Pathway databases and tools for their exploitation: benefits, current limitations and challenges

Detecting and dissecting signaling crosstalk via the multilayer network integration of signaling and regulatory interactions

The versatility of cellular response arises from the communication, or crosstalk, of signaling pathways in a complex network of signaling and transcriptional regulatory interactions. Understanding the various mechanisms underlying crosstalk on a global scale requires untargeted computational approaches. We present a network-based statistical approach, MuXTalk, that uses high-dimensional edges called multilinks to model the unique ways in which signaling and regulatory interactions can interface. We demonstrate that the signaling-regulatory interface is located primarily in the intermediary region between signaling pathways where crosstalk occurs, and that multilinks can differentiate between distinct signaling-transcriptional mechanisms. Using statistically over-represented multilinks as proxies of crosstalk, we infer crosstalk among 60 signaling pathways, expanding currently available crosstalk databases by more than five-fold. MuXTalk surpasses existing methods in terms of model performance metrics, identifies additions to manual curation efforts, and pinpoints potential mediators of crosstalk. Moreover, it accommodates the inherent context-dependence of crosstalk, allowing future applications to cell type- and disease-specific crosstalk.

Using published pathway figures in enrichment analysis and machine learning

Pathway Figure OCR (PFOCR) is a novel kind of pathway database approaching the breadth and depth of Gene Ontology while providing rich, mechanistic diagrams and direct literature support. Here, we highlight the utility of PFOCR in disease research in comparison with popular pathway databases through an assessment of disease coverage and analytical applications. In addition to common pathway analysis use cases, we present two advanced case studies demonstrating unique advantages of PFOCR in terms of cancer subtype and grade prediction analyses.

Using Published Pathway Figures in Enrichment Analysis and Machine Learning

Pathway Figure OCR (PFOCR) is a novel kind of pathway database approaching the breadth and depth of Gene Ontology while providing rich, mechanistic diagrams and direct literature support. PFOCR content is extracted from published pathway figures currently emerging at a rate of 1000 new pathways each month. Here, we compare the pathway information contained in PFOCR against popular pathway databases with respect to overall and disease-specific coverage. In addition to common pathways analysis use cases, we present two advanced case studies demonstrating unique advantages of PFOCR in terms of cancer subtype and grade prediction analyses.

Machine Learning Methods for Cancer Classification Using Gene Expression Data: A Review

Cancer is a term that denotes a group of diseases caused by the abnormal growth of cells that can spread in different parts of the body. According to the World Health Organization (WHO), cancer is the second major cause of death after cardiovascular diseases. Gene expression can play a fundamental role in the early detection of cancer, as it is indicative of the biochemical processes in tissue and cells, as well as the genetic characteristics of an organism. Deoxyribonucleic acid (DNA) microarrays and ribonucleic acid (RNA)-sequencing methods for gene expression data allow quantifying the expression levels of genes and produce valuable data for computational analysis. This study reviews recent progress in gene expression analysis for cancer classification using machine learning methods. Both conventional and deep learning-based approaches are reviewed, with an emphasis on the application of deep learning models due to their comparative advantages for identifying gene patterns that are distinctive for various types of cancers. Relevant works that employ the most commonly used deep neural network architectures are covered, including multi-layer perceptrons, as well as convolutional, recurrent, graph, and transformer networks. This survey also presents an overview of the data collection methods for gene expression analysis and lists important datasets that are commonly used for supervised machine learning for this task. Furthermore, we review pertinent techniques for feature engineering and data preprocessing that are typically used to handle the high dimensionality of gene expression data, caused by a large number of genes present in data samples. The paper concludes with a discussion of future research directions for machine learning-based gene expression analysis for cancer classification.

ProtInteract: A deep learning framework for predicting protein-protein interactions

Proteins mainly perform their functions by interacting with other proteins. Protein-protein interactions underpin various biological activities such as metabolic cycles, signal transduction, and immune response. However, due to the sheer number of proteins, experimental methods for finding interacting and non-interacting protein pairs are time-consuming and costly. We therefore developed the ProtInteract framework to predict protein-protein interaction. ProtInteract comprises two components: first, a novel autoencoder architecture that encodes each protein's primary structure to a lower-dimensional vector while preserving its underlying sequence attributes. This leads to faster training of the second network, a deep convolutional neural network (CNN) that receives encoded proteins and predicts their interaction under three different scenarios. In each scenario, the deep CNN predicts the class of a given encoded protein pair. Each class indicates different ranges of confidence scores corresponding to the probability of whether a predicted interaction occurs or not. The proposed framework features significantly low computational complexity and relatively fast response. The contributions of this work are twofold. First, ProtInteract assimilates the protein's primary structure into a pseudo-time series. Therefore, we leverage the nature of the time series of proteins and their physicochemical properties to encode a protein's amino acid sequence into a lower-dimensional vector space. This approach enables extracting highly informative sequence attributes while reducing computational complexity. Second, the ProtInteract framework utilises this information to identify protein interactions with other proteins based on its amino acid configuration. Our results suggest that the proposed framework performs with high accuracy and efficiency in predicting protein-protein interactions.

DBHR: a collection of databases relevant to human research

BACKGROUND

The achievement of the human genome project provides a basis for the systematic study of the human genome from evolutionary history to disease-specific medicine. With the explosive growth of biological data, a growing number of biological databases are being established to support human-related research.

OBJECTIVE

The main objective of our study is to store, organize and share data in a structured and searchable manner. In short, we have planned the future development of new features in the database research area.

MATERIALS & METHODS

In total, we collected and integrated 680 human databases from scientific published work. Multiple options are presented for accessing the data, while original links and short descriptions are also presented for each database.

RESULTS & DISCUSSION

We have provided the latest collection of human research databases on a single platform with six categories: DNA database, RNA database, protein database, expression database, pathway database and disease database.

CONCLUSION

Taken together, our database will be useful for further human research study and will be modified over time. The database has been implemented in PHP, HTML, CSS and MySQL and is available freely at https://habdsk.org/database.php.

