Tumor Necrosis Factor-Regulated Granuloma Formation in Tuberculosis: Multi-Scale Modeling and Experiments.

Tuberculosis is a deadly infectious disease caused by Mycobacterium tuberculosis (Mtb). Multiple immune factors control host responses to Mtb infection, including the formation of granulomas in the lung, which are aggregates of bacteria, infected and uninfected immune cells whose function may reflect success or failure of the host to control infection. One such factor is tumor necrosis factor-α (TNF). TNF has been experimentally characterized to affect macrophage activation, apoptosis, chemokine and cytokine production during Mtb infection. Measurement of TNF concentrations and TNF activities within a granuloma to determine the relevant mechanisms for control of infection are difficult to assess in vivo. Further, processes that control TNF availability and activities within a granuloma remain unknown. We developed a multi-scale computational model that describes the immune response to Mtb in lung over three biological length scales: tissue, cellular and molecular. We used the results of sensitivity analysis as a tool to identify which experiments were needed to measure critical model parameters in an experimental system. This system is a model of a granuloma induced in the lungs of mice following injection of mycobacterial antigen-coated beads. Using these parameters in the model, we identified processes that regulate TNF availability and cellular behaviors and thus influence the outcome of infection within a granuloma. At the level of TNF/TNF receptor dynamics, TNF receptor internalization kinetics were shown to significantly influence TNF concentration dynamics, macrophage and T cell recruitment to site of infection, macrophage activation and apoptosis. These processes play a critical role in control of inflammation and bacterial levels within a granuloma. At the level of intracellular signaling, our analysis elucidated intracellular NF-κB associated signaling molecules and processes that may be new targets for control of infection and inflammation. We also used the model to explain what mechanisms lead to clinically observed differential effects of TNF-neutralizing drugs (generally used to treat inflammatory diseases) on reactivation of tuberculosis. Ultimately, these results can help to elaborate relevant features of the immune response to Mtb infection, identifying new strategies for therapy and prevention of tuberculosis as well as for development of safer anti-TNF drugs to treat inflammatory diseases.Ph.D.Chemical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/91477/1/fallahi_1.pd

Lipid Raft-Mediated Regulation of G-Protein Coupled Receptor Signaling by Ligands which Influence Receptor Dimerization: A Computational Study

G-protein coupled receptors (GPCRs) are the largest family of cell surface receptors; they activate heterotrimeric G-proteins in response to ligand stimulation. Although many GPCRs have been shown to form homo- and/or heterodimers on the cell membrane, the purpose of this dimerization is not known. Recent research has shown that receptor dimerization may have a role in organization of receptors on the cell surface. In addition, microdomains on the cell membrane termed lipid rafts have been shown to play a role in GPCR localization. Using a combination of stochastic (Monte Carlo) and deterministic modeling, we propose a novel mechanism for lipid raft partitioning of GPCRs based on reversible dimerization of receptors and then demonstrate that such localization can affect GPCR signaling. Modeling results are consistent with a variety of experimental data indicating that lipid rafts have a role in amplification or attenuation of G-protein signaling. Thus our work suggests a new mechanism by which dimerization-inducing or inhibiting characteristics of ligands can influence GPCR signaling by controlling receptor organization on the cell membrane

A Review on Epigenome Editing using CRISPR-based Tools to Rejuvenate Skin Tissues

Genomic activity is controlled by a sophisticated series of cell functions known as the epigenome. The creation of tools capable of directly altering various processes is required to unravel this intricacy. Additionally, by employing tailored DNA-binding platforms connected with effector domains to serve as targeted transcription factors or epigenetic modifiers, it is possible to control the chemical modifiers that regulate the genome's activity. Neoplastic disorders have received the most attention in the study of epigenetics, though the epigenome's significance in a variety of disease processes is now well acknowledged. Researchers are inspired to investigate novel approaches to revert these pathogenic alterations to their normal patterns by considering the fact that the epigenome profile of individuals with aging skin cells or other skin disorders, including atopic dermatitis, differs from that of healthy individuals. Here in this review, we discuss the use of CRISPR/dCas9 as a cutting-edge and flexible tool for fundamental studies on chromatin structure, transcription regulation, and epigenetic landscapes, as well as the potential of this method in these fields. Furthermore, we review on common and recently invented methods to make epigenetic alterations possible in daughter cells after any mitotic differentiations. In the very near future, CRISPR-based epigenomic editing will become a potent tool for comprehending and regulating biological functions

Tuneable resolution as a systems biology approach for multi-scale, multi-compartment computational models

