uptake

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)0,073
score de la tête « metaresearch » (Gemma)0,082
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesÉtudes des sciences et des technologies
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Théorique ou conceptuel · Signal consensuel: Théorique ou conceptuel
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,997
Score d'incertitude au seuil0,385

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0730,082
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0030,007
Communication savante0,0120,009
Science ouverte0,0030,014
Intégrité de la recherche0,0070,007
Charge utile insuffisante (le modèle a refusé de juger)0,0090,003

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 Opus0,054
Tête enseignante GPT0,287
Écart entre enseignants0,232 · 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.

Devis d'étudeThéorique ou conceptuel
Domainenon disponible
GenreEmpirique

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
Publié2024
Routes d'admission1
Résumé présentoui

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