Towards a molecular characterisation of pathological pathways

Systematic identification of genetic systems associated with phenotypes in patients with rare genomic copy number variations

Copy number variation (CNV) related disorders tend to show complex phenotypic profiles that do not match known diseases. This makes it difficult to ascertain their underlying molecular basis. A potential solution is to compare the affected genomic regions for multiple patients that share a pathological phenotype, looking for commonalities. Here, we present a novel approach to associate phenotypes with functional systems, in terms of GO categories and KEGG and Reactome pathways, based on patient data. The approach uses genomic and phenomic data from the same patients, finding shared genomic regions between patients with similar phenotypes. These regions are mapped to genes to find associated functional systems. We applied the approach to analyse patients in the DECIPHER database with de novo CNVs, finding functional systems associated with most phenotypes, often due to mutations affecting related genes in the same genomic region. Manual inspection of the ten top-scoring phenotypes found multiple FunSys connections supported by the previous studies for seven of them. The workflow also produces reports focussed on the genes and FunSys connected to the different phenotypes, alongside patient-specific reports, which give details of the associated genes and FunSys for each individual in the cohort. These can be run in "confidential" mode, preserving patient confidentiality. The workflow presented here can be used to associate phenotypes with functional systems using data at the level of a whole cohort of patients, identifying important connections that could not be found when considering them individually. The full workflow is available for download, enabling it to be run on any patient cohort for which phenotypic and CNV data are available.

Muscle molecular adaptations to endurance exercise training are conditioned by glycogen availability: a proteomics-based analysis in the McArdle mouse model

KEY POINTS

Although they are unable to utilize muscle glycogen, McArdle mice adapt favourably to an individualized moderate-intensity endurance exercise training regime. Yet, they fail to reach the performance capacity of healthy mice with normal glycogen availability. There is a remarkable difference in the protein networks involved in muscle tissue adaptations to endurance exercise training in mice with and without glycogen availability. Indeed, endurance exercise training promoted the expression of only three proteins common to both McArdle and wild-type mice: LIMCH1, PARP1 and TIGD4. In turn, trained McArdle mice presented strong expression of mitogen-activated protein kinase 12 (MAPK12).

ABSTRACT

McArdle's disease is an inborn disorder of skeletal muscle glycogen metabolism that results in blockade of glycogen breakdown due to mutations in the myophosphorylase gene. We recently developed a mouse model carrying the homozygous p.R50X common human mutation (McArdle mouse), facilitating the study of how glycogen availability affects muscle molecular adaptations to endurance exercise training. Using quantitative differential analysis by liquid chromatography with tandem mass spectrometry, we analysed the quadriceps muscle proteome of 16-week-old McArdle (n = 5) and wild-type (WT) (n = 4) mice previously subjected to 8 weeks' moderate-intensity treadmill training or to an equivalent control (no training) period. Protein networks enriched within the differentially expressed proteins with training in WT and McArdle mice were assessed by hypergeometric enrichment analysis. Whereas endurance exercise training improved the estimated maximal aerobic capacity of both WT and McArdle mice as compared with controls, it was ∼50% lower than normal in McArdle mice before and after training. We found a remarkable difference in the protein networks involved in muscle tissue adaptations induced by endurance exercise training with and without glycogen availability, and training induced the expression of only three proteins common to McArdle and WT mice: LIM and calponin homology domains-containing protein 1 (LIMCH1), poly (ADP-ribose) polymerase 1 (PARP1 - although the training effect was more marked in McArdle mice), and tigger transposable element derived 4 (TIGD4). Trained McArdle mice presented strong expression of mitogen-activated protein kinase 12 (MAPK12). Through an in-depth proteomic analysis, we provide mechanistic insight into how glycogen availability affects muscle protein signalling adaptations to endurance exercise training.

Computational Systems Biology of Psoriasis: Are We Ready for the Age of Omics and Systems Biomarkers?

