NDEx: A Community Resource for Sharing and Publishing of Biological Networks

Bioinformatics Goes Viral: I. Databases, Phylogenetics and Phylodynamics Tools for Boosting Virus Research

Computer-aided analysis of proteins or nucleic acids seems like a matter of course nowadays; however, the history of Bioinformatics and Computational Biology is quite recent. The advent of high-throughput sequencing has led to the production of "big data", which has also affected the field of virology. The collaboration between the communities of bioinformaticians and virologists already started a few decades ago and it was strongly enhanced by the recent SARS-CoV-2 pandemics. In this article, which is the first in a series on how bioinformatics can enhance virus research, we show that highly useful information is retrievable from selected general and dedicated databases. Indeed, an enormous amount of information-both in terms of nucleotide/protein sequences and their annotation-is deposited in the general databases of international organisations participating in the International Nucleotide Sequence Database Collaboration (INSDC). However, more and more virus-specific databases have been established and are progressively enriched with the contents and features reported in this article. Since viruses are intracellular obligate parasites, a special focus is given to host-pathogen protein-protein interaction databases. Finally, we illustrate several phylogenetic and phylodynamic tools, combining information on algorithms and features with practical information on how to use them and case studies that validate their usefulness. Databases and tools for functional inference will be covered in the next article of this series: Bioinformatics goes viral: II. Sequence-based and structure-based functional analyses for boosting virus research.

Role of death-associated protein kinase 1 (DAPK1) in retinal degenerative diseases: an in-silico approach towards therapeutic intervention

The Death-associated protein kinase 1 (DAPK1) has emerged as a crucial player in the pathogenesis of degenerative diseases. As a serine/threonine kinase family member, DAPK1 regulates critical signaling pathways, such as apoptosis and autophagy. In this study, we comprehensively analyzed DAPK1 interactors and enriched molecular functions, biological processes, phenotypic expression, disease associations, and aging signatures to elucidate the molecular networks of DAPK1. Furthermore, we employed a structure-based virtual screening approach using the PubChem database, which enabled the identification of potential bioactive compounds capable of inhibiting DAPK1, including caspase inhibitors and synthetic analogs. Three selected compounds, CID24602687, CID8843795, and CID110869998, exhibited high docking affinity and selectivity towards DAPK1, which were further investigated using molecular dynamics simulations to understand their binding patterns. Our findings establish a connection between DAPK1 and retinal degenerative diseases and highlight the potential of these selected compounds for the development of novel therapeutic strategies. This study provides valuable insights into the molecular mechanisms underlying DAPK1-related diseases, and offers new opportunities for the discovery of effective treatments for retinal degeneration.Communicated by Ramaswamy H. Sarma.

Calmodulin-Domain Protein Kinase PiCDPK1 Interacts with the 14-3-3-like Protein NtGF14 to Modulate Pollen Tube Growth

Calcium-mediated signaling pathways are known to play important roles in the polar growth of pollen tubes. The calcium-dependent protein kinase, PiCDPK1, has been shown to be involved in regulating this process through interaction with a guanine dissociation inhibitor, PiRhoGDI1. To more fully understand the role of PiCDPK1 in pollen tube extension, we designed a pull-down study to identify additional substrates of this kinase. These experiments identified 123 putative interactors. Two of the identified proteins were predicted to directly interact with PiCDPK1, and this possibility was investigated in planta. The first, NtGF14, a 14-3-3-like protein, did not produce a noticeable phenotype when overexpressed in pollen alone but partially rescued the spherical tube phenotype caused by PiCDPK1 over-expression when co-over-expressed with the kinase. The second, NtREN1, a GTPase activating protein (GAP), severely inhibited pollen tube germination when over-expressed, and its co-over-expression with PiCDPK1 did not substantially affect this phenotype. These results suggest a novel in vivo interaction between NtGF14 and PiCDPK1 but do not support the direct interaction between PiCDPK1 and NtREN1. We demonstrate the utility of the methodology used to identify potential protein interactions while confirming the necessity of additional studies to confirm their validity. Finally, additional support was found for intersection between PiCDPK1 and RopGTPase pathways to control polar growth at the pollen tube tip.

Network propagation for GWAS analysis: a practical guide to leveraging molecular networks for disease gene discovery

MOTIVATION

Genome-wide association studies (GWAS) have enabled large-scale analysis of the role of genetic variants in human disease. Despite impressive methodological advances, subsequent clinical interpretation and application remains challenging when GWAS suffer from a lack of statistical power. In recent years, however, the use of information diffusion algorithms with molecular networks has led to fruitful insights on disease genes.

RESULTS

We present an overview of the design choices and pitfalls that prove crucial in the application of network propagation methods to GWAS summary statistics. We highlight general trends from the literature, and present benchmark experiments to expand on these insights selecting as case study three diseases and five molecular networks. We verify that the use of gene-level scores based on GWAS P-values offers advantages over the selection of a set of 'seed' disease genes not weighted by the associated P-values if the GWAS summary statistics are of sufficient quality. Beyond that, the size and the density of the networks prove to be important factors for consideration. Finally, we explore several ensemble methods and show that combining multiple networks may improve the network propagation approach.

WikiPathways 2024: next generation pathway database

WikiPathways (wikipathways.org) is an open-source biological pathway database. Collaboration and open science are pivotal to the success of WikiPathways. Here we highlight the continuing efforts supporting WikiPathways, content growth and collaboration among pathway researchers. As an evolving database, there is a growing need for WikiPathways to address and overcome technical challenges. In this direction, WikiPathways has undergone major restructuring, enabling a renewed approach for sharing and curating pathway knowledge, thus providing stability for the future of community pathway curation. The website has been redesigned to improve and enhance user experience. This next generation of WikiPathways continues to support existing features while improving maintainability of the database and facilitating community input by providing new functionality and leveraging automation.

KG-Hub-building and exchanging biological knowledge graphs

MOTIVATION

Knowledge graphs (KGs) are a powerful approach for integrating heterogeneous data and making inferences in biology and many other domains, but a coherent solution for constructing, exchanging, and facilitating the downstream use of KGs is lacking.

RESULTS

Here we present KG-Hub, a platform that enables standardized construction, exchange, and reuse of KGs. Features include a simple, modular extract-transform-load pattern for producing graphs compliant with Biolink Model (a high-level data model for standardizing biological data), easy integration of any OBO (Open Biological and Biomedical Ontologies) ontology, cached downloads of upstream data sources, versioned and automatically updated builds with stable URLs, web-browsable storage of KG artifacts on cloud infrastructure, and easy reuse of transformed subgraphs across projects. Current KG-Hub projects span use cases including COVID-19 research, drug repurposing, microbial-environmental interactions, and rare disease research. KG-Hub is equipped with tooling to easily analyze and manipulate KGs. KG-Hub is also tightly integrated with graph machine learning (ML) tools which allow automated graph ML, including node embeddings and training of models for link prediction and node classification.

AVAILABILITY AND IMPLEMENTATION

https://kghub.org.

Distinct Patterns of Gene Expression Changes in the Colon and Striatum of Young Mice Overexpressing Alpha-Synuclein Support Parkinson's Disease as a Multi-System Process

BACKGROUND

Evidence supports a role for the gut-brain axis in Parkinson's disease (PD). Mice overexpressing human wild type α- synuclein (Thy1-haSyn) exhibit slow colonic transit prior to motor deficits, mirroring prodromal constipation in PD. Identifying molecular changes in the gut could provide both biomarkers for early diagnosis and gut-targeted therapies to prevent progression.

OBJECTIVE

To identify early molecular changes in the gut-brain axis in Thy1-haSyn mice through gene expression profiling.

METHODS

Gene expression profiling was performed on gut (colon) and brain (striatal) tissue from Thy1-haSyn and wild-type (WT) mice aged 1 and 3 months using 3' RNA sequencing. Analysis included differential expression, gene set enrichment and weighted gene co-expression network analysis (WGCNA).

