Angela M. Jarrett

CCR7 Modulates the Generation of Thymic Regulatory T Cells by Altering the Composition of the Thymic Dendritic Cell Compartment

Upon recognition of auto-antigens, thymocytes are negatively selected or diverted to a regulatory Txa0cell (Treg) fate. CCR7 is required for negative selection of auto-reactive thymocytes in the thymic medulla. Here, we describe an unanticipated contribution of CCR7 to intrathymic Treg generation. Ccr7-/- mice have increased Treg cellularity because of a hematopoietic but non-T cell autonomous CCR7 function. CCR7 expression by thymic dendritic cells (DCs) promotes survival of mature Sirpα- DCs. Thus, CCR7 deficiency results in apoptosis of Sirpα- DCs, which is counterbalanced by expansion of immature Sirpα+ DCs that efficiently induce Treg generation. CCR7 deficiency results in enhanced intrathymic generation of Tregs at the neonatal stage and in lymphopenic adults, when Treg differentiation is critical for establishing self-tolerance. Together, these results reveal a complex function for CCR7 in thymic tolerance induction, where CCR7 not only promotes negative selection but also governs intrathymic Treg generation via non-thymocyte intrinsic mechanisms.

Sensitivity Analysis of a Pharmacokinetic Model of Vaginal Anti-HIV Microbicide Drug Delivery.

Uncertainties in parameter values in microbicide pharmacokinetics (PK) models confound the models use in understanding the determinants of drug delivery and in designing and interpreting dosing and sampling in PK studies. A global sensitivity analysis (Sobol indices) was performed for a compartmental model of the pharmacokinetics of gel delivery of tenofovir to the vaginal mucosa. The models parameter space was explored to quantify model output sensitivities to parameters characterizing properties for the gel-drug product (volume, drug transport, initial loading) and host environment (thicknesses of the mucosal epithelium and stroma and the role of ambient vaginal fluid in diluting gel). Greatest sensitivities overall were to the initial drug concentration in gel, gel-epithelium partition coefficient for drug, and rate constant for gel dilution by vaginal fluid. Sensitivities for 3 PK measures of drug concentration values were somewhat different than those for the kinetic PK measure. Sensitivities in the stromal compartment (where tenofovir acts against host cells) and a simulated biopsy also depended on thicknesses of epithelium and stroma. This methodology and results here contribute an approach to help interpret uncertainties in measures of vaginal microbicide gel properties and their host environment. In turn, this will inform rational gel design and optimization.

Modelling the interaction between the host immune response, bacterial dynamics and inflammatory damage in comparison with immunomodulation and vaccination experiments.

The immune system is a complex system of chemical and cellular interactions that responds quickly to queues that signal infection and then reverts to a basal level once the challenge is eliminated. Here, we present a general, four-component model of the immune systems response to a Staphylococcal aureus (S. aureus) infection, using ordinary differential equations. To incorporate both the infection and the immune system, we adopt the style of compartmenting the system to include bacterial dynamics, damage and inflammation to the host, and the host response. We incorporate interactions not previously represented including cross-talk between inflammation/damage and the infection and the suppression of the anti-inflammatory pathway in response to inflammation/damage. As a result, the most relevant equilibrium of the system, representing the health state, is an all-positive basal level. The model is able to capture eight different experimental outcomes for mice challenged with intratibial osteomyelitis due to S. aureus, primarily involving immunomodulation and vaccine therapies. For further validation and parameter exploration, we perform a parameter sensitivity analysis which suggests that the model is very stable with respect to variations in parameters, indicates potential immunomodulation strategies and provides a possible explanation for the difference in immune potential for different mouse strains.

Global sensitivity analysis used to interpret biological experimental results

Modeling host/pathogen interactions provides insight into immune defects that allow bacteria to overwhelm the host, mechanisms that allow vaccine strategies to be successful, and illusive interactions between immune components that govern the immune response to a challenge. However, even simplified models require a fairly high dimensional parameter space to be explored. Here we use global sensitivity analysis for parameters in a simple model for biofilm infections in mice. The results indicate which parameters are insignificant and are ‘frozen’ to yield a reduced model. The reduced model replicates the full model with high accuracy, using approximately half of the parameter space. We used the sensitivity to investigate the results of the combined biological and mathematical experiments for osteomyelitis. We are able to identify parts of the compartmentalized immune system that were responsible for each of the experimental outcomes. This model is one example for a technique that can be used generally.