Computational biology and 'omics' systems sciences are greatly impacting research on common diseases such as cancer. By contrast, dermatology covering an array of skin diseases with high prevalence in society, has received relatively less attention from 'omics' and computational biosciences. We are focusing on psoriasis, a common and debilitating autoimmune disease involving skin and joints. Using computational systems biology and reconstruction, topological, modular, and a novel correlational analyses (based on fold changes) of biological and transcriptional regulatory networks, we analyzed and integrated data from a total of twelve studies from the Gene Expression Omnibus (sample size = 534). Samples represented a comprehensive continuum from lesional and nonlesional skin, as well as bone marrow and dermal mesenchymal stem cells. We identified and propose here a JAK/STAT signaling pathway significant for psoriasis. Importantly, cytokines, interferon-stimulated genes, antimicrobial peptides, among other proteins, were involved in intrinsic parts of the proposed pathway. Several biomarker and therapeutic candidates such as SUB1 are discussed for future experimental studies. The integrative systems biology approach presented here illustrates a comprehensive perspective on the molecular basis of psoriasis. This also attests to the promise of systems biology research in skin diseases, with psoriasis as a systemic component. The present study reports, to the best of our knowledge, the largest set of microarray datasets on psoriasis, to offer new insights into the disease mechanisms with a proposal of a disease pathway. We call for greater computational systems biology research and analyses in dermatology and skin diseases in general.

Integrated analysis identifies interaction patterns between small molecules and pathways

Previous studies have indicated that the downstream proteins in a key pathway can be potential drug targets and that the pathway can play an important role in the action of drugs. So pathways could be considered as targets of small molecules. A link map between small molecules and pathways was constructed using gene expression profile, pathways, and gene expression of cancer cell line intervened by small molecules and then we analysed the topological characteristics of the link map. Three link patterns were identified based on different drug discovery implications for breast, liver, and lung cancer. Furthermore, molecules that significantly targeted the same pathways tended to treat the same diseases. These results can provide a valuable reference for identifying drug candidates and targets in molecularly targeted therapy.

Microarray analysis of genes and gene functions in disc degeneration

The aim of the present study was to screen differentially expressed genes (DEGs) in human degenerative intervertebral discs (IVDs), and to perform functional analysis on these DEGs. The gene expression profile was downloaded from the Gene Expression Omnibus database (GSE34095)and included six human IVD samples: three degenerative and three non-degenerative. The DEGs between the normal and disease samples were identified using R packages. The online software WebGestalt was used to perform the functional analysis of the DEGs, followed by Osprey software to search for interactions between the DEGs. The Database for Annotation, Visualization and Integrated Discovery was utilized to annotate the DEGs in the interaction network and then the DEGs were uploaded to the Connectivity Map database to search for small molecules. In addition, the active binding sites for the hub genes in the network were obtained, based on the Universal Protein database. By comparing the gene expression profiles of the non-degenerative and degenerative IVDs, the DEGs between the samples were identified. The DEGs were significantly associated with transforming growth factor β and the extracellular matrix. Matrix metalloproteinase 2 (MMP2) was identified as the hub gene of the interaction network of DEGs. In addition, MMP2 was found to be upregulated in degenerative IVDs. The screened small molecules and the active binding sites of MMP2 may facilitate the development of methods to inhibit overexpression of MMP2.

Network-based approaches for anticancer therapy (Review)

Cancer is a complex disease resulting from alterations of multiple signaling networks. Cancer networks have been identified as scale-free networks and may contain a functionally important key player called a hub that is linked to a large number of interactors. Since a hub can serve as a biological marker in a given network, targeting the hub could be an effective strategy for enhancing the efficacy of cancer treatment. Chemotherapies and radiotherapies are generally used to treat tumors not amenable to resection, and target single or multiple molecules associated with hubs. However, these therapies may unexpectedly induce the resistance of cancer cells to drugs and radiation. Cancer cells can overcome therapy-induced damage via the activation of back-up signaling pathways and flexible modulation of affected networks. These activities are considered to be the main reasons for chemoresistance and radioresistance, and subsequent failure of cancer therapies. Much effort is required to identify the key molecules that control the modulation of signaling networks in response to drugs and radiation. Network-based therapy that affects network flexibility, including rewired network structures and hub molecules in these networks, could minimize the occurrence of side-effects and be a promising strategy for enhancing the therapeutic efficacy of cancer treatments. This review is intended to offer an overview of current research efforts including ones focused on cancer-associated complex networks, their modulation in response to cancer therapy, and further strategies targeting networks that may improve cancer treatment efficacy.

