Systems-level interactions between insulin-EGF networks amplify mitogenic signaling

A PINK1 input threshold arises from positive feedback in the PINK1/Parkin mitophagy decision circuit

Mechanisms that prevent accidental activation of the PINK1/Parkin mitophagy circuit on healthy mitochondria are poorly understood. On the surface of damaged mitochondria, PINK1 accumulates and acts as the input signal to a positive feedback loop of Parkin recruitment, which in turn promotes mitochondrial degradation via mitophagy. However, PINK1 is also present on healthy mitochondria, where it could errantly recruit Parkin and thereby activate this positive feedback loop. Here, we explore emergent properties of the PINK1/Parkin circuit by quantifying the relationship between mitochondrial PINK1 concentrations and Parkin recruitment dynamics. We find that Parkin is recruited to mitochondria only if PINK1 levels exceed a threshold and then only after a delay that is inversely proportional to PINK1 levels. Furthermore, these two regulatory properties arise from the input-coupled positive feedback topology of the PINK1/Parkin circuit. These results outline an intrinsic mechanism by which the PINK1/Parkin circuit can avoid errant activation on healthy mitochondria.

PI3K signaling through a biochemical systems lens

Following 3 decades of extensive research into PI3K signaling, it is now evidently clear that the underlying network does not equate to a simple ON/OFF switch. This is best illustrated by the multifaceted nature of the many diseases associated with aberrant PI3K signaling, including common cancers, metabolic disease, and rare developmental disorders. However, we are still far from a complete understanding of the fundamental control principles that govern the numerous phenotypic outputs that are elicited by activation of this well-characterized biochemical signaling network, downstream of an equally diverse set of extrinsic inputs. At its core, this is a question on the role of PI3K signaling in cellular information processing and decision making. Here, we review the determinants of accurate encoding and decoding of growth factor signals and discuss outstanding questions in the PI3K signal relay network. We emphasize the importance of quantitative biochemistry, in close integration with advances in single-cell time-resolved signaling measurements and mathematical modeling.

Human Blood Serum Can Diminish EGFR-Targeted Inhibition of Squamous Carcinoma Cell Growth through Reactivation of MAPK and EGFR Pathways

Regardless of the presence or absence of specific diagnostic mutations, many cancer patients fail to respond to EGFR-targeted therapeutics, and a personalized approach is needed to identify putative (non)responders. We found previously that human peripheral blood and EGF can modulate the activities of EGFR-specific drugs on inhibiting clonogenity in model EGFR-positive A431 squamous carcinoma cells. Here, we report that human serum can dramatically abolish the cell growth rate inhibition by EGFR-specific drugs cetuximab and erlotinib. We show that this phenomenon is linked with derepression of drug-induced G1S cell cycle transition arrest. Furthermore, A431 cell growth inhibition by cetuximab, erlotinib, and EGF correlates with a decreased activity of ERK1/2 proteins. In turn, the EGF- and human serum-mediated rescue of drug-treated A431 cells restores ERK1/2 activity in functional tests. RNA sequencing revealed 1271 and 1566 differentially expressed genes (DEGs) in the presence of cetuximab and erlotinib, respectively. Erlotinib- and cetuximab-specific DEGs significantly overlapped. Interestingly, the expression of 100% and 75% of these DEGs restores to the no-drug level when EGF or a mixed human serum sample, respectively, is added along with cetuximab. In the case of erlotinib, EGF and human serum restore the expression of 39% and 83% of DEGs, respectively. We further assessed differential molecular pathway activation levels and propose that EGF/human serum-mediated A431 resistance to EGFR drugs can be largely explained by reactivation of the MAPK signaling cascade.

An integrated mechanistic and data-driven computational model predicts cell responses to high- and low-affinity EGFR ligands

The biophysical properties of ligand binding heavily influence the ability of receptors to specify cell fates. Understanding the rules by which ligand binding kinetics impact cell phenotype is challenging, however, because of the coupled information transfers that occur from receptors to downstream signaling effectors and from effectors to phenotypes. Here, we address that issue by developing an integrated mechanistic and data-driven computational modeling platform to predict cell responses to different ligands for the epidermal growth factor receptor (EGFR). Experimental data for model training and validation were generated using MCF7 human breast cancer cells treated with the high- and low-affinity ligands epidermal growth factor (EGF) and epiregulin (EREG), respectively. The integrated model captures the unintuitive, concentration-dependent abilities of EGF and EREG to drive signals and phenotypes differently, even at similar levels of receptor occupancy. For example, the model correctly predicts the dominance of EREG over EGF in driving a cell differentiation phenotype through AKT signaling at intermediate and saturating ligand concentrations and the ability of EGF and EREG to drive a broadly concentration-sensitive migration phenotype through cooperative ERK and AKT signaling. Parameter sensitivity analysis identifies EGFR endocytosis, which is differentially regulated by EGF and EREG, as one of the most important determinants of the alternative phenotypes driven by different ligands. The integrated model provides a new platform to predict how phenotypes are controlled by the earliest biophysical rate processes in signal transduction and may eventually be leveraged to understand receptor signaling system performance depends on cell context.

One-sentence summary

Integrated kinetic and data-driven EGFR signaling model identifies the specific signaling mechanisms that dictate cell responses to EGFR activation by different ligands.

Modeling Cellular Signaling Variability Based on Single-Cell Data: The TGFβ-SMAD Signaling Pathway

Nongenetic heterogeneity is key to cellular decisions, as even genetically identical cells respond in very different ways to the same external stimulus, e.g., during cell differentiation or therapeutic treatment of disease. Strong heterogeneity is typically already observed at the level of signaling pathways that are the first sensors of external inputs and transmit information to the nucleus where decisions are made. Since heterogeneity arises from random fluctuations of cellular components, mathematical models are required to fully describe the phenomenon and to understand the dynamics of heterogeneous cell populations. Here, we review the experimental and theoretical literature on cellular signaling heterogeneity, with special focus on the TGFβ/SMAD signaling pathway.

Insulin Resistance and Acne: The Role of Metformin as Alternative Therapy in Men

The association between acne and insulin resistance has not been investigated as thoroughly in males as it has been in women, despite the fact that in adult men, acne prevalence has grown. On the face, sebaceous glands produce and secrete sebum, which lubricates the skin and protects it from friction. Metformin, an insulin-sensitizing medication, may modify the association between acne vulgaris and insulin resistance (IR). Individuals with IR, metabolic syndrome or with impaired glucose tolerance are sometimes treated 'off label' with Metformin. In these conditions, IR may be a leading factor in the pathogenesis of acne, and in men, Metformin treatment may reduce the Global Acne Grading System (GAGS) score by enhancing insulin sensitivity. However, additional clinical studies are required to corroborate these assumptions.

Relating individual cell division events to single-cell ERK and Akt activity time courses

Biochemical correlates of stochastic single-cell fates have been elusive, even for the well-studied mammalian cell cycle. We monitored single-cell dynamics of the ERK and Akt pathways, critical cell cycle progression hubs and anti-cancer drug targets, and paired them to division events in the same single cells using the non-transformed MCF10A epithelial line. Following growth factor treatment, in cells that divide both ERK and Akt activities are significantly higher within the S-G2 time window (~ 8.5-40 h). Such differences were much smaller in the pre-S-phase, restriction point window which is traditionally associated with ERK and Akt activity dependence, suggesting unappreciated roles for ERK and Akt in S through G2. Simple metrics of central tendency in this time window are associated with subsequent cell division fates. ERK activity was more strongly associated with division fates than Akt activity, suggesting Akt activity dynamics may contribute less to the decision driving cell division in this context. We also find that ERK and Akt activities are less correlated with each other in cells that divide. Network reconstruction experiments demonstrated that this correlation behavior was likely not due to crosstalk, as ERK and Akt do not interact in this context, in contrast to other transformed cell types. Overall, our findings support roles for ERK and Akt activity throughout the cell cycle as opposed to just before the restriction point, and suggest ERK activity dynamics may be more important than Akt activity dynamics for driving cell division in this non-transformed context.