Mathematical Model for MRSA Nasal Carriage

An interesting biological phenomenon that is a factor for the spread of antibiotic-resistant strains, such as MRSA, is human nasal carriage. Here, we evaluate several biological hypotheses for this problem in an effort to better understand and narrow the scope of the dominant factors that allow these bacteria to persist in otherwise healthy individuals. First, we set up and analyze a simple PDE model created to generally mimic the interactions of the microbes and nasal immune response. This includes looking at different types of diffusion and chemotaxis terms as well as different boundary conditions. Then, using sensitivity analysis, we walk through several biological hypotheses and compare to the model’s results looking for persistent infection scenarios indicated by the model’s bacteria component surviving over time.

Incorporating drug delivery into an imaging-driven, mechanics-coupled reaction diffusion model for predicting the response of breast cancer to neoadjuvant chemotherapy: Theory and preliminary clinical results

Clinical methods for assessing tumor response to therapy are largely rudimentary, monitoring only temporal changes in tumor size. Our goal is to predict the response of breast tumors to therapy using a mathematical model that utilizes magnetic resonance imaging (MRI) data obtained non-invasively from individual patients. We extended a previously established, mechanically coupled, reaction-diffusion model for predicting tumor response initialized with patient-specific diffusion weighted MRI (DW-MRI) data by including the effects of chemotherapy drug delivery, which is estimated using dynamic contrast-enhanced (DCE-) MRI data. The extended, drug incorporated, model is initialized using patient-specific DW-MRI and DCE-MRI data. Data sets from five breast cancer patients were used-obtained before, after one cycle, and at mid-point of neoadjuvant chemotherapy. The DCE-MRI data was used to estimate spatiotemporal variations in tumor perfusion with the extended Kety-Tofts model. The physiological parameters derived from DCE-MRI were used to model changes in delivery of therapy drugs within the tumor for incorporation in the extended model. We simulated the original model and the extended model in both 2D and 3D and compare the results for this five-patient cohort. Preliminary results show reductions in the error of model predicted tumor cellularity and size compared to the experimentally-measured results for the third MRI scan when therapy was incorporated. Comparing the two models for agreement between the predicted total cellularity and the calculated total cellularity (from the DW-MRI data) reveals an increased concordance correlation coefficient from 0.81 to 0.98 for the 2D analysis and 0.85 to 0.99 for the 3D analysis (pu2009u2009<u2009u20090.01 for each) when the extended model was used in place of the original model. This study demonstrates the plausibility of using DCE-MRI data as a means to estimate drug delivery on a patient-specific basis in predictive models and represents a step toward the goal of achieving individualized prediction of tumor response to therapy.

Repeatability, reproducibility, and accuracy of quantitative mri of the breast in the community radiology setting: Repeatability of Breast MRI in Community

Quantitative diffusion‐weighted MRI (DW‐MRI) and dynamic contrast‐enhanced MRI (DCE‐MRI) have the potential to impact patient care by providing noninvasive biological information in breast cancer.

Mathematical modelling of trastuzumab-induced immune response in an in vivo murine model of HER2+ breast cancer

The goal of this study is to develop an integrated, mathematical-experimental approach for understanding the interactions between the immune system and the effects of trastuzumab on breast cancer that overexpresses the human epidermal growth factor receptor 2 (HER2+). A system of coupled, ordinary differential equations was constructed to describe the temporal changes in tumour growth, along with intratumoural changes in the immune response, vascularity, necrosis and hypoxia. The mathematical model is calibrated with serially acquired experimental data of tumour volume, vascularity, necrosis and hypoxia obtained from either imaging or histology from a murine model of HER2+ breast cancer. Sensitivity analysis shows that model components are sensitive for 12 of 13 parameters, but accounting for uncertainty in the parameter values, model simulations still agree with the experimental data. Given theinitial conditions, the mathematical model predicts an increase in the immune infiltrates over time in the treated animals. Immunofluorescent staining results are presented that validate this prediction by showing an increased co-staining of CD11c and F4/80 (proteins expressed by dendritic cells and/or macrophages) in the total tissue for the treated tumours compared to the controls (

The ups and downs of S. aureus nasal carriage

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Mathematical models of tumor cell proliferation: A review of the literature

). We posit that the proposed mathematical-experimental approach can be used to elucidate driving interactions between the trastuzumab-induced responses in the tumour and the immune system that drive the stabilization of vasculature while simultaneously decreasing tumour growth-conclusions revealed by the mathematical model that were not deducible from the experimental data alone.