RESULTS

At one month, differential expression (Thy1-haSyn vs. WT) of mitochondrial genes and pathways related to PD was discordant between gut and brain, with negative enrichment in brain (enriched in WT) but positive enrichment in gut. Linear regression of WGCNA modules showed partial independence of gut and brain gene expression changes. Thy1-haSyn-associated WGCNA modules in the gut were enriched for PD risk genes and PD-relevant pathways including inflammation, autophagy, and oxidative stress. Changes in gene expression were modest at 3 months.

CONCLUSIONS

Overexpression of haSyn acutely disrupts gene expression in the colon. While changes in colon gene expression are highly related to known PD-relevant mechanisms, they are distinct from brain changes, and in some cases, opposite in direction. These findings are in line with the emerging view of PD as a multi-system disease.

Multilayered Networks of SalmoNet2 Enable Strain Comparisons of the Salmonella Genus on a Molecular Level

Serovars of the genus Salmonella primarily evolved as gastrointestinal pathogens in a wide range of hosts. Some serotypes later evolved further, adopting a more invasive lifestyle in a narrower host range associated with systemic infections. A system-level knowledge of these pathogens could identify the complex adaptations associated with the evolution of serovars with distinct pathogenicity, host range, and risk to human health. This promises to aid the design of interventions and serve as a knowledge base in the Salmonella research community. Here, we present SalmoNet2, a major update to SalmoNet1, the first multilayered interaction resource for Salmonella strains, containing protein-protein, transcriptional regulatory, and enzyme-enzyme interactions. The new version extends the number of Salmonella networks from 11 to 20. We now include a strain from the second species in the Salmonella genus, a strain from the Salmonella enterica subspecies arizonae and additional strains of importance from the subspecies enterica, including S. Typhimurium strain D23580, an epidemic multidrug-resistant strain associated with invasive nontyphoidal salmonellosis (iNTS). The database now uses strain specific metabolic models instead of a generalized model to highlight differences between strains. The update has increased the coverage of high-quality protein-protein interactions, and enhanced interoperability with other computational resources by adopting standardized formats. The resource website has been updated with tutorials to help researchers analyze their Salmonella data using molecular interaction networks from SalmoNet2. SalmoNet2 is accessible at http://salmonet.org/. IMPORTANCE Multilayered network databases collate interaction information from multiple sources, and are powerful both as a knowledge base and subject of analysis. Here, we present SalmoNet2, an integrated network resource containing protein-protein, transcriptional regulatory, and metabolic interactions for 20 Salmonella strains. Key improvements to the update include expanding the number of strains, strain-specific metabolic networks, an increase in high-quality protein-protein interactions, community standard computational formats to help interoperability, and online tutorials to help users analyze their data using SalmoNet2.

Data-dependent visualization of biological networks in the web-browser with NDExEdit

Networks are a common methodology used to capture increasingly complex associations between biological entities. They serve as a resource of biological knowledge for bioinformatics analyses, and also comprise the subsequent results. However, the interpretation of biological networks is challenging and requires suitable visualizations dependent on the contained information. The most prominent software in the field for the visualization of biological networks is Cytoscape, a desktop modeling environment also including many features for analysis.

A further challenge when working with networks is their distribution. Within a typical collaborative workflow, even slight changes of the network data force one to repeat the visualization step as well. Also, just minor adjustments to the visual representation not only need the networks to be transferred back and forth. Collaboration on the same resources requires specific infrastructure to avoid redundancies, or worse, the corruption of the data. A well-established solution is provided by the NDEx platform where users can upload a network, share it with selected colleagues or make it publicly available.

NDExEdit is a web-based application where simple changes can be made to biological networks within the browser, and which does not require installation. With our tool, plain networks can be enhanced easily for further usage in presentations and publications. Since the network data is only stored locally within the web browser, users can edit their private networks without concerns of unintentional publication. The web tool is designed to conform to the Cytoscape Exchange (CX) format as a data model, which is used for the data transmission by both tools, Cytoscape and NDEx. Therefore the modified network can be directly exported to the NDEx platform or saved as a compatible CX file, additionally to standard image formats like PNG and JPEG.

Relations in biological research are often visualized as networks. For instance, if two proteins interact with each other during a certain process, the corresponding network would show two nodes connected by one edge. But the fact that the interaction between the two exists, may not be enough. With established software solutions like Cytoscape we can add all the information we have about our nodes and their interaction to our data foundation. Furthermore, we can change the visual appearance of our nodes and their interaction based on this information.

For example, if our network contains 20 nodes, that all interact with each other, but the strength of these interactions each range between 0 and 1, we can illustrate that by making the edges wider for strong interactions and slimmer for weak interactions. Thus, our visualization is enriched with valuable information. As of now these data-dependent modifications can only be made with a desktop client.

We introduce NDExEdit, a web-based solution for visualization changes to networks that conform to the CX data format. It allows us to import networks directly from the NDEx platform and apply changes to the visualization—including all types of mappings, one of which was briefly described above.

KeyPathwayMineR: De Novo Pathway Enrichment in the R Ecosystem

De novo pathway enrichment is a systems biology approach in which OMICS data are projected onto a molecular interaction network to identify subnetworks representing condition-specific functional modules and molecular pathways. Compared to classical pathway enrichment analysis methods, de novo pathway enrichment is not limited to predefined lists of pathways from (curated) databases and thus particularly suited for discovering novel disease mechanisms. While several tools have been proposed for pathway enrichment, the integration of de novo pathway enrichment in end-to-end OMICS analysis workflows in the R programming language is currently limited to a single tool. To close this gap, we have implemented an R package KeyPathwayMineR (KPM-R). The package extends the features and usability of existing versions of KeyPathwayMiner by leveraging the power, flexibility and versatility of R and by providing various novel functionalities for performing data preparation, visualization, and comparison. In addition, thanks to its interoperability with a plethora of existing R packages in e.g., Bioconductor, CRAN, and GitHub, KPM-R allows carrying out the initial preparation of the datasets and to meaningfully interpret the extracted subnetworks. To demonstrate the package's potential, KPM-R was applied to bulk RNA-Seq data of nasopharyngeal swabs from SARS-CoV-2 infected individuals, and on single cell RNA-Seq data of aging mice tissue from the Tabula Muris Senis atlas.

Computational analyses of mechanism of action (MoA): data, methods and integration

The elucidation of a compound's Mechanism of Action (MoA) is a challenging task in the drug discovery process, but it is important in order to rationalise phenotypic findings and to anticipate potential side-effects. Bioinformatic approaches, advances in machine learning techniques and the increasing deposition of high-throughput data in public databases have significantly contributed to recent advances in the field, but it is not straightforward to decide which data and methods are most suitable to use in a given case. In this review, we focus on these methods and data and their applications in generating MoA hypotheses for subsequent experimental validation. We discuss compound-specific data such as -omics, cell morphology and bioactivity data, as well as commonly used supplementary prior knowledge such as network and pathway data, and provide information on databases where this data can be accessed. In terms of methodologies, we discuss both well-established methods (connectivity mapping, pathway enrichment) as well as more developing methods (neural networks and multi-omics integration). Finally, we review case studies where the MoA of a compound was successfully suggested from computational analysis by incorporating multiple data modalities and/or methodologies. Our aim for this review is to provide researchers with insights into the benefits and drawbacks of both the data and methods in terms of level of understanding, biases and interpretation - and to highlight future avenues of investigation which we foresee will improve the field of MoA elucidation, including greater public access to -omics data and methodologies which are capable of data integration.

