Human Signaling Network Research Articles

Signaling network analysis reveals fostamatinib as a potential drug to control platelet hyperactivation during SARS-CoV-2 infection.

Pro-thrombotic events are one of the prevalent causes of intensive care unit (ICU) admissions among COVID-19 patients, although the signaling events in the stimulated platelets are still unclear. We conducted a comparative analysis of platelet transcriptome data from healthy donors, ICU, and non-ICU COVID-19 patients to elucidate these mechanisms. To surpass previous analyses, we constructed models of involved networks and control cascades by integrating a global human signaling network with transcriptome data. We investigated the control of platelet hyperactivation and the specific proteins involved. Our study revealed that control of the platelet network in ICU patients is significantly higher than in non-ICU patients. Non-ICU patients require control over fewer proteins for managing platelet hyperactivity compared to ICU patients. Identification of indispensable proteins highlighted key subnetworks, that are targetable for system control in COVID-19-related platelet hyperactivity. We scrutinized FDA-approved drugs targeting indispensable proteins and identified fostamatinib as a potent candidate for preventing thrombosis in COVID-19 patients. Our findings shed light on how SARS-CoV-2 efficiently affects host platelets by targeting indispensable and critical proteins involved in the control of platelet activity. We evaluated several drugs for specific control of platelet hyperactivity in ICU patients suffering from platelet hyperactivation. The focus of our approach is repurposing existing drugs for optimal control over the signaling network responsible for platelet hyperactivity in COVID-19 patients. Our study offers specific pharmacological recommendations, with drug prioritization tailored to the distinct network states observed in each patient condition. Interactive networks and detailed results can be accessed at https://fostamatinib.bioinfo-wuerz.eu/.

Crocin Protects Against Atrazine-induced Toxicity in Primary Rat Hepatocytes

Background: Atrazine is a typical herbicide that has domestic and agricultural uses all over the world. A review of the literature, however, revealed evidence of its destructive effects on many human tissues and signaling networks. As such, this study explored the impact of crocin, a natural pigment, on reducing the liver damage caused by atrazine in primary rat hepatocytes. Methods: As biochemical cytotoxicity indicators, cell death, lactate dehydrogenase (LDH) leakage, reactive oxygen species (ROS) generation, lipid peroxidation (LPO), glutathione (GSH) level, and mitochondrial membrane potential were assessed. Results: In the first step, LD50 concentration of atrazine was evaluated using a methyl thiazolyl tetrazolium (MTT) test in rat hepatocytes. The findings indicated that cellular function declines at LC50 concentration (400 M). On the contrary, crocin (50 µM) substantially boosted hepatocyte viability, decreased ROS production and LPO, replenished cellular GSH pools, and improved mitochondrial function. Conclusion: Overall, the data suggest that crocin may play a protective function in atrazine-induced liver injury in which the main mechanisms of toxicity appear to be the generation of ROS and mitochondrial damage.

Exploring attractor bifurcations in Boolean networks

BackgroundBoolean networks (BNs) provide an effective modelling formalism for various complex biochemical phenomena. Their long term behaviour is represented by attractors–subsets of the state space towards which the BN eventually converges. These are then typically linked to different biological phenotypes. Depending on various logical parameters, the structure and quality of attractors can undergo a significant change, known as a bifurcation. We present a methodology for analysing bifurcations in asynchronous parametrised Boolean networks.ResultsIn this paper, we propose a computational framework employing advanced symbolic graph algorithms that enable the analysis of large networks with hundreds of Boolean variables. To visualise the results of this analysis, we developed a novel interactive presentation technique based on decision trees, allowing us to quickly uncover parameters crucial to the changes in the attractor landscape. As a whole, the methodology is implemented in our tool AEON. We evaluate the method’s applicability on a complex human cell signalling network describing the activity of type-1 interferons and related molecules interacting with SARS-COV-2 virion. In particular, the analysis focuses on explaining the potential suppressive role of the recently proposed drug molecule GRL0617 on replication of the virus.ConclusionsThe proposed method creates a working analogy to the concept of bifurcation analysis widely used in kinetic modelling to reveal the impact of parameters on the system’s stability. The important feature of our tool is its unique capability to work fast with large-scale networks with a relatively large extent of unknown information. The results obtained in the case study are in agreement with the recent biological findings.

