Statistical analysis
The transgenic lines were statistically compared to WT using a Student’st test at 5% of probability (P < 0.05) by using
Microsoft Excel (Microsoft, Redmond, WA, USA). Regression analysis were
carried out using SIGMAPLOT 14 (Systat Software Inc., San Jose,
CA, USA). The equations from stomatal kinetics regressions were derived
and the rate of change were fit to linear plateau using easyreg package
in R 3.6.3 (Arnhold, 2018; R Core Team, 2020). Correlation analysis were
carried out by Pearson correlation analysis using the Java-based
CorrelationCalculator software (Basu et al., 2017). The metabolomics
data were analyzed using the MetaboAnalyst platform (Chong et al.,
2018). Multivariate analysis such as partial least square-discriminant
analysis (PLS-DA) and orthogonal PLS-DA (orthoPLS-DA) was performed in
Cube root-transformed and mean-centered data by using both Cube root and
Pareto-scaling mode of the MetaboAnalyst platform, which is recommended
to reduce the scale variability of metabolomics datasets (Xia &
Wishart, 2011). Correlation-based metabolic networks were designed by
using MetScape on CYTOSCAPE v.3.7.2 software (Karnovsky et al., 2012;
Shannon et al., 2003).