Platelet aggregation pathway network-based approach for evaluating compounds efficacy

Traditional Chinese medicines (TCMs) contain a large quantity of compounds with multiple biological activities. By using multitargets docking and network analysis in the context of pathway network of platelet aggregation, we proposed network efficiency and network flux model to screen molecules which can be used as drugs for antiplatelet aggregation. Compared with traditional single-target screening methods, network efficiency and network flux take into account the influences which compounds exert on the whole pathway network. The activities of antiplatelet aggregation of 19 active ingredients separated from TCM and 14 nonglycoside compounds predicated from network efficiency and network flux model show good agreement with experimental results (correlation coefficient = 0.73 and 0.90, resp.). This model can be used to evaluate the potential bioactive compounds and thus bridges the gap between computation and clinical indicator.

Personalized medicine: the impact on chemistry

An effective strategy for personalized medicine requires a major conceptual change in the development and application of therapeutics. In this article, we argue that further advances in this field should be made with reference to another conceptual shift, that of network pharmacology. We examine the intersection of personalized medicine and network pharmacology to identify strategies for the development of personalized therapies that are fully informed by network pharmacology concepts. This provides a framework for discussion of the impact personalized medicine will have on chemistry in terms of drug discovery, formulation and delivery, the adaptations and changes in ideology required and the contribution chemistry is already making. New ways of conceptualizing chemistry's relationship with medicine will lead to new approaches to drug discovery and hold promise of delivering safer and more effective therapies.

A novel framework for the comparative analysis of biological networks

Genome sequencing projects provide nearly complete lists of the individual components present in an organism, but reveal little about how they work together. Follow-up initiatives have deciphered thousands of dynamic and context-dependent interrelationships between gene products that need to be analyzed with novel bioinformatics approaches able to capture their complex emerging properties. Here, we present a novel framework for the alignment and comparative analysis of biological networks of arbitrary topology. Our strategy includes the prediction of likely conserved interactions, based on evolutionary distances, to counter the high number of missing interactions in the current interactome networks, and a fast assessment of the statistical significance of individual alignment solutions, which vastly increases its performance with respect to existing tools. Finally, we illustrate the biological significance of the results through the identification of novel complex components and potential cases of cross-talk between pathways and alternative signaling routes.

Interspecies translation of disease networks increases robustness and predictive accuracy

Gene regulatory networks give important insights into the mechanisms underlying physiology and pathophysiology. The derivation of gene regulatory networks from high-throughput expression data via machine learning strategies is problematic as the reliability of these models is often compromised by limited and highly variable samples, heterogeneity in transcript isoforms, noise, and other artifacts. Here, we develop a novel algorithm, dubbed Dandelion, in which we construct and train intraspecies Bayesian networks that are translated and assessed on independent test sets from other species in a reiterative procedure. The interspecies disease networks are subjected to multi-layers of analysis and evaluation, leading to the identification of the most consistent relationships within the network structure. In this study, we demonstrate the performance of our algorithms on datasets from animal models of oculopharyngeal muscular dystrophy (OPMD) and patient materials. We show that the interspecies network of genes coding for the proteasome provide highly accurate predictions on gene expression levels and disease phenotype. Moreover, the cross-species translation increases the stability and robustness of these networks. Unlike existing modeling approaches, our algorithms do not require assumptions on notoriously difficult one-to-one mapping of protein orthologues or alternative transcripts and can deal with missing data. We show that the identified key components of the OPMD disease network can be confirmed in an unseen and independent disease model. This study presents a state-of-the-art strategy in constructing interspecies disease networks that provide crucial information on regulatory relationships among genes, leading to better understanding of the disease molecular mechanisms.