A systems genomics approach to uncover patient-specific pathogenic pathways and proteins in ulcerative colitis

We describe a precision medicine workflow, the integrated single nucleotide polymorphism network platform (iSNP), designed to determine the mechanisms by which SNPs affect cellular regulatory networks, and how SNP co-occurrences contribute to disease pathogenesis in ulcerative colitis (UC). Using SNP profiles of 378 UC patients we map the regulatory effects of the SNPs to a human signalling network containing protein-protein, miRNA-mRNA and transcription factor binding interactions. With unsupervised clustering algorithms we group these patient-specific networks into four distinct clusters driven by PRKCB, HLA, SNAI1/CEBPB/PTPN1 and VEGFA/XPO5/POLH hubs. The pathway analysis identifies calcium homeostasis, wound healing and cell motility as key processes in UC pathogenesis. Using transcriptomic data from an independent patient cohort, with three complementary validation approaches focusing on the SNP-affected genes, the patient specific modules and affected functions, we confirm the regulatory impact of non-coding SNPs. iSNP identified regulatory effects for disease-associated non-coding SNPs, and by predicting the patient-specific pathogenic processes, we propose a systems-level way to stratify patients.

A graph centrality-based approach for candidate gene prediction for type 1 diabetes.

Type 1 diabetes mellitus (T1DM) or insulin-dependent diabetes is an autoimmune disease that may pose life-threatening situations to individuals. In most cases, cytotoxic T lymphocytes (CTLs) promotes killing of islets of Langerhans in the pancreas, which harbour insulin-producing beta cells. The trigger for autoimmune attack is still unclear; therefore, identifying and targeting candidate genes are imperative to hinder its deleterious effects. In the present study, we focused on identification of new candidate genes for T1DM. For our study, we exclusively selected immune-related genes as they play a crucial role in T1DM. We constructed and analysed a human immunome signalling network (directed network) to identify the new candidate genes through various graph centrality measures combining with Gene Ontology (GO). As a result, we identified 4 new candidate genes which may act as potential drug targets for T1DM. We further validated for their disease relevance through literature survey and pathway analysis and found that 3 out of 4 predicted genes mirrored their well-established roles as potential targets for T1DM.

Modular Reorganization of Signaling Networks during the Development of Colon Adenoma and Carcinoma.

Network science is an emerging tool in systems biology and oncology, providing novel, system-level insight into the development of cancer. The aim of this project was to study the signaling networks in the process of oncogenesis to explore the adaptive mechanisms taking part in the cancerous transformation of healthy cells. For this purpose, colon cancer proved to be an excellent candidate as the preliminary phase, and adenoma has a long evolution time. In our work, transcriptomic data have been collected from normal colon, colon adenoma, and colon cancer samples to calculating link (i.e., network edge) weights as approximative proxies for protein abundances, and link weights were included in the Human Cancer Signaling Network. Here we show that the adenoma phase clearly differs from the normal and cancer states in terms of a more scattered link weight distribution and enlarged network diameter. Modular analysis shows the rearrangement of the apoptosis- and the cell-cycle-related modules, whose pathway enrichment analysis supports the relevance of targeted therapy. Our work enriches the system-wide assessment of cancer development, showing specific changes for the adenoma state.

Expression profile analysis of dermal papilla cells mRNA in response to WNT10B treatment.

