Manipulation of Seed Carbon Flow from Cellulose to Lipid and Protein Biosynthesis in Arabidopsis to Accelerate the Characterization of Protein-Related Genes in Canola
Notice bibliographique
Résumé
Canola (Brassica napus L.) is the major oilseed crop in Canada. After oil extraction, the protein-rich seed meal (around 40% protein) serves as a nutritious feedstock for animals and the protein fraction has potential for human consumption. However, excess fiber in the seed meal (about 33%) can reduce the efficiency of digestion in animals. Therefore, it would be beneficial to partially reallocate seed carbon from cellulose, the major component of seed fibre, to storage protein biosynthesis without penalizing the seed oil content via engineering of multiple genes in these pathways. However, direct screening of different gene stacking combinations in canola is time and labor intensive. This work aimed to find promising gene combinations in the model plant Arabidopsis thaliana L. (henceforth Arabidopsis) to accelerate the genetic work needed to effectively manipulate seed carbon in canola. The screening process requires lipid analysis of a large number of seed samples with direct transmethylation, followed by analysis with GC-FID. In Chapter 2, I optimized the lab protocol of direct transmethylation and the optimized method reduced the reaction time from overnight to no more than 2 hours, as well as replaced the expensive commercial methanolic HCl with self-prepared 2% H2SO4 in methanol. A three-pronged strategy was used to find the best gene combinations: 1) seed-specific RNAi-down-regulation of Arabidopsis CELLULOSE SYNTHASE 1 (AtCESA1) to partially reduce seed cellulose, 2) overexpression of B. napus DIACYLGLYCEROL ACYLTRANSFERASE 1 (BnDGAT1), and its performance-enhanced variants, to restore the seed oil content, and 3) overexpression of several protein biosynthesis-related genes from Arabidopsis to determine whether seed carbon reallocation is effective for increasing seed protein content. Our previous work has successfully generated heterozygous Arabidopsis with AtCESA1 down-regulation and overexpression of BnDGAT1 and its performance-enhanced variants. In this thesis project, we generated homozygous lines with AtCESA1-RNAi and BnDGAT1-OE and measured the seed cellulose, oil, and protein contents. Ideal lines with equal or increased oil, equal or increased protein, and reduced cellulose were chosen for further study. Subsequently, we overexpressed Arabidopsis amino acid permease 1 (AtAAP1), alanine aminotransferase 1 (AtALALAAT1) and asparagine synthase 1 (AtASN1) in those lines, respectively. Overexpression of AtAAP1, AtALAAT1, and AtASN1 in the AtCESA1-RNAi/ BnDGAT1-OE background successfully increased the seed protein content beyond overexpression of protein-related genes alone (by up to +2.7%), as well as reduced the seed cellulose by an average of 21%. Importantly, the seed lipid contents were increased slightly in these lines relative to the empty vector (EV) controls, though this finding did not reach statistical significance for the lines with overexpression of AtALAAT1 and AtASN1. There were several modest changes in the fatty acid composition, most notably a reduced proportion of 18:3 in all lines with BnDGAT1-OE. The plant phenotype analysis found some evidence indicating increased seed yield, with a few small changes in 100 seed size, and seed weight in the carbon reallocated lines. No obvious or severe phenotypic abnormalities were reported during the growth of the transgenic lines, although some differences were detected in the seedling early growth assays, such as reduced hypocotyl length, root length, fresh seedling weight, and germination differences. Overall, the seed carbon reallocation strategy away from cellulose biosynthesis and towards lipid and protein biosynthesis is successful, with the AtCESA1-RNAi/BnDGAT1-OE/AtAAP1-OE stacking approach yielding the most positive seed composition outcomes overall.
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 enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».