Control of cell state transitions

Understanding cell state transitions and purposefully controlling them is a longstanding challenge in biology. Here we present cell state transition assessment and regulation (cSTAR), an approach for mapping cell states, modelling transitions between them and predicting targeted interventions to convert cell fate decisions. cSTAR uses omics data as input, classifies cell states, and develops a workflow that transforms the input data into mechanistic models that identify a core signalling network, which controls cell fate transitions by influencing whole-cell networks. By integrating signalling and phenotypic data, cSTAR models how cells manoeuvre in Waddington's landscape1 and make decisions about which cell fate to adopt. Notably, cSTAR devises interventions to control the movement of cells in Waddington's landscape. Testing cSTAR in a cellular model of differentiation and proliferation shows a high correlation between quantitative predictions and experimental data. Applying cSTAR to different types of perturbation and omics datasets, including single-cell data, demonstrates its flexibility and scalability and provides new biological insights. The ability of cSTAR to identify targeted perturbations that interconvert cell fates will enable designer approaches for manipulating cellular development pathways and mechanistically underpinned therapeutic interventions.

Structural insights into Ras regulation by SIN1

Both the mTORC2 and Ras-ERK pathways respond to growth factor stimulation and play critical roles in cell growth and proliferation, disarray of these pathways leads to many diseases, especially cancer. These two signaling pathways crosstalk at many levels; recently it's become clear that the SIN1 component of mTORC2 could interact with Ras family small GTPases, but how these two proteins interact at the molecular level and the functional outcomes of this interaction remain to be addressed. In this work we determined the high-resolution structure of Ras-SIN1 complexes and revealed the detailed interaction mechanism. We also showed that Ras-SIN1 association inhibits insulin-induced ERK activation. Insights from this work could improve our understanding of the disease-causing mechanism of errant mTORC2 or Ras proteins.

Over the years it has been established that SIN1, a key component of mTORC2, could interact with Ras family small GTPases through its Ras-binding domain (RBD). The physical association of Ras and SIN1/mTORC2 could potentially affect both mTORC2 and Ras-ERK pathways. To decipher the precise molecular mechanism of this interaction, we determined the high-resolution structures of HRas/KRas-SIN1 RBD complexes, showing the detailed interaction interface. Mutation of critical interface residues abolished Ras-SIN1 interaction and in SIN1 knockout cells we demonstrated that Ras-SIN1 association promotes SGK1 activity but inhibits insulin-induced ERK activation. With structural comparison and competition fluorescence resonance energy transfer (FRET) assays we showed that HRas-SIN1 RBD association is much weaker than HRas-Raf1 RBD but is slightly stronger than HRas-PI3K RBD interaction, providing a possible explanation for the different outcome of insulin or EGF stimulation. We also found that SIN1 isoform lacking the PH domain binds stronger to Ras than other longer isoforms and the PH domain appears to have an inhibitory effect on Ras-SIN1 binding. In addition, we uncovered a Ras dimerization interface that could be critical for Ras oligomerization. Our results advance our understanding of Ras-SIN1 association and crosstalk between growth factor-stimulated pathways.

Potential and Therapeutic Roles of Diosmin in Human Diseases

Because of their medicinal characteristics, effectiveness, and importance, plant-derived flavonoids have been a possible subject of research for many years, particularly in the last decade. Plants contain a huge number of flavonoids, and Diosmin, a flavone glycoside, is one of them. Numerous in-vitro and in-vivo studies have validated Diosmin’s extensive range of biological capabilities which present antioxidative, antihyperglycemic, anti-inflammatory, antimutagenic, and antiulcer properties. We have presented this review work because of the greater biological properties and influences of Diosmin. We have provided a brief overview of Diosmin, its pharmacology, major biological properties, such as anti-cancer, anti-diabetic, antibacterial, anticardiovascular, liver protection, and neuroprotection, therapeutic approach, potential Diosmin targets, and pathways that are known to be associated with it.

OncoboxPD: human 51 672 molecular pathways database with tools for activity calculating and visualization

OncoboxPD (Oncobox pathway databank) available at https://open.oncobox.com is the collection of 51 672 uniformly processed human molecular pathways. Superposition of all pathways formed interactome graph of protein–protein interactions and metabolic reactions containing 361 654 interactions and 64 095 molecular participants. Pathways are uniformly classified by biological processes, and each pathway node is algorithmically functionally annotated by specific activator/repressor role. This enables online calculation of statistically supported pathway activation levels (PALs) with the built-in bioinformatic tool using custom RNA/protein expression profiles. Each pathway can be visualized as static or dynamic graph, where vertices are molecules participating in a pathway and edges are interactions or reactions between them. Differentially expressed nodes in a pathway can be visualized in two-color mode with user-defined color scale. For every comparison, OncoboxPD also generates a graph summarizing top up- and downregulated pathways.

Mechanisms of Functional Pleiotropy of p73 in Cancer and Beyond

The transcription factor p73 is a structural and functional homolog of TP53, the most famous and frequently mutated tumor-suppressor gene. The TP73 gene can synthesize an overwhelming number of isoforms via splicing events in 5′ and 3′ ends and alternative promoter usage. Although it originally came into the spotlight due to the potential of several of these isoforms to mimic p53 functions, it is now clear that TP73 has its own unique identity as a master regulator of multifaceted processes in embryonic development, tissue homeostasis, and cancer. This remarkable functional pleiotropy is supported by a high degree of mechanistic heterogeneity, which extends far-beyond the typical mode of action by transactivation and largely relies on the ability of p73 isoforms to form protein–protein interactions (PPIs) with a variety of nuclear and cytoplasmic proteins. Importantly, each p73 isoform carries a unique combination of functional domains and residues that facilitates the establishment of PPIs in a highly selective manner. Herein, we summarize the expanding functional repertoire of TP73 in physiological and oncogenic processes. We emphasize how TP73’s ability to control neurodevelopment and neurodifferentiation is co-opted in cancer cells toward neoneurogenesis, an emerging cancer hallmark, whereby tumors promote their own innervation. By further exploring the canonical and non-canonical mechanistic patterns of p73, we apprehend its functional diversity as the result of a sophisticated and coordinated interplay of: (a) the type of p73 isoforms (b) the presence of p73 interaction partners in the cell milieu, and (c) the architecture of target gene promoters. We suppose that dysregulation of one or more of these parameters in tumors may lead to cancer initiation and progression by reactivating p73 isoforms and/or p73-regulated differentiation programs thereof in a spatiotemporally inappropriate manner. A thorough understanding of the mechanisms supporting p73 functional diversity is of paramount importance for the efficient and precise p73 targeting not only in cancer, but also in other pathological conditions where TP73 dysregulation is causally involved.

