WikiPathways: capturing the full diversity of pathway knowledge

Understanding the Early Biological Effects of Isoprene-Derived Particulate Matter Enhanced by Anthropogenic Pollutants

INTRODUCTION

Airborne fine particulate matter (PM2.5; particulate matter ≤ 2.5 μm in aerodynamic diameter) plays a key role in air quality, climate, and public health. Globally, the largest mass fraction of PM2.5 is organic, dominated by secondary organic aerosol (SOA) formed from atmospheric oxidation of volatile organic compounds (VOCs). Isoprene from vegetation is the most abundant nonmethane VOC emitted into Earth's atmosphere. Isoprene has been recently recognized as one of the major sources of global SOA production that is enhanced by the presence of anthropogenic pollutants, such as acidic sulfate derived from sulfur dioxide (SO2), through multiphase chemistry of its oxidation products. Considering the abundance of isoprene-derived SOA in the atmosphere, understanding mechanisms of adverse health effects through inhalation exposure is critical to mitigating its potential impact on public health. Although previous studies have examined the toxicological effects of certain isoprene-derived gas-phase oxidation products, to date, no systematic studies have examined the potential toxicological effects of isoprene-derived SOA, its constituents, or its SOA precursors on human lung cells.

SPECIFIC AIMS

The overall objective of this study was to investigate the early biological effects of isoprene-derived SOA and its subtypes on BEAS-2B cells (a human bronchial epithelial cell line), with a particular focus on the alteration of oxidative stress- and inflammation-related genes. To achieve this objective, there were two specific aims.

1. Examine toxicity and early biological effects of SOA derived from the photochemical oxidation of isoprene, considering both urban and downwind-urban types of chemistry.

2. Examine toxicity and early biological effects of SOA derived directly from downstream oxidation products of isoprene (i.e., epoxides and hydroperoxides).

METHODS

Isoprene-derived SOA was first generated by photooxidation of isoprene under natural sunlight in the presence of nitric oxide (NO) and acidified sulfate aerosols. Experiments were conducted in a 120-m3 outdoor Teflon-film chamber located on the roof of the Gillings School of Global Public Health, University of North Carolina at Chapel Hill (UNC-Chapel Hill). BEAS-2B cells were exposed to chamber- generated isoprene-derived SOA using the Electrostatic Aerosol in Vitro Exposure System (EAVES). This approach allowed us to generate atmospherically relevant compositions of isoprene-derived SOA and to examine its toxicity through in vitro exposures at an air-liquid interface, providing a more biologically relevant exposure model. Isoprene-derived SOA samples were also collected, concurrently with EAVES sampling, onto Teflon membrane filters for in vitro resuspension exposures and for analysis of aerosol chemical composition by gas chromatography/electron ionization-quadrupole mass spectrometry (GC/EI-MS) with prior trimethylsilylation and ultra-performance liquid-chromatography coupled to high-resolution quadrupole time-of-flight mass spectrometry equipped with electrospray ionization (UPLC/ESI-HR-QTOFMS). Isoprene-derived SOA samples were also analyzed by the dithiothreitol (DTT) assay in order to characterize their reactive oxygen species (ROS)-generation potential.

Organic synthesis of known isoprene-derived SOA precursors, which included isoprene epoxydiols (IEPOX), methacrylic acid epoxide (MAE), and isoprene-derived hydroxyhydroperoxides (ISOPOOH), was conducted in order to isolate major isoprene-derived SOA formation pathways from each other and to determine which of these pathways (or SOA types) is potentially more toxic. Since IEPOX and MAE produce SOA through multiphase chemistry onto acidic sulfate aerosol, dark reactive uptake experiments of IEPOX and MAE in the presence of acidic sulfate aerosol were performed in a 10-m3 flexible Teflon indoor chamber at UNC-Chapel Hill. Since the generation of SOA from ISOPOOH (through a non-IEPOX route) requires a hydroxyl radical (•OH)-initiated oxidation, ozonolysis of tetramethylethylene (TME) was used to form the needed •OH radicals in the indoor chamber. The resultant low-volatility multifunctional hydroperoxides condensed onto nonacidified sulfate aerosol, yielding the ISOPOOH-derived SOA needed for exposures. Similar to the outdoor chamber SOAs, IEPOX, MAE- and ISOPOOH-derived SOAs were collected onto Teflon membrane filters and were subsequently chemically characterized by GC/EI-MS and UPLC/ESI-HR-QTOFMS as well as for ROS-generation potential using the DTT assay. These filters were also used for resuspension in vitro exposures.

By conducting gene expression profiling, we provided mechanistic insights into the potential health effects of isoprene-derived SOA. First, gene expression profiling of 84 oxidative stress- and 249 inflammation-associated human genes was performed for cells exposed to isoprene-derived SOA generated in our outdoor chamber experiments in EAVES or by resuspension. Two pathway-focused panels were utilized for this purpose: (1) nCounter GX Human Inflammation Kit comprised of 249 human genes (NanoString), and (2) Human Oxidative Stress Plus RT2 Profiler PCR Array (Qiagen) comprised of 84 oxidative stress-associated genes. We compared the gene expression levels in cells exposed to SOA generated in an outdoor chamber from photochemical oxidation of isoprene in the presence of NO and acidified sulfate seed aerosol to cells exposed to a dark control mixture of isoprene, NO, and acidified sulfate seed aerosol to isolate the effects of the isoprene-derived SOA on the cells using the EAVES and resuspension exposure methods. Pathway-based analysis was performed for significantly altered genes using the ConsensusPathDB database, which is a database system for the integration of human gene functional interactions to provide biological pathway information for a gene set of interest. Pathway annotation was performed to provide biological pathway information for each gene set. The gene-gene interaction networks were constructed and visualized using the GeneMANIA Cytoscape app (version 3.4.1) to predict the putative function of altered genes. Lastly, isoprene-derived SOA collected onto filters was used in resuspension exposures to measure select inflammatory biomarkers, including interleukin 8 (IL-8) and prostaglandin-endoperoxide synthase 2 (PTGS2) genes, in BEAS-2B cells to ensure that effects observed from EAVES exposures were attributable to particle-phase organic products. Since EAVES and resuspension exposures compared well, gene expression profiling for IEPOX-, MAE- and ISOPOOH-derived SOA were conducted using only resuspension exposures.

RESULTS AND CONCLUSIONS

Chemical characterization coupled with biological analyses show that atmospherically relevant compositions of isoprene-derived SOA alter the levels of 41 oxidative stress-related genes. Of the different composition types of isoprene-derived SOA, MAE- and ISOPOOH-derived SOA altered the greatest number of genes, suggesting that carbonyl and hydroperoxide functional groups are oxidative stress promoters. Taken together, the different composition types accounted for 34 of the genes altered by the total isoprene-derived SOA mixture, while 7 remained unique to the total mixture exposures, indicating that there is either a synergistic effect of the different isoprene-derived SOA components or an unaccounted component in the mixture.

The high-oxides of nitrogen (NOx) regime, which yielded MAE- and methacrolein (MACR)-derived SOA, had a higher ROS-generation potential (as measured by the DTT assay) than the low- NOx regime, which included IEPOX- and isoprene-derived SOA. However, ISOPOOH-derived SOA, which also formed in the low- NOx regime, had the highest ROS-generation potential, similar to 1,4-naphthoquinone (1,4-NQ). This suggests that aerosol-phase organic peroxides contribute significantly to particulate matter (PM) oxidative potential. MAE- and MACR- derived SOA showed equal or greater ROS-generation potential than was reported in prior UNC-Chapel Hill studies on diesel exhaust PM, highlighting the importance of a comprehensive investigation of the toxicity of isoprene-derived SOA. Notably, ISOPOOH-derived SOA was one order of magnitude higher in ROS-generation potential than diesel exhaust particles previously examined at UNC-Chapel Hill. As an acellular assay, the DTT assay may not be predictive of oxidative stress; therefore, we also focused on the gene expression results from the cellular exposures.

We have demonstrated that the nuclear factor (erythroid-derived 2)-like 2 (Nrf2) and the redox-sensitive activation protein-1 (AP-1) transcription factor networks have been significantly altered upon exposure to isoprene-derived SOA. The identification of Nrf2 pathway in cells exposed to isoprene-derived SOA is in accordance with our findings using the DTT assay, which measures the thiol reactivity of PM samples as a surrogate for their ROS-generation potential. Specifically, our results point to the cysteine-thiol modifications within cells that lead to activation of Nrf2-related gene expression.

However, based on our gene expression results showing no clear relationship between DTT activity and the number of altered oxidative stress-related genes, the DTT activity of isoprene-derived SOA may not be directly indicative of toxicity relative to other SOA types. While activation of Nrf2-associated genes has been identified with responses to oxidative stress and linked to traffic related air pollution exposure in both toxicological and epidemiological studies, their implicit involvement in this study suggests that activation of Nrf2-related gene expression may occur with exposures to all sorts of PM types.

