AIDS and HIV Research Group, State Key Laboratory of Virology, Wuhan Institute of Virology, Chinese Academy of Sciences, Wuhan 430071, China.
Virol Sin. 2013 Aug;28(4):228-38. doi: 10.1007/s12250-013-3348-z. Epub 2013 Jul 27.
The epidemiology of HIV-1 varies in different areas of the world, and it is possible that this complexity may leave unique footprints in the viral genome. Thus, we attempted to find significant patterns in global HIV-1 genome sequences. By applying the rule inference algorithm RIPPER (Repeated Incremental Pruning to Produce Error Reduction) to multiple sequence alignments of Env sequences from four classes of compiled datasets, we generated four sets of signature patterns. We found that these patterns were able to distinguish southeastern Asian from nonsoutheastern Asian sequences with 97.5% accuracy, Chinese from non-Chinese sequences with 98.3% accuracy, African from non-African sequences with 88.4% accuracy, and southern African from non-southern African sequences with 91.2% accuracy. These patterns showed different associations with subtypes and with amino acid positions. In addition, some signature patterns were characteristic of the geographic area from which the sample was taken. Amino acid features corresponding to the phylogenetic clustering of HIV-1 sequences were consistent with some of the deduced patterns. Using a combination of patterns inferred from subtypes B, C, and all subtypes chimeric with CRF01_AE worldwide, we found that signature patterns of subtype C were extremely common in some sampled countries (for example, Zambia in southern Africa), which may hint at the origin of this HIV-1 subtype and the need to pay special attention to this area of Africa. Signature patterns of subtype B sequences were associated with different countries. Even more, there are distinct patterns at single position 21 with glycine, leucine and isoleucine corresponding to subtype C, B and all possible recombination forms chimeric with CRF01_AE, which also indicate distinct geographic features. Our method widens the scope of inference of signature from geographic, genetic, and genomic viewpoints. These findings may provide a valuable reference for epidemiological research or vaccine design.
HIV-1 的流行病学在世界不同地区有所不同，这种复杂性可能在病毒基因组中留下独特的痕迹。因此，我们试图在全球 HIV-1 基因组序列中找到显著的模式。通过将规则推理算法 RIPPER（通过重复递增修剪产生误差减少）应用于四个数据集的 Env 序列的多重序列比对，我们生成了四组签名模式。我们发现，这些模式能够以 97.5%的准确率区分东南亚和非东南亚序列，以 98.3%的准确率区分中国和非中国序列，以 88.4%的准确率区分非洲和非非洲序列，以 91.2%的准确率区分南部非洲和非南部非洲序列。这些模式与亚型和氨基酸位置具有不同的关联。此外，一些签名模式具有采样地区的特征。与 HIV-1 序列系统发育聚类相对应的氨基酸特征与一些推断的模式一致。使用从全球 B、C 和所有与 CRF01_AE 嵌合的亚型推断出的模式的组合，我们发现亚型 C 的签名模式在一些采样国家（例如南部非洲的赞比亚）非常普遍，这可能暗示了这种 HIV-1 亚型的起源，需要特别关注该非洲地区。B 亚型序列的签名模式与不同的国家有关。更重要的是，在单一位置 21 处存在明显的模式，甘氨酸、亮氨酸和异亮氨酸分别对应于 C、B 和所有可能与 CRF01_AE 嵌合的重组形式，这也表明了明显的地理特征。我们的方法从地理、遗传和基因组的角度扩大了签名推断的范围。这些发现可能为流行病学研究或疫苗设计提供有价值的参考。
