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Research Article
Salinity Depression in Vitro Impress the Growth Potential and, Mediates the Antioxidants Pool and the Tolerance Genes Expression of Grapevine
Mohammad Aazami
University of Maragheh
Corresponding Author
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Undoubtedly, salinity is the major environmental stress affecting the crops growth, development and yield. Grapevine is a dominant horticulture crop and mildly sensitive to the salinity. Salinity triggers variations at the cellular and molecular levels and hence induces some specific genes expression. The responses of two grapevine cultivars (Fakhri and Sultanin) were evaluated for its tolerance to the salinity stress in vitro. The results for the explants exposed to the salinity revealed that the viability, fresh weight of the regenerated explant and the proliferation rate were declined compared to the control ones. The activity of SOD enzyme and MDA content were increased with salinity. However, protein content declined. There was no significant difference in CAT and APX activities with the salinity treatment. With salinity adding up, the DREB/CBFs genes expression pattern was significantly increased in both cultivars. ‘Fakhri’ was more responsive in growth parameters and the activity of antioxidant enzymes and higher expression rate of DREB/CBFs under salinity compared to the variety ‘Sultanin’.
Keywords
Vitis vinifera L., Salinity, In vitro, Antioxidant enzymes, CBFs
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Table 1. Reaction mixture for cDNA synthesis
|
Volume |
Reactive |
|
0.5 µl |
Vivantis RT Enzyme Mix |
|
2 µl |
Buffer RT Enzyme |
|
0.5 µl |
Oligo RT Primer (50 µM) |
|
0.5 µl |
Random 6 mers (100 µM) |
|
1 µl |
dNTP |
|
11.5 µl |
DDW |
|
5 µl |
Total RNA (500 ng) |
|
20 µl |
Total |
Due to technical limitations, table 2 is only available as a download in the Supplemental Files section.
Table 3. The composition of reaction mixture for RT-RCR
|
Volume |
Reactive |
|
2 µl |
RT reaction solution (cDNA) |
|
0.4 µl |
Primer F |
|
0.4 µl |
Primer R |
|
10 µl |
Power SYBR Green PCR Master Mix |
|
7.2 µl |
DDW |
|
20 µl |
Total |
Table 4. The effects of NaCl treatments on some physiological traits in two grape cultivars
|
Cultivar |
NaCl(gL-1) |
Shoot length (mm) |
Shoot weight (mg) |
Viability (%) |
Number of shoots |
Proliferation ratio |
Number of leaves/shoots |
Number of 3-node shoots |
|
0 |
35.13±0.99a |
324.79±41.66a |
100±0a |
6±0.61a |
14.25±3.36a |
4.5±0.25a |
2.75±0.21a |
|
|
0.25 |
23.62±0.77b |
267.04±17.87ab |
87.50±2.16b |
5±0.35a |
8.75±0.41bc |
3±0.35bcde |
2.25±0.21ab |
|
|
Sultanin |
0.5 |
16.33±0.81c |
182.12±31.72bc |
86.25±3.75b |
4.25±0.89ab |
5.5±0.55cde |
2.75±0.21cdef |
1.75±0.41abcd |
|
0.75 |
10.18±0.45d |
93.05±6.82cd |
76.25±2.72c |
2.25±0.41bc |
3±0.61de |
2.25±0.21ef |
1.5±0.25abcd |
|
|
1 |
7.93±0.34d |
27.59±4.64d |
21.25±3.69e |
1.5±0.43c |
1.75±0.41e |
1.75±0.41f |
0.5±0.25d |
|
|
0 |
33.38±3.25a |
314.72±16.14a |
100±0a |
6.25±0.96a |
11.75±2.21ab |
4±0.35ab |
2.75±0.21a |
|
|
0.25 |
22.08±0.59b |
294.67±70.3ab |
100±0a |
4.5±0.55ab |
7.5±0.55bcd |
3.75±0.21abc |
2.75±0.21a |
|
|
Fakhri |
0.5 |
16.49±0.71c |
263.74±37.74ab |
100±0a |
4.25±0.96ab |
5.25±0.21cde |
3.5±0.25abcd |
2±0.5abc |
|
0.75 |
11.96±0.52d |
105.86±11.09cd |
83.75±3.69bc |
2.75±0.41bc |
2.75±0.21de |
2.75±0.41cdef |
1.25±0.54bcd |
|
|
1 |
9.10±0.64d |
49.92±11.01d |
33.75±2.72d |
2.25±0.21bc |
1.25±0.21e |
2.5±0.25def |
0.75±0.41cd |
Means by the same latter within each column are not significantly different at 0.05 level, according to the Duncan's multiple range test. Data are mean±SD (n=4 replicates)
Table 5. Coefficients of correlation values for the pairs of studied characters in two grape cultivars under NaCl