Therapeutic radiation exposure of the abdomen during childhood induces chronic adipose tissue dysfunction

BACKGROUNDChildhood cancer survivors who received abdominal radiotherapy (RT) or total body irradiation (TBI) are at increased risk for cardiometabolic disease, but the underlying mechanisms are unknown. We hypothesize that RT-induced adipose tissue dysfunction contributes to the development of cardiometabolic disease in the expanding population of childhood cancer survivors.METHODSWe performed clinical metabolic profiling of adult childhood cancer survivors previously exposed to TBI, abdominal RT, or chemotherapy alone, alongside a group of healthy controls. Study participants underwent abdominal s.c. adipose biopsies to obtain tissue for bulk RNA sequencing. Transcriptional signatures were analyzed using pathway and network analyses and cellular deconvolution.RESULTSIrradiated adipose tissue is characterized by a gene expression signature indicative of a complex macrophage expansion. This signature includes activation of the TREM2-TYROBP network, a pathway described in diseases of chronic tissue injury. Radiation exposure of adipose is further associated with dysregulated adipokine secretion, specifically a decrease in insulin-sensitizing adiponectin and an increase in insulin resistance-promoting plasminogen activator inhibitor-1. Accordingly, survivors exhibiting these changes have early signs of clinical metabolic derangement, such as increased fasting glucose and hemoglobin A1c.CONCLUSIONChildhood cancer survivors exposed to abdominal RT or TBI during treatment exhibit signs of chronic s.c. adipose tissue dysfunction, manifested as dysregulated adipokine secretion that may negatively impact their systemic metabolic health.FUNDINGThis study was supported by Rockefeller University Hospital; National Institute of General Medical Sciences (T32GM007739); National Center for Advancing Translational Sciences (UL1 TR001866); National Cancer Institute (P30CA008748); American Cancer Society (133831-CSDG-19-117-01-CPHPS); American Diabetes Association (1-17-ACE-17); and an anonymous donor (MSKCC).

Humans and machines in biomedical knowledge curation: hypertrophic cardiomyopathy molecular mechanisms' representation

Background

Biomedical knowledge is dispersed in scientific literature and is growing constantly. Curation is the extraction of knowledge from unstructured data into a computable form and could be done manually or automatically. Hypertrophic cardiomyopathy (HCM) is the most common inherited cardiac disease, with genotype–phenotype associations still incompletely understood. We compared human- and machine-curated HCM molecular mechanisms’ models and examined the performance of different machine approaches for that task.

Results

We created six models representing HCM molecular mechanisms using different approaches and made them publicly available, analyzed them as networks, and tried to explain the models’ differences by the analysis of factors that affect the quality of machine-curated models (query constraints and reading systems’ performance). A result of this work is also the Interactive HCM map, the only publicly available knowledge resource dedicated to HCM. Sizes and topological parameters of the networks differed notably, and a low consensus was found in terms of centrality measures between networks. Consensus about the most important nodes was achieved only with respect to one element (calcium). Models with a reduced level of noise were generated and cooperatively working elements were detected. REACH and TRIPS reading systems showed much higher accuracy than Sparser, but at the cost of extraction performance. TRIPS proved to be the best single reading system for text segments about HCM, in terms of the compromise between accuracy and extraction performance.

Conclusions

Different approaches in curation can produce models of the same disease with diverse characteristics, and they give rise to utterly different conclusions in subsequent analysis. The final purpose of the model should direct the choice of curation techniques. Manual curation represents the gold standard for information extraction in biomedical research and is most suitable when only high-quality elements for models are required. Automated curation provides more substance, but high level of noise is expected. Different curation strategies can reduce the level of human input needed. Biomedical knowledge would benefit overwhelmingly, especially as to its rapid growth, if computers were to be able to assist in analysis on a larger scale.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13040-021-00279-2.

COVID19 Disease Map, a computational knowledge repository of virus-host interaction mechanisms

We need to effectively combine the knowledge from surging literature with complex datasets to propose mechanistic models of SARS-CoV-2 infection, improving data interpretation and predicting key targets of intervention. Here, we describe a large-scale community effort to build an open access, interoperable and computable repository of COVID-19 molecular mechanisms. The COVID-19 Disease Map (C19DMap) is a graphical, interactive representation of disease-relevant molecular mechanisms linking many knowledge sources. Notably, it is a computational resource for graph-based analyses and disease modelling. To this end, we established a framework of tools, platforms and guidelines necessary for a multifaceted community of biocurators, domain experts, bioinformaticians and computational biologists. The diagrams of the C19DMap, curated from the literature, are integrated with relevant interaction and text mining databases. We demonstrate the application of network analysis and modelling approaches by concrete examples to highlight new testable hypotheses. This framework helps to find signatures of SARS-CoV-2 predisposition, treatment response or prioritisation of drug candidates. Such an approach may help deal with new waves of COVID-19 or similar pandemics in the long-term perspective.

NDEx: Accessing Network Models and Streamlining Network Biology Workflows

NDEx, the Network Data Exchange (https://www.ndexbio.org) is a web-based resource where users can find, store, share and publish network models of any type and size. NDEx is integrated with Cytoscape, the widely used desktop application for network analysis and visualization. NDEx and Cytoscape are the pillars of the Cytoscape Ecosystem, a diverse environment of resources, tools, applications and services for network biology workflows. In this article, we introduce researchers to NDEx and highlight how it can simplify common tasks in network biology workflows as well as streamline publication and access to). Finally, we show how NDEx can be used programmatically via Python with the 'ndex2' client library, and point readers to additional examples for other popular programming languages such as JavaScript and R. © 2021 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Getting started with NDEx Basic Protocol 2: Using NDEx and Cytoscape in a publication-oriented workflow Basic Protocol 3: Manipulating networks in NDEx via Python.

KLF10 integrates circadian timing and sugar signaling to coordinate hepatic metabolism

The mammalian circadian timing system and metabolism are highly interconnected, and disruption of this coupling is associated with negative health outcomes. Krüppel-like factors (KLFs) are transcription factors that govern metabolic homeostasis in various organs. Many KLFs show a circadian expression in the liver. Here, we show that the loss of the clock-controlled KLF10 in hepatocytes results in extensive reprogramming of the mouse liver circadian transcriptome, which in turn alters the temporal coordination of pathways associated with energy metabolism. We also show that glucose and fructose induce Klf10, which helps mitigate glucose intolerance and hepatic steatosis in mice challenged with a sugar beverage. Functional genomics further reveal that KLF10 target genes are primarily involved in central carbon metabolism. Together, these findings show that in the liver KLF10 integrates circadian timing and sugar metabolism-related signaling, and serves as a transcriptional brake that protects against the deleterious effects of increased sugar consumption.

Shared Molecular Mechanisms of Hypertrophic Cardiomyopathy and Its Clinical Presentations: Automated Molecular Mechanisms Extraction Approach

Hypertrophic cardiomyopathy (HCM) is the most common inherited cardiovascular disease with a prevalence of 1 in 500 people and varying clinical presentations. Although there is much research on HCM, underlying molecular mechanisms are poorly understood, and research on the molecular mechanisms of its specific clinical presentations is scarce. Our aim was to explore the molecular mechanisms shared by HCM and its clinical presentations through the automated extraction of molecular mechanisms. Molecular mechanisms were congregated by a query of the INDRA database, which aggregates knowledge from pathway databases and combines it with molecular mechanisms extracted from abstracts and open-access full articles by multiple machine-reading systems. The molecular mechanisms were extracted from 230,072 articles on HCM and 19 HCM clinical presentations, and their intersections were found. Shared molecular mechanisms of HCM and its clinical presentations were represented as networks; the most important elements in the intersections’ networks were found, centrality scores for each element of each network calculated, networks with reduced level of noise generated, and cooperatively working elements detected in each intersection network. The identified shared molecular mechanisms represent possible mechanisms underlying different HCM clinical presentations. Applied methodology produced results consistent with the information in the scientific literature.

Extremes of age are associated with differences in the expression of selected pattern recognition receptor genes and ACE2, the receptor for SARS-CoV-2: implications for the epidemiology of COVID-19 disease

Background

Older aged adults and those with pre-existing conditions are at highest risk for severe COVID-19 associated outcomes.