By controlling the exposure time, method, and dose we demonstrated that among the SOA derived from previously identified individual precursors of isoprene-derived SOA, ISOPOOH-derived SOA alters more oxidative stress related genes than does IEPOX-derived SOA, but fewer than MAE-derived SOA. This suggests that the composition of MAE-derived SOA may be the greatest contributor to alterations of oxidative stress-related gene expression observed due to isoprene-derived SOA exposure. Further study on induced levels of protein expression and specific toxicological endpoints is necessary to determine if the observed gene expression changes lead to adverse health effects. In addition, such studies have implications for pollution-control strategies because NOx and SO2 are controllable pollutants that can alter the composition of SOA, and in turn alter its effects on gene expression. The mass fraction of different components of atmospheric isoprene derived SOA should be considered, but altering the fraction of high- NOx isoprene-derived SOA (e.g., MAE derived SOA) may yield greater changes in gene expression than altering the fraction of low- NOx isoprene derived SOA types (ISOPOOH- or IEPOX-derived SOA). Finally, this study confirms that total isoprene-derived SOA alters the expression of a greater number of genes than does SOA derived from the tested precursors. This warrants further work to determine the underlying explanation for this observation, which may be uncharacterized components of isoprene-derived SOA or the potential for synergism between the studied components.

Visualization and Analysis of Epiproteome Dynamics

The epiproteome describes the set of all post-translational modifications (PTMs) made to the proteins comprising a cell or organism. The extent of the epiproteome is still largely unknown; however, advances in experimental techniques are beginning to produce a deluge of data, tracking dynamic changes to the epiproteome in response to cellular stimuli. These data have potential to revolutionize our understanding of biology and disease. This review covers a range of recent visualization methods and tools developed specifically for dynamic epiproteome data sets. These methods have been designed primarily for data sets on phosphorylation, as this the most studied PTM; however, most of these methods are also applicable to other types of PTMs. Unfortunately, the currently available methods are often inadequate for existing data sets; thus, realizing the potential buried in epiproteome data sets will require new, tailored bioinformatics methods that will help researchers analyze, visualize, and interactively explore these complex data sets.

A network biology approach to unraveling inherited axonopathies

Inherited axonopathies represent a spectrum of disorders unified by the common pathological mechanism of length-dependent axonal degeneration. Progressive axonal degeneration can lead to both Charcot-Marie-Tooth type 2 (CMT2) and Hereditary Spastic Paraplegia (HSP) depending on the affected neurons: peripheral motor and sensory nerves or central nervous system axons of the corticospinal tract and dorsal columns, respectively. Inherited axonopathies display an extreme degree of genetic heterogeneity of Mendelian high-penetrance genes. High locus heterogeneity is potentially advantageous to deciphering disease etiology by providing avenues to explore biological pathways in an unbiased fashion. Here, we investigate ‘gene modules’ in inherited axonopathies through a network-based analysis of the Human Integrated Protein-Protein Interaction rEference (HIPPIE) database. We demonstrate that CMT2 and HSP disease proteins are significantly more connected than randomly expected. We define these connected disease proteins as ‘proto-modules’ and show the topological relationship of these proto-modules by evaluating their overlap through a shortest-path based measurement. In particular, we observe that the CMT2 and HSP proto-modules significantly overlapped, demonstrating a shared genetic etiology. Comparison of both modules with other diseases revealed an overlapping relationship between HSP and hereditary ataxia and between CMT2 + HSP and hereditary ataxia. We then use the DIseAse Module Detection (DIAMOnD) algorithm to expand the proto-modules into comprehensive disease modules. Analysis of disease modules thus obtained reveals an enrichment of ribosomal proteins and pathways likely central to inherited axonopathy pathogenesis, including protein processing in the endoplasmic reticulum, spliceosome, and mRNA processing. Furthermore, we determine pathways specific to each axonopathy by analyzing the difference of the axonopathy modules. CMT2-specific pathways include glycolysis and gluconeogenesis-related processes, while HSP-specific pathways include processes involved in viral infection response. Unbiased characterization of inherited axonopathy disease modules will provide novel candidate disease genes, improve interpretation of candidate genes identified through patient data, and guide therapy development.

Proteomics and Metabolomics for AKI Diagnosis

Acute kidney injury (AKI) is a severe and frequent condition in hospitalized patients. Currently, no efficient therapy of AKI is available. Therefore, efforts focus on early prevention and potentially early initiation of renal replacement therapy to improve the outcome in AKI. The detection of AKI in hospitalized patients implies the need for early, accurate, robust, and easily accessible biomarkers of AKI evolution and outcome prediction because only a narrow window exists to implement the earlier-described measures. Even more challenging is the multifactorial origin of AKI and the fact that the changes of molecular expression induced by AKI are difficult to distinguish from those of the diseases associated or causing AKI as shock or sepsis. During the past decade, a considerable number of protein biomarkers for AKI have been described and we expect from recent advances in the field of omics technologies that this number will increase further in the future and be extended to other sorts of biomolecules, such as RNAs, lipids, and metabolites. However, most of these biomarkers are poorly defined by their AKI-associated molecular context. In this review, we describe the state-of-the-art tissue and biofluid proteomic and metabolomic technologies and new bioinformatics approaches for proteomic and metabolomic pathway and molecular interaction analysis. In the second part of the review, we focus on AKI-associated proteomic and metabolomic biomarkers and briefly outline their pathophysiological context in AKI.

An atlas of genetic influences on osteoporosis in humans and mice

Osteoporosis is a common aging-related disease diagnosed primarily using bone mineral density (BMD). We assessed genetic determinants of BMD as estimated by heel quantitative ultrasound in 426,824 individuals, identifying 518 genome-wide significant loci (301 novel), explaining 20% of its variance. We identified 13 bone fracture loci, all associated with estimated BMD (eBMD), in ~1.2 million individuals. We then identified target genes enriched for genes known to influence bone density and strength (maximum odds ratio (OR) = 58, P = 1 × 10-75) from cell-specific features, including chromatin conformation and accessible chromatin sites. We next performed rapid-throughput skeletal phenotyping of 126 knockout mice with disruptions in predicted target genes and found an increased abnormal skeletal phenotype frequency compared to 526 unselected lines (P < 0.0001). In-depth analysis of one gene, DAAM2, showed a disproportionate decrease in bone strength relative to mineralization. This genetic atlas provides evidence linking associated SNPs to causal genes, offers new insight into osteoporosis pathophysiology, and highlights opportunities for drug development.

Prediction of skin anti-aging clinical benefits of an association of ingredients from marine and maritime origins: Ex vivo evaluation using a label-free quantitative proteomic and customized data processing approach

BACKGROUND

The application of ingredients from marine and maritime origins is increasingly common in skin care products, driven by consumer expectations for natural ingredients. However, these ingredients are typically studied for a few isolated in vitro activities.

OBJECTIVES

The purpose of this study was to carry out a comprehensive evaluation of the activity on the skin of an association of ingredients from marine and maritime origins using label-free quantitative proteomic analysis, in order to predict the clinical benefits if used in a skin care product.

METHODS

An aqueous gel containing 6.1% of ingredients from marine and maritime origins (amino acid-enriched giant kelp extract, trace element-enriched seawater, dedifferentiated sea fennel cells) was topically applied on human skin explants. The skin explants' proteome was analyzed in a label-free manner by high-performance liquid nano-chromatography coupled with tandem mass spectrometry. A specific data processing pipeline (CORAVALID) providing an objective and comprehensive interpretation of the statistically relevant biological activities processed the results.

RESULTS

Compared to untreated skin explants, 64 proteins were significantly regulated by the gel treatment (q-value ≤ 0.05). Computer data processing revealed an activity of the ingredients on the epidermis and the dermis. These significantly regulated proteins are involved in gene expression, cell survival and metabolism, inflammatory processes, dermal extracellular matrix synthesis, melanogenesis and keratinocyte proliferation, migration, and differentiation.

CONCLUSIONS

These results suggest that the tested ingredients could help to preserve a healthy epidermis and dermis, and possibly to prevent the visible signs of skin aging.

Stress dynamically regulates co-expression networks of glucocorticoid receptor-dependent MDD and SCZ risk genes

Early-life adversity is an important risk factor for major depressive disorder (MDD) and schizophrenia (SCZ) that interacts with genetic factors to confer disease risk through mechanisms that are still insufficiently understood. One downstream effect of early-life adversity is the activation of glucocorticoid receptor (GR)-dependent gene networks that drive acute and long-term adaptive behavioral and cellular responses to stress. We have previously shown that genetic variants that moderate GR-induced gene transcription (GR-response eSNPs) are significantly enriched among risk variants from genome-wide association studies (GWASs) for MDD and SCZ. Here, we show that the 63 transcripts regulated by these disease-associated functional genetic variants form a tight glucocorticoid-responsive co-expression network (termed GCN). We hypothesized that changes in the correlation structure of this GCN may contribute to early-life adversity-associated disease risk. Therefore, we analyzed the effects of different qualities of social support and stress throughout life on GCN formation across distinct brain regions using a translational mouse model. We observed that different qualities of social experience substantially affect GCN structure in a highly brain region-specific manner. GCN changes were predominantly found in two functionally interconnected regions, the ventral hippocampus and the hypothalamus, two brain regions previously shown to be of relevance for the stress response, as well as psychiatric disorders. Overall, our results support the hypothesis that a subset of genetic variants may contribute to risk for MDD and SCZ by altering circuit-level effects of early and adult social experiences on GCN formation and structure.

