Jennifer J. Linderman - US grants

This research centers on the application of engineering principles to a set of fundamental problems in cell biology: the biochemical and biophysical mechanisms used by mammalian cells to sense and respond to their environment. Coordinated mathematical modeling and experimental approaches will be used to address two general types of problems in the category of receptor-mediated cell phenomena. First, a quantitative understanding of the mechanisms and kinetics of the processing (transport/reaction/separation) of receptors and their ligands will be developed. Efforts in this area will focus on the intracellular processing of these molecules in endocytosis and the trafficking of these molecules during antigen presentation. Second, the mechanisms and kinetics of biological signal transduction, particularly the involvement of receptors in the inositol trisphosphate/calcium ion signal transduction pathway, will be investigated. An important aspect of cell function is the ability of cells to sense and respond to their environment. This communication between cells and their surroundings is critical to normal cell functioning, to the immune response, and to the action of pharmacologic agents, and is impaired in many disease states. An ability to quantitatively understand and manipulate these communication mechanisms is thus crucial to many areas of modern biotechnology and has application to the maintenance of cell cultures, the development of new pharmaceuticals, and the treatment of disease. In this research, engineering principles will be applied to these fundamental problems in cell biology, with the goal of discovering the relevant mechanisms and kinetics involved in these communication processes.

9420567 Montgomery ABSTRACT The goals are: The developed materials to expose all undergraduate chemical engineering students to current industrial applications of biotechnology within the context of the core curriculum; and we will train interested undergraduate and graduate chemical engineering students to meet the new challenges in biotechnology by integrating current research into the curriculum. The accomplished of these goals are by fulfilling the following specific aims: 1. Develop a new biotechnology curriculum - An upper level undergraduate/lower level graduate course, entitled "Biomolecular Engineering", will focus on engineering analyses and applications at the molecular, cellular, and tissue level. It will be based largely on on-going research efforts in laboratories of the University of Michigan and some of its industrial research partners. The existing upper level biochemical engineering courses will be modified to include more advanced concepts. The resulting bioengineering sequence will provide students with breadth as well as depth of knowledge in biotechnology. 2. Develop interactive multimedia-based software for the new curriculum and for national integration into the core undergraduate chemical engineering curriculum - This software will be used in the new course (Aim 1) as well as in an existing biotechnology course which covers the more traditional aspects of biotechnology, e.g. fermentation process design. Abbreviated versions of the software will be prepared for use in core undergraduate chemical engineering courses. These modules, which will be distributed nationally, will serve both as motivators to provide exposure to the biotechnology field as well as facilitators to enable students to analyze engineering fundamentals as they are applied to new situations. ***

9410403 Linderman This project is on research to study the relationship between receptor number, ligand-receptor binding kinetics, G-protein number and cellular responses. This involves the systematic manipulation of binding kinetics and the quantitative characterization of complex intracellular responses. The neutrophil cell responses to N-formyl peptides will be used as a model system for this study. The specific objectives of the research are: (1) to examine the role of N-formyl peptide receptors by varying the number participating in signal transduction; (2) to examine the role of G-proteins by varying the number participating in signal transduction; (3) to develop a mathematical model of receptor binding and G-protein activation; and (4) to verify the model by predicting cell responses when receptor and G-protein are manipulated. This research could lead to both improved cell response and the design of pharmaceuticals. ***

9713856 Linderman The responses of cells are governed to a large extent by the binding of ligands to cell surface receptors and the signal transduction steps that follow. Multiple ligands which bind to the same receptors often show differences in their abilities to elict cellular responses despite equivalent occupancy of receptors (i.e. there are differences in ligand efficacy). An estimated 80% of all signaling receptors transmit their signal through guanine nucleotide binding proteins (G-proteins). A quantitative understanding of the determinants of ligand efficacy in G-protein coupled receptor systems is thus vital to biotechnological applications such as rational drug design and cell and tissue engineering. The objective of this project is to use and integrated engineering/cell biology approach to demonstrate that three critical parameters determining ligand efficacy and cellular responses in G-protein couple receptor systems are: (a) the rate of transition between receptor states; (b) the lifetime of an individual receptor/ligand complex; and (c) the extent of receptor/G- protein precoupling. The primary methods to be used are flow cytometry and mathematical modeling. ***

