Channeling macrophage polarization by rocaglates increases macrophage resistance to Mycobacterium tuberculosis

Macrophages contribute to host immunity and tissue homeostasis via alternative activation programs. M1-like macrophages control intracellular bacterial pathogens and tumor progression. In contrast, M2-like macrophages shape reparative microenvironments that can be conducive for pathogen survival or tumor growth. An imbalance of these macrophages phenotypes may perpetuate sites of chronic unresolved inflammation, such as infectious granulomas and solid tumors. We have found that plant-derived and synthetic rocaglates sensitize macrophages to low concentrations of the M1-inducing cytokine IFN-gamma and inhibit their responsiveness to IL-4, a prototypical activator of the M2-like phenotype. Treatment of primary macrophages with rocaglates enhanced phagosome-lysosome fusion and control of intracellular mycobacteria. Thus, rocaglates represent a novel class of immunomodulators that can direct macrophage polarization toward the M1-like phenotype in complex microenvironments associated with hypofunction of type 1 and/or hyperactivation of type 2 immunity, e.g., chronic bacterial infections, allergies, and, possibly, certain tumors.R35 GM118173 - NIGMS NIH HHS; R01 HL126066 - NHLBI NIH HHS; R01 GM120272 - NIGMS NIH HHS; R01 CA218500 - NCI NIH HHS; R01 HL133190 - NHLBI NIH HHS; R33 AI105944 - NIAID NIH HHSPublished versio

MAPK kinase signalling dynamics regulate cell fate decisions and drug resistance

The RAS/RAF/MEK/MAPK kinase pathway has been extensively studied for more than 25 years, yet we continue to be puzzled by its intricate dynamic control and plasticity. Different spatiotemporal MAPK dynamics bring about distinct cell fate decisions in normal vs cancer cells and developing organisms. Recent modelling and experimental studies provided novel insights in the versatile MAPK dynamics concerted by a plethora of feedforward/feedback regulations and crosstalk on multiple timescales. Multiple cancer types and various developmental disorders arise from persistent alterations of the MAPK dynamics caused by RAS/RAF/MEK mutations. While a key role of the MAPK pathway in multiple diseases made the development of novel RAF/MEK inhibitors a hot topic of drug development, these drugs have unexpected side-effects and resistance inevitably occurs. We review how RAF dimerization conveys drug resistance and recent breakthroughs to overcome this resistance.European Commission Horizon 2020European Commission - Seventh Framework Programme (FP7

Reconstructing static and dynamic models of signaling pathways using Modular Response Analysis

In this review we discuss the origination and evolution of Modular Response Analysis (MRA), which is a physics-based method for reconstructing quantitative topological models of biochemical pathways. We first focus on the core theory of MRA, demonstrating how both the direction and the strength of local, causal connections between network modules can be precisely inferred from the global responses of the entire network to a sufficient number of perturbations, under certain conditions. Subsequently, we analyze statistical reformulations of MRA and show how MRA is used to build and calibrate mechanistic models of biological networks. We further discuss what sets MRA apart from other network reconstruction methods and outline future directions for MRA-based methods of network reconstruction.European Commission Horizon 2020Irish Cancer Societ

The complexities and versatility of the RAS-to-ERK signalling system in normal and cancer cells

The intricate dynamic control and plasticity of RAS to ERK mitogenic, survival and apoptotic signalling has mystified researches for more than 30 years. Therapeutics targeting the oncogenic aberrations within this pathway often yield unsatisfactory, even undesired results, as in the case of paradoxical ERK activation in response to RAF inhibition. A direct approach of inhibiting single oncogenic proteins misses the dynamic network context governing the network signal processing. In this review, we discuss the signalling behaviour of RAS and RAF proteins in normal and in cancer cells, and the emerging systems-level properties of the RAS-to-ERK signalling network. We argue that to understand the dynamic complexities of this control system, mathematical models including mechanistic detail are required. Looking into the future, these dynamic models will build the foundation upon which more effective, rational approaches to cancer therapy will be developed.European Commission Horizon 2020European Commission - Seventh Framework Programme (FP7)Science Foundation Irelan

The complexities and versatility of the RAS-to-ERK signalling system in normal and cancer cells

The intricate dynamic control and plasticity of RAS to ERK mitogenic, survival and apoptotic signalling has mystified researches for more than 30 years. Therapeutics targeting the oncogenic aberrations within this pathway often yield unsatisfactory, even undesired results, as in the case of paradoxical ERK activation in response to RAF inhibition. A direct approach of inhibiting single oncogenic proteins misses the dynamic network context governing the network signal processing. In this review, we discuss the signalling behaviour of RAS and RAF proteins in normal and in cancer cells, and the emerging systems-level properties of the RAS-to-ERK signalling network. We argue that to understand the dynamic complexities of this control system, mathematical models including mechanistic detail are required. Looking into the future, these dynamic models will build the foundation upon which more effective, rational approaches to cancer therapy will be developed.European Commission Horizon 2020European Commission - Seventh Framework Programme (FP7)Science Foundation Irelan

