丙型肝炎病毒(HCV)高变区1变异性在急性和慢性丙型肝炎感染阶段的差异

Q2 Medicine
I V Astrakhantseva, D S Campo, A Araujo, C-G Teo, Y Khudyakov, S Kamili
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引用次数: 22

摘要

区分急性和慢性丙型肝炎病毒感染在临床上很重要,因为感染患者的早期治疗导致持续病毒学反应的高发率。对急性(n = 49)和慢性(n = 102) HCV感染患者的2179个HCV基因组高变区1 (HVR1)克隆序列的分析表明,慢性HCV感染患者的宿主内HVR1多样性是急性感染患者的1.8倍。利用分子方差分析,发现宿主内HVR1变异的5个氨基酸(位置5、7、12、16和18)的频率和平均遗传距离存在显著差异。10个氨基酸(位置1、4、5、12、14、15、16、21、22和29)的极性、体积和疏水性也存在差异。基于这些属性,可以构建一个分类模型,该模型允许区分急性和慢性病例的HVR1变异,准确率为88%。从急性到慢性HCV感染的进展伴随着HVR1氨基酸组成的特征性变化。识别这些变化可能有助于诊断最近的HCV感染。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Differences in variability of hypervariable region 1 of hepatitis C virus (HCV) between acute and chronic stages of HCV infection.

Distinguishing between acute and chronic HCV infections is clinically important given that early treatment of infected patients leads to high rates of sustained virological response. Analysis of 2179 clonal sequences derived from hypervariable region 1 (HVR1) of the HCV genome in samples obtained from patients with acute (n = 49) and chronic (n = 102) HCV infection showed that intra-host HVR1 diversity was 1.8 times higher in patients with chronic than acute infection. Significant differences in frequencies of 5 amino acids (positions 5, 7, 12, 16 and 18) and the average genetic distances among intra-host HVR1 variants were found using analysis of molecular variance. Differences were also observed in the polarity, volume and hydrophobicity of 10 amino acids (at positions 1, 4, 5, 12, 14, 15, 16, 21, 22 and 29). Based on these properties, a classification model could be constructed, which permitted HVR1 variants from acute and chronic cases to be discriminated with an accuracy of 88%. Progression from acute to chronic stage of HCV infection is accompanied by characteristic changes in amino acid composition of HVR1. Identifying these changes may permit diagnosis of recent HCV infection.

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来源期刊
In Silico Biology
In Silico Biology Computer Science-Computational Theory and Mathematics
CiteScore
2.20
自引率
0.00%
发文量
1
期刊介绍: The considerable "algorithmic complexity" of biological systems requires a huge amount of detailed information for their complete description. Although far from being complete, the overwhelming quantity of small pieces of information gathered for all kind of biological systems at the molecular and cellular level requires computational tools to be adequately stored and interpreted. Interpretation of data means to abstract them as much as allowed to provide a systematic, an integrative view of biology. Most of the presently available scientific journals focus either on accumulating more data from elaborate experimental approaches, or on presenting new algorithms for the interpretation of these data. Both approaches are meritorious.
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