Concentrated growth factor regulates the macrophage-mediated immune response

Concentrated growth factor (CGF) is a promising regenerative material that serves as a scaffold and adjunct growth factor for tissue engineering. The host immune response, particularly macrophage activity, plays a critical role in injury repair and tissue regeneration. However, the biological effect of CGF on the immune response is not clear. To enrich the theoretical groundwork for clinical application, the present study examined the immunoregulatory role of CGF in macrophage functional activities in vitro. The CGF scaffold appeared as a dense fibrin network with multiple embedded leukocytes and platelets, and it was biocompatible with macrophages. Concentrated bioactive factors in the CGF extract enhanced THP-1 monocyte recruitment and promoted the maturation of suspended monocytes into adherent macrophages. CGF extract also promoted THP-1 macrophage polarization toward the M2 phenotype with upregulated CD163 expression, as detected by cell morphology and surface marker expression. A cytokine antibody array showed that CGF extract exerted a regulatory effect on macrophage functional activities by reducing secretion of the inflammatory factor interleukin-1β while inducing expression of the chemokine regulated on activation, normal T cell expressed and secreted. Mechanistically, the AKT signaling pathway was activated, and an AKT inhibitor partially suppressed the immunomodulatory effect of CGF. Our findings reveal that CGF induces a favorable immune response mediated by macrophages, which represents a promising strategy for functional tissue regeneration.

Endothelial Insulin Receptors Promote VEGF-A Signaling via ERK1/2 and Sprouting Angiogenesis

Endothelial insulin receptors (Insr) promote sprouting angiogenesis, although the underpinning cellular and molecular mechanisms are unknown. Comparing mice with whole-body insulin receptor haploinsufficiency (Insr+/-) against littermate controls, we found impaired limb perfusion and muscle capillary density after inducing hind-limb ischemia; this was in spite of increased expression of the proangiogenic growth factor Vegfa. Insr+/- neonatal retinas exhibited reduced tip cell number and branching complexity during developmental angiogenesis, which was also found in separate studies of mice with endothelium-restricted Insr haploinsufficiency. Functional responses to vascular endothelial growth factor A (VEGF-A), including in vitro angiogenesis, were also impaired in aortic rings and pulmonary endothelial cells from Insr+/- mice. Human umbilical vein endothelial cells with shRNA-mediated knockdown of Insr also demonstrated impaired functional angiogenic responses to VEGF-A. VEGF-A signaling to Akt and endothelial nitric oxide synthase was intact, but downstream signaling to extracellular signal-reduced kinase 1/2 (ERK1/2) was impaired, as was VEGF receptor-2 (VEGFR-2) internalization, which is required specifically for signaling to ERK1/2. Hence, endothelial insulin receptors facilitate the functional response to VEGF-A during angiogenic sprouting and are required for appropriate signal transduction from VEGFR-2 to ERK1/2.

Programmed Death-Ligand 1 (PD-L1) Expression Is Induced by Insulin in Pancreatic Ductal Adenocarcinoma Cells Pointing to Its Role in Immune Checkpoint Control

Type-2 diabetes (T2DM) is a risk factor for the development of pancreatic ductal adenocarcinoma (PDAC) and is characterized by insulin resistance and hyperinsulinemia. Besides the well-known growth-promoting activity of insulin or the other members of the Insulin/Insulin-like Growth factor (IGF) axis, we here describe an inducing effect of insulin on PD-L1 expression in PDAC cells. Treatment of the PDAC cell lines BxPc3, A818-6, and T3M4 with insulin increased PD-L1 expression in a time- and dose dependent fashion, as shown by Western blot and qPCR analysis. siRNA mediated knock-down showed that the effects of insulin on PD-L1 depend on the insulin and IGF receptors (InsR and IGFR, respectively). In addition, a crosstalk of insulin-induced ERK activation and Epidermal Growth Factor (EGF) triggered PD-L1 expression. This involves different mechanisms in the three cell lines including upregulation of InsR-A expression in A818-6 and modulation of the adaptor protein Gab1 in BxPc3 cells. As a consequence of the insulin-induced PD-L1 expression, PDAC cells suppress the proliferation of activated human CD8+ T-cells in coculture experiments. The suppression of CD8+ cell proliferation by insulin-pretreated PDAC cells was reversed by PD-1 blockade with Pembrolizumab or by PD-L1 siRNA. Furthermore, the clinical relevance of these observations was supported by detecting a coexpression of cytoplasmic InsR (characteristic for its activation) and PD-L1 in tumor tissues from PDAC patients. Our findings provide a novel insight into the protumorigenic role of insulin in PDAC. Recognizing the impact of insulin on PD-L1 expression as part of the immune privilege, strategies to interfere with this mechanism could pave the way towards a more efficient immunotherapy of PDAC.

Inhibition of RPS6K reveals context-dependent Akt activity in luminal breast cancer cells

Aberrant signaling through insulin (Ins) and insulin-like growth factor I (IGF1) receptors contribute to the risk and advancement of many cancer types by activating cell survival cascades. Similarities between these pathways have thus far prevented the development of pharmacological interventions that specifically target either Ins or IGF1 signaling. To identify differences in early Ins and IGF1 signaling mechanisms, we developed a dual receptor (IGF1R & InsR) computational response model. The model suggested that ribosomal protein S6 kinase (RPS6K) plays a critical role in regulating MAPK and Akt activation levels in response to Ins and IGF1 stimulation. As predicted, perturbing RPS6K kinase activity led to an increased Akt activation with Ins stimulation compared to IGF1 stimulation. Being able to discern differential downstream signaling, we can explore improved anti-IGF1R cancer therapies by eliminating the emergence of compensation mechanisms without disrupting InsR signaling.

Using proteomic and transcriptomic data to assess activation of intracellular molecular pathways

Analysis of molecular pathway activation is the recent instrument that helps to quantize activities of various intracellular signaling, structural, DNA synthesis and repair, and biochemical processes. This may have a deep impact in fundamental research, bioindustry, and medicine. Unlike gene ontology analyses and numerous qualitative methods that can establish whether a pathway is affected in principle, the quantitative approach has the advantage of exactly measuring the extent of a pathway up/downregulation. This results in emergence of a new generation of molecular biomarkers-pathway activation levels, which reflect concentration changes of all measurable pathway components. The input data can be the high-throughput proteomic or transcriptomic profiles, and the output numbers take both positive and negative values and positively reflect overall pathway activation. Due to their nature, the pathway activation levels are more robust biomarkers compared to the individual gene products/protein levels. Here, we review the current knowledge of the quantitative gene expression interrogation methods and their applications for the molecular pathway quantization. We consider enclosed bioinformatic algorithms and their applications for solving real-world problems. Besides a plethora of applications in basic life sciences, the quantitative pathway analysis can improve molecular design and clinical investigations in pharmaceutical industry, can help finding new active biotechnological components and can significantly contribute to the progressive evolution of personalized medicine. In addition to the theoretical principles and concepts, we also propose publicly available software for the use of large-scale protein/RNA expression data to assess the human pathway activation levels.

System, Method and Software for Calculation of a Cannabis Drug Efficiency Index for the Reduction of Inflammation

There are many varieties of Cannabis sativa that differ from each other by composition of cannabinoids, terpenes and other molecules. The medicinal properties of these cultivars are often very different, with some being more efficient than others. This report describes the development of a method and software for the analysis of the efficiency of various cannabis extracts to detect the anti-inflammatory properties of the various cannabis extracts. The method uses high-throughput gene expression profiling data but can potentially use other omics data as well. According to the signaling pathway topology, the gene expression profiles are convoluted into the signaling pathway activities using a signaling pathway impact analysis (SPIA) method. The method was tested by inducing inflammation in human 3D epithelial tissues, including intestine, oral and skin, and then exposing these tissues to various extracts and then performing transcriptome analysis. The analysis showed a different efficiency of the various extracts in restoring the transcriptome changes to the pre-inflammation state, thus allowing to calculate a different cannabis drug efficiency index (CDEI).