Network neighbors of drug targets contribute to drug side-effect similarity

In pharmacology, it is essential to identify the molecular mechanisms of drug action in order to understand adverse side effects. These adverse side effects have been used to infer whether two drugs share a target protein. However, side-effect similarity of drugs could also be caused by their target proteins being close in a molecular network, which as such could cause similar downstream effects. In this study, we investigated the proportion of side-effect similarities that is due to targets that are close in the network compared to shared drug targets. We found that only a minor fraction of side-effect similarities (5.8 %) are caused by drugs targeting proteins close in the network, compared to side-effect similarities caused by overlapping drug targets (64%). Moreover, these targets that cause similar side effects are more often in a linear part of the network, having two or less interactions, than drug targets in general. Based on the examples, we gained novel insight into the molecular mechanisms of side effects associated with several drug targets. Looking forward, such analyses will be extremely useful in the process of drug development to better understand adverse side effects.

Revealing the molecular relationship between type 2 diabetes and the metabolic changes induced by a very-low-carbohydrate low-fat ketogenic diet

Background

The prevalence of type 2 diabetes is increasing worldwide, accounting for 85-95% of all diagnosed cases of diabetes. Clinical trials provide evidence of benefits of low-carbohydrate ketogenic diets in terms of clinical outcomes on type 2 diabetes patients. However, the molecular events responsible for these improvements still remain unclear in spite of the high amount of knowledge on the primary mechanisms of both the diabetes and the metabolic state of ketosis. Molecular network analysis of conditions, diseases and treatments might provide new insights and help build a better understanding of clinical, metabolic and molecular relationships among physiological conditions. Accordingly, our aim is to reveal such a relationship between a ketogenic diet and type 2 diabetes through systems biology approaches.

Methods

Our systemic approach is based on the creation and analyses of the cell networks representing the metabolic state in a very-low-carbohydrate low-fat ketogenic diet. This global view might help identify unnoticed relationships often overlooked in molecule or process-centered studies.

Results

A strong relationship between the insulin resistance pathway and the ketosis main pathway was identified, providing a possible explanation for the improvement observed in clinical trials. Moreover, the map analyses permit the formulation of some hypothesis on functional relationships between the molecules involved in type 2 diabetes and induced ketosis, suggesting, for instance, a direct implication of glucose transporters or inflammatory processes. The molecular network analysis performed in the ketogenic-diet map, from the diabetes perspective, has provided insights on the potential mechanism of action, but also has opened new possibilities to study the applications of the ketogenic diet in other situations such as CNS or other metabolic dysfunctions.

Proof of concept: network and systems biology approaches aid in the discovery of potent anticancer drug combinations

Cancer therapies that target key molecules have not fulfilled expected promises for most common malignancies. Major challenges include the incomplete understanding and validation of these targets in patients, the multiplicity and complexity of genetic and epigenetic changes in the majority of cancers, and the redundancies and cross-talk found in key signaling pathways. Collectively, the uses of single-pathway targeted approaches are not effective therapies for human malignancies. To overcome these barriers, it is important to understand the molecular cross-talk among key signaling pathways and how they may be altered by targeted agents. Innovative approaches are needed, such as understanding the global physiologic environment of target proteins and the effects of modifying them without losing key molecular details. Such strategies will aid the design of novel therapeutics and their combinations against multifaceted diseases, in which efficacious combination therapies will focus on altering multiple pathways rather than single proteins. Integrated network modeling and systems biology have emerged as powerful tools benefiting our understanding of drug mechanisms of action in real time. This review highlights the significance of the network and systems biology-based strategy and presents a proof of concept recently validated in our laboratory using the example of a combination treatment of oxaliplatin and the MDM2 inhibitor MI-219 in genetically complex and incurable pancreatic adenocarcinoma.

DockAnalyse: an application for the analysis of protein-protein interactions

BACKGROUND

Is it possible to identify what the best solution of a docking program is? The usual answer to this question is the highest score solution, but interactions between proteins are dynamic processes, and many times the interaction regions are wide enough to permit protein-protein interactions with different orientations and/or interaction energies. In some cases, as in a multimeric protein complex, several interaction regions are possible among the monomers. These dynamic processes involve interactions with surface displacements between the proteins to finally achieve the functional configuration of the protein complex. Consequently, there is not a static and single solution for the interaction between proteins, but there are several important configurations that also have to be analyzed.