Tripartite collaboration of blood-derived endothelial cells, next generation RNA sequencing and bioengineered vessel-chip may distinguish vasculopathy and thrombosis among sickle cell disease patients

Sickle cell disease (SCD) is the most prevalent inherited blood disorder in the world. But the clinical manifestations of the disease are highly variable. In particular, it is currently difficult to predict the adverse outcomes within patients with SCD, such as, vasculopathy, thrombosis, and stroke. Therefore, for most effective and timely interventions, a predictive analytic strategy is desirable. In this study, we evaluate the endothelial and prothrombotic characteristics of blood outgrowth endothelial cells (BOECs) generated from blood samples of SCD patients with known differences in clinical severity of the disease. We present a method to evaluate patient-specific vaso-occlusive risk by combining novel RNA-seq and organ-on-chip approaches. Through differential gene expression (DGE) and pathway analysis we find that BOECs from SCD patients exhibit an activated state through cell adhesion molecule (CAM) and cytokine signaling pathways among many others. In agreement with clinical symptoms of patients, DGE analyses reveal that patient with severe SCD had a greater extent of endothelial activation compared to patient with milder symptoms. This difference is confirmed by performing qRT-PCR of endothelial adhesion markers like E-selectin, P-selectin, tissue factor, and Von Willebrand factor. Finally, the differential regulation of the proinflammatory phenotype is confirmed through platelet adhesion readouts in our BOEC vessel-chip. Taken together, we hypothesize that these easily blood-derived endothelial cells evaluated through RNA-seq and organ-on-chips may serve as a biotechnique to predict vaso-occlusive episodes in SCD patients and will ultimately allow better therapeutic interventions.

Electroretinography and Gene Expression Measures Implicate Phototransduction and Metabolic Shifts in Chick Myopia and Hyperopia Models

The Retinal Ion-Driven Fluid Efflux (RIDE) model theorizes that phototransduction-driven changes in trans-retinal ion and fluid transport underlie the development of myopia (short-sightedness). In support of this model, previous functional studies have identified the attenuation of outer retinal contributions to the global flash electroretinogram (gfERG) following weeks of myopia induction in chicks, while discovery-driven transcriptome studies have identified changes to the expression of ATP-driven ion transport and mitochondrial metabolism genes in the retina/RPE/choroid at the mid- to late-induction time-points. Less is known about the early time-points despite biometric analyses demonstrating changes in eye growth by 3 h in the chick lens defocus model. Thus, the present study compared gfERG and transcriptome profiles between 3 h and 3 days of negative lens-induced myopia and positive lens-induced hyperopia in chicks. Photoreceptor (a-wave and d-wave) and bipolar (b-wave and late-stage d-wave) cell responses were suppressed following negative lens-wear, particularly at the 3–4 h and 3-day time-points when active shifts in the rate of ocular growth were expected. Transcriptome measures revealed the up-regulation of oxidative phosphorylation genes following 6 h of negative lens-wear, concordant with previous reports at 2 days in this model. Signal transduction pathways, with core genes involved in glutamate and G-protein coupled receptor signalling, were down-regulated at 6 h. These findings contribute to a growing body of evidence for the dysregulation of phototransduction and mitochondrial metabolism in animal models of myopia.

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.

Predicting lincRNA-Disease Association in Heterogeneous Networks Using Co-regularized Non-negative Matrix Factorization

Long intergenic non-coding ribonucleic acids (lincRNAs) are critical regulators for many complex diseases, and identification of disease-lincRNA association is both costly and time-consuming. Therefore, it is necessary to design computational approaches to predict the disease-lincRNA associations that shed light on the mechanisms of diseases. In this study, we develop a co-regularized non-negative matrix factorization (aka Cr-NMF) to identify potential disease-lincRNA associations by integrating the gene expression of lincRNAs, genetic interaction network for mRNA genes, gene-lincRNA associations, and disease-gene associations. The Cr-NMF algorithm factorizes the disease-lincRNA associations, while the other associations/interactions are integrated using regularization. Furthermore, the regularization does not only preserve the topological structure of the lincRNA co-expression network, but also maintains the links “lincRNA → gene → disease.” Experimental results demonstrate that the proposed algorithm outperforms state-of-the-art methods in terms of accuracy on predicting the disease-lincRNA associations. The model and algorithm provide an effective way to explore disease-lncRNA associations.

Pathways Enrichment Analysis of Gene Expression Data in Type 2 Diabetes

Profiling genome-wide transcriptional changes with advanced high-throughput transcriptional profiling techniques has led to a revolution in biomedical science. It has been challenging to handle the massive data generated by these techniques and draw meaningful conclusions from it. Therefore, computational biologists have developed a number of innovative methods of varying complexity and effectiveness to analyze such complex data. Over the past decade, rich information in pathway repositories has attracted and motivated researchers to incorporate such existing biological knowledge into computational analysis tools to develop what is known as pathway enrichment analysis tools. This chapter describes a new sophisticated pathway enrichment tool that exploits topology of pathway as well as expression of significantly changed genes to identify biologically significant pathways for high-dimensional gene expression datasets. Also, we demonstrate the use of this tool to analyze gene expression data from a type 2 diabetes dataset to identify a list of significantly enriched metabolic pathways.

Effects of Sphingosine-1-Phosphate on Cell Viability, Differentiation, and Gene Expression of Adipocytes

Sphingosine-1-phosphate (S1P) is a highly potent sphingolipid metabolite, which controls numerous physiological and pathological process via its extracellular and intracellular functions. The breast is mainly composed of epithelial cells (mammary gland) and adipocytes (stroma). Adipocytes play an important role in regulating the normal functions of the breast. Compared to the vast amount studies on breast epithelial cells, the functions of S1P in breast adipocytes are much less known. Thus, in the current study, we used human preadipocyte cell lines SGBS and mouse preadipocyte cell line 3T3-L1 as in vitro models to evaluate the effects of S1P on cell viability, differentiation, and gene expression in adipocytes. Our results showed that S1P increased cell viability in SGBS and 3T3-L1 preadipocytes but moderately reduced cell viability in differentiated SGBS and 3T3-L1 adipocytes. S1P was also shown to inhibit adipogenic differentiation of SGBS and 3T3-L1 at concentration higher than 1000 nM. Transcriptome analyses showed that S1P was more influential on gene expression in differentiated adipocytes. Furthermore, our network analysis in mature adipocytes showed that the upregulated DEGs (differentially expressed genes) were related to regulation of lipolysis, PPAR (peroxisome proliferator-activated receptor) signaling, alcoholism, and toll-like receptor signaling, whereas the downregulated DEGs were overrepresented in cytokine-cytokine receptor interaction, focal adhesion, starch and sucrose metabolism, and nuclear receptors pathways. Together previous studies on the functions of S1P in breast epithelial cells, the current study implicated that S1P may play a critical role in modulating the bidirectional regulation of adipocyte-extracellular matrix-epithelial cell axis and maintaining the normal physiological functions of the breast.

Differential Gene Expression in Brain and Liver Tissue of Wistar Rats after Rapid Eye Movement Sleep Deprivation

Sleep is essential for the survival of most living beings. Numerous researchers have identified a series of genes that are thought to regulate "sleep-state" or the "deprived state". As sleep has a significant effect on physiology, we believe that lack of total sleep, or particularly rapid eye movement (REM) sleep, for a prolonged period would have a profound impact on various body tissues. Therefore, using the microarray method, we sought to determine which genes and processes are affected in the brain and liver of rats following nine days of REM sleep deprivation. Our findings showed that REM sleep deprivation affected a total of 652 genes in the brain and 426 genes in the liver. Only 23 genes were affected commonly, 10 oppositely, and 13 similarly across brain and liver tissue. Our results suggest that nine-day REM sleep deprivation differentially affects genes and processes in the brain and liver of rats.