Underlying Mechanism of Insulin Resistance: A Bioinformatics Analysis Based on Validated Related-Genes from Public Disease Databases

Background

The underlying mechanism of insulin resistance is complex; bioinformatics analysis is used to explore the mechanism based differential expression genes (DEGs) obtained from omics analysis. However, the expression and role of most DEGs involved in bioinformatics analysis are invalidated. This study aimed to disclose the mechanism of insulin resistance via bioinformatics analysis based on validated insulin resistance-related genes (IRRGs) collected from public disease-gene databases.

Material/Methods

IRRGs were collected from 4 disease databases including NCBI-Gene, CTD, RGD, and Phenopedia. GO and KEGG analysis of IRRGs were performed by DAVID. Then, the STRING database was employed to construct a protein–protein interaction (PPI) network of IRRGs. The module analysis and hub genes identification were carried out by MCODE and cytoHubba plugin of Cytoscape based on the primary PPI network, respectively.

Results

A total of 1195 IRRGs were identified. Response to drug, hypoxia, insulin, positive regulation of transcription from RNA polymerase II promoter, cell proliferation, inflammatory response, negative regulation of apoptotic process, glucose homeostasis, cellular response to insulin stimulus, and aging were proposed as the crucial functions related to insulin resistance. Ten insulin resistance-related pathways included the pathways of insulin resistance, pathways in cancer, adipocytokine, prostate cancer, PI3K-Akt, insulin, AMPK, HIF-1, prolactin, and pancreatic cancer signaling pathway were revealed. INS, AKT1, IL-6, TP53, TNF, VEGFA, MAPK3, EGFR, EGF, and SRC were identified as the top 10 hub genes.

Conclusions

The current study presented a landscape view of possible underlying mechanism of insulin resistance by bioinformatics analysis based on validated IRRGs.

Applications of personalised signalling network models in precision oncology

As our ability to provide in-depth, patient-specific characterisation of the molecular alterations within tumours rapidly improves, it is becoming apparent that new approaches will be required to leverage the power of this data and derive the full benefit for each individual patient. Systems biology approaches are beginning to emerge within this field as a potential method of incorporating large volumes of network level data and distilling a coherent, clinically-relevant prediction of drug response. However, the initial promise of this developing field is yet to be realised. Here we argue that in order to develop these precise models of individual drug response and tailor treatment accordingly, we will need to develop mathematical models capable of capturing both the dynamic nature of drug-response signalling networks and key patient-specific information such as mutation status or expression profiles. We also review the modelling approaches commonly utilised within this field, and outline recent examples of their use in furthering the application of systems biology for a precision medicine approach to cancer treatment.

Patient-specific logic models of signaling pathways from screenings on cancer biopsies to prioritize personalized combination therapies

Mechanistic modeling of signaling pathways mediating patient-specific response to therapy can help to unveil resistance mechanisms and improve therapeutic strategies. Yet, creating such models for patients, in particular for solid malignancies, is challenging. A major hurdle to build these models is the limited material available that precludes the generation of large-scale perturbation data. Here, we present an approach that couples ex vivo high-throughput screenings of cancer biopsies using microfluidics with logic-based modeling to generate patient-specific dynamic models of extrinsic and intrinsic apoptosis signaling pathways. We used the resulting models to investigate heterogeneity in pancreatic cancer patients, showing dissimilarities especially in the PI3K-Akt pathway. Variation in model parameters reflected well the different tumor stages. Finally, we used our dynamic models to efficaciously predict new personalized combinatorial treatments. Our results suggest that our combination of microfluidic experiments and mathematical model can be a novel tool toward cancer precision medicine.

Mechanisms of resistance to mTOR inhibitors

In several tumors the PI3K/AKT/mTOR pathway is frequently disrupted, an event that results in uncontrolled cell proliferation and tumor growth. Through the years, several compounds have been developed to inhibit the pathway at different steps: the mammalian target of rapamycin (mTOR) seemed to be the most qualified target. However, this kinase has such a key role in cell survival that mechanisms of resistance are rapidly developed. Nevertheless, clinical results obtained with mTOR inhibitors in breast cancer, renal cell carcinoma, neuroendocrine tumors and mantle cell lymphoma push oncologists to actively further develop these drugs, maybe by better selecting the population to which they are offered, through the research of predictive factors of responsiveness. In this review, we aim to describe mechanisms of resistance to mTOR inhibitors, from preclinical and clinical perspectives.

Quantitation of Molecular Pathway Activation Using RNA Sequencing Data

Intracellular molecular pathways (IMPs) control all major events in the living cell. IMPs are considered hotspots in biomedical sciences and thousands of IMPs have been discovered for humans and model organisms. Knowledge of IMPs activation is essential for understanding biological functions and differences between the biological objects at the molecular level. Here we describe the Oncobox system for accurate quantitative scoring activities of up to several thousand molecular pathways based on high throughput molecular data. Although initially designed for gene expression and mainly RNA sequencing data, Oncobox is now also applicable for quantitative proteomics, microRNA and transcription factor binding sites mapping data. The Oncobox system includes modules of gene expression data harmonization, aggregation and comparison and a recursive algorithm for automatic annotation of molecular pathways. The universal rationale of Oncobox enables scoring of signaling, metabolic, cytoskeleton, immunity, DNA repair, and other pathways in a multitude of biological objects. The Oncobox system can be helpful to all those working in the fields of genetics, biochemistry, interactomics, and big data analytics in molecular biomedicine.

An investigation of the equine epidermal growth factor system during hyperinsulinemic laminitis

Equine laminitis is a disease of the digital epidermal lamellae typified by epidermal cell proliferation and structural collapse. Most commonly the disease is caused by hyperinsulinemia, although the pathogenesis is incompletely understood. Insulin can activate the epidermal growth factor (EGF) system in other species and the present study tested the hypothesis that upregulation of EGF receptor (EGFR) signalling is a key factor in laminitis pathophysiology. First, we examined lamellar tissue from healthy Standardbred horses and those with induced hyperinsulinemia and laminitis for EGFR distribution and quantity using immunostaining and gene expression, respectively. Phosphorylation of EGFR was also quantified. Next, plasma EGF concentrations were compared in healthy and insulin-infused horses, and in healthy and insulin-dysregulated ponies before and after feeding. The EGFR were localised to the secondary epidermal lamellae, with stronger staining in parabasal, rather than basal, cells. No change in EGFR gene expression occurred with laminitis, although the receptor showed some phosphorylation. No difference was seen in EGF concentrations in horses, but in insulin-dysregulated ponies mean, post-prandial EGF concentrations were almost three times higher than in healthy ponies (274 ± 90 vs. 97.4 ± 20.9 pg/mL, P = 0.05). Although the EGFR does not appear to play a major pathogenic role in hyperinsulinemic laminitis, the significance of increased EGF in insulin-dysregulated ponies deserves further investigation.

Elevated Expression of A-Raf and FA2H in Hepatocellular Carcinoma is Associated with Lipid Metabolism Dysregulation and Cancer Progression

BACKGROUND

Identification of events leading to hepatocellular carcinoma (HCC) progression is essential for understanding its pathophysiology. The aims of this study are to identify and characterize differentially expressed proteins in serum of HCC-bearing rats and the corresponding controls during cancer initiation, progression and tumorigenesis.