Integration of Metabolomics and Transcriptomics to Identify Gene-Metabolite Relationships Specific to Phenotype

Metabolomics plays an increasingly large role in translational research, with metabolomics data being generated in large cohorts, alongside other omics data such as gene expression. With this in mind, we provide a review of current approaches that integrate metabolomic and transcriptomic data. Furthermore, we provide a detailed framework for integrating metabolomic and transcriptomic data using a two-step approach: (1) numerical integration of gene and metabolite levels to identify phenotype (e.g., cancer)-specific gene-metabolite relationships using IntLIM and (2) knowledge-based integration, using pathway overrepresentation analysis through RaMP, a comprehensive database of biological pathways. Each step makes use of publicly available R packages ( https://github.com/mathelab/IntLIM and https://github.com/mathelab/RaMP-DB ), and provides a user-friendly web interface for analysis. These interfaces can be run locally through the package or can be accessed through our servers ( https://intlim.bmi.osumc.edu and https://ramp-db.bmi.osumc.edu ). The goal of this chapter is to provide step-by-step instructions on how to install the software and use the commands within the R framework, without the user interface (which is slower than running the commands through command line). Both packages are in continuous development so please refer to the GitHub sites to check for updates.

Epstein-Barr virus microRNAs regulate B cell receptor signal transduction and lytic reactivation

MicroRNAs (miRNAs) are post-transcriptional regulatory RNAs that can modulate cell signaling and play key roles in cell state transitions. Epstein-Barr virus (EBV) expresses >40 viral miRNAs that manipulate both viral and cellular gene expression patterns and contribute to reprogramming of the host environment during infection. Here, we identified a subset of EBV miRNAs that desensitize cells to B cell receptor (BCR) stimuli, and attenuate the downstream activation of NF-kappaB or AP1-dependent transcription. Bioinformatics and pathway analysis of Ago PAR-CLIP datasets identified multiple EBV miRNA targets related to BCR signal transduction, including GRB2, SOS1, MALT1, RAC1, and INPP5D, which we validated in reporter assays. BCR signaling is critical for B cell activation, proliferation, and differentiation, and for EBV, is linked to reactivation. In functional assays, we demonstrate that EBV miR-BHRF1-2-5p contributes to the growth of latently infected B cells through GRB2 regulation. We further determined that activities of EBV miR-BHRF1-2-5p, EBV miR-BART2-5p, and a cellular miRNA, miR-17-5p, directly regulate virus reactivation triggered by BCR engagement. Our findings provide mechanistic insight into some of the key miRNA interactions impacting the proliferation of latently infected B cells and importantly, governing the latent to lytic switch.

Integration of miRNA and mRNA Expression Data for Understanding Etiology of Gynecologic Cancers

Dysregulation of miRNA-mRNA regulatory networks is very common phenomenon in any diseases including cancer. Altered expression of biomarkers leads to these gynecologic cancers. Therefore, understanding the underlying biological mechanisms may help in developing a robust diagnostic as well as a prognostic tool. It has been demonstrated in various studies that the pathways associated with gynecologic cancer have dysregulated miRNA as well as mRNA expression. Identification of miRNA-mRNA regulatory modules may help in understanding the mechanism of altered gynecologic cancer pathways. In this regard, an existing robust mutual information-based Maximum-Relevance Maximum-Significance algorithm has been used for identification of miRNA-mRNA regulatory modules in gynecologic cancer. A set of miRNA-mRNA modules are identified first than their association with gynecologic cancer are studied exhaustively. The effectiveness of the proposed approach is compared with the existing methods. The proposed approach is found to generate more robust integrated networks of miRNA-mRNA in gynecologic cancer.

GWAS of Behavioral Traits

Over the past decade, genome-wide association studies (GWAS) have evolved into a powerful tool to investigate genetic risk factors for human diseases via a hypothesis-free scan of the genome. The success of GWAS for psychiatric disorders and behavioral traits have been somewhat mixed, partly owing to the complexity and heterogeneity of these traits. Significant progress has been made in the last few years in the development and implementation of complex statistical methods and algorithms incorporating GWAS. Such advanced statistical methods applied to GWAS hits in combination with incorporation of different layers of genomics data have catapulted the search for novel genes for behavioral traits and improved our understanding of the complex polygenic architecture of these traits.This chapter will give a brief overview on GWAS and statistical methods currently used in GWAS. The chapter will focus on reviewing the current literature and highlight some of the most important GWAS on psychiatric and other behavioral traits and will conclude with a discussion on future directions.

Proteins Altered by Surgical Weight Loss Highlight Biomarkers of Insulin Resistance in the Community

Objective- Mechanisms of early and late improvements in cardiovascular risk after bariatric surgery and applicability to larger, at-risk populations remain unclear. We aimed to identify proteins altered after bariatric surgery and their relations to metabolic syndrome and diabetes mellitus. Approach and Results- We identified 19 proteins altered in 32 nonfasting plasma samples from a study of patients undergoing bariatric surgery who were evaluated preoperatively (visit 1) versus both early (visit 2; ≈3 months) and late (visit 3; ≈12 months) postoperative follow-up using predefined protein panels (Olink). Using in silico methods and publicly available gene expression repositories, we found that genes encoding 8 out of 19 proteins had highest expression in liver relative to other assayed tissues, with the top biological and disease processes, including major obesity-related vascular diseases. Of 19 candidate proteins in the surgical cohort, 6 were previously measured in >3000 FHS (Framingham Heart Study) participants (IGFBP [insulin-like growth factor binding protein]-1, IGFBP-2, P-selectin, CD163, LDL (low-density lipoprotein)-receptor, and PAI [plasminogen activator inhibitor]-1). A higher concentration of IGFBP-2 at baseline was associated with a lower risk of incident metabolic syndrome (odds ratio per log-normal unit, 0.45; 95% CI, 0.32-0.64; P=7.7×10-6) and diabetes mellitus (odds ratio, 0.63; 95% CI, 0.49-0.79; P=0.0001) after multivariable adjustment. Conclusions- Using a directed protein quantification platform (Olink), we identified known and novel proteins altered after surgical weight loss, including IGFBP-2. Future efforts in well-defined obesity intervention settings may further define and validate novel targets for the prevention of vascular disease in obesity.

Introducing WikiPathways as a Data-Source to Support Adverse Outcome Pathways for Regulatory Risk Assessment of Chemicals and Nanomaterials

A paradigm shift is taking place in risk assessment to replace animal models, reduce the number of economic resources, and refine the methodologies to test the growing number of chemicals and nanomaterials. Therefore, approaches such as transcriptomics, proteomics, and metabolomics have become valuable tools in toxicological research, and are finding their way into regulatory toxicity. One promising framework to bridge the gap between the molecular-level measurements and risk assessment is the concept of adverse outcome pathways (AOPs). These pathways comprise mechanistic knowledge and connect biological events from a molecular level toward an adverse effect outcome after exposure to a chemical. However, the implementation of omics-based approaches in the AOPs and their acceptance by the risk assessment community is still a challenge. Because the existing modules in the main repository for AOPs, the AOP Knowledge Base (AOP-KB), do not currently allow the integration of omics technologies, additional tools are required for omics-based data analysis and visualization. Here we show how WikiPathways can serve as a supportive tool to make omics data interoperable with the AOP-Wiki, part of the AOP-KB. Manual matching of key events (KEs) indicated that 67% could be linked with molecular pathways. Automatic connection through linkage of identifiers between the databases showed that only 30% of AOP-Wiki chemicals were found on WikiPathways. More loose linkage through gene names in KE and Key Event Relationships descriptions gave an overlap of 70 and 71%, respectively. This shows many opportunities to create more direct connections, for example with extended ontology annotations, improving its interoperability. This interoperability allows the needed integration of omics data linked to the molecular pathways with AOPs. A new AOP Portal on WikiPathways is presented to allow the community of AOP developers to collaborate and populate the molecular pathways that underlie the KEs of AOP-Wiki. We conclude that the integration of WikiPathways and AOP-Wiki will improve risk assessment because omics data will be linked directly to KEs and therefore allow the comprehensive understanding and description of AOPs. To make this assessment reproducible and valid, major changes are needed in both WikiPathways and AOP-Wiki.

iDEP: an integrated web application for differential expression and pathway analysis of RNA-Seq data

BACKGROUND

RNA-seq is widely used for transcriptomic profiling, but the bioinformatics analysis of resultant data can be time-consuming and challenging, especially for biologists. We aim to streamline the bioinformatic analyses of gene-level data by developing a user-friendly, interactive web application for exploratory data analysis, differential expression, and pathway analysis.

