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Significance of pretreatment analysis of hepatitis C virus genotype 1b hypervariable region 1 sequences to predict antiviral outcome.

ABSTRACT: The heterogeneity of hypervariable region 1 (HVR1), located at the amino terminus of the E2 envelope, may be involved in resistance to alpha interferon (IFN-alpha) treatment. We investigated whether peculiar HVR1 domain profiles before treatment were associated with the maintenance of sensitivity or the appearance of resistance to treatment. Fifteen patients infected with hepatitis C virus genotype 1b and treated with IFN with or without ribavirin were selected. Ten responded to treatment (groups R1 and R2) and five did not (group NR). The amino acid sequences of 150 naturally occurring HVR1 variants present in the serum before therapy were compared in relation to treatment outcome. HVR1 variants from the NR group contained a constant nonantigenic amino acid segment that was not found in HVR1 variants from the R groups.

SUBMITTER: Gaudy C

PROVIDER: S-EPMC179854 | biostudies-literature | 2003 Aug

REPOSITORIES: biostudies-literature

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Significance of pretreatment analysis of hepatitis C virus genotype 1b hypervariable region 1 sequences to predict antiviral outcome.

Gaudy Catherine C Moreau Alain A Veillon Pascal P Temoin Stephanie S Lunel Francoise F Goudeau Alain A

Journal of clinical microbiology 20030801 8

The heterogeneity of hypervariable region 1 (HVR1), located at the amino terminus of the E2 envelope, may be involved in resistance to alpha interferon (IFN-alpha) treatment. We investigated whether peculiar HVR1 domain profiles before treatment were associated with the maintenance of sensitivity or the appearance of resistance to treatment. Fifteen patients infected with hepatitis C virus genotype 1b and treated with IFN with or without ribavirin were selected. Ten responded to treatment (group ...[more]

PMID: 12904364

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Project description:Human identification of unknown samples following disaster and mass casualty events is essential, especially to bring closure to family and friends of the deceased. Unfortunately, victim identification is often challenging for forensic investigators as analysis becomes complicated when biological samples are degraded or of poor quality as a result of exposure to harsh environmental factors. Mitochondrial DNA becomes the ideal option for analysis, particularly for determining the origin of the samples. In such events, the estimation of genetic parameters plays an important role in modelling and predicting genetic relatedness and is useful in assigning unknown individuals to an ethnic group. Various techniques exist for the estimation of genetic relatedness, but the use of Machine learning (ML) algorithms are novel and presently the least used in forensic genetic studies. In this study, we investigated the ability of ML algorithms to predict genetic relatedness using hypervariable region I sequences; that were retrieved from the GenBank database for three race groups, namely African, Asian and Caucasian. Four ML classification algorithms; Support vector machines (SVM), Linear discriminant analysis (LDA), Quadratic discriminant analysis (QDA) and Random Forest (RF) were hybridised with one-hot encoding, Principal component analysis (PCA) and Bags of Words (BoW), and were compared for inferring genetic relatedness. The findings from this study on WEKA showed that genetic inferences based on PCA-SVM achieved an overall accuracy of 80-90% and consistently outperformed PCA-LDA, PCA-RF and PCA-QDA, while in Python BoW-PCA-RF achieved 94.4% accuracy which outperformed BoW-PCA-SVM, BoW-PCA-LDA and BoW-PCA-QDA respectively. ML results from the use of WEKA and Python software tools displayed higher accuracies as compared to the Analysis of molecular variance results. Given the results, SVM and RF algorithms are likely to also be useful in other sequence classification applications, making it a promising tool in genetics and forensic science. The study provides evidence that ML can be utilized as a supplementary tool for forensic genetics casework analysis.

Dataset Information

Significance of pretreatment analysis of hepatitis C virus genotype 1b hypervariable region 1 sequences to predict antiviral outcome.

Publications

Significance of pretreatment analysis of hepatitis C virus genotype 1b hypervariable region 1 sequences to predict antiviral outcome.

Similar Datasets

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