Integration, visualization and analysis of human interactome

Explainable biology for improved therapies in precision medicine: AI is not enough

Technological advances and high-throughput bio-chemical assays are rapidly changing ways how we formulate and test biological hypotheses, and how we treat patients. Most complex diseases arise on a background of genetics, lifestyle and environment factors, and manifest themselves as a spectrum of symptoms. To fathom intricate biological processes and their changes from healthy to disease states, we need to systematically integrate and analyze multi-omics datasets, ontologies, and diverse annotations. Without proper management of such complex biological and clinical data, artificial intelligence (AI) algorithms alone cannot be effectively trained, validated, and successfully applied to provide trustworthy and patient-centric diagnosis, prognosis and treatment. Precision medicine requires to use multi-omics approaches effectively, and offers many opportunities for using AI, "big data" analytics, and integrative computational biology workflows. Advances in optical and biochemical assay technologies including sequencing, mass spectrometry and imaging modalities have transformed research by empowering us to simultaneously view all genes expressed, identify proteome-wide changes, and assess interacting partners of each individual protein within a dynamically changing biological system, at an individual cell level. While such views are already having an impact on our understanding of healthy and disease conditions, it remains challenging to extract useful information comprehensively and systematically from individual studies, ensure that signal is separated from noise, develop models, and provide hypotheses for further research. Data remain incomplete and are often poorly connected using fragmented biological networks. In addition, statistical and machine learning models are developed at a cohort level and often not validated at the individual patient level. Combining integrative computational biology and AI has the potential to improve understanding and treatment of diseases by identifying biomarkers and building explainable models characterizing individual patients. From systematic data analysis to more specific diagnostic, prognostic and predictive biomarkers, drug mechanism of action, and patient selection, such analyses influence multiple steps from prevention to disease characterization, and from prognosis to drug discovery. Data mining, machine learning, graph theory and advanced visualization may help identify diagnostic, prognostic and predictive biomarkers, and create causal models of disease. Intertwining computational prediction and modeling with biological experiments leads to faster, more biologically and clinically relevant discoveries. However, computational analysis results and models are going to be only as accurate and useful as correct and comprehensive are the networks, ontologies and datasets used to build them. High quality, curated data portals provide the necessary foundation for translational research. They help to identify better biomarkers, new drugs, precision treatments, and should lead to improved patient outcomes and their quality of life. Intertwining computational prediction and modeling with biological experiments, efficiently and effectively leads to more useful findings faster.

Reconstruction of Eriocheir sinensis Protein-Protein Interaction Network Based on DGO-SVM Method

Eriocheir sinensis is an economically important aquatic animal. Its regulatory mechanisms underlying many biological processes are still vague due to the lack of systematic analysis tools. The protein-protein interaction network (PIN) is an important tool for the systematic analysis of regulatory mechanisms. In this work, a novel machine learning method, DGO-SVM, was applied to predict the protein-protein interaction (PPI) in E. sinensis, and its PIN was reconstructed. With the domain, biological process, molecular functions and subcellular locations of proteins as the features, DGO-SVM showed excellent performance in Bombyx mori, humans and five aquatic crustaceans, with 92-96% accuracy. With DGO-SVM, the PIN of E. sinensis was reconstructed, containing 14,703 proteins and 7,243,597 interactions, in which 35,604 interactions were associated with 566 novel proteins mainly involved in the response to exogenous stimuli, cellular macromolecular metabolism and regulation. The DGO-SVM demonstrated that the biological process, molecular functions and subcellular locations of proteins are significant factors for the precise prediction of PPIs. We reconstructed the largest PIN for E. sinensis, which provides a systematic tool for the regulatory mechanism analysis. Furthermore, the novel-protein-related PPIs in the PIN may provide important clues for the mechanism analysis of the underlying specific physiological processes in E. sinensis.

Analysis of Protein-Protein Interaction Networks through Computational Approaches

The interactions among proteins and genes are extremely important for cellular functions. Molecular interactions at protein or gene levels can be used to construct interaction networks in which the interacting species are categorized based on direct interactions or functional similarities. Compared with the limited experimental techniques, various computational tools make it possible to analyze, filter, and combine the interaction data to get comprehensive information about the biological pathways. By the efficient way of integrating experimental findings in discovering PPIs and computational techniques for prediction, the researchers have been able to gain many valuable data on PPIs, including some advanced databases. Moreover, many useful tools and visualization programs enable the researchers to establish, annotate, and analyze biological networks. We here review and list the computational methods, databases, and tools for protein-protein interaction prediction.