DESCRIPTION (Verbatim from the Applicant's Abstract): The overall, long term goal of this work is to develop ways to manipulate cellular responses initiated by ligand/receptor binding; this is done through the combination of mathematical modeling and experiment. The proposed work focuses on developing mathematical models for guanine nucleotide binding protein (G-protein) coupled receptors and G-protein activation. G-proteins are found in virtually every tissue, and they play key roles in, for example, the immune system, vision, brain function, and heart regulation. The activation of G-proteins by receptors at the cell surface initiates a signal transduction pathway that is complex and poorly understood. Receptors can exist in multiple states (active, inactive, ligand-bound, desensitized, internalized, etc.) and these states influence G-protein activation. Further, the kinetics of the transitions between receptor states appear to be important in determining levels and dynamics of G-protein activation and thus a variety of cellular responses. Despite the obvious complexity and dynamics of these signaling processes, most work in the field concentrates on relatively simple equilibrium models of the system. More accurate models of G-protein coupled receptors and G-protein activation are essential to understanding how effective bound ligands are at eliciting cellular responses. Such information is critical to the rational manipulation of cell function for purposes of cell and tissue engineering, and for the development of methods for the development and/or discovery of new phamaceuticals. In this proposal, kinetic models of the G-protein coupled receptor signaling pathway will be developed. Specifically, we will use these models to (1) test the hypothesis that ligand efficacy may be dramatically manipulated by altering cellular parameters, (2) demonstrate the influence that ligand-specific parameters have on signaling and desensitization, and (3) demonstrate conditions under which receptor dimerization may cause larger scale clustering and thus influence signaling. Finally, (4) we will test the hypothesis that common high throughput drug screening assays may be biased against the detection of a class of ligands known as inverse agonists. In each case, models will be used to make predictions that are experimentally accessible and have application to a wide range of G-protein coupled receptor systems.

[unreadable] DESCRIPTION (provided by applicant): Tuberculosis is responsible for 2 million deaths per year. The interplay between host and bacterial factors leads to different disease outcomes (latency, primary tuberculosis, reactivation tuberculosis). A key outcome is the formation of a collection of immune cells termed the granuloma. This structure acts not only as an immune microenvironment and a barrier to dissemination but also as a niche for long-term bacterial survival. The long- term goal of this project is to identify factors that contribute to different outcomes of M. tuberculosis infection. We hypothesize that these different infection outcomes are reflected locally at the level of the granuloma and that granuloma structure is the result of the interplay of events at organ, tissue, cellular, and molecular scales over the time course of minutes to years. Several models of granuloma formation in tuberculosis will be integrated: pulmonary granulomas induced by M. tuberculosis antigen (PPD) coated beads in vivo, M. tuberculosis infection in mice and non-human primates, and multi-scale in silico models. Our studies will include multiple spatial and temporal scales to address the following aims. Aim 1: Determine how specific immune cells and effector molecules in the lung influence the formation of different granuloma structures. Aim 2: Determine the role of dendritic cell and T cell trafficking between lung granuloma and draining lymph nodes in influencing granuloma development. Aim 3: Identify the mechanisms that determine TNF availability for the purpose of understanding how granulomas form as well as how treatment with anti-TNF-therapies leads to TB reactivation. Our interdisciplinary team's approach for integrating data and in silico models over the relevant biological and temporal scales will allow us to predict and test hypotheses regarding key factors that influence granuloma formation and structure. These factors are likely central to determining different disease outcomes following M. tuberculosis infection and will provide a new tool for testing therapies and vaccines against M. tuberculosis. Tuberculosis (TB) is a world health issue. The immune response to TB is unique, resulting in the formation of structures called granulomas in the lungs of infected people. We seek to understand the formation and function of these structures using integrated data generated from a variety of animal and computational models. (End of Abstract) [unreadable] [unreadable] [unreadable] [unreadable]