|
|
Y1 (Shoot length) |
Y2 (Shoot weight) |
Y3 (Viability) |
Y4 (Number of shoots) |
Y5 (Proliferation ratio) |
Y6 (Number of leaves/shoots) |
Y7 (Number of 3-node shoots) |
Y8 (APX) |
Y9 (GPX) |
Y10 (CAT) |
Y11 (SOD) |
Y12 (MDA) |
Y13 (Protein) |
|
Y1 |
1 |
|
|
|
|
|
|
|
|
|
|
|
|
|
Y2 |
0.769** |
1 |
|
|
|
|
|
|
|
|
|
|
|
|
Y3 |
0.670** |
0.734** |
1 |
|
|
|
|
|
|
|
|
|
|
|
Y4 |
0.766** |
0.742** |
0.626** |
1 |
|
|
|
|
|
|
|
|
|
|
Y5 |
0.841** |
0.728** |
0.575** |
0.692** |
1 |
|
|
|
|
|
|
|
|
|
Y6 |
0.711** |
0.689** |
0.617** |
0.544** |
0.591** |
1 |
|
|
|
|
|
|
|
|
Y7 |
0.718** |
0.760** |
0.633** |
0.597** |
0.545** |
0.652** |
1 |
|
|
|
|
|
|
|
Y8 |
0.033 |
-0.047 |
-0.207 |
-0.016 |
0.090 |
-0.025 |
-0.071 |
1 |
|
|
|
|
|
|
Y9 |
0.102 |
-0.013 |
-0.041 |
-0.035 |
0.178 |
-0.057 |
0.043 |
0.466** |
1 |
|
|
|
|
|
Y10 |
-0.053 |
-0.135 |
-0.114 |
-0.058 |
0.040 |
-0.126 |
0.017 |
0.679** |
0.399* |
1 |
|
|
|
|
Y11 |
0.076 |
0.098 |
0.126 |
-0.028 |
0.215 |
-0.022 |
0.197 |
0.302 |
0.507** |
0.261 |
1 |
|
|
|
Y12 |
0.134 |
0.074 |
-0.106 |
0.272 |
0.038 |
0.121 |
0.188 |
-0.272 |
-0.377* |
-0.253 |
-0.328* |
1 |
|
|
Y13 |
0.376* |
0.098 |
0.065 |
0.130 |
0.377* |
0.128 |
0.132 |
0.230 |
0.025 |
0.116 |
0.278 |
-0.046 |
1 |
**, * significant at p≤0.01 probability and significant at p≤0.05 probability, respectively.
|
Table 6. Total variance explained |
|||
|
|
Initial Eigenvalues |
||
|
Components |
Total |
% of Variance |
Cumulative % |
|
1 |
5.172 |
39.783 |
39.783 |
|
2 |
2.680 |
20.612 |
60.395 |
|
3 |
1.132 |
8.710 |
69.105 |
|
4 |
0.991 |
7.620 |
76.725 |
|
5 |
0.739 |
5.688 |
82.412 |
|
6 |
0.587 |
4.512 |
86.925 |
|
7 |
0.483 |
3.716 |
90.641 |
|
8 |
0.358 |
2.753 |
93.394 |
|
9 |
0.273 |
2.102 |
95.496 |
|
10 |
0.243 |
1.872 |
97.368 |
|
11 |
0.156 |
1.201 |
98.570 |
|
12 |
0.123 |
0.946 |
99.516 |
|
13 |
0.063 |
0.484 |
100.000 |
|
Extraction Method: Principal Component Analysis. |
|||
|
Table 7. Total variance explained |
||
|
Component Matrixa |
||
|
|
1 |
2 |
|
Y1 |
0.930 |
0.060 |
|
Y2 |
0.905 |
-0.053 |
|
Y3 |
0.847 |
0.198 |
|
Y4 |
0.832 |
-0.097 |
|
Y5 |
0.823 |
0.002 |
|
Y6 |
0.802 |
-0.070 |
|
Y7 |
0.797 |
-0.099 |
|
Y8 |
-0.040 |
0.788 |
|
Y9 |
0.032 |
0.758 |
|
Y10 |
-0.083 |
0.733 |
|
Y11 |
0.138 |
0.672 |
|
Y12 |
0.139 |
-0.560 |
|
Y13 |
0.274 |
0.334 |
|