Neighborhoods to Nucleotides - Advances and gaps for an obesity disparities systems epidemiology model

Purpose of Review

Disparities in obesity rates in the US continue to increase. Here we review progress and highlight gaps in understanding disparities in obesity with a focus on the Hispanic/Latino population from a systems epidemiology framework. We review seven domains: environment, behavior, biomarkers, nutrition, microbiome, genomics, and epigenomics/transcriptomics. We focus on recent advances that include at least two or more of these domains, and then provide a real world example of data collection efforts that reflect these domains.

Recent Findings

Research into DNA methylation related to discrimination and microbiome relating to eating behaviors and food content is furthering understanding of why disparities in obesity persist. Environmental and neighborhood level research is uncovering the importance of exposures such as air and noise pollution and systematic or structural racism for obesity and related outcomes through behaviors such as sleep.

Deciphering the Potential Pharmaceutical Mechanism of GUI-ZHI-FU-LING-WAN on Systemic Sclerosis based on Systems Biology Approaches

Systemic sclerosis (SSc; scleroderma) is a complicated idiopathic connective tissue disease with seldom effective treatment. GUI-ZHI-FU-LING-WAN (GFW) is a classic Traditional Chinese Medicine (TCM) formula widely used for the treatment of SSc. However, the mechanism of how the GFW affects SSc remains unclear. In this study, the system biology approach was utilized to analyze herb compounds and related targets to get the general information of GFW. The KEGG enrichment analysis of 1645 related targets suggested that the formula is involved in the VEGF signaling pathway, the Toll-like receptor signaling pathway, etc. Quantitative and qualitative analysis of the relationship among the 3 subsets (formula targets, drug targets and disease genes) showed that the formula targets overlapped with 38.0% drug targets and 26.0% proteins encoded by disease genes. Through the analysis of SSc related microarray statistics from the GEO database, we also validated the consistent expression behavior among the 3 subsets before and after treatment. To further reveal the mechanism of prescription, we constructed a network among 3 subsets and decomposed it into 24 modules to decipher how GFW interfere in the progress of SSc. The modules indicated that the intervention may come into effect through following pathogenic processes: vasculopathy, immune dysregulation and tissue fibrosis. Vitro experiments confirmed that GFW could suppress the proliferation of fibroblasts and decrease the Th1 cytokine (TNF-α, MIP-2 and IL-6) expression for lipopolysaccharide (LPS) and bleomycin (BLM) stimulation in macrophages, which is consistent with previous conclusion that GFW is able to relieve SSc. The systems biology approach provides a new insight for deepening understanding about TCM.

Petri net siphon analysis and graph theoretic measures for identifying combination therapies in cancer

Epidermal Growth Factor Receptor (EGFR) signaling to the Ras-MAPK pathway is implicated in the development and progression of cancer and is a major focus of targeted combination therapies. Physiochemical models have been used for identifying and testing the signal-inhibiting potential of targeted therapies, however, their application to larger multi-pathway networks is limited by the availability of experimentally-determined rate and concentration parameters. An alternate strategy for identifying and evaluating drug-targetable nodes is proposed. A physiochemical model of EGFR-Ras-MAPK signaling is implemented and calibrated to experimental data. Essential topological features of the model are converted into a Petri net and nodes that behave as siphons-a structural property of Petri nets-are identified. Siphons represent potential drug-targets since they are unrecoverable if their values fall below a threshold. Centrality measures are then used to prioritize siphons identified as candidate drug-targets. Single and multiple drug-target combinations are identified which correspond to clinically relevant drug targets and exhibit inhibition synergy in physiochemical simulations of EGF-induced EGFR-Ras-MAPK signaling. Taken together, these studies suggest that siphons and centrality analyses are a promising computational strategy to identify and rank drug-targetable nodes in larger networks as they do not require knowledge of the dynamics of the system, but rely solely on topology.

Establishment of a Strong Link Between Smoking and Cancer Pathogenesis through DNA Methylation Analysis

Smoking is a well-documented risk factor in various cancers, especially lung cancer. In the current study, we tested the hypothesis that abnormal DNAm loci associated with smoking are enriched in genes and pathways that convey a risk of cancer by determining whether smoking-related methylated genes led to enrichment in cancer-related pathways. We analyzed two sets of smoking-related methylated genes from 28 studies originating from blood and buccal samples. By analyzing 320 methylated genes from 26 studies on blood samples (N = 17,675), we found 57 enriched pathways associated with different types of cancer (FDR < 0.05). Of these, 11 were also significantly overrepresented in the 661 methylated genes from two studies of buccal samples (N = 1,002). We further found the aryl hydrocarbon receptor signaling pathway plays an important role in the initiation of smoking-attributable cancer. Finally, we constructed a subnetwork of genes important for smoking-attributable cancer from the 48 non-redundant genes in the 11 oncogenic pathways. Of these, genes such as DUSP4 and AKT3 are well documented as being involved in smoking-related lung cancer. In summary, our findings provide robust and systematic evidence in support of smoking’s impact on the epigenome, which may be an important contributor to cancer.

