scholarly journals Assembling Disease Networks From Causal Interaction Resources

2021 ◽  
Vol 12 ◽  
Author(s):  
Gianni Cesareni ◽  
Francesca Sacco ◽  
Livia Perfetto
Keyword(s):  
Biological Networks ◽  
Expression Profiles ◽  
Directed Graphs ◽  
Protein A ◽  
Boolean Networks ◽  
Disease Networks ◽  
Data Content ◽  
Functional Consequences ◽  
Or Gene ◽  
Network Approaches

The development of high-throughput high-content technologies and the increased ease in their application in clinical settings has raised the expectation of an important impact of these technologies on diagnosis and personalized therapy. Patient genomic and expression profiles yield lists of genes that are mutated or whose expression is modulated in specific disease conditions. The challenge remains of extracting from these lists functional information that may help to shed light on the mechanisms that are perturbed in the disease, thus setting a rational framework that may help clinical decisions. Network approaches are playing an increasing role in the organization and interpretation of patients' data. Biological networks are generated by connecting genes or gene products according to experimental evidence that demonstrates their interactions. Till recently most approaches have relied on networks based on physical interactions between proteins. Such networks miss an important piece of information as they lack details on the functional consequences of the interactions. Over the past few years, a number of resources have started collecting causal information of the type protein A activates/inactivates protein B, in a structured format. This information may be represented as signed directed graphs where physiological and pathological signaling can be conveniently inspected. In this review we will (i) present and compare these resources and discuss the different scope in comparison with pathway resources; (ii) compare resources that explicitly capture causality in terms of data content and proteome coverage (iii) review how causal-graphs can be used to extract disease-specific Boolean networks.

scholarly journals de novo identification of maximally deregulated subnetworks based on multi-omics data with DeRegNet

2021 ◽  
Author(s):  
Sebastian S Winkler ◽  
Ivana Winkler ◽  
Mirjam Figaschewski ◽  
Thorsten Tiede ◽  
Alfred Nordheim ◽  
...  
Keyword(s):  
Biological Networks ◽  
De Novo ◽  
Directed Graphs ◽  
Simulated Data ◽  
Likelihood Estimation ◽  
Difficult Problem ◽  
Omics Data ◽  
Cancer Dataset ◽  
Gene Sets ◽  
Or Gene

With a growing amount of (multi-)omics data being available, the extraction of knowledge from these datasets is still a difficult problem. Classical enrichment-style analyses require predefined pathways or gene sets that are tested for significant deregulation to assess whether the pathway is functionally involved in the biological process under study. De novo identification of these pathways can reduce the bias inherent in predefined pathways or gene sets. At the same time, the definition and efficient identification of these pathways de novo from large biological networks is a challenging problem. We present a novel algorithm, DeRegNet, for the identification of maximally deregulated subnetworks on directed graphs based on deregulation scores derived from (multi-)omics data. DeRegNet can be interpreted as maximum likelihood estimation given a certain probabilistic model for de-novo subgraph identification. We use fractional integer programming to solve the resulting combinatorial optimization problem. We can show that the approach outperforms related algorithms on simulated data with known ground truths. On a publicly available liver cancer dataset we can show that DeRegNet can identify biologically meaningful subgraphs suitable for patient stratification. DeRegNet is freely available as open-source software.

Analysis of Protein-Protein Interaction Networks through Computational Approaches

2020 ◽  
Vol 27 (4) ◽  
pp. 265-278 ◽  
Author(s):  
Ying Han ◽  
Liang Cheng ◽  
Weiju Sun
Keyword(s):  
Protein Interaction ◽  
Biological Networks ◽  
Interaction Networks ◽  
Computational Techniques ◽  
Cellular Functions ◽  
Protein Protein Interaction ◽  
Comprehensive Information ◽  
Protein Interaction Prediction ◽  
Or Gene ◽  
Experimental Findings

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

Algorithmic and Stochastic Representations of Gene Regulatory Networks and Protein-Protein Interactions

2019 ◽  
Vol 19 (6) ◽  
pp. 413-425 ◽  
Author(s):  
Athanasios Alexiou ◽  
Stylianos Chatzichronis ◽  
Asma Perveen ◽  
Abdul Hafeez ◽  
Ghulam Md. Ashraf
Keyword(s):  
Protein Interactions ◽  
Biological Networks ◽  
Regulatory Networks ◽  
Review Paper ◽  
Boolean Networks ◽  
Diagnostic Tools ◽  
Complex Nature ◽  
Cellular Interactions ◽  
Protein Protein Interactions ◽  
Deterministic Models

