A network approach to analyzing highly recombinant malaria parasite genes.
A network approach to analyzing highly recombinant malaria parasite genes.
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DOI：
10.1371/journal.pcbi.1003268
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发表时间：
2013
影响因子：
4.3
通讯作者：
Buckee CO
中科院分区：
文献类型：
--
作者：
Larremore DB;Clauset A;Buckee CO
人类疟疾寄生虫恶性疟原虫的 var 基因由于其极端多样性（由高重组率产生）给群体遗传学家带来了挑战。这些基因编码一种称为 PfEMP1 的初级抗原蛋白，该蛋白在受感染的红细胞表面表达并引发保护性免疫反应。 Var 基因序列的特点是明显的镶嵌现象，无法使用需要分叉树状进化关系的传统系统发育工具。我们提出了一种新方法，可以识别高度可变区域（HVR），然后将每个 HVR 映射到一个复杂的网络，其中每个序列都是一个节点，如果两个节点共享显着长度的精确匹配，则两个节点被链接。在这里，自由重组的 var 基因网络预计具有均匀的随机结构，但对重组的限制将产生我们使用随机块模型识别的网络社区。我们在合成数据上验证了这种方法，表明它在应用于 var 基因的 Duffy Binding Like-α (DBLα) 结构域之前可以正确恢复受限重组群体。我们发现 9 个 HVR，其网络社区以独特的方式映射到已知的 DBLα 分类和临床表型。我们表明，一些 HVR 的重组约束是相关的，而另一些则是独立的。这些发现表明，这种微模块结构促进了相邻镶嵌区域的独立进化轨迹，使寄生虫能够保留蛋白质功能，同时产生巨大的序列多样性。因此，我们的方法提供了一种严格的方法来分析 var 基因的进化限制，并且也足够灵活，可以轻松地更广泛地应用于任何高度重组的序列。人类疟原虫每年导致全球近一百万人死亡。疟疾寄生虫之间频繁的基因交换创造了巨大的遗传多样性，这在很大程度上解释了缺乏针对该疾病的有效疫苗的原因。然而，传统的系统发育工具无法适应这种类型的多样性，并且仍然缺乏能够理解频繁重组的基因序列的严格分析工具。在这里，我们使用物理和网络科学界开发的网络技术来分析疟原虫基因序列，使我们能够自动识别序列数据中高度可变的镶嵌区域并推导出重组事件的网络。我们将我们的方法应用于七个完全测序的寄生虫基因组，并表明我们的方法为寄生虫的复杂进化模式提供了新的见解。我们的结果表明，这些序列的结构允许寄生虫快速多样化以逃避免疫反应，同时保持抗原结构和功能。
The var genes of the human malaria parasite Plasmodium falciparum present a challenge to population geneticists due to their extreme diversity, which is generated by high rates of recombination. These genes encode a primary antigen protein called PfEMP1, which is expressed on the surface of infected red blood cells and elicits protective immune responses. Var gene sequences are characterized by pronounced mosaicism, precluding the use of traditional phylogenetic tools that require bifurcating tree-like evolutionary relationships. We present a new method that identifies highly variable regions (HVRs), and then maps each HVR to a complex network in which each sequence is a node and two nodes are linked if they share an exact match of significant length. Here, networks of var genes that recombine freely are expected to have a uniformly random structure, but constraints on recombination will produce network communities that we identify using a stochastic block model. We validate this method on synthetic data, showing that it correctly recovers populations of constrained recombination, before applying it to the Duffy Binding Like-α (DBLα) domain of var genes. We find nine HVRs whose network communities map in distinctive ways to known DBLα classifications and clinical phenotypes. We show that the recombinational constraints of some HVRs are correlated, while others are independent. These findings suggest that this micromodular structuring facilitates independent evolutionary trajectories of neighboring mosaic regions, allowing the parasite to retain protein function while generating enormous sequence diversity. Our approach therefore offers a rigorous method for analyzing evolutionary constraints in var genes, and is also flexible enough to be easily applied more generally to any highly recombinant sequences. The human malaria parasite kills nearly 1 million people each year globally. Frequent genetic exchange between malaria parasites creates enormous genetic diversity that largely explains the lack of an effective vaccine for the disease. Traditional phylogenetic tools cannot accommodate this type of diversity, however, and rigorous analytical tools capable of making sense of gene sequences that recombine frequently are still lacking. Here, we use network techniques that have been developed by the physics and network science communities to analyze malaria parasite gene sequences, allowing us to automatically identify highly variable mosaic regions in sequence data and to derive the network of recombination events. We apply our method to seven fully-sequenced parasite genomes, and show that our method provides new insights into the complex evolutionary patterns of the parasite. Our results suggest that the structure of these sequences allows the parasite to rapidly diversify to evade immune responses while maintaining antigen structure and function.
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影响因子：
10.7
作者：
Castresana, J
通讯作者：
Castresana, J
影响因子：
82.9
作者：
通讯作者：
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影响因子：
15.9
作者：
Chan, Jo-Anne;Howell, Katherine B.;Beeson, James G.
通讯作者：
Beeson, James G.
影响因子：
3.1
作者：
Dodoo, D;Staalsoe, T;Theander, TG
通讯作者：
Theander, TG
影响因子：
64.8
作者：
Freitas-Junior, LH;Bottius, E;Scherf, A
通讯作者：
Scherf, A