Extraction Method: Principal Component Analysis (PCA). 1 (PCA1) and 2 (PCA2) a: 3 components extracted. Y1 (Shoot length),Y2 (Shoot weight),Y3 (Viability),Y4 (Number of shoots),Y5 (Proliferation ratio),Y6 (Number of leaves/shoots),Y7 (Number of 3-node shoots),Y8 (APX),Y9 (GPX),Y10 (CAT), Y11 (SOD),Y12 (MDA),Y13 (Protein) |
||
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This preprint is under consideration at Scientific Reports. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research Article
Salinity Depression in Vitro Impress the Growth Potential and, Mediates the Antioxidants Pool and the Tolerance Genes Expression of Grapevine
Mohammad Aazami
University of Maragheh
Corresponding Author
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Under Review
Version 1
Posted 21 Dec, 2020
No community comments so far
Reviewers invited
Invitations sent on 14 Feb, 2021
Editor assigned
On 06 Jan, 2021
Editor invited
On 16 Dec, 2020
Submission checks complete
On 16 Dec, 2020
First submitted
On 15 Dec, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Undoubtedly, salinity is the major environmental stress affecting the crops growth, development and yield. Grapevine is a dominant horticulture crop and mildly sensitive to the salinity. Salinity triggers variations at the cellular and molecular levels and hence induces some specific genes expression. The responses of two grapevine cultivars (Fakhri and Sultanin) were evaluated for its tolerance to the salinity stress in vitro. The results for the explants exposed to the salinity revealed that the viability, fresh weight of the regenerated explant and the proliferation rate were declined compared to the control ones. The activity of SOD enzyme and MDA content were increased with salinity. However, protein content declined. There was no significant difference in CAT and APX activities with the salinity treatment. With salinity adding up, the DREB/CBFs genes expression pattern was significantly increased in both cultivars. ‘Fakhri’ was more responsive in growth parameters and the activity of antioxidant enzymes and higher expression rate of DREB/CBFs under salinity compared to the variety ‘Sultanin’.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Table 1. Reaction mixture for cDNA synthesis
|
Volume |
Reactive |
|
0.5 µl |
Vivantis RT Enzyme Mix |
|
2 µl |
Buffer RT Enzyme |
|
0.5 µl |
Oligo RT Primer (50 µM) |
|
0.5 µl |
Random 6 mers (100 µM) |
|
1 µl |
dNTP |
|
11.5 µl |
DDW |
|
5 µl |
Total RNA (500 ng) |
|
20 µl |
Total |
Due to technical limitations, table 2 is only available as a download in the Supplemental Files section.