DESCRIPTION (provided by applicant): A multi-scale model to predict outcomes of immunomodulation and drug therapy during tuberculosis Mycobacterium tuberculosis (Mtb) is the most successful pathogen known to humans;it is responsible for ~2 million deaths/year and infects an estimated 1/3 of the world. Despite decades of study, our understanding of the interplay of various pathogen and immune processes that allow for different outcomes in tuberculosis (TB), i.e. primary TB, latency and reactivation TB, remains incomplete. The hallmark of TB is the formation of a spherical collection of immune cells in the lung and lymph node that both immunologically restrains and physically contains the bacteria. Yet bacilli can survive within granuloma for years. Current therapy requires 6 months of treatment with multiple antibiotics;immunomodulation may be able to augment this treatment, shortening treatment time and reducing side effects. There is a crucial need for an in silico platform to provide a cost-effective means of predicting the outcome of new treatment strategies. The long-term goals of this project are to integrate knowledge about immune system dynamics in these organs into a realistic, multi-scale, multi-organ model of the immune response during Mtb infection and to use this model to identify optimal approaches for immunomodulation/antibiotic therapy. The specific aims are: Aim 1: Incorporate new components (IL-10, bacterial population dynamics) into our existing multi-scale lung granuloma model, and use the model to predict factors affecting control of infection in the lung. Aim 2: Incorporate new information (lymph node anatomy, key cytokines, and bacterial populations) into our existing multi-scale lymph node model, and use the model to predict factors leading to initiation of the immune response and granuloma formation and maintenance in a lymph node. Aim 3. Build a multi-compartment, multi-scale model that includes the models of Aims 1 and 2 and trafficking events between the organs, and use this model to predict infection control and pathology at the level of individual granulomas during immunodulation/antibiotic therapy. Data generated herein from non-human primates will inform our models and be used to validate predictions. Our systems biology approach - incorporating both computational and experimental tools - will allow us to predict and test hypotheses regarding key mechanisms that influence immunity to TB. Our interdisciplinary approach will also serve the broader community of researchers investigating areas related to TB, immunity and multi-scale modeling by providing data and tools that will be made readily available. PUBLIC HEALTH RELEVANCE: Using a combined experimental/computational systems biology approach, we will develop a realistic multi-scale multi-compartment model that describes the immune response to infection with the bacteria that causes tuberculosis. The model will be used to predict the outcome of treatment strategies that boost immunity during antibiotic treatment, providing a cost-effective means of evaluating therapeutic interventions.

DESCRIPTION (provided by applicant): Tuberculosis is responsible for 2 million deaths every year. Even after decades of research, there are still numerous gaps in our understanding of the mechanisms that provide protection against tuberculosis in humans. In this proposal, we take a unique multipronged systems biology approach, combining a non-human primate model of M. tuberculosis infection that closely mimics all aspects of human M. tuberculosis infection, with a vaccine that is only partially effective (resulting in protected and non-protected animals), and state-of- the-art live animal imaging, immunologic monitoring, and computational models, to identify and predict the immunologic mechanisms that are protective against TB. This proposal will develop a multi-disciplinary tool- box for dissecting human immune responses that provide protection against bacterial infections, even beyond TB. We build on considerable experience in computational modeling of tuberculosis and will generate a first- time 3 physiologic compartment model of blood, lung, lymph node with multi-scale interactions (molecular to cellular to organ to host scales), incorporating data generated in this project from the non-human primate, as well as our previous data and that from the literature. These models will be used to identify surrogate markers of protection, which will be used to predict the animals that are protected by this vaccine. In turn, the experimental animals will be the source of granuloma and lymph node tissue to probe the actual protective responses at the site of infection, studies that are impossible to do in humans. Through iteration between computational and experimental models, we will identify the factors that together can contribute to a protective immune response. These studies are a necessary step to a more detailed understanding of host immune responses protective against tuberculosis, and development and testing of new therapeutic and intervention strategies to prevent this disease.