The use of multi-scale mathematical and computational models to study complex biological processes is becoming increasingly productive. Multi-scale models span a range of spatial and/or temporal scales and can encompass multi-compartment (e.g., multi-organ) models. Modeling advances are enabling virtual experiments to explore and answer questions that are problematic to address in the wet-lab. Wet-lab experimental technologies now allow scientists to observe, measure, record, and analyze experiments focusing on different system aspects at a variety of biological scales. We need the technical ability to mirror that same flexibility in virtual experiments using multi-scale models. Here we present a new approach, tuneable resolution, which can begin providing that flexibility. Tuneable resolution involves fine- or coarse-graining existing multi-scale models at the user's discretion, allowing adjustment of the level of resolution specific to a question, an experiment, or a scale of interest. Tuneable resolution expands options for revising and validating mechanistic multi-scale models, can extend the longevity of multi-scale models, and may increase computational efficiency. The tuneable resolution approach can be applied to many model types, including differential equation, agent-based, and hybrid models. We demonstrate our tuneable resolution ideas with examples relevant to infectious disease modeling, illustrating key principles at work. WIREs Syst Biol Med 2014, 6:225–245. doi:10.1002/wsbm.1270 How to cite this article: WIREs Syst Biol Med 2014, 6:289–309. doi:10.1002/wsbm.127

Adaptive resistance of melanoma cells to RAF inhibition via reversible induction of a slowly dividing de‐differentiated state

Abstract Treatment of BRAF‐mutant melanomas with MAP kinase pathway inhibitors is paradigmatic of the promise of precision cancer therapy but also highlights problems with drug resistance that limit patient benefit. We use live‐cell imaging, single‐cell analysis, and molecular profiling to show that exposure of tumor cells to RAF/MEK inhibitors elicits a heterogeneous response in which some cells die, some arrest, and the remainder adapt to drug. Drug‐adapted cells up‐regulate markers of the neural crest (e.g., NGFR), a melanocyte precursor, and grow slowly. This phenotype is transiently stable, reverting to the drug‐naïve state within 9 days of drug withdrawal. Transcriptional profiling of cell lines and human tumors implicates a c‐Jun/ECM/FAK/Src cascade in de‐differentiation in about one‐third of cell lines studied; drug‐induced changes in c‐Jun and NGFR levels are also observed in xenograft and human tumors. Drugs targeting the c‐Jun/ECM/FAK/Src cascade as well as BET bromodomain inhibitors increase the maximum effect (E max) of RAF/MEK kinase inhibitors by promoting cell killing. Thus, analysis of reversible drug resistance at a single‐cell level identifies signaling pathways and inhibitory drugs missed by assays that focus on cell populations

Substrate Activity Screening with Kinases: Discovery of Small‐Molecule Substrate‐Competitive c‐Src Inhibitors

Substrate‐competitive kinase inhibitors represent a promising class of kinase inhibitors, however, there is no methodology to selectively identify this type of inhibitor. Substrate activity screening was applied to tyrosine kinases. By using this methodology, the first small‐molecule substrates for any protein kinase were discovered, as well as the first substrate‐competitive inhibitors of c‐Src with activity in both biochemical and cellular assays. Characterization of the lead inhibitor demonstrates that substrate‐competitive kinase inhibitors possess unique properties, including cellular efficacy that matches biochemical potency and synergy with ATP‐competitive inhibitors. SASsy inhibitors : Small‐molecule substrate‐competitive inhibitors of the tyrosine kinase c‐Src were discovered through the application of substrate activity screening (SAS). Characterization of the lead inhibitor demonstrates that substrate‐competitive kinase inhibitors possess unique properties, including cellular efficacy that matches biochemical potency and synergy with ATP‐competitive inhibitors.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/107499/1/7010_ftp.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/107499/2/anie_201311096_sm_miscellaneous_information.pd

Uptake of oxLDL and IL-10 production by macrophages requires PAFR and CD36 recruitment into the same lipid rafts

Macrophage interaction with oxidized low-density lipoprotein (oxLDL) leads to its differentiation into foam cells and cytokine production, contributing to atherosclerosis development. In a previous study, we showed that CD36 and the receptor for platelet-activating factor (PAFR) are required for oxLDL to activate gene transcription for cytokines and CD36. Here, we investigated the localization and physical interaction of CD36 and PAFR in macrophages stimulated with oxLDL. We found that blocking CD36 or PAFR decreases oxLDL uptake and IL-10 production. OxLDL induces IL-10 mRNA expression only in HEK293T expressing both receptors (PAFR and CD36). OxLDL does not induce IL-12 production. The lipid rafts disruption by treatment with βCD reduces the oxLDL uptake and IL-10 production. OxLDL induces co-immunoprecipitation of PAFR and CD36 with the constitutive raft protein flotillin-1, and colocalization with the lipid raft-marker GM1-ganglioside. Finally, we found colocalization of PAFR and CD36 in macrophages from human atherosclerotic plaques. Our results show that oxLDL induces the recruitment of PAFR and CD36 into the same lipid rafts, which is important for oxLDL uptake and IL-10 production. This study provided new insights into how oxLDL interact with macrophages and contributing to atherosclerosis development