Table 3. The composition of reaction mixture for RT-RCR
|
Volume |
Reactive |
|
2 µl |
RT reaction solution (cDNA) |
|
0.4 µl |
Primer F |
|
0.4 µl |
Primer R |
|
10 µl |
Power SYBR Green PCR Master Mix |
|
7.2 µl |
DDW |
|
20 µl |
Total |
Table 4. The effects of NaCl treatments on some physiological traits in two grape cultivars
|
Cultivar |
NaCl(gL-1) |
Shoot length (mm) |
Shoot weight (mg) |
Viability (%) |
Number of shoots |
Proliferation ratio |
Number of leaves/shoots |
Number of 3-node shoots |
|
0 |
35.13±0.99a |
324.79±41.66a |
100±0a |
6±0.61a |
14.25±3.36a |
4.5±0.25a |
2.75±0.21a |
|
|
0.25 |
23.62±0.77b |
267.04±17.87ab |
87.50±2.16b |
5±0.35a |
8.75±0.41bc |
3±0.35bcde |
2.25±0.21ab |
|
|
Sultanin |
0.5 |
16.33±0.81c |
182.12±31.72bc |
86.25±3.75b |
4.25±0.89ab |
5.5±0.55cde |
2.75±0.21cdef |
1.75±0.41abcd |
|
0.75 |
10.18±0.45d |
93.05±6.82cd |
76.25±2.72c |
2.25±0.41bc |
3±0.61de |
2.25±0.21ef |
1.5±0.25abcd |
|
|
1 |
7.93±0.34d |
27.59±4.64d |
21.25±3.69e |
1.5±0.43c |
1.75±0.41e |
1.75±0.41f |
0.5±0.25d |
|
|
0 |
33.38±3.25a |
314.72±16.14a |
100±0a |
6.25±0.96a |
11.75±2.21ab |
4±0.35ab |
2.75±0.21a |
|
|
0.25 |
22.08±0.59b |
294.67±70.3ab |
100±0a |
4.5±0.55ab |
7.5±0.55bcd |
3.75±0.21abc |
2.75±0.21a |
|
|
Fakhri |
0.5 |
16.49±0.71c |
263.74±37.74ab |
100±0a |
4.25±0.96ab |
5.25±0.21cde |
3.5±0.25abcd |
2±0.5abc |
|
0.75 |
11.96±0.52d |
105.86±11.09cd |
83.75±3.69bc |
2.75±0.41bc |
2.75±0.21de |
2.75±0.41cdef |
1.25±0.54bcd |
|
|
1 |
9.10±0.64d |
49.92±11.01d |
33.75±2.72d |
2.25±0.21bc |
1.25±0.21e |
2.5±0.25def |
0.75±0.41cd |
Means by the same latter within each column are not significantly different at 0.05 level, according to the Duncan's multiple range test. Data are mean±SD (n=4 replicates)
Table 5. Coefficients of correlation values for the pairs of studied characters in two grape cultivars under NaCl
|
|
Y1 (Shoot length) |
Y2 (Shoot weight) |
Y3 (Viability) |
Y4 (Number of shoots) |
Y5 (Proliferation ratio) |
Y6 (Number of leaves/shoots) |
Y7 (Number of 3-node shoots) |
Y8 (APX) |
Y9 (GPX) |
Y10 (CAT) |
Y11 (SOD) |
Y12 (MDA) |
Y13 (Protein) |
|
Y1 |
1 |
|
|
|
|
|
|
|
|
|
|
|
|
|
Y2 |
0.769** |
1 |
|
|
|
|
|
|
|
|
|
|
|
|
Y3 |
0.670** |
0.734** |
1 |
|
|
|
|
|
|
|
|
|
|
|
Y4 |
0.766** |
