How to Steer and Control ERK and the ERK Signaling Cascade Exemplified by Looking at Cardiac Insufficiency

DataXflow: Synergizing data-driven modeling with best parameter fit and optimal control - An efficient data analysis for cancer research

Building data-driven models is an effective strategy for information extraction from empirical data. Adapting model parameters specifically to data with a best fitting approach encodes the relevant information into a mathematical model. Subsequently, an optimal control framework extracts the most efficient targets to steer the model into desired changes via external stimuli. The DataXflow software framework integrates three software pipelines, D2D for model fitting, a framework solving optimal control problems including external stimuli and JimenaE providing graphical user interfaces to employ the other frameworks lowering the barriers for the need of programming skills, and simultaneously automating reoccurring modeling tasks. Such tasks include equation generation from a graph and script generation allowing also to approach systems with many agents, like complex gene regulatory networks. A desired state of the model is defined, and therapeutic interventions are modeled as external stimuli. The optimal control framework purposefully exploits the model-encoded information by providing those external stimuli that effect the desired changes most efficiently. The implementation of DataXflow is available under https://github.com/MarvelousHopefull/DataXflow. We showcase its application by detecting specific drug targets for a therapy of lung cancer from measurement data to lower proliferation and increase apoptosis. By an iterative modeling process refining the topology of the model, the regulatory network of the tumor is generated from the data. An application of the optimal control framework in our example reveals the inhibition of AURKA and the activation of CDH1 as the most efficient drug target combination. DataXflow paves the way to an agile interplay between data generation and its analysis potentially accelerating cancer research by an efficient drug target identification, even in complex networks.

Software JimenaE allows efficient dynamic simulations of Boolean networks, centrality and system state analysis

The signal modelling framework JimenaE simulates dynamically Boolean networks. In contrast to SQUAD, there is systematic and not just heuristic calculation of all system states. These specific features are not present in CellNetAnalyzer and BoolNet. JimenaE is an expert extension of Jimena, with new optimized code, network conversion into different formats, rapid convergence both for system state calculation as well as for all three network centralities. It allows higher accuracy in determining network states and allows to dissect networks and identification of network control type and amount for each protein with high accuracy. Biological examples demonstrate this: (i) High plasticity of mesenchymal stromal cells for differentiation into chondrocytes, osteoblasts and adipocytes and differentiation-specific network control focusses on wnt-, TGF-beta and PPAR-gamma signaling. JimenaE allows to study individual proteins, removal or adding interactions (or autocrine loops) and accurately quantifies effects as well as number of system states. (ii) Dynamical modelling of cell-cell interactions of plant Arapidopsis thaliana against Pseudomonas syringae DC3000: We analyze for the first time the pathogen perspective and its interaction with the host. We next provide a detailed analysis on how plant hormonal regulation stimulates specific proteins and who and which protein has which type and amount of network control including a detailed heatmap of the A.thaliana response distinguishing between two states of the immune response. (iii) In an immune response network of dendritic cells confronted with Aspergillus fumigatus, JimenaE calculates now accurately the specific values for centralities and protein-specific network control including chemokine and pattern recognition receptors.

Progeria and Aging-Omics Based Comparative Analysis

Since ancient times aging has also been regarded as a disease, and humankind has always strived to extend the natural lifespan. Analyzing the genes involved in aging and disease allows for finding important indicators and biological markers for pathologies and possible therapeutic targets. An example of the use of omics technologies is the research regarding aging and the rare and fatal premature aging syndrome progeria (Hutchinson-Gilford progeria syndrome, HGPS). In our study, we focused on the in silico analysis of differentially expressed genes (DEGs) in progeria and aging, using a publicly available RNA-Seq dataset (GEO dataset GSE113957) and a variety of bioinformatics tools. Despite the GSE113957 RNA-Seq dataset being well-known and frequently analyzed, the RNA-Seq data shared by Fleischer et al. is far from exhausted and reusing and repurposing the data still reveals new insights. By analyzing the literature citing the use of the dataset and subsequently conducting a comparative analysis comparing the RNA-Seq data analyses of different subsets of the dataset (healthy children, nonagenarians and progeria patients), we identified several genes involved in both natural aging and progeria (KRT8, KRT18, ACKR4, CCL2, UCP2, ADAMTS15, ACTN4P1, WNT16, IGFBP2). Further analyzing these genes and the pathways involved indicated their possible roles in aging, suggesting the need for further in vitro and in vivo research. In this paper, we (1) compare "normal aging" (nonagenarians vs. healthy children) and progeria (HGPS patients vs. healthy children), (2) enlist genes possibly involved in both the natural aging process and progeria, including the first mention of IGFBP2 in progeria, (3) predict miRNAs and interactomes for WNT16 (hsa-mir-181a-5p), UCP2 (hsa-mir-26a-5p and hsa-mir-124-3p), and IGFBP2 (hsa-mir-124-3p, hsa-mir-126-3p, and hsa-mir-27b-3p), (4) demonstrate the compatibility of well-established R packages for RNA-Seq analysis for researchers interested but not yet familiar with this kind of analysis, and (5) present comparative proteomics analyses to show an association between our RNA-Seq data analyses and corresponding changes in protein expression.

