Biology subjects

Stewart, W.

Publications and source records attributed to Stewart, W..

8 recordsLinked to original sources

bioRxiv2026

Increased CD3 Immunoreactive Cells Persist Chronically in the Brain Parenchyma in Association with Focal Cortical Contusion following Experimental TBI

While a history of TBI is associated with an increased risk of neurodegenerative disease, associated mechanisms remain largely unknown. Neuroinflammation is commonly implicated as playing a role in progressive neurodegeneration in general, yet little is known about the adaptive response of neuroinflammation in TBI or how it may contribute to progressive pathologies. To parse out components of the adaptive response, we assessed for intraparenchymal T-cell infiltration in two different translational large animal (swine) models of TBI, inertial injury and focal contusion. We characterized the extent and distribution of T cells post-injury and their association with blood-brain barrier disruption and axonal pathology. T-cell infiltration following focal TBI followed a spatiotemporal progression from gray matter at 72 hours to both gray and white matter at 6 months post-injury, consistent with recruitment into the parenchyma and then white matter. Inertial injury did not lead to substantial T-cell infiltration despite BBB breakdown and axonal pathology. We did not find a spatial correlation between blood-brain barrier breakdown or axonal pathology and T-cell infiltration in focal TBI. These data suggest that there is an active adaptive response to TBI, particularly in tissue proximal to contusions. A large animal model that reproducibly demonstrates chronic T-cell infiltration may allow for examination of the downstream effects of the adaptive response to TBI, and whether targeting this adaptive response may reduce chronic inflammation and improve recovery.

neuroscience↗

bioRxiv2023

Spatial organization and stochastic fluctuations of immune cells impact clinical responsiveness to immune checkpoint inhibitors in patients with melanoma

High-dimensional, spatial single-cell technologies such as CyTOF imaging mass cytometry (IMC) provide detailed information regarding locations of a large variety of cancer and immune cells in microscopic scales in tumor microarray (TMA) slides obtained from patients prior to immune checkpoint inhibitor (ICI) therapy. An important question is how the initial spatial organization of these cells in the tumor microenvironment (TME) change with time, regulate tumor growth and eventually outcomes as patients undergo ICI therapy. Utilizing IMC data of melanomas of patients who later underwent ICI therapy, we develop a spatially resolved interacting cell systems model that is calibrated against patient response data to address the above question. We find that the tumor fate in these patients is determined by the spatial organization of activated CD8+ T cells, macrophages, and melanoma cells and the interplay between these cells that regulate exhaustion of CD8+ T cells. We find that fencing of tumor cell boundaries by exhausted CD8+T cells is dynamically generated from the initial conditions that can play a pro-tumor role. Furthermore, we find that specific spatial features such as co-clustering of activated CD8+ T cells and macrophages in the pre-treatment samples determine the fate of the tumor progression, despite stochastic fluctuations and changes over the treatment course. Our framework enables determination of mechanisms of interplay between a key subset of tumor and immune cells in the TME that regulate clinical response to ICIs. SignificanceRecent advances in single cell technologies allows for spatial imaging a wide variety of cancer and immune cells in tissue samples obtained from solid tumors. This detailed snapshot data of microscale organization of tumor and immune cells could provide valuable insights into underlying biology and clinical responsiveness to cancer immunotherapy. By combining published data from imaging mass-cytometry and patient response against ICI drugs with data analysis rooted in statistical physics and statistical inference theory, we developed and studied the dynamics of mechanistic spatially resolved models: we show that tumor growth during ICI treatment is regulated by non-intuitive interplay between CD8+ T cells and tumor associated macrophages, formation of a pro-tumor fencing of exhausted CD8+ T cells around melanoma cells, specific features of spatial organization of these cells prior to treatment, and stochastic fluctuations in the dynamics. The mechanisms unveiled in our studies are general and can pertain to the response of other solid tumors to ICI therapy.

immunology↗

bioRxiv2023

Clonal stochasticity in early NK cell response to mouse cytomegalovirus is generated by mature subsets of varying proliferative ability

Natural killer (NK) cells are classically defined as innate immune cells, but experiments show that mouse cytomegalovirus (MCMV) infection in C57BL/6 mice can cause NK cells to undergo antigen-specific proliferation and memory formation, similar to adaptive CD8+ T cells. One shared behavior between CD8+ T cells and NK cells is clonal expansion, where a single stimulated cell proliferates rapidly to form a diverse population of cells. For example, clones derived from single cells are most abundant during expansion when they are primarily CD27- for NK cells and CD62L- for T cells, phenotypes derived from precursor CD27+ and CD62L+ cells, respectively. Here we determined the mechanistic rules involving proliferation, cell death, and differentiation of endogenous and adoptively transferred NK cells in the expansion phase of the response to MCMV infection. We found that the interplay between cell proliferation and cell death of mature CD27- NK cells and a highly proliferative CD27-Ly6C- mature subtype and intrinsic stochastic fluctuations in these processes play key roles in regulating the heterogeneity and population of the NK cell subtypes. Furthermore, we estimate rates for maturation of endogenous NK cells in homeostasis and in MCMV infection and found that only NK cell growth rates, and not differentiation rates, are appreciably increased by MCMV. Taken together, these results quantify the differences between the kinetics of NK cell antigen-specific expansion from that of CD8+ T cells and unique mechanisms that give rise to the observed heterogeneity in NK cell clones generated from single NK cells in the expansion phase.

