NK cell dysregulation may potentiate cardiovascular disease in adolescents with perinatally acquired HIV on antiretroviral therapy

Figures

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  Fig. 1. Cardiovascular- and plasma- biomarker profiling reveal increased cardiovascular disease risk in APHIV. Schematic diagrams of the vascular measurements and group comparisons between perinatally HIV aquired adolescents (APHIV) and HIV-unexposed adolescents (HIV − ) (a) Carotid intima-media thickness (IMT; maximum IMT, mm) and (b) pulse wave velocity (PWV, m/s) are shown with accompanying schematics. c Oxidized LDL (Ox-LDL, mIU/L) and (d) soluble CD163 (sCD163, ng/mL) are shown as violin plots. Each dot represents one participant. For IMT and PWV (a, b), HIV − , n = 19; APHIV, n = 18. For Ox-LDL and sCD163 (c, d), HIV − , n = 20; APHIV, n = 18. For box-and-whisker plots (a, b), the centre line indicates the median, the box bounds indicate the 25th and 75th percentiles, and the whiskers extend to the minimum and maximum values; individual participant values are overlaid as points. For violin plots (c, d), violin width represents the distribution of participant values, with individual participant values overlaid as points. P values shown above group comparisons were calculated using a two-tailed Mann–Whitney test. In the PWV schematic, d denotes the distance between femoral and…

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  Fig. 2. Differentially expressed genes (DEGs) and enriched transcriptional pathways of cellular biological processes of PHIV adolescents vs HIV- controls. a Volcano plot of DEGs between PHIVs and controls (screening thresholds: p value < 0.05). b Gene Ontology (GO) analysis was performed and revealed significantly enriched pathways in PBMC samples from PHIV adolescent vs HIV unexposed participants related to biological processes (screening threshold: adjusted p value < 0.05). c Protein-protein interaction network highlighting enriched pathways among APHIVs. The top 777 upregulated genes in APHIVs and the STRING interactome database were used as input. Upregulated genes were selected with a threshold of p value < 0.05 and putative potential protein-protein interactions were predicted using an interaction confidence threshold of 0.700.

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  Fig. 3. Enrichment of memory-like CD57⁺ NK cells and selective activation in adolescents with perinatally acquired HIV (APHIV). a A representative pseudocolor flow-cytometry plot illustrates the gating strategy used to define six NK cell subsets based on CD56 and CD16 expression.b Surface expression of CD69, HLA-DR, NKp44, CD57, and CXCR3 across the indicated NK subsets in HIV-negative controls (HIV − ) and APHIV adolescents, displayed as dot plots with individual participant values overlaid (one dot = one participant). Horizontal lines indicate the median for each group. c To further characterize these changes, t-SNE projections visualize the distribution of immune markers within the CD57⁺CD56dimCD16dim NK cell subset, highlighting differences between the two study populations. d A focused t-SNE analysis of CXCR3 expression within this memory-like (CD57⁺CD56dimCD16dim)NK subset demonstrates its marked upregulation in APHIV, suggesting enhanced tissue-homing potential. e Quantification of CXCR3 within the CD57⁺CD56dimCD16dim subset shown as box-and-whisker plots with individual participant values overlaid. The centre line indicates the median, the box bounds indicate the 25th and 75th…

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  Fig. 4. Machine learning-based statistical algorithms were used to explore relationships between markers of cardiovascular risk and innate immune signatures in APHIV. XGBoost algorithm model-based associations between immune signatures/cardiometabolic biomarkers and (a) carotid IMT and (b) PWV in the APHIV population. Glasso network plots visualizing the dependencies among the cardiometabolic and immune marker networks for (c) IMT max and (d) PWV. The size of each node in the network plot is proportional to the magnitude of feature importance for the dependent cardiovascular risk, as determined by XGBoost. The colors of the edges reflect the sign of dependencies: pink for positive and blue for negative. The black borders of the nodes indicate biomarkers that are important for distinguishing HIV status based on OPLS-DA analysis.

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  Fig. 5. Associations between activated, pro-inflammatory NK cell subsets and plasma Ox-LDL levels among PHIV adolescents. a Heatmap showing Pearson correlation coefficient (r) between NK cell subsets and plasma Ox-LDL in HIV- and APHIV. Colors ranging from blue to red depict the strength of the correlation, with asterisks indicating significant correlations (two-sided p < 0.05). b–e Scatter plots depicting Pearson correlation analysis between plasma Ox-LDL and CCR5+ NK cell subsets in APHIV (one dot per participant): (b)CD56dimCD16- NK cell subset, (c) CD56-CD16dim NK cell, (d) CD56dimCD16dim NK cell and (e) CD56briCD16lo NK cell subsets. f Scatter plot depicting Pearson correlation analysis between plasma Ox-LDL and CD57+CD56dimCD16dim NK cell subset in APHIV. Pearson r and exact two-sided P values are shown in each panel. g Schematic representation of an in-vitro experiment designed to investigate the role of activated NK cells in Ox-LDL uptake by monocyte-derived macrophages (MDMs). h Histogram depicting the impact of activated NK cells with and without NKG2D receptor blocking on Ox-LDL uptake by macrophages, assessed using flow cytometry. i Quantification of Dil-Ox-LDL uptake by MDMs…

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  Fig. 6. Conceptual model of lipid interactions driving atherogenesis in HIV-infected individuals. Schematic illustrating the proposed mechanisms linking altered lipid metabolism and innate immune activation to accelerated atherogenesis in individuals. In healthy conditions, low-density lipoprotein (LDL) undergoes limited oxidation, and resident macrophages (Mφ) and natural killer (NK) cells maintain vascular homeostasis. In PHIV, increased LDL oxidation leads to accumulation of oxidized LDL (OxLDL), promoting enhanced uptake by macrophages and formation of lipid-laden foam cells. Concurrently, NK cells exhibit an activated, pro-inflammatory phenotype (e.g., CD69⁺, CXCR3⁺), contributing to endothelial inflammation and amplifying vascular immune responses. With aging into adulthood, comorbid factors including diabetes, hypertension, smoking, sedentary lifestyle, and dyslipidemia further exacerbate lipid dysregulation, increasing OxLDL levels and altering the lipid landscape. These changes promote macrophage lipid uptake, impaired egress, and sustained inflammation, ultimately accelerating atherogenesis compared to healthy individuals. This panel was created in BioRender.com. I. De Silva,…