Dermal papilla cells (DPCs) are associated with the development of hair follicles (HFs) and the regulation of the hair growth cycle. Previous studies have shown that Wnt family member 10B (WNT10B) plays an important role in the proliferation and survival of DPCs in vitro, and promotes the growth of HFs. However, the underlying mechanisms have not been fully elucidated. The present study evaluated the role of WNT10B in regulating HF morphogenesis by characterizing the differential gene expression profiles between WNT10B-treated DPCs and control DPCs using RNA-sequencing (RNA-seq). A total of 1,073 and 451 genes were upregulated and downregulated, respectively. The RNA-seq data was subsequently validated by reverse-transcription quantitative PCR. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis revealed that 442 GO terms and 21 KEGG pathways were significantly enriched. Further functional analysis revealed that WNT10B decreased translation initiation, elongation and termination, and RNA metabolic processes in cultured DPCs compared with controls in vitro. Human signaling networks were compared using pathway analysis, and treatment of DPCs with WNT10B was revealed to downregulate the ribosome biogenesis pathway and decrease protein synthesis in vitro. KEGG pathway analysis showed that WNT10B upregulated the phosphoinositide 3-kinase/protein kinase B signaling pathway. The present study analyzed the expression of mRNA in WNT10B-treated DPCs using next-generation sequencing and uncovered mechanisms regulating the induction of HFs.

Identifying qualitative differences in PPARα signaling networks in human and rat hepatocytes and their significance for next generation chemical risk assessment methods

In this paper, we evaluate the PPARα signaling network in rats, examining transcriptional responses in primary hepatocytes exposed to a PPARα specific ligand, GW7647. These transcriptomic studies were complemented with ChIP-seq studies of PPARα binding and transcription binding motif identification for PPARα responsive genes. We also conducted a limited study of GW7647 dosing the in intact rat to examine differences in transcriptional responses for primary hepatocytes in vitro and in the intact liver. The rat network has a much larger number of down-regulated genes and pathways than we had found in the human and the PPARα binding motifs in rat differed for upregulated and down regulated genes. Based on these results and comparison with our previous work with the human PPARα signaling network, we identified qualitative differences in the transcriptional networks controlled by PPARα activation in the two species that provide an explanation of the interspecies differences in the responses of humans and rodents to GW7647 and likely to other PPARα agonists. These studies also allow some observations on the manner in which in vitro, fit-for-purpose assays in human hepatocytes could form the basis for risk assessment without recourse to in-life studies in rodents or other test species.

Computational systems pharmacology reveals an antiplatelet and neuroprotective mechanism of Deng-Zhan-Xi-Xin injection in the treatment of ischemic stroke

Herbs are typically prescribed in traditional Chinese medicine (TCM) to treat complex diseases. The multicomponent nature of herbal drug ingredients makes it difficult to readily understand their mode of action. To decipher their molecular mechanisms, here we proposed a novel computational systems pharmacology based approach, which consisted of transcriptome profiling, data collection, statistical analysis, network algorithm, bioinformatics analysis and pharmacological validation. The network algorithm called signed random walk with restart (SRWR) was used to simulate the propagation of drugs’ effects on networks. This algorithm could identify proteins either positively or negatively regulated (activated or inhibited) by drugs on human signaling networks. To establish proof of principle, the herbal product Deng-Zhan-Xi-Xin injection (DZXXI), which exhibits pharmacological effects in ischemic stroke but its mechanism was unclear, was analyzed. Eighty-three targets were predicted with high confidence for DZXXI’s active compounds in plasma, and 87 differentially expressed genes (DEGs) were identified in MCF7 cells treated with DZXXI. These target genes were further found to be associated with pathways involved in neuronal apoptosis in ischemic stroke, such as NF-κB signaling, TNF signaling, and PI3K-Akt signaling. Intersection analysis between DZXXI’s putative targets with ischemic stroke-associated genes identified two important targets (PTGS1, PTGS2) corresponding to four DZXXI compounds, which were further validated using in silico and in vitro/vivo models. The most inhibited genes identified by the SRWR algorithm were significantly enriched with ischemic stroke-associated disease genes, antiplatelet associated pathways, and their encoded proteins were enriched in brain, vascular endothelium and platelets. The CMAP analysis based on DEGs suggested that DZXXI could function as both an anti-inflammatory and anti-platelet agent. Taken together, the computational analysis suggested that DZXXI exhibited anti-platelet and neuroprotective effects in the treatment of ischemic stroke. These deductions were preliminarily confirmed by subsequent in vitro/vivo studies. This approach provides a systems perspective to study the relevance between herbal drugs and disease processes, and can reveal possible pharmacological effects of multiple ingredients within herbal product.

