Close
Help




JOURNAL

Evolutionary Bioinformatics

CNL Disease Resistance Genes in Soybean and Their Evolutionary Divergence

Submit a Paper


Evolutionary Bioinformatics 2015:11 49-63

Original Research

Published on 07 Apr 2015

DOI: 10.4137/EBO.S21782


Further metadata provided in PDF



Sign up for email alerts to receive notifications of new articles published in Evolutionary Bioinformatics

Abstract

Disease resistance genes (R-genes) encode proteins involved in detecting pathogen attack and activating downstream defense molecules. Recent availability of soybean genome sequences makes it possible to examine the diversity of gene families including disease-resistant genes. The objectives of this study were to identify coiled-coil NBS-LRR ( = CNL) R-genes in soybean, infer their evolutionary relationships, and assess structural as well as functional divergence of the R-genes. Profile hidden Markov models were used for sequence identification and model-based maximum likelihood was used for phylogenetic analysis, and variation in chromosomal positioning, gene clustering, and functional divergence were assessed. We identified 188 soybean CNL genes nested into four clades consistent to their orthologs in Arabidopsis. Gene clustering analysis revealed the presence of 41 gene clusters located on 13 different chromosomes. Analyses of the Ks-values and chromosomal positioning suggest duplication events occurring at varying timescales, and an extrapericentromeric positioning may have facilitated their rapid evolution. Each of the four CNL clades exhibited distinct patterns of gene expression. Phylogenetic analysis further supported the extrapericentromeric positioning effect on the divergence and retention of the CNL genes. The results are important for understanding the diversity and divergence of CNL genes in soybean, which would have implication in soybean crop improvement in future.



Downloads

PDF  (7.19 MB PDF FORMAT)

RIS citation   (ENDNOTE, REFERENCE MANAGER, PROCITE, REFWORKS)

Supplementary Files 1  (212.06 KB ZIP FORMAT)

BibTex citation   (BIBDESK, LATEX)

XML

PMC HTML


Sharing


What Your Colleagues Say About Evolutionary Bioinformatics
It's an excellent experience to publish my work in Evolutionary Bioinformatics. The whole process including submission, peer review and publication was easy and efficient. I am pleased to recommend Evolutionary Bioinformatics to my colleagues.
Dr Bin Ai (Chinese Academy of Sciences, Guangzhou, China)
More Testimonials

Quick Links


New article and journal news notification services
Email Alerts RSS Feeds
Facebook Google+ Twitter
Pinterest Tumblr YouTube