RESULTS

iDEP (integrated Differential Expression and Pathway analysis) seamlessly connects 63 R/Bioconductor packages, 2 web services, and comprehensive annotation and pathway databases for 220 plant and animal species. The workflow can be reproduced by downloading customized R code and related pathway files. As an example, we analyzed an RNA-Seq dataset of lung fibroblasts with Hoxa1 knockdown and revealed the possible roles of SP1 and E2F1 and their target genes, including microRNAs, in blocking G1/S transition. In another example, our analysis shows that in mouse B cells without functional p53, ionizing radiation activates the MYC pathway and its downstream genes involved in cell proliferation, ribosome biogenesis, and non-coding RNA metabolism. In wildtype B cells, radiation induces p53-mediated apoptosis and DNA repair while suppressing the target genes of MYC and E2F1, and leads to growth and cell cycle arrest. iDEP helps unveil the multifaceted functions of p53 and the possible involvement of several microRNAs such as miR-92a, miR-504, and miR-30a. In both examples, we validated known molecular pathways and generated novel, testable hypotheses.

CONCLUSIONS

Combining comprehensive analytic functionalities with massive annotation databases, iDEP ( http://ge-lab.org/idep/ ) enables biologists to easily translate transcriptomic and proteomic data into actionable insights.

Reactome diagram viewer: data structures and strategies to boost performance

Motivation

Reactome is a free, open-source, open-data, curated and peer-reviewed knowledgebase of biomolecular pathways. For web-based pathway visualization, Reactome uses a custom pathway diagram viewer that has been evolved over the past years. Here, we present comprehensive enhancements in usability and performance based on extensive usability testing sessions and technology developments, aiming to optimize the viewer towards the needs of the community.

Results

The pathway diagram viewer version 3 achieves consistently better performance, loading and rendering of 97% of the diagrams in Reactome in less than 1 s. Combining the multi-layer html5 canvas strategy with a space partitioning data structure minimizes CPU workload, enabling the introduction of new features that further enhance user experience. Through the use of highly optimized data structures and algorithms, Reactome has boosted the performance and usability of the new pathway diagram viewer, providing a robust, scalable and easy-to-integrate solution to pathway visualization. As graph-based visualization of complex data is a frequent challenge in bioinformatics, many of the individual strategies presented here are applicable to a wide range of web-based bioinformatics resources.

Availability and implementation

Reactome is available online at: https://reactome.org. The diagram viewer is part of the Reactome pathway browser (https://reactome.org/PathwayBrowser/) and also available as a stand-alone widget at: https://reactome.org/dev/diagram/. The source code is freely available at: https://github.com/reactome-pwp/diagram.

Supplementary information

Supplementary data are available at Bioinformatics online.

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/.

New models of atherosclerosis and multi-drug therapeutic interventions

Motivation

Atherosclerosis is amongst the leading causes of death globally. However, it is challenging to study in vivo or in vitro and no detailed, openly-available computational models exist. Clinical studies hint that pharmaceutical therapy may be possible. Here, we develop the first detailed, computational model of atherosclerosis and use it to develop multi-drug therapeutic hypotheses.

Results

We assembled a network describing atheroma development from the literature. Maps and mathematical models were produced using the Systems Biology Graphical Notation and Systems Biology Markup Language, respectively. The model was constrained against clinical and laboratory data. We identified five drugs that together potentially reverse advanced atheroma formation.

Availability and implementation

The map is available in the Supplementary Material in SBGN-ML format. The model is available in the Supplementary Material and from BioModels, a repository of SBML models, containing CellDesigner markup.

Supplementary information

Supplementary data are available at Bioinformatics online.

Analysis of the Effect of Intestinal Ischemia and Reperfusion on the Rat Neutrophils Proteome

Intestinal ischemia and reperfusion injury is a model system of possible consequences of severe trauma and surgery, which might result into tissue dysfunction and organ failure. Neutrophils contribute to the injuries preceded by ischemia and reperfusion. However, the mechanisms by which intestinal ischemia and reperfusion stimulate and activate circulating neutrophils is still not clear. In this work, we used proteomics approach to explore the underlying regulated mechanisms in Wistar rat neutrophils after ischemia and reperfusion. We isolated neutrophils from three different biological groups; control, sham laparotomy, and intestinal ischemia/reperfusion. In the workflow, we included iTRAQ-labeling quantification and peptide fractionation using HILIC prior to LC-MS/MS analysis. From proteomic analysis, we identified 2,045 proteins in total that were grouped into five different clusters based on their regulation trend between the experimental groups. A total of 417 proteins were found as significantly regulated in at least one of the analyzed conditions. Interestingly, the enzyme prediction analysis revealed that ischemia/reperfusion significantly reduced the relative abundance of most of the antioxidant and pro-survival molecules to cause more tissue damage and ROS production whereas some of the significantly up regulated enzymes were involved in cytoskeletal rearrangement, adhesion and migration. Clusters based KEGG pathways analysis revealed high motility, phagocytosis, directional migration, and activation of the cytoskeletal machinery in neutrophils after ischemia and reperfusion. Increased ROS production and decreased phagocytosis were experimentally validated by microscopy assays. Taken together, our findings provide a characterization of the rat neutrophil response to intestinal ischemia and reperfusion and the possible mechanisms involved in the tissue injury by neutrophils after intestinal ischemia and reperfusion.

Cooperative interaction among BMAL1, HSF1, and p53 protects mammalian cells from UV stress

The circadian clock allows physiological systems to adapt to their changing environment by synchronizing their timings in response to external stimuli. Previously, we reported clock-controlled adaptive responses to heat-shock and oxidative stress and showed how the circadian clock interacts with BMAL1 and HSF1. Here, we present a similar clock-controlled adaptation to UV damage. In response to UV irradiation, HSF1 and tumor suppressor p53 regulate the expression of the clock gene Per2 in a time-dependent manner. UV irradiation first activates the HSF1 pathway, which subsequently activates the p53 pathway. Importantly, BMAL1 regulates both HSF1 and p53 through the BMAL1-HSF1 interaction to synchronize the cellular clock. Based on these findings and transcriptome analysis, we propose that the circadian clock protects cells against the UV stress through sequential and hierarchical interactions between the circadian clock, the heat shock response, and a tumor suppressive mechanism.

Explicit interaction information from WikiPathways in RDF facilitates drug discovery in the Open PHACTS Discovery Platform

Open PHACTS is a pre-competitive project to answer scientific questions developed recently by the pharmaceutical industry. Having high quality biological interaction information in the Open PHACTS Discovery Platform is needed to answer multiple pathway related questions. To address this, updated WikiPathways data has been added to the platform. This data includes information about biological interactions, such as stimulation and inhibition. The platform's Application Programming Interface (API) was extended with appropriate calls to reference these interactions.  These new methods of the Open PHACTS API are available now.

The role of TCF3 as potential master regulator in blastemal Wilms tumors

Wilms tumors are the most common type of pediatric kidney tumors. While the overall prognosis for patients is favorable, especially tumors that exhibit a blastemal subtype after preoperative chemotherapy have a poor prognosis. For an improved risk assessment and therapy stratification, it is essential to identify the driving factors that are distinctive for this aggressive subtype. In our study, we compared gene expression profiles of 33 tumor biopsies (17 blastemal and 16 other tumors) after neoadjuvant chemotherapy. The analysis of this dataset using the Regulator Gene Association Enrichment algorithm successfully identified several biomarkers and associated molecular mechanisms that distinguish between blastemal and nonblastemal Wilms tumors. Specifically, regulators involved in embryonic development and epigenetic processes like chromatin remodeling and histone modification play an essential role in blastemal tumors. In this context, we especially identified TCF3 as the central regulatory element. Furthermore, the comparison of ChIP-Seq data of Wilms tumor cell cultures from a blastemal mouse xenograft and a stromal tumor provided further evidence that the chromatin states of blastemal cells share characteristics with embryonic stem cells that are not present in the stromal tumor cell line. These stem-cell like characteristics could potentially add to the increased malignancy and chemoresistance of the blastemal subtype. Along with TCF3, we detected several additional biomarkers that are distinctive for blastemal Wilms tumors after neoadjuvant chemotherapy and that may provide leads for new therapeutic regimens.

SyNDI: synchronous network data integration framework

BACKGROUND

Systems biology takes a holistic approach by handling biomolecules and their interactions as big systems. Network based approach has emerged as a natural way to model these systems with the idea of representing biomolecules as nodes and their interactions as edges. Very often the input data come from various sorts of omics analyses. Those resulting networks sometimes describe a wide range of aspects, for example different experiment conditions, species, tissue types, stimulating factors, mutants, or simply distinct interaction features of the same network produced by different algorithms. For these scenarios, synchronous visualization of more than one distinct network is an excellent mean to explore all the relevant networks efficiently. In addition, complementary analysis methods are needed and they should work in a workflow manner in order to gain maximal biological insights.

