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Insights to HIV-1 coreceptor usage by estimating HLA adaptation with Bayesian generalized linear mixed models.
Hake, Anna; Germann, Anja; de Beer, Corena; Thielen, Alexander; Däumer, Martin; Preiser, Wolfgang; von Briesen, Hagen; Pfeifer, Nico.
Afiliación
  • Hake A; Research Group Computational Biology, Max Planck Institute for Informatics, Saarbrücken, Germany.
  • Germann A; Saarbrücken Graduate School of Computer Science, Saarland University, Saarbrücken, Germany.
  • de Beer C; Main Department Medical Biotechnology, Fraunhofer Institute for Biomedical Engineering, Sulzbach, Germany.
  • Thielen A; Division of Medical Virology, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa.
  • Däumer M; National Health Laboratory Service, Tygerberg Business Unit, Cape Town, South Africa.
  • Preiser W; Seq IT GmbH & Co.KG, Kaiserslautern, Germany.
  • von Briesen H; Institute of Immunology and Genetics, Kaiserslautern, Germany.
  • Pfeifer N; Division of Medical Virology, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa.
PLoS Comput Biol ; 19(12): e1010355, 2023 Dec.
Article en En | MEDLINE | ID: mdl-38127856
ABSTRACT
The mechanisms triggering the human immunodeficiency virus type I (HIV-1) to switch the coreceptor usage from CCR5 to CXCR4 during the course of infection are not entirely understood. While low CD4+ T cell counts are associated with CXCR4 usage, a predominance of CXCR4 usage with still high CD4+ T cell counts remains puzzling. Here, we explore the hypothesis that viral adaptation to the human leukocyte antigen (HLA) complex, especially to the HLA class II alleles, contributes to the coreceptor switch. To this end, we sequence the viral gag and env protein with corresponding HLA class I and II alleles of a new cohort of 312 treatment-naive, subtype C, chronically-infected HIV-1 patients from South Africa. To estimate HLA adaptation, we develop a novel computational approach using Bayesian generalized linear mixed models (GLMMs). Our model allows to consider the entire HLA repertoire without restricting the model to pre-learned HLA-polymorphisms. In addition, we correct for phylogenetic relatedness of the viruses within the model itself to account for founder effects. Using our model, we observe that CXCR4-using variants are more adapted than CCR5-using variants (p-value = 1.34e-2). Additionally, adapted CCR5-using variants have a significantly lower predicted false positive rate (FPR) by the geno2pheno[coreceptor] tool compared to the non-adapted CCR5-using variants (p-value = 2.21e-2), where a low FPR is associated with CXCR4 usage. Consequently, estimating HLA adaptation can be an asset in predicting not only coreceptor usage, but also an approaching coreceptor switch in CCR5-using variants. We propose the usage of Bayesian GLMMs for modeling virus-host adaptation in general.
Asunto(s)

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Infecciones por VIH / VIH-1 Límite: Humans Idioma: En Revista: PLoS Comput Biol Asunto de la revista: BIOLOGIA / INFORMATICA MEDICA Año: 2023 Tipo del documento: Article País de afiliación: Alemania

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Infecciones por VIH / VIH-1 Límite: Humans Idioma: En Revista: PLoS Comput Biol Asunto de la revista: BIOLOGIA / INFORMATICA MEDICA Año: 2023 Tipo del documento: Article País de afiliación: Alemania