Biology subjects

Petrucciani, A.

Publications and source records attributed to Petrucciani, A..

6 recordsLinked to original sources

bioRxiv2025

Partially-functional exhausted CD8+ T cells can contribute to short-term viral suppression: a computational prediction for children with perinatal HIV

We and others have reported evidence of T cell exhaustion in children with perinatal HIV with increased expression of inhibitory receptors PD-1, CD160, and TIM-3, but there is limited data on the virologic functional consequences of this immune exhaustion. We address this by using an immune database from Kenyan children with perinatal HIV and unexposed controls. We computationally integrate T cell profiles of differentiation, activation and exhaustion in an agent-based model (ABM) to predict how T cell exhaustion impacts viral control following HIV exposure in vitro. Our ABM includes macrophages, CD4 and CD8 T cells, cytokines, and HIV. Model mechanisms include viral dynamics, macrophage activation, T cell activation and proliferation, cytotoxic T cell killing, and cytokine/HIV diffusion and degradation. Participants are grouped by HIV plasma viremia and by age, less than 5 years or 5-18 years. Our findings indicate that cells from virally active participants, who have the highest levels of exhaustion, have lower predicted viral concentrations and infected cells compared to other participant groups during new infection. However, this coincides with higher cell death, suggesting that short-term viral control is associated with excessive inflammation, which could be detrimental long-term. Cells from virally suppressed participants older than 5 years can maintain lower viral concentrations while limiting cell death, reflecting a more sustainable short-term immune response. In virally suppressed children younger than 5 years, immune response patterns strongly resemble the age-matched healthy control group, suggesting early viral suppression may preserve antiviral immune responses. Our model predicts unique patterns of cell death for each participant group, with CD8 T cell death being dominant in virally active groups and CD4 T cell and macrophage death being dominant in healthy and virally suppressed groups. Finally, exhausted CD8 T cells are predicted to contribute significantly to CD8 T cell killing, proliferation, and activation in the virally active group, indicating partially functional CD8 T cells can still contribute to short-term viral control. Our analysis functionally integrates participant-specific immunophenotypic data to allow quantification of the extent, mechanisms, and impact of immune dysfunction in perinatal HIV and could inform pediatric HIV remission and cure strategies. Author SummaryCytotoxic CD8+ T cells are vital for our ability to kill infected cells. However, chronic stimulation of CD8+ T cells can result in T cell exhaustion, leading to cellular dysfunction and reduced ability to fight infections. Here we ask: What impact does CD8+ T cell exhaustion have on the ability to control HIV replication over the short term? We answer this using a computational model, anchored in experimental data from children with perinatal HIV. We use participant-specific data on dozens of immune markers that characterize the state of their immune system. We carefully map these markers to known immune cell functions and use our model to predict how immune cells with these participant-specific markers would respond to HIV infection. Our results suggest that earlier viral suppression can lead to better immune function in children with perinatal HIV. Our findings also suggest that exhausted CD8+ T cells could still contribute to fighting HIV infection, but only if there are enough of them to compensate for their reduced functionality. Our work uses computational models to put valuable participant-specific data into context, allowing us to predict short-term infection outcomes and better understand immune function in children with perinatal HIV.

systems biology↗

bioRxiv2024

Injury-Specific Muscle Regeneration: A Computational Blueprint for Cellular and Cytokine Drivers

Skeletal muscle regeneration is crucial for maintaining muscle health and mobility, making it a key research focus. Common experimental models of muscle injury used to study this process in vivo include cardiotoxin injury (CTX), freeze-induced (FI) injury, and eccentric contraction (EC) injury. While the response to injury varies across these models, these variations are often overlooked in experimental designs. The cellular dynamics throughout the time course of muscle regeneration differ significantly between these injury types, presenting challenges for both experimental investigation and literature analysis. To enhance understanding of how regeneration responses differ across injury types, we extend our previously validated computational model of skeletal muscle regeneration to simulate muscle fiber remodeling during regeneration following CTX, FI, and EC injuries. We further validate our model against multiple literature-derived metrics of regeneration for each injury type. Analysis of model outcomes reveals that recovery from each injury type is sensitive to unique combinations of cells and cytokines acting at different critical time points, exerting the greatest influence on regeneration at 28 days post-injury. Notably, the cytokines responsible for satellite cell (SSC) recruitment, proliferation, and differentiation required for regeneration vary across injury types, driven by biological redundancy and feedback mechanisms. Specifically, for EC injuries, cross-sectional area (CSA) recovery was predominantly associated with hepatocyte growth factor (HGF) and vascular endothelial growth factor A (VEGF-A) during the early stages of regeneration, with SSC dynamics playing a critical role throughout. FI injuries demonstrated a consistent reliance on HGF across all phases, with additional influences from transforming growth factor beta (TGF-{beta}), tumor necrosis factor-alpha (TNF-), monocyte chemoattractant protein-1 (MCP-1), and SSC dynamics in later stages. CTX injuries showed early dependence on TGF-{beta}, with significant contributions from SSC, TNF-, VEGF-A, and fibroblast dynamics over time. Our findings emphasize the importance of considering the molecular and cellular mechanisms relevant to each injury type used in pre-clinical in vivo studies, and motivates the development of therapeutic strategies that are designed for specific injury types to optimize recovery outcomes.