Background:Latest studies reveal the importance of Protein-Protein interactions on physiologic functions and biological structures. Several stochastic and algorithmic methods have been published until now, for the modeling of the complex nature of the biological systems.Objective:Biological Networks computational modeling is still a challenging task. The formulation of the complex cellular interactions is a research field of great interest. In this review paper, several computational methods for the modeling of GRN and PPI are presented analytically.Methods:Several well-known GRN and PPI models are presented and discussed in this review study such as: Graphs representation, Boolean Networks, Generalized Logical Networks, Bayesian Networks, Relevance Networks, Graphical Gaussian models, Weight Matrices, Reverse Engineering Approach, Evolutionary Algorithms, Forward Modeling Approach, Deterministic models, Static models, Hybrid models, Stochastic models, Petri Nets, BioAmbients calculus and Differential Equations.Results:GRN and PPI methods have been already applied in various clinical processes with potential positive results, establishing promising diagnostic tools.Conclusion:In literature many stochastic algorithms are focused in the simulation, analysis and visualization of the various biological networks and their dynamics interactions, which are referred and described in depth in this review paper.

scholarly journals Community Detection in Large-Scale Bipartite Biological Networks

2021 ◽  
Vol 12 ◽  
Author(s):  
Genís Calderer ◽  
Marieke L. Kuijjer
Keyword(s):  
Biological Networks ◽  
Large Scale ◽  
Interaction Network ◽  
Gene Interaction ◽  
Community Structures ◽  
Gene Interaction Network ◽  
Structure Detection ◽  
Wide Range ◽  
Disease Associations ◽  
Or Gene

Networks are useful tools to represent and analyze interactions on a large, or genome-wide scale and have therefore been widely used in biology. Many biological networks—such as those that represent regulatory interactions, drug-gene, or gene-disease associations—are of a bipartite nature, meaning they consist of two different types of nodes, with connections only forming between the different node sets. Analysis of such networks requires methodologies that are specifically designed to handle their bipartite nature. Community structure detection is a method used to identify clusters of nodes in a network. This approach is especially helpful in large-scale biological network analysis, as it can find structure in networks that often resemble a “hairball” of interactions in visualizations. Often, the communities identified in biological networks are enriched for specific biological processes and thus allow one to assign drugs, regulatory molecules, or diseases to such processes. In addition, comparison of community structures between different biological conditions can help to identify how network rewiring may lead to tissue development or disease, for example. In this mini review, we give a theoretical basis of different methods that can be applied to detect communities in bipartite biological networks. We introduce and discuss different scores that can be used to assess the quality of these community structures. We then apply a wide range of methods to a drug-gene interaction network to highlight the strengths and weaknesses of these methods in their application to large-scale, bipartite biological networks.

scholarly journals MicroRNA-132-3p suppresses type I IFN response through targeting IRF1 to facilitate H1N1 influenza A virus infection

2019 ◽  
Vol 39 (12) ◽  
Author(s):  
Fangyi Zhang ◽  
Xuefeng Lin ◽  
Xiaodong Yang ◽  
Guangjian Lu ◽  
Qunmei Zhang ◽  
...  
Keyword(s):  
Influenza A Virus ◽  
Peripheral Blood ◽  
Influenza A ◽  
Expression Profiles ◽  
Protein A ◽  
H1n1 Influenza ◽  
Type I ◽  
Dependent Manner ◽  
Type I Ifn

Abstract Increasing evidence has indicated that microRNAs (miRNAs) have essential roles in innate immune responses to various viral infections; however, the role of miRNAs in H1N1 influenza A virus (IAV) infection is still unclear. The present study aimed to elucidate the role and mechanism of miRNAs in IAV replication in vitro. Using a microarray assay, we analyzed the expression profiles of miRNAs in peripheral blood from IAV patients. It was found that miR-132-3p was significantly up-regulated in peripheral blood samples from IAV patients. It was also observed that IAV infection up-regulated the expression of miR-132-3p in a dose- and time-dependent manner. Subsequently, we investigated miR-132-3p function and found that up-regulation of miR-132-3p promoted IAV replication, whereas knockdown of miR-132-3p repressed replication. Meanwhile, overexpression of miR-132-3p could inhibit IAV triggered INF-α and INF-β production and IFN-stimulated gene (ISG) expression, including myxovirus protein A (MxA), 2′,5′-oligoadenylate synthetases (OAS), and double-stranded RNA-dependent protein kinase (PKR), while inhibition of miR-132-3p enhanced IAV triggered these effects. Of note, interferon regulatory factor 1 (IRF1), a well-known regulator of the type I IFN response, was identified as a direct target of miR-132-3p during HIN1 IAV infection. Furthermore, knockdown of IRF1 by si-IRF1 reversed the promoting effects of miR-132-3p inhibition on type I IFN response. Taken together, up-regulation of miR-132-3p promotes IAV replication by suppressing type I IFN response through its target gene IRF1, suggesting that miR-132-3p could represent a novel potential therapeutic target of IAV treatment.