Genome-wide analysis of therapeutic response uncovers molecular pathways governing tamoxifen resistance in ER+ breast cancer

Background

Prioritization of breast cancer patients based on the risk of resistance to tamoxifen plays a significant role in personalized therapeutic planning and improving disease course and outcomes.

Methods

In this work, we demonstrate that a genome-wide pathway-centric computational framework elucidates molecular pathways as markers of tamoxifen resistance in ER+ breast cancer patients. In particular, we associated activity levels of molecular pathways with a wide spectrum of response to tamoxifen, which defined markers of tamoxifen resistance in patients with ER+ breast cancer.

Findings

We identified five biological pathways as markers of tamoxifen failure and demonstrated their ability to predict the risk of tamoxifen resistance in two independent patient cohorts (Test cohort1: log-rank p-value = 0.02, adjusted HR = 3.11; Test cohort2: log-rank p-value = 0.01, adjusted HR = 4.24). We have shown that these pathways are not markers of aggressiveness and outperform known markers of tamoxifen response. Furthermore, for adoption into clinic, we derived a list of pathway read-out genes and their associated scoring system, which assigns a risk of tamoxifen resistance for new incoming patients.

Interpretation

We propose that the identified pathways and their read-out genes can be utilized to prioritize patients who would benefit from tamoxifen treatment and patients at risk of tamoxifen resistance that should be offered alternative regimens.

Funding

This work was supported by the Rutgers SHP Dean's research grant, Rutgers start-up funds, Libyan Ministry of Higher Education and Scientific Research, and Katrina Kehlet Graduate Award from The NJ Chapter of the Healthcare Information Management Systems Society.

Comprehensive Analysis of Differentially Expressed Circular RNAs in Patients with Senile Osteoporotic Vertebral Compression Fracture

Aim

Circular RNAs (circRNAs) have been found to contribute to the regulation of many diseases and are abundantly expressed in various organisms. The present study is aimed at systematically characterizing the circRNA expression profiles in patients with senile osteoporotic vertebral compression fracture (OVCF) and predicting the potential functions of the regulatory networks correlated with these differentially expressed circRNAs.

Methods

The circRNA expression profile in patients with senile OVCF was explored by using RNA sequencing. The prediction of the enriched signaling pathways and circRNA-miRNA networks was conducted by bioinformatics analysis. Real-time quantitative PCR was used to validate the selected differentially expressed circRNAs from 20 patients with senile OVCF relative to 20 matched healthy controls.

Results

A total of 884 differentially expressed circRNAs were identified, of which 554 were upregulated and 330 were downregulated. The top 15 signaling pathways associated with these differentially expressed circRNAs were predicted. The result of qRT-PCR of the selected circRNAs was consistent with RNA sequencing.

Conclusions

CircRNAs are differentially expressed in patients with senile OVCF, which might contribute to the pathophysiological mechanism of senile osteoporosis.

Functional contexts of adipose and gluteal muscle tissue gene co-expression networks in the domestic horse

A gene's response to an environment is tightly bound to the underlying genetic variation present in an individual's genome and varies greatly depending on the tissue it is being expressed in. Gene co-expression networks provide a mechanism to understand and interpret the collective transcriptional responses of genes. Here, we use the Camoco co-expression network framework to characterize the transcriptional landscape of adipose and gluteal muscle tissue in 83 domestic horses (Equus caballus) representing 5 different breeds. In each tissue, gene expression profiles, capturing transcriptional response due to variation across individuals, were used to build two separate, tissue-focused, genotypically-diverse gene co-expression networks. The aim of our study was to identify significantly co-expressed clusters of genes in each tissue, then compare the clusters across networks to quantify the extent that clusters were found in both networks as well as to identify clusters found in a single network. The known and unknown functions for each network were quantified using complementary, supervised and unsupervised approaches. First, supervised ontological enrichment was utilized to quantify biological functions represented by each network. Curated ontologies (GO and KEGG) were used to measure the known biological functions present in each tissue. Overall, a large percentage of terms (40.3% of GO and 41% of KEGG) were co-expressed in at least one tissue. Many terms were co-expressed in both tissues, however a small proportion of terms exhibited single tissue co-expression suggesting functional differentiation based on curated, functional annotation. To complement this, an unsupervised approach not relying on ontologies was employed. Strongly co-expressed sets of genes defined by Markov clustering identified sets of unannotated genes showing similar patterns of co-expression within a tissue. We compared gene sets across tissues and identified clusters of genes the either segregate in co-expression by tissue or exhibit high levels of co-expression in both tissues. Clusters were also integrated with GO and KEGG ontologies to identify gene sets containing previously curated annotations versus unannotated gene sets indicating potentially novel biological function. Coupling together these transcriptional datasets, we mapped the transcriptional landscape of muscle and adipose setting up a generalizable framework for interpreting gene function for additional tissues in the horse and other species.

Transcriptomic profiling in Cutaneous Leishmaniasis patients

INTRODUCTION

Cutaneous leishmaniasis (CL), caused by different Leishmania parasite species, is associated with parasite-induced immune-mediated skin inflammation and ulceration. Whereas many CL studies focus on gene expression signatures in mouse models, the transcriptional response driving human patients in the field is less characterized. Human studies in CL disease provide the opportunity to directly investigate the host-pathogen interaction in the cutaneous lesion site.

AREAS COVERED

Advances in high-throughput sequencing technologies, particularly their application for evaluation of the global gene expression changes, have made transcriptomics as a powerful tool to understand the pathogen-host molecular interactions.

EXPERT COMMENTARY

In this review, we focus on the transcriptomics studies that have been performed so far on human blood or tissue-driven samples to investigate Leishmania parasites interplay with the CL patients. Further, we summarize microarray and RNA-seq studies associated with lesion biopsies of CL patients to discuss how current whole genome analysis along with systems biology approaches have developed novel CL biomarkers for further applications, not only for research, but also for accelerating vaccine development.