METHODS

Chemical carcinogens, N-Nitrosodiethylamine and 2-aminoacetylfluorine are administered to induce HCC to male Wistar rats. The 2D-Electrophoresis and PD-Quest analyses are performed to identify several differentially expressed proteins in serum of HCC-bearing animals. These proteins are further characterized by MALDI-TOF-MS/MS analyses. Using pathwaylinker a HCC-specific network is analyzed among the MALDITOF- MS/MS characterized proteins and their interactors.

RESULTS

Carcinogen administration caused inflammation leading to liver injury and HCC development. Liver inflammation was confirmed by increase in the levels of TNF-α and IL-6 in carcinogen treated rats. We report significant increase in expression of two differentially expressed proteins, namely, A-Raf and Fatty Acid 2- Hydroxylase (FA2H), at early stage of HCC initiation, during its progression and at tumor stage. Real-time PCR analysis of mRNA for these proteins confirmed up-regulation of their transcripts. Further, we validated our experimental data with sera of clinically confirmed liver cancer patients.

CONCLUSION

The study suggests that FA2H and A-Raf play a major role in the progression of HCC.

ModelBricks-modules for reproducible modeling improving model annotation and provenance

Most computational models in biology are built and intended for "single-use"; the lack of appropriate annotation creates models where the assumptions are unknown, and model elements are not uniquely identified. Simply recreating a simulation result from a publication can be daunting; expanding models to new and more complex situations is a herculean task. As a result, new models are almost always created anew, repeating literature searches for kinetic parameters, initial conditions and modeling specifics. It is akin to building a brick house starting with a pile of clay. Here we discuss a concept for building annotated, reusable models, by starting with small well-annotated modules we call ModelBricks. Curated ModelBricks, accessible through an open database, could be used to construct new models that will inherit ModelBricks annotations and thus be easier to understand and reuse. Key features of ModelBricks include reliance on a commonly used standard language (SBML), rule-based specification describing species as a collection of uniquely identifiable molecules, association with model specific numerical parameters, and more common annotations. Physical bricks can vary substantively; likewise, to be useful the structure of ModelBricks must be highly flexible-it should encapsulate mechanisms from single reactions to multiple reactions in a complex process. Ultimately, a modeler would be able to construct large models by using multiple ModelBricks, preserving annotations and provenance of model elements, resulting in a highly annotated model. We envision the library of ModelBricks to rapidly grow from community contributions. Persistent citable references will incentivize model creators to contribute new ModelBricks.

Signal flow control of complex signaling networks

Complex disease such as cancer is often caused by genetic mutations that eventually alter the signal flow in the intra-cellular signaling network and result in different cell fate. Therefore, it is crucial to identify control targets that can most effectively block such unwanted signal flow. For this purpose, systems biological analysis provides a useful framework, but mathematical modeling of complicated signaling networks requires massive time-series measurements of signaling protein activity levels for accurate estimation of kinetic parameter values or regulatory logics. Here, we present a novel method, called SFC (Signal Flow Control), for identifying control targets without the information of kinetic parameter values or regulatory logics. Our method requires only the structural information of a signaling network and is based on the topological estimation of signal flow through the network. SFC will be particularly useful for a large-scale signaling network to which parameter estimation or inference of regulatory logics is no longer applicable in practice. The identified control targets have significant implication in drug development as they can be putative drug targets.

Aging-induced Akt activation involves in aging-related pathologies and Aβ-induced toxicity

Multicellular signals are altered in the processes of both aging and neurodegenerative diseases, including Alzheimer's disease (AD). Similarities in behavioral and cellular functional changes suggest a common regulator between aging and AD that remains undetermined. Our genetics and behavioral approaches revealed the regulatory role of Akt in both aging and AD pathogenesis. In this study, we found that the activity of Akt is upregulated during aging through epidermal growth factor receptor activation by using the fruit fly as an in vivo model. Downregulation of Akt in neurons improved cell survival, locomotor activity, and starvation challenge in both aged and Aβ42‐expressing flies. Interestingly, increased cAMP levels attenuated both Akt activation‐induced early death and Aβ42‐induced learning deficit in flies. At the molecular level, overexpression of Akt promoted Notch cleavage, suggesting that Akt is an endogenous activity regulator of γ‐secretase. Taken together, this study revealed that Akt is involved in the aging process and Aβ toxicity, and manipulating Akt can restore both neuronal functions and improve behavioral activity during the processes of aging and AD pathogenesis.

Coupled feedback regulation of nuclear factor of activated T-cells (NFAT) modulates activation-induced cell death of T cells

A properly functioning immune system is vital for an organism’s wellbeing. Immune tolerance is a critical feature of the immune system that allows immune cells to mount effective responses against exogenous pathogens such as viruses and bacteria, while preventing attack to self-tissues. Activation-induced cell death (AICD) in T lymphocytes, in which repeated stimulations of the T-cell receptor (TCR) lead to activation and then apoptosis of T cells, is a major mechanism for T cell homeostasis and helps maintain peripheral immune tolerance. Defects in AICD can lead to development of autoimmune diseases. Despite its importance, the regulatory mechanisms that underlie AICD remain poorly understood, particularly at an integrative network level. Here, we develop a dynamic multi-pathway model of the integrated TCR signalling network and perform model-based analysis to characterize the network-level properties of AICD. Model simulation and analysis show that amplified activation of the transcriptional factor NFAT in response to repeated TCR stimulations, a phenomenon central to AICD, is tightly modulated by a coupled positive-negative feedback mechanism. NFAT amplification is predominantly enabled by a positive feedback self-regulated by NFAT, while opposed by a NFAT-induced negative feedback via Carabin. Furthermore, model analysis predicts an optimal therapeutic window for drugs that help minimize proliferation while maximize AICD of T cells. Overall, our study provides a comprehensive mathematical model of TCR signalling and model-based analysis offers new network-level insights into the regulation of activation-induced cell death in T cells.

Short-term cellular memory tunes the signaling responses of the chemokine receptor CXCR4

The chemokine receptor CXCR4 regulates fundamental processes in development, normal physiology, and diseases, including cancer. Small subpopulations of CXCR4-positive cells drive the local invasion and dissemination of malignant cells during metastasis, emphasizing the need to understand the mechanisms controlling responses at the single-cell level to receptor activation by the chemokine ligand CXCL12. Using single-cell imaging, we discovered that short-term cellular memory of changes in environmental conditions tuned CXCR4 signaling to Akt and ERK, two kinases activated by this receptor. Conditioning cells with growth stimuli before CXCL12 exposure increased the number of cells that initiated CXCR4 signaling and the amplitude of Akt and ERK activation. Data-driven, single-cell computational modeling revealed that growth factor conditioning modulated CXCR4-dependent activation of Akt and ERK by decreasing extrinsic noise (preexisting cell-to-cell differences in kinase activity) in PI3K and mTORC1. Modeling established mTORC1 as critical for tuning single-cell responses to CXCL12-CXCR4 signaling. Our single-cell model predicted how combinations of extrinsic noise in PI3K, Ras, and mTORC1 superimposed on different driver mutations in the ERK and/or Akt pathways to bias CXCR4 signaling. Computational experiments correctly predicted that selected kinase inhibitors used for cancer therapy shifted subsets of cells to states that were more permissive to CXCR4 activation, suggesting that such drugs may inadvertently potentiate pro-metastatic CXCR4 signaling. Our work establishes how changing environmental inputs modulate CXCR4 signaling in single cells and provides a framework to optimize the development and use of drugs targeting this signaling pathway.

