PathWalks: identifying pathway communities using a disease-related map of integrated information

Abstract Motivation Understanding the underlying biological mechanisms and respective interactions of a disease remains an elusive, time consuming and costly task. Computational methodologies that propose pathway/mechanism communities and reveal respective relationships can be of great value as they can help expedite the process of identifying how perturbations in a single pathway can affect other pathways. Results We present a random-walks-based methodology called PathWalks, where a walker crosses a pathway-to-pathway network under the guidance of a disease-related map. The latter is a gene network that we construct by integrating multi-source information regarding a specific disease. The most frequent trajectories highlight communities of pathways that are expected to be strongly related to the disease under study. We apply the PathWalks methodology on Alzheimer's disease and idiopathic pulmonary fibrosis and establish that it can highlight pathways that are also identified by other pathway analysis tools as well as are backed through bibliographic references. More importantly, PathWalks produces additional new pathways that are functionally connected with those already established, giving insight for further experimentation. Availability and implementation https://github.com/vagkaratzas/PathWalks. Supplementary information Supplementary data are available at Bioinformatics online.

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PathWalks: Identifying pathway communities using a disease-related map of integrated information

10.1101/2020.01.27.921270 ◽

2020 ◽

Cited By ~ 1

Author(s):

Evangelos Karatzas ◽

Margarita Zachariou ◽

Marilena Bourdakou ◽

George Minadakis ◽

Anastasios Oulas ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Idiopathic Pulmonary Fibrosis ◽

Pulmonary Fibrosis ◽

Random Walks ◽

Mapk Signaling ◽

Strong Interactions ◽

Receptor Interaction ◽

Receptor Signaling ◽

Source Information

AbstractUnderstanding disease underlying biological mechanisms and respective interactions remains an elusive, time consuming and costly task. The realization of computational methodologies that can propose pathway/mechanism communities and reveal respective relationships can be of great value as it can help expedite the process of identifying how perturbations in a single pathway can affect other pathways.Random walks is a stochastic approach that can be used for both efficient discovery of strong connections and identification of communities formed in networks. The approach has grown in popularity as it efficiently exposes key network components and reveals strong interactions among genes, proteins, metabolites, pathways and drugs. Using random walks in biology, we need to overcome two key challenges: 1) construct disease-specific biological networks by integrating information from available data sources as they become available, and 2) provide guidance to the walker so as it can follow plausible trajectories that comply with inherent biological constraints.In this work, we present a methodology called PathWalks, where a random walker crosses a pathway-to-pathway network under the guidance of a disease-related map. The latter is a gene network that we construct by integrating multi-source information regarding a specific disease. The most frequent trajectories highlight communities of pathways that are expected to be strongly related to the disease under study. We present maps for Alzheimer’s Disease and Idiopathic Pulmonary Fibrosis and we use them as case-studies for identifying pathway communities through the application of PathWalks.In the case of Alzheimer’s Disease, the most visited pathways are the “Alzheimer’s disease” and the “Calcium signaling” pathways which have indeed the strongest association with Alzheimer’s Disease. Interestingly however, in the top-20 visited pathways we identify the “Kaposi sarcoma-associated herpesvirus infection” (HHV-8) and the “Human papillomavirus infection” (HPV) pathways suggesting that viruses may be involved in the development and progression of Alzheimer’s. Similarly, most of the highlighted pathways in Idiopathic Pulmonary Fibrosis are backed by the bibliography. We establish that “MAPK signaling” and “Cytokine-cytokine receptor interaction” pathways are the most visited. However, the “NOD receptor signaling” pathway is also in the top-40 edges. In Idiopathic Pulmonary Fibrosis samples, increased NOD receptor signaling has been associated with augmented concentrations of certain strains of Streptococcus. Additional experimental evidence is required however to further explore and ascertain the above indications.

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Reviews and prospectives of signaling pathway analysis in idiopathic pulmonary fibrosis

Autoimmunity Reviews ◽

10.1016/j.autrev.2014.08.028 ◽

2014 ◽

Vol 13 (10) ◽

pp. 1020-1025 ◽

Cited By ~ 39

Author(s):

Zheng Yan ◽

Zhang kui ◽

Zhu Ping

Keyword(s):

Idiopathic Pulmonary Fibrosis ◽

Pulmonary Fibrosis ◽

Signaling Pathway ◽

Pathway Analysis

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PAFway: pairwise associations between functional annotations in biological networks and pathways

Bioinformatics ◽

10.1093/bioinformatics/btaa639 ◽

2020 ◽

Vol 36 (19) ◽

pp. 4963-4964

Author(s):

Mahiar Mahjoub ◽

Daphne Ezer

Keyword(s):

Arabidopsis Thaliana ◽

Biological Networks ◽

Gene Networks ◽

Gene Network ◽

Supplementary Information ◽

Supplementary Data ◽

Specific Function ◽

Biological Functions ◽

Functional Annotations ◽

Large Gene

Abstract Motivation Large gene networks can be dense and difficult to interpret in a biologically meaningful way. Results Here, we introduce PAFway, which estimates pairwise associations between functional annotations in biological networks and pathways. It answers the biological question: do genes that have a specific function tend to regulate genes that have a different specific function? The results can be visualized as a heatmap or a network of biological functions. We apply this package to reveal associations between functional annotations in an Arabidopsis thaliana gene network. Availability and implementation PAFway is submitted to CRAN. Currently available here: https://github.com/ezer/PAFway. Supplementary information Supplementary data are available at Bioinformatics online.

