A plasma biomarker signature of immune activation in HIV patients on antiretroviral therapy

Abstract

Background: Immune activation is a strong predictor of disease progression in HIV infection. Combinatorial plasma biomarker signatures that represent surrogate markers of immune activation in both viremic and aviremic HIV patients on combination antiretroviral therapy (cART) have not been defined. Here, we identify a plasma inflammatory biomarker signature that distinguishes between both viremic and aviremic HIV patients on cART and healthy controls and examine relationships of this signature to markers of disease progression.

Methods: Multiplex profiling and ELISA were used to detect 15 cytokines/chemokines, soluble IL-2R (sIL-2R), and soluble CD14 (sCD14) in plasma from 57 HIV patients with CD4 nadir <300 cells/µl and 29 healthy controls. Supervised and unsupervised analyses were used to identify biomarkers explaining variance between groups defined by HIV status or drug abuse. Relationships between biomarkers and disease markers were examined by Spearman correlation.

Results: The majority (91%) of HIV subjects were on cART, with 38% having undetectable viral loads (VL). Hierarchical clustering identified a biomarker cluster in plasma consisting of two interferon-stimulated gene products (CXCL9 and CXCL10), T cell activation marker (sIL-2R), and monocyte activation marker (sCD14) that distinguished both viremic and aviremic HIV patients on cART from controls (p<0.0001) and were top-ranked in variables important in projection plots. IL-12 and CCL4 were also elevated in viremic and aviremic patients compared to controls (p<0.05). IL-12 correlated with IFNα, IFNγ, CXCL9, and sIL-2R (p<0.05). CXCL10 correlated positively with plasma VL and percentage of CD16+ monocytes, and inversely with CD4 count (p = 0.001, <0.0001, and 0.04, respectively).

Conclusion: A plasma inflammatory biomarker signature consisting of CXCL9, CXCL10, sIL-2R, and sCD14 may be useful as a surrogate marker to monitor immune activation in both viremic and aviremic HIV patients on cART during disease progression and therapeutic responses.

Figure 1. Plasma CXCL9, CXCL10, sIL-2R, and sCD14 represent a biomarker cluster that distinguishes HIV subjects from healthy uninfected controls.

(A) Hierarchical clustering (supervised only by sample) by Euclidean distance and average linkage of 17 plasma biomarkers in 57 HIV patients and 29 healthy uninfected controls shows increased plasma levels and clustering of CXCL9, CXCL10, sIL-2R, and sCD14 (highlighted in box) in HIV subjects compared to healthy controls. In heat maps, red and blue represent increased and decreased levels relative to the mean level of a biomarker, respectively. Each column and row defines individual patient and biomarkers, respectively.

Figure 2. Increased plasma levels of CXCL9, CXCL10, sIL-2R, and sCD14 distinguish viremic and aviremic HIV subjects from controls.

Supervised hierarchical clustering of plasma CXCL9, CXCL10, sIL-2R, and sCD14 levels measured in viremic (panel A) or aviremic, (panel B) HIV subjects compared to healthy controls. In heat maps, red and blue represent increased and decreased levels relative to the mean level of a biomarker, respectively. Each column and row defines individual patient and biomarkers, respectively.

Figure 3. PLS-DA of 17 biomarkers shows separation of HIV subjects and aviremics from controls and identifies top-ranked biomarkers accounting for separation between groups.

(A) PLS-DA represented as a three dimensional scatter plot shows the top 3 components in the matrix of biomarker data (n = 17 biomarkers) measured in HIV (green dots, n = 57) and uninfected control subjects (red dots, n = 29). 46.5% of the variance observed in the matrix of biomarker data is explained by the first 3 components. Plot on the right shows variables important in projection (VIP) plot ranking sCD14, CXCL9, CXCL10, and sIL-2R as the top 4 biomarkers accounting for the variance between all HIV subjects and controls. (B) PLS-DA represented as a three dimensional scatter plot showing the top 3 components in the matrix of biomarker levels measured in aviremic HIV subjects (red dots, n = 22) and uninfected control samples (green dots, n = 29). 48.3% of the variance in the matrix of biomarkers is explained by the first 3 components. Plot on the right represents VIP plot ranking sCD14, CXCL9, CXCL10, and sIL-2R as the top 4 biomarkers explaining the variance between aviremic HIV subjects and uninfected controls.

Figure 4. Inter-relationships between plasma inflammatory biomarkers and interferons in HIV subjects on cART.

(A) Plasma sCD14 shows positive correlation with IL-6, IL-15, and trend towards significant correlation with CCL3. (B) CXCL10 correlated positively with CXCL9 andsIL-2R, while IL-12 was significantly associated with CXCL9. (C) IL-12 correlated positively with IFNα, IFNγ, and sIL-2R. Shown are log2 transformed values of measurements normalized to the mean of healthy controls. Data was analyzed by Spearman correlation, with p<0.05 considered significant.

Figure 5. Relationships between plasma CXCL10 levels and CD4 count, plasma viral load, and frequency of CD16+ monocytes.

Plasma CXCL10 levels show positive correlation with plasma viral load and frequency of CD16+ monocytes, and negative correlation with CD4 counts. Data was analyzed by Spearman correlation, with p<0.05 considered significant.