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Allelic variants in durum wheat (Triticum turgidum L. var. durum) DREB genes conferring tolerance to abiotic stresses

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Abstract

To identify potential and useful markers able to discriminate promising lines of durum wheat (Triticum turgidum L. var durum) tolerant to salt and drought stresses, nucleotide sequences of Dehydration-Responsive-Element Binding Factor (DREB) genes were used to design primers probed with High Resolution Melting technology for the identification of allelic variants. DREB1, DREB2, DREB3, DREB4 and DREB5 conserved regions corresponding to EREBP/AP2 domain and containing the conserved core sequence (5′-TACCGACAT-3′), the protein site directly involved in DNA recognition, were analyzed. The validated primers were probed on four lines of durum wheat differentially tolerant to salt and drought stresses treated with solutions containing different salt concentrations. Some SNPs mutations were identified in the highly tolerant durum cultivar Jennah Khetifa treated with the maximum salt concentration (1.5 M). The SNPs mutations identified were non-synonymous (nsSNPs) causing changes in peptide sequences. These concerned amino acid residues directly involved in the maintenance of protein geometry, the recognition of the specific cis-element, and the contacts between the protein and DNA. A validation of the found SNPs was carried out by analyzing the regressions between DREBs SNPs allelic variants and some morpho-physiological characters in a RIL population, deriving from a cross between the two durum wheat genotypes utilized for SNPs detection, grown under contrasting environments. Several phenotypical characters have been assessed in the progeny across all the localities evaluating the different performances under different stress levels and related with SNPs occurrence. Significant relations between SNPs variants and morpho-physiological characteristics were found in the progeny growth in very severe drought environments, suggesting a role of the identified SNPs in conferring a superior capability to adverse stress conditions and, at the same time, the key role of these genes in empowering salt tolerance.

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Author information

Authors and Affiliations

  1. Department of Science and Technologies for Agriculture, Forestry, Nature and Energy (DAFNE), Tuscia University, via S. C. de Lellis, 01100, Viterbo, Italy

    Linda Mondini & Mario Augusto Pagnotta

  2. ICARDA Biodiversity and Integrated Gene Management (BIGM), B.P. 6299, Station Exp. INRA-Quich, Rue Hafiane Cherkaoui. Agdal, Rabat-Institutes, Rabat, Morocco

    Miloudi Mikael Nachit

Authors
  1. Linda Mondini
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  2. Miloudi Mikael Nachit
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  3. Mario Augusto Pagnotta
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Corresponding author

Correspondence to Mario Augusto Pagnotta.

Additional information

Communicated by S. Hohmann.

Electronic supplementary material

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Figure 1S Amino acid and nucleotide sequences of DREB1 gene of wheat. In the squarely boxes are indicated the amino acid changed and the dbSNPs identified

Figure 2S Amino acid and nucleotide sequences of DREB2 gene of wheat. In the squarely boxes are indicated the amino acid changed and the SNPs identified

Figure 3S Amino acid and nucleotide sequences of DREB4 gene of wheat. In the squarely boxes are indicated the amino acid changed and the SNPs identified

Figure 4S Amino acid and nucleotide sequences of DREB5 gene of wheat. In the squarely boxes are indicated the amino acid residues changed and the SNPs identified

Appendices

Appendix 1

Analysis of variance for morphological traits measured on seedling under hydroponic solution and factorial experiment of 3 genotypes, 3 salt concentrations, and 2 reps. Only ANOVA tables with significant interaction genotype × salt are here reported

Dependent variable: root growth

Source

Type III sum of squares

df

Mean square

F

Sig.

Intercept

415.681

1

415.681

1,662.722

0.000

Replicate

0.125

1

0.125

0.500

0.500

Salt molarity

466.361

2

233.181

932.722

0.000

Genotypes

5.361

2

2.681

10.722

0.005

Salt molaritya genotypes

3.722

4

0.931

3.722

0.050

Error

2.000

8

0.250

  

Total

893.250

18

   

Corrected total

477.569

17

   
  1. aR squared = 0.996 (adjusted R squared = 0.991)

Dependent variable: turgidity after 1hA

Source

Type III sum of squares

df

Mean square

F

Sig.

Intercept

696.889

1

696.889

3,136.000

0.000

Replicate

0.222

1

0.222

1.000

0.347

Salt molarity

77.778

2

38.889

175.000

0.000

Genotypes

19.444

2

9.722

43.750

0.000

Salt molaritya genotypes

9.889

4

2.472

11.125

0.002

Error

1.778

8

0.222

  

Total

806.000

18

   

Corrected Total

109.111

17

   
  1. aR squared = 0.984 (adjusted R squared = 0.965)

Dependent variable: turgidity after 5DA

Source

Type III sum of squares

df

Mean square

F

Sig.

Intercept

480.500

1

480.500

2,661.231

0.000

Replicate

0.056

1

0.056

0.308

0.594

Salt molarity

132.333

2

66.167

366.462

0.000

Genotypes

26.333

2

13.167

72.923

0.000

Salt molaritya genotypes

14.333

4

3.583

19.846

0.000

Error

1.444

8

0.181

  

Total

655.000

18

   

Corrected total

174.500

17

   
  1. aR squared = 0.992 (adjusted R squared = 0.982)

Appendix 2

Tables of the statistical significant ANOVA for the regressions between DREB variants and morphological traits on RIL populations grown under different drought climatic conditions

ANOVA of Dependent variable: GYKF

Model

Sum of squares

df

Mean square

F

Sig.