immunology↗

bioRxiv2022

Generalized Method of Moments improves parameter estimation in biochemical signaling models of time-stamped single-cell snapshot data

MotivationOrdinary differential equations are commonly used to model the sub-cellular dynamics of average values of proteins and mRNAs. New single-cell technologies provide cell-to-cell differences in protein/mRNA abundances that allow for the evaluation of higher order moments. However, using this additional information to improve parameter estimation can be challenging since the magnitudes of single-cell abundances can vary widely between proteins/mRNA. ResultsWe employ Generalized Method of Moments (GMM) and Particle Swarm Optimization to address the above challenges in mechanistic modeling of signaling kinetics data. Using synthetic data from linear and non-linear models, we show that the proposed method improves parameter estimation and enables construction of approximate confidence intervals. Furthermore, our approach exploits parallel computation to scale with increasing data size and dimensions. We apply our software CyGMM to estimate parameters in a linear ODE model for publicly available longitudinal single-cell cytometry data for CD8+ T cells. Our results demonstrate substantial improvements for modeling data from single-cell cytometry and RNA-seq experiments. AvailabilityWe also make freely available our estimation software CyGMM written in C++ on github (https://github.com/jhnwu3/CyGMM). Contactjayajit@gmail.com Supplementary informationSupplementary data are available at Bioinformatics online.

systems biology↗

bioRxiv2021

Increased apoptotic priming of glioblastoma enables therapeutic targeting by BH3-mimetics

IDH wild-type glioblastoma (GBM) is the most prevalent malignant primary brain tumour in adults. GBM typically has a poor prognosis, mainly due to a lack of effective treatment options leading to tumour persistence or recurrence. Tackling this, we investigated the therapeutic potential of targeting anti-apoptotic BCL-2 proteins in GBM. Levels of anti- apoptotic BCL-xL and MCL-1 were consistently increased in GBM compared with non- malignant cells and tissue. Moreover, we found that relative to their differentiated counterparts, patient-derived GBM stem-like cells also displayed higher expression of anti- apoptotic BCL-2 family members. Surprisingly, high anti-apoptotic BCL-xL and MCL-1 expression correlated with heightened susceptibility of GBM to BCL-2 family protein- targeting BH3-mimetics. This is indicative of increased apoptotic priming. Indeed, GBM displayed an obligate requirement for MCL-1 expression in both tumour development and maintenance. Investigating this apoptotic sensitivity, we found that sequential inhibition of BCL-xL and MCL-1 led to robust anti-tumour responses in vivo, in the absence of overt toxicity. These data demonstrate that BCL-xL and MCL-1 pro-survival function is a fundamental prerequisite for GBM survival that can be therapeutically exploited by BH3- mimetics.

cancer biology↗

bioRxiv2018

Mutations in bacterial genes induce unanticipated changes in the relationship between bacterial pathogens in experimental otitis media

Otitis media (OM) is a common polymicrobial infection of the middle ear in children under the age of fifteen years. A widely used experimental strategy to analyze roles of specific phenotypes of bacterial pathogens of OM is to study changes in co-infection kinetics of bacterial populations in animal models when a wild type bacterial strain is replaced by a specific isogenic mutant strain in the co-inoculating mixtures. Since relationships between the OM bacterial pathogens within the host are regulated by many interlinked processes, connecting the changes in the co-infection kinetics to a bacterial phenotype can be challenging. We investigated middle ear co-infections in adult chinchillas (Chinchilla lanigera) by two major OM pathogens: nontypeable Haemophilus influenzae (NTHi) and Moraxella catarrhalis (Mcat), as well as isogenic mutant strains in each bacterial species. We analyzed the infection kinetic data using Lotka-Volterra population dynamics, Maximum Entropy inference, and Akaike Information Criteria (AIC) based model selection. We found that changes in relationships between the bacterial pathogens that were not anticipated in the design of the co-infection experiments involving mutant strains are common and were strong regulators of the co-infecting bacterial populations. The framework developed here allows for a systematic analysis of host-host variations of bacterial populations and small sizes of animal cohorts in co-infection experiments to quantify the role of specific mutant strains in changing the infection kinetics. Our combined approach can be used to analyze the functional footprint of mutant strains in regulating co-infection kinetics in models of experimental OM and other polymicrobial diseases.

