High frequency of CD8 escape mutations in elite controllers as new obstacle for HIV cure

Accumulation of mutations in epitopes of cytolytic-T-lymphocytes immune response (CTL) in HIV-reservoir seems to be one of the reasons for shock-and-kill strategy failure. Ten non-controller patients on successful cART (TX) and seven elite controllers (EC) were included. HIV-Gag gene from purified resting memory CD4+ T-cells was sequenced by Next-Generation-Sequencing. HLA class-I alleles were typed to predict optimal HIV-Gag CTL epitopes. For each subject, the frequency of mutated epitopes in the HIV-Gag gene, the proportion of them considered as CTL-escape variants as well as their effect on antigen recognition by HLA were assessed. The proportion (%) of mutated HIV-Gag CTL epitopes in the reservoir was high and similar in EC and TX (86%[50–100] and 57%[48–82] respectively, p=0.315). Many of them were predicted to negatively impact antigen recognition. Moreover, the proportion of mutated epitopes considered to be CTL-escape variants was also similar in TX and EC (77%[49–92] vs. 50%[33–75] respectively, p=0.117). Thus, the most relevant finding of our study was the high and similar proportions of HIV-Gag CTL-escape mutations in the reservoir of both HIV-noncontroller patients with cART (TX) and patients with spontaneous HIV-control (EC). Our findings suggest that escape mutations of CTL-response may be another obstacle to eliminate the HIV reservoir and constitute a proof of concept that challenges HIV cure strategies focused on the reactivation of reservoirs. Due to the small sample size that could impact the robustness of the study, further studies with larger cohorts of elite controller patients are needed to confirm these results.</p

Delta values of different subsets of CD8 T-cells after long-term follow up, in cART naïve (boxes in light grey) and in cART (boxes in dark grey) groups of patients.

<p>Delta values of subsets defined by HLADR and CD38 markers are shown in the upper row graph. Delta values of PD1 and Tim3 expression in subsets defined according to HLADR and CD38 markers are shown in the lower row graph. Delta values significantly different from zero are marked with an asterisk.</p

Delta values of different subsets of CD4 T-cells after long-term follow up, in cART naïve (boxes in light grey) and in cART (boxes in dark grey) groups of patients.

<p>Delta values of subsets defined by CD45RA, KI67 and CD31 markers are shown in the upper row graph. Delta values of PD1 and Tim3 expression in subsets defined according to CD45RA and CD31 markers are shown in the middle row graph. Delta values of CD95 and CD57 expression on subsets defined according to CD31 and Ki67 markers are shown in lower row graph. Delta values significantly different from zero are marked with an asterisk.</p

Scatter-plot graphs showing the association between variation of CD4 counts (Δ CD4 counts) and variation of immune parameters after long-term follow up (ΔLT) in patients on cART.

<p>Spearman Rho (σ) coefficient and p-value are shown in each scatter plot.</p

Box-plot graphs showing the delta values of different CD4 and CD8 T-cell subsets after long-term follow up in the cART naïve group of HIV patients.

<p>Patients were stratified according to the significance of their individual CD4 slope during follow up into two groups: those without (light grey boxes) and with (dark grey boxes) a significant CD4 slope. p-values for the comparison between groups (Mann-Whitney test) are given.</p

Characteristics of patients included in the study.

<p>Characteristics of patients included in the study.</p

HCV coinfection contributes to HIV pathogenesis by increasing immune exhaustion in CD8 T-cells

<div><p>Background</p><p>There are several contributors to HIV-pathogenesis or insufficient control of the infection. However, whether HIV/HCV-coinfected population exhibits worst evolution of HIV-pathogenesis remains unclear. Recently, some markers of immune exhaustion have been proposed as preferentially upregulated on T-cells during HIV-infection. Herein, we have analyzed T-cell exhaustion together with several other contributors to HIV-pathogenesis that could be affected by HCV-coinfection.</p><p>Patients and methods</p><p>Ninety-six patients with chronic HIV-infection (60 HIV-monoinfected and 36 HIV/HCV-coinfected), and 20 healthy controls were included in the study. All patients were untreated for both infections. Several CD4 and CD8 T-cell subsets involved in HIV-pathogenesis were investigated. Non-parametric tests were used to establish differences between groups and associations between variables. Multivariate linear regression was used to ascertain the variables independently associated with CD4 counts.</p><p>Results</p><p>HIV-patients presented significant differences compared to healthy controls in most of the parameters analyzed. Both HIV and HIV/HCV groups were comparable in terms of age, CD4 counts and HIV-viremia. Compared to HIV group, HIV/HCV group presented significantly higher levels of exhaustion (Tim3⁺PD1^- subset) in total CD8⁺ T-cells (p = 0.003), and higher levels of exhaustion in CD8⁺HLADR⁺CD38⁺ (p = 0.04), CD8⁺HLADR^-CD38⁺ (p = 0.009) and CD8⁺HLADR^-CD38^- (p = 0.006) subsets of CD8⁺ T-cells. Interestingly these differences were maintained after adjusting by CD4 counts and HIV-viremia.</p><p>Conclusions</p><p>We show a significant impact of HCV-coinfection on CD8 T-cells exhaustion, an important parameter associated with CD8 T-cell dysfunction in the setting of chronic HIV-infection. The relevance of this phenomenon on immunological and/or clinical HIV progression prompts HCV treatment to improve management of coinfected patients.</p></div

Levels of CD4 T-cell subsets.

<p>Box-plot graphs showing the levels of CD4 T-cell subsets in healthy controls (clear boxes), HIV (light grey boxes) and HIV/HCV (dark grey boxes) patients. Upper graph shows levels of different CD4 subsets on the basis of CD45RA, CD31 and Ki67 expression. Right vertical axis applies only for subsets defined by Ki67 and CD31. Middle graph shows levels of apoptosis (CD95) and senescence (CD57) on different subsets of CD4 cells; and lower graph the levels of exhaustion (PD1 and Tim3 markers) on different subsets of CD4 cells. Statistically significant differences between healthy controls and all patients are marked by an asterisk; and differences between the two groups of patients by ¶ symbol.</p

Characteristics of patients included in the study.

<p>Characteristics of patients included in the study.</p

Linear regression model showing the parameters significantly and independently associated with CD4 counts in the whole population of patients.

<p>Linear regression model showing the parameters significantly and independently associated with CD4 counts in the whole population of patients.</p