scholarly journals Deriving a Boolean dynamics to reveal macrophage activation with in vitro temporal cytokine expression profiles

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Ricardo Ramirez ◽  
Allen Michael Herrera ◽  
Joshua Ramirez ◽  
Chunjiang Qian ◽  
David W. Melton ◽  
...  
Keyword(s):  
Boolean Network ◽  
Macrophage Activation ◽  
Expression Profiles ◽  
Network Models ◽  
Cytokine Expression ◽  
Boolean Networks ◽  
Experimental Results ◽  
Activated Macrophages ◽  
Activation Patterns

Abstract Background Macrophages show versatile functions in innate immunity, infectious diseases, and progression of cancers and cardiovascular diseases. These versatile functions of macrophages are conducted by different macrophage phenotypes classified as classically activated macrophages and alternatively activated macrophages due to different stimuli in the complex in vivo cytokine environment. Dissecting the regulation of macrophage activations will have a significant impact on disease progression and therapeutic strategy. Mathematical modeling of macrophage activation can improve the understanding of this biological process through quantitative analysis and provide guidance to facilitate future experimental design. However, few results have been reported for a complete model of macrophage activation patterns. Results We globally searched and reviewed literature for macrophage activation from PubMed databases and screened the published experimental results. Temporal in vitro macrophage cytokine expression profiles from published results were selected to establish Boolean network models for macrophage activation patterns in response to three different stimuli. A combination of modeling methods including clustering, binarization, linear programming (LP), Boolean function determination, and semi-tensor product was applied to establish Boolean networks to quantify three macrophage activation patterns. The structure of the networks was confirmed based on protein-protein-interaction databases, pathway databases, and published experimental results. Computational predictions of the network evolution were compared against real experimental results to validate the effectiveness of the Boolean network models. Conclusion Three macrophage activation core evolution maps were established based on the Boolean networks using Matlab. Cytokine signatures of macrophage activation patterns were identified, providing a possible determination of macrophage activations using extracellular cytokine measurements.

scholarly journals Pregnancy-Associated Plasma Protein-A (PAPP-A) in Ewing Sarcoma: Role in Tumor Growth and Immune Evasion

2019 ◽  
Vol 111 (9) ◽  
pp. 970-982 ◽  
Author(s):  
Sabine Heitzeneder ◽  
Elena Sotillo ◽  
Jack F Shern ◽  
Sivasish Sindiri ◽  
Peng Xu ◽  
...  
Keyword(s):  
Cell Surface ◽  
Cell Lines ◽  
Plasma Protein ◽  
Immune Evasion ◽  
Ewing Sarcoma ◽  
Antigen Processing ◽  
Expression Profiles ◽  
Statistical Tests ◽  
Protein A ◽  
Differentially Expressed

AbstractBackgroundEwing sarcoma (EWS) manifests one of the lowest somatic mutation rates of any cancer, leading to a scarcity of druggable mutations and neoantigens. Immunotherapeutics targeting differentially expressed cell surface antigens could provide therapeutic benefit for such tumors. Pregnancy-associated plasma protein A (PAPP-A) is a cell membrane-associated proteinase produced by the placenta that promotes fetal growth by inducing insulinlike growth factor (IGF) signaling.MethodsBy comparing RNA expression of cell surface proteins in EWS (n = 120) versus normal tissues (n = 42), we comprehensively characterized the surfaceome of EWS to identify highly differentially expressed molecules. Using CRISPR/Cas-9 and anti-PAPP-A antibodies, we investigated biological roles for PAPP-A in EWS in vitro and in vivo in NSG xenograft models and performed RNA-sequencing on PAPPA knockout clones (n = 5) and controls (n = 3). All statistical tests were two-sided.ResultsEWS surfaceome analysis identified 11 highly differentially overexpressed genes, with PAPPA ranking second in differential expression. In EWS cell lines, genetic knockout of PAPPA and treatment with anti-PAPP-A antibodies revealed an essential survival role by regulating local IGF-1 bioavailability. MAb-mediated PAPPA inhibition diminished EWS growth in orthotopic xenografts (leg area mm2 at day 49 IgG2a control (CTRL) [n = 14], mean = 397.0, SD = 86.1 vs anti-PAPP-A [n = 14], mean = 311.7, SD = 155.0; P = .03; median OS anti-PAPP-A = 52.5 days, 95% CI = 46.0 to 63.0 days vs IgG2a = 45.0 days, 95% CI = 42.0 to 52.0 days; P = .02) and improved the efficacy of anti-IGF-1R treatment (leg area mm2 at day 49 anti-PAPP-A + anti-IGF-1R [n = 15], mean = 217.9, SD = 148.5 vs IgG2a-CTRL; P < .001; median OS anti-PAPP-A + anti-IGF1R = 63.0 days, 95% CI = 52.0 to 67.0 days vs IgG2a-CTRL; P < .001). Unexpectedly, PAPPA knockout in EWS cell lines induced interferon (IFN)-response genes, including proteins associated with antigen processing/presentation. Consistently, gene expression profiles in PAPPA-low EWS tumors were enriched for immune response pathways.ConclusionThis work provides a comprehensive characterization of the surfaceome of EWS, credentials PAPP-A as a highly differentially expressed therapeutic target, and discovers a novel link between IGF-1 signaling and immune evasion in cancer, thus implicating shared mechanisms of immune evasion between EWS and the placenta.

