Using machine learning and big data to explore the drug resistance landscape in HIV.

Drug resistance mutations (DRMs) appear in HIV under treatment pressure. DRMs are commonly transmitted to naive patients. The standard approach to reveal new DRMs is to test for significant frequency differences of mutations between treated and naive patients. However, we then consider each mutation...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Luc Blassel, Anna Tostevin, Christian Julian Villabona-Arenas, Martine Peeters, Stéphane Hué, Olivier Gascuel, UK HIV Drug Resistance Database
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/f24dab1ce4724b9696dc6e25ea878cb0
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:f24dab1ce4724b9696dc6e25ea878cb0
record_format	dspace
spelling	oai:doaj.org-article:f24dab1ce4724b9696dc6e25ea878cb02021-12-02T19:58:00ZUsing machine learning and big data to explore the drug resistance landscape in HIV.1553-734X1553-735810.1371/journal.pcbi.1008873https://doaj.org/article/f24dab1ce4724b9696dc6e25ea878cb02021-08-01T00:00:00Zhttps://doi.org/10.1371/journal.pcbi.1008873https://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358Drug resistance mutations (DRMs) appear in HIV under treatment pressure. DRMs are commonly transmitted to naive patients. The standard approach to reveal new DRMs is to test for significant frequency differences of mutations between treated and naive patients. However, we then consider each mutation individually and cannot hope to study interactions between several mutations. Here, we aim to leverage the ever-growing quantity of high-quality sequence data and machine learning methods to study such interactions (i.e. epistasis), as well as try to find new DRMs. We trained classifiers to discriminate between Reverse Transcriptase Inhibitor (RTI)-experienced and RTI-naive samples on a large HIV-1 reverse transcriptase (RT) sequence dataset from the UK (n ≈ 55, 000), using all observed mutations as binary representation features. To assess the robustness of our findings, our classifiers were evaluated on independent data sets, both from the UK and Africa. Important representation features for each classifier were then extracted as potential DRMs. To find novel DRMs, we repeated this process by removing either features or samples associated to known DRMs. When keeping all known resistance signal, we detected sufficiently prevalent known DRMs, thus validating the approach. When removing features corresponding to known DRMs, our classifiers retained some prediction accuracy, and six new mutations significantly associated with resistance were identified. These six mutations have a low genetic barrier, are correlated to known DRMs, and are spatially close to either the RT active site or the regulatory binding pocket. When removing both known DRM features and sequences containing at least one known DRM, our classifiers lose all prediction accuracy. These results likely indicate that all mutations directly conferring resistance have been found, and that our newly discovered DRMs are accessory or compensatory mutations. Moreover, apart from the accessory nature of the relationships we found, we did not find any significant signal of further, more subtle epistasis combining several mutations which individually do not seem to confer any resistance.Luc BlasselAnna TostevinChristian Julian Villabona-ArenasMartine PeetersStéphane HuéOlivier GascuelUK HIV Drug Resistance DatabasePublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 17, Iss 8, p e1008873 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Luc Blassel Anna Tostevin Christian Julian Villabona-Arenas Martine Peeters Stéphane Hué Olivier Gascuel UK HIV Drug Resistance Database Using machine learning and big data to explore the drug resistance landscape in HIV.
description	Drug resistance mutations (DRMs) appear in HIV under treatment pressure. DRMs are commonly transmitted to naive patients. The standard approach to reveal new DRMs is to test for significant frequency differences of mutations between treated and naive patients. However, we then consider each mutation individually and cannot hope to study interactions between several mutations. Here, we aim to leverage the ever-growing quantity of high-quality sequence data and machine learning methods to study such interactions (i.e. epistasis), as well as try to find new DRMs. We trained classifiers to discriminate between Reverse Transcriptase Inhibitor (RTI)-experienced and RTI-naive samples on a large HIV-1 reverse transcriptase (RT) sequence dataset from the UK (n ≈ 55, 000), using all observed mutations as binary representation features. To assess the robustness of our findings, our classifiers were evaluated on independent data sets, both from the UK and Africa. Important representation features for each classifier were then extracted as potential DRMs. To find novel DRMs, we repeated this process by removing either features or samples associated to known DRMs. When keeping all known resistance signal, we detected sufficiently prevalent known DRMs, thus validating the approach. When removing features corresponding to known DRMs, our classifiers retained some prediction accuracy, and six new mutations significantly associated with resistance were identified. These six mutations have a low genetic barrier, are correlated to known DRMs, and are spatially close to either the RT active site or the regulatory binding pocket. When removing both known DRM features and sequences containing at least one known DRM, our classifiers lose all prediction accuracy. These results likely indicate that all mutations directly conferring resistance have been found, and that our newly discovered DRMs are accessory or compensatory mutations. Moreover, apart from the accessory nature of the relationships we found, we did not find any significant signal of further, more subtle epistasis combining several mutations which individually do not seem to confer any resistance.
format	article
author	Luc Blassel Anna Tostevin Christian Julian Villabona-Arenas Martine Peeters Stéphane Hué Olivier Gascuel UK HIV Drug Resistance Database
author_facet	Luc Blassel Anna Tostevin Christian Julian Villabona-Arenas Martine Peeters Stéphane Hué Olivier Gascuel UK HIV Drug Resistance Database
author_sort	Luc Blassel
title	Using machine learning and big data to explore the drug resistance landscape in HIV.
title_short	Using machine learning and big data to explore the drug resistance landscape in HIV.
title_full	Using machine learning and big data to explore the drug resistance landscape in HIV.
title_fullStr	Using machine learning and big data to explore the drug resistance landscape in HIV.
title_full_unstemmed	Using machine learning and big data to explore the drug resistance landscape in HIV.
title_sort	using machine learning and big data to explore the drug resistance landscape in hiv.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/f24dab1ce4724b9696dc6e25ea878cb0
work_keys_str_mv	AT lucblassel usingmachinelearningandbigdatatoexplorethedrugresistancelandscapeinhiv AT annatostevin usingmachinelearningandbigdatatoexplorethedrugresistancelandscapeinhiv AT christianjulianvillabonaarenas usingmachinelearningandbigdatatoexplorethedrugresistancelandscapeinhiv AT martinepeeters usingmachinelearningandbigdatatoexplorethedrugresistancelandscapeinhiv AT stephanehue usingmachinelearningandbigdatatoexplorethedrugresistancelandscapeinhiv AT oliviergascuel usingmachinelearningandbigdatatoexplorethedrugresistancelandscapeinhiv AT ukhivdrugresistancedatabase usingmachinelearningandbigdatatoexplorethedrugresistancelandscapeinhiv
_version_	1718375815136149504

Using machine learning and big data to explore the drug resistance landscape in HIV.

Ejemplares similares