Impact of measurement noise, experimental design, and estimation methods on Modular Response Analysis based network reconstruction

Modular Response Analysis (MRA) is a method to reconstruct signalling networks from steady-state perturbation data which has frequently been used in different settings. Since these data are usually noisy due to multi-step measurement procedures and biological variability, it is important to investigate the effect of this noise onto network reconstruction. Here we present a systematic study to investigate propagation of noise from concentration measurements to network structures. Therefore, we design an in silico study of the MAPK and the p53 signalling pathways with realistic noise settings. We make use of statistical concepts and measures to evaluate accuracy and precision of individual inferred interactions and resulting network structures. Our results allow to derive clear recommendations to optimize the performance of MRA based network reconstruction: First, large perturbations are favorable in terms of accuracy even for models with non-linear steady-state response curves. Second, a single control measurement for different perturbation experiments seems to be sufficient for network reconstruction, and third, we recommend to execute the MRA workflow with the mean of different replicates for concentration measurements rather than using computationally more involved regression strategies.European Commission Horizon 2020German Research Foundation via the Cluster of Excellence in Simulation Technolog

Impact of measurement noise, experimental design, and estimation methods on Modular Response Analysis based network reconstruction

Modular Response Analysis (MRA) is a method to reconstruct signalling networks from steady-state perturbation data which has frequently been used in different settings. Since these data are usually noisy due to multi-step measurement procedures and biological variability, it is important to investigate the effect of this noise onto network reconstruction. Here we present a systematic study to investigate propagation of noise from concentration measurements to network structures. Therefore, we design an in silico study of the MAPK and the p53 signalling pathways with realistic noise settings. We make use of statistical concepts and measures to evaluate accuracy and precision of individual inferred interactions and resulting network structures. Our results allow to derive clear recommendations to optimize the performance of MRA based network reconstruction: First, large perturbations are favorable in terms of accuracy even for models with non-linear steady-state response curves. Second, a single control measurement for different perturbation experiments seems to be sufficient for network reconstruction, and third, we recommend to execute the MRA workflow with the mean of different replicates for concentration measurements rather than using computationally more involved regression strategies.European Commission Horizon 2020German Research Foundation via the Cluster of Excellence in Simulation Technolog

Successive stages of a solid thrombus formation.

<p>a—thrombus nucleation, b—formation of fibre-like fibrin structure, c—fibre-like structure thickening, d—solid thrombus. Shown are the color maps of <i>N</i>_<i>w</i>—the weight-average number of monomers in fibrin-polymer in the vessel, with red areas representing regions of fibrin gel formation (</p><p></p><p></p><p></p><p><mi>N</mi><mi>w</mi></p><mo>≥</mo><p><mi>N</mi><mi>w</mi><mi>s</mi></p><p></p><p></p><p></p>). Streamlines are plotted to visualize the flow, and the separatrix, which divides the core of the flow from the recirculation zone, is shown with a dashed line. Parameters used in the simulations are: <i>Re</i> = 130, <i>h</i> = 0.5, <p></p><p></p><p></p><p><mi>d</mi><mo>~</mo></p><mo>=</mo><mn>0</mn><mo>.</mo><mn>5</mn><p></p><p></p><p></p>, <p></p><p></p><p></p><p></p><p><mi>μ</mi><mo>~</mo></p><mn>2</mn><p></p><mo>=</mo><mn>9</mn><mo>.</mo><mn>5</mn><p></p><p></p><p></p>. Note that only a fragment of the vessel closest to the plaque is depicted.<p></p

Parametric diagram of blood coagulation system regimes in the (Re,μ~2) parameter space.

<p><i>Re</i> is the Reynolds number and </p><p></p><p></p><p></p><p></p><p><mi>μ</mi><mo>~</mo></p><mn>2</mn><p></p><p></p><p></p><p></p> is the non-dimensional vessel wall permeability. Label “I” is used to denote stationary regimes with no coagulation in the vessel while “II” marks the regimes with thrombi formation. Also shown are two typical values <i>Re</i>₁, <i>Re</i>₂ marking the boundary of the coagulation regime for a given <p></p><p></p><p></p><p></p><p><mi>μ</mi><mo>~</mo></p><mn>2</mn><p></p><p></p><p></p><p></p> and <p></p><p></p><p></p><p></p><p><mi>μ</mi><mo>~</mo></p><p><mi>m</mi><mi>i</mi><mi>n</mi></p><p></p><p></p><p></p><p></p>—the permeability of the vessel wall below which clotting does not occur under any hydrodynamic conditions. Finally, <i>Re</i> = <i>Re</i>_<i>τ</i>2 indicates the lowest Reynolds number at which the shear stress reaches <i>τ</i>₂ and the local permeability of the vessel reaches <p></p><p></p><p></p><p></p><p><mi>μ</mi><mn>2</mn></p><mo>~</mo><p></p><p></p><p></p><p></p>. <i>h</i> = 0.6, <i>d</i> = 0.4.<p></p