Biology subjects

Gourves, M.

Publications and source records attributed to Gourves, M..

3 recordsLinked to original sources

bioRxiv2026

Modeling how memory CD8 T cells can elicit post-treatment control of HIV infection

While most people living with HIV suffer progressive disease following cessation of antiretroviral therapy, a small fraction elicits lasting post-treatment control. Understanding the mechanisms underlying this control is key to devising effective HIV remission strategies. Although recent studies implicate memory CD8 T cells, how these cells establish lasting viremic control remains unknown. Here, we combine mathematical modeling and analysis of data from SIV-infected non-human primates to elucidate the underlying mechanisms. We recognized that sustained antigenic stimulation leads to heritable epigenetic remodeling of the CD8 T cell pool, impairing memory cell survivability. Antiretroviral therapy rapidly suppresses viremia, thereby arresting antigenic stimulation and preserving memory potential. The greater this preservation is, the better would be the memory recall response following viral rebound post-treatment. Our mathematical model based on this hypothesis predicts that post-treatment control is an alternative steady state to progressive infection, realized by strong memory-driven recall responses. Our model fits longitudinal virological data spanning the pre-, during-, and post-antiretroviral treatment phases of infection, and recapitulates the outcomes of progressive disease and long-term remission realized, the latter predominantly with early treatment initiation. It shows, consistently with data, that memory CD8 T cells could drive post-treatment control independently of the size of the latent reservoir, explaining how such control may be realized more widely than estimated with prevalent hypotheses. Our model further explains the existence of a window of treatment initiation times that maximizes the chances of post-treatment control. Finally, model predictions inform interventions targeting memory CD8 T cells for HIV remission.

immunology↗

bioRxiv2026

Dual blockade of LILRB1 and LILRB2 enhances antiviral immune responses in SIV infection

Restoring effective antiviral immunity remains a major challenge in HIV infection. Among emerging immune checkpoint molecules, the inhibitory receptors LILRB1 and LILRB2 have been proposed as therapeutic targets, yet their in vivo function remains undefined due to the lack of cross-reactive blocking antibodies for relevant preclinical models. To address this, we developed a dual-specific blocking monoclonal antibody, mac20G10, targeting cynomolgus macaque LILRB1 and LILRB2 and assessed its immunomodulatory activity in an SIV model of infection. Pharmacodynamics analyses demonstrated that mac20G10 persisted in circulation and engaged target myeloid cells for up to 14 days without detectable adverse effects. A single administration prior to SIVmac251 infection enhanced early myeloid immune activation, characterized by increased frequencies of CD80+ pDC and CD80+ monocyte/macrophage subsets in blood and lymphoid tissues. These changes were accompanied by increased plasma levels of IFN-{lambda}, IL8, and IL-1RA during acute infection. Although viral replication remained unchanged, mac20G10 treatment promoted the development of SIV-specific memory CD8 T-cell responses. Together, these findings provide in vivo evidence that LILRB1 and LILRB2 function as myeloid immune checkpoints restraining antiviral priming, supporting this pathway as a rational target for combination immunotherapeutic strategies aimed at achieving durable HIV remission during analytic treatment interruption.

immunology↗

bioRxiv2026

IMAGENE: Single-cell association of live cell imaging and gene expression profiles of non-adherent cells through photoactivatable adhesives

Live cell imaging is uniquely placed to study cell behavior as it preserves spatial context and enables non-destructive observations over time. Integrating live cell imaging and molecular phenotypes with single-cell resolution is key to uncovering the relationship between the behavioral and morphological signatures of cells, and their molecular states. Non-adherent cells - as are most immune cells - however, present unique challenges in linking live cell imaging and fixed cell assays with single-cell resolution due to the difficulty of identifying individual cells across experimental modalities. To overcome this issue, we developed IMAGENE, an experimental and computational pipeline that leverages previously reported photoactivatable biocompatible adhesive material (PA-BAM) coatings for on-the-fly cell immobilization. We demonstrate the IMAGENE experimental and computational pipeline by generating a dataset of label-free time-lapse videos of primary human naive CD8+ T cells following 24 hours of polyclonal stimulation. Individual cells, including highly motile cells, can be matched to expression profiles of genes of interest obtained through KrakenFISH, a modified version of the previously reported autoFISH setup for automated, single-molecule fluorescence in situ hybridization (smFISH) experiments that supports sample parallelization. We use this data to train explainable machine learning models that predict expression levels of individual genes, with variable performance, from hand-crafted dynamic and spatial features obtained from live cell imaging.

bioengineering↗