Optimization of synthetic molecular reporters for a mesenchymal glioblastoma transcriptional program by integer programing

MOTIVATION

A recent approach to perform genetic tracing of complex biological problems involves the generation of synthetic deoxyribonucleic acid (DNA) probes that specifically mark cells with a phenotype of interest. These synthetic locus control regions (sLCRs), in turn, drive the expression of a reporter gene, such as fluorescent protein. To build functional and specific sLCRs, it is critical to accurately select multiple bona fide cis-regulatory elements from the target cell phenotype cistrome. This selection occurs by maximizing the number and diversity of transcription factors (TFs) within the sLCR, yet the size of the final sLCR should remain limited.

RESULTS

In this work, we discuss how optimization, in particular integer programing, can be used to systematically address the construction of a specific sLCR and optimize pre-defined properties of the sLCR. Our presented instance of a linear optimization problem maximizes the activation potential of the sLCR such that its size is limited to a pre-defined length and a minimum number of all TFs deemed sufficiently characteristic for the phenotype of interest is covered. We generated an sLCR to trace the mesenchymal glioblastoma program in patients by solving our corresponding linear program with the software optimizer Gurobi. Considering the binding strength of transcription factor binding sites (TFBSs) with their TFs as a proxy for activation potential, the optimized sLCR scores similarly to an sLCR experimentally validated in vivo, and is smaller in size while having the same coverage of TFBSs.

AVAILABILITY AND IMPLEMENTATION

We provide a Python implementation of the presented framework in the Supplementary Material with which an optimal selection of cis-regulatory elements can be calculated once the target set of TFs and their binding strength with their TFBSs is known.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

A modular systems biological modelling framework studies cyclic nucleotide signaling in platelets

BACKGROUND

The cyclic nucleotides cAMP and cGMP inhibit platelet activation. Different platelet signaling modules work together. We develop here a modelling framework to integrate different signaling modules and apply it to platelets.

RESULTS

We introduce a novel standardized bilinear coupling mechanism allowing sub model debugging and standardization of coupling with optimal data driven modelling by methods from optimization. Besides cAMP signaling our model considers specific cGMP effects including external stimuli by drugs. Moreover, the output of the cGMP module serves as input for a modular model of VASP phosphorylation and for the activity of cAMP and cGMP pathways in platelets. Experimental data driven modeling allows us to design models with quantitative output. We use the condensed information about involved regulation and system responses for modeling drug effects and obtaining optimal experimental settings. Stepwise further validation of our model is given by direct experimental data.

CONCLUSIONS

We present a general framework for model integration using modules and their stimulus responses. We demonstrate it by a multi-modular model for platelet signaling focusing on cGMP and VASP phosphorylation. Moreover, this allows to estimate drug action on any of the inhibitory cyclic nucleotide pathways (cGMP, cAMP) and is supported by experimental data.

Mechanisms Underlying Mu Opioid Receptor Effects on Parallel Fiber-Purkinje Cell Synaptic Transmission in Mouse Cerebellar Cortex

μ-opioid receptors (MOR) are widely expressed in the brain, varying in density in different areas. Activation of MORs underlies analgesia, euphoria, but may lead to tolerance, dependence, and ultimately opioid addiction. The Purkinje cell (PC) is the only efferent neuron in the cerebellar cortex and receives glutamatergic synaptic inputs from the parallel fibers formed by the axons of granule cells. Studies have shown that MORs are expressed during the development of cerebellar cells. However, the distribution of MOR and their effects on PF-PC synaptic transmission remain unclear. To examine these questions, we used whole-cell patch clamp recordings and pharmacological methods to determine the effects and mechanisms of MOR activation on synaptic transmission at PF-PC synapses. The MOR-selective agonist DAMGO significantly reduced the amplitude and area under the curve (AUC) of PF-PC evoked (e) EPSCs, and increased the paired-pulse ratio (PPR).DAMGO-induced inhibitory effects on PF-PC eEPSCs and PPR were abolished by MOR specific blocker CTOP. Further, DAMGO significantly reduced the frequency of PF-PC mEPSCs, but had no obvious effect on their amplitude, suggesting a presynaptic site of action. The DAMGO-induced reduction in the frequency of PF-PC mEPSCs also was blocked by CTOP. A protein kinase A (PKA) inhibitor PKI added in the pipette solution did not affect the inhibitory effects on PF-PC mEPSCs induced by DAMGO. Both the PKA inhibitor K5720 and MEK inhibitor U0126 in artificial cerebrospinal fluid (ACSF) prevented the inhibitory effects of DAMGO on PF-PC mEPSCs. These findings reveal that MORs are expressed in presynaptic PF axon terminals, where DAMGO can activate presynaptic MORs to inhibit PF-PC synaptic transmission by regulating the release of glutamate. G-protein-dependent cAMP-PKA signaling pathway may be involved in this process.