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Use of Chou’s 5-Steps Rule to Reveal Active Compound and Mechanism of Shuangshen Pingfei San on Idiopathic Pulmonary Fibrosis

Current Molecular Medicine ◽

10.2174/1566524019666191011160543 ◽

2020 ◽

Vol 20 (3) ◽

pp. 220-230 ◽

Cited By ~ 1

Author(s):

Yeqing Chen ◽

Xinsheng Fan

Keyword(s):

Idiopathic Pulmonary Fibrosis ◽

Pulmonary Fibrosis ◽

Active Compound ◽

Interaction Network ◽

Core Gene ◽

Tanshinone Iia ◽

Gene Target ◽

Active Compounds ◽

Pathway Network ◽

Target Network

Background: Shuangshen Pingfei San (SPS) is the derivative from the classic formula Renshen Pingfei San in treating idiopathic pulmonary fibrosis (IPF). Methods: In this study, Chou’s 5-steps rule was performed to explore the potential active compound and mechanism of SPS on IPF. Compound–target network, target– pathway network, herb–target network and the core gene target interaction network were established and analyzed. A total of 296 compounds and 69 candidate therapeutic targets of SPS in treating IPF were obtained. Network analysis revealed that the main active compounds were flavonoids (such as apigenin, quercetin, naringenin, luteolin), other clusters (such as ginsenoside Rh2, diosgenin, tanshinone IIa), which might also play significant roles. SPS regulated multiple IPF relative genes, which affect fibrosis (PTGS2, KDR, FGFR1, TGFB, VEGFA, MMP2/9) and inflammation (PPARG, TNF, IL13, IL4, IL1B, etc.). Conclusion: In conclusion, anti-pulmonary fibrosis effect of SPS might be related to the regulation of inflammation and pro-fibrotic signaling pathways. These findings revealed that the potential active compounds and mechanisms of SPS on IPF were a benefit to further study.

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Genes with high network connectivity are enriched for disease heritability

10.1101/442582 ◽

2018 ◽

Author(s):

Samuel S. Kim ◽

Chengzhen Dai ◽

Farhad Hormozdiari ◽

Bryce van de Geijn ◽

Steven Gazal ◽

...

Keyword(s):

Complex Traits ◽

Gene Networks ◽

Gene Network ◽

Network Connectivity ◽

Biological Mechanisms ◽

Pathway Network ◽

Network Analyses ◽

Disease Biology ◽

Significant Pathway

AbstractRecent studies have highlighted the role of gene networks in disease biology. To formally assess this, we constructed a broad set of pathway, network, and pathway+network annotations and applied stratified LD score regression to 42 independent diseases and complex traits (average N=323K) to identify enriched annotations. First, we constructed annotations from 18,119 biological pathways, including 100kb windows around each gene. We identified 156 pathway-trait pairs whose disease enrichment was statistically significant (FDR < 5%) after conditioning on all genes and on annotations from the baseline-LD model, a stringent step that greatly reduced the number of pathways detected; most of the significant pathway-trait pairs were previously unreported. Next, for each of four published gene networks, we constructed probabilistic annotations based on network connectivity using closeness centrality, a measure of how close a gene is to other genes in the network. For each gene network, the network connectivity annotation was strongly significantly enriched. Surprisingly, the enrichments were fully explained by excess overlap between network annotations and regulatory annotations from the baseline-LD model, validating the informativeness of the baseline-LD model and emphasizing the importance of accounting for regulatory annotations in gene network analyses. Finally, for each of the 156 enriched pathway-trait pairs, for each of the four gene networks, we constructed pathway+network annotations by annotating genes with high network connectivity to the input pathway. For each gene network, these pathway+network annotations were strongly significantly enriched for the corresponding traits. Once again, the enrichments were largely explained by the baseline-LD model. In conclusion, gene network connectivity is highly informative for disease architectures, but the information in gene networks may be subsumed by regulatory annotations, such that accounting for known annotations is critical to robust inference of biological mechanisms.

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PAPA: a flexible tool for identifying pleiotropic pathways using genome-wide association study summaries

Bioinformatics ◽

10.1093/bioinformatics/btv668 ◽

2015 ◽

Vol 32 (6) ◽

pp. 946-948 ◽

Cited By ~ 5

Author(s):

Yan Wen ◽

Wenyu Wang ◽

Xiong Guo ◽

Feng Zhang

Keyword(s):

Body Mass Index ◽

Association Study ◽

Pathway Analysis ◽

Genome Wide Association Study ◽

Genome Wide Association ◽

Supplementary Information ◽

Analysis Tool ◽

Supplementary Data ◽

Flexible Tool ◽

Genome Wide

Abstract Summary: Pleiotropy is common in the genetic architectures of complex diseases. To the best of our knowledge, no analysis tool has been developed for identifying pleiotropic pathways using multiple genome-wide association study (GWAS) summaries by now. Here, we present PAPA, a flexible tool for pleiotropic pathway analysis utilizing GWAS summary results. The performance of PAPA was validated using publicly available GWAS summaries of body mass index and waist-hip ratio of the GIANT datasets. PAPA identified a set of pleiotropic pathways, which have been demonstrated to be involved in the development of obesity. Availability and implementation : PAPA program, document and illustrative example are available at http://sourceforge.net/projects/papav1/files/. Contact : [email protected] Supplementary information: Supplementary data are available at Bioinformatics online.

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