Dynamics and Correlates of CD8 T-Cell Counts in Africans with Primary Human Immunodeficiency Virus Type 1 Infection

Abstract

In individuals with HIV-1 infection, depletion of CD4؉ T cells is often accompanied by a malfunction of CD8 ؉ T cells that are persistently activated and/or exhausted. While the dynamics and correlates of CD4 counts have been well documented, the same does not apply to CD8 counts. Here, we examined the CD8 counts in a cohort of 497 Africans with primary HIV-1 infection evaluated in monthly to quarterly follow-up visits for up to 3 years in the absence of antiretroviral therapy. Statistical models revealed that (i) CD8 counts were relatively steady in the 3-to 36-month period of infection and similar between men and women; (ii) neither geography nor heterogeneity in the HIV-1 set-point viral load could account for the roughly 10-fold range of CD8 counts in the cohort (P > 0.25 in all tests); and (iii) factors independently associated with relatively high CD8 counts included demographics (age < 40 years, adjusted P ‫؍‬ 0.010) and several human leukocyte antigen class I (HLA-I) alleles, including HLA-A*03:01 (P ‫؍‬ 0.013), B*15:10 (P ‫؍‬ 0.007), and B*58:02 (P < 0.001). Multiple sensitivity analyses provided supporting evidence for these novel relationships. Overall, these findings suggest that factors associated with the CD8 count have little overlap with those previously reported for other HIV-1-related outcome measures, including viral load, CD4 count, and CD4/CD8 ratio. IMPORTANCELongitudinal data from 497 HIV-1 seroconverters allowed us to systematically evaluate the dynamics and correlates of CD8 ؉ T-cell counts during untreated primary HIV-1 infection in eastern and southern Africans. Our findings suggest that individuals with certain HLA-I alleles, including A*03 (exclusively A*03:01), persistently maintain relatively high CD8 counts following HIV-1 infection, a finding which may offer an intriguing explanation for the recently reported, negative association of A*03 with HIV-1-specific, broadly neutralizing antibody responses. In future studies, attention to HLA-I genotyping data may benefit indepth understanding of both cellular and humoral immunity, as well as the intrinsic balances of these types of immunity, especially in settings where there is emerging evidence of antagonism between the two arms of adaptive immunity.

Immunogenetic factors in early immune control of human immunodeficiency virus type 1 (HIV-1) infection: Evaluation of HLA class I amino acid variants in two African populations

“…As summarized earlier [9, 10, 17], Rwandan and Zambian SCs ( n = 76 and 196, respectively) were similar in terms of sex ratio, age, EDI, frequency of VL measures, and DOI (Table 1). The only notable distinction between the two SC groups was the HIV-1 subtype ― predominantly subtype A1 in Rwanda and subtype C in Zambia, which was also expected [9, 10, 17]. In all, Rwandan and Zambian SCs contributed 669 and 1,636 person-visits, respectively, for analyses of repeated VL measurements.…”

“…The research procedures were approved by institutional review boards at University of Alabama at Birmingham and Emory University, with further compliance to guidelines set forth by the United States Department of Health and Human Services. DNA samples derived from buffy coats or peripheral blood mononuclear cells were used for high-resolution HLA-I genotyping, as described elsewhere [9, 10, 14, 16, 17]. The individual amino acid residues (AARs) corresponding to each HLA allele were inferred from the IPD-IMGT/HLA Database [18] compiled by the International ImMunoGeneTics Project (http://www.ebi.ac.uk/ipd/imgt/hla/index.html, last accessed in January 2017), as described in the SNP2HLA program [19].…”

“…For SCs identified during frequent (monthly to quarterly) testing [20–22], the estimated dates of HIV-1 infection (EDI) were defined using four criteria that have been detailed before [9, 10, 17]. Viral subtype was defined by partial sequencing of the HIV-1 pol gene [9, 23].…”

“…Using software from SAS version 9.2 (SAS Institute, Cary, NC) [6, 28], we first tested existing hypotheses regarding four HLA-B AARs (residues 63, 97, 116 and 245). Mixed models evaluated these AARs individually and collectively for their relative impact on repeated measures of VL (after log 10 -transformation), as reflected by the effect size (mean beta estimate (β) and standard error (SE)) after statistical adjustments for duration of infection (DOI) and demographics (sex and age) [9, 10, 17]. Alternative models assessed the impact of AAR on set-point VL, with a focus on the proportion of variance attributable to each AAR variant.…”

“…The challenge to gathering robust and generalizable immunogenetic data is even greater in African populations because of the diverse circulating HIV-1 subtypes [8–10], as well as heterogeneities in local and regional genetic architectures [11–13]. On the other hand, these populations hold an important key to fine-mapping of confirmed genomic regions because variable structure of linkage disequilibrium (LD) can help resolve local haplotype blocks for in-depth interrogation of causal variants, including those that are not fully captured by routine genotyping [6].…”