Seeking Windows of Opportunity to Shape Lifelong Immune Health: A Network-Based Strategy to Predict and Prioritize Markers of Early Life Immune Modulation

A healthy immune status is strongly conditioned during early life stages. Insights into the molecular drivers of early life immune development and function are prerequisite to identify strategies to enhance immune health. Even though several starting points for targeted immune modulation have been identified and are being developed into prophylactic or therapeutic approaches, there is no regulatory guidance on how to assess the risk and benefit balance of such interventions. Six early life immune causal networks, each compromising a different time period in early life (the 1st, 2nd, 3rd trimester of gestations, birth, newborn, and infant period), were generated. Thereto information was extracted and structured from early life literature using the automated text mining and machine learning tool: Integrated Network and Dynamical Reasoning Assembler (INDRA). The tool identified relevant entities (e.g., genes/proteins/metabolites/processes/diseases), extracted causal relationships among these entities, and assembled them into early life-immune causal networks. These causal early life immune networks were denoised using GeneMania, enriched with data from the gene-disease association database DisGeNET and Gene Ontology resource tools (GO/GO-SLIM), inferred missing relationships and added expert knowledge to generate information-dense early life immune networks. Analysis of the six early life immune networks by PageRank, not only confirmed the central role of the "commonly used immune markers" (e.g., chemokines, interleukins, IFN, TNF, TGFB, and other immune activation regulators (e.g., CD55, FOXP3, GATA3, CD79A, C4BPA), but also identified less obvious candidates (e.g., CYP1A2, FOXK2, NELFCD, RENBP). Comparison of the different early life periods resulted in the prediction of 11 key early life genes overlapping all early life periods (TNF, IL6, IL10, CD4, FOXP3, IL4, NELFCD, CD79A, IL5, RENBP, and IFNG), and also genes that were only described in certain early life period(s). Concluding, here we describe a network-based approach that provides a science-based and systematical method to explore the functional development of the early life immune system through time. This systems approach aids the generation of a testing strategy for the safety and efficacy of early life immune modulation by predicting the key candidate markers during different phases of early life immune development.

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.

Expression Profile Analysis of Differentially Expressed Circular RNAs in Steroid-Induced Osteonecrosis of the Femoral Head

Background

A growing number of studies have suggested that circular RNAs (circRNAs) serve as potential diagnostic biomarkers in many diseases. However, the role of circRNAs in steroid-induced osteonecrosis of the femoral head (SONFH) has not been reported.

Methods

Secondary sequencing was performed to profile circRNA expression in peripheral blood samples from three SONFH patients and three healthy individuals. We confirmed our preliminary findings by qRT-PCR. Bioinformatics analysis was conducted to predict their functions.

Results

The result showed 345 dysregulated circRNAs. qRT-PCR of eight selected circRNAs preliminarily confirmed the results, which were consistent with RNA sequencing. Bioinformatics analyses were performed to predict the functions of circRNAs to target the genes of miRNAs and the networks of circRNA-miRNA-mRNA interactions.

Conclusions

This study provides a new and fundamental circRNA profile of SONFH and a theoretical basis for further studies on the functions of circRNAs in SONFH.

Quantifying mechanisms in neurodegenerative diseases (NDDs) using candidate mechanism perturbation amplitude (CMPA) algorithm

BACKGROUND

Literature derived knowledge assemblies have been used as an effective way of representing biological phenomenon and understanding disease etiology in systems biology. These include canonical pathway databases such as KEGG, Reactome and WikiPathways and disease specific network inventories such as causal biological networks database, PD map and NeuroMMSig. The represented knowledge in these resources delineates qualitative information focusing mainly on the causal relationships between biological entities. Genes, the major constituents of knowledge representations, tend to express differentially in different conditions such as cell types, brain regions and disease stages. A classical approach of interpreting a knowledge assembly is to explore gene expression patterns of the individual genes. However, an approach that enables quantification of the overall impact of differentially expressed genes in the corresponding network is still lacking.

RESULTS

Using the concept of heat diffusion, we have devised an algorithm that is able to calculate the magnitude of regulation of a biological network using expression datasets. We have demonstrated that molecular mechanisms specific to Alzheimer (AD) and Parkinson Disease (PD) regulate with different intensities across spatial and temporal resolutions. Our approach depicts that the mitochondrial dysfunction in PD is severe in cortex and advanced stages of PD patients. Similarly, we have shown that the intensity of aggregation of neurofibrillary tangles (NFTs) in AD increases as the disease progresses. This finding is in concordance with previous studies that explain the burden of NFTs in stages of AD.

CONCLUSIONS

This study is one of the first attempts that enable quantification of mechanisms represented as biological networks. We have been able to quantify the magnitude of regulation of a biological network and illustrate that the magnitudes are different across spatial and temporal resolution.

Enabling Web-scale data integration in biomedicine through Linked Open Data

The biomedical data landscape is fragmented with several isolated, heterogeneous data and knowledge sources, which use varying formats, syntaxes, schemas, and entity notations, existing on the Web. Biomedical researchers face severe logistical and technical challenges to query, integrate, analyze, and visualize data from multiple diverse sources in the context of available biomedical knowledge. Semantic Web technologies and Linked Data principles may aid toward Web-scale semantic processing and data integration in biomedicine. The biomedical research community has been one of the earliest adopters of these technologies and principles to publish data and knowledge on the Web as linked graphs and ontologies, hence creating the Life Sciences Linked Open Data (LSLOD) cloud. In this paper, we provide our perspective on some opportunities proffered by the use of LSLOD to integrate biomedical data and knowledge in three domains: (1) pharmacology, (2) cancer research, and (3) infectious diseases. We will discuss some of the major challenges that hinder the wide-spread use and consumption of LSLOD by the biomedical research community. Finally, we provide a few technical solutions and insights that can address these challenges. Eventually, LSLOD can enable the development of scalable, intelligent infrastructures that support artificial intelligence methods for augmenting human intelligence to achieve better clinical outcomes for patients, to enhance the quality of biomedical research, and to improve our understanding of living systems.

Predicting instances of pathway ontology classes for pathway integration

BACKGROUND

To improve the outcomes of biological pathway analysis, a better way of integrating pathway data is needed. Ontologies can be used to organize data from disparate sources, and we leverage the Pathway Ontology as a unifying ontology for organizing pathway data. We aim to associate pathway instances from different databases to the appropriate class in the Pathway Ontology.

RESULTS

Using a supervised machine learning approach, we trained neural networks to predict mappings between Reactome pathways and Pathway Ontology (PW) classes. For 2222 Reactome classes, the neural network (NN) model generated 10,952 class recommendations. We compared against a baseline bag-of-words (BOW) model for predicting correct PW classes. A 5% subset of Reactome pathways (111 pathways) was randomly selected, and the corresponding class recommendations from both models were evaluated by two curators. The precision of the BOW model was higher (0.49 for BOW and 0.39 for NN), but the recall was lower (0.42 for BOW and 0.78 for NN). Around 78% of Reactome pathways received pertinent recommendations from the NN model.