Methods

Using a large dataset of genome-wide RNA-seq profiles derived from human dermal fibroblasts (GSE113957) we investigated whether age affects the expression of pattern recognition receptor (PRR) genes and ACE2, the receptor for SARS-CoV-2.

Results

Extremes of age are associated with increased expression of selected PRR genes, ACE2 and four genes that encode proteins that have been shown to interact with SAR2-CoV-2 proteins.

Conclusions

Assessment of PRR expression might provide a strategy for stratifying the risk of severe COVID-19 disease at both the individual and population levels.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12920-021-00970-7.

The DisGeNET cytoscape app: Exploring and visualizing disease genomics data

Thanks to the unbiased exploration of genomic variants at large scale, hundreds of thousands of disease-associated loci have been uncovered. In parallel, network-based approaches have proven to be essential to understand the molecular mechanisms underlying human diseases. The use of these approaches has been boosted by the abundance of information about disease associated genes and variants, high quality human interactomics data, and the emergence of new types of omics data. The DisGeNET Cytoscape App combines the capabilities of Cytoscape with those of DisGeNET, a knowledge platform based on a comprehensive catalogue of disease-associated genes and variants. The DisGeNET Cytoscape App contains functions to query, analyze, and visualize different network representations of the gene-disease and variant-disease associations available in DisGeNET. It supports a wide variety of applications through its query and filter functionalities, including the annotation of foreign networks generated by other apps or uploaded by the user. The new release of the DisGeNET Cytoscape App has been designed to support Cytoscape 3.x and incorporates novel distinctive features such as visualization and analysis of variant-disease networks, disease enrichment analysis for genes and variants, and analytic support through Cytoscape Automation. Moreover, the DisGeNET Cytoscape App features an API to access its core functionalities via the REST protocol fostering the development of reproducible and scalable analysis workflows based on DisGeNET data.

On the limits of active module identification

In network and systems medicine, active module identification methods (AMIMs) are widely used for discovering candidate molecular disease mechanisms. To this end, AMIMs combine network analysis algorithms with molecular profiling data, most commonly, by projecting gene expression data onto generic protein-protein interaction (PPI) networks. Although active module identification has led to various novel insights into complex diseases, there is increasing awareness in the field that the combination of gene expression data and PPI network is problematic because up-to-date PPI networks have a very small diameter and are subject to both technical and literature bias. In this paper, we report the results of an extensive study where we analyzed for the first time whether widely used AMIMs really benefit from using PPI networks. Our results clearly show that, except for the recently proposed AMIM DOMINO, the tested AMIMs do not produce biologically more meaningful candidate disease modules on widely used PPI networks than on random networks with the same node degrees. AMIMs hence mainly learn from the node degrees and mostly fail to exploit the biological knowledge encoded in the edges of the PPI networks. This has far-reaching consequences for the field of active module identification. In particular, we suggest that novel algorithms are needed which overcome the degree bias of most existing AMIMs and/or work with customized, context-specific networks instead of generic PPI networks.

Mucociliary Respiratory Epithelium Integrity in Molecular Defense and Susceptibility to Pulmonary Viral Infections

Mucociliary defense, mediated by the ciliated and goblet cells, is fundamental to respiratory fitness. The concerted action of ciliary movement on the respiratory epithelial surface and the pathogen entrapment function of mucus help to maintain healthy airways. Consequently, genetic or acquired defects in lung defense elicit respiratory diseases and secondary microbial infections that inflict damage on pulmonary function and may even be fatal. Individuals living with chronic and acute respiratory diseases are more susceptible to develop severe coronavirus disease-19 (COVID-19) illness and hence should be proficiently managed. In light of the prevailing pandemic, we review the current understanding of the respiratory system and its molecular components with a major focus on the pathophysiology arising due to collapsed respiratory epithelium integrity such as abnormal ciliary movement, cilia loss and dysfunction, ciliated cell destruction, and changes in mucus rheology. The review includes protein interaction networks of coronavirus infection-manifested implications on the molecular machinery that regulates mucociliary clearance. We also provide an insight into the alteration of the transcriptional networks of genes in the nasopharynx associated with the mucociliary clearance apparatus in humans upon infection by severe acute respiratory syndrome coronavirus-2.

WikiPathways: connecting communities

WikiPathways (https://www.wikipathways.org) is a biological pathway database known for its collaborative nature and open science approaches. With the core idea of the scientific community developing and curating biological knowledge in pathway models, WikiPathways lowers all barriers for accessing and using its content. Increasingly more content creators, initiatives, projects and tools have started using WikiPathways. Central in this growth and increased use of WikiPathways are the various communities that focus on particular subsets of molecular pathways such as for rare diseases and lipid metabolism. Knowledge from published pathway figures helps prioritize pathway development, using optical character and named entity recognition. We show the growth of WikiPathways over the last three years, highlight the new communities and collaborations of pathway authors and curators, and describe various technologies to connect to external resources and initiatives. The road toward a sustainable, community-driven pathway database goes through integration with other resources such as Wikidata and allowing more use, curation and redistribution of WikiPathways content.

Multi-omics integration in biomedical research - A metabolomics-centric review

Recent advances in high-throughput technologies have enabled the profiling of multiple layers of a biological system, including DNA sequence data (genomics), RNA expression levels (transcriptomics), and metabolite levels (metabolomics). This has led to the generation of vast amounts of biological data that can be integrated in so-called multi-omics studies to examine the complex molecular underpinnings of health and disease. Integrative analysis of such datasets is not straightforward and is particularly complicated by the high dimensionality and heterogeneity of the data and by the lack of universal analysis protocols. Previous reviews have discussed various strategies to address the challenges of data integration, elaborating on specific aspects, such as network inference or feature selection techniques. Thereby, the main focus has been on the integration of two omics layers in their relation to a phenotype of interest. In this review we provide an overview over a typical multi-omics workflow, focusing on integration methods that have the potential to combine metabolomics data with two or more omics. We discuss multiple integration concepts including data-driven, knowledge-based, simultaneous and step-wise approaches. We highlight the application of these methods in recent multi-omics studies, including large-scale integration efforts aiming at a global depiction of the complex relationships within and between different biological layers without focusing on a particular phenotype.

Comparative host-coronavirus protein interaction networks reveal pan-viral disease mechanisms

The COVID-19 pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is a grave threat to public health and the global economy. SARS-CoV-2 is closely related to the more lethal but less transmissible coronaviruses SARS-CoV-1 and Middle East respiratory syndrome coronavirus (MERS-CoV). Here, we have carried out comparative viral-human protein-protein interaction and viral protein localization analyses for all three viruses. Subsequent functional genetic screening identified host factors that functionally impinge on coronavirus proliferation, including Tom70, a mitochondrial chaperone protein that interacts with both SARS-CoV-1 and SARS-CoV-2 ORF9b, an interaction we structurally characterized using cryo-electron microscopy. Combining genetically validated host factors with both COVID-19 patient genetic data and medical billing records identified molecular mechanisms and potential drug treatments that merit further molecular and clinical study.

LL-37-Induced Autophagy Contributed to the Elimination of Live Porphyromonas gingivalis Internalized in Keratinocytes

Porphyromonas gingivalis (P. gingivalis), one of the most important pathogens of periodontitis, is closely associated with the aggravation and recurrence of periodontitis and systemic diseases. Antibacterial peptide LL-37, transcribed from the cathelicidin antimicrobial peptide (CAMP) gene, exhibits a broad spectrum of antibacterial activity and regulates the immune system. In this study, we demonstrated that LL-37 reduced the number of live P. gingivalis (ATCC 33277) in HaCaT cells in a dose-dependent manner via an antibiotic-protection assay. LL-37 promoted autophagy of HaCaT cells internalized with P. gingivalis. Inhibition of autophagy with 3-methyladenine (3-MA) weakened the inhibitory effect of LL-37 on the number of intracellular P. gingivalis. A cluster of orthologous groups (COGs) and a gene ontology (GO) functional analysis were used to individually assign 65 (10%) differentially expressed genes (DEGs) to an "Intracellular trafficking, secretion, and vesicular transport" cluster and 306 (47.08%) DEGs to metabolic processes including autophagy. Autophagy-related genes, a tripartite motif-containing 22 (TRIM22), and lysosomal-associated membrane protein 3 (LAMP3) were identified as potentially involved in LL-37-induced autophagy. Finally, bioinformatics software was utilized to construct and predict the protein-protein interaction (PPI) network of CAMP-TRIM22/LAMP3-Autophagy. The findings indicated that LL-37 can reduce the quantity of live P. gingivalis internalized in HaCaT cells by promoting autophagy in these cells. The transcriptome sequencing and analysis also revealed the potential molecular pathway of LL-37-induced autophagy.

