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Enregistrement W3203123333 · doi:10.1093/plphys/kiab357

Gene discovery in plant metabolism: Listening to the sound of silence, but where?

2021· article· en· W3203123333 sur OpenAlexaff
Trinh‐Don Nguyen

Notice bibliographique

RevuePLANT PHYSIOLOGY · 2021
Typearticle
Langueen
DomaineBiochemistry, Genetics and Molecular Biology
ThématiquePlant tissue culture and regeneration
Établissements canadiensUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
Organismes subventionnairesnon disponible
Mots-clésSilenceSound (geography)Active listeningPlant metabolismBiologyGeneComputational biologyCommunicationGeneticsPsychologyAcousticsRNAPhysics

Résumé

récupéré en direct d'OpenAlex

An essential strategy for studying a gene’s function is to silence its expression in the living organism and observe the resulting phenotype. To this end, virus-induced gene silencing (VIGS) is a popular tool that can selectively and transiently knockdown genes of interest (GOIs; Ratcliff et al., 1997; Courdavault and Besseau, 2020). VIGS analysis looks for association between gene expression levels and phenotypic changes in the plant, and identifying the tissues in which the silencing occurs is thus critical. This localization task, however, is more often than not precarious. In this issue of Plant Physiology, Yamamoto et al. (2021) report an innovative VIGS method that allows simple, high-throughput, and accurate localization of silencing. The researchers demonstrated the use of the improved tool in discovering serpentine synthase, a long-elusive and important gene in monoterpenoid indole alkaloid (MIA) biosynthesis in the medicinal plant Madagascar periwinkle (Catharanthus roseus). VIGS essentially hijacks the plant’s natural RNA-mediated machinery against viruses to silence GOIs in a sequence-specific manner. DNA constructs harboring a viral genome, usually from tobacco rattle virus, and a GOI fragment are transformed into a plant via Agrobacterium tumefaciens infection. The plant’s defense mechanism responds by cutting the double-stranded RNA formed during the viral replication. This digestion releases small interfering RNA fragments which are then used by the plant to scan and destroy any complementary RNA molecules, including those derived from the GOI (Ratcliff et al., 1997; Burch-Smith et al., 2004). VIGS has been exploited to elucidate genes involved in specialized metabolism of many non-model plants, notably the biosynthesis of MIAs in C. roseus and benzylisoquinoline alkaloids in opium poppy (Hagel and Facchini, 2010; Liscombe and O’Connor, 2011; Courdavault and Besseau, 2020). Nevertheless, the interpretation of VIGS experiments is not always straightforward as partial and off-target silencing significantly limit its sensitivity. The matter is complicated further as systematic silencing does not always occur, and the inability to precisely pinpoint the sporadic organs or tissues in which the GOI is downregulated leads to poor reproducibility. To improve the detection of silencing location, Yamamoto et al. (2021) incorporated a sequence of the marker gene phytoene desaturase into their VIGS construct together with the virus-GOI sequences. The visible photobleaching phenotype due to phytoene desaturase knockdown has been routinely used to verify VIGS efficacy (Courdavault and Besseau, 2020), and, in this study, Yamamoto and coworkers’ double silencing approach effectively couples this marker with the GOI. The coupling allowed the authors to effortlessly identify where VIGS took effect (Figure 1), and from there narrow their metabolic and biochemical analyses to these visibly defined tissues. Photobleaching as a visible marker for VIGS in the discovery of serpentine synthase in Madagascar periwinkle (C. roseus). The simultaneous knockdown of individual candidate genes with phytoene desaturase, an essential gene in chlorophyll biosynthesis, allows straightforward, high-throughput, and accurate localization of candidate gene silencing. Image adapted from Yamamoto et al. (2021; Figure 1) with the C. roseus artwork created by Katharine Davis (University of Cambridge). Photobleaching as a visible marker for VIGS in the discovery of serpentine synthase in Madagascar periwinkle (C. roseus). The simultaneous knockdown of individual candidate genes with phytoene desaturase, an essential gene in chlorophyll biosynthesis, allows straightforward, high-throughput, and accurate localization of candidate gene silencing. Image adapted from Yamamoto et al. (2021; Figure 1) with the C. roseus artwork created by Katharine Davis (University of Cambridge). The team used this improved VIGS tool to investigate a key missing step in MIA metabolism: the conversion of ajmalicine to serpentine, a potential anti-cancer compound. In C. roseus, an enzyme named alstonine synthase catalyzes the oxidative conversion of tetrahydroalstonine and its diastereomer ajmalicine to alstonine and serpentine, respectively (Figure 1; Dang et al., 2018). Alstonine synthase’s poor catalytic efficiency toward serpentine synthesis and different expression pattern compared to that of serpentine occurrence suggests a more dedicated serpentine synthase exists. Candidate genes in C. roseus were thus selected among close homologs of alstonine synthase for silencing. VIGS with marker-incorporated constructs guided Yamamoto and colleagues to tissues where silencing occurred as subsequently confirmed by transcriptomic data. The distinct accumulation of ajmalicine (substrate) and reduction of serpentine (product) observed in plants silenced in one of the candidate genes allowed the team to identify that gene as serpentine synthase (Figure 1). The gene’s function was verified by in vitro enzyme assay using its encoded enzyme heterologously expressed in yeast and further confirmed by transient overexpression in planta, putting in place the last piece of the serpentine biosynthetic pathway puzzle (Stavrinides et al., 2016; Dang et al., 2018; Yamamoto et al., 2021). As only the bleached leaves were harvested for analysis rather than all leaves emerging after infection, this elegant method displayed markedly enhanced sensitivity in comparison to conventional VIGS. The straightforward silencing localization and sensitive analysis method developed by Yamamoto et al. (2021) improves upon VIGS as a powerful tool that can target multiple unrelated genes to elucidate their physiological roles in the plant. Such in planta data are essential in untangling the complex biosynthetic networks of plant natural products in which functional redundancy and catalytic promiscuity are common and pathways are highly compartmentalized across tissues and organs. In this case, serpentine synthase and its previously characterized homolog, alstonine synthase, display some cross-reactivities albeit with different specificities (Dang et al., 2018; Yamamoto et al., 2021). VIGS together with other in planta functional characterization tools, such as expression pattern analysis and cellular localization, help put the role of each gene in context. It should be noted that this silencing approach does not address other common predicaments of a VIGS experiment, such as infection efficacy and target selectivity. The possibility of plant metabolism being disrupted by silencing a marker and physiologically essential gene such as phytoene desaturase should also be considered (Naing et al., 2019), although it did not cause any major issues in this study. In any case, with the convenience and effectiveness of the improved VIGS method, one can expect to see simultaneous marker-GOI(s) knockdown becoming a routine practice in the coming years.

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)0,005
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Théorique ou conceptuel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,005
Score d'incertitude au seuil0,024

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0050,005
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0010,004
Communication savante0,0030,004
Science ouverte0,0010,002
Intégrité de la recherche0,0020,004
Charge utile insuffisante (le modèle a refusé de juger)0,0030,001

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 Opus0,011
Tête enseignante GPT0,226
Écart entre enseignants0,215 · 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'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
Publié2021
Routes d'admission1
Résumé présentnon

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