Assembly and comparative analysis of the first complete mitochondrial genome of Acer truncatum Bunge: a woody oil-tree species producing nervonic acid.

BACKGROUND: Acer truncatum (purpleblow maple) is a woody tree species that produces seeds with high levels of valuable fatty acids (especially nervonic acid). The species is admired as a landscape plant with high developmental prospects and scientific research value. The A. truncatum chloroplast genome has recently been reported; however, the mitochondrial genome (mitogenome) is still unexplored. RESULTS: We characterized the A. truncatum mitogenome, which was assembled using reads from PacBio and Illumina sequencing platforms, performed a comparative analysis against different species of Acer. The circular mitogenome of A. truncatum has a length of 791,052 bp, with a base composition of 27.11% A, 27.21% T, 22.79% G, and 22.89% C. The A. truncatum mitogenome contains 62 genes, including 35 protein-coding genes, 23 tRNA genes and 4 rRNA genes. We also examined codon usage, sequence repeats, RNA editing and selective pressure in the A. truncatum mitogenome. To determine the evolutionary and taxonomic status of A. truncatum, we conducted a phylogenetic analysis based on the mitogenomes of A. truncatum and 25 other taxa. In addition, the gene migration from chloroplast and nuclear genomes to the mitogenome were analyzed. Finally, we developed a novel NAD1 intron indel marker for distinguishing several Acer species. CONCLUSIONS: In this study, we assembled and annotated the mitogenome of A. truncatum, a woody oil-tree species producing nervonic acid. The results of our analyses provide comprehensive information on the A. truncatum mitogenome, which would facilitate evolutionary research and molecular barcoding in Acer.

Characterization of the Complete Chloroplast Genome of Acer truncatum Bunge (Sapindales: Aceraceae): A New Woody Oil Tree Species Producing Nervonic Acid.

Acer truncatum, which is a new woody oil tree species, is an important ornamental and medicinal plant in China. To assess the genetic diversity and relationships of A. truncatum, we analyzed its complete chloroplast (cp) genome sequence. The A. truncatum cp genome comprises 156,492 bp, with the large single-copy, small single-copy, and inverted repeat (IR) regions consisting of 86,010, 18,050, and 26,216 bp, respectively. The A. truncatum cp genome contains 112 unique functional genes (i.e., 4 rRNA, 30 tRNA, and 78 protein-coding genes) as well as 78 simple sequence repeats, 9 forward repeats, 1 reverse repeat, 5 palindromic repeats, and 7 tandem repeats. We analyzed the expansion/contraction of the IR regions in the cp genomes of six Acer species. A comparison of these cp genomes indicated the noncoding regions were more diverse than the coding regions. A phylogenetic analysis revealed that A. truncatum is closely related to A. miaotaiense. Moreover, a novel ycf4-cemA indel marker was developed for distinguishing several Acer species (i.e., A. buergerianum, A. truncatum, A. henryi, A. negundo, A. ginnala, and A. tonkinense). The results of the current study provide valuable information for future evolutionary studies and the molecular barcoding of Acer species.

Using pseudo amino acid composition to predict protein structural class: approached by incorporating 400 dipeptide components.

The proteins structure can be mainly classified into four classes: all-alpha, all-beta, alpha/beta, and alpha + beta protein according to their chain fold topologies. For the purpose of predicting the protein structural class, a new predicting algorithm, in which the increment of diversity combines with Quadratic Discriminant analysis, is presented to study and predict protein structural class. On the basis of the concept of the pseudo amino acid composition (Chou, Proteins: Struct Funct Genet 2001, 43, 246; Erratum: Proteins Struct Funct Genet 2001, 44, 60), 400 dipeptide components and 20 amino acid composition are, respectively, selected as parameters of diversity source. Total of 204 nonhomologous proteins constructed by Chou (Chou, Biochem Biophys Res Commun 1999, 264, 216) are used for training and testing the predictive model. The predicted results by using the pseudo amino acids approach as proposed in this paper can remarkably improve the success rates, and hence the current method may play a complementary role to other existing methods for predicting protein structural classification.