CONCLUSIONS

The neural predictive model produced meaningful class recommendations that assisted PW curators in selecting appropriate class mappings for Reactome pathways. Our methods can be used to reduce the manual effort associated with ontology curation, and more broadly, for augmenting the curators' ability to organize and integrate data from pathway databases using the Pathway Ontology.

In Silico Toxicology Data Resources to Support Read-Across and (Q)SAR

A plethora of databases exist online that can assist in in silico chemical or drug safety assessment. However, a systematic review and grouping of databases, based on purpose and information content, consolidated in a single source, has been lacking. To resolve this issue, this review provides a comprehensive listing of the key in silico data resources relevant to: chemical identity and properties, drug action, toxicology (including nano-material toxicity), exposure, omics, pathways, Absorption, Distribution, Metabolism and Elimination (ADME) properties, clinical trials, pharmacovigilance, patents-related databases, biological (genes, enzymes, proteins, other macromolecules etc.) databases, protein-protein interactions (PPIs), environmental exposure related, and finally databases relating to animal alternatives in support of 3Rs policies. More than nine hundred databases were identified and reviewed against criteria relating to accessibility, data coverage, interoperability or application programming interface (API), appropriate identifiers, types of in vitro, in vivo,-clinical or other data recorded and suitability for modelling, read-across, or similarity searching. This review also specifically addresses the need for solutions for mapping and integration of databases into a common platform for better translatability of preclinical data to clinical data.

Up-regulated mRNA expression of some anti-inflammatory mediators in bovine oviduct epithelial cells by urea in vitro: Cellular pathways by Reactome analysis

Increased urea concentration is a major cause of low fertility in dairy cows fed high-protein diets. A strong correlation exists between the urea concentration in the blood and oviduct fluid of dairy cows. In this study, bovine oviduct epithelial cells (BOECs) were incubated with varying concentrations of urea (0, 20, 40, and 80 mg/dL) in the absence of ovarian sex steroids (estradiol and progesterone) and luteinizing hormone. The 80 mg/dL urea reduced the cell viability, and thus was excluded in further analysis. Compared to the control (U0), the 20 mg/dL urea (U20) increased the mRNA expression of Toll-like receptor (TLR) 4, interleukin (IL) 10, IL4, and prostaglandin (PG) E synthase (mPGES) but decreased the mRNA expression of tumor necrosis factor α (TNFA). Compared to U0, the 40 mg/dL urea (U40) decreased the mRNA expression of TNFA and increased alpha-1-acid glycoprotein (AGP). U40 also increased TLR2, IL10, and IL4 mRNA expression compared to U0. In addition, compared to U20, the U40 decreased the mRNA expression of TLR4 and IL1B but increased that of AGP and TLR2. Subsequently, the mRNA expression data were then projected into the Reactome database. The Reactome analysis showed that pathways, including cytokine signaling in the immune system (i.e., TNFs bind their physiological receptors) and death receptor signaling (i.e., TNF signaling), were down-regulated in the presence of urea compared to the U0 group. These in vitro data implied that high urea level can alter the balance between pro- and anti-inflammatory responses in BOECs, thus providing a suboptimal environment for the early reproductive events or a weakened innate immune system, predisposing the oviduct to infections.

ComPath: an ecosystem for exploring, analyzing, and curating mappings across pathway databases

Although pathways are widely used for the analysis and representation of biological systems, their lack of clear boundaries, their dispersion across numerous databases, and the lack of interoperability impedes the evaluation of the coverage, agreements, and discrepancies between them. Here, we present ComPath, an ecosystem that supports curation of pathway mappings between databases and fosters the exploration of pathway knowledge through several novel visualizations. We have curated mappings between three of the major pathway databases and present a case study focusing on Parkinson’s disease that illustrates how ComPath can generate new biological insights by identifying pathway modules, clusters, and cross-talks with these mappings. The ComPath source code and resources are available at https://github.com/ComPath and the web application can be accessed at https://compath.scai.fraunhofer.de/.

Enter the Matrix: Factorization Uncovers Knowledge from Omics

Omics data contain signals from the molecular, physical, and kinetic inter- and intracellular interactions that control biological systems. Matrix factorization (MF) techniques can reveal low-dimensional structure from high-dimensional data that reflect these interactions. These techniques can uncover new biological knowledge from diverse high-throughput omics data in applications ranging from pathway discovery to timecourse analysis. We review exemplary applications of MF for systems-level analyses. We discuss appropriate applications of these methods, their limitations, and focus on the analysis of results to facilitate optimal biological interpretation. The inference of biologically relevant features with MF enables discovery from high-throughput data beyond the limits of current biological knowledge - answering questions from high-dimensional data that we have not yet thought to ask.

Synthesizing Signaling Pathways from Temporal Phosphoproteomic Data

We present a method for automatically discovering signaling pathways from time-resolved phosphoproteomic data. The Temporal Pathway Synthesizer (TPS) algorithm uses constraint-solving techniques first developed in the context of formal verification to explore paths in an interaction network. It systematically eliminates all candidate structures for a signaling pathway where a protein is activated or inactivated before its upstream regulators. The algorithm can model more than one hundred thousand dynamic phosphosites and can discover pathway members that are not differentially phosphorylated. By analyzing temporal data, TPS defines signaling cascades without needing to experimentally perturb individual proteins. It recovers known pathways and proposes pathway connections when applied to the human epidermal growth factor and yeast osmotic stress responses. Independent kinase mutant studies validate predicted substrates in the TPS osmotic stress pathway.

Analysis of Critical and Redundant Vertices in Controlling Directed Complex Networks Using Feedback Vertex Sets

Controlling complex networks through a small number of controller vertices is of great importance in wide-ranging research fields. Recently, a new approach based on the minimum feedback vertex set (MFVS) has been proposed to find such vertices in directed networks in which the target states are restricted to steady states. However, multiple MFVS configurations may exist and thus the selection of vertices may depend on algorithms and input data representations. Our attempts to address this ambiguity led us to adopt an existing approach that classifies vertices into three categories. This approach has been successfully applied to maximum matching-based and minimum dominating set-based controllability analysis frameworks. In this article, we present an algorithm as well as its implementation to compute and evaluate the critical, intermittent, and redundant vertices under the MFVS-based framework, where these three categories include vertices belonging to all MFVSs, some (but not all) MFVSs, and none of the MFVSs, respectively. The results of computational experiments using artificially generated networks and real-world biological networks suggest that the proposed algorithm is useful for identifying these three kinds of vertices for relatively large-scale networks, and that the fraction of critical and intermittent vertices is considerably small. Moreover, an analysis of the signal pathways indicates that critical and intermittent MFVSs tend to be enriched by essential genes.

