Protein-Protein interactions uncover candidate ‘core genes’ within omnigenic disease networks

Ratnakumar, Abhirami; Weinhold, Nils; Mar, Jessica C.; Riaz, Nadeem

doi:10.1371/journal.pgen.1008903

Cited by 19 publications

(13 citation statements)

References 60 publications

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“…Previous methods have combined GWAS hits with PPI networks. For example, Ratnakumar and colleagues identify proteins enriched for PPI with GWAS hits (Ratnakumar et al 2020). Recently, Dey and colleagues have demonstrated advantage in using strategies capturing both distal and proximal gene regulation to prioritize genes for disease (Dey et al 2022).…”

Section: Discussionmentioning

confidence: 99%

The PENGUIN approach to reconstruct protein interactions at enhancer-promoter regions and its application to prostate cancer

Armaos

Serra

Núñez-Carpintero

et al. 2022

Preprint

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DNA-binding proteins (DBPs) and in particular transcription factors interact with enhancers and their target genes through enhancer-promoter (E-P) interactions. Technological advancements such as chromosome conformation capture allow to identify E-P interactions, but the protein networks involved have not yet been characterized. Most importantly, the role of nuclear protein networks in human diseases has been so far poorly investigated. Prostate cancer (PrCa) heritability is associated with variations in enhancers that affect specific gene expression. Here, we introduce a novel approach, called Promoter-ENhancer-GUided Interaction Networks (PENGUIN), to identify protein-protein interactions (PPI) in E-P interactions and apply it to our PrCa dataset. PENGUIN integrates chromatin interactions between a promoter and its enhancers defined by high-coverage H3K27ac-HiChIP data, with a tissue-specific PPI network inferred from DNA-binding motifs and refined with gene expression. Among a total of 4,314 E-P networks, PENGUIN performed unsupervised clustering. We functionally validated this clustering procedure by searching for enrichments of specific biological features. We first validated PENGUIN structural classification of E-P networks by showing a clear differential enrichment of the architectural protein CTCF. Next, and directly related to our PrCa case study, we observed that one of our 8 main clusters, containing 273 promoters, is particularly enriched for PrCA associated single nucleotide polymorphisms (SNPs) and oncogenes. Our approach proposes a mechanistic explanation for 208 PrCa SNPs falling either inside the binding sites of DNA-binding proteins (DBPs) or within genes encoding for intermediate proteins bridging E-P contacts. PENGUIN not only confirmed the relevance of key regulators in PrCa, but also identified new candidates for intervention, opening up new directions to identify molecular targets for disease treatment.

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Section: Discussionmentioning

confidence: 99%

The PENGUIN approach to reconstruct protein interactions at enhancer-promoter regions and its application to prostate cancer

Armaos

Serra

Núñez-Carpintero

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Previous methods combined GWAS hits with PPI networks. For example, Ratnakumar and colleagues identi ed proteins enriched for PPI with GWAS hits 44 . Recently, Dey and colleagues have demonstrated advantage in using strategies capturing both distal and proximal gene regulation to prioritize genes for disease 45 .…”

Section: Discussionmentioning

confidence: 99%

Reconstructing protein interactions at enhancer-promoter regions in prostate cancer

Armaos

Serra

Núñez-Carpintero

et al. 2022

Preprint

View full text Add to dashboard Cite

DNA-binding proteins (DBPs) and in particular transcription factors interact with enhancers and their target genes through enhancer-promoter (E-P) interactions. Technological advancements such as chromosome conformation capture allow to identify E-P interactions, but the protein networks involved have not yet been characterized. Most importantly, the role of nuclear protein networks in human diseases has been so far poorly investigated. Prostate cancer (PrCa) heritability is associated with variations in enhancers that affect specific gene expression. Here, we introduce a novel approach, called Promoter-ENhancer-GUided Interaction Networks (PENGUIN), to identify protein-protein interactions (PPI) in E-P interactions and apply it to our PrCa dataset. PENGUIN integrates chromatin interactions between a promoter and its enhancers defined by high-coverage H3K27ac-HiChIP data, with a tissue-specific PPI network inferred from DNA-binding motifs and refined with gene expression. Among a total of 4,314 E-P networks, PENGUIN performed unsupervised clustering. We functionally validated this clustering procedure by searching for enrichments of specific biological features. We confirmed PENGUIN structural classification of E-P networks by showing a clear differential enrichment of the architectural protein CTCF. Next, and directly related to our PrCa case study, we observed that one of our 8 main clusters, containing 273 promoters, is particularly enriched for PrCA associated single nucleotide polymorphisms (SNPs) and oncogenes. Our approach proposes a mechanistic explanation for 208 PrCa SNPs falling either inside the binding sites of DNA-binding proteins (DBPs) or within genes encoding for intermediate proteins bridging E-P contacts. PENGUIN not only confirmed the relevance of key regulators in PrCa, but also identified new candidates for intervention, opening up new directions to identify molecular targets for disease treatment.

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“…In addition to gene-set-based analysis, there are other methods that focuse on the topology of biological networks. These approaches evaluate various network structure parameters and analyze the connectivity of two genes or the size and interconnection of their neighbors [64,65]. According to the underlying idea, the mutated gene will show dysfunction and can affect its neighboring genes.…”

Section: Biological Network Analysis For Biomarker Validationmentioning

confidence: 99%

Integrative Analysis of Next-Generation Sequencing for Next-Generation Cancer Research toward Artificial Intelligence

Park

Heider

Hauschild

2021

Cancers

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The rapid improvement of next-generation sequencing (NGS) technologies and their application in large-scale cohorts in cancer research led to common challenges of big data. It opened a new research area incorporating systems biology and machine learning. As large-scale NGS data accumulated, sophisticated data analysis methods became indispensable. In addition, NGS data have been integrated with systems biology to build better predictive models to determine the characteristics of tumors and tumor subtypes. Therefore, various machine learning algorithms were introduced to identify underlying biological mechanisms. In this work, we review novel technologies developed for NGS data analysis, and we describe how these computational methodologies integrate systems biology and omics data. Subsequently, we discuss how deep neural networks outperform other approaches, the potential of graph neural networks (GNN) in systems biology, and the limitations in NGS biomedical research. To reflect on the various challenges and corresponding computational solutions, we will discuss the following three topics: (i) molecular characteristics, (ii) tumor heterogeneity, and (iii) drug discovery. We conclude that machine learning and network-based approaches can add valuable insights and build highly accurate models. However, a well-informed choice of learning algorithm and biological network information is crucial for the success of each specific research question.

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Protein-Protein interactions uncover candidate ‘core genes’ within omnigenic disease networks

Cited by 19 publications

References 60 publications

The PENGUIN approach to reconstruct protein interactions at enhancer-promoter regions and its application to prostate cancer

The PENGUIN approach to reconstruct protein interactions at enhancer-promoter regions and its application to prostate cancer

Reconstructing protein interactions at enhancer-promoter regions in prostate cancer

Integrative Analysis of Next-Generation Sequencing for Next-Generation Cancer Research toward Artificial Intelligence

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