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. 2023 Jan;95(1):e28389.
doi: 10.1002/jmv.28389.

Analysis of mutational history of multidrug-resistant genotypes with a mutagenetic tree model

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Analysis of mutational history of multidrug-resistant genotypes with a mutagenetic tree model

Martin Pirkl et al. J Med Virol. 2023 Jan.

Abstract

Human immunodeficiency virus (HIV) can develop resistance to all antiretroviral drugs. Multidrug resistance, however, is a rare event in modern HIV treatment, but can be life-threatening, particular in patients with very long therapy histories and in areas with limited access to novel drugs. To understand the evolution of multidrug resistance, we analyzed the EuResist database to uncover the accumulation of mutations over time. We hypothesize that the accumulation of resistance mutations is not acquired simultaneously and randomly across viral genotypes but rather tends to follow a predetermined order. The knowledge of this order might help to elucidate potential mechanisms of multidrug resistance. Our evolutionary model shows an almost monotonic increase of resistance with each acquired mutation, including less well-known nucleoside reverse transcriptase (RT) inhibitor-related mutations like K223Q, L228H, and Q242H. Mutations within the integrase (IN) (T97A, E138A/K G140S, Q148H, N155H) indicate high probability of multidrug resistance. Hence, these IN mutations also tend to be observed together with mutations in the protease (PR) and RT. We followed up with an analysis of the mutation-specific error rates of our model given the data. We identified several mutations with unusual rates (PR: M41L, L33F, IN: G140S). This could imply the existence of previously unknown virus variants in the viral quasispecies. In conclusion, our bioinformatics model supports the analysis and understanding of multidrug resistance.

Keywords: antiretrovirus drug; antiviral agents; disease control; evolution; human immunodeficiency virus; infection; mutation/mutation rate; reservoir; resistance; virus classification.

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References

REFERENCES

    1. Beyrer C, Pozniak A. HIV drug resistance-an emerging threat to epidemic control. N Engl J Med. 2017;377(17):1605-1607.
    1. Bertagnolio S, Beanland RL, Jordan MR, Doherty M, Hirnschall G. The world health organization's response to emerging human immunodeficiency virus drug resistance and a call for global action. J Infect Dis. 2017;216(suppl 9):S801-S804.
    1. Zazzi M, Hu H, Prosperi M. The global burden of HIV-1 drug resistance in the past 20 years. PeerJ. 2018;6:e4848.
    1. Marcus JL, Leyden WA, Alexeeff SE, et al. Comparison of overall and comorbidity-free life expectancy between insured adults with and without HIV infection, 2000-2016. JAMA Network Open. 2020;3(6):e207954.
    1. Feder AF, Harper KN, Brumme CJ, Pennings PS. Understanding patterns of HIV multi-drug resistance through models of temporal and spatial drug heterogeneity. eLife. 2021;10:e69032.

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