Consensus transcriptional regulatory networks of coronavirus-infected human cells

Establishing consensus around the transcriptional interface between coronavirus (CoV) infection and human cellular signaling pathways can catalyze the development of novel anti-CoV therapeutics. Here, we used publicly archived transcriptomic datasets to compute consensus regulatory signatures, or consensomes, that rank human genes based on their rates of differential expression in MERS-CoV (MERS), SARS-CoV-1 (SARS1) and SARS-CoV-2 (SARS2)-infected cells. Validating the CoV consensomes, we show that high confidence transcriptional targets (HCTs) of MERS, SARS1 and SARS2 infection intersect with HCTs of signaling pathway nodes with known roles in CoV infection. Among a series of novel use cases, we gather evidence for hypotheses that SARS2 infection efficiently represses E2F family HCTs encoding key drivers of DNA replication and the cell cycle; that progesterone receptor signaling antagonizes SARS2-induced inflammatory signaling in the airway epithelium; and that SARS2 HCTs are enriched for genes involved in epithelial to mesenchymal transition. The CoV infection consensomes and HCT intersection analyses are freely accessible through the Signaling Pathways Project knowledgebase, and as Cytoscape-style networks in the Network Data Exchange repository.

From Persian Gulf to Indonesia: interrelated phylogeographic distance and chemistry within the genus Peronia (Onchidiidae, Gastropoda, Mollusca)

The knowledge of relationships between taxa is essential to understand and explain the chemical diversity of the respective groups. Here, twelve individuals of the panpulmonate slug Peronia persiae from two localities in Persian Gulf, and one animal of P. verruculata from Bangka Island, Indonesia, were analyzed in a phylogenetic and chemotaxonomic framework. Based on the ABGD test and haplotype networking using COI gene sequences of Peronia specimens, nine well-supported clades were found. Haplotype network analysis highlighted a considerable distance between the specimens of P. persiae and other clades. Metabolomic analysis of both species using tandem mass spectrometry-based GNPS molecular networking revealed a large chemical diversity within Peronia of different clades and localities. While P. persiae from different localities showed a highly similar metabolome, only few identical chemical features were found across the clades. The main common metabolites in both Peronia species were assigned as polypropionate esters of onchitriols and ilikonapyrones, and osmoprotectant amino acid-betaine compounds. On the other hand, the isoflavonoids genistein and daidzein were exclusively detected in P. persiae, while cholesterol and conjugated chenodeoxycholic acids were only found in P. verruculata. Flavonoids, bile acids, and amino acid-betaine compounds were not reported before from Onchidiidae, some are even new for panpulmonates. Our chemical analyses indicate a close chemotaxonomic relation between phylogeographically distant Peronia species.

Voltage-Dependent Anion Channels Influence Cytotoxicity of ME-344, a Therapeutic Isoflavone

ME-344 is a second-generation cytotoxic isoflavone with anticancer activity promulgated through interference with mitochondrial functions. Using a click chemistry version of the drug together with affinity-enriched mass spectrometry, voltage-dependent anion channels (VDACs) 1 and 2 were identified as drug targets. To determine the importance of VDAC1 or 2 to cytotoxicity, we used lung cancer cells that were either sensitive (H460) or intrinsically resistant (H596) to the drug. In H460 cells, depletion of VDAC1 and VDAC2 by small interfering RNA impacted ME-344 effects by diminishing generation of reactive oxygen species (ROS), preventing mitochondrial membrane potential dissipation, and moderating ME-344-induced cytotoxicity and mitochondrial-mediated apoptosis. Mechanistically, VDAC1 and VDAC2 knockdown prevented ME-344-induced apoptosis by inhibiting Bax mitochondrial translocation and cytochrome c release as well as apoptosis in these H460 cells. We conclude that VDAC1 and 2, as mediators of the response to oxidative stress, have roles in modulating ROS generation, Bax translocation, and cytochrome c release during mitochondrial-mediated apoptosis caused by ME-344. SIGNIFICANCE STATEMENT: Dissecting preclinical drug mechanisms are of significance in development of a drug toward eventual Food and Drug Administration approval.

Consensus transcriptional regulatory networks of coronavirus-infected human cells

Establishing consensus around the transcriptional interface between coronavirus (CoV) infection and human cellular signaling pathways can catalyze the development of novel anti-CoV therapeutics. Here, we used publicly archived transcriptomic datasets to compute consensus regulatory signatures, or consensomes, that rank human genes based on their rates of differential expression in MERS-CoV (MERS), SARS-CoV-1 (SARS1) and SARS-CoV-2 (SARS2)-infected cells. Validating the CoV consensomes, we show that high confidence transcriptional targets (HCTs) of CoV infection intersect with HCTs of signaling pathway nodes with known roles in CoV infection. Among a series of novel use cases, we gather evidence for hypotheses that SARS2 infection efficiently represses E2F family target genes encoding key drivers of DNA replication and the cell cycle; that progesterone receptor signaling antagonizes SARS2-induced inflammatory signaling in the airway epithelium; and that SARS2 HCTs are enriched for genes involved in epithelial to mesenchymal transition. The CoV infection consensomes and HCT intersection analyses are freely accessible through the Signaling Pathways Project knowledgebase, and as Cytoscape-style networks in the Network Data Exchange repository.

ERRα Expression in Bone Metastases Leads to an Exacerbated Antitumor Immune Response

Bone is the most common metastatic site for breast cancer. Although the estrogen-related receptor alpha (ERRα) has been implicated in breast cancer cell dissemination to the bone from the primary tumor, its role after tumor cell anchorage in the bone microenvironment remains elusive. Here, we reveal that ERRα inhibits the progression of bone metastases of breast cancer cells by increasing the immune activity of the bone microenvironment. Overexpression of ERRα in breast cancer bone metastases induced expression of chemokines CCL17 and CCL20 and repressed production of TGFβ3. Subsequently, CD8+ T lymphocytes recruited to bone metastases escaped TGFβ signaling control and were endowed with exacerbated cytotoxic features, resulting in significant reduction in metastases. The clinical relevance of our findings in mice was confirmed in over 240 patients with breast cancer. Thus, this study reveals that ERRα regulates immune properties in the bone microenvironment that contributes to decreasing metastatic growth. SIGNIFICANCE: This study places ERRα at the interplay between the immune response and bone metastases of breast cancer, highlighting a potential target for intervention in advanced disease.

A systems approach to infectious disease

Ongoing social, political and ecological changes in the 21st century have placed more people at risk of life-threatening acute and chronic infections than ever before. The development of new diagnostic, prophylactic, therapeutic and curative strategies is critical to address this burden but is predicated on a detailed understanding of the immensely complex relationship between pathogens and their hosts. Traditional, reductionist approaches to investigate this dynamic often lack the scale and/or scope to faithfully model the dual and co-dependent nature of this relationship, limiting the success of translational efforts. With recent advances in large-scale, quantitative omics methods as well as in integrative analytical strategies, systems biology approaches for the study of infectious disease are quickly forming a new paradigm for how we understand and model host-pathogen relationships for translational applications. Here, we delineate a framework for a systems biology approach to infectious disease in three parts: discovery - the design, collection and analysis of omics data; representation - the iterative modelling, integration and visualization of complex data sets; and application - the interpretation and hypothesis-based inquiry towards translational outcomes.

