cSTAR analysis identifies endothelial cell cycle as a key regulator of flow-dependent artery remodeling.

Fluid shear stress (FSS) from blood flow sensed by vascular endothelial cells (ECs) determines vessel behavior, but regulatory mechanisms are only partially understood. We used cell state transition assessment and regulation (cSTAR), a powerful computational method, to elucidate EC transcriptomic states under low shear stress (LSS), physiological shear stress (PSS), high shear stress (HSS), and oscillatory shear stress (OSS) that induce vessel inward remodeling, stabilization, outward remodeling, or disease susceptibility, respectively. Combined with a publicly available database on EC transcriptomic responses to drug treatments, this approach inferred a regulatory network controlling EC states and made several notable predictions.

Cell-specific models reveal conformation-specific RAF inhibitor combinations that synergistically inhibit ERK signaling in pancreatic cancer cells.

Pancreatic ductal adenocarcinoma (PDAC) presents significant challenges for targeted clinical interventions due to prevalent KRAS mutations, rendering PDAC resistant to RAF and MEK inhibitors (RAFi and MEKi). In addition, responses to targeted therapies vary between patients. Here, we explored the differential sensitivities of PDAC cell lines to RAFi and MEKi and developed an isogenic pair comprising the most sensitive and resistant PDAC cells.

Lipid Peroxidation and Type I Interferon Coupling Fuels Pathogenic Macrophage Activation Causing Tuberculosis Susceptibility.

A quarter of human population is infected with Mycobacterium tuberculosis, but less than 10% of those infected develop pulmonary TB. We developed a genetically defined sst1-susceptible mouse model that uniquely reproduces a defining feature of human TB: the development of necrotic lung granulomas and determined that the sst1-susceptible phenotype was driven by the aberrant macrophage activation. This study demonstrates that the aberrant response of the sst1-susceptible macrophages to prolonged stimulation with TNF is primarily driven by conflicting Myc and antioxidant response pathways leading to a coordinated failure 1) to properly sequester intracellular iron and 2) to activate ferroptosis inhibitor enzymes.

VEGF counteracts shear stress-determined arterial fate specification during capillary remodeling.

A key feature of arteriogenesis is capillary-to-arterial endothelial cell fate transition. Although a number of studies in the past two decades suggested this process is driven by VEGF activation of Notch signaling, how arteriogenesis is regulated remains poorly understood. Here we report that arterial specification is mediated by fluid shear stress (FSS) independent of VEGFR2 signaling and that a decline in VEGFR2 signaling is required for arteriogenesis to fully take place.

cSTAR analysis identifies endothelial cell cycle as a key regulator of flow-dependent artery remodeling.

Fluid shear stress (FSS) from blood flow is sensed by vascular endothelial cells (ECs) to determine vessel stability, remodeling and susceptibility to atherosclerosis and other inflammatory diseases but the regulatory networks that govern these behaviors are only partially understood. We used cSTAR, a powerful new computational method, to define EC transcriptomic states under low shear stress (LSS) that triggers vessel inward remodeling, physiological shear stress (PSS) that stabilizes vessels, high shear stress (HSS) that triggers outward remodeling, and oscillatory shear stress (OSS) that confers disease susceptibility, all in comparison to cells under static conditions (STAT). We combined these results with the LINCS database where EC transcriptomic responses to drug treatments to define a preliminary regulatory network in which the cyclin-dependent kinases CDK1/2 play a central role in promoting vessel stability.

Cell state transition analysis identifies interventions that improve control of infection by susceptible macrophages.

Understanding cell state transitions and purposefully controlling them to improve therapies is a longstanding challenge in biological research and medicine. Here, we identify a transcriptional signature that distinguishes activated macrophages from the tuberculosis (TB) susceptible and resistant mice. We then apply the cSTAR (cell state transition assessment and regulation) approach to data from screening-by-RNA sequencing to identify chemical perturbations that shift the transcriptional state of tumor necrosis factor (TNF)-activated TB-susceptible macrophages toward that of TB-resistant cells, i.

A Combination of Conformation-Specific RAF Inhibitors Overcome Drug Resistance Brought about by RAF Overexpression.

Cancer cells often adapt to targeted therapies, yet the molecular mechanisms underlying adaptive resistance remain only partially understood. Here, we explore a mechanism of RAS/RAF/MEK/ERK (MAPK) pathway reactivation through the upregulation of RAF isoform (RAFs) abundance. Using computational modeling and in vitro experiments, we show that the upregulation of RAFs changes the concentration range of paradoxical pathway activation upon treatment with conformation-specific RAF inhibitors.

Cell state transition analysis identifies interventions that improve control of infection by susceptible macrophages.

Understanding cell state transitions and purposefully controlling them to improve therapies is a longstanding challenge in biological research and medicine. Here, we identify a transcriptional signature that distinguishes activated macrophages from TB-susceptible and TB-resistant mice. We then apply the cSTAR (cell State Transition Assessment and Regulation) approach to data from screening-by-RNA sequencing to identify chemical perturbations that shift the.