Harnessing RKIP to Combat Heart Disease and Cancer

Cancer and heart disease are leading causes of morbidity and mortality worldwide. These diseases have common risk factors, common molecular signaling pathways that are central to their pathogenesis, and even some disease phenotypes that are interdependent. Thus, a detailed understanding of common regulators is critical for the development of new and synergistic therapeutic strategies. The Raf kinase inhibitory protein (RKIP) is a regulator of the cellular kinome that functions to maintain cellular robustness and prevent the progression of diseases including heart disease and cancer. Two of the key signaling pathways controlled by RKIP are the β-adrenergic receptor (βAR) signaling to protein kinase A (PKA), particularly in the heart, and the MAP kinase cascade Raf/MEK/ERK1/2 that regulates multiple diseases. The goal of this review is to discuss how we can leverage RKIP to suppress cancer without incurring deleterious effects on the heart. Specifically, we discuss: (1) How RKIP functions to either suppress or activate βAR (PKA) and ERK1/2 signaling; (2) How we can prevent cancer-promoting kinase signaling while at the same time avoiding cardiotoxicity.

ERK1/2 Activity Is Critical for the Outcome of Ischemic Stroke

Ischemic disorders are the leading cause of death worldwide. The extracellular signal-regulated kinases 1 and 2 (ERK1/2) are thought to affect the outcome of ischemic stroke. However, it is under debate whether activation or inhibition of ERK1/2 is beneficial. In this study, we report that the ubiquitous overexpression of wild-type ERK2 in mice (ERK2^wt) is detrimental after transient occlusion of the middle cerebral artery (tMCAO), as it led to a massive increase in infarct volume and neurological deficits by increasing blood–brain barrier (BBB) leakiness, inflammation, and the number of apoptotic neurons. To compare ERK1/2 activation and inhibition side-by-side, we also used mice with ubiquitous overexpression of the Raf-kinase inhibitor protein (RKIP^wt) and its phosphorylation-deficient mutant RKIP^S153A, known inhibitors of the ERK1/2 signaling cascade. RKIP^wt and RKIP^S153A attenuated ischemia-induced damages, in particular via anti-inflammatory signaling. Taken together, our data suggest that stimulation of the Raf/MEK/ERK1/2-cascade is severely detrimental and its inhibition is rather protective. Thus, a tight control of the ERK1/2 signaling is essential for the outcome in response to ischemic stroke.

A novel β2-AR agonist, Higenamine, induces β-arrestin-biased signaling

The biased ligands in G protein-coupled receptors (GPCRs) have opened new avenues for developing safer and more effective drugs. However, the identification of such biased ligands as drug candidates is highly desirable. Here, we report that Higenamine, a compound isolated from a Chinese herb, functions as a novel β-arrestin-biased ligand of the β₂-adrenergic receptor (β₂-AR). The radioligand binding assays demonstrated that Higenamine was the ligand of β₂-AR. Higenamine induced phosphorylation of extracellular signal-regulated kinase 1/2 (ERK1/2), which can be blocked by propranolol, an inhibitor of β₂-AR. The Gi protein inhibitor, pertussis toxin, had no effect on the phosphorylation of ERK1/2 induced by Higenamine. Furthermore, Higenamine induced ERK1/2 phosphorylation through transactivation of Epithelial growth factor receptor (EGFR). We also found that Higenamine-induced-ERK1/2 phosphorylation is dependent on β-arrestin1/2, and HG inhibits Doxorubicin-induced cardiomyocyte apoptosis. Our results identify Higenamine as a novel biased ligand via the β-arrestin-dependent pathway. These findings give us a better understanding of Higenamine's potential role in designing diagnostic and therapeutic strategies.

Alveolar Regeneration in COVID-19 Patients: A Network Perspective

A viral infection involves entry and replication of viral nucleic acid in a host organism, subsequently leading to biochemical and structural alterations in the host cell. In the case of SARS-CoV-2 viral infection, over-activation of the host immune system may lead to lung damage. Albeit the regeneration and fibrotic repair processes being the two protective host responses, prolonged injury may lead to excessive fibrosis, a pathological state that can result in lung collapse. In this review, we discuss regeneration and fibrosis processes in response to SARS-CoV-2 and provide our viewpoint on the triggering of alveolar regeneration in coronavirus disease 2019 (COVID-19) patients.