Encircling the regions of the pharmacogenomic landscape that determine drug response

BACKGROUND

The integration of large-scale drug sensitivity screens and genome-wide experiments is changing the field of pharmacogenomics, revealing molecular determinants of drug response without the need for previous knowledge about drug action. In particular, transcriptional signatures of drug sensitivity may guide drug repositioning, prioritize drug combinations, and point to new therapeutic biomarkers. However, the inherent complexity of transcriptional signatures, with thousands of differentially expressed genes, makes them hard to interpret, thus giving poor mechanistic insights and hampering translation to clinics.

METHODS

To simplify drug signatures, we have developed a network-based methodology to identify functionally coherent gene modules. Our strategy starts with the calculation of drug-gene correlations and is followed by a pathway-oriented filtering and a network-diffusion analysis across the interactome.

RESULTS

We apply our approach to 189 drugs tested in 671 cancer cell lines and observe a connection between gene expression levels of the modules and mechanisms of action of the drugs. Further, we characterize multiple aspects of the modules, including their functional categories, tissue-specificity, and prevalence in clinics. Finally, we prove the predictive capability of the modules and demonstrate how they can be used as gene sets in conventional enrichment analyses.

CONCLUSIONS

Network biology strategies like module detection are able to digest the outcome of large-scale pharmacogenomic initiatives, thereby contributing to their interpretability and improving the characterization of the drugs screened.

New insights into RAS biology reinvigorate interest in mathematical modeling of RAS signaling

RAS is the most frequently mutated gene across human cancers, but developing inhibitors of mutant RAS has proven to be challenging. Given the difficulties of targeting RAS directly, drugs that impact the other components of pathways where mutant RAS operates may potentially be effective. However, the system-level features, including different localizations of RAS isoforms, competition between downstream effectors, and interlocking feedback and feed-forward loops, must be understood to fully grasp the opportunities and limitations of inhibiting specific targets. Mathematical modeling can help us discern the system-level impacts of these features in normal and cancer cells. New technologies enable the acquisition of experimental data that will facilitate development of realistic models of oncogenic RAS behavior. In light of the wealth of empirical data accumulated over decades of study and the advancement of experimental methods for gathering new data, modelers now have the opportunity to advance progress toward realization of targeted treatment for mutant RAS-driven cancers.

Mechanisms of Insulin Action and Insulin Resistance

The 1921 discovery of insulin was a Big Bang from which a vast and expanding universe of research into insulin action and resistance has issued. In the intervening century, some discoveries have matured, coalescing into solid and fertile ground for clinical application; others remain incompletely investigated and scientifically controversial. Here, we attempt to synthesize this work to guide further mechanistic investigation and to inform the development of novel therapies for type 2 diabetes (T2D). The rational development of such therapies necessitates detailed knowledge of one of the key pathophysiological processes involved in T2D: insulin resistance. Understanding insulin resistance, in turn, requires knowledge of normal insulin action. In this review, both the physiology of insulin action and the pathophysiology of insulin resistance are described, focusing on three key insulin target tissues: skeletal muscle, liver, and white adipose tissue. We aim to develop an integrated physiological perspective, placing the intricate signaling effectors that carry out the cell-autonomous response to insulin in the context of the tissue-specific functions that generate the coordinated organismal response. First, in section II, the effectors and effects of direct, cell-autonomous insulin action in muscle, liver, and white adipose tissue are reviewed, beginning at the insulin receptor and working downstream. Section III considers the critical and underappreciated role of tissue crosstalk in whole body insulin action, especially the essential interaction between adipose lipolysis and hepatic gluconeogenesis. The pathophysiology of insulin resistance is then described in section IV. Special attention is given to which signaling pathways and functions become insulin resistant in the setting of chronic overnutrition, and an alternative explanation for the phenomenon of ‟selective hepatic insulin resistanceˮ is presented. Sections V, VI, and VII critically examine the evidence for and against several putative mediators of insulin resistance. Section V reviews work linking the bioactive lipids diacylglycerol, ceramide, and acylcarnitine to insulin resistance; section VI considers the impact of nutrient stresses in the endoplasmic reticulum and mitochondria on insulin resistance; and section VII discusses non-cell autonomous factors proposed to induce insulin resistance, including inflammatory mediators, branched-chain amino acids, adipokines, and hepatokines. Finally, in section VIII, we propose an integrated model of insulin resistance that links these mediators to final common pathways of metabolite-driven gluconeogenesis and ectopic lipid accumulation.

Fitting mathematical models of biochemical pathways to steady state perturbation response data without simulating perturbation experiments

Fitting Ordinary Differential Equation (ODE) models of signal transduction networks (STNs) to experimental data is a challenging problem. Computational parameter fitting algorithms simulate a model many times with different sets of parameter values until the simulated STN behaviour match closely with experimental data. This process can be slow when the model is fitted to measurements of STN responses to numerous perturbations, since this requires simulating the model as many times as the number of perturbations for each set of parameter values. Here, I propose an approach that avoids simulating perturbation experiments when fitting ODE models to steady state perturbation response (SSPR) data. Instead of fitting the model directly to SSPR data, it finds model parameters which provides a close match between the scaled Jacobian matrices (SJM) of the model, which are numerically calculated using the model's rate equations and estimated from SSPR data using modular response analysis (MRA). The numerical estimation of SJM of an ODE model does not require simulating perturbation experiments, saving significant computation time. The effectiveness of this approach is demonstrated by fitting ODE models of the Mitogen Activated Protein Kinase (MAPK) pathway using simulated and real SSPR data.

Changes in cardiac resident fibroblast physiology and phenotype in aging

The cardiac fibroblast plays a central role in tissue homeostasis and in repair after injury. With aging, dysregulated cardiac fibroblasts have a reduced capacity to activate a canonical transforming growth factor-β-Smad pathway and differentiate poorly into contractile myofibroblasts. That results in the formation of an insufficient scar after myocardial infarction. In contrast, in the uninjured aged heart, fibroblasts are activated and acquire a profibrotic phenotype that leads to interstitial fibrosis, ventricular stiffness, and diastolic dysfunction, all conditions that may lead to heart failure. There is an apparent paradox in aging, wherein reparative fibrosis is impaired but interstitial, adverse fibrosis is augmented. This could be explained by analyzing the effectiveness of signaling pathways in resident fibroblasts from young versus aged hearts. Whereas defective signaling by transforming growth factor-β leads to insufficient scar formation by myofibroblasts, enhanced activation of the ERK1/2 pathway may be responsible for interstitial fibrosis mediated by activated fibroblasts. Listen to this article's corresponding podcast at https://ajpheart.podbean.com/e/fibroblast-phenotypic-changes-in-the-aging-heart/ .

The extracellular matrix as a key regulator of intracellular signalling networks

The extracellular matrix (ECM) is a salient feature of all solid tissues within the body. This complex, acellular entity is composed of hundreds of individual molecules whose assembly, architecture and biomechanical properties are critical to controlling the behaviour and phenotype of the different cell types residing within tissues. Cells are the basic unit of life and the core building block of tissues and organs. At their simplest, they follow a set of rules, governed by their genetic code and effected through the complex protein signalling networks that these genes encode. These signalling networks assimilate and process the information received by the cell to control cellular decisions that govern cell fate. The ECM is the biggest provider of external stimuli to cells and as such is responsible for influencing intracellular signalling dynamics. In this review, we discuss the inclusion of ECM as a central regulatory signalling sub-network in computational models of cellular decision making, with a focus on its role in diseases such as cancer. LINKED ARTICLES: This article is part of a themed section on Translating the Matrix. To view the other articles in this section visit http://onlinelibrary.wiley.com/doi/10.1111/bph.v176.1/issuetoc.