Mechanisms of Action of Autophagy Modulators Dissected by Quantitative Systems Pharmacology Analysis

Autophagy plays an essential role in cell survival/death and functioning. Modulation of autophagy has been recognized as a promising therapeutic strategy against diseases/disorders associated with uncontrolled growth or accumulation of biomolecular aggregates, organelles, or cells including those caused by cancer, aging, neurodegeneration, and liver diseases such as α1-antitrypsin deficiency. Numerous pharmacological agents that enhance or suppress autophagy have been discovered. However, their molecular mechanisms of action are far from clear. Here, we collected a set of 225 autophagy modulators and carried out a comprehensive quantitative systems pharmacology (QSP) analysis of their targets using both existing databases and predictions made by our machine learning algorithm. Autophagy modulators include several highly promiscuous drugs (e.g., artenimol and olanzapine acting as activators, fostamatinib as an inhibitor, or melatonin as a dual-modulator) as well as selected drugs that uniquely target specific proteins (~30% of modulators). They are mediated by three layers of regulation: (i) pathways involving core autophagy-related (ATG) proteins such as mTOR, AKT, and AMPK; (ii) upstream signaling events that regulate the activity of ATG pathways such as calcium-, cAMP-, and MAPK-signaling pathways; and (iii) transcription factors regulating the expression of ATG proteins such as TFEB, TFE3, HIF-1, FoxO, and NF-κB. Our results suggest that PKA serves as a linker, bridging various signal transduction events and autophagy. These new insights contribute to a better assessment of the mechanism of action of autophagy modulators as well as their side effects, development of novel polypharmacological strategies, and identification of drug repurposing opportunities.

Identification of microRNAs in skeletal muscle associated with lung cancer cachexia

BACKGROUND

Cachexia, highly prevalent in patients with non-small cell lung cancer (NSCLC), impairs quality of life and is associated with reduced tolerance and responsiveness to cancer therapy and decreased survival. MicroRNAs (miRNAs) are small non-coding RNAs that play a central role in post-transcriptional gene regulation. Changes in intramuscular levels of miRNAs have been implicated in muscle wasting conditions. Here, we aimed to identify miRNAs that are differentially expressed in skeletal muscle of cachectic lung cancer patients to increase our understanding of cachexia and to allow us to probe their potential as therapeutic targets.

METHODS

A total of 754 unique miRNAs were profiled and analysed in vastus lateralis muscle biopsies of newly diagnosed treatment-naïve NSCLC patients with cachexia (n = 8) and age-matched and sex-matched healthy controls (n = 8). miRNA expression analysis was performed using a TaqMan MicroRNA Array. In silico network analysis was performed on all significant differentially expressed miRNAs. Differential expression of the top-ranked miRNAs was confirmed using reverse transcription-quantitative real-time PCR in an extended group (n = 48) consisting of NSCLC patients with (n = 15) and without cachexia (n = 11) and healthy controls (n = 22). Finally, these miRNAs were subjected to univariate and multivariate Cox proportional hazard analysis using overall survival and treatment-induced toxicity data obtained during the follow-up of this group of patients.

RESULTS

We identified 28 significant differentially expressed miRNAs, of which five miRNAs were up-regulated and 23 were down-regulated. In silico miRNA-target prediction analysis showed 158 functional gene targets, and pathway analysis identified 22 pathways related to the degenerative or regenerative processes of muscle tissue. Subsequently, the expression of six top-ranked miRNAs was measured in muscle biopsies of the entire patient group. Five miRNAs were detectable with reverse transcription-quantitative real-time PCR analysis, and their altered expression (expressed as fold change, FC) was confirmed in muscle of cachectic NSCLC patients compared with healthy control subjects: miR-424-5p (FC = 4.5), miR-424-3p (FC = 12), miR-450a-5p (FC = 8.6), miR-144-5p (FC = 0.59), and miR-451a (FC = 0.57). In non-cachectic NSCLC patients, only miR-424-3p was significantly increased (FC = 5.6) compared with control. Although the statistical support was not sufficient to imply these miRNAs as individual predictors of overall survival or treatment-induced toxicity, when combined in multivariate analysis, miR-450-5p and miR-451a resulted in a significant stratification between short-term and long-term survival.

CONCLUSIONS

We identified differentially expressed miRNAs putatively involved in lung cancer cachexia. These findings call for further studies to investigate the causality of these miRNAs in muscle atrophy and the mechanisms underlying their differential expression in lung cancer cachexia.

Enhancing reproducibility of gene expression analysis with known protein functional relationships: The concept of well-associated protein

Identification of differentially expressed genes (DEGs) is well recognized to be variable across independent replications of genome-wide transcriptional studies. These are often employed to characterize disease state early in the process of discovery and prioritize novel targets aimed at addressing unmet medical need. Increasing reproducibility of biological findings from these studies could potentially positively impact the success rate of new clinical interventions. This work demonstrates that statistically sound combination of gene expression data with prior knowledge about biology in the form of large protein interaction networks can yield quantitatively more reproducible observations from studies characterizing human disease. The novel concept of Well-Associated Proteins (WAPs) introduced herein-gene products significantly associated on protein interaction networks with the differences in transcript levels between control and disease-does not require choosing a differential expression threshold and can be computed efficiently enough to enable false discovery rate estimation via permutation. Reproducibility of WAPs is shown to be on average superior to that of DEGs under easily-quantifiable conditions suggesting that they can yield a significantly more robust description of disease. Enhanced reproducibility of WAPs versus DEGs is first demonstrated with four independent data sets focused on systemic sclerosis. This finding is then validated over thousands of pairs of data sets obtained by random partitions of large studies in several other diseases. Conditions that individual data sets must satisfy to yield robust WAP scores are examined. Reproducible identification of WAPs can potentially benefit drug target selection and precision medicine studies.

A Guide to Conquer the Biological Network Era Using Graph Theory

Networks are one of the most common ways to represent biological systems as complex sets of binary interactions or relations between different bioentities. In this article, we discuss the basic graph theory concepts and the various graph types, as well as the available data structures for storing and reading graphs. In addition, we describe several network properties and we highlight some of the widely used network topological features. We briefly mention the network patterns, motifs and models, and we further comment on the types of biological and biomedical networks along with their corresponding computer- and human-readable file formats. Finally, we discuss a variety of algorithms and metrics for network analyses regarding graph drawing, clustering, visualization, link prediction, perturbation, and network alignment as well as the current state-of-the-art tools. We expect this review to reach a very broad spectrum of readers varying from experts to beginners while encouraging them to enhance the field further.

Expression of SMARCD1 interacts with age in association with asthma control on inhaled corticosteroid therapy

Background

Global gene expression levels are known to be highly dependent upon gross demographic features including age, yet identification of age-related genomic indicators has yet to be comprehensively undertaken in a disease and treatment-specific context.

Methods

We used gene expression data from CD4+ lymphocytes in the Asthma BioRepository for Integrative Genomic Exploration (Asthma BRIDGE), an open-access collection of subjects participating in genetic studies of asthma with available gene expression data. Replication population participants were Puerto Rico islanders recruited as part of the ongoing Genes environments & Admixture in Latino Americans (GALA II), who provided nasal brushings for transcript sequencing. The main outcome measure was chronic asthma control as derived by questionnaires. Genomic associations were performed using regression of chronic asthma control score on gene expression with age in years as a covariate, including a multiplicative interaction term for gene expression times age.

Results

The SMARCD1 gene (SWI/SNF-related matrix-associated actin-dependent regulator of chromatin subfamily D member 1) interacted with age to influence chronic asthma control on inhaled corticosteroids, with a doubling of expression leading to an increase of 1.3 units of chronic asthma control per year (95% CI [0.86, 1.74], p = 6 × 10^− 9), suggesting worsening asthma control with increasing age. This result replicated in GALA II (p = 3.8 × 10^− 8). Cellular assays confirmed the role of SMARCD1 in glucocorticoid response in airway epithelial cells.

