RIKEN Plant Science Center, 1-7-22, Suehiro-cho, Tsurumi, Yokohama 230-0045, Japan.
BMC Genomics. 2009 Dec 1;10:568. doi: 10.1186/1471-2164-10-568.
It is essential to elucidate the relationship between metabolic and genomic diversity to understand the genetic regulatory networks associated with the changing metabolo-phenotype among natural variation and/or populations. Recent innovations in metabolomics technologies allow us to grasp the comprehensive features of the metabolome. Metabolite quantitative trait analysis is a key approach for the identification of genetic loci involved in metabolite variation using segregated populations. Although several attempts have been made to find correlative relationships between genetic and metabolic diversity among natural populations in various organisms, it is still unclear whether it is possible to discover such correlations between each metabolite and the polymorphisms found at each chromosomal location. To assess the correlative relationship between the metabolic and genomic diversity found in rice accessions, we compared the distance matrices for these two "omics" patterns in the rice accessions.
We selected 18 accessions from the world rice collection based on their population structure. To determine the genomic diversity of the rice genome, we genotyped 128 restriction fragment length polymorphism (RFLP) markers to calculate the genetic distance among the accessions. To identify the variations in the metabolic fingerprint, a soluble extract from the seed grain of each accession was analyzed with one dimensional (1)H-nuclear magnetic resonance (NMR). We found no correlation between global metabolic diversity and the phylogenetic relationships among the rice accessions (r(s) = 0.14) by analyzing the distance matrices (calculated from the pattern of the metabolic fingerprint in the 4.29- to 0.71-ppm (1)H chemical shift) and the genetic distance on the basis of the RFLP markers. However, local correlation analysis between the distance matrices (derived from each 0.04-ppm integral region of the (1)H chemical shift) against genetic distance matrices (derived from sets of 3 adjacent markers along each chromosome), generated clear correlations (r(s) > 0.4, p < 0.001) at 34 RFLP markers.
This combinatorial approach will be valuable for exploring the correlative relationships between metabolic and genomic diversity. It will facilitate the elucidation of complex regulatory networks and those of evolutionary significance in plant metabolic systems.
阐明代谢和基因组多样性之间的关系对于理解自然变异和/或群体中代谢表型变化相关的遗传调控网络至关重要。代谢组学技术的最新创新使我们能够掌握代谢组的全面特征。代谢物定量性状分析是使用分离群体鉴定涉及代谢物变异的遗传基因座的关键方法。尽管已经有几项尝试在各种生物体的自然种群中寻找遗传和代谢多样性之间的相关性，但仍不清楚是否有可能发现每个代谢物与每个染色体位置发现的多态性之间的这种相关性。为了评估水稻品种中代谢和基因组多样性之间的相关性，我们比较了水稻品种中这两种“组学”模式的距离矩阵。
我们根据种群结构从世界水稻收藏中选择了 18 个品种。为了确定水稻基因组的基因组多样性，我们对 128 个限制片段长度多态性（RFLP）标记进行了基因分型，以计算品种之间的遗传距离。为了鉴定代谢指纹图谱的变化，对每个品种的种子谷物的可溶提取物进行了一维（1）H 核磁共振（NMR）分析。通过分析距离矩阵（基于代谢指纹图谱在 4.29 至 0.71ppm（1）H 化学位移的模式计算）和基于 RFLP 标记的遗传距离，我们发现全球代谢多样性与水稻品种的系统发育关系之间没有相关性（r(s) = 0.14）。然而，在距离矩阵（源自（1）H 化学位移的每个 0.04ppm 积分区域）与遗传距离矩阵（源自每条染色体上 3 个相邻标记的集合）之间的局部相关分析中，在 34 个 RFLP 标记处产生了清晰的相关性（r(s)>0.4，p<0.001）。
这种组合方法将有助于探索代谢和基因组多样性之间的相关性。它将有助于阐明植物代谢系统中复杂的调控网络和具有进化意义的网络。