Extending in Silico Protein Target Prediction Models to Include Functional Effects

In silico protein target deconvolution is frequently used for mechanism-of-action investigations; however existing protocols usually do not predict compound functional effects, such as activation or inhibition, upon binding to their protein counterparts. This study is hence concerned with including functional effects in target prediction. To this end, we assimilated a bioactivity training set for 332 targets, comprising 817,239 active data points with unknown functional effect (binding data) and 20,761,260 inactive compounds, along with 226,045 activating and 1,032,439 inhibiting data points from functional screens. Chemical space analysis of the data first showed some separation between compound sets (binding and inhibiting compounds were more similar to each other than both binding and activating or activating and inhibiting compounds), providing a rationale for implementing functional prediction models. We employed three different architectures to predict functional response, ranging from simplistic random forest models ('Arch1') to cascaded models which use separate binding and functional effect classification steps ('Arch2' and 'Arch3'), differing in the way training sets were generated. Fivefold stratified cross-validation outlined cascading predictions provides superior precision and recall based on an internal test set. We next prospectively validated the architectures using a temporal set of 153,467 of in-house data points (after a 4-month interim from initial data extraction). Results outlined Arch3 performed with the highest target class averaged precision and recall scores of 71% and 53%, which we attribute to the use of inactive background sets. Distance-based applicability domain (AD) analysis outlined that Arch3 provides superior extrapolation into novel areas of chemical space, and thus based on the results presented here, propose as the most suitable architecture for the functional effect prediction of small molecules. We finally conclude including functional effects could provide vital insight in future studies, to annotate cases of unanticipated functional changeover, as outlined by our CHRM1 case study.

In silico identification of drug target pathways in breast cancer subtypes using pathway cross-talk inhibition

Background

Despite great development in genome and proteome high-throughput methods, treatment failure is a critical point in the management of most solid cancers, including breast cancer (BC). Multiple alternative mechanisms upon drug treatment are involved to offset therapeutic effects, eventually causing drug resistance or treatment failure.

Methods

Here, we optimized a computational method to discover novel drug target pathways in cancer subtypes using pathway cross-talk inhibition (PCI). The in silico method is based on the detection and quantification of the pathway cross-talk for distinct cancer subtypes. From a BC data set of The Cancer Genome Atlas, we have identified different networks of cross-talking pathways for different BC subtypes, validated using an independent BC dataset from Gene Expression Omnibus. Then, we predicted in silico the effects of new or approved drugs on different BC subtypes by silencing individual or combined subtype-derived pathways with the aim to find new potential drugs or more effective synergistic combinations of drugs.

Results

Overall, we identified a set of new potential drug target pathways for distinct BC subtypes on which therapeutic agents could synergically act showing antitumour effects and impacting on cross-talk inhibition.

Conclusions

We believe that in silico methods based on PCI could offer valuable approaches to identifying more tailored and effective treatments in particular in heterogeneous cancer diseases.

Electronic supplementary material

The online version of this article (10.1186/s12967-018-1535-2) contains supplementary material, which is available to authorized users.

A molecule-based genetic association approach implicates a range of voltage-gated calcium channels associated with schizophrenia

Traditional genome-wide association studies (GWAS) have successfully detected genetic variants associated with schizophrenia. However, only a small fraction of heritability can be explained. Gene-set/pathway-based methods can overcome limitations arising from single nucleotide polymorphism (SNP)-based analysis, but most of them place constraints on size which may exclude highly specific and functional sets, like macromolecules. Voltage-gated calcium (Ca_v ) channels, belonging to macromolecules, are composed of several subunits whose encoding genes are located far away or even on different chromosomes. We combined information about such molecules with GWAS data to investigate how functional channels associated with schizophrenia. We defined a biologically meaningful SNP-set based on channel structure and performed an association study by using a validated method: SNP-set (sequence) kernel association test. We identified eight subtypes of Ca_v channels significantly associated with schizophrenia from a subsample of published data (N = 56,605), including the L-type channels (Ca_v 1.1, Ca_v 1.2, Ca_v 1.3), P-/Q-type Ca_v 2.1, N-type Ca_v 2.2, R-type Ca_v 2.3, T-type Ca_v 3.1, and Ca_v 3.3. Only genes from Ca_v 1.2 and Ca_v 3.3 have been implicated by the largest GWAS (N = 82,315). Each subtype of Ca_v channels showed relatively high chip heritability, proportional to the size of its constituent gene regions. The results suggest that abnormalities of Ca_v channels may play an important role in the pathophysiology of schizophrenia and these channels may represent appropriate drug targets for therapeutics. Analyzing subunit-encoding genes of a macromolecule in aggregate is a complementary way to identify more genetic variants of polygenic diseases. This study offers the potential of power for discovery the biological mechanisms of schizophrenia.

BIOPYDB: A Dynamic Human Cell Specific Biochemical Pathway Database with Advanced Computational Analyses Platform

BIOPYDB: BIOchemical PathwaY DataBase is developed as a manually curated, readily updatable, dynamic resource of human cell specific pathway information along with integrated computational platform to perform various pathway analyses. Presently, it comprises of 46 pathways, 3189 molecules, 5742 reactions and 6897 different types of diseases linked with pathway proteins, which are referred by 520 literatures and 17 other pathway databases. With its repertoire of biochemical pathway data, and computational tools for performing Topological, Logical and Dynamic analyses, BIOPYDB offers both the experimental and computational biologists to acquire a comprehensive understanding of signaling cascades in the cells. Automated pathway image reconstruction, cross referencing of pathway molecules and interactions with other databases and literature sources, complex search operations to extract information from other similar resources, integrated platform for pathway data sharing and computation, etc. are the novel and useful features included in this database to make it more acceptable and attractive to the users of pathway research communities. The RESTful API service is also made available to the advanced users and developers for accessing this database more conveniently through their own computer programmes.

Novel evidence for complement system activation in chick myopia and hyperopia models: a meta-analysis of transcriptome datasets

Myopia (short-sightedness) and hyperopia (long-sightedness) occur when the eye grows too long or short, respectively, for its refractive power. There are currently approximately 1.45 billion myopes worldwide and prevalence is rising dramatically. Although high myopia significantly increases the risk of developing a range of sight-threatening disorders, the molecular mechanisms underlying ocular growth regulation and its relationship to these secondary complications remain poorly understood. Thus, this study meta-analyzed transcriptome datasets collected in the commonly used chick model of optically-induced refractive error. Fifteen datasets (collected across five previous studies) were obtained from GEO, preprocessed in Bioconductor, and divided into 4 conditions representing early (≤1 day) and late (>1 day) myopia and hyperopia induction. Differentially expressed genes in each condition were then identified using Rank Product meta-analysis. The results provide novel evidence for transcriptional activation of the complement system during both myopia and hyperopia induction, and confirm existing literature implicating cell signaling, mitochondrial, and structural processes in refractive error. Further comparisons demonstrated that the meta-analysis results also significantly improve concordance with broader omics data types (i.e., human genetic association and animal proteomics studies) relative to previous transcriptome studies, and show extensive similarities with the genes linked to age-related macular degeneration, choroidal neovascularization, and cataract.