Prediction of Protein-Protein Interactions by Evidence Combining Methods

Most cellular functions involve proteins' features based on their physical interactions with other partner proteins. Sketching a map of protein-protein interactions (PPIs) is therefore an important inception step towards understanding the basics of cell functions. Several experimental techniques operating in vivo or in vitro have made significant contributions to screening a large number of protein interaction partners, especially high-throughput experimental methods. However, computational approaches for PPI predication supported by rapid accumulation of data generated from experimental techniques, 3D structure definitions, and genome sequencing have boosted the map sketching of PPIs. In this review, we shed light on in silico PPI prediction methods that integrate evidence from multiple sources, including evolutionary relationship, function annotation, sequence/structure features, network topology and text mining. These methods are developed for integration of multi-dimensional evidence, for designing the strategies to predict novel interactions, and for making the results consistent with the increase of prediction coverage and accuracy.

Bifunctional Ligands for Inhibition of Tight-Binding Protein-Protein Interactions

The acknowledged potential of small-molecule therapeutics targeting disease-related protein-protein interactions (PPIs) has promoted active research in this field. The strategy of using small molecule inhibitors (SMIs) to fight strong (tight-binding) PPIs tends to fall short due to the flat and wide interfaces of PPIs. Here we propose a biligand approach for disruption of strong PPIs. The potential of this approach was realized for disruption of the tight-binding (KD = 100 pM) tetrameric holoenzyme of cAMP-dependent protein kinase (PKA). Supported by X-ray analysis of cocrystals, bifunctional inhibitors (ARC-inhibitors) were constructed that simultaneously associated with both the ATP-pocket and the PPI interface area of the catalytic subunit of PKA (PKAc). Bifunctional inhibitor ARC-1411, possessing a KD value of 3 pM toward PKAc, induced the dissociation of the PKA holoenzyme with a low-nanomolar IC50, whereas the ATP-competitive inhibitor H89 bound to the PKA holoenzyme without disruption of the protein tetramer.

CDH1/E-cadherin and solid tumors. An updated gene-disease association analysis using bioinformatics tools

Cancer is a group of diseases that causes millions of deaths worldwide. Among cancers, Solid Tumors (ST) stand-out due to their high incidence and mortality rates. Disruption of cell-cell adhesion is highly relevant during tumor progression. Epithelial-cadherin (protein: E-cadherin, gene: CDH1) is a key molecule in cell-cell adhesion and an abnormal expression or/and function(s) contributes to tumor progression and is altered in ST. A systematic study was carried out to gather and summarize current knowledge on CDH1/E-cadherin and ST using bioinformatics resources. The DisGeNET database was exploited to survey CDH1-associated diseases. Reported mutations in specific ST were obtained by interrogating COSMIC and IntOGen tools. CDH1 Single Nucleotide Polymorphisms (SNP) were retrieved from the dbSNP database. DisGeNET analysis identified 609 genes annotated to ST, among which CDH1 was listed. Using CDH1 as query term, 26 disease concepts were found, 21 of which were neoplasms-related terms. Using DisGeNET ALL Databases, 172 disease concepts were identified. Of those, 80 ST disease-related terms were subjected to manual curation and 75/80 (93.75%) associations were validated. On selected ST, 489 CDH1 somatic mutations were listed in COSMIC and IntOGen databases. Breast neoplasms had the highest CDH1-mutation rate. CDH1 was positioned among the 20 genes with highest mutation frequency and was confirmed as driver gene in breast cancer. Over 14,000 SNP for CDH1 were found in the dbSNP database. This report used DisGeNET to gather/compile current knowledge on gene-disease association for CDH1/E-cadherin and ST; data curation expanded the number of terms that relate them. An updated list of CDH1 somatic mutations was obtained with COSMIC and IntOGen databases and of SNP from dbSNP. This information can be used to further understand the role of CDH1/E-cadherin in health and disease.

Incorporating High-Throughput Exposure Predictions With Dosimetry-Adjusted In Vitro Bioactivity to Inform Chemical Toxicity Testing

We previously integrated dosimetry and exposure with high-throughput screening (HTS) to enhance the utility of ToxCast HTS data by translating in vitro bioactivity concentrations to oral equivalent doses (OEDs) required to achieve these levels internally. These OEDs were compared against regulatory exposure estimates, providing an activity-to-exposure ratio (AER) useful for a risk-based ranking strategy. As ToxCast efforts expand (ie, Phase II) beyond food-use pesticides toward a wider chemical domain that lacks exposure and toxicity information, prediction tools become increasingly important. In this study, in vitro hepatic clearance and plasma protein binding were measured to estimate OEDs for a subset of Phase II chemicals. OEDs were compared against high-throughput (HT) exposure predictions generated using probabilistic modeling and Bayesian approaches generated by the U.S. Environmental Protection Agency (EPA) ExpoCast program. This approach incorporated chemical-specific use and national production volume data with biomonitoring data to inform the exposure predictions. This HT exposure modeling approach provided predictions for all Phase II chemicals assessed in this study whereas estimates from regulatory sources were available for only 7% of chemicals. Of the 163 chemicals assessed in this study, 3 or 13 chemicals possessed AERs < 1 or < 100, respectively. Diverse bioactivities across a range of assays and concentrations were also noted across the wider chemical space surveyed. The availability of HT exposure estimation and bioactivity screening tools provides an opportunity to incorporate a risk-based strategy for use in testing prioritization.