systems biology↗

bioRxiv2024

Timing matters in Macrophage / CD4+ T cell interactions: An agent-based model comparing Mycobacterium tuberculosis host-pathogen interactions between latently infected and naïve individuals

Tuberculosis (TB), caused by Mycobacterium tuberculosis (Mtb), remains a significant health challenge. Clinical manifestations of TB exist across a spectrum with a majority of infected individuals remaining asymptomatic, commonly referred to as latent TB infection (LTBI). In vitro models have demonstrated that cells from individuals with LTBI can better control Mtb growth, and form granuloma-like structures more quickly, compared to cells from uninfected (Mtb-naive) individuals. These in vitro results agree with animal and clinical evidence that LTBI protects, to some degree, against reinfection. However, the mechanisms by which LTBI might offer protection against reinfection remain unclear, and quantifying the relative contributions of multiple control mechanisms is challenging using experimental methods alone. To complement in vitro models, we have developed an in silico agent-based model to help elucidate host responses that might contribute to protection against reinfection. Our simulations indicate that earlier contact between macrophages and CD4+ T cells leads to LTBI simulations having more activated CD4+ T cells and, in turn, more activated infected macrophages which all contribute to decreased bacterial load early on. Our simulations also demonstrate that granuloma-like structures support this early macrophage activation in LTBI simulations. We find that differences between LTBI and Mtb-naive simulations are driven by TNF and IFN{gamma}-associated mechanisms as well as macrophage phagocytosis and killing mechanisms. Together, our simulations show how important the timing of the first interactions between innate and adaptive immune cells is, how this impacts infection progression, and why cells from LTBI individuals might be faster to respond to reinfection. ImportanceTuberculosis (TB) remains a significant global health challenge, with millions of new infections and deaths annually. Despite extensive research, the mechanisms by which latent TB infection (LTBI) confers protection against reinfection remain unclear. In this study, we developed an in silico agent-based model to simulate early immune responses to Mycobacterium tuberculosis (Mtb) infection based on experimental in vitro infection of human donor cells. Our simulations reveal that early interactions between macrophages and CD4+ T cells, driven by TNF and IFN{gamma}, are critical for bacterial control and granuloma formation in LTBI. These findings offer new insights into the immune processes involved in TB, which could inform the development of targeted vaccines and host-directed therapies. By integrating experimental data with computational predictions, our research provides a robust framework for understanding TB immunity and guiding future interventions to mitigate the global TB burden.

immunology↗

bioRxiv2023

Agent-based model predicts that layered structure and 3D movement work synergistically to reduce bacterial load in 3D in vitro models of tuberculosis granuloma

Tuberculosis (TB) remains a global public health threat with increasing prevalence. Understanding the dynamics of host-pathogen interactions within TB granulomas will assist in identifying what leads to successful elimination of infection. In vitro TB models provide a controllable environment to study these granuloma dynamics. Previously we developed a biomimetic 3D spheroid granuloma model that controls bacteria better than a traditional monolayer culture counterpart. We used agent-based simulations to predict the mechanistic reason for this difference. Our calibrated simulations were able to predict heterogeneous bacterial dynamics that are consistent with experimental data. In one group of simulations, spheroids are found to have a higher macrophage activation than their traditional counterparts, leading to better bacterial control. This higher macrophage activation in the spheroids was not due to higher T cell activation, rather fewer activated T cells were able to activate more macrophages due to the proximity of these cells within the spheroid. In a second group of simulations, spheroids again have more macrophage activation but also more T cell activation, specifically CD8+ T cells. This higher level of CD8+ T cell activation is predicted to be due to the proximity of these cells to the cells that activate them. Multiple mechanisms of control were predicted. Virtual knockouts show one group has a CD4+ T cell dominant response, while the other has a mixed/CD8+ T cell dominant response. Lastly, we demonstrated that the initial structure and movement rules work synergistically to reduce bacterial load. These findings provide valuable insights into how the structural complexity of in vitro models impacts immune responses. Moreover, our study has implications for engineering more physiologically relevant in vitro models and advancing our understanding of TB pathogenesis and potential therapeutic interventions.

