HIV-1

HIV-1 identifies novel drug resistance mutations in HIV-1 by analyzing deep sequencing data with support vector machine feature selection to reveal mutations associated with raltegravir (RAL) susceptibility, particularly in non-subtype B infections.

Key Features:

• Deep Sequencing Analysis: Utilizes next-generation sequencing (MiSeq, Illumina) on archived plasma samples to generate comprehensive HIV-1 sequence data.
• Support Vector Machine (SVM) Analysis: Applies SVMs for feature selection to identify mutations associated with drug resistance and treatment outcomes.
• Multiple Imputation: Implements multiple imputation techniques to handle partial reads and low-quality base calls and improve variant detection robustness.
• Experimental Validation: Introduces candidate mutations into an NL4-3 (subtype B) backbone and assesses raltegravir susceptibility in U87.CD4.CXCR4 cells and replication capacity in TZM-bl cells with and without RAL.
• Mutation Detection: Detects known integrase mutations such as N155H, G163R, and V151I, and identifies novel mutations I203M and I208L associated with decreased RAL susceptibility.
• Non-subtype B Focus: Emphasizes analysis of non-subtype B infections, which constitute the majority of global HIV-1 diversity.

Scientific Applications:

• Detection of Novel Resistance Mutations: Identifies and prioritizes candidate integrase mutations (including I203M and I208L) that correlate with decreased susceptibility to raltegravir.
• Resistance Surveillance in Diverse Populations: Addresses gaps in resistance knowledge by characterizing mutations in non-subtype B infections prevalent in global HIV-1 epidemics.

Methodology:

Deep sequencing data (MiSeq, Illumina) are processed through a bioinformatic pipeline for alignment and variant calling, with SVM feature selection and multiple imputation to handle partial reads and low-quality base calls.