Endocytosed HIV-1 Envelope Glycoprotein Traffics to Rab14+ Late Endosomes and Lysosomes to Regulate Surface Levels in T-Cell Lines.

Production of infectious HIV-1 particles requires incorporation of the viral envelope glycoprotein (Env) at the plasma membrane (PM) of infected CD4+ T cells. Env trafficking to the PM exposes viral epitopes that can be exploited by the host immune system; however, HIV-1 can evade this response by endocytosis of excess Env from the PM. The fate of Env after internalization remains unclear, with evidence suggesting several different vesicular trafficking steps may be involved, including recycling pathways. To date, there have been very few studies documenting the trafficking pathways of native Env in infected T cells. Furthermore, it remains unclear whether there are T-cell-specific endosomal pathways regulating the fate of endocytic Env. Here, we use a pulse-labeling approach with a monovalent anti-Env Fab probe to characterize the trafficking of internalized Env within infected CD4+ T-cell lines, together with CRISPR/Cas9-mediated endogenous protein tagging, to assess the role of host cell Rab GTPases in Env trafficking. We show that endocytosed Env traffics to Rab14+ compartments that possess hallmarks of late endosomes and lysosomes. We also demonstrate that Env can recycle back to the PM, although we find that recycling does not occur at high rates when compared to the model recycling protein transferrin. These results help to resolve open questions about the fate and relevance of endocytosed Env in HIV-infected cells and suggest a novel role for Rab14 in a cell-type-specific late-endosomal/lysosomal trafficking pathway in T cells. IMPORTANCE HIV-1 envelope glycoprotein (Env) evades immune neutralization through many mechanisms. One immune evasion strategy may result from the internalization of excess surface-exposed Env to prevent antibody-dependent cellular cytotoxicity or neutralization. Characterization of the fate of endocytosed Env is critical to understand which vesicular pathways could be targeted to promote display of Env epitopes to the immune system. In this study, we characterize the endocytic fate of native Env, expressed from infected human T-cell lines. We demonstrate that Env is rapidly trafficked to a late-endosome/lysosome-like compartment and can be recycled to the cell surface for incorporation into virus assembly sites. This study implicates a novel intracellular compartment, marked by host-cell Rab14 GTPases, for the sequestration of Env. Therapeutic approaches aimed at mobilizing this intracellular pool of Env could lead to stronger immune control of HIV-1 infection via antibody-dependent cell-mediated cytotoxicity.

Critical Comparison of MaxCal and Other Stochastic Modeling Approaches in Analysis of Gene Networks.

Learning the underlying details of a gene network with feedback is critical in designing new synthetic circuits. Yet, quantitative characterization of these circuits remains limited. This is due to the fact that experiments can only measure partial information from which the details of the circuit must be inferred. One potentially useful avenue is to harness hidden information from single-cell stochastic gene expression time trajectories measured for long periods of time-recorded at frequent intervals-over multiple cells. This raises the feasibility vs. accuracy dilemma while deciding between different models of mining these stochastic trajectories. We demonstrate that inference based on the Maximum Caliber (MaxCal) principle is the method of choice by critically evaluating its computational efficiency and accuracy against two other typical modeling approaches: (i) a detailed model (DM) with explicit consideration of multiple molecules including protein-promoter interaction, and (ii) a coarse-grain model (CGM) using Hill type functions to model feedback. MaxCal provides a reasonably accurate model while being significantly more computationally efficient than DM and CGM. Furthermore, MaxCal requires minimal assumptions since it is a top-down approach and allows systematic model improvement by including constraints of higher order, in contrast to traditional bottom-up approaches that require more parameters or ad hoc assumptions. Thus, based on efficiency, accuracy, and ability to build minimal models, we propose MaxCal as a superior alternative to traditional approaches (DM, CGM) when inferring underlying details of gene circuits with feedback from limited data.