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首页> 外文期刊>AIDS Research and Human Retroviruses >Use of principal components analysis and protein microarray to explore the association of HIV-1-specific IgG responses with disease progression
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Use of principal components analysis and protein microarray to explore the association of HIV-1-specific IgG responses with disease progression

机译：使用主成分分析和蛋白质芯片研究HIV-1特异性IgG反应与疾病进展的关系

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The role of HIV-1-specific antibody responses in HIV disease progression is complex and would benefit from analysis techniques that examine clusterings of responses. Protein microarray platforms facilitate the simultaneous evaluation of numerous protein-specific antibody responses, though excessive data are cumbersome in analyses. Principal components analysis (PCA) reduces data dimensionality by generating fewer composite variables that maximally account for variance in a dataset. To identify clusters of antibody responses involved in disease control, we investigated the association of HIV-1-specific antibody responses by protein microarray, and assessed their association with disease progression using PCA in a nested cohort design. Associations observed among collections of antibody responses paralleled protein-specific responses. At baseline, greater antibody responses to the transmembrane glycoprotein (TM) and reverse transcriptase (RT) were associated with higher viral loads, while responses to the surface glycoprotein (SU), capsid (CA), matrix (MA), and integrase (IN) proteins were associated with lower viral loads. Over 12 months greater antibody responses were associated with smaller decreases in CD4 count (CA, MA, IN), and reduced likelihood of disease progression (CA, IN). PCA and protein microarray analyses highlighted a collection of HIV-specific antibody responses that together were associated with reduced disease progression, and may not have been identified by examining individual antibody responses. This technique may be useful to explore multifaceted host-disease interactions, such as HIV coinfections.
机译：HIV-1特异性抗体应答在HIV疾病进展中的作用非常复杂，可以从检查应答簇的分析技术中受益。蛋白质微阵列平台有助于同时评估多种蛋白质特异性抗体反应，尽管过多的数据在分析中很麻烦。主成分分析（PCA）通过生成较少的复合变量来减少数据维，这些复合变量最大程度地说明了数据集中的差异。为了确定与疾病控制有关的抗体应答簇，我们通过蛋白质微阵列研究了HIV-1特异性抗体应答的关联，并在巢式队列设计中使用PCA评估了它们与疾病进展的关联。在抗体反应的集合中观察到的关联与蛋白质特异性反应平行。在基线时，对跨膜糖蛋白（TM）和逆转录酶（RT）的抗体反应更大，与较高的病毒载量相关，而对表面糖蛋白（SU），衣壳（CA），基质（MA）和整合酶（IN ）蛋白与较低的病毒载量相关。在超过12个月的时间内，更大的抗体反应与CD4计数（CA，MA，IN）的减少较小相关，并且疾病进展的可能性降低（CA，IN）。 PCA和蛋白质微阵列分析突出显示了HIV特异性抗体反应的集合，这些反应与疾病进展减少相关，并且可能无法通过检查单个抗体反应来鉴定。该技术可能对探索多方面的宿主-疾病相互作用（例如HIV合并感染）很有用。

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