Network controllability analysis of intracellular signalling reveals viruses are actively controlling molecular systems

In recent years control theory has been applied to biological systems with the aim of identifying the minimum set of molecular interactions that can drive the network to a required state. However, in an intra-cellular network it is unclear how control can be achieved in practice. To address this limitation we use viral infection, specifically human immunodeficiency virus type 1 (HIV-1) and hepatitis C virus (HCV), as a paradigm to model control of an infected cell. Using a large human signalling network comprised of over 6000 human proteins and more than 34000 directed interactions, we compared two states: normal/uninfected and infected. Our network controllability analysis demonstrates how a virus efficiently brings the dynamically organised host system into its control by mostly targeting existing critical control nodes, requiring fewer nodes than in the uninfected network. The lower number of control nodes is presumably to optimise exploitation of specific sub-systems needed for virus replication and/or involved in the host response to infection. Viral infection of the human system also permits discrimination between available network-control models, which demonstrates that the minimum dominating set (MDS) method better accounts for how the biological information and signals are organised during infection by identifying most viral proteins as critical driver nodes compared to the maximum matching (MM) method. Furthermore, the host driver nodes identified by MDS are distributed throughout the pathways enabling effective control of the cell via the high ‘control centrality’ of the viral and targeted host nodes. Our results demonstrate that control theory gives a more complete and dynamic understanding of virus exploitation of the host system when compared with previous analyses limited to static single-state networks.

Interrogating the ccm-3 Gene Network

Cerebral cavernous malformations (CCMs) are neurovascular lesions caused by mutations in one of three genes (CCM1-3). Loss of CCM3 causes the poorest prognosis, and little is known about how it regulates vascular integrity. The C.elegans ccm-3 gene regulates the development of biological tubes that resemble mammalian vasculature, and in a genome-wide reverse genetic screen, we identified more than 500 possible CCM-3 pathway genes. With a phenolog-like approach, we generated a human CCM signaling network and identified 29 genes in common, of which 14 are required for excretory canal extension and membrane integrity, similar to ccm-3. Notably, depletion of the MO25 ortholog mop-25.2 causes severe defects in tube integrity by preventing CCM-3 localization to apical membranes. Furthermore, loss of MO25 phenocopies CCM3 ablation by causing stress fiber formation in endothelial cells. This work deepens our understanding of howCCM3 regulates vascular integrity and may help identify therapeutic targets for treating CCM3 patients.

Synthetic Lethality-based Identification of Targets for Anticancer Drugs in the Human Signaling Network

Chemotherapy agents can cause serious adverse effects by attacking both cancer tissues and normal tissues. Therefore, we proposed a synthetic lethality (SL) concept-based computational method to identify specific anticancer drug targets. First, a 3-step screening strategy (network-based, frequency-based and function-based screening) was proposed to identify the SL gene pairs by mining 697 cancer genes and the human signaling network, which had 6306 proteins and 62937 protein-protein interactions. The network-based screening was composed of a stability score constructed using a network information centrality measure (the average shortest path length) and the distance-based screening between the cancer gene and the non-cancer gene. Then, the non-cancer genes were extracted and annotated using drug-target interaction and drug description information to obtain potential anticancer drug targets. Finally, the human SL data in SynLethDB, the existing drug sensitivity data and text-mining were utilized for target validation. We successfully identified 2555 SL gene pairs and 57 potential anticancer drug targets. Among them, CDK1, CDK2, PLK1 and WEE1 were verified by all three aspects and could be preferentially used in specific targeted therapy in the future.

Method for identification of sensitive nodes in Boolean models of biological networks.

Biological systems are often represented as Boolean networks and analysed to identify sensitive nodes which on perturbation disproportionately change a predefined output. There exist different kinds of perturbation methods: perturbation of function, perturbation of state and perturbation in update scheme. Nodes may have defects in interpretation of the inputs from other nodes and calculation of the node output. To simulate these defects and systematically assess their effect on the system output, two new function perturbations, referred to as ‘not of function’ and ‘function of not’, are introduced. In the former, the inputs are assumed to be correctly interpreted but the output of the update rule is perturbed; and in the latter, each input is perturbed but the correct update rule is applied. These and previously used perturbation methods were applied to two existing Boolean models, namely the human melanogenesis signalling network and the fly segment polarity network. Through mathematical simulations, it was found that these methods successfully identified nodes earlier found to be sensitive using other methods, and were also able to identify sensitive nodes which were previously unreported.

