Centre for Epidemiology and Biostatistics, Department of Food Safety and Infection Biology, Norwegian School of Veterinary Science, Oslo, Norway.
PLoS One. 2013 Jul 26;8(7):e69878. doi: 10.1371/journal.pone.0069878. Print 2013.
Genomic base composition ranges from less than 25% AT to more than 85% AT in prokaryotes. Since only a small fraction of prokaryotic genomes is not protein coding even a minor change in genomic base composition will induce profound protein changes. We examined how amino acid and codon frequencies were distributed in over 2000 microbial genomes and how these distributions were affected by base compositional changes. In addition, we wanted to know how genome-wide amino acid usage was biased in the different genomes and how changes to base composition and mutations affected this bias. To carry this out, we used a Generalized Additive Mixed-effects Model (GAMM) to explore non-linear associations and strong data dependences in closely related microbes; principal component analysis (PCA) was used to examine genomic amino acid- and codon frequencies, while the concept of relative entropy was used to analyze genomic mutation rates.
We found that genomic amino acid frequencies carried a stronger phylogenetic signal than codon frequencies, but that this signal was weak compared to that of genomic %AT. Further, in contrast to codon usage bias (CUB), amino acid usage bias (AAUB) was differently distributed in AT- and GC-rich genomes in the sense that AT-rich genomes did not prefer specific amino acids over others to the same extent as GC-rich genomes. AAUB was also associated with relative entropy; genomes with low AAUB contained more random mutations as a consequence of relaxed purifying selection than genomes with higher AAUB.
Genomic base composition has a substantial effect on both amino acid- and codon frequencies in bacterial genomes. While phylogeny influenced amino acid usage more in GC-rich genomes, AT-content was driving amino acid usage in AT-rich genomes. We found the GAMM model to be an excellent tool to analyze the genomic data used in this study.
原核生物的基因组碱基组成范围从低于 25%的 AT 到高于 85%的 AT。由于即使是一小部分原核基因组不是蛋白质编码，基因组碱基组成的微小变化也会引起深刻的蛋白质变化。我们研究了超过 2000 个微生物基因组中氨基酸和密码子频率的分布方式，以及这些分布方式如何受到碱基组成变化的影响。此外，我们还想知道在不同的基因组中，全基因组的氨基酸使用情况是如何偏向的，以及碱基组成的变化和突变如何影响这种偏向。为此，我们使用广义加性混合效应模型（GAMM）来探索密切相关微生物中非线性关联和强数据依赖性；主成分分析（PCA）用于研究基因组的氨基酸和密码子频率，而相对熵的概念用于分析基因组的突变率。
我们发现，基因组氨基酸频率比密码子频率携带更强的系统发育信号，但与基因组%AT 的信号相比，这种信号很弱。此外，与密码子使用偏性（CUB）不同，氨基酸使用偏性（AAUB）在 AT 丰富和 GC 丰富的基因组中的分布不同，因为 AT 丰富的基因组不像 GC 丰富的基因组那样，对特定氨基酸的偏好程度相同。AAUB 还与相对熵有关；与具有较高 AAUB 的基因组相比，AAUB 较低的基因组由于放松了净化选择，包含更多的随机突变。
基因组碱基组成对细菌基因组中的氨基酸和密码子频率都有很大的影响。虽然在 GC 丰富的基因组中，系统发育对氨基酸使用的影响更大，但 AT 含量是 AT 丰富的基因组中氨基酸使用的驱动因素。我们发现 GAMM 模型是分析本研究中使用的基因组数据的优秀工具。
