Photoheterotroph improved the growth and nutrient levels of Chlorella vulgaris and the related molecular mechanism
Notice bibliographique
Résumé
Microalgae are rich in fatty acids, proteins, and other nutrients, which have gained the general attention of researchers all over the world. For the development of Chlorella vulgaris in food and feed industry, this study was conducted to investigate the differences in C. vulgaris' growth and nutritional components under different culture conditions (autotrophic, heterotrophic, photoheterotrophic) and the internal factors through cell counting in combination with transcriptome and nutrient analyses. The results showed that, under the photoheterotrophic condition, Chlorella's growth and the contents of lipid and protein were significantly higher than that under the heterotrophic condition, and the moisture content was lower than that under the heterotrophic condition. The saturated fatty acid content under the photoheterotrophic condition was the lowest, while the polyunsaturated fatty acid content was significantly higher than those under the other two conditions. There were 46,583 differentially expressed genes (DEGs), including 33,039 up-regulated DEGs (70.93%) and 13,544 down-regulated DEGs (29.07%), under the photoheterotrophic condition in comparison with the autotrophic condition. The fold change between the two conditions of samples of up-regulated genes was higher than that of the down-regulated genes. The KEGG enrichment showed that the up-regulated DEGs in the photoheterotrophic condition were significantly enriched in 5 pathways, including protein processing in endoplasmic reticulum pathway, photosynthesis pathway, photosynthesis-antenna protein pathway, endocytosis pathway, and phosphonate and phosphinate metabolism pathway. DEGs related to fatty acid metabolic pathways were significantly enriched in the fatty acid biosynthesis pathway and the biosynthesis of unsaturated fatty acid pathway. The qPCR analysis showed that the expression pattern of the selected genes was consistent with that of transcriptome analysis. The results of this study lay a theoretical foundation for the large-scale production of Chlorella and its application in food, feed, and biodiesel. KEY POINTS: • Nutrient levels under photoheterotrophic condition were higher than other conditions. • Six important pathways were discovered that affect changes in nutritional composition. • Explored genes encode important enzymes in the differential metabolic pathways.
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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 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,003 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| 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 ».