Novel approaches to develop community-built biological network models for potential drug discovery

INTRODUCTION

Hundreds of thousands of data points are now routinely generated in clinical trials by molecular profiling and NGS technologies. A true translation of this data into knowledge is not possible without analysis and interpretation in a well-defined biology context. Currently, there are many public and commercial pathway tools and network models that can facilitate such analysis. At the same time, insights and knowledge that can be gained is highly dependent on the underlying biological content of these resources. Crowdsourcing can be employed to guarantee the accuracy and transparency of the biological content underlining the tools used to interpret rich molecular data. Areas covered: In this review, the authors describe crowdsourcing in drug discovery. The focal point is the efforts that have successfully used the crowdsourcing approach to verify and augment pathway tools and biological network models. Technologies that enable the building of biological networks with the community are also described. Expert opinion: A crowd of experts can be leveraged for the entire development process of biological network models, from ontologies to the evaluation of their mechanistic completeness. The ultimate goal is to facilitate biomarker discovery and personalized medicine by mechanistically explaining patients' differences with respect to disease prevention, diagnosis, and therapy outcome.

Working with Ontologies

Ontologies are powerful and popular tools to encode data in a structured format and manage knowledge. A large variety of existing ontologies offer users access to biomedical knowledge. This chapter contains a short theoretical background of ontologies and introduces two notable examples: The Gene Ontology and the ontology for Biological Pathways Exchange. For both ontologies a short overview and working bioinformatic applications, i.e., Gene Ontology enrichment analyses and pathway data visualization, are provided.

Bioinformatics Meets Biomedicine: OncoFinder, a Quantitative Approach for Interrogating Molecular Pathways Using Gene Expression Data

We propose a biomathematical approach termed OncoFinder (OF) that enables performing both quantitative and qualitative analyses of the intracellular molecular pathway activation. OF utilizes an algorithm that distinguishes the activator/repressor role of every gene product in a pathway. This method is applicable for the analysis of any physiological, stress, malignancy, and other conditions at the molecular level. OF showed a strong potential to neutralize background-caused differences between experimental gene expression data obtained using NGS, microarray and modern proteomics techniques. Importantly, in most cases, pathway activation signatures were better markers of cancer progression compared to the individual gene products. OF also enables correlating pathway activation with the success of anticancer therapy for individual patients. We further expanded this approach to analyze impact of micro RNAs (miRs) on the regulation of cellular interactome. Many alternative sources provide information about miRs and their targets. However, instruments elucidating higher level impact of the established total miR profiles are still largely missing. A variant of OncoFinder termed MiRImpact enables linking miR expression data with its estimated outcome on the regulation of molecular processes, such as signaling, metabolic, cytoskeleton, and DNA repair pathways. MiRImpact was used to establish cancer-specific and cytomegaloviral infection-linked interactomic signatures for hundreds of molecular pathways. Interestingly, the impact of miRs appeared orthogonal to pathway regulation at the mRNA level, which stresses the importance of combining all available levels of gene regulation to build a more objective molecular model of cell.

Network-Assisted Disease Classification and Biomarker Discovery

Developing improved approaches for diagnosis, treatment, and prevention of diseases is a major goal of biomedical research. Therefore, the discovery of biomarker signatures from high-throughput "omics" data is an active research topic in the field of bioinformatics and systems medicine. A major issue is the low reproducibility and the limited biological interpretability of candidate biomarker signatures identified from high-throughput data. This impedes the use of discovered biomarker signatures into clinical applications. Currently, much focus is placed on developing strategies to improve reproducibility and interpretability. Researchers have fruitfully started to incorporate prior knowledge derived from pathways and molecular networks into the process of biomarker identification. In this chapter, after giving a general introduction to the problem of disease classification and biomarker discovery, we will review two types of network-assisted approaches: (1) approaches inferring activity scores for specific pathways which are subsequently used for classification and (2) approaches identifying subnetworks or modules of molecular networks by differential network analysis which can serve as biomarker signatures.

Bidirectional Expression of Metabolic, Structural, and Immune Pathways in Early Myopia and Hyperopia

Myopia (short-sightedness) affects 1.45 billion people worldwide, many of whom will develop sight-threatening secondary disorders. Myopic eyes are characterized by excessive size while hyperopic (long-sighted) eyes are typically small. The biological and genetic mechanisms underpinning the retina's local control of these growth patterns remain unclear. In the present study, we used RNA sequencing to examine gene expression in the retina/RPE/choroid across 3 days of optically-induced myopia and hyperopia induction in chick. Data were analyzed for differential expression of single genes, and Gene Set Enrichment Analysis (GSEA) was used to identify gene sets correlated with ocular axial length and refraction across lens groups. Like previous studies, we found few single genes that were differentially-expressed in a sign-of-defocus dependent manner (only BMP2 at 1 day). Using GSEA, however, we are the first to show that more subtle shifts in structural, metabolic, and immune pathway expression are correlated with the eye size and refractive changes induced by lens defocus. Our findings link gene expression with the morphological characteristics of refractive error, and suggest that physiological stress arising from metabolic and inflammatory pathway activation could increase the vulnerability of myopic eyes to secondary pathologies.

Reactome from a WikiPathways Perspective

Reactome and WikiPathways are two of the most popular freely available databases for biological pathways. Reactome pathways are centrally curated with periodic input from selected domain experts. WikiPathways is a community-based platform where pathways are created and continually curated by any interested party. The nascent collaboration between WikiPathways and Reactome illustrates the mutual benefits of combining these two approaches. We created a format converter that converts Reactome pathways to the GPML format used in WikiPathways. In addition, we developed the ComplexViz plugin for PathVisio which simplifies looking up complex components. The plugin can also score the complexes on a pathway based on a user defined criterion. This score can then be visualized on the complex nodes using the visualization options provided by the plugin. Using the merged collection of curated and converted Reactome pathways, we demonstrate improved pathway coverage of relevant biological processes for the analysis of a previously described polycystic ovary syndrome gene expression dataset. Additionally, this conversion allows researchers to visualize their data on Reactome pathways using PathVisio's advanced data visualization functionalities. WikiPathways benefits from the dedicated focus and attention provided to the content converted from Reactome and the wealth of semantic information about interactions. Reactome in turn benefits from the continuous community curation available on WikiPathways. The research community at large benefits from the availability of a larger set of pathways for analysis in PathVisio and Cytoscape. The pathway statistics results obtained from PathVisio are significantly better when using a larger set of candidate pathways for analysis. The conversion serves as a general model for integration of multiple pathway resources developed using different approaches.