0.742** |
0.626** |
1 |
|
|
|
|
|
|
|
|
|
|
Y5 |
0.841** |
0.728** |
0.575** |
0.692** |
1 |
|
|
|
|
|
|
|
|
|
Y6 |
0.711** |
0.689** |
0.617** |
0.544** |
0.591** |
1 |
|
|
|
|
|
|
|
|
Y7 |
0.718** |
0.760** |
0.633** |
0.597** |
0.545** |
0.652** |
1 |
|
|
|
|
|
|
|
Y8 |
0.033 |
-0.047 |
-0.207 |
-0.016 |
0.090 |
-0.025 |
-0.071 |
1 |
|
|
|
|
|
|
Y9 |
0.102 |
-0.013 |
-0.041 |
-0.035 |
0.178 |
-0.057 |
0.043 |
0.466** |
1 |
|
|
|
|
|
Y10 |
-0.053 |
-0.135 |
-0.114 |
-0.058 |
0.040 |
-0.126 |
0.017 |
0.679** |
0.399* |
1 |
|
|
|
|
Y11 |
0.076 |
0.098 |
0.126 |
-0.028 |
0.215 |
-0.022 |
0.197 |
0.302 |
0.507** |
0.261 |
1 |
|
|
|
Y12 |
0.134 |
0.074 |
-0.106 |
0.272 |
0.038 |
0.121 |
0.188 |
-0.272 |
-0.377* |
-0.253 |
-0.328* |
1 |
|
|
Y13 |
0.376* |
0.098 |
0.065 |
0.130 |
0.377* |
0.128 |
0.132 |
0.230 |
0.025 |
0.116 |
0.278 |
-0.046 |
1 |
**, * significant at p≤0.01 probability and significant at p≤0.05 probability, respectively.
|
Table 6. Total variance explained |
|||
|
|
Initial Eigenvalues |
||
|
Components |
Total |
% of Variance |
Cumulative % |
|
1 |
5.172 |
39.783 |
39.783 |
|
2 |
2.680 |
20.612 |
60.395 |
|
3 |
1.132 |
8.710 |
69.105 |
|
4 |
0.991 |
7.620 |
76.725 |
|
5 |
0.739 |
5.688 |
82.412 |
|
6 |
0.587 |
4.512 |
86.925 |
|
7 |
0.483 |
3.716 |
90.641 |
|
8 |
0.358 |
2.753 |
93.394 |
|
9 |
0.273 |
2.102 |
95.496 |
|
10 |
0.243 |
1.872 |
97.368 |
|
11 |
0.156 |
1.201 |
98.570 |
|
12 |
0.123 |
0.946 |
99.516 |
|
13 |
0.063 |
0.484 |
100.000 |
|
Extraction Method: Principal Component Analysis. |
|||
|
Table 7. Total variance explained |
||
|
Component Matrixa |
||
|
|
1 |
2 |
|
Y1 |
0.930 |
0.060 |
|
Y2 |
0.905 |
-0.053 |
|
Y3 |
0.847 |
0.198 |
|
Y4 |
0.832 |
-0.097 |
|
Y5 |
0.823 |
0.002 |
|
Y6 |
0.802 |
-0.070 |
|
Y7 |
0.797 |
-0.099 |
|
Y8 |
-0.040 |
0.788 |
|
Y9 |
0.032 |
0.758 |
|
Y10 |
-0.083 |
0.733 |
|
Y11 |
0.138 |
0.672 |
|
Y12 |
0.139 |
-0.560 |
|
Y13 |
0.274 |
0.334 |
|
Extraction Method: Principal Component Analysis (PCA). 1 (PCA1) and 2 (PCA2) a: 3 components extracted. Y1 (Shoot length),Y2 (Shoot weight),Y3 (Viability),Y4 (Number of shoots),Y5 (Proliferation ratio),Y6 (Number of leaves/shoots),Y7 (Number of 3-node shoots),Y8 (APX),Y9 (GPX),Y10 (CAT), Y11 (SOD),Y12 (MDA),Y13 (Protein) |
||