RESULTS

To extract those representative solutions from the docking output datafile, we have developed an unsupervised and automatic clustering application, named DockAnalyse. This application is based on the already existing DBscan clustering method, which searches for continuities among the clusters generated by the docking output data representation. The DBscan clustering method is very robust and, moreover, solves some of the inconsistency problems of the classical clustering methods like, for example, the treatment of outliers and the dependence of the previously defined number of clusters.

CONCLUSIONS

DockAnalyse makes the interpretation of the docking solutions through graphical and visual representations easier by guiding the user to find the representative solutions. We have applied our new approach to analyze several protein interactions and model the dynamic protein interaction behavior of a protein complex. DockAnalyse might also be used to describe interaction regions between proteins and, therefore, guide future flexible dockings. The application (implemented in the R package) is accessible.

Systems biology and modeling in neuroblastoma: practicalities and perspectives

Neuroblastoma (NB) is a common pediatric malignancy characterized by clinical and biological heterogeneity. A host of prognostic markers are available, contributing to accurate risk stratification and appropriate treatment allocation. Unfortunately, outcome is still poor for many patients, indicating the need for a new approach with enhanced utilization of the available biological data. Systems biology is a holistic approach in which all components of a biological system carry equal importance. Systems biology uses mathematical modeling and simulation to investigate dynamic interactions between system components, as a means of explaining overall system behavior. Systems biology can benefit the biomedical sciences by providing a more complete understanding of human disease, enhancing the development of targeted therapeutics. Systems biology is largely contiguous with current approaches in NB, which already employ an integrative and pseudo-holistic approach to disease management. Systems modeling of NB offers an optimal method for continuing progression in this field, and conferring additional benefit to current risk stratification and management. Likewise, NB provides an opportunity for systems biology to prove its utility in the context of human disease, since the biology of NB is comprehensively characterized and, therefore, suited to modeling. The purpose of this review is to outline the benefits, challenges and fundamental workings of systems modeling in human disease, using a specific example of bottom-up modeling in NB. The intention is to demonstrate practical requirements to begin bridging the gap between biological research and applied mathematical approaches for the mutual gain of both fields, and with additional benefits for clinical management.

Unveiling the role of network and systems biology in drug discovery

Network and systems biology offer a novel way of approaching drug discovery by developing models that consider the global physiological environment of protein targets, and the effects of modifying them, without losing the key molecular details. Here we review some recent advances in network and systems biology applied to human health, and discuss how they can have a big impact on some of the most interesting areas of drug discovery. In particular, we claim that network biology will play a central part in the development of novel polypharmacology strategies to fight complex multifactorial diseases, where efficacious therapies will need to center on altering entire pathways rather than single proteins. We briefly present new developments in the two areas where we believe network and system biology strategies are more likely to have an immediate contribution: predictive toxicology and drug repurposing.

A network medicine approach to human disease

High-throughput interaction discovery initiatives are providing thousands of novel protein interactions which are unveiling many unexpected links between apparently unrelated biological processes. In particular, analyses of the first draft human interactomes highlight a strong association between protein network connectivity and disease. Indeed, recent exciting studies have exploited the information contained within protein networks to disclose some of the molecular mechanisms underlying complex pathological processes. These findings suggest that both protein-protein interactions and the networks themselves could emerge as a new class of targetable entities, boosting the quest for novel therapeutic strategies.

DrugBank and its relevance to pharmacogenomics

DrugBank is a freely available web-enabled database that combines detailed drug data with comprehensive drug-target and drug-action information. It was specifically designed to facilitate in silico drug-target discovery, drug design, drug-metabolism prediction, drug-interaction prediction, and general pharmaceutical education. One of the most unique and useful components of the DrugBank database is the information it contains on drug metabolism, drug-metabolizing enzymes and drug-target polymorphisms. As pharmacogenomics is fundamentally concerned with the role of genes and genetic variation of how an individual responds to a drug, DrugBank is able to offer a convenient venue to explore pharmacogenomic questions in silico. This paper provides a brief overview on DrugBank and how it can facilitate pharmacogenomic research.