MAPPING AND ANALYSIS OF THE HUMAN INTERACTOME NETWORK

AbstractIn this study, we integrate the outcomes of co-expression network analysis with the human interactome network to predict novel putative disease genes and modules. We first apply the SWItch Miner (SWIM) methodology, which predicts important (switch) genes within the co-expression network that regulate disease state transitions, then map them to the human protein–protein interaction network (PPI, or interactome) to predict novel disease–disease relationships (i.e., a SWIM-informed diseasome). Although the relevance of switch genes to an observed phenotype has been recently assessed, their performance at the system or network level constitutes a new, potentially fascinating territory yet to be explored. Quantifying the interplay between switch genes and human diseases in the interactome network, we found that switch genes associated with specific disorders are closer to each other than to other nodes in the network, and tend to form localized connected subnetworks. These subnetworks overlap between similar diseases and are situated in different neighborhoods for pathologically distinct phenotypes, consistent with the well-known topological proximity property of disease genes. These findings allow us to demonstrate how SWIM-based correlation network analysis can serve as a useful tool for efficient screening of potentially new disease gene associations. When integrated with an interactome-based network analysis, it not only identifies novel candidate disease genes, but also may offer testable hypotheses by which to elucidate the molecular underpinnings of human disease and reveal commonalities between seemingly unrelated diseases.

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A network-based approach to uncover microRNA-mediated disease comorbidities and potential pathobiological implications

npj Systems Biology and Applications ◽

10.1038/s41540-019-0115-2 ◽

2019 ◽

Vol 5 (1) ◽

Cited By ~ 2

Author(s):

Shuting Jin ◽

Xiangxiang Zeng ◽

Jiansong Fang ◽

Jiawei Lin ◽

Stephen Y. Chan ◽

...

Keyword(s):

Biological Networks ◽

High Performance ◽

Disease Gene ◽

Mirna Gene ◽

Airway Epithelial ◽

Disease Network ◽

Human Interactome ◽

Interactome Network ◽

Protein Interactome ◽

Meta Path

Abstract Disease–disease relationships (e.g., disease comorbidities) play crucial roles in pathobiological manifestations of diseases and personalized approaches to managing those conditions. In this study, we develop a network-based methodology, termed meta-path-based Disease Network (mpDisNet) capturing algorithm, to infer disease–disease relationships by assembling four biological networks: disease–miRNA, miRNA–gene, disease–gene, and the human protein–protein interactome. mpDisNet is a meta-path-based random walk to reconstruct the heterogeneous neighbors of a given node. mpDisNet uses a heterogeneous skip-gram model to solve the network representation of the nodes. We find that mpDisNet reveals high performance in inferring clinically reported disease–disease relationships, outperforming that of traditional gene/miRNA-overlap approaches. In addition, mpDisNet identifies network-based comorbidities for pulmonary diseases driven by underlying miRNA-mediated pathobiological pathways (i.e., hsa-let-7a- or hsa-let-7b-mediated airway epithelial apoptosis and pro-inflammatory cytokine pathways) as derived from the human interactome network analysis. The mpDisNet offers a powerful tool for network-based identification of disease–disease relationships with miRNA-mediated pathobiological pathways.

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Towards a global investigation of transcriptomic signatures through co-expression networks and pathway knowledge for the identification of disease mechanisms

10.1101/2021.03.02.433520 ◽

2021 ◽

Author(s):

Rebeca Queiroz Figueiredo ◽

Tamara Raschka ◽

Alpha Tom Kodamullil ◽

Martin Hofmann-Apitius ◽

Sarah Mubeen ◽

...

Keyword(s):

Molecular Interactions ◽

Protein Interactions ◽

Systematic Approach ◽

Joint Analysis ◽

Protein Protein Interactions ◽

Case Scenario ◽

Human Interactome ◽

Disease Mechanisms ◽

Interactome Network ◽

Different Levels

AbstractIn this work, we attempt to address a key question in the joint analysis of transcriptomic data: can we correlate the patterns we observe in transcriptomic datasets to known molecular interactions and pathway knowledge to broaden our understanding of disease pathophysiology? We present a systematic approach that sheds light on the patterns observed in hundreds of transcriptomic datasets from over sixty indications by using pathways and molecular interactions as a template. Our analysis employs transcriptomic datasets to construct dozens of disease specific co-expression networks, alongside a human interactome network of protein-protein interactions described in the literature. Leveraging the interoperability between these two network templates, we explore patterns both common and particular to these diseases on three different levels. Firstly, at the node-level, we identify the most and least common proteins in these diseases and evaluate their consistency against the interactome as a proxy for their prevalence in the scientific literature. Secondly, we overlay both network templates to analyze common correlations and interactions across diseases at the edge-level. Thirdly, we explore the similarity between patterns observed at the disease level and pathway knowledge to identify pathway signatures associated with specific diseases and indication areas. Finally, we present a case scenario in the context of schizophrenia, where we show how our approach can be used to investigate disease pathophysiology.