An effective model of endogenous clocks and external stimuli determining circadian rhythms

Circadian endogenous clocks of eukaryotic organisms are an established and rapidly developing research field. To investigate and simulate in an effective model the effect of external stimuli on such clocks and their components we developed a software framework for download and simulation. The application is useful to understand the different involved effects in a mathematical simple and effective model. This concerns the effects of Zeitgebers, feedback loops and further modifying components. We start from a known mathematical oscillator model, which is based on experimental molecular findings. This is extended with an effective framework that includes the impact of external stimuli on the circadian oscillations including high dose pharmacological treatment. In particular, the external stimuli framework defines a systematic procedure by input-output-interfaces to couple different oscillators. The framework is validated by providing phase response curves and ranges of entrainment. Furthermore, Aschoffs rule is computationally investigated. It is shown how the external stimuli framework can be used to study biological effects like points of singularity or oscillators integrating different signals at once. The mathematical framework and formalism is generic and allows to study in general the effect of external stimuli on oscillators and other biological processes. For an easy replication of each numerical experiment presented in this work and an easy implementation of the framework the corresponding Mathematica files are fully made available. They can be downloaded at the following link: https://www.biozentrum.uni-wuerzburg.de/bioinfo/computing/circadian/ .

Interactions between the ERK1/2 signaling pathway and PCAF play a key role in PE‑induced cardiomyocyte hypertrophy

Cardiomyocyte hypertrophy is a compensatory phase of chronic heart failure that is induced by the activation of multiple signaling pathways. The extracellular signal-regulated protein kinase (ERK) signaling pathway is an important regulator of cardiomyocyte hypertrophy. In our previous study, it was demonstrated that phenylephrine (PE)-induced cardiomyocyte hypertrophy involves the hyperacetylation of histone H3K9ac by P300/CBP-associated factor (PCAF). However, the upstream signaling pathway has yet to be fully identified. In the present study, the role of the extracellular signal-regulated protein kinase (ERK)1/2 signaling pathway in PE-induced cardiomyocyte hypertrophy was investigated. The mice cardiomyocyte hypertrophy model was successfully established by treating cells with PE in vitro. The results showed that phospho-(p-)ERK1/2 interacted with PCAF and modified the pattern of histone H3K9ac acetylation. An ERK inhibitor (U0126) and/or a histone acetylase inhibitor (anacardic acid; AA) attenuated the overexpression of phospho-ERK1/2 and H3K9ac hyperacetylation by inhibiting the expression of PCAF in PE-induced cardiomyocyte hypertrophy. Moreover, U0126 and/or AA could attenuate the overexpression of several biomarker genes related to cardiac hypertrophy (myocyte enhancer factor 2C, atrial natriuretic peptide, brain natriuretic peptide and β-myosin heavy chain) and prevented cardiomyocyte hypertrophy. These results revealed a novel mechanism in that AA protects against PE-induced cardiomyocyte hypertrophy in mice via the ERK1/2 signaling pathway, and by modifying the acetylation of H3K9ac. These findings may assist in the development of novel methods for preventing and treating hypertrophic cardiomyopathy.

Role of extracellular signal-regulated kinase 1/2 signaling underlying cardiac hypertrophy

Cardiac hypertrophy is the result of increased myocardial cell size responding to an increased workload and developmental signals. These extrinsic and intrinsic stimuli as key drivers of cardiac hypertrophy have spurred efforts to target their associated signaling pathways. The extracellular signal-regulated kinases 1/2 (ERK1/2), as an essential member of mitogen-activated protein kinases (MAPKs), has been widely recognized for promoting cardiac growth. Several modified transgenic mouse models have been generated through either affecting the upstream kinase to change ERK1/2 activity, manipulating the direct role of ERK1/2 in the heart, or targeting phosphatases or MAPK scaffold proteins to alter total ERK1/2 activity in response to an increased workload. Using these models, both regulation of the upstream events and modulation of each isoform and indirect effector could provide important insights into how ERK1/2 modulates cardiomyocyte biology. Furthermore, a plethora of compounds, inhibitors, and regulators have emerged in consideration of ERK, or its MAPK kinases, are possible therapeutic targets against cardiac hypertrophic diseases. Herein, is a review of the available evidence regarding the exact role of ERK1/2 in regulating cardiac hypertrophy and a discussion of pharmacological strategy for treatment of cardiac hypertrophy.