Conclusion

Focusing on age-dependent factors may help identify novel indicators of asthma medication response. Age appears to modulate the effect of SMARCD1 on asthma control with inhaled corticosteroids.

SIGNOR 2.0, the SIGnaling Network Open Resource 2.0: 2019 update

The SIGnaling Network Open Resource 2.0 (SIGNOR 2.0) is a public repository that stores signaling information as binary causal relationships between biological entities. The captured information is represented graphically as a signed directed graph. Each signaling relationship is associated to an effect (up/down-regulation) and to the mechanism (e.g. binding, phosphorylation, transcriptional activation, etc.) causing the up/down-regulation of the target entity. Since its first release, SIGNOR has undergone a significant content increase and the number of annotated causal interactions have almost doubled. SIGNOR 2.0 now stores almost 23 000 manually-annotated causal relationships between proteins and other biologically relevant entities: chemicals, phenotypes, complexes, etc. We describe here significant changes in curation policy and a new confidence score, which is assigned to each interaction. We have also improved the compliance to the FAIR data principles by providing (i) SIGNOR stable identifiers, (ii) programmatic access through REST APIs, (iii) bioschemas and (iv) downloadable data in standard-compliant formats, such as PSI-MI CausalTAB and GMT. The data are freely accessible and downloadable at https://signor.uniroma2.it/.

A Coordinated Approach by Public Domain Bioinformatics Resources to Aid the Fight Against Alzheimer's Disease Through Expert Curation of Key Protein Targets

BACKGROUND

The analysis and interpretation of data generated from patient-derived clinical samples relies on access to high-quality bioinformatics resources. These are maintained and updated by expert curators extracting knowledge from unstructured biological data described in free-text journal articles and converting this into more structured, computationally-accessible forms. This enables analyses such as functional enrichment of sets of genes/proteins using the Gene Ontology, and makes the searching of data more productive by managing issues such as gene/protein name synonyms, identifier mapping, and data quality.

OBJECTIVE

To undertake a coordinated annotation update of key public-domain resources to better support Alzheimer's disease research.

METHODS

We have systematically identified target proteins critical to disease process, in part by accessing informed input from the clinical research community.

RESULTS

Data from 954 papers have been added to the UniProtKB, Gene Ontology, and the International Molecular Exchange Consortium (IMEx) databases, with 299 human proteins and 279 orthologs updated in UniProtKB. 745 binary interactions were added to the IMEx human molecular interaction dataset.

CONCLUSION

This represents a significant enhancement in the expert curated data pertinent to Alzheimer's disease available in a number of biomedical databases. Relevant protein entries have been updated in UniProtKB and concomitantly in the Gene Ontology. Molecular interaction networks have been significantly extended in the IMEx Consortium dataset and a set of reference protein complexes created. All the resources described are open-source and freely available to the research community and we provide examples of how these data could be exploited by researchers.

Vascular PPARβ/δ Promotes Tumor Angiogenesis and Progression

Peroxisome proliferator-activated receptors (PPARs) are nuclear receptors, which function as transcription factors. Among them, PPARβ/δ is highly expressed in endothelial cells. Pharmacological activation with PPARβ/δ agonists had been shown to increase their angiogenic properties. PPARβ/δ has been suggested to be involved in the regulation of the angiogenic switch in tumor progression. However, until now, it is not clear to what extent the expression of PPARβ/δ in tumor endothelium influences tumor progression and metastasis formation. We addressed this question using transgenic mice with an inducible conditional vascular-specific overexpression of PPARβ/δ. Following specific over-expression of PPARβ/δ in endothelial cells, we induced syngenic tumors. We observed an enhanced tumor growth, a higher vessel density, and enhanced metastasis formation in the tumors of animals with vessel-specific overexpression of PPARβ/δ. In order to identify molecular downstream targets of PPARβ/δ in the tumor endothelium, we sorted endothelial cells from the tumors and performed RNA sequencing. We identified platelet-derived growth factor receptor beta (Pdgfrb), platelet-derived growth factor subunit B (Pdgfb), and the tyrosinkinase KIT (c-Kit) as new PPARβ/δ -dependent molecules. We show here that PPARβ/δ activation, regardless of its action on different cancer cell types, leads to a higher tumor vascularization which favors tumor growth and metastasis formation.

PHI-Nets: A Network Resource for Ascomycete Fungal Pathogens to Annotate and Identify Putative Virulence Interacting Proteins and siRNA Targets

Interactions between proteins underlie all aspects of complex biological mechanisms. Therefore, methodologies based on complex network analyses can facilitate identification of promising candidate genes involved in phenotypes of interest and put this information into appropriate contexts. To facilitate discovery and gain additional insights into globally important pathogenic fungi, we have reconstructed computationally inferred interactomes using an interolog and domain-based approach for 15 diverse Ascomycete fungal species, across nine orders, specifically Aspergillus fumigatus, Bipolaris sorokiniana, Blumeria graminis f. sp. hordei, Botrytis cinerea, Colletotrichum gloeosporioides, Colletotrichum graminicola, Fusarium graminearum, Fusarium oxysporum f. sp. lycopersici, Fusarium verticillioides, Leptosphaeria maculans, Magnaporthe oryzae, Saccharomyces cerevisiae, Sclerotinia sclerotiorum, Verticillium dahliae, and Zymoseptoria tritici. Network cartography analysis was associated with functional patterns of annotated genes linked to the disease-causing ability of each pathogen. In addition, for the best annotated organism, namely F. graminearum, the distribution of annotated genes with respect to network structure was profiled using a random walk with restart algorithm, which suggested possible co-location of virulence-related genes in the protein–protein interaction network. In a second ‘use case’ study involving two networks, namely B. cinerea and F. graminearum, previously identified small silencing plant RNAs were mapped to their targets. The F. graminearum phenotypic network analysis implicates eight B. cinerea targets and 35 F. graminearum predicted interacting proteins as prime candidate virulence genes for further testing. All 15 networks have been made accessible for download at www.phi-base.org providing a rich resource for major crop plant pathogens.

Proteogenomic Network Analysis of Context-Specific KRAS Signaling in Mouse-to-Human Cross-Species Translation

The highest frequencies of KRAS mutations occur in colorectal carcinoma (CRC) and pancreatic ductal adenocarcinoma (PDAC). The ability to target downstream pathways mediating KRAS oncogenicity is limited by an incomplete understanding of the contextual cues modulating the signaling output of activated K-RAS. We performed mass spectrometry on mouse tissues expressing wild-type or mutant Kras to determine how tissue context and genetic background modulate oncogenic signaling. Mutant Kras dramatically altered the proteomes and phosphoproteomes of preneoplastic and neoplastic colons and pancreases in a context-specific manner. We developed an approach to statistically humanize the mouse networks with data from human cancer and identified genes within the humanized CRC and PDAC networks synthetically lethal with mutant KRAS. Our studies demonstrate the context-dependent plasticity of oncogenic signaling, identify non-canonical mediators of KRAS oncogenicity within the KRAS-regulated signaling network, and demonstrate how statistical integration of mouse and human datasets can reveal cross-species therapeutic insights.

Classifying tumors by supervised network propagation

Motivation

Network propagation has been widely used to aggregate and amplify the effects of tumor mutations using knowledge of molecular interaction networks. However, propagating mutations through interactions irrelevant to cancer leads to erosion of pathway signals and complicates the identification of cancer subtypes.