The Pathogenesis of Atherosclerosis Based on Human Signaling Networks and Stem Cell Expression Data.

Atherosclerosis is a common and complex disease, whose morbidity increased significantly. Here, an integrated approach was proposed to elucidate systematically the pathogenesis of atherosclerosis from a systems biology point of view. Two weighted human signaling networks were constructed based on atherosclerosis related gene expression data of stem cells. Then, 37 candidate Atherosclerosis-risk Modules were detected using four kinds of permutation tests. Five Atherosclerosis-risk Modules (three Absent Modules and two Emerging Modules) enriched in functions significantly associated with disease genes were identified and verified to be associated with the maintenance of normal biological process and the pathogenesis and development of atherosclerosis. Especially for Atherosclerosis-risk Emerging Module P96, it could distinguish between normal and disease samples by Supporting Vector Machine with the average expression value of the module as classification feature. These identified modules and their genes may act as potential atherosclerosis biomarkers. Our study would shed light on the signal transduction of atherosclerosis, and provide new insights to its pathogenesis from the perspective of stem cells.

WDNfinder: A method for minimum driver node set detection and analysis in directed and weighted biological network.

Structural controllability is the generalization of traditional controllability for dynamical systems. During the last decade, interesting biological discoveries have been inferred by applied structural controllability analysis to biological networks. However, false positive/negative information (i.e. nodes and edges) widely exists in biological networks that documented in public data sources, which can hinder accurate analysis of structural controllability. In this study, we propose WDNfinder, a comprehensive analysis package that provides structural controllability with consideration of node connection strength in biological networks. When applied to the human cancer signaling network and p53-mediate DNA damage response network, WDNfinder shows high accuracy on essential nodes prediction in these networks. Compared to existing methods, WDNfinder can significantly narrow down the set of minimum driver node set (MDS) under the restriction of domain knowledge. When using p53-mediate DNA damage response network as illustration, we find more meaningful MDSs by WDNfinder. The source code is implemented in python and publicly available together with relevant data on GitHub: https://github.com/dustincys/WDNfinder .

Identification of critical regulatory genes in cancer signaling network using controllability analysis

Cancer is characterized by a complex web of regulatory mechanisms which makes it difficult to identify features that are central to its control. Molecular integrative models of cancer, generated with the help of data from experimental assays, facilitate use of control theory to probe for ways of controlling the state of such a complex dynamic network. We modeled the human cancer signaling network as a directed graph and analyzed it for its controllability, identification of driver nodes and their characterization. We identified the driver nodes using the maximum matching algorithm and classified them as backbone, peripheral and ordinary based on their role in regulatory interactions and control of the network. We found that the backbone driver nodes were key to driving the regulatory network into cancer phenotype (via mutations) as well as for steering into healthy phenotype (as drug targets). This implies that while backbone genes could lead to cancer by virtue of mutations, they are also therapeutic targets of cancer. Further, based on their impact on the size of the set of driver nodes, genes were characterized as indispensable, dispensable and neutral. Indispensable nodes within backbone of the network emerged as central to regulatory mechanisms of control of cancer. In addition to probing the cancer signaling network from the perspective of control, our findings suggest that indispensable backbone driver nodes could be potentially leveraged as therapeutic targets. This study also illustrates the application of structural controllability for studying the mechanisms underlying the regulation of complex diseases.

Identification of potential drug targets based on a computational biology algorithm for venous thromboembolism.

Venous thromboembolism (VTE) is a common, fatal and frequently recurrent disease. Changes in the activity of different coagulation factors serve as a pathophysiological basis for the recurrent risk of VTE. Systems biology approaches provide a better understanding of the pathological mechanisms responsible for recurrent VTE. In this study, a novel computational method was presented to identify the recurrent risk modules(RRMs) based on the integration of expression profiles and human signaling network, which hold promise for achieving new and deeper insights into the mechanisms responsible for VTE. The results revealed that the RRMs had good classification performance to discriminate patients with recurrent VTE. The functional annotation analysis demonstrated that the RRMs played a crucial role in the pathogenesis of VTE. Furthermore, a variety of approved drug targets in the RRMM5 were related to VTE. Thus, the M5may be applied to select potential drug targets for combination therapy and the extended treatment of VTE.