Topological estimation of signal flow in complex signaling networks

In a cell, any information about extra- or intra-cellular changes is transferred and processed through a signaling network and dysregulation of signal flow often leads to disease such as cancer. So, understanding of signal flow in the signaling network is critical to identify drug targets. Owing to the development of high-throughput measurement technologies, the structure of a signaling network is becoming more available, but detailed kinetic parameter information about molecular interactions is still very limited. A question then arises as to whether we can estimate the signal flow based only on the structure information of a signaling network. To answer this question, we develop a novel algorithm that can estimate the signal flow using only the topological information and apply it to predict the direction of activity change in various signaling networks. Interestingly, we find that the average accuracy of the estimation algorithm is about 60–80% even though we only use the topological information. We also find that this predictive power gets collapsed if we randomly alter the network topology, showing the importance of network topology. Our study provides a basis for utilizing the topological information of signaling networks in precision medicine or drug target discovery.

A mechanistic pan-cancer pathway model informed by multi-omics data interprets stochastic cell fate responses to drugs and mitogens

Most cancer cells harbor multiple drivers whose epistasis and interactions with expression context clouds drug and drug combination sensitivity prediction. We constructed a mechanistic computational model that is context-tailored by omics data to capture regulation of stochastic proliferation and death by pan-cancer driver pathways. Simulations and experiments explore how the coordinated dynamics of RAF/MEK/ERK and PI-3K/AKT kinase activities in response to synergistic mitogen or drug combinations control cell fate in a specific cellular context. In this MCF10A cell context, simulations suggest that synergistic ERK and AKT inhibitor-induced death is likely mediated by BIM rather than BAD, which is supported by prior experimental studies. AKT dynamics explain S-phase entry synergy between EGF and insulin, but simulations suggest that stochastic ERK, and not AKT, dynamics seem to drive cell-to-cell proliferation variability, which in simulations is predictable from pre-stimulus fluctuations in C-Raf/B-Raf levels. Simulations suggest MEK alteration negligibly influences transformation, consistent with clinical data. Tailoring the model to an alternate cell expression and mutation context, a glioma cell line, allows prediction of increased sensitivity of cell death to AKT inhibition. Our model mechanistically interprets context-specific landscapes between driver pathways and cell fates, providing a framework for designing more rational cancer combination therapy.

Cancer is a complex and diverse disease. Two people with the same cancer type often respond differently to the same treatment. These differences are primarily driven by the fact that two type-matched tumors can possess distinct sets of mutations and gene expression profiles, provoking differential sensitivity to drugs. Over the past few decades, we have seen a shift away from more broadly cytotoxic drugs to more targeted molecules therapies; but how to match a patient with a specific drug or drug cocktail remains a difficult problem. Here, we build a mechanistic ordinary differential equation model describing the interactions between commonly mutated pan-cancer signaling pathways—receptor tyrosine kinases, Ras/RAF/ERK, PI3K/AKT, mTOR, cell cycle, DNA damage, and apoptosis. We develop methods for how to tailor the model to multi-omics data from a specific biological context, devise a novel stochastic algorithm to induce non-genetic cell-to-cell fluctuations in mRNA and protein quantities over time, and train the model against a wealth of biochemical and cell fate data to gain insight into the systems-level, context-specific control of proliferation and death. One day, we hope models of this kind could be tailored to patient-derived tumor mRNA sequencing data and used to prioritize patient-specific drug regimens.

Profiling of Human Molecular Pathways Affected by Retrotransposons at the Level of Regulation by Transcription Factor Proteins

Endogenous retroviruses and retrotransposons also termed retroelements (REs) are mobile genetic elements that were active until recently in human genome evolution. REs regulate gene expression by actively reshaping chromatin structure or by directly providing transcription factor binding sites (TFBSs). We aimed to identify molecular processes most deeply impacted by the REs in human cells at the level of TFBS regulation. By using ENCODE data, we identified ~2 million TFBS overlapping with putatively regulation-competent human REs located in 5-kb gene promoter neighborhood (~17% of all TFBS in promoter neighborhoods; ~9% of all RE-linked TFBS). Most of REs hosting TFBS were highly diverged repeats, and for the evolutionary young (0–8% diverged) elements we identified only ~7% of all RE-linked TFBS. The gene-specific distributions of RE-linked TFBS generally correlated with the distributions for all TFBS. However, several groups of molecular processes were highly enriched in the RE-linked TFBS regulation. They were strongly connected with the immunity and response to pathogens, with the negative regulation of gene transcription, ubiquitination, and protein degradation, extracellular matrix organization, regulation of STAT signaling, fatty acids metabolism, regulation of GTPase activity, protein targeting to Golgi, regulation of cell division and differentiation, development and functioning of perception organs and reproductive system. By contrast, the processes most weakly affected by the REs were linked with the conservative aspects of embryo development. We also identified differences in the regulation features by the younger and older fractions of the REs. The regulation by the older fraction of the REs was linked mainly with the immunity, cell adhesion, cAMP, IGF1R, Notch, Wnt, and integrin signaling, neuronal development, chondroitin sulfate and heparin metabolism, and endocytosis. The younger REs regulate other aspects of immunity, cell cycle progression and apoptosis, PDGF, TGF beta, EGFR, and p38 signaling, transcriptional repression, structure of nuclear lumen, catabolism of phospholipids, and heterocyclic molecules, insulin and AMPK signaling, retrograde Golgi-ER transport, and estrogen signaling. The immunity-linked pathways were highly represented in both categories, but their functional roles were different and did not overlap. Our results point to the most quickly evolving molecular pathways in the recent and ancient evolution of human genome.

Model Based Targeting of IL-6-Induced Inflammatory Responses in Cultured Primary Hepatocytes to Improve Application of the JAK Inhibitor Ruxolitinib

IL-6 is a central mediator of the immediate induction of hepatic acute phase proteins (APP) in the liver during infection and after injury, but increased IL-6 activity has been associated with multiple pathological conditions. In hepatocytes, IL-6 activates JAK1-STAT3 signaling that induces the negative feedback regulator SOCS3 and expression of APPs. While different inhibitors of IL-6-induced JAK1-STAT3-signaling have been developed, understanding their precise impact on signaling dynamics requires a systems biology approach. Here we present a mathematical model of IL-6-induced JAK1-STAT3 signaling that quantitatively links physiological IL-6 concentrations to the dynamics of IL-6-induced signal transduction and expression of target genes in hepatocytes. The mathematical model consists of coupled ordinary differential equations (ODE) and the model parameters were estimated by a maximum likelihood approach, whereas identifiability of the dynamic model parameters was ensured by the Profile Likelihood. Using model simulations coupled with experimental validation we could optimize the long-term impact of the JAK-inhibitor Ruxolitinib, a therapeutic compound that is quickly metabolized. Model-predicted doses and timing of treatments helps to improve the reduction of inflammatory APP gene expression in primary mouse hepatocytes close to levels observed during regenerative conditions. The concept of improved efficacy of the inhibitor through multiple treatments at optimized time intervals was confirmed in primary human hepatocytes. Thus, combining quantitative data generation with mathematical modeling suggests that repetitive treatment with Ruxolitinib is required to effectively target excessive inflammatory responses without exceeding doses recommended by the clinical guidelines.