RESULTS

In order to address the aforementioned needs, we have developed a Synchronous Network Data Integration (SyNDI) framework. This framework contains SyncVis, a Cytoscape application for user-friendly synchronous and simultaneous visualization of multiple biological networks, and it is seamlessly integrated with other bioinformatics tools via the Galaxy platform. We demonstrated the functionality and usability of the framework with three biological examples - we analyzed the distinct connectivity of plasma metabolites in networks associated with high or low latent cardiovascular disease risk; deeper insights were obtained from a few similar inflammatory response pathways in Staphylococcus aureus infection common to human and mouse; and regulatory motifs which have not been reported associated with transcriptional adaptations of Mycobacterium tuberculosis were identified.

CONCLUSIONS

Our SyNDI framework couples synchronous network visualization seamlessly with additional bioinformatics tools. The user can easily tailor the framework for his/her needs by adding new tools and datasets to the Galaxy platform.

Fibrin-targeting immunotherapy protects against neuroinflammation and neurodegeneration

Activation of innate immunity and deposition of blood-derived fibrin in the central nervous system (CNS) occur in autoimmune and neurodegenerative diseases, including multiple sclerosis (MS) and Alzheimer's disease (AD). However, the mechanisms that link disruption of the blood-brain barrier (BBB) to neurodegeneration are poorly understood, and exploration of fibrin as a therapeutic target has been limited by its beneficial clotting functions. Here we report the generation of monoclonal antibody 5B8, targeted against the cryptic fibrin epitope γ377-395, to selectively inhibit fibrin-induced inflammation and oxidative stress without interfering with clotting. 5B8 suppressed fibrin-induced nicotinamide adenine dinucleotide phosphate (NADPH) oxidase activation and the expression of proinflammatory genes. In animal models of MS and AD, 5B8 entered the CNS and bound to parenchymal fibrin, and its therapeutic administration reduced the activation of innate immunity and neurodegeneration. Thus, fibrin-targeting immunotherapy inhibited autoimmunity- and amyloid-driven neurotoxicity and might have clinical benefit without globally suppressing innate immunity or interfering with coagulation in diverse neurological diseases.

Identification of cancer biomarkers of prognostic value using specific gene regulatory networks (GRN): a novel role of RAD51AP1 for ovarian and lung cancers

To date, microarray analyses have led to the discovery of numerous individual ‘molecular signatures’ associated with specific cancers. However, there are serious limitations for the adoption of these multi-gene signatures in the clinical environment for diagnostic or prognostic testing as studies with more power need to be carried out. This may involve larger richer cohorts and more advanced analyses. In this study, we conduct analyses—based on gene regulatory network—to reveal distinct and common biomarkers across cancer types. Using microarray data of triple-negative and medullary breast, ovarian and lung cancers applied to a combination of glasso and Bayesian networks (BNs), we derived a unique network-containing genes that are uniquely involved: small proline-rich protein 1A (SPRR1A), follistatin like 1 (FSTL1), collagen type XII alpha 1 (COL12A1) and RAD51 associated protein 1 (RAD51AP1). RAD51AP1 and FSTL1 are significantly overexpressed in ovarian cancer patients but only RAD51AP1 is upregulated in lung cancer patients compared with healthy controls. The upregulation of RAD51AP1 was mirrored in the bloods of both ovarian and lung cancer patients, and Kaplan–Meier (KM) plots predicted poorer overall survival (OS) in patients with high expression of RAD51AP1. Suppression of RAD51AP1 by RNA interference reduced cell proliferation in vitro in ovarian (SKOV3) and lung (A549) cancer cells. This effect appears to be modulated by a decrease in the expression of mTOR-related genes and pro-metastatic candidate genes. Our data describe how an initial in silico approach can generate novel biomarkers that could potentially support current clinical practice and improve long-term outcomes.

Dietary cholesterol promotes steatohepatitis related hepatocellular carcinoma through dysregulated metabolism and calcium signaling

The underlining mechanisms of dietary cholesterol and nonalcoholic steatohepatitis (NASH) in contributing to hepatocellular carcinoma (HCC) remain undefined. Here we demonstrated that high-fat-non-cholesterol-fed mice developed simple steatosis, whilst high-fat-high-cholesterol-fed mice developed NASH. Moreover, dietary cholesterol induced larger and more numerous NASH-HCCs than non-cholesterol-induced steatosis-HCCs in diethylnitrosamine-treated mice. NASH-HCCs displayed significantly more aberrant gene expression-enriched signaling pathways and more non-synonymous somatic mutations than steatosis-HCCs (335 ± 84/sample vs 43 ± 13/sample). Integrated genetic and expressional alterations in NASH-HCCs affected distinct genes pertinent to five pathways: calcium, insulin, cell adhesion, axon guidance and metabolism. Some of the novel aberrant gene expression, mutations and core oncogenic pathways identified in cholesterol-associated NASH-HCCs in mice were confirmed in human NASH-HCCs, which included metabolism-related genes (ALDH18A1, CAD, CHKA, POLD4, PSPH and SQLE) and recurrently mutated genes (RYR1, MTOR, SDK1, CACNA1H and RYR2). These findings add insights into the link of cholesterol to NASH and NASH-HCC and provide potential therapeutic targets.

Genomic data integration systematically biases interactome mapping

Elucidating the complete network of protein-protein interactions, or interactome, is a fundamental goal of the post-genomic era, yet existing interactome maps are far from complete. To increase the throughput and resolution of interactome mapping, methods for protein-protein interaction discovery by co-migration have been introduced. However, accurate identification of interacting protein pairs within the resulting large-scale proteomic datasets is challenging. Consequently, most computational pipelines for co-migration data analysis incorporate external genomic datasets to distinguish interacting from non-interacting protein pairs. The effect of this procedure on interactome mapping is poorly understood. Here, we conduct a rigorous analysis of genomic data integration for interactome recovery across a large number of co-migration datasets, spanning diverse experimental and computational methods. We find that genomic data integration leads to an increase in the functional coherence of the resulting interactome maps, but this comes at the expense of a decrease in power to discover novel interactions. Importantly, putative novel interactions predicted by genomic data integration are no more likely to later be experimentally discovered than those predicted from co-migration data alone. Our results reveal a widespread and unappreciated limitation in a methodology that has been widely used to map the interactome of humans and model organisms.

Turning omics data into therapeutic insights

Omics technologies have made it easier and cheaper to evaluate thousands of biological molecules at once. These advances have led to novel therapies approved for use in the clinic, elucidated the mechanisms behind disease-associated mutations, led to increased accuracy in disease subtyping and personalized medicine, and revealed novel uses and treatment regimes for existing drugs through drug repurposing and pharmacology studies. In this review, we summarize some of these milestones and discuss the potential of integrative analyses that combine multiple data types for further advances.

The Proteomic Landscape in the Vitreous of Patients With Age-Related and Diabetic Retinal Disease

Purpose

In contrast to neovascular AMD (nAMD), no treatment option exists for dry AMD. Hence, the identification of specific biomarkers is required to facilitate diagnosis and therapy of dry AMD.

Methods

The proteome of 34 vitreous humor samples (dry AMD: n = 6; nAMD: n = 10; proliferative diabetic retinopathy [PDR]: n = 9; epiretinal membrane [ERM]: n = 9) was analyzed by liquid chromatography coupled mass spectrometry. Then, label-free relative quantification of dry AMD, nAMD, and PDR relative to ERM, which was defined as the reference group, was performed. Application of a bioinformatics pipeline further analyzed the vitreous proteome by cluster and gene set enrichment analysis. A selection of differentially regulated proteins was validated by ELISA.

Results

A total of 677 proteins were identified in the vitreous of the four patient groups and quantified relatively to ERM. Different clusters of regulated proteins for each patient group were identified and showed characteristic enrichment of specific pathways including "oxidative stress" for dry AMD, "focal adhesion" for nAMD, and "complement and coagulation cascade" for PDR patients. We identified cholinesterase (CHLE) to be specifically upregulated in dry AMD and ribonuclease (pancreatic; RNAS1) together with serine carboxypeptidase (probable; CPVL) to be upregulated in both forms of AMD.

Conclusions

The described pathways specific for the different patient groups and the identification of characteristic differentially regulated proteins provide a first step toward the definition of biomarkers for dry AMD. The presented data will facilitate the investigation of mechanistic connections of proteins to the respective disease.

Clustering approaches for visual knowledge exploration in molecular interaction networks

BACKGROUND

Biomedical knowledge grows in complexity, and becomes encoded in network-based repositories, which include focused, expert-drawn diagrams, networks of evidence-based associations and established ontologies. Combining these structured information sources is an important computational challenge, as large graphs are difficult to analyze visually.