? DESCRIPTION (provided by applicant): Directional migration of malignant cells toward a gradient of one or more signaling molecules underlies fundamental steps in metastasis, including local invasion of cancer cells, vascular intravasation, and extravasation of cancer cells at secondary sites. Understanding formation of gradients in complex environments with multiple cells and extracellular matrix molecules remains a central challenge in cell migration not only in cancer but also in normal physiology and other diseases. The challenge of understanding gradient formation and cell migration becomes even more difficult in the disordered cellular and extracellular matrix architecture of a tumor. We will meet this challenge through an integrated systems bioengineering approach combining microscale technologies for cell migration, in vitro and in vivo cellular and molecular imaging, and sophisticated multi-scale computational models. This approach will enable us to investigate gradient formation and cell migration in increasingly complex environments, ranging from a 2D system with defined positions of three different cell types to the disorganized structure of a tumor. Using computational modeling to identify key parameters controlling gradient formation and cell migration, we also will experimentally test and validate interventions to block cell migration, which will provide new targets for anti-metastatic therapies. Our research will focus on gradient formation and cell migration controlled by chemokine CXCL12, a signaling molecule that drives metastasis in more than 20 human cancers. CXCL12 exists as six alternatively-spliced isoforms, four of which are expressed in human breast cancers. We recently have shown CXCL12-isoform specific differences in cell migration, resistance to targeted inhibitors, and correlations with disease recurrence and survival in breast cancer. We propose that CXCL12 molecules bound to the extracellular environment drive cell migration, a process referred to as haptotaxis, and differences in binding to the extracellular matrix underlie isoform-specific differences in gradient formation and cell migration. To investigate CXCL12 isoforms in cell migration, we will complete the following specific aims: 1) derive basic cell migration response parameters under simple, defined gradients; 2) using tissue-like geometries, test effects of extracellular matrix composition on migration potency of CXCL12 isoforms; and 3) Quantify in vivo migration in tumor environments with different CXCL12 isoforms. Collectively, this research will advance knowledge of gradient formation in cell migration and point to new treatment strategies for targeting CXCL12 in cancer.

? DESCRIPTION (provided by applicant): Tuberculosis (TB) is a pulmonary disease resulting from infection with Mycobacterium tuberculosis (Mtb). TB is treatable but requires multiple antibiotics taken for >6 months, and the emergence of drug resistant Mtb (MDR and XDR-TB) has strained our current small arsenal of effective TB drugs. The situation is desperate considering there are 9 million new cases of active TB every year. The pathological hallmarks of TB are granulomas, dense spherical collections of immune cells that serve to protect the host but also isolate and shelter the pathogen. Granulomas pose a two- fold challenge to TB treatment: granulomas present a physical barrier for antibiotic penetration, and bacterial subpopulations with diminished antibiotic susceptibility emerge within granulomas. These difficulties contribute to the challenge of devising new and more effective treatment strategies for TB: getting the right drugs at the right concentration to the right location to kill the appropiate bacterial subpopulation. Processes that participate in these dynamics act across scales ranging from molecular (e.g. drug diffusion), cellular (e.g. macrophage activation), tissue (e.g. granuloma formation), organs (e.g. blood delivery of antibiotics) up to the entire host. To elaborate mechanisms driving dynamics in this complex system and to answer this vital challenge, we propose a multi-scale systems pharmacology approach. We use multi-scale computational modeling to track drug distributions in granulomas and development of resistance. We identify a novel bridge between the scale of host lung granulomas to the entire host scale where the disease manifests, and we use new approaches to predict better treatment options. We partner this with state-of-the-art experimental methods for imaging drug distribution within granulomas from humans, non-human primates (NHP) and rabbits. We perform Virtual Clinical Trials and test our prediction of a specific regimen for an efficacy trial in NHP models o TB with human-like pathology. To tackle this challenging proposition, we propose to: (1) Determine the spatial and temporal distributions of TB antibiotics within granulomas, and predict the development of resistance; (2) Identify optimal antibiotic treatment regimens for TB using genetic algorithms to narrow the combinatorial design space of antibiotics (e.g. drug classes, dosing, schedule); (3) Perform virtual clinical trials at a population level to test treatment regimens we identify, and test the optimal regimen in the NHP system against a standard regimen. Our outstanding interdisciplinary team and unique approach will allow for rapid assessment of new strategies and ultimately reduce the number of TB deaths world-wide.