Modeling Drug Resistance Emergence and Transmission in HIV-1 in the UK
A deeper understanding of HIV-1 transmission and drug resistance mechanisms can lead to improvements in current treatment policies. However, the rates at which HIV-1 drug resistance mutations (DRMs) are acquired and which transmitted DRMs persist are multi-factorial and vary considerably between dif...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-05-01
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| Series: | Viruses |
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| Online Access: | https://www.mdpi.com/1999-4915/15/6/1244 |
| _version_ | 1797592338635161600 |
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| author | Anna Zhukova David Dunn Olivier Gascuel |
| author_facet | Anna Zhukova David Dunn Olivier Gascuel |
| author_sort | Anna Zhukova |
| collection | DOAJ |
| description | A deeper understanding of HIV-1 transmission and drug resistance mechanisms can lead to improvements in current treatment policies. However, the rates at which HIV-1 drug resistance mutations (DRMs) are acquired and which transmitted DRMs persist are multi-factorial and vary considerably between different mutations. We develop a method for the estimation of drug resistance acquisition and transmission patterns. The method uses maximum likelihood ancestral character reconstruction informed by treatment roll-out dates and allows for the analysis of very large datasets. We apply our method to transmission trees reconstructed on the data obtained from the UK HIV Drug Resistance Database to make predictions for known DRMs. Our results show important differences between DRMs, in particular between polymorphic and non-polymorphic DRMs and between the B and C subtypes. Our estimates of reversion times, based on a very large number of sequences, are compatible but more accurate than those already available in the literature, with narrower confidence intervals. We consistently find that large resistance clusters are associated with polymorphic DRMs and DRMs with long loss times, which require special surveillance. As in other high-income countries (e.g., Switzerland), the prevalence of sequences with DRMs is decreasing, but among these, the fraction of transmitted resistance is clearly increasing compared to the fraction of acquired resistance mutations. All this indicates that efforts to monitor these mutations and the emergence of resistance clusters in the population must be maintained in the long term. |
| first_indexed | 2024-03-11T01:49:44Z |
| format | Article |
| id | doaj.art-10ee6efadd47485e92a5535ad701aeb1 |
| institution | Directory Open Access Journal |
| issn | 1999-4915 |
| language | English |
| last_indexed | 2024-03-11T01:49:44Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Viruses |
| spelling | doaj.art-10ee6efadd47485e92a5535ad701aeb12023-11-18T13:01:01ZengMDPI AGViruses1999-49152023-05-01156124410.3390/v15061244Modeling Drug Resistance Emergence and Transmission in HIV-1 in the UKAnna Zhukova0David Dunn1Olivier Gascuel2Bioinformatics and Biostatistics Hub, Institut Pasteur, Université Paris Cité, 75015 Paris, FranceUK MRC Clinical Trials Unit, University College London, London WC1V 6LJ, UKInstitut de Systématique, Evolution, Biodiversité (ISYEB)—URM 7205 CNRS, Muséum National d’Histoire Naturelle, SU, EPHE & UA, 75005 Paris, FranceA deeper understanding of HIV-1 transmission and drug resistance mechanisms can lead to improvements in current treatment policies. However, the rates at which HIV-1 drug resistance mutations (DRMs) are acquired and which transmitted DRMs persist are multi-factorial and vary considerably between different mutations. We develop a method for the estimation of drug resistance acquisition and transmission patterns. The method uses maximum likelihood ancestral character reconstruction informed by treatment roll-out dates and allows for the analysis of very large datasets. We apply our method to transmission trees reconstructed on the data obtained from the UK HIV Drug Resistance Database to make predictions for known DRMs. Our results show important differences between DRMs, in particular between polymorphic and non-polymorphic DRMs and between the B and C subtypes. Our estimates of reversion times, based on a very large number of sequences, are compatible but more accurate than those already available in the literature, with narrower confidence intervals. We consistently find that large resistance clusters are associated with polymorphic DRMs and DRMs with long loss times, which require special surveillance. As in other high-income countries (e.g., Switzerland), the prevalence of sequences with DRMs is decreasing, but among these, the fraction of transmitted resistance is clearly increasing compared to the fraction of acquired resistance mutations. All this indicates that efforts to monitor these mutations and the emergence of resistance clusters in the population must be maintained in the long term.https://www.mdpi.com/1999-4915/15/6/1244HIV-1drug resistance mutationsancestral character reconstruction |
| spellingShingle | Anna Zhukova David Dunn Olivier Gascuel Modeling Drug Resistance Emergence and Transmission in HIV-1 in the UK Viruses HIV-1 drug resistance mutations ancestral character reconstruction |
| title | Modeling Drug Resistance Emergence and Transmission in HIV-1 in the UK |
| title_full | Modeling Drug Resistance Emergence and Transmission in HIV-1 in the UK |
| title_fullStr | Modeling Drug Resistance Emergence and Transmission in HIV-1 in the UK |
| title_full_unstemmed | Modeling Drug Resistance Emergence and Transmission in HIV-1 in the UK |
| title_short | Modeling Drug Resistance Emergence and Transmission in HIV-1 in the UK |
| title_sort | modeling drug resistance emergence and transmission in hiv 1 in the uk |
| topic | HIV-1 drug resistance mutations ancestral character reconstruction |
| url | https://www.mdpi.com/1999-4915/15/6/1244 |
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