Bioinformatic analysis of maize gene encoding starch branching enzyme SBEIIb.

Authors

DOI:

https://doi.org/10.21498/2518-1017.3(32).2016.75972

Keywords:

ae1 gene, starch branching enzyme SBEIIb, maize, bioinformatic

Abstract

Purpose. Investigation of maize ae1 gene polymorphism by bioinformatic methods.

Methods. Global and local alignment of the nucleotide and amino acid sequences, in silico translation and transcription, translates modeling, pri­mers design, phylogenetic analysis.

Results. 255 nucleotide sequences of maize аe1 gene, 500 amino acid sequences of homology translates of maize ae1 gene (SBEIIb enzyme homologs) and 100 mRNA expressed from the maize ae1 gene were analyzed to establish phylogenetic relationships. Polymorphism of maize ae1 gene different regions was investigated by bioinformatic methods. Modeling of the maize enzyme SBEIIb was performed.

Conclusions. According to the results of amino acid sequences of SBEIIb enzyme homologs alignment, it was found that ae1 gene orthologs are pre­sent only in monocots, paralogs – in monocots, dicots, and other taxa, including algae and animals. Based on the results of alignment of plants mRNA from which enzyme SBEIIb is translated, maize ae1 gene orthologs and the nearest paralogs encoding starch branching enzymes with chloroplast localization were defined; this suggests a possible origin of ae1 gene due to duplication of the gene encoding the 1,4-alpha-glucan-branching enzyme 2 with chloroplast or amyloplast localization. In the maize ae1 gene structure, regions were found that include polymorphic sites not defined previously. For the polymorphic sites design primers were developed that allowed to differentiate the maize lines. It was determined that the detection of polymorphism in theory can influence the enzyme function and, as a result, change the concentration of amylopectin in maize grain.

Author Biographies

Г. І. Сліщук, Plant Breeding and Genetics Institute – National center of Seed and Cultivar Investigation

Slishchuk, G. I.

Т. Ю. Жернаков, Plant Breeding and Genetics Institute – National center of Seed and Cultivar Investigation

Zhernakov, T. Yu.

Н. Е. Волкова, Plant Breeding and Genetics Institute – National Center of Seed and Cultivar Investigation

Volkova, N. E.

References

Farooq, M., Wahid, A., & Siddique, K. H. M. (2014). Physiology of grain development in cereals. In M. Pessarakli (Ed.), Handbook of Plant and Crop Physiology. (3rd ed.). (pp. 301–308). Boca Raton, FL, USA: CRC Press, Taylor & Francis Publishing Group.

James M., & Myers, A. (2009). Seed starch synthesis. In J. L. Bennetzen & S. C. Hake (Eds), Handbook of Maize: Its Biology. (pp. 439–456). New York, USA: Springer. doi: 10.1007/978-0-387-79418-1_22.

Agrawal, G. K., & Rakwal, R. (Eds). (2012). Seed development: OMICS technologies toward improvement of seed quality and crop yield. Dordrecht, NL: Springer. doi: 10.1007/978-94-007-4749-4

Hennen-Bierwagen, T. A, Liu, F., Marsh, R. S., Kim, S., Gan, Q., Tetlow, I. J., … Myers, A. M. (2008). Starch biosynthetic enzymes from developing maize endosperm associate in multisubunit complexes. Plant Physiol., 146(4), 1892–1908. doi: 10.1104/pp.108.116285

Hennen-Bierwagen, T. A., & Myers, A. M. (2013). Genomic specification of starch biosynthesis in maize endosperm. In P. W. Becraft (Ed.), Seed Genomics. (pp. 123–137). Hoboken, NJ: John Wiley & Sons. doi: 10.1002/9781118525524.ch7

National Center for Biotechnology Information (NCBI) data base. (n.d.). Retrieved from http://www.ncbi.nlm.nih.gov

Smith, T. F., & Waterman, M. S. (1981). Identification of common molecular subsequences. J. Mol. Biol., 147(1), 195–197. doi: 10.1016/0022-2836(81)90087-5

Needleman, S. B., & Wunsch, C. D. (1970). A general method applicable to the search for similarities in the amino acid sequence of two proteins. J. Mol. Biol., 48(3), 443–453.

Okonechnikov, K., Golosova, O., & Fursov, M. (2012). Unipro UGENE: a unified bioinformatics toolkit. Bioinformatics, 28(8), 1166–1167. doi: 10.1093/bioinformatics/bts091

Arnold, K., Bordoli, L., Kopp, F., & Schwede, T. (2006). The SWISS-MODEL workspace: a web-based environment for protein structure homology modeling. Bioinformatics, 22(2), 195–201. doi: 10.1093/bioinformatics/bti770

Sneath, P. H. A., & Sokal, R. R. (1973). Numerical taxonomy: The principles and practice of numerical classification. San Francisco: W. H. Freeman & Co.

Tamura, K., Peterson, D., Peterson, N., Stecher, G., Nei, M., & Kumar, S. (2011). MEGA5: molecular evolutionary genetics analysis using maximum likelihood, evolutionary distance, and maximum parsimony methods. Mol. Biol. Evol., 28(10), 2731–2739. doi: 10.1093/molbev/msr121

Tajima, F. (1989). Statistical method for testing the neutral mutation hypothesis by DNA polymorphism. Genetics, 123(3), 585–595.

Published

2016-07-28

How to Cite

Сліщук, Г. І., Жернаков, Т. Ю., & Волкова, Н. Е. (2016). Bioinformatic analysis of maize gene encoding starch branching enzyme SBEIIb. Plant Varieties Studying and Protection, (3(32), 13–18. https://doi.org/10.21498/2518-1017.3(32).2016.75972

Issue

Section

GENETICS