- Takahashi, Naofumi;
- Ardeshir, Amir;
- Holder, Gerard E;
- Cai, Yanhui;
- Sugimoto, Chie;
- Mori, Kazuyasu;
- Araínga, Mariluz;
- He, Ziyuan;
- Fukuyo, Yayoi;
- Kim, Woong-Ki;
- Didier, Elizabeth S;
- Kuroda, Marcelo J
Objectives
CD4+ T-cell decline and increasing virus levels are considered hallmarks of HIV/AIDS pathogenesis but we previously demonstrated in rhesus macaques that tissue macrophage destruction by simian immunodeficiency virus (SIV) infection associated with increased monocyte turnover also appear to impact pathogenesis. It remains unclear, however, which factors best predict onset of terminal disease progression and survival time. The objective of this study, therefore, was to directly compare these co-variates of infection for predicting survival times in retrospective studies of SIV/simian-HIV (SHIV)-infected adult rhesus macaques.Methods
Rhesus macaques were infected with various strains of SIV/SHIV and evaluated longitudinally for monocyte turnover, CD4+ T-cell loss, plasma viral load, and SIV/SHIV strain. Correlation analyses and machine learning algorithm modeling were applied to compare relative contributions of each of the co-variates to survival time.Results
All animals with AIDS-related clinical signs requiring euthanasia exhibited increased monocyte turnover regardless of CD4+ T-cell level, viral strain, or plasma viral load. Regression analyses and machine learning algorithms indicated a stronger correlation and contribution between increased monocyte turnover and reduced survival time than between CD4+ T-cell decline, plasma viral load, or virus strain and reduced survival time. Decision tree modeling categorized monocyte turnover of 13.2% as the initial significant threshold that best predicted decreased survival time.Conclusion
These results demonstrate that monocytes/macrophages significantly affect HIV/SIV pathogenesis outcomes. Monocyte turnover analyses are not currently feasible in humans, so there is a need to identify surrogate biomarkers reflecting tissue macrophage damage that predict HIV infection disease progression.