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Brumme Z, Wang B, Nair K, Brumme C, de Pierres C, Reddy S, Julg B, Moodley E, Thobakgale C, Lu Z, van der Stok M, Bishop K, Mncube Z, Chonco F, Yuki Y, Frahm N, Brander C, Carrington M, Freedberg K, Kiepiela P, Goulder P, Walker B, Ndung’u T, Losina E. Impact of select immunologic and virologic biomarkers on CD4 cell count decrease in patients with chronic HIV-1 subtype C infection: results from Sinikithemba Cohort, Durban, South Africa. Clin Infect Dis 2009; 49:956-64. [PMID: 19663693 PMCID: PMC2777678 DOI: 10.1086/605503] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022] Open
Abstract
BACKGROUND The extent to which immunologic and clinical biomarkers influence human immunodeficiency virus type 1 (HIV-1) infection outcomes remains incompletely characterized, particularly for non-B subtypes. On the basis of data supporting in vitro HIV-1 protein-specific CD8 T lymphocyte responses as correlates of immune control in cross-sectional studies, we assessed the relationship of these responses, along with established HIV-1 biomarkers, with rates of CD4 cell count decrease in individuals infected with HIV-1 subtype C. METHODS Bivariate and multivariate mixed-effects models were used to assess the relationship of baseline CD4 cell count, plasma viral load, human leukocyte antigen (HLA) class I alleles, and HIV-1 protein-specific CD8 T cell responses with the rate of CD4 cell count decrease in a longitudinal population-based cohort of 300 therapy-naive, chronically infected adults with baseline CD4 cell counts >200 cells/mm(3) and plasma viral loads >500 copies/mL over a median of 25 months of follow-up. RESULTS In bivariate analyses, baseline CD4 cell count, plasma viral load, and possession of a protective HLA allele correlated significantly with the rate of CD4 cell count decrease. No relationship was observed between HIV-1 protein-specific CD8 T cell responses and CD4 cell count decrease. Results from multivariate models incorporating baseline CD4 cell counts (201-350 vs >350 cells/mm(3)), plasma viral load (< or =100,000 vs >100,000 copies/mL), and HLA (protective vs not protective) yielded the ability to discriminate CD4 cell count decreases over a 10-fold range. The fastest decrease was observed among individuals with CD4 cell counts >350 cells/mm(3) and plasma viral loads >100,000 copies/mL with no protective HLA alleles (-59 cells/mm(3) per year), whereas the slowest decrease was observed among individuals with CD4 cell counts 201-350 cells/mm(3), plasma viral loads < or =100,000 copies/mL, and a protective HLA allele (-6 cells/mm(3) per year). CONCLUSIONS The combination of plasma viral load and HLA class I type, but not in vitro HIV-1 protein-specific CD8 T cell responses, differentiates rates of CD4 cell count decrease in patients with chronic subtype-C infection better than either marker alone.
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Affiliation(s)
- Zabrina Brumme
- Ragon Institute of Massachusetts General Hospital, Massachusetts Institute of Technology and Harvard University, Boston MA, USA
| | - Bingxia Wang
- Program in HIV Outcomes Research, Massachusetts General Hospital, Boston MA, USA
| | - Kriebashne Nair
- HIV Pathogenesis Programme, Doris Duke Medical Research Institute, University of KwaZulu-Natal, Durban South Africa
| | - Chanson Brumme
- Ragon Institute of Massachusetts General Hospital, Massachusetts Institute of Technology and Harvard University, Boston MA, USA
| | - Chantal de Pierres
- HIV Pathogenesis Programme, Doris Duke Medical Research Institute, University of KwaZulu-Natal, Durban South Africa
| | - Shabashini Reddy
- HIV Pathogenesis Programme, Doris Duke Medical Research Institute, University of KwaZulu-Natal, Durban South Africa
| | - Boris Julg
- Ragon Institute of Massachusetts General Hospital, Massachusetts Institute of Technology and Harvard University, Boston MA, USA
- HIV Pathogenesis Programme, Doris Duke Medical Research Institute, University of KwaZulu-Natal, Durban South Africa
| | - Eshia Moodley
- HIV Pathogenesis Programme, Doris Duke Medical Research Institute, University of KwaZulu-Natal, Durban South Africa
| | - Christina Thobakgale
- HIV Pathogenesis Programme, Doris Duke Medical Research Institute, University of KwaZulu-Natal, Durban South Africa
| | - Zhigang Lu
- Program in HIV Outcomes Research, Massachusetts General Hospital, Boston MA, USA
| | - Mary van der Stok
- HIV Pathogenesis Programme, Doris Duke Medical Research Institute, University of KwaZulu-Natal, Durban South Africa
| | - Karen Bishop
- HIV Pathogenesis Programme, Doris Duke Medical Research Institute, University of KwaZulu-Natal, Durban South Africa
| | - Zenele Mncube