SHP2 regulates proliferation and tumorigenicity of glioma stem cells

SHP2 is a cytoplasmic protein tyrosine phosphatase (PTPase) involved in multiple signaling pathways and was the first identified proto-oncogene PTPase. Previous work in glioblastoma (GBM) has demonstrated the role of SHP2 PTPase activity in modulating the oncogenic phenotype of adherent GBM cell lines. Mutations in PTPN11, the gene encoding SHP2, have been identified with increasing frequency in GBM. Given the importance of SHP2 in developing neural stem cells, and the importance of glioma stem cells (GSCs) in GBM oncogenesis, we explored the functional role of SHP2 in GSCs. Using paired differentiated and stem cell primary cultures, we investigated the association of SHP2 expression with the tumor stem cell compartment. Proliferation and soft agar assays were used to demonstrate the functional contribution of SHP2 to cell growth and transformation. SHP2 expression correlated with SOX2 expression in GSC lines and was decreased in differentiated cells. Forced differentiation of GSCs by removal of growth factors, as confirmed by loss of SOX2 expression, also resulted in decreased SHP2 expression. Lentiviral-mediated knockdown of SHP2 inhibited proliferation. Finally, growth in soft-agar was similarly inhibited by loss of SHP2 expression. Our results show that SHP2 function is required for cell growth and transformation of the GSC compartment in GBM.

Data aggregation at the level of molecular pathways improves stability of experimental transcriptomic and proteomic data

High throughput technologies opened a new era in biomedicine by enabling massive analysis of gene expression at both RNA and protein levels. Unfortunately, expression data obtained in different experiments are often poorly compatible, even for the same biologic samples. Here, using experimental and bioinformatic investigation of major experimental platforms, we show that aggregation of gene expression data at the level of molecular pathways helps to diminish cross- and intra-platform bias otherwise clearly seen at the level of individual genes. We created a mathematical model of cumulative suppression of data variation that predicts the ideal parameters and the optimal size of a molecular pathway. We compared the abilities to aggregate experimental molecular data for the 5 alternative methods, also evaluated by their capacity to retain meaningful features of biologic samples. The bioinformatic method OncoFinder showed optimal performance in both tests and should be very useful for future cross-platform data analyses.

The spatiotemporal regulation of RAS signalling

Nearly 30% of human tumours harbour mutations in RAS family members. Post-translational modifications and the localisation of RAS within subcellular compartments affect RAS interactions with regulator, effector and scaffolding proteins. New insights into the control of spatiotemporal RAS signalling reveal that activation kinetics and subcellular compartmentalisation are tightly coupled to the generation of specific biological outcomes. Computational modelling can help utilising these insights for the identification of new targets and design of new therapeutic approaches.

Understanding the mTOR signaling pathway via mathematical modeling

The mechanistic target of rapamycin (mTOR) is a central regulatory pathway that integrates a variety of environmental cues to control cellular growth and homeostasis by intricate molecular feedbacks. In spite of extensive knowledge about its components, the molecular understanding of how these function together in space and time remains poor and there is a need for Systems Biology approaches to perform systematic analyses. In this work, we review the recent progress how the combined efforts of mathematical models and quantitative experiments shed new light on our understanding of the mTOR signaling pathway. In particular, we discuss the modeling concepts applied in mTOR signaling, the role of multiple feedbacks and the crosstalk mechanisms of mTOR with other signaling pathways. We also discuss the contribution of principles from information and network theory that have been successfully applied in dissecting design principles of the mTOR signaling network. We finally propose to classify the mTOR models in terms of the time scale and network complexity, and outline the importance of the classification toward the development of highly comprehensive and predictive models. WIREs Syst Biol Med 2017, 9:e1379. doi: 10.1002/wsbm.1379 For further resources related to this article, please visit the WIREs website.

Clinical responses to ERK inhibition in BRAFV600E-mutant colorectal cancer predicted using a computational model

Approximately 10% of colorectal cancers harbor BRAFV600E mutations, which constitutively activate the MAPK signaling pathway. We sought to determine whether ERK inhibitor (GDC-0994)-containing regimens may be of clinical benefit to these patients based on data from in vitro (cell line) and in vivo (cell- and patient-derived xenograft) studies of cetuximab (EGFR), vemurafenib (BRAF), cobimetinib (MEK), and GDC-0994 (ERK) combinations. Preclinical data was used to develop a mechanism-based computational model linking cell surface receptor (EGFR) activation, the MAPK signaling pathway, and tumor growth. Clinical predictions of anti-tumor activity were enabled by the use of tumor response data from three Phase 1 clinical trials testing combinations of EGFR, BRAF, and MEK inhibitors. Simulated responses to GDC-0994 monotherapy (overall response rate = 17%) accurately predicted results from a Phase 1 clinical trial regarding the number of responding patients (2/18) and the distribution of tumor size changes ("waterfall plot"). Prospective simulations were then used to evaluate potential drug combinations and predictive biomarkers for increasing responsiveness to MEK/ERK inhibitors in these patients.

Three-factor models versus time series models: quantifying time-dependencies of interactions between stimuli in cell biology and psychobiology for short longitudinal data

Signal integration determines cell fate on the cellular level, affects cognitive processes and affective responses on the behavioural level, and is likely to be involved in psychoneurobiological processes underlying mood disorders. Interactions between stimuli may subjected to time effects. Time-dependencies of interactions between stimuli typically lead to complex cell responses and complex responses on the behavioural level. We show that both three-factor models and time series models can be used to uncover such time-dependencies. However, we argue that for short longitudinal data the three factor modelling approach is more suitable. In order to illustrate both approaches, we re-analysed previously published short longitudinal data sets. We found that in human embryonic kidney 293 cells cells the interaction effect in the regulation of extracellular signal-regulated kinase (ERK) 1 signalling activation by insulin and epidermal growth factor is subjected to a time effect and dramatically decays at peak values of ERK activation. In contrast, we found that the interaction effect induced by hypoxia and tumour necrosis factor-alpha for the transcriptional activity of the human cyclo-oxygenase-2 promoter in HEK293 cells is time invariant at least in the first 12-h time window after stimulation. Furthermore, we applied the three-factor model to previously reported animal studies. In these studies, memory storage was found to be subjected to an interaction effect of the beta-adrenoceptor agonist clenbuterol and certain antagonists acting on the alpha-1-adrenoceptor / glucocorticoid-receptor system. Our model-based analysis suggests that only if the antagonist drug is administer in a critical time window, then the interaction effect is relevant.

Inferring Intracellular Signal Transduction Circuitry from Molecular Perturbation Experiments

The development of network inference methodologies that accurately predict connectivity in dysregulated pathways may enable the rational selection of patient therapies. Accurately inferring an intracellular network from data remains a very challenging problem in molecular systems biology. Living cells integrate extremely robust circuits that exhibit significant heterogeneity, but still respond to external stimuli in predictable ways. This phenomenon allows us to introduce a network inference methodology that integrates measurements of protein activation from perturbation experiments. The methodology relies on logic-based networks to provide a predictive approximation of the transfer of signals in a network. The approach presented was validated in silico with a set of test networks and applied to investigate the epidermal growth factor receptor signaling of a breast epithelial cell line, MFC10A. In our analysis, we predict the potential signaling circuitry most likely responsible for the experimental readouts of several proteins in the mitogen-activated protein kinase and phosphatidylinositol-3 kinase pathways. The approach can also be used to identify additional necessary perturbation experiments to distinguish between a set of possible candidate networks.