Relational grounding facilitates development of scientifically useful multiscale models

We review grounding issues that influence the scientific usefulness of any biomedical multiscale model (MSM). Groundings are the collection of units, dimensions, and/or objects to which a variable or model constituent refers. To date, models that primarily use continuous mathematics rely heavily on absolute grounding, whereas those that primarily use discrete software paradigms (e.g., object-oriented, agent-based, actor) typically employ relational grounding. We review grounding issues and identify strategies to address them. We maintain that grounding issues should be addressed at the start of any MSM project and should be reevaluated throughout the model development process. We make the following points. Grounding decisions influence model flexibility, adaptability, and thus reusability. Grounding choices should be influenced by measures, uncertainty, system information, and the nature of available validation data. Absolute grounding complicates the process of combining models to form larger models unless all are grounded absolutely. Relational grounding facilitates referent knowledge embodiment within computational mechanisms but requires separate model-to-referent mappings. Absolute grounding can simplify integration by forcing common units and, hence, a common integration target, but context change may require model reengineering. Relational grounding enables synthesis of large, composite (multi-module) models that can be robust to context changes. Because biological components have varying degrees of autonomy, corresponding components in MSMs need to do the same. Relational grounding facilitates achieving such autonomy. Biomimetic analogues designed to facilitate translational research and development must have long lifecycles. Exploring mechanisms of normal-to-disease transition requires model components that are grounded relationally. Multi-paradigm modeling requires both hyperspatial and relational grounding

Modelling the effects of environmental heterogeneity within the lung on the tuberculosis life-cycle

Funding: This work was supported by the Medical Research Council [grant number MR/P014704/1] and the PreDiCT-TB consortium (IMI Joint undertaking grant agreement number 115337, resources of which are composed of financial contribution from the European Union’s Seventh Framework Programme (FP7/2007-2013) and EF-PIA companies’ in kind contribution.Progress in shortening the duration of tuberculosis (TB) treatment is hampered by the lack of a predictive model that accurately reflects the diverse environment within the lung. This is important as TB has been shown to produce distinct localisations to different areas of the lung during different disease stages, with the environmental heterogeneity within the lung of factors such as air ventilation, blood perfusion and oxygen tension believed to contribute to the apical localisation witnessed during the post-primary form of the disease. Building upon our previous model of environmental lung heterogeneity, we present a networked metapopulation model that simulates TB across the whole lung, incorporating these notions of environmental heterogeneity across the whole TB life-cycle to show how different stages of the disease are influenced by different environmental and immunological factors. The alveolar tissue in the lung is divided into distinct patches, with each patch representing a portion of the total tissue and containing environmental attributes that reflect the internal conditions at that location. We include populations of bacteria and immune cells in various states, and events are included which determine how the members of the model interact with each other and the environment. By allowing some of these events to be dependent on environmental attributes, we create a set of heterogeneous dynamics, whereby the location of the tissue within the lung determines the disease pathological events that occur there. Our results show that the environmental heterogeneity within the lung is a plausible driving force behind the apical localisation during post-primary disease. After initial infection, bacterial levels will grow in the initial infection location at the base of the lung until an adaptive immune response is initiated. During this period, bacteria are able to disseminate and create new lesions throughout the lung. During the latent stage, the lesions that are situated towards the apex are the largest in size, and once a post-primary immune-suppressing event occurs, it is the uppermost lesions that reach the highest levels of bacterial proliferation. Our sensitivity analysis also shows that it is the differential in blood perfusion, causing reduced immune activity towards the apex, which has the biggest influence of disease outputs.Publisher PDFPeer reviewe

Impact of receptor clustering on ligand binding

<p>Abstract</p> <p>Background</p> <p>Cellular response to changes in the concentration of different chemical species in the extracellular medium is induced by ligand binding to dedicated transmembrane receptors. Receptor density, distribution, and clustering may be key spatial features that influence effective and proper physical and biochemical cellular responses to many regulatory signals. Classical equations describing this kind of binding kinetics assume the distributions of interacting species to be homogeneous, neglecting by doing so the impact of clustering. As there is experimental evidence that receptors tend to group in clusters inside membrane domains, we investigated the effects of receptor clustering on cellular receptor ligand binding.</p> <p>Results</p> <p>We implemented a model of receptor binding using a Monte-Carlo algorithm to simulate ligand diffusion and binding. In some simple cases, analytic solutions for binding equilibrium of ligand on clusters of receptors are provided, and supported by simulation results. Our simulations show that the so-called "apparent" affinity of the ligand for the receptor decreases with clustering although the microscopic affinity remains constant.</p> <p>Conclusions</p> <p>Changing membrane receptors clustering could be a simple mechanism that allows cells to change and adapt its affinity/sensitivity toward a given stimulus.</p