Identification of critical paralog groups with indispensable roles in the regulation of signaling flow.

Extensive cross-talk between signaling pathways is required to integrate the myriad of extracellular signal combinations at the cellular level. Gene duplication events may lead to the emergence of novel functions, leaving groups of similar genes - termed paralogs - in the genome. To distinguish critical paralog groups (CPGs) from other paralogs in human signaling networks, we developed a signaling network-based method using cross-talk annotation and tissue-specific signaling flow analysis. 75 CPGs were found with higher degree, betweenness centrality, closeness, and ‘bowtieness’ when compared to other paralogs or other proteins in the signaling network. CPGs had higher diversity in all these measures, with more varied biological functions and more specific post-transcriptional regulation than non-critical paralog groups (non-CPG). Using TGF-beta, Notch and MAPK pathways as examples, SMAD2/3, NOTCH1/2/3 and MEK3/6-p38 CPGs were found to regulate the signaling flow of their respective pathways. Additionally, CPGs showed a higher mutation rate in both inherited diseases and cancer, and were enriched in drug targets. In conclusion, the results revealed two distinct types of paralog groups in the signaling network: CPGs and non-CPGs. Thus highlighting the importance of CPGs as compared to non-CPGs in drug discovery and disease pathogenesis.

Attractor landscape analysis of colorectal tumorigenesis and its reversion.

BackgroundColorectal cancer arises from the accumulation of genetic mutations that induce dysfunction of intracellular signaling. However, the underlying mechanism of colorectal tumorigenesis driven by genetic mutations remains yet to be elucidated.ResultsTo investigate colorectal tumorigenesis at a system-level, we have reconstructed a large-scale Boolean network model of the human signaling network by integrating previous experimental results on canonical signaling pathways related to proliferation, metastasis, and apoptosis. Throughout an extensive simulation analysis of the attractor landscape of the signaling network model, we found that the attractor landscape changes its shape by expanding the basin of attractors for abnormal proliferation and metastasis along with the accumulation of driver mutations. A further hypothetical study shows that restoration of a normal phenotype might be possible by reversely controlling the attractor landscape. Interestingly, the targets of approved anti-cancer drugs were highly enriched in the identified molecular targets for the reverse control.ConclusionsOur results show that the dynamical analysis of a signaling network based on attractor landscape is useful in acquiring a system-level understanding of tumorigenesis and developing a new therapeutic strategy.Electronic supplementary materialThe online version of this article (doi:10.1186/s12918-016-0341-9) contains supplementary material, which is available to authorized users.

Exploring the interactions of the RAS family in the human protein network and their potential implications in RAS-directed therapies.

RAS proteins are the founding members of the RAS superfamily of GTPases. They are involved in key signaling pathways regulating essential cellular functions such as cell growth and differentiation. As a result, their deregulation by inactivating mutations often results in aberrant cell proliferation and cancer. With the exception of the relatively well-known KRAS, HRAS and NRAS proteins, little is known about how the interactions of the other RAS human paralogs affect cancer evolution and response to treatment. In this study we performed a comprehensive analysis of the relationship between the phylogeny of RAS proteins and their location in the protein interaction network. This analysis was integrated with the structural analysis of conserved positions in available 3D structures of RAS complexes. Our results show that many RAS proteins with divergent sequences are found close together in the human interactome. We found specific conserved amino acid positions in this group that map to the binding sites of RAS with many of their signaling effectors, suggesting that these pairs could share interacting partners. These results underscore the potential relevance of cross-talking in the RAS signaling network, which should be taken into account when considering the inhibitory activity of drugs targeting specific RAS oncoproteins. This study broadens our understanding of the human RAS signaling network and stresses the importance of considering its potential cross-talk in future therapies.