Temporal and spatial transcriptional programs in murine kidney development

2005 ◽  
Vol 23 (2) ◽  
pp. 159-171 ◽  
Author(s):  
G. Challen ◽  
B. Gardiner ◽  
G. Caruana ◽  
X. Kostoulias ◽  
G. Martinez ◽  
...  
Keyword(s):  
Spatial Data ◽  
Biological Networks ◽  
Expression Profiling ◽  
Kidney Development ◽  
Expression Profiles ◽  
Rat Kidney ◽  
Genetic Network ◽  
Metanephric Mesenchyme ◽  
Spatial Profiling ◽  
Temporal And Spatial

We have performed a systematic temporal and spatial expression profiling of the developing mouse kidney using Compugen long-oligonucleotide microarrays. The activity of 18,000 genes was monitored at 24-h intervals from 10.5-day-postcoitum (dpc) metanephric mesenchyme (MM) through to neonatal kidney, and a cohort of 3,600 dynamically expressed genes was identified. Early metanephric development was further surveyed by directly comparing RNA from 10.5 vs. 11.5 vs. 13.5dpc kidneys. These data showed high concordance with the previously published dynamic profile of rat kidney development (Stuart RO, Bush KT, and Nigam SK. Proc Natl Acad Sci USA 98: 5649–5654, 2001) and our own temporal data. Cluster analyses were used to identify gene ontological terms, functional annotations, and pathways associated with temporal expression profiles. Genetic network analysis was also used to identify biological networks that have maximal transcriptional activity during early metanephric development, highlighting the involvement of proliferation and differentiation. Differential gene expression was validated using whole mount and section in situ hybridization of staged embryonic kidneys. Two spatial profiling experiments were also undertaken. MM (10.5dpc) was compared with adjacent intermediate mesenchyme to further define metanephric commitment. To define the genes involved in branching and in the induction of nephrogenesis, expression profiling was performed on ureteric bud (GFP+) FACS sorted from HoxB7-GFP transgenic mice at 15.5dpc vs. the GFP− mesenchymal derivatives. Comparisons between temporal and spatial data enhanced the ability to predict function for genes and networks. This study provides the most comprehensive temporal and spatial survey of kidney development to date, and the compilation of these transcriptional surveys provides important insights into metanephric development that can now be functionally tested.

Old and new findings on lipopolysaccharide-binding protein: a soluble pattern-recognition molecule

2011 ◽  
Vol 39 (4) ◽  
pp. 989-993 ◽  
Author(s):  
Ralf R. Schumann
Keyword(s):  
Binding Protein ◽  
Protein A ◽  
Lipopolysaccharide Binding Protein ◽  
New Findings ◽  
Substantial Progress ◽  
Large Protein Family ◽  
Functional Consequences ◽  
Health And Disease ◽  
Lipopolysaccharide Binding ◽  
Key Questions

LBP [LPS (lipopolysaccharide)-binding protein] was discovered approximately 25 years ago. Since then, substantial progress has been made towards our understanding of its function in health and disease. Furthermore, the discovery of a large protein family sharing functional and structural attributes has helped in our knowledge. Still, key questions are unresolved, and here an overview on the old and new findings on LBP is given. LBP is an acute-phase protein of the liver, but is also synthesized in other cells of the organism. While LBP is named after the ability to bind to LPS of Gram-negative bacteria, it also can recognize other bacterial compounds, such as lipopeptides. It has been shown that LBP is needed to combat infections; however, the main mechanism of action is still not clear. New findings on natural genetic variations of LBP leading to functional consequences may help in further elucidating the mechanism of LBP and its role in innate immunity and disease.

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