Results

To address this problem we introduce a propagation algorithm, Network-Based Supervised Stratification (NBS²), which learns the mutated subnetworks underlying tumor subtypes using a supervised approach. Given an annotated molecular network and reference tumor mutation profiles for which subtypes have been predefined, NBS² is trained by adjusting the weights on interaction features such that network propagation best recovers the provided subtypes. After training, weights are fixed such that mutation profiles of new tumors can be accurately classified. We evaluate NBS² on breast and glioblastoma tumors, demonstrating that it outperforms the best network-based approaches in classifying tumors to known subtypes for these diseases. By interpreting the interaction weights, we highlight characteristic molecular pathways driving selected subtypes.

Availability and implementation

The NBS² package is freely available at: https://github.com/wzhang1984/NBSS.

Supplementary information

Supplementary data are available at Bioinformatics online.

Methodologies and Applications of Proteomics for Study of Yeast Strains: An Update

Yeasts are one of the mostly used microorganisms as models in several studies. A wide range of applications in different processes can be attributed to their intrinsic characteristics. They are eukaryotes and therefore valuable expression hosts that require elaborate post-translational modifications. Their arsenal of proteins has become a valuable biochemical tool for the catalysis of several reactions of great value to the food (beverages), pharmaceutical and energy industries. Currently, the main challenge in systemic yeast biology is the understanding of the expression, function and regulation of the protein pool encoded by such microorganisms. In this review, we will provide an overview of the proteomic methodologies used in the analysis of yeasts. This research focuses on the advantages and improvements in their most recent applications with an understanding of the functionality of the proteins of these microorganisms, as well as an update of the advances of methodologies employed in mass spectrometry.

A bioinformatics workflow to decipher transcriptomic data from vitamin D studies

The link between the experimental laboratory studies and bioinformatic approaches aims to find procedures to connect tools from both branches producing workflows that bring together different techniques that are capable of exploiting data at many levels. Thanks to the open access sources and the numerous tools available, it is possible to create various pipelines capable of solving specific problems. Nevertheless the lack of connectivity between them that interconnect different approaches complicates the exploitation of these workflows. Here, we present a detailed description of a workflow composed of different bioinformatics tools that exploits data from large-scale gene expression experiments, contextualizing them at many biological levels. To illustrate the relevance of our workflow for the vitamin D community we applied it to data from myeloid cell models treated with the hormonally active form of vitamin. From raw files of functional genomic studies it is possible to utilize the whole information to obtain a biological insight. Different software and algorithms are included to analyse at pathway, metabolic, ontology and molecular biology level the effects on gene expression. The usage of different databases to analyse gene expression data allows to perform a complete interpretation of functional genomic studies and the implementation of analysis and visualization software tools gives a better understanding of the biological meaning of the results. This review is an example of how to select and bring together several software modules to create one pipeline that processes and analyses genomic data at many biological levels making it open, reproducible and user friendly. Finally, the application of our bioinformatic pipeline revealed that vitamin D modulates crucial metabolic pathways in different myeloid cells that may play an important role in their immune function.

New perspectives: systems medicine in cardiovascular disease

Background

Cardiovascular diseases (CVD) represent one of the most important causes of morbidity and mortality worldwide. Innovative approaches to increase the understanding of the underpinnings of CVD promise to enhance CVD risk assessment and might pave the way to tailored therapies. Within the last years, systems medicine has emerged as a novel tool to study the genetic, molecular and physiological interactions.

Conclusion

In this review, we provide an overview of the current molecular-experimental, epidemiological and bioinformatical tools applied in systems medicine in the cardiovascular field. We will discuss the status and challenges in implementing interdisciplinary systems medicine approaches in CVD.

Systematic Evaluation of Molecular Networks for Discovery of Disease Genes

Gene networks are rapidly growing in size and number, raising the question of which networks are most appropriate for particular applications. Here, we evaluate 21 human genome-wide interaction networks for their ability to recover 446 disease gene sets identified through literature curation, gene expression profiling, or genome-wide association studies. While all networks have some ability to recover disease genes, we observe a wide range of performance with STRING, ConsensusPathDB, and GIANT networks having the best performance overall. A general tendency is that performance scales with network size, suggesting that new interaction discovery currently outweighs the detrimental effects of false positives. Correcting for size, we find that the DIP network provides the highest efficiency (value per interaction). Based on these results, we create a parsimonious composite network with both high efficiency and performance. This work provides a benchmark for selection of molecular networks in human disease research.

NSAID use and somatic exomic mutations in Barrett's esophagus

Background

Use of aspirin and other non-steroidal anti-inflammatory drugs (NSAIDs) has been shown to protect against tetraploidy, aneuploidy, and chromosomal alterations in the metaplastic condition Barrett’s esophagus (BE) and to lower the incidence and mortality of esophageal adenocarcinoma (EA). The esophagus is exposed to both intrinsic and extrinsic mutagens resulting from gastric reflux, chronic inflammation, and exposure to environmental carcinogens such as those found in cigarettes. Here we test the hypothesis that NSAID use inhibits accumulation of point mutations/indels during somatic genomic evolution in BE.

Methods

Whole exome sequences were generated from 82 purified epithelial biopsies and paired blood samples from a cross-sectional study of 41 NSAID users and 41 non-users matched by sex, age, smoking, and continuous time using or not using NSAIDs.

Results

NSAID use reduced overall frequency of point mutations across the spectrum of mutation types, lowered the frequency of mutations even when adjusted for both TP53 mutation and smoking status, and decreased the prevalence of clones with high variant allele frequency. Never smokers who consistently used NSAIDs had fewer point mutations in signature 17, which is commonly found in EA. NSAID users had, on average, a 50% reduction in functional gene mutations in nine cancer-associated pathways and also had less diversity in pathway mutational burden compared to non-users.

Conclusions

These results indicate NSAID use functions to limit overall mutations on which selection can act and supports a model in which specific mutant cell populations survive or expand better in the absence of NSAIDs.

Electronic supplementary material

The online version of this article (10.1186/s13073-018-0520-y) contains supplementary material, which is available to authorized users.

NDEx 2.0: A Clearinghouse for Research on Cancer Pathways

We present NDEx 2.0, the latest release of the Network Data Exchange (NDEx) online data commons (www.ndexbio.org) and the ways in which it can be used to (i) improve the quality and abundance of biological networks relevant to the cancer research community; (ii) provide a medium for collaboration involving networks; and (iii) facilitate the review and dissemination of networks. We describe innovations addressing the challenges of an online data commons: scalability, data integration, data standardization, control of content and format by authors, and decentralized mechanisms for review. The practical use of NDEx is presented in the context of a novel strategy to foster network-oriented communities of interest in cancer research by adapting methods from academic publishing and social media. Cancer Res; 77(21); e58-61. ©2017 AACR.

Graphettes: Constant-time determination of graphlet and orbit identity including (possibly disconnected) graphlets up to size 8

Graphlets are small connected induced subgraphs of a larger graph G. Graphlets are now commonly used to quantify local and global topology of networks in the field. Methods exist to exhaustively enumerate all graphlets (and their orbits) in large networks as efficiently as possible using orbit counting equations. However, the number of graphlets in G is exponential in both the number of nodes and edges in G. Enumerating them all is already unacceptably expensive on existing large networks, and the problem will only get worse as networks continue to grow in size and density. Here we introduce an efficient method designed to aid statistical sampling of graphlets up to size k = 8 from a large network. We define graphettes as the generalization of graphlets allowing for disconnected graphlets. Given a particular (undirected) graphette g, we introduce the idea of the canonical graphette [Formula: see text] as a representative member of the isomorphism group Iso(g) of g. We compute the mapping [Formula: see text], in the form of a lookup table, from all 2k(k - 1)/2 undirected graphettes g of size k ≤ 8 to their canonical representatives [Formula: see text], as well as the permutation that transforms g to [Formula: see text]. We also compute all automorphism orbits for each canonical graphette. Thus, given any k ≤ 8 nodes in a graph G, we can in constant time infer which graphette it is, as well as which orbit each of the k nodes belongs to. Sampling a large number N of such k-sets of nodes provides an approximation of both the distribution of graphlets and orbits across G, and the orbit degree vector at each node.