**1. Foundational Methods**
Standard plant tissue culture techniques, including sterilization, callus induction, and plant regeneration, will be used
[1]. These methods underpin genetic transformation and subsequent analysis. Standard molecular biology protocols, such as DNA extraction, PCR amplification, and restriction enzyme digestion, will facilitate gene manipulation and validation
[2]. Quantitative real-time PCR (qPCR) will be used to assess gene expression levels under stress conditions. Physiological measurements of drought and salinity tolerance will encompass water potential, stomatal conductance, and relative water content
[3]. Growth assays will quantify shoot length, root length, fresh weight, and dry weight under controlled stress conditions to analyze phenotypic effects of genome editing on stress tolerance. These parameters were selected based on their established roles as indicators of plant stress response and tolerance
[4].
**2. Proposed Method Description**
This methodology comprises three sequential steps: target gene selection, CRISPR-Cas9-mediated gene editing, and phenotypic characterization. First, candidate genes will be selected based on existing literature
[5] describing genes involved in *Arabidopsis thaliana* drought and salinity stress responses and bioinformatic analysis of publicly available genomic databases, such as TAIR
[6]. Selection criteria will prioritize genes with known roles in osmotic adjustment, ion homeostasis, stress signaling pathways, and those exhibiting differential expression under stress conditions. Genes will be selected to represent diverse functional categories within the stress response pathways. At least five candidate genes will be selected. This selection aims to identify genes whose editing enhances stress tolerance. Second, CRISPR-Cas9 technology will edit the selected genes. This involves designing specific guide RNAs (gRNAs) targeting these genes, cloning them into a suitable CRISPR-Cas9 expression vector (pCAMBIA1300 with a constitutive CaMV 35S promoter driving Cas9 expression)
[7], and transforming the construct into *A. thaliana* using *Agrobacterium tumefaciens*-mediated transformation
[8]. The Cas9 variant used will be SpCas9. Multiple gRNAs (at least three) will be designed per target gene to maximize editing efficiency and minimize off-target effects. The efficiency of each gRNA will be assessed in silico using tools such as CRISPR design tools. Gene-editing efficiency will be assessed using PCR, sequencing, and TILLING. Off-target effects will be assessed using whole-genome sequencing. Editing efficiency will be calculated as shown in
(Eq. 1):
Editing Efficiency=Total number of transformed plantsNumber of edited plantsâĂ100
(1)
(Eq. 1)
where the numerator represents plants exhibiting successful gene editing, and the denominator represents the total number of plants successfully transformed with the CRISPR-Cas9 construct. Third, transgenic plants will undergo drought and salinity stress treatments to evaluate their phenotypic response. Stress levels will be defined by the medium's osmotic potential
(Eq. 2):
Osmotic Potential(Κsâ)=âiCRT
(2)
(Eq. 2)
where
i is the ionization constant,
C is the solute's molar concentration,
R is the ideal gas constant, and
T is the temperature in Kelvin. A range of stress levels (-0.2 MPa, -0.4 MPa, -0.6 MPa) will be tested to determine the optimal stress conditions for phenotypic characterization. Stress treatments will be applied for a period of 2 weeks. Phenotypic characterization will involve quantitative measurements of growth parameters and physiological traits under control (no stress) and stress conditions.
**3. Data & Statistical Analysis**
Collected data will include phenotypic traits (e.g., shoot length, root length, fresh and dry weight, water potential, stomatal conductance, chlorophyll fluorescence, ion content) and genotypic data (sequencing results confirming gene edits and assessing off-target effects). Statistical analysis will compare edited plants' phenotypic performance to wild-type controls under stress and non-stress conditions
[9]. Data normality and homogeneity of variance will be assessed before applying parametric tests. If data meet the assumptions of normality and homogeneity of variance, two-way ANOVA
(Eq. 3) will be used to assess the impact of gene editing and stress treatment on plant growth and physiological parameters. If assumptions are violated, non-parametric alternatives such as the Kruskal-Wallis test will be used.
F=MSEMSTâ
(3)
(Eq. 3)
where
F is the F-statistic,
MST is the mean sum of squares due to treatment, and
MSE is the mean sum of squares due to error. Post-hoc tests (e.g., Tukey's HSD) will determine significant differences between groups if ANOVA reveals significant overall differences. Effect sizes will be calculated using Cohen's d
(Eq. 4):
d=spââŁxË1ââxË2ââŁâ
(4)
(Eq. 4)
where
xË1â and
xË2â represent the means of the control and experimental groups, respectively, and
spâ is the pooled standard deviation. A power analysis will be conducted to determine the appropriate sample size to detect meaningful effects.
**4. Evaluation Metrics**
Primary metrics for evaluating gene-editing success and its impact on stress tolerance include the percentage of successfully edited plants
(Eq. 1), improvements in growth parameters (e.g., shoot length, root length, biomass) under stress compared to controls, and changes in physiological parameters (e.g., water potential, stomatal conductance, relative water content, chlorophyll fluorescence, ion content). A significant improvement in stress tolerance will be defined as a minimum of 20% increase in biomass under stress conditions compared to wild-type controls. Additional metrics include survival rate under stress and the degree of phenotypic changes. All data will determine the extent to which gene editing enhances tolerance.
**5. Method Complexity**
The bioinformatic analysis's computational complexity depends on the genomic database size and the search algorithms' complexity. CRISPR-Cas9 gene editing is not computationally intensive; phenotypic characterization is primarily experimental. Statistical analysis involves straightforward calculations; thus, overall computational complexity is low.