- HIV Pathogenesis Programme, Doris Duke Medical Research Institute, University of KwaZulu-Natal, Durban South Africa
| | - Fundisiwe Chonco
- HIV Pathogenesis Programme, Doris Duke Medical Research Institute, University of KwaZulu-Natal, Durban South Africa
| | - Yuko Yuki
- Cancer and Inflammation Program, Laboratory of Experimental Immunology, SAIC-Frederick, Inc., NCI-Frederick, Frederick, MD
| | - Nicole Frahm
- Ragon Institute of Massachusetts General Hospital, Massachusetts Institute of Technology and Harvard University, Boston MA, USA
| | - Christian Brander
- Ragon Institute of Massachusetts General Hospital, Massachusetts Institute of Technology and Harvard University, Boston MA, USA
| | - Mary Carrington
- Cancer and Inflammation Program, Laboratory of Experimental Immunology, SAIC-Frederick, Inc., NCI-Frederick, Frederick, MD
| | - Kenneth Freedberg
- Program in HIV Outcomes Research, Massachusetts General Hospital, Boston MA, USA
| | - Photini Kiepiela
- HIV Pathogenesis Programme, Doris Duke Medical Research Institute, University of KwaZulu-Natal, Durban South Africa
| | - Philip Goulder
- Ragon Institute of Massachusetts General Hospital, Massachusetts Institute of Technology and Harvard University, Boston MA, USA
- HIV Pathogenesis Programme, Doris Duke Medical Research Institute, University of KwaZulu-Natal, Durban South Africa
- Department of Pediatrics, Nuffield Department of Medicine, Oxford, UK
| | - Bruce Walker
- Ragon Institute of Massachusetts General Hospital, Massachusetts Institute of Technology and Harvard University, Boston MA, USA
- HIV Pathogenesis Programme, Doris Duke Medical Research Institute, University of KwaZulu-Natal, Durban South Africa
- Howard Hughes Medical Institute, Chevy Chase, MD, USA
| | - Thumbi Ndung’u
- Ragon Institute of Massachusetts General Hospital, Massachusetts Institute of Technology and Harvard University, Boston MA, USA
- HIV Pathogenesis Programme, Doris Duke Medical Research Institute, University of KwaZulu-Natal, Durban South Africa
| | - Elena Losina
- Program in HIV Outcomes Research, Massachusetts General Hospital, Boston MA, USA
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Serwanga J, Shafer LA, Pimego E, Auma B, Watera C, Rowland S, Yirrell D, Pala P, Grosskurth H, Whitworth J, Gotch F, Kaleebu P. Host HLA B*allele-associated multi-clade Gag T-cell recognition correlates with slow HIV-1 disease progression in antiretroviral therapy-naïve Ugandans. PLoS One 2009; 4:e4188. [PMID: 19142234 PMCID: PMC2617765 DOI: 10.1371/journal.pone.0004188] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2008] [Accepted: 11/17/2008] [Indexed: 01/04/2023] Open
Abstract
BACKGROUND Some HIV infected individuals remain asymptomatic for protracted periods of time in the absence of antiretroviral therapy (ART). Virological control, CD4 T cell loss and HIV-specific responses are some of the key interrelated determinants of HIV-1 disease progression. In this study, possible interactions between viral load, CD4 T cell slopes, host genetics and HIV-specific IFN-gamma responses were evaluated in chronically HIV-1-infected adults. METHODOLOGY/PRINCIPAL FINDINGS Multilevel regression modeling was used to stratify clade A or D HIV-infected individuals into disease progression groups based on CD4 T cell slopes. ELISpot assays were used to quantify the frequency and magnitude of HIV-induced IFN-gamma responses in 7 defined rapid progressors (RPs) and 14 defined slow progressors (SPs) at a single time point. HLA typing was performed using reference strand conformational analysis (RSCA). Although neither the breadth nor the magnitude of the proteome-wide HIV-specific IFN-gamma response correlated with viral load, slow disease progression was associated with over-representation of host immunogenetic protective HLA B* alleles (10 of 14 SPs compared to 0 of 7; p = 0.004, Fisher's Exact) especially B*57 and B*5801, multiclade Gag T-cell targeting (71%, 10 of 14 SPs compared to 14%, 1 of 7 RPs); p = 0.029, Fisher's Exact test and evident virological control (3.65 compared to 5.46 log10 copies/mL in SPs and RPs respectively); p<0.001, unpaired student's t-test CONCLUSIONS These data are consistent with others that associated protection from HIV disease with inherent host HLA B allele-mediated ability to induce broader Gag T-cell targeting coupled with apparent virological control. These immunogenetic features of Gag-specific immune response which could influence disease progression may provide useful insight in future HIV vaccine design.
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Affiliation(s)
- Jennifer Serwanga
- MRC/UVRI Uganda Research Unit on AIDS, c/o Uganda Virus Research Institute, Entebbe, Uganda.
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