DYVIPAC: an integrated analysis and visualisation framework to probe multi-dimensional biological networks

Biochemical networks are dynamic and multi-dimensional systems, consisting of tens or hundreds of molecular components. Diseases such as cancer commonly arise due to changes in the dynamics of signalling and gene regulatory networks caused by genetic alternations. Elucidating the network dynamics in health and disease is crucial to better understand the disease mechanisms and derive effective therapeutic strategies. However, current approaches to analyse and visualise systems dynamics can often provide only low-dimensional projections of the network dynamics, which often does not present the multi-dimensional picture of the system behaviour. More efficient and reliable methods for multi-dimensional systems analysis and visualisation are thus required. To address this issue, we here present an integrated analysis and visualisation framework for high-dimensional network behaviour which exploits the advantages provided by parallel coordinates graphs. We demonstrate the applicability of the framework, named "Dynamics Visualisation based on Parallel Coordinates" (DYVIPAC), to a variety of signalling networks ranging in topological wirings and dynamic properties. The framework was proved useful in acquiring an integrated understanding of systems behaviour.

An integrative computational approach for prioritization of genomic variants

An essential step in the discovery of molecular mechanisms contributing to disease phenotypes and efficient experimental planning is the development of weighted hypotheses that estimate the functional effects of sequence variants discovered by high-throughput genomics. With the increasing specialization of the bioinformatics resources, creating analytical workflows that seamlessly integrate data and bioinformatics tools developed by multiple groups becomes inevitable. Here we present a case study of a use of the distributed analytical environment integrating four complementary specialized resources, namely the Lynx platform, VISTA RViewer, the Developmental Brain Disorders Database (DBDB), and the RaptorX server, for the identification of high-confidence candidate genes contributing to pathogenesis of spina bifida. The analysis resulted in prediction and validation of deleterious mutations in the SLC19A placental transporter in mothers of the affected children that causes narrowing of the outlet channel and therefore leads to the reduced folate permeation rate. The described approach also enabled correct identification of several genes, previously shown to contribute to pathogenesis of spina bifida, and suggestion of additional genes for experimental validations. The study demonstrates that the seamless integration of bioinformatics resources enables fast and efficient prioritization and characterization of genomic factors and molecular networks contributing to the phenotypes of interest.

In silico prediction of physical protein interactions and characterization of interactome orphans

Protein-protein interactions (PPIs) are useful for understanding signaling cascades, predicting protein function, associating proteins with disease and fathoming drug mechanism of action. Currently, only ∼ 10% of human PPIs may be known, and about one-third of human proteins have no known interactions. We introduce FpClass, a data mining-based method for proteome-wide PPI prediction. At an estimated false discovery rate of 60%, we predicted 250,498 PPIs among 10,531 human proteins; 10,647 PPIs involved 1,089 proteins without known interactions. We experimentally tested 233 high- and medium-confidence predictions and validated 137 interactions, including seven novel putative interactors of the tumor suppressor p53. Compared to previous PPI prediction methods, FpClass achieved better agreement with experimentally detected PPIs. We provide an online database of annotated PPI predictions (http://ophid.utoronto.ca/fpclass/) and the prediction software (http://www.cs.utoronto.ca/~juris/data/fpclass/).

Toward a systems-level view of dynamic phosphorylation networks

To better understand how cells sense and respond to their environment, it is important to understand the organization and regulation of the phosphorylation networks that underlie most cellular signal transduction pathways. These networks, which are composed of protein kinases, protein phosphatases and their respective cellular targets, are highly dynamic. Importantly, to achieve signaling specificity, phosphorylation networks must be regulated at several levels, including at the level of protein expression, substrate recognition, and spatiotemporal modulation of enzymatic activity. Here, we briefly summarize some of the traditional methods used to study the phosphorylation status of cellular proteins before focusing our attention on several recent technological advances, such as protein microarrays, quantitative mass spectrometry, and genetically-targetable fluorescent biosensors, that are offering new insights into the organization and regulation of cellular phosphorylation networks. Together, these approaches promise to lead to a systems-level view of dynamic phosphorylation networks.