1

Regression

4,522,305.573

1

4,522,305.573

5.273

0.023a

Residual

149,225,429.528

174

857,617.411

Total

153,747,735.101

175

 
  1. aPredictors: (constant), DREB2A

ANOVA of Dependent variable: MDKF

Model

Sum of squares

df

Mean square

F

Sig.

1

Regression

10.131

1

10.131

4.265

0.040a

Residual

413.286

174

2.375

Total

423.417

175

 
  1. aPredictors: (constant), DREB2A

ANOVA of Dependent variable: SCLP

Model

 

Sum of squares

df

Mean square

F

Sig.

1

Regression

0.036

1

0.036

13.676

0.000a

Residual

0.457

174

0.003

Total

0.493

175

 
  1. aPredictors: (constant), DREB2A

ANOVA of Dependent variable: WI KF

Model

Sum of Squares

df

Mean Square

F

Sig.

1

Regression

0.005

1

0.005

3.790

0.053a

Residual

0.214

174

0.001

Total

0.218

175

 
  1. aPredictors: (constant), DREB2A

ANOVA of Dependent variable: GYBR

Model

Sum of squares

df

Mean square

F

Sig.

1

Regression

323,971.956

1

323,971.956

7.606

0.006a

Residual

7,410,973.431

174

42,591.801

Total

7,734,945.388

175

 
  1. aPredictors: (constant), DREB3B

ANOVA of Dependent variable: SCLP

Model

Sum of squares

df

Mean square

F

Sig.

1

Regression

0.428

1

0.428

13.143

0.000a

Residual

5.668

174

0.033

Total

6.097

175

 
  1. aPredictors: (constant), DREB3B

ANOVA of Dependent variable: PHLP

Model

Sum of squares

df

Mean square

F

Sig.

1

Regression

377.343

1

377.343

4.531

0.035a

Residual

14,490.816

174

83.281

Total

14,868.159

175

 
  1. aPredictors: (constant), DREB3B

ANOVA of Dependent variable: WIKF

Model

Sum of squares

df

Mean square

F

Sig.

1

Regression

0.007

1

0.007

4.047

0.046a

Residual

0.287

174

0.002

Total

0.294

175

 
  1. aPredictors: (constant), DREB3B

ANOVA of Dependent variable: TSINC

Model

Sum of squares

df

Mean square

F

Sig.

1

Regression

5.248

1

5.248

4.840

0.029a

Residual

188.646

174

1.084

Total

193.894

175

 
  1. aPredictors: (constant), DREB3B

ANOVA of Dependent variable: GYKF

Model

Sum of squares

df

Mean square

F

Sig.

1

Regression

5,481,397.505

1

5,481,397.505

5.226

0.023a

Residual

182,513,490.883

174

1,048,928.109

Total

187,994,888.388

175

 
  1. aPredictors: (constant), DREB4B

ANOVA of Dependent variable: GYRF

Model

Sum of squares

df

Mean square

F

Sig.

1

Regression

799,302.596

1

799,302.596

6.379

0.012a

Residual

21,803,721.199

174

125,308.743

Total

22,603,023.795

175

 
  1. aPredictors: (constant), DREB4B

ANOVA of Dependent variable: GYBR

Model

Sum of squares

df

Mean square

F

Sig.

1

Regression

210,605.420

1

210,605.420

4.870

0.029a

Residual

7,524,339.967

174

43,243.333

Total

7,734,945.388

175

 
  1. aPredictors: (constant), DREB4B

ANOVA of Dependent variable: GYINC

Model

Sum of squares

df

Mean square

F

Sig.

1

Regression

2,924,938.031

1

2,924,938.031

4.022

0.046a

Residual

126,531,644.735

174

727,193.361

Total

129,456,582.765

175

 
  1. aPredictors: (constant), DREB4B

ANOVA of Dependent variable: HDLP

Model

Sum of squares

df

Mean square

F

Sig.

1

Regression

30.308

1

30.308

4.778

0.030a

Residual

1,103.639

174

6.343

Total

1,133.946

175

 
  1. aPredictors: (constant), DREB4B

ANOVA of Dependent variable: HDRF

Model

Sum of squares

df

Mean square

F

Sig.

1

Regression

188.805

1

188.805

3.916

0.049a

Residual

8,389.393

174

48.215

Total

8,578.198

175

 
  1. aPredictors: (constant), DREB4B

ANOVA of Dependent variable: VIGBR

Model

Sum of squares

df

Mean square

F

Sig.

1

Regression

6.360

1

6.360

4.004

0.047a

Residual

276.388

174

1.588

Total

282.748

175

 
  1. aPredictors: (constant), DREB4B

ANOVA of Dependent variable: WI KF

Model

Sum of squares

df

Mean square

F

Sig.

1

Regression

0.012

1

0.012

3.915

0.049a

Residual

0.544

174

0.003

Total

0.556

175

 
  1. aPredictors: (constant), DREB4B

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Mondini, L., Nachit, M.M. & Pagnotta, M.A. Allelic variants in durum wheat (Triticum turgidum L. var. durum) DREB genes conferring tolerance to abiotic stresses. Mol Genet Genomics 290, 531–544 (2015). https://doi.org/10.1007/s00438-014-0933-2

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