Protein Interaction Networks Define the Genetic Architecture of Preterm Birth

Rather than pathogenic variants in single genes, the likely genetic architecture of complex diseases is that subgroups of patients share variants in genes in specific networks and pathways sufficient to give rise to a shared phenotype. We combined high throughput sequencing with advanced bioinformatic approaches to identify subgroups of patients with shared networks and pathways associated with preterm birth (PTB). We previously identified genes, gene sets and haplotype blocks that were highly associated with preterm birth. We performed targeted sequencing on these genes and genomic regions on highly phenotyped patients with 2 or 3 generations of preterm birth, and term controls with no family history of preterm birth. We performed a genotype test for differential abundance of variants between cases and controls. We used the genotype association statistics for ranking purposes in order to analyze the data using a multi-sample, protein-protein interaction (PPI) tool to identify significant clusters of patients associated with preterm birth. We identified shared interaction networks of proteins among 45 preterm cases in two statistically significant clusters, p<0.001. We also found two small control-dominated clusters. For replication, we compared our data to an independent, large birth cohort. Sequence data on 60 cases and 321 controls identified 34 preterm cases with shared networks of proteins distributed in two significant clusters. Analysis of the layered PPI networks of these clusters showed significant similarity scores between the clusters from the two independent cohorts of patients. Canonical pathway analysis of the unique genes defining these clusters demonstrated enrichment in inflammatory signaling pathways, the glucocorticoid receptor, the insulin receptor, EGF and B-cell signaling, These results provide insights into the genetics of PTB and support a genetic architecture defined by subgroups of patients that share variants in genes in specific networks and pathways which are sufficient to give rise to the disease phenotype.

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Protein interaction networks define the genetic architecture of preterm birth

Scientific Reports ◽

10.1038/s41598-021-03427-0 ◽

2022 ◽

Vol 12 (1) ◽

Author(s):

Alper Uzun ◽

Jessica S. Schuster ◽

Joan Stabila ◽

Valeria Zarate ◽

George A. Tollefson ◽

...

Keyword(s):

Preterm Birth ◽

Protein Interaction ◽

Genetic Architecture ◽

High Throughput Sequencing ◽

Protein Interaction Networks ◽

Interaction Networks ◽

Significant Similarity ◽

Protein Protein Interaction ◽

B Cell Signaling ◽

Shared Networks

AbstractThe likely genetic architecture of complex diseases is that subgroups of patients share variants in genes in specific networks sufficient to express a shared phenotype. We combined high throughput sequencing with advanced bioinformatic approaches to identify such subgroups of patients with variants in shared networks. We performed targeted sequencing of patients with 2 or 3 generations of preterm birth on genes, gene sets and haplotype blocks that were highly associated with preterm birth. We analyzed the data using a multi-sample, protein–protein interaction (PPI) tool to identify significant clusters of patients associated with preterm birth. We identified shared protein interaction networks among preterm cases in two statistically significant clusters, p < 0.001. We also found two small control-dominated clusters. We replicated these data on an independent, large birth cohort. Separation testing showed significant similarity scores between the clusters from the two independent cohorts of patients. Canonical pathway analysis of the unique genes defining these clusters demonstrated enrichment in inflammatory signaling pathways, the glucocorticoid receptor, the insulin receptor, EGF and B-cell signaling, These results support a genetic architecture defined by subgroups of patients that share variants in genes in specific networks and pathways which are sufficient to give rise to the disease phenotype.

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PPInfer: a Bioconductor package for inferring functionally related proteins using protein interaction networks

F1000Research ◽

10.12688/f1000research.12947.1 ◽

2017 ◽

Vol 6 ◽

pp. 1969

Author(s):

Dongmin Jung ◽

Xijin Ge

Keyword(s):

Protein Interaction ◽

Protein Interaction Networks ◽

Interaction Networks ◽

Biological Processes ◽

Bioconductor Package ◽

Biological Functions ◽

Ppi Network ◽

Protein Protein Interaction ◽

Ppi Networks ◽

Related Proteins

Interactions between proteins occur in many, if not most, biological processes. This fact has motivated the development of a variety of experimental methods for the identification of protein-protein interaction (PPI) networks. Leveraging PPI data available STRING database, we use network-based statistical learning methods to infer the putative functions of proteins from the known functions of neighboring proteins on a PPI network. This package identifies such proteins often involved in the same or similar biological functions. The package is freely available at the Bioconductor web site (http://bioconductor.org/packages/PPInfer/).

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PPInfer: a Bioconductor package for inferring functionally related proteins using protein interaction networks

F1000Research ◽

10.12688/f1000research.12947.3 ◽

2018 ◽

Vol 6 ◽

pp. 1969 ◽

Cited By ~ 3

Author(s):

Dongmin Jung ◽

Xijin Ge

Keyword(s):

Protein Interaction ◽

Protein Interaction Networks ◽

Interaction Networks ◽

Biological Processes ◽

Bioconductor Package ◽

Biological Functions ◽

Ppi Network ◽

Protein Protein Interaction ◽

Ppi Networks ◽

Related Proteins

Interactions between proteins occur in many, if not most, biological processes. This fact has motivated the development of a variety of experimental methods for the identification of protein-protein interaction (PPI) networks. Leveraging PPI data available in the STRING database, we use a network-based statistical learning methods to infer the putative functions of proteins from the known functions of neighboring proteins on a PPI network. This package identifies such proteins often involved in the same or similar biological functions. The package is freely available at the Bioconductor web site (http://bioconductor.org/packages/PPInfer/).

