Comparative Genomics of Domesticated and Wild Sunflower: Complete Chloroplast and Mitochondrial Genomes

M S Makarenko,K V Azarin,O F Gorbachenko,A V Usatov,N A Usatov,N V Markin

doi:10.3844/ojbsci.2016.71.75

Abstract

The entire chloroplast and mitochondrial genomes of domesticated and wild type sunflower were sequenced. The comparative analysis of chloroplast genomes revealed 43 variant sites, including 21 polymorphic SSR loci and 22 SNPs. About 14 variant sites were found by collation of mitochondrial DNA (mtDNA), among them 4 SSRs, 8 SNPs and 2 deletions. About 9 SNPs were located in coding region of chloroplast DNA (cpDNA) and single SNP was mapped in mitochondrial gene. Only three SNPs caused amino acid changes: Two SNPs in cpDNA and one mtDNA SNP. Despite the fact that sunflower mitochondrial genome sequence is twice as long as chloroplast genome sequence, mtDNA has one third as much variant sites than cpDNA.

Highlights

Whole Genome Sequencing (WGS) is a common technique in contemporary plant research (Balakrishnan et al, 2015)
WGS data are used in phylogenetic analysis
According to data obtained from extranuclear genomes analysis of domestic and wild sunflower, a few assumptions could be established

Summary

Introduction

Whole Genome Sequencing (WGS) is a common technique in contemporary plant research (Balakrishnan et al, 2015). WGS provides an opportunity to investigate nucleotide diversity much faster and more accurate than hybridization-based methods (molecular beacons, microarrays etc.), enzymebased methods (RFLP, many PCR methods, HRM) or Sanger sequencing technique. WGS data are used in phylogenetic analysis. For understanding of plant phylogeny, mitochondrial DNA (mtDNA) sequences are significant (Knoop et al, 2011), for such purpose, complete mitochondrial genomes are used less often than chloroplast. Few papers provide phylogenetic analysis on data of both extranuclear genomes. WGS data make possible creation of DNA markers (SSR, CAPS etc). As well as complete genome sequences can be used for developing specific transformation vectors (Chen et al, 2011) or other genetic engineering applications

Methods

Results

Discussion

Conclusion