The Agronomic Traits, Alkaloids Analysis, FT-IR and 2DCOS-IR Spectroscopy Identification of the Low-Nicotine-Content Nontransgenic Tobacco Edited by CRISPR–Cas9
Abstract
:1. Introduction
2. Materials and Methods
2.1. Plant Materials
2.2. Alkaloids Analysis
2.3. FT-IR and 2DCOS-IR Analysis
3. Results and Discussion
3.1. Effects of BBLs Knockout on Agronomic Traits
3.2. Effects of BBLs Knockout on Alkaloid Metabolism
3.3. FT-IR Analysis of Typical Tobacco Samples
3.4. PCA Analysis of Roots and Leaves of Tobacco Samples
- (a)
- Let the optimal projection direction be w. Then, the principal component vector is obtained after projection:
- (b)
- The optimal projection direction should be such that the projection vector on it has the maximum differentiation, i.e., the optimization objective is to maximize the variance of t after projection; then, the problem is transformed to solve for the eigenvector w of the square matrix .
- (c)
- The projection X can be expressed in the form of a regression on the vector t:
- (d)
- The residual matrix E is used as the new X. Repeat the process of (a)~(c) until the first R principal components are all determined.
- (e)
- After the first R principal components are selected, all vectors t form the principal component matrix T to complete the data dimensionality reduction.
3.5. 2DCOS-IR Spectroscopy and PCA Classification of Tobacco Samples
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Sample Availability
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Analysis Part | Code Name | Sample Numbers | |
---|---|---|---|
Hongda wild-type tobacco (CK) | Root | CK-G | 8 |
Leaf | CK-Y | 6 | |
Gene knockout mutants (YJ) | Root | YJ-G | 6 |
Leaf | YJ-Y | 8 |
ID | PH (cm) | GS (cm) | LTL (cm) | WTL (cm) | NL | |
---|---|---|---|---|---|---|
Max | 130.0 | 14.0 | 68.0 | 25.0 | 15.0 | |
YJ | Ave | 126.8 | 13.3 | 61.4 | 24.0 | 14.8 |
Min | 110.0 | 12.8 | 56.0 | 19.0 | 14.0 | |
Max | 134.0 | 13.8 | 67.0 | 28.0 | 16.0 | |
CK | Ave | 130.8 | 13.4 | 65.8 | 25.0 | 15.2 |
Min | 128.0 | 11.8 | 62.0 | 24.0 | 15.0 |
Peak Position/cm−1 | Functional Group | Main Attribution | |
---|---|---|---|
Root | Leaf | ||
3389 | 3374 | sν (O−H, N−H) | OH, NH |
2928 | 2928 | νas (CH2) | CH2 |
1628 | 1621 | ν (C=O−O), ν (Ar) | calcium oxalate, carboxyl |
- | 1416 | δ (CH2), δ (C−O−H) | lignin, cellulose |
1375 | - | νas (C−N−C), δ (C−O−H) | lignin, cellulose |
1317 | 1317 | ν (C−O−H) | calcium oxalate |
1153 | 1153 | ν (C−C,C−O), δ (C−O−H) | cellulose |
1077 | 1079 | ν (C−C,C−O), δ (C−O−H) | lignin |
1031 | 1039 | ν (C−C,C−O), δ (C−O−H) | cellulose |
850 | ν (C−C,C−O), δ (C−O−H) | cellulose | |
781 | 781 | ν (C−C) | calcium oxalate |
518 | - | C−O−H | calcium oxalate |
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Zhang, J.; Zhou, Q.; Zhang, D.; Yang, G.; Zhang, C.; Wu, Y.; Xu, Y.; Chen, J.; Kong, W.; Kong, G.; et al. The Agronomic Traits, Alkaloids Analysis, FT-IR and 2DCOS-IR Spectroscopy Identification of the Low-Nicotine-Content Nontransgenic Tobacco Edited by CRISPR–Cas9. Molecules 2022, 27, 3817. https://doi.org/10.3390/molecules27123817
Zhang J, Zhou Q, Zhang D, Yang G, Zhang C, Wu Y, Xu Y, Chen J, Kong W, Kong G, et al. The Agronomic Traits, Alkaloids Analysis, FT-IR and 2DCOS-IR Spectroscopy Identification of the Low-Nicotine-Content Nontransgenic Tobacco Edited by CRISPR–Cas9. Molecules. 2022; 27(12):3817. https://doi.org/10.3390/molecules27123817
Chicago/Turabian StyleZhang, Jianduo, Qun Zhou, Dongheyu Zhang, Guangyu Yang, Chengming Zhang, Yuping Wu, Yong Xu, Jianhua Chen, Weisong Kong, Guanghui Kong, and et al. 2022. "The Agronomic Traits, Alkaloids Analysis, FT-IR and 2DCOS-IR Spectroscopy Identification of the Low-Nicotine-Content Nontransgenic Tobacco Edited by CRISPR–Cas9" Molecules 27, no. 12: 3817. https://doi.org/10.3390/molecules27123817