ArticleAIDS research and human retroviruses2014
In vivo HIV-1 hypermutation and viral loads among antiretroviral-naive Brazilian patients.
Article in AIDS research and human retroviruses, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed, 9 citations in OpenAlex.
- Investigating alignment-free machine learning methods for HIV-1 subtype classification.Bioinformatics advances · 2024Article
- Reduced HIV-1 latent reservoir outgrowth and distinct immune correlates among women in Rakai, Uganda.JCI insight · 2020Article
- Impact of Suboptimal APOBEC3G Neutralization on the Emergence of HIV Drug Resistance in Humanized Mice.Journal of virology · 2020Article
- Pace of Coreceptor Tropism Switch in HIV-1-Infected Individuals after Recent Infection.Journal of virology · 2017Article
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Authors and funding
10 authors at 2 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
Hypermutation alludes to an excessive number of specific guanine-to-adenine (G- >A) substitutions in proviral DNA and this phenomenon is attributed to the catalytic activity of cellular APOBECs. Population studies relating hypermutation and the progression of infection by human immunodeficiency virus type 1 (HIV-1) have been performed to elucidate the effect of hypermutation on the natural course of HIV-1 infection. However, the many different approaches employed to assess hypermutation in nucleotide sequences render the comparison of results difficult. This study selected 157 treatment-naive patients and sought to correlate the hypermutation level of the proviral sequences in clinical samples with demographic variables, HIV-1 RNA viral load, and the level of CD4(+) T cells. Nested touchdown polymerase chain reaction (PCR) was performed with specific primers to detect hypermutation in the region of HIV-1 integrase, and the amplified sequences were run in agarose gels with HA-Yellow. The analysis of gel migration patterns using the k-means clustering method was validated by its agreement with the results obtained with the software Hypermut. Hypermutation was found in 31.2% of the investigated samples, and a correlation was observed between higher hypermutation levels and higher viral load levels. These findings suggest a high frequency of hypermutation detection in a Brazilian cohort, which can reflect a particular characteristic of this population, but also can result from the method approach by aiming at hypermutation-sensitive sites. Furthermore, we found that hypermutation events are pervasive during HIV-1 infection as a consequence of high viral replication, reflecting its role during disease progression.
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