title: Modeling adaptive regulatory T-cell dynamics during early HIV infection.
abstract: Regulatory T-cells (Tregs) are a subset of CD4(+) T-cells that have been found to suppress the immune response. During HIV viral infection, Treg activity has been observed to have both beneficial and deleterious effects on patient recovery; however, the extent to which this is regulated is poorly understood. We hypothesize that this dichotomy in behavior is attributed to Treg dynamics changing over the course of infection through the proliferation of an 'adaptive' Treg population which targets HIV-specific immune responses. To investigate the role Tregs play in HIV infection, a delay differatial equation model was constructed to examine (1) the possible existence of two distinct Treg populations, normal (nTregs) and adaptive (aTregs), and (2) their respective effects in limiting viral load. Sensitivity analysis was performed to test parameter regimes that show the proportionality of viral load with adaptive regulatory populations and also gave insight into the importance of downregulation of CD4(+) cells by normal Tregs on viral loads. Through the inclusion of Treg populations in the model, a diverse array of viral dynamics was found. Specifically, oscillatory and steady state behaviors were both witnessed and it was seen that the model provided a more accurate depiction of the effector cell population as compared with previous models. Through further studies of adaptive and normal Tregs, improved treatments for HIV can be constructed for patients and the viral mechanisms of infection can be further elucidated.
authors.affiliation: Department of Mathematics, University of Michigan, Ann Arbor, Michigan, United States of America.
authors.forename: Michael
authors.initials: M
authors.lastname: Simonov
authors.forename: Renata A
authors.initials: RA
authors.lastname: Rawlings
authors.forename: Nick
authors.initials: N
authors.lastname: Comment
authors.forename: Scott E
authors.initials: SE
authors.lastname: Reed
authors.forename: Xiaoyu
authors.initials: X
authors.lastname: Shi
authors.forename: Patrick W
authors.initials: PW
authors.lastname: Nelson
mesh_terms: D000465:Algorithms; D015496:CD4-Positive T-Lymphocytes / Q000276:immunology / Q000502:physiology; D018414:CD8-Positive T-Lymphocytes / Q000276:immunology / Q000502:physiology; D016000:Cluster Analysis; D003198:Computer Simulation; D015658:HIV Infections / Q000276:immunology* / Q000473:pathology; D006801:Humans; D008213:Lymphocyte Activation; D018448:Models, Immunological; D012680:Sensitivity and Specificity; D050378:T-Lymphocytes, Regulatory / Q000276:immunology / Q000502:physiology*; D019562:Viral Load