A Six-Stage Workflow for Robust Application of Systems Pharmacology

Quantitative and systems pharmacology (QSP) is increasingly being applied in pharmaceutical research and development. One factor critical to the ultimate success of QSP is the establishment of commonly accepted language, technical criteria, and workflows. We propose an integrated workflow that bridges conceptual objectives with underlying technical detail to support the execution, communication, and evaluation of QSP projects.

Scientific Knowledge Engineering: a conceptual delineation and overview of the state of the art

As a community work, scientific contributions are usually built incrementally, involving some transformation, expansion or refutation of existing conceptual and propositional networks. As the body of knowledge increases, scientists concentrate more effort on ensuring that new hypotheses and observations are needed and consistent with previous findings. In this paper, we will characterize Knowledge Engineering as an important groundwork for structuring scientific knowledge. We argue that knowledge-based computational infrastructures can support researchers in organizing and making explicit the main aspects needed to make inferences or extract conclusions from an existing body of knowledge. This view is also comparatively built, contrasting it with alternatives for manipulating scientific knowledge, namely data-intensive approaches and the computational discovery of scientific knowledge. The current state of the art is presented with 22 knowledge representations and computational infrastructure implementations, with their main relevant properties analyzed and compared. Based on this review and on the theoretical foundations of Knowledge Engineering, a high level step-by-step approach for specifying and constructing scientific computational environments is described. The paper concludes by indicating paths for further development of the view initiated here, especially related to the technical specificities that originates from applying Knowledge Engineering to scientific knowledge.

Pathway Analysis: State of the Art

Pathway analysis is a set of widely used tools for research in life sciences intended to give meaning to high-throughput biological data. The methodology of these tools settles in the gathering and usage of knowledge that comprise biomolecular functioning, coupled with statistical testing and other algorithms. Despite their wide employment, pathway analysis foundations and overall background may not be fully understood, leading to misinterpretation of analysis results. This review attempts to comprise the fundamental knowledge to take into consideration when using pathway analysis as a hypothesis generation tool. We discuss the key elements that are part of these methodologies, their capabilities and current deficiencies. We also present an overview of current and all-time popular methods, highlighting different classes across them. In doing so, we show the exploding diversity of methods that pathway analysis encompasses, point out commonly overlooked caveats, and direct attention to a potential new class of methods that attempt to zoom the analysis scope to the sample scale.

Drug target prioritization by perturbed gene expression and network information

Drugs bind to their target proteins, which interact with downstream effectors and ultimately perturb the transcriptome of a cancer cell. These perturbations reveal information about their source, i.e., drugs’ targets. Here, we investigate whether these perturbations and protein interaction networks can uncover drug targets and key pathways. We performed the first systematic analysis of over 500 drugs from the Connectivity Map. First, we show that the gene expression of drug targets is usually not significantly affected by the drug perturbation. Hence, expression changes after drug treatment on their own are not sufficient to identify drug targets. However, ranking of candidate drug targets by network topological measures prioritizes the targets. We introduce a novel measure, local radiality, which combines perturbed genes and functional interaction network information. The new measure outperforms other methods in target prioritization and proposes cancer-specific pathways from drugs to affected genes for the first time. Local radiality identifies more diverse targets with fewer neighbors and possibly less side effects.

Automatically visualise and analyse data on pathways using PathVisioRPC from any programming environment

BACKGROUND

Biological pathways are descriptive diagrams of biological processes widely used for functional analysis of differentially expressed genes or proteins. Primary data analysis, such as quality control, normalisation, and statistical analysis, is often performed in scripting languages like R, Perl, and Python. Subsequent pathway analysis is usually performed using dedicated external applications. Workflows involving manual use of multiple environments are time consuming and error prone. Therefore, tools are needed that enable pathway analysis directly within the same scripting languages used for primary data analyses. Existing tools have limited capability in terms of available pathway content, pathway editing and visualisation options, and export file formats. Consequently, making the full-fledged pathway analysis tool PathVisio available from various scripting languages will benefit researchers.

RESULTS

We developed PathVisioRPC, an XMLRPC interface for the pathway analysis software PathVisio. PathVisioRPC enables creating and editing biological pathways, visualising data on pathways, performing pathway statistics, and exporting results in several image formats in multiple programming environments. We demonstrate PathVisioRPC functionalities using examples in Python. Subsequently, we analyse a publicly available NCBI GEO gene expression dataset studying tumour bearing mice treated with cyclophosphamide in R. The R scripts demonstrate how calls to existing R packages for data processing and calls to PathVisioRPC can directly work together. To further support R users, we have created RPathVisio simplifying the use of PathVisioRPC in this environment. We have also created a pathway module for the microarray data analysis portal ArrayAnalysis.org that calls the PathVisioRPC interface to perform pathway analysis. This module allows users to use PathVisio functionality online without having to download and install the software and exemplifies how the PathVisioRPC interface can be used by data analysis pipelines for functional analysis of processed genomics data.

CONCLUSIONS

PathVisioRPC enables data visualisation and pathway analysis directly from within various analytical environments used for preliminary analyses. It supports the use of existing pathways from WikiPathways or pathways created using the RPC itself. It also enables automation of tasks performed using PathVisio, making it useful to PathVisio users performing repeated visualisation and analysis tasks. PathVisioRPC is freely available for academic and commercial use at http://projects.bigcat.unimaas.nl/pathvisiorpc.

Discerning mechanistically rewired biological pathways by cumulative interaction heterogeneity statistics

Changes in response of a biological pathway could be a consequence of either pathway rewiring, changed input, or a combination of both. Most pathway analysis methods are not designed for mechanistic rewiring such as regulatory element variations. This limits our understanding of biological pathway evolution. Here we present a Q-method to discern whether changed pathway response is caused by mechanistic rewiring of pathways due to evolution. The main innovation is a cumulative pathway interaction heterogeneity statistic accounting for rewiring-specific effects on the rate of change of each molecular variable across conditions. The Q-method remarkably outperformed differential-correlation based approaches on data from diverse biological processes. Strikingly, it also worked well in differentiating rewired chaotic systems, whose dynamics are notoriously difficult to predict. Applying the Q-method on transcriptome data of four yeasts, we show that pathway interaction heterogeneity for known metabolic and signaling pathways is indeed a predictor of interspecies genetic rewiring due to unbalanced TATA box-containing genes among the yeasts. The demonstrated effectiveness of the Q-method paves the way to understanding network evolution at the resolution of functional biological pathways.