Can Systems Biology Advance Clinical Precision Oncology?

Precision oncology is perceived as a way forward to treat individual cancer patients. However, knowing particular cancer mutations is not enough for optimal therapeutic treatment, because cancer genotype-phenotype relationships are nonlinear and dynamic. Systems biology studies the biological processes at the systems' level, using an array of techniques, ranging from statistical methods to network reconstruction and analysis, to mathematical modeling.

Relationship Between Dimensionality and Convergence of Optimization Algorithms: A Comparison Between Data-Driven Normalization and Scaling Factor-Based Methods Using PEPSSBI.

Ordinary differential equation models are used to represent intracellular signaling pathways in silico, aiding and guiding biological experiments to elucidate intracellular regulation. To construct such quantitative and predictive models of intracellular interactions, unknown parameters need to be estimated. Most of biological data are expressed in relative or arbitrary units, raising the question of how to compare model simulations with data.

Modeling the Nonlinear Dynamics of Intracellular Signaling Networks.

This protocol illustrates a pipeline for modeling the nonlinear behavior of intracellular signaling pathways. At fixed spatial points, nonlinear signaling dynamics are described by ordinary differential equations (ODEs). At constant parameters, these ODEs may have multiple attractors, such as multiple steady states or limit cycles.

Channeling macrophage polarization by rocaglates increases macrophage resistance to .

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.

A systematic analysis of signaling reactivation and drug resistance.

Increasing evidence suggests that the reactivation of initially inhibited signaling pathways causes drug resistance. Here, we analyze how network topologies affect signaling responses to drug treatment. Network-dependent drug resistance is commonly attributed to negative and positive feedback loops.

Inhaled multi-walled carbon nanotubes differently modulate global gene and protein expression in rat lungs.

Inhalation of multi-walled carbon nanotubes (MWCNTs) induces lung inflammation. Depending on industrial applications, CNTs with different physicochemical characteristics are produced and workers can potentially be exposed. This raises concerns about the long-term health effects of these nanomaterials.

Systems biology approaches to macromolecules: the role of dynamic protein assemblies in information processing.

Macromolecular protein assemblies govern many cellular processes and are disturbed in many diseases including cancer. Often seen as static molecular machines, protein complexes involved in signal transduction networks exhibit intricate dynamics that are critical for their function. Using the RAS-RAF-MEK-ERK pathway as example we discuss recent progress in our understanding of protein complex dynamics achieved through mathematical modelling, computational simulations and structural studies.

Periodic propagating waves coordinate RhoGTPase network dynamics at the leading and trailing edges during cell migration.

Migrating cells need to coordinate distinct leading and trailing edge dynamics but the underlying mechanisms are unclear. Here, we combine experiments and mathematical modeling to elaborate the minimal autonomous biochemical machinery necessary and sufficient for this dynamic coordination and cell movement. RhoA activates Rac1 via DIA and inhibits Rac1 via ROCK, while Rac1 inhibits RhoA through PAK.

Acute Phase Response as a Biological Mechanism-of-Action of (Nano)particle-Induced Cardiovascular Disease.

Inhaled nanoparticles constitute a potential health hazard due to their size-dependent lung deposition and large surface to mass ratio. Exposure to high levels contributes to the risk of developing respiratory and cardiovascular diseases, as well as of lung cancer. Particle-induced acute phase response may be an important mechanism of action of particle-induced cardiovascular disease.

An Integrative Computational Approach for a Prioritization of Key Transcription Regulators Associated With Nanomaterial-Induced Toxicity.

A rapid increase of new nanomaterial (NM) products poses new challenges for their risk assessment. Current traditional methods for estimating potential adverse health effect of NMs are complex, time consuming, and expensive. In order to develop new prediction tests for nanotoxicity evaluation, a systems biology approach, and data from high-throughput omics experiments can be used.

Mapping connections in signaling networks with ambiguous modularity.

Modular Response Analysis (MRA) is a suite of methods that under certain assumptions permits the precise reconstruction of both the directions and strengths of connections between network modules from network responses to perturbations. Standard MRA assumes that modules are insulated, thereby neglecting the existence of inter-modular protein complexes. Such complexes sequester proteins from different modules and propagate perturbations to the protein abundance of a downstream module retroactively to an upstream module.

Modeling cell line-specific recruitment of signaling proteins to the insulin-like growth factor 1 receptor.

Receptor tyrosine kinases (RTKs) typically contain multiple autophosphorylation sites in their cytoplasmic domains. Once activated, these autophosphorylation sites can recruit downstream signaling proteins containing Src homology 2 (SH2) and phosphotyrosine-binding (PTB) domains, which recognize phosphotyrosine-containing short linear motifs (SLiMs). These domains and SLiMs have polyspecific or promiscuous binding activities.