systems biology↗

bioRxiv2023

Agent-based model demonstrates the impact of nonlinear, complex interactions between cytokines on muscle regeneration

Muscle regeneration is a complex process due to dynamic and multiscale biochemical and cellular interactions, making it difficult to identify microenvironmental conditions that are beneficial to muscle recovery from injury using experimental approaches alone. To understand the degree to which individual cellular behaviors impact endogenous mechanisms of muscle recovery, we developed an agent-based model (ABM) using the Cellular Potts framework to simulate the dynamic microenvironment of a cross-section of murine skeletal muscle tissue. We referenced more than 100 published studies to define over 100 parameters and rules that dictate the behavior of muscle fibers, satellite stem cells (SSC), fibroblasts, neutrophils, macrophages, microvessels, and lymphatic vessels, as well as their interactions with each other and the microenvironment. We utilized parameter density estimation to calibrate the model to temporal biological datasets describing cross-sectional area (CSA) recovery, SSC, and fibroblast cell counts at multiple time points following injury. The calibrated model was validated by comparison of other model outputs (macrophage, neutrophil, and capillaries counts) to experimental observations. Predictions for eight model perturbations that varied cell or cytokine input conditions were compared to published experimental studies to validate model predictive capabilities. We used Latin hypercube sampling and partial rank correlation coefficient to identify in silico perturbations of cytokine diffusion coefficients and decay rates to enhance CSA recovery. This analysis suggests that combined alterations of specific cytokine decay and diffusion parameters result in greater fibroblast and SSC proliferation compared to individual perturbations with a 13% increase in CSA recovery compared to unaltered regeneration at 28 days. These results enable guided development of therapeutic strategies that similarly alter muscle physiology (i.e. converting ECM-bound cytokines into freely diffusible forms as studied in cancer therapeutics or delivery of exogenous cytokines) during regeneration to enhance muscle recovery after injury.

systems biology↗

bioRxiv2023

In silico agent-based modeling approach to characterize multiple in vitro tuberculosis infection models

In vitro models of Mycobacterium tuberculosis (Mtb) infection are a valuable tool to examine host-pathogen interactions and screen drugs. With the development of more complex in vitro models, there is a need for tools to help analyze and integrate data from these models. We introduce an agent-based model (ABM) representation of the interactions between immune cells and bacteria in an in vitro setting. This in silico model was used to independently simulate both traditional and spheroid cell culture models by changing the movement rules and initial spatial layout of the cells. These two setups were calibrated to published experimental data in a paired manner, by using the same parameters in both simulations. Within the calibrated set, heterogeneous outputs are seen for outputs of interest including bacterial count and T cell infiltration into the macrophage core of the spheroid. The simulations are also able to predict many outputs with high time resolution, including spatial structure. The structure of a single spheroid can be followed across the time course of the simulation, allowing the relationship between cell localization and immune activation to be explored. Uncertainty analyses are performed for both model setups using latin hypercube sampling and partial rank correlation coefficients to allow for easier comparison, which can provide insight into ideal use cases for the independent setups. Future model iterations can be guided by the limitations of the current model, specifically which parts of the output space were harder to reach. This ABM can be used to represent more in vitro Mtb infection models due to its flexible structure, providing a powerful analysis tool that can be used in tandem with experiments. Author SummaryTuberculosis is an infectious disease that causes over 1.4 million deaths every year. During infection, immune cells surround the bacteria forming structures called granulomas in the lungs. New laboratory models generate spheroids that aim to recreate these structures to help understand infection and find new ways to treat tuberculosis. Computational modeling is used to compare these newer spheroid models to traditional models, which dont recreate the structure of the cell clusters. After calibration to data from laboratory experiments to ensure that the computational model can represent both systems, the structures were characterized over time. The traditional and spheroid model were also compared by looking at how model inputs impact outputs, allowing users to figure out when one model should be used over the other. This computational tool can be used to help integrate data from different laboratory models, generate hypothesis to be tested in laboratory models, and predict pathways to be targeted by drugs.

systems biology↗