HIV coreceptor tropism determination and mutational pattern identification.

Hui-Shuang Shen,Fei Leng,Xian-Ming Pan,Chao Xu,Rui-Fang Teng,Jason Yin,Xia-Yu Xia

doi:10.1038/srep21280

Abstract

In the early stages of infection, Human Immunodeficiency Virus Type 1 (HIV-1) generally selects CCR5 as the primary coreceptor for entering the host cell. As infection progresses, the virus evolves and may exhibit a coreceptor-switch to CXCR4. Accurate determination coreceptor usage and identification key mutational patterns associated tropism switch are essential for selection of appropriate therapies and understanding mechanism of coreceptor change. We developed a classifier composed of two coreceptor-specific weight matrices (CMs) based on a full-scale dataset. For this classifier, we found an AUC of 0.97, an accuracy of 95.21% and an MCC of 0.885 (sensitivity 92.92%; specificity 95.54%) in a ten-fold cross-validation, outperforming all other methods on an independent dataset (13% higher MCC value than geno2pheno and 15% higher MCC value than PSSM). A web server (http://spg.med.tsinghua.edu.cn/CM.html) based on our classifier was provided. Patterns of genetic mutations that occur along with coreceptor transitions were further identified based on the score of each sequence. Six pairs of one-AA mutational patterns and three pairs of two-AA mutational patterns were identified to associate with increasing propensity for X4 tropism. These mutational patterns offered new insights into the mechanism of coreceptor switch and aided in monitoring coreceptor switch.

Highlights

Been developed, such as methods based on Support Vector Machine (SVMs)[15,16,17], Position-Specific Weight Matrix (PSSM)[18] and artificial neural networks[19]
We found area under the curve (AUC) values of 0.98 and 0.97 for self-consistency and ten-fold cross validation, respectively (Fig. 1)
Hivcopred[16], dskernel[31], PSSM18, the 11/25 rule[14], the combined charge rule[46] and WetCat15) using an independent test set comprised of 221 R5 and 91 × 4 V3 sequences extracted from previous studies

Summary

Introduction

Been developed, such as methods based on Support Vector Machine (SVMs)[15,16,17], Position-Specific Weight Matrix (PSSM)[18] and artificial neural networks[19]. These machine learning based methods yielded higher accuracies on coreceptor tropism identification compared to the 11/25 rule, they do not explore genetic mutations associated with tropism switch. Our method couples the classification using CMs, incorporating charge rules, alongside mutational pattern search to better elucidate the tropism switch process

Methods

Results

Conclusion