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Analysis of Pan-Omics Data in Human Interactome Network (APODHIN)

10.1101/2020.04.18.048207 ◽

2020 ◽

Author(s):

Nupur Biswas ◽

Krishna Kumar ◽

Sarpita Bose ◽

Raisa Bera ◽

Saikat Chakrabarti

Keyword(s):

Target Gene ◽

Metabolic Reprogramming ◽

Signaling Proteins ◽

Omics Data ◽

Breast Cancers ◽

Human Interactome ◽

Metabolomics Data ◽

Interactome Network ◽

3D Network ◽

Gene Regulatory

AbstractAnalysis of Pan-Omics Data in Human Interactome Network (APODHIN) is a platform for integrative analysis of transcriptomics, proteomics, genomics, and metabolomics data for identification of key molecular players and their interconnections exemplified in cancer scenario. APODHIN works on a meta-interactome networks consisting of human protein-protein interactions, miRNA-target gene regulatory interactions, and transcription factor-target gene regulatory relationships, respectively. In its first module, APODHIN maps proteins/genes/miRNAs from different omics data in its meta-interactome network and extracts the network of biomolecules that are differentially altered in the given scenario. Using this context specific, filtered interaction network, APODHIN identifies topologically important nodes (TINs) implementing graph theory based network topology analysis and further justifies their role via pathway and disease marker mapping. These TINs could be used as prospective diagnostic and/or prognostic biomarkers and/or potential therapeutic targets. In its second module, APODHIN attempts to identify cross pathway regulatory and protein-protein interaction (PPI) links connecting signaling proteins, transcription factors, and miRNAs to metabolic enzymes via utilization of single-omics and/or pan-omics data and implementation of mathematical modeling. Interconnections between regulatory components such as signaling proteins/TFs/miRNAs and metabolic pathways need to be elucidated more elaborately in order to understand the role of oncogene and tumor suppressors in regulation of metabolic reprogramming during cancer.APODHIN platform contains a web server component where users can upload single/multi omics data to identify TINs and cross-pathway links. Tabular, graphical and 3D network representations of the identified TINs and cross-pathway links are provided for better appreciation. Additionally, this platform also provides a database part where cancer specific, single and/or multi omics dataset centric meta-interactome networks, TINs, and cross-pathway links are provided for cervical, ovarian, and breast cancers, respectively. APODHIN platform is freely available at http://www.hpppi.iicb.res.in/APODHIN/home.html.

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A Proteome-Scale Map of the Human Interactome Network

Cell ◽

10.1016/j.cell.2014.10.050 ◽

2014 ◽

Vol 159 (5) ◽

pp. 1212-1226 ◽

Cited By ~ 796

Author(s):

Thomas Rolland ◽

Murat Taşan ◽

Benoit Charloteaux ◽

Samuel J. Pevzner ◽

Quan Zhong ◽

...

Keyword(s):

Human Interactome ◽

Interactome Network

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Analysis of Pan-omics Data in Human Interactome Network (APODHIN)

Frontiers in Genetics ◽

10.3389/fgene.2020.589231 ◽

2020 ◽

Vol 11 ◽

Author(s):

Nupur Biswas ◽

Krishna Kumar ◽

Sarpita Bose ◽

Raisa Bera ◽

Saikat Chakrabarti

Keyword(s):

Target Gene ◽

Metabolic Reprogramming ◽

Signaling Proteins ◽

Omics Data ◽

Breast Cancers ◽

Human Interactome ◽

Metabolomics Data ◽

Interactome Network ◽

3D Network ◽

Gene Regulatory

Analysis of Pan-omics Data in Human Interactome Network (APODHIN) is a platform for integrative analysis of transcriptomics, proteomics, genomics, and metabolomics data for identification of key molecular players and their interconnections exemplified in cancer scenario. APODHIN works on a meta-interactome network consisting of human protein–protein interactions (PPIs), miRNA-target gene regulatory interactions, and transcription factor-target gene regulatory relationships. In its first module, APODHIN maps proteins/genes/miRNAs from different omics data in its meta-interactome network and extracts the network of biomolecules that are differentially altered in the given scenario. Using this context specific, filtered interaction network, APODHIN identifies topologically important nodes (TINs) implementing graph theory based network topology analysis and further justifies their role via pathway and disease marker mapping. These TINs could be used as prospective diagnostic and/or prognostic biomarkers and/or potential therapeutic targets. In its second module, APODHIN attempts to identify cross pathway regulatory and PPI links connecting signaling proteins, transcription factors (TFs), and miRNAs to metabolic enzymes via utilization of single-omics and/or pan-omics data and implementation of mathematical modeling. Interconnections between regulatory components such as signaling proteins/TFs/miRNAs and metabolic pathways need to be elucidated more elaborately in order to understand the role of oncogene and tumor suppressors in regulation of metabolic reprogramming during cancer. APODHIN platform contains a web server component where users can upload single/multi omics data to identify TINs and cross-pathway links. Tabular, graphical and 3D network representations of the identified TINs and cross-pathway links are provided for better appreciation. Additionally, this platform also provides few example data analysis of cancer specific, single and/or multi omics dataset for cervical, ovarian, and breast cancers where meta-interactome networks, TINs, and cross-pathway links are provided. APODHIN platform is freely available at http://www.hpppi.iicb.res.in/APODHIN/home.html.

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