PMID- 38127856
OWN - NLM
STAT- MEDLINE
DCOM- 20240108
LR  - 20240108
IS  - 1553-7358 (Electronic)
IS  - 1553-734X (Print)
IS  - 1553-734X (Linking)
VI  - 19
IP  - 12
DP  - 2023 Dec
TI  - Insights to HIV-1 coreceptor usage by estimating HLA adaptation with Bayesian 
      generalized linear mixed models.
PG  - e1010355
LID - 10.1371/journal.pcbi.1010355 [doi]
LID - e1010355
AB  - 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.
CI  - Copyright: (c) 2023 Hake et al. This is an open access article distributed under 
      the terms of the Creative Commons Attribution License, which permits unrestricted 
      use, distribution, and reproduction in any medium, provided the original author 
      and source are credited.
FAU - Hake, Anna
AU  - Hake A
AUID- ORCID: 0000-0001-7288-5088
AD  - Research Group Computational Biology, Max Planck Institute for Informatics, 
      Saarbrucken, Germany.
AD  - Saarbrucken Graduate School of Computer Science, Saarland University, 
      Saarbrucken, Germany.
FAU - Germann, Anja
AU  - Germann A
AD  - Main Department Medical Biotechnology, Fraunhofer Institute for Biomedical 
      Engineering, Sulzbach, Germany.
FAU - de Beer, Corena
AU  - de Beer C
AD  - Division of Medical Virology, Faculty of Medicine and Health Sciences, 
      Stellenbosch University, Cape Town, South Africa.
AD  - National Health Laboratory Service, Tygerberg Business Unit, Cape Town, South 
      Africa.
FAU - Thielen, Alexander
AU  - Thielen A
AD  - Seq IT GmbH & Co.KG, Kaiserslautern, Germany.
FAU - Daumer, Martin
AU  - Daumer M
AD  - Institute of Immunology and Genetics, Kaiserslautern, Germany.
FAU - Preiser, Wolfgang
AU  - Preiser W
AD  - Division of Medical Virology, Faculty of Medicine and Health Sciences, 
      Stellenbosch University, Cape Town, South Africa.
AD  - National Health Laboratory Service, Tygerberg Business Unit, Cape Town, South 
      Africa.
FAU - von Briesen, Hagen
AU  - von Briesen H
AD  - Main Department Medical Biotechnology, Fraunhofer Institute for Biomedical 
      Engineering, Sulzbach, Germany.
FAU - Pfeifer, Nico
AU  - Pfeifer N
AUID- ORCID: 0000-0002-4647-8566
AD  - Research Group Computational Biology, Max Planck Institute for Informatics, 
      Saarbrucken, Germany.
AD  - German Center for Infection Research, Partner Site Tubingen, Tubingen, Germany.
AD  - Methods in Medical Informatics, Department of Computer Science, University of 
      Tubingen, Tubingen, Germany.
LA  - eng
PT  - Journal Article
DEP - 20231221
PL  - United States
TA  - PLoS Comput Biol
JT  - PLoS computational biology
JID - 101238922
RN  - 0 (Receptors, CCR5)
RN  - 0 (Receptors, CXCR4)
RN  - 0 (Histocompatibility Antigens)
SB  - IM
MH  - Humans
MH  - Receptors, CCR5/genetics/metabolism
MH  - *HIV-1
MH  - Phylogeny
MH  - Bayes Theorem
MH  - *HIV Infections
MH  - Receptors, CXCR4/genetics/metabolism
MH  - Histocompatibility Antigens
PMC - PMC10769057
COIS- The authors have declared that no competing interests exist.
EDAT- 2023/12/21 18:41
MHDA- 2024/01/08 06:42
PMCR- 2023/12/21
CRDT- 2023/12/21 16:31
PHST- 2022/07/05 00:00 [received]
PHST- 2023/11/06 00:00 [accepted]
PHST- 2024/01/05 00:00 [revised]
PHST- 2024/01/08 06:42 [medline]
PHST- 2023/12/21 18:41 [pubmed]
PHST- 2023/12/21 16:31 [entrez]
PHST- 2023/12/21 00:00 [pmc-release]
AID - PCOMPBIOL-D-22-01028 [pii]
AID - 10.1371/journal.pcbi.1010355 [doi]
PST - epublish
SO  - PLoS Comput Biol. 2023 Dec 21;19(12):e1010355. doi: 10.1371/journal.pcbi.1010355. 
      eCollection 2023 Dec.