RESULTS

We investigate knowledge discovery in manually curated and annotated molecular interaction diagrams. To evaluate similarity of content we use: i) Euclidean distance in expert-drawn diagrams, ii) shortest path distance using the underlying network and iii) ontology-based distance. We employ clustering with these metrics used separately and in pairwise combinations. We propose a novel bi-level optimization approach together with an evolutionary algorithm for informative combination of distance metrics. We compare the enrichment of the obtained clusters between the solutions and with expert knowledge. We calculate the number of Gene and Disease Ontology terms discovered by different solutions as a measure of cluster quality. Our results show that combining distance metrics can improve clustering accuracy, based on the comparison with expert-provided clusters. Also, the performance of specific combinations of distance functions depends on the clustering depth (number of clusters). By employing bi-level optimization approach we evaluated relative importance of distance functions and we found that indeed the order by which they are combined affects clustering performance. Next, with the enrichment analysis of clustering results we found that both hierarchical and bi-level clustering schemes discovered more Gene and Disease Ontology terms than expert-provided clusters for the same knowledge repository. Moreover, bi-level clustering found more enriched terms than the best hierarchical clustering solution for three distinct distance metric combinations in three different instances of disease maps.

CONCLUSIONS

In this work we examined the impact of different distance functions on clustering of a visual biomedical knowledge repository. We found that combining distance functions may be beneficial for clustering, and improve exploration of such repositories. We proposed bi-level optimization to evaluate the importance of order by which the distance functions are combined. Both combination and order of these functions affected clustering quality and knowledge recognition in the considered benchmarks. We propose that multiple dimensions can be utilized simultaneously for visual knowledge exploration.

Comprehensive innate immune profiling of chikungunya virus infection in pediatric cases

Chikungunya virus (CHIKV) is a mosquito-borne alphavirus that causes global epidemics of debilitating disease worldwide. To gain functional insight into the host cellular genes required for virus infection, we performed whole-blood RNA-seq, 37-plex mass cytometry of peripheral blood mononuclear cells (PBMCs), and serum cytokine measurements of acute- and convalescent-phase samples obtained from 42 children naturally infected with CHIKV Semi-supervised classification and clustering of single-cell events into 57 sub-communities of canonical leukocyte phenotypes revealed a monocyte-driven response to acute infection, with the greatest expansions in "intermediate" CD14++CD16+ monocytes and an activated subpopulation of CD14+ monocytes. Increases in acute-phase CHIKV envelope protein E2 expression were highest for monocytes and dendritic cells. Serum cytokine measurements confirmed significant acute-phase upregulation of monocyte chemoattractants. Distinct transcriptomic signatures were associated with infection timepoint, as well as convalescent-phase anti-CHIKV antibody titer, acute-phase viremia, and symptom severity. We present a multiscale network that summarizes all observed modulations across cellular and transcriptomic levels and their interactions with clinical outcomes, providing a uniquely global view of the biomolecular landscape of human CHIKV infection.

Simultaneous Extraction of RNA and Metabolites from Single Kidney Tissue Specimens for Combined Transcriptomic and Metabolomic Profiling

Tissue analysis represents a powerful tool for the investigation of disease pathophysiology. However, the heterogeneous nature of tissue samples, in particular of neoplastic, may affect the outcome of such analysis and hence obscure interpretation of results. Thus, comprehensive isolation and extraction of transcripts and metabolites from an identical tissue specimen would minimize variations and enable the economic use of biopsy material which is usually available in limited amounts. Here we demonstrate a fast and simple protocol for combined transcriptomics and metabolomics analysis in homogenates prepared from one single tissue sample. Metabolites were recovered by protein precipitation from lysates originally prepared for RNA isolation and were analyzed by LC-QTOF-MS after HILIC and RPLC separation, respectively. Strikingly, although ion suppression was observed, over 80% of the 2885 detected metabolic features could be extracted and analyzed with high reproducibility (CV ≤ 20%). Moreover fold changes of different tumor and nontumor kidney tissues were correlated to an established metabolomics protocol and revealed a strong correlation ( r_p ≥ 0.75). In order to demonstrate the feasibility of the combined analysis of RNA and metabolites, the protocol was applied to kidney tissue of metformin treated mice to investigate drug induced alterations.

Predictive classification models and targets identification for betulin derivatives as Leishmania donovani inhibitors

Betulin derivatives have been proven effective in vitro against Leishmania donovani amastigotes, which cause visceral leishmaniasis. Identifying the molecular targets and molecular mechanisms underlying their action is a currently an unmet challenge. In the present study, we tackle this problem using computational methods to establish properties essential for activity as well as to screen betulin derivatives against potential targets. Recursive partitioning classification methods were explored to develop predictive models for 58 diverse betulin derivatives inhibitors of L. donovani amastigotes. The established models were validated on a testing set, showing excellent performance. Molecular fingerprints FCFP_6 and ALogP were extracted as the physicochemical properties most extensively involved in separating inhibitors from non-inhibitors. The potential targets of betulin derivatives inhibitors were predicted by in silico target fishing using structure-based pharmacophore searching and compound-pharmacophore-target-pathway network analysis, first on PDB and then among L. donovani homologs using a PSI-BLAST search. The essential identified proteins are all related to protein kinase family. Previous research already suggested members of the cyclin-dependent kinase family and MAP kinases as Leishmania potential drug targets. The PSI-BLAST search suggests two L. donovani proteins to be especially attractive as putative betulin target, heat shock protein 83 and membrane transporter D1.

The latent geometry of the human protein interaction network

Motivation

A series of recently introduced algorithms and models advocates for the existence of a hyperbolic geometry underlying the network representation of complex systems. Since the human protein interaction network (hPIN) has a complex architecture, we hypothesized that uncovering its latent geometry could ease challenging problems in systems biology, translating them into measuring distances between proteins.

Results

We embedded the hPIN to hyperbolic space and found that the inferred coordinates of nodes capture biologically relevant features, like protein age, function and cellular localization. This means that the representation of the hPIN in the two-dimensional hyperbolic plane offers a novel and informative way to visualize proteins and their interactions. We then used these coordinates to compute hyperbolic distances between proteins, which served as likelihood scores for the prediction of plausible protein interactions. Finally, we observed that proteins can efficiently communicate with each other via a greedy routing process, guided by the latent geometry of the hPIN. We show that these efficient communication channels can be used to determine the core members of signal transduction pathways and to study how system perturbations impact their efficiency.

Availability and implementation

An R implementation of our network embedder is available at https://github.com/galanisl/NetHypGeom. Also, a web tool for the geometric analysis of the hPIN accompanies this text at http://cbdm-01.zdv.uni-mainz.de/~galanisl/gapi.

Supplementary information

Supplementary data are available at Bioinformatics online.

Developments in toxicogenomics: understanding and predicting compound-induced toxicity from gene expression data

The toxicogenomics field aims to understand and predict toxicity by using 'omics' data in order to study systems-level responses to compound treatments. In recent years there has been a rapid increase in publicly available toxicological and 'omics' data, particularly gene expression data, and a corresponding development of methods for its analysis. In this review, we summarize recent progress relating to the analysis of RNA-Seq and microarray data, review relevant databases, and highlight recent applications of toxicogenomics data for understanding and predicting compound toxicity. These include the analysis of differentially expressed genes and their enrichment, signature matching, methods based on interaction networks, and the analysis of co-expression networks. In the future, these state-of-the-art methods will likely be combined with new technologies, such as whole human body models, to produce a comprehensive systems-level understanding of toxicity that reduces the necessity of in vivo toxicity assessment in animal models.

Ubiquitin ligase RNF8 suppresses Notch signaling to regulate mammary development and tumorigenesis

The E3 ubiquitin ligase RNF8 plays critical roles in maintaining genomic stability by promoting the repair of DNA double-strand breaks (DSBs) through ubiquitin signaling. Abnormal activation of Notch signaling and defective repair of DSBs promote breast cancer risk. Here, we found that low expression of the full-length RNF8 correlated with poor prognosis for breast cancer patients. Our data revealed that in addition to its role in the repair of DSBs, RNF8 regulated Notch1 signaling and cell-fate determination of mammary luminal progenitors. Mechanistically, RNF8 acted as a negative regulator of Notch signaling by ubiquitylating the active NOTCH1 protein (N1ICD), leading to its degradation. Consistent with abnormal activation of Notch signaling and impaired repair of DSBs in Rnf8-mutant mammary epithelial cells, we observed increased risk of mammary tumorigenesis in mouse models for RNF8 deficiency. Notably, deficiency of RNF8 sensitized breast cancer cells to combination of pharmacological inhibitors of Notch signaling and poly(ADP-ribose) polymerase (PARP), suggesting implications for treatment of breast cancer associated with impaired RNF8 expression or function.

Reactome enhanced pathway visualization

Motivation

Reactome is a free, open-source, open-data, curated and peer-reviewed knowledge base of biomolecular pathways. Pathways are arranged in a hierarchical structure that largely corresponds to the GO biological process hierarchy, allowing the user to navigate from high level concepts like immune system to detailed pathway diagrams showing biomolecular events like membrane transport or phosphorylation. Here, we present new developments in the Reactome visualization system that facilitate navigation through the pathway hierarchy and enable efficient reuse of Reactome visualizations for users’ own research presentations and publications.