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A Central Edge Selection Based Overlapping Community Detection Algorithm for the Detection of Overlapping Structures in Protein–Protein Interaction Networks

Molecules ◽

10.3390/molecules23102633 ◽

2018 ◽

Vol 23 (10) ◽

pp. 2633 ◽

Cited By ~ 6

Author(s):

Fang Zhang ◽

Anjun Ma ◽

Zhao Wang ◽

Qin Ma ◽

Bingqiang Liu ◽

...

Keyword(s):

Community Detection ◽

Protein Interaction ◽

Protein Interaction Networks ◽

Interaction Networks ◽

Overlapping Community Detection ◽

Protein Protein Interaction ◽

Central Node ◽

Ppi Networks ◽

Overlapping Community ◽

Protein Protein Interaction Networks

Overlapping structures of protein–protein interaction networks are very prevalent in different biological processes, which reflect the sharing mechanism to common functional components. The overlapping community detection (OCD) algorithm based on central node selection (CNS) is a traditional and acceptable algorithm for OCD in networks. The main content of CNS is the central node selection and the clustering procedure. However, the original CNS does not consider the influence among the nodes and the importance of the division of the edges in networks. In this paper, an OCD algorithm based on a central edge selection (CES) algorithm for detection of overlapping communities of protein–protein interaction (PPI) networks is proposed. Different from the traditional CNS algorithms for OCD, the proposed algorithm uses community magnetic interference (CMI) to obtain more reasonable central edges in the process of CES, and employs a new distance between the non-central edge and the set of the central edges to divide the non-central edge into the correct cluster during the clustering procedure. In addition, the proposed CES improves the strategy of overlapping nodes pruning (ONP) to make the division more precisely. The experimental results on three benchmark networks and three biological PPI networks of Mus. musculus, Escherichia coli, and Cerevisiae show that the CES algorithm performs well.

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GASOLINE: a Cytoscape app for multiple local alignment of PPI networks

F1000Research ◽

10.12688/f1000research.4537.2 ◽

2014 ◽

Vol 3 ◽

pp. 140 ◽

Cited By ~ 7

Author(s):

Giovanni Micale ◽

Andrea Continella ◽

Alfredo Ferro ◽

Rosalba Giugno ◽

Alfredo Pulvirenti

Keyword(s):

Protein Interaction ◽

Sampling Strategy ◽

Protein Interaction Networks ◽

Interaction Networks ◽

Local Alignment ◽

Stochastic Algorithm ◽

Protein Protein Interaction ◽

Ppi Networks ◽

Alignment Task ◽

Protein Protein Interaction Networks

Comparing protein interaction networks can reveal interesting patterns of interactions for a specific function or process in distantly related species. In this paper we present GASOLINE, a Cytoscape app for multiple local alignments of PPI (protein-protein interaction) networks. The app is based on the homonymous greedy and stochastic algorithm. GASOLINE starts with the identification of sets of similar nodes, called seeds of the alignment. Alignments are then extended in a greedy manner and finally refined. Both the identification of seeds and the extension of alignments are performed through an iterative Gibbs sampling strategy. GASOLINE is a Cytoscape app for computing and visualizing local alignments, without requiring any post-processing operations. GO terms can be easily attached to the aligned proteins for further functional analysis of alignments. GASOLINE can perform the alignment task in few minutes, even for a large number of input networks.

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GASOLINE: a Cytoscape app for multiple local alignment of PPI networks

F1000Research ◽

10.12688/f1000research.4537.1 ◽

2014 ◽

Vol 3 ◽

pp. 140 ◽

Cited By ~ 1

Author(s):

Giovanni Micale ◽

Andrea Continella ◽

Alfredo Ferro ◽

Rosalba Giugno ◽

Alfredo Pulvirenti

Keyword(s):

Protein Interaction ◽

Protein Interaction Networks ◽

Interaction Networks ◽

Local Alignment ◽

Stochastic Algorithms ◽

Protein Protein Interaction ◽

Ppi Networks ◽

Alignment Task ◽

Protein Protein Interaction Networks ◽

Go Terms

Comparing protein interaction networks can reveal interesting patterns of interactions for a specific function or process in distantly related species. In this paper we present GASOLINE, a Cytoscape app for multiple local alignments of PPI (protein-protein interaction) networks. The app is based on the homonymous greedy and stochastic algorithms. To the authors knowledge, it is the first Cytoscape app for computing and visualizing local alignments, without requiring any post-processing operations. GO terms can be easily attached to the aligned proteins for further functional analysis of alignments. GASOLINE can perform the alignment task in few minutes, even for a large number of input networks.

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Incorporating Network Topology Improves Prediction of Protein Interaction Networks from Transcriptomic Data

Computational Knowledge Discovery for Bioinformatics Research ◽

10.4018/978-1-4666-1785-8.ch012 ◽

2013 ◽

pp. 203-221

Author(s):

Peter E. Larsen ◽

Frank Collart ◽

Yang Dai

Keyword(s):

Experimental Data ◽

Protein Interaction ◽

Network Topology ◽

Biological Networks ◽

Dimensional Space ◽

Interaction Networks ◽

Protein Protein Interactions ◽

Transcriptomic Data ◽

Protein Protein Interaction ◽

Ppi Networks

The reconstruction of protein-protein interaction (PPI) networks from high-throughput experimental data is one of the most challenging problems in bioinformatics. These biological networks have specific topologies defined by the functional and evolutionary relationships between the proteins and the physical limitations imposed on proteins interacting in the three-dimensional space. In this paper, the authors propose a novel approach for the identification of potential protein-protein interactions based on the integration of known PPI network topology and transcriptomic data. The proposed method, Function Restricted Value Neighborhood (FRV-N), was used to reconstruct PPI networks using an experimental data set consisting of 170 yeast microarray profiles. The results of this analysis demonstrate that incorporating knowledge of interactome topology improves the ability of transcriptome analysis to reconstruct interaction networks with a high degree of biological relevance.

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