CyTargetLinker app update: A flexible solution for network extension in Cytoscape

Here, we present an update of the open-source CyTargetLinker app for Cytoscape ( http://apps.cytoscape.org/apps/cytargetlinker) that introduces new automation features. CyTargetLinker provides a simple interface to extend networks with links to relevant data and/or knowledge extracted from so-called linksets. The linksets are provided on the CyTargetLinker website ( https://cytargetlinker.github.io/) or can be custom-made for specific use cases. The new automation feature enables users to programmatically execute the app's functionality in Cytoscape (command line tool) and with external tools (e.g. R, Jupyter, Python, etc). This allows users to share their analysis workflows and therefore increase repeatability and reproducibility. Three use cases demonstrate automated workflows, combinations with other Cytoscape apps and core Cytoscape functionality. We first extend a protein-protein interaction network created with the stringApp, with compound-target interactions and disease-gene annotations. In the second use case, we created a workflow to load differentially expressed genes from an experimental dataset and extend it with gene-pathway associations. Lastly, we chose an example outside the biological domain and used CyTargetLinker to create an author-article-journal network for the five authors of this manuscript using a two-step extension mechanism. With 400 downloads per month in the last year and nearly 20,000 downloads in total, CyTargetLinker shows the adoption and relevance of the app in the field of network biology. In August 2019, the original publication was cited in 83 articles demonstrating the applicability in biomedical research

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 &lt;As &lt;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.</p

Tolerogenic effects of 1,25-dihydroxyvitamin D on dendritic cells involve induction of fatty acid synthesis

The active form of vitamin D, 1,25-dihydroxyvitamin D (1,25D) is a potent regulator of immune function, promoting anti-inflammatory, tolerogenic T cell responses by modulating antigen presentation by dendritic cells (DC). Transcriptomic analyses indicate that DC responses to 1,25D involve changes in glycolysis, oxidative phosphorylation, electron transport and the TCA cycle. To determine the functional impact of 1,25D-mediated metabolic remodelling, human monocyte-derived DC were differentiated to immature (+vehicle, iDC), mature (+LPS, mDC), and immature tolerogenic DC (+1,25D, itolDC) and characterised for metabolic function. In contrast to mDC which showed no change in respiration, itolDC showed increased basal and ATP-linked respiration relative to iDC. Tracer metabolite analyses using (13)C -labeled glucose showed increased lactate and TCA cycle metabolites. Analysis of lipophilic metabolites of (13)C-glucose revealed significant incorporation of label in palmitate and palmitoleate, indicating that 1,25D promotes metabolic fatty acid synthesis in itolDC. Inhibition of fatty acid synthesis in itolDC altered itolDC morphology and suppressed expression of CD14 and IL-10 by these cells. These data indicate that the ability of 1,25D to induce tolerogenic DC involves metabolic remodelling leading to synthesis of fatty acids

Bioinformatics for the NuGO proof of principle study: analysis of gene expression in muscle of ApoE3*Leiden mice on a high-fat diet using PathVisio

Insulin resistance is a characteristic of type-2 diabetes and its development is associated with an increased fat consumption. Muscle is one of the tissues that becomes insulin resistant after high fat (HF) feeding. The aim of the present study is to identify processes involved in the development of HF-induced insulin resistance in muscle of ApOE3*Leiden mice by using microarrays. These mice are known to become insulin resistant on a HF diet. Differential gene expression was measured in muscle using the Affymetrix mouse plus 2.0 array. To get more insight in the processes, affected pathway analysis was performed with a new tool, PathVisio. PathVisio is a pathway editor customized with plug-ins (1) to visualize microarray data on pathways and (2) to perform statistical analysis to select pathways of interest. The present study demonstrated that with pathway analysis, using PathVisio, a large variety of processes can be investigated. The significantly regulated genes in muscle of ApOE3*Leiden mice after 12 weeks of HF feeding were involved in several biological pathways including fatty acid beta oxidation, fatty acid biosynthesis, insulin signaling, oxidative stress and inflammation

2D-electrophoresis and multiplex immunoassay proteomic analysis of different body fluids and cellular components reveal known and novel markers for extended fasting

Proteomic technologies applied for profiling human biofluids and blood cells are considered to reveal new biomarkers of exposure or provide insights into novel mechanisms of adaptation

Inferring causal molecular networks: empirical assessment through a community-based effort

Inferring molecular networks is a central challenge in computational biology. However, it has remained unclear whether causal, rather than merely correlational, relationships can be effectively inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge that focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results constitute the most comprehensive assessment of causal network inference in a mammalian setting carried out to date and suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess the causal validity of inferred molecular networks

Inferring causal molecular networks: empirical assessment through a community-based effort

It remains unclear whether causal, rather than merely correlational, relationships in molecular networks can be inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge, which focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective, and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess inferred molecular networks in a causal sense

Prader-Willi syndrome and Angelman syndrome:Visualisation of the molecular pathways for two chromosomal disorders

Objectives: Prader-Willi syndrome (PWS) and Angelman syndrome (AS) are two syndromes that are caused by the same chromosomal deletion on 15q11.2-q13. Due to methylation patterns, different genes are responsible for the two distinct phenotypes resulting in the disorders. Patients of both disorders exhibit hypotonia in neonatal stage, delay in development and hypopigmentation. Typical features for PWS include hyperphagia, which leads to obesity, the major cause of mortality, and hypogonadism. In AS, patients suffer from a more severe developmental delay, they have a distinctive behaviour that is often described as unnaturally happy, and a tendency for epileptic seizures. For both syndromes, we identified and visualised molecular downstream pathways of the deleted genes that could give insight on the development of the clinical features. Methods: This was done by consulting literature, genome browsers and pathway databases to identify molecular interactions and to construct downstream pathways. Results: A pathway visualisation was created and uploaded to the open pathway database WikiPathways covering all molecular pathways that were found. Conclusions: The visualisation of the downstream pathways of PWS- and AS-deleted genes shows that some of the typical symptoms are caused by multiple genes and reveals critical gaps in the current knowledge