Results

For the higher levels of the hierarchy, Reactome now provides scalable, interactive textbook-style diagrams in SVG format, which are also freely downloadable and editable. Repeated diagram elements like ‘mitochondrion’ or ‘receptor’ are available as a library of graphic elements. Detailed lower-level diagrams are now downloadable in editable PPTX format as sets of interconnected objects.

Availability and implementation

http://reactome.org

Immunophenotype and Transcriptome Profile of Patients With Multiple Sclerosis Treated With Fingolimod: Setting Up a Model for Prediction of Response in a 2-Year Translational Study

BACKGROUND

Fingolimod is a functional sphingosine-1-phosphate antagonist approved for the treatment of multiple sclerosis (MS). Fingolimod affects lymphocyte subpopulations and regulates gene expression in the lymphocyte transcriptome. Translational studies are necessary to identify cellular and molecular biomarkers that might be used to predict the clinical response to the drug. In MS patients, we aimed to clarify the differential effects of fingolimod on T, B, and natural killer (NK) cell subsets and to identify differentially expressed genes in responders and non-responders (NRs) to treatment.

MATERIALS AND METHODS

Samples were obtained from relapsing-remitting multiple sclerosis patients before and 6 months after starting fingolimod. Forty-eight lymphocyte subpopulations were measured by flow cytometry based on surface and intracellular marker analysis. Transcriptome sequencing by next-generation technologies was used to define the gene expression profiling in lymphocytes at the same time points. NEDA-3 (no evidence of disease activity) and NEDA-4 scores were measured for all patients at 1 and 2 years after beginning fingolimod treatment to investigate an association with cellular and molecular characteristics.

RESULTS

Fingolimod affects practically all lymphocyte subpopulations and exerts a strong effect on genetic transcription switching toward an anti-inflammatory and antioxidant response. Fingolimod induces a differential effect in lymphocyte subpopulations after 6 months of treatment in responder and NR patients. Patients who achieved a good response to the drug compared to NR patients exhibited higher percentages of NK bright cells and plasmablasts, higher levels of FOXP3, glucose phosphate isomerase, lower levels of FCRL1, and lower Expanded Disability Status Scale at baseline. The combination of these possible markers enabled us to build a probabilistic linear model to predict the clinical response to fingolimod.

CONCLUSION

MS patients responsive to fingolimod exhibit a recognizable distribution of lymphocyte subpopulations and a different pretreatment gene expression signature that might be useful as a biomarker.

Differential protein expression of hippocampal cells associated with heavy metals (Pb, As, and MeHg) neurotoxicity: Deepening into the molecular mechanism of neurodegenerative diseases

Chronic exposure to heavy metals such as Pb, As, and MeHg can be associated with an increased risk of developing neurodegenerative diseases. Our in vitro bioassays results showed the potency of heavy metals in the order of Pb < As < MeHg on hippocampal cells. The main objective of this study was combining in vitro label free proteomics and systems biology approach for elucidating patterns of biological response, discovering underlying mechanisms of Pb, As, and MeHg toxicity in hippocampal cells. The omics data was refined by using different filters and normalization and multilevel analysis tools were employed to explore the data visualization. The functional and pathway visualization was performed by using Gene ontology and PathVisio tools. Using these all integrated approaches, we identified significant proteins across treatments within the mitochondrial dysfunction, oxidative stress, ubiquitin proteome dysfunction, and mRNA splicing related to neurodegenerative diseases. The systems biology analysis revealed significant alterations in proteins implicated in Parkinson's disease (PD) and Alzheimer's disease (AD). The current proteomics analysis of three metals support the insight into the proteins involved in neurodegeneration and the altered proteins can be useful for metal-specific biomarkers of exposure and its adverse effects.

SIGNIFICANCE

The proteomics techniques have been claimed to be more sensitive than the conventional toxicological assays, facilitating the measurement of responses to heavy metals (Pb, As, and MeHg) exposure before obvious harm has occurred demonstrating their predictive value. Also, proteomics allows for the comparison of responses between Pb, As, and MeHg metals, permitting the evaluation of potency differences hippocampal cells of the brain. Hereby, the molecular information provided by pathway and gene functional analysis can be used to develop a more thorough understanding of each metal mechanism at the protein level for different neurological adverse outcomes (e.g. Parkinson's disease, Alzheimer's diseases). Efforts are put into developing proteomics based toxicity testing methods using in vitro models for improving human risk assessment. Some of the key proteins identified can also potentially be used as biomarkers in epidemiologic studies. These heavy metal response patterns shed new light on the mechanisms of mRNA splicing, ubiquitin pathway role in neurodegeneration, and can be useful for the development of molecular biomarkers of heavy metals exposure.

Network Rewiring in Cancer: Applications to Melanoma Cell Lines and the Cancer Genome Atlas Patients

Genes do not work in isolation, but rather as part of networks that have many feedback and redundancy mechanisms. Studying the properties of genetic networks and how individual genes contribute to overall network functions can provide insight into genetically-mediated disease processes. Most analytical techniques assume a network topology based on normal state networks. However, gene perturbations often lead to the rewiring of relevant networks and impact relationships among other genes. We apply a suite of analysis methodologies to assess the degree of transcriptional network rewiring observed in different sets of melanoma cell lines using whole genome gene expression microarray profiles. We assess evidence for network rewiring in melanoma patient tumor samples using RNA-sequence data available from The Cancer Genome Atlas. We make a distinction between “unsupervised” and “supervised” network-based methods and contrast their use in identifying consistent differences in networks between subsets of cell lines and tumor samples. We find that different genes play more central roles within subsets of genes within a broader network and hence are likely to be better drug targets in a disease state. Ultimately, we argue that our results have important implications for understanding the molecular pathology of melanoma as well as the choice of treatments to combat that pathology.

NetControl4BioMed: a pipeline for biomedical data acquisition and analysis of network controllability

BACKGROUND

Network controllability focuses on discovering combinations of external interventions that can drive a biological system to a desired configuration. In practice, this approach translates into finding a combined multi-drug therapy in order to induce a desired response from a cell; this can lead to developments of novel therapeutic approaches for systemic diseases like cancer.

RESULT

We develop a novel bioinformatics data analysis pipeline called NetControl4BioMed based on the concept of target structural control of linear networks. Our pipeline generates novel molecular interaction networks by combining pathway data from various public databases starting from the user's query. The pipeline then identifies a set of nodes that is enough to control a given, user-defined set of disease-specific essential proteins in the network, i.e., it is able to induce a change in their configuration from any initial state to any final state. We provide both the source code of the pipeline as well as an online web-service based on this pipeline http://combio.abo.fi/nc/net_control/remote_call.php .

CONCLUSION

The pipeline can be used by researchers for controlling and better understanding of molecular interaction networks through combinatorial multi-drug therapies, for more efficient therapeutic approaches and personalised medicine.

Phenotype-driven gene prioritization for rare diseases using graph convolution on heterogeneous networks

BACKGROUND

One of the major goals of genomic medicine is the identification of causal genomic variants in a patient and their relation to the observed clinical phenotypes. Prioritizing the genomic variants by considering only the genotype information usually identifies a few hundred potential variants. Narrowing it down further to find the causal disease genes and relating them to the observed clinical phenotypes remains a significant challenge, especially for rare diseases.

METHODS

We propose a phenotype-driven gene prioritization approach using heterogeneous networks in the context of rare diseases. Towards this, we first built a heterogeneous network consisting of ontological associations as well as curated associations involving genes, diseases, phenotypes and pathways from multiple sources. Motivated by the recent progress in spectral graph convolutions, we developed a graph convolution based technique to infer new phenotype-gene associations from this initial set of associations. We included these inferred associations in the initial network and termed this integrated network HANRD (Heterogeneous Association Network for Rare Diseases). We validated this approach on 230 recently published rare disease clinical cases using the case phenotypes as input.

RESULTS

When HANRD was queried with the case phenotypes as input, the causal genes were captured within Top-50 for more than 31% of the cases and within Top-200 for more than 56% of the cases. The results showed improved performance when compared to other state-of-the-art tools.

CONCLUSIONS

In this study, we showed that the heterogeneous network HANRD, consisting of curated, ontological and inferred associations, helped improve causal gene identification in rare diseases. HANRD allows future enhancements by supporting incorporation of new entity types and additional information sources.

Exploring the cellular network of metabolic flexibility in the adipose tissue

Background

Metabolic flexibility is the ability of cells to change substrates for energy production based on the nutrient availability and energy requirement. It has been shown that metabolic flexibility is impaired in obesity and chronic diseases such as type 2 diabetes mellitus, cardiovascular diseases, and metabolic syndrome, although, whether it is a cause or an effect of these conditions remains to be elucidated.