microbiology↗

bioRxiv2018

Mathematical Modeling Identifies the Role of Adaptive Immunity as a Key Controller of Respiratory Syncytial Virus (RSV) Titer in Cotton Rats

Respiratory syncytial virus (RSV) is a common virus that can have varying effects ranging from mild cold-like symptoms to mortality depending on the age and immune status of the individual. We combined mathematical modeling using ordinary differential equations (ODEs) with measurement of RSV infection kinetics in primary well differentiated human airway epithelial (HAE) cultures in vitro and in immunocompetent and immunosuppressed cotton rats to glean mechanistic details that underlie RSV infection kinetics in the lung. Quantitative analysis of viral titer kinetics in our mathematical model showed that the elimination of infected cells by the adaptive immune response generates unique RSV titer kinetic features including a faster time scale of viral titer clearance than viral production, and a monotonic decrease in the peak RSV titer with decreasing inoculum dose. Parameter estimation in the ODE model using a non-linear mixed effects approach revealed a very low rate (average single cell lifetime > 10 days) of cell lysis by RSV before the adaptive immune response is initiated. Our model predicted negligible changes in the RSV titer kinetics on earlier days (< 5 d.p.i) but a slower decay in RSV titer at later days (>5 d.p.i) in immunosuppressed cotton rats compared to that in non-suppressed cotton rats in silico. These predictions were in excellent agreement with the experimental results. Our combined approach quantified the importance of the adaptive immune response in suppressing RSV infection in cotton rats, which could be useful in testing RSV vaccine candidates.\n\nImportanceA major difficulty in developing vaccines against RSV infection is our rudimentary understanding of the mechanisms that underlie RSV infection. We addressed this challenge by developing a mechanistic computational model with predictive powers for describing RSV infection kinetics in cotton rats. The model was constructed synergistically with in vitro and in vivo measurements. The combined framework determined an important role for CD8+ T cells responses in reducing RSV titers in cotton rats. The framework can be used to design future experiments to elucidate mechanisms underlying RSV infection and test outcomes for potential vaccine candidates. In addition, estimation of the model parameters provides quantitative values for parameters of biological and clinical interest such as the replication rate of RSV, the death rate of infected cells, and the average number of new infections initiated by a single infected cell.

microbiology↗

bioRxiv2018

Mathematical Modeling Identifies the Role of Adaptive Immunity as a Key Controller of Respiratory Syncytial Virus (RSV) Titer in Cotton Rats

Respiratory syncytial virus (RSV) is a common virus that can have varying effects ranging from mild cold-like symptoms to mortality depending on the age and immune status of the individual. We combined mathematical modeling using ordinary differential equations (ODEs) with measurement of RSV infection kinetics in primary well differentiated human airway epithelial (HAE) cultures in vitro and in immunocompetent and immunosuppressed cotton rats to glean mechanistic details that underlie RSV infection kinetics in the lung. Quantitative analysis of viral titer kinetics in our mathematical model showed that the elimination of infected cells by the adaptive immune response generates unique RSV titer kinetic features including a faster time scale of viral titer clearance than viral production, and a monotonic decrease in the peak RSV titer with decreasing inoculum dose. Parameter estimation in the ODE model using a non-linear mixed effects approach revealed a very low rate (average single cell lifetime > 10 days) of cell lysis by RSV before the adaptive immune response is initiated. Our model predicted negligible changes in the RSV titer kinetics on earlier days (< 5 d.p.i) but a slower decay in RSV titer in immunosuppressed cotton rats compared to that in non-suppressed cotton rats at later days (>5 d.p.i) in silico. These predictions were in excellent agreement with the experimental results. Our combined approach quantified the importance of the adaptive immune response in suppressing RSV infection in cotton rats, which could be useful in testing RSV vaccine candidates.\n\nImportanceA major difficulty in developing vaccines against RSV infection is our rudimentary understanding of the mechanisms that underlie RSV infection. We addressed this challenge by developing a mechanistic computational model with predictive powers for describing RSV infection kinetics in cotton rats. The model was constructed synergistically with in vitro and in vivo measurements. The combined framework determined an important role for CD8+ T cells responses in reducing RSV titers in cotton rats. The framework can be used to design future experiments to elucidate mechanisms underlying RSV infection and test outcomes for potential vaccine candidates. In addition, estimation of the model parameters provides quantitative values for parameters of biological and clinical interest such as the replication rate of RSV, the death rate of infected cells, and the average number of new infections initiated by a single infected cell.

microbiology↗