Main body

In this paper, we have reviewed the literature on metabolic flexibility and curated pathways and processes resulting in a network resource to investigate the interplay between these processes in the subcutaneous adipose tissue. The adipose tissue has been shown to be responsible, not only for energy storage but also for maintaining energy homeostasis through oxidation of glucose and fatty acids. We highlight the role of pyruvate dehydrogenase complex–pyruvate dehydrogenase kinase (PDC-PDK) interaction as a regulatory switch which is primarily responsible for changing substrates in energy metabolism from glucose to fatty acids and back. Baseline gene expression of the subcutaneous adipose tissue, along with a publicly available obesity data set, are visualised on the cellular network of metabolic flexibility to highlight the genes that are expressed and which are differentially affected in obesity.

Conclusion

We have constructed an abstracted network covering glucose and fatty acid oxidation, as well as the PDC-PDK regulatory switch. In addition, we have shown how the network can be used for data visualisation and as a resource for follow-up studies.

Electronic supplementary material

The online version of this article (10.1186/s12263-018-0609-3) contains supplementary material, which is available to authorized users.

Autonomous Multimodal Metabolomics Data Integration for Comprehensive Pathway Analysis and Systems Biology

Comprehensive metabolomic data can be achieved using multiple orthogonal separation and mass spectrometry (MS) analytical techniques. However, drawing biologically relevant conclusions from this data and combining it with additional layers of information collected by other omic technologies present a significant bioinformatic challenge. To address this, a data processing approach was designed to automate the comprehensive prediction of dysregulated metabolic pathways/networks from multiple data sources. The platform autonomously integrates multiple MS-based metabolomics data types without constraints due to different sample preparation/extraction, chromatographic separation, or MS detection method. This multimodal analysis streamlines the extraction of biological information from the metabolomics data as well as the contextualization within proteomics and transcriptomics data sets. As a proof of concept, this multimodal analysis approach was applied to a colorectal cancer (CRC) study, in which complementary liquid chromatography-mass spectrometry (LC-MS) data were combined with proteomic and transcriptomic data. Our approach provided a highly resolved overview of colon cancer metabolic dysregulation, with an average 17% increase of detected dysregulated metabolites per pathway and an increase in metabolic pathway prediction confidence. Moreover, 95% of the altered metabolic pathways matched with the dysregulated genes and proteins, providing additional validation at a systems level. The analysis platform is currently available via the XCMS Online ( XCMSOnline.scripps.edu ).

Identification of an immune gene expression signature associated with favorable clinical features in Treg-enriched patient tumor samples

Immune heterogeneity within the tumor microenvironment undoubtedly adds several layers of complexity to our understanding of drug sensitivity and patient prognosis across various cancer types. Within the tumor microenvironment, immunogenicity is a favorable clinical feature in part driven by the antitumor activity of CD8+ T cells. However, tumors often inhibit this antitumor activity by exploiting the suppressive function of regulatory T cells (Tregs), thus suppressing the adaptive immune response. Despite the seemingly intuitive immunosuppressive biology of Tregs, prognostic studies have produced contradictory results regarding the relationship between Treg enrichment and survival. We therefore analyzed RNA-seq data of Treg-enriched tumor samples to derive a pan-cancer gene signature able to help reconcile the inconsistent results of Treg studies, by better understanding the variable clinical association of Tregs across alternative tumor contexts. We show that increased expression of a 32-gene signature in Treg-enriched tumor samples (n = 135) is able to distinguish a cohort of patients associated with chemosensitivity and overall survival. This cohort is also enriched for CD8+ T cell abundance, as well as the antitumor M1 macrophage subtype. With a subsequent validation in a larger TCGA pool of Treg-enriched patients (n = 626), our results reveal a gene signature able to produce unsupervised clusters of Treg-enriched patients, with one cluster of patients uniquely representative of an immunogenic tumor microenvironment. Ultimately, these results support the proposed gene signature as a putative biomarker to identify certain Treg-enriched patients with immunogenic tumors that are more likely to be associated with features of favorable clinical outcome.

A new genetic test could help predict responses to therapy and survival outcomes among cancer patients with tumors that are infiltrated with large numbers of regulatory T cells (Tregs). Kevin B. Givechian of NantOmics in Los Angeles, California, USA, and colleagues measured gene activity levels in tumor samples taken from 135 patients with Treg-enriched cancers of all kinds. They singled out genes with particularly variable expression levels to create a 32-gene signature that revealed two distinct clusters of patients: those who responded to their prescribed drugs and lived longer, and those who were treatment-resistant and died sooner. The researchers also validated the gene panel in a larger, independent cohort of 626 tumor samples, showing that it could identify patients with immunogenic tumors who are more likely have favorable clinical outcomes.

Using Regularization to Infer Cell Line Specificity in Logical Network Models of Signaling Pathways

Understanding the functional properties of cells of different origins is a long-standing challenge of personalized medicine. Especially in cancer, the high heterogeneity observed in patients slows down the development of effective cures. The molecular differences between cell types or between healthy and diseased cellular states are usually determined by the wiring of regulatory networks. Understanding these molecular and cellular differences at the systems level would improve patient stratification and facilitate the design of rational intervention strategies. Models of cellular regulatory networks frequently make weak assumptions about the distribution of model parameters across cell types or patients. These assumptions are usually expressed in the form of regularization of the objective function of the optimization problem. We propose a new method of regularization for network models of signaling pathways based on the local density of the inferred parameter values within the parameter space. Our method reduces the complexity of models by creating groups of cell line-specific parameters which can then be optimized together. We demonstrate the use of our method by recovering the correct topology and inferring accurate values of the parameters of a small synthetic model. To show the value of our method in a realistic setting, we re-analyze a recently published phosphoproteomic dataset from a panel of 14 colon cancer cell lines. We conclude that our method efficiently reduces model complexity and helps recovering context-specific regulatory information.

Integrated multi-omics data analysis identifying novel drug sensitivity-associated molecular targets of hepatocellular carcinoma cells

Hepatocellular carcinoma (HCC) is the most common type of liver cancer and the third-leading cause of malignancy-associated mortality worldwide. HCC cells are highly resistant to chemotherapeutic agents. Therefore, there are currently only two US Food and Drug Administration-approved drugs available for the treatment of HCC. The objective of the present study was to analyze the results of previously published high-throughput drug screening, and in vitro genomic and transcriptomic data from HCC cell lines, and to integrate the obtained results to define the underlying molecular mechanisms of drug sensitivity and resistance in HCC cells. The results of treatment with 225 different small molecules on 14 different HCC cell lines were retrieved from the Genomics of Drug Sensitivity in Cancer database and analyzed. Cluster analysis using the treatment results determined that HCC cell lines consist of two groups, according to their drug response profiles. Continued analyses of these two groups with Gene Set Enrichment Analysis method revealed 6 treatment-sensitive molecular targets (epidermal growth factor receptor, mechanistic target of rapamycin, deoxyribonucleic acid-dependent protein kinase, the Aurora kinases, Bruton's tyrosine kinase and phosphoinositide 3-kinase; all P<0.05) and partially effective drugs. Genetic and genome-wide gene expression data analyses of the determined targets and their known biological partners revealed 2 somatically mutated and 13 differentially expressed genes, which differed between drug-resistant and drug-sensitive HCC cells. Integration of the obtained data into a short molecular pathway revealed a drug treatment-sensitive signaling axis in HCC cells. In conclusion, the results of the present study provide novel drug sensitivity-associated molecular targets for the development of novel personalized and targeted molecular therapies against HCC.

LitPathExplorer: a confidence-based visual text analytics tool for exploring literature-enriched pathway models

Motivation

Pathway models are valuable resources that help us understand the various mechanisms underpinning complex biological processes. Their curation is typically carried out through manual inspection of published scientific literature to find information relevant to a model, which is a laborious and knowledge-intensive task. Furthermore, models curated manually cannot be easily updated and maintained with new evidence extracted from the literature without automated support.

Results

We have developed LitPathExplorer, a visual text analytics tool that integrates advanced text mining, semi-supervised learning and interactive visualization, to facilitate the exploration and analysis of pathway models using statements (i.e. events) extracted automatically from the literature and organized according to levels of confidence. LitPathExplorer supports pathway modellers and curators alike by: (i) extracting events from the literature that corroborate existing models with evidence; (ii) discovering new events which can update models; and (iii) providing a confidence value for each event that is automatically computed based on linguistic features and article metadata. Our evaluation of event extraction showed a precision of 89% and a recall of 71%. Evaluation of our confidence measure, when used for ranking sampled events, showed an average precision ranging between 61 and 73%, which can be improved to 95% when the user is involved in the semi-supervised learning process. Qualitative evaluation using pair analytics based on the feedback of three domain experts confirmed the utility of our tool within the context of pathway model exploration.

Availability and implementation

LitPathExplorer is available at http://nactem.ac.uk/LitPathExplorer_BI/.

Contact

sophia.ananiadou@manchester.ac.uk.

Supplementary information

Supplementary data are available at Bioinformatics online.