Socio-physical interaction network (SPIN)

Abstract Motivation The relationship between gene co-expression and chromatin conformation is of great biological interest. Thanks to high-throughput chromosome conformation capture technologies (Hi-C), researchers are gaining insights on the tri-dimensional organization of the genome. Given the high complexity of Hi-C data and the difficult definition of gene co-expression networks, the development of proper computational tools to investigate such relationship is rapidly gaining the interest of researchers. One of the most fascinating questions in this context is how chromatin topology correlates with gene co-expression and which physical interaction patterns are most predictive of co-expression relationships. Results To address these questions, we developed a computational framework for the prediction of co-expression networks from chromatin conformation data. We first define a gene chromatin interaction network where each gene is associated to its physical interaction profile; then, we apply two graph embedding techniques to extract a low-dimensional vector representation of each gene from the interaction network; finally, we train a classifier on gene embedding pairs to predict if they are co-expressed. Both graph embedding techniques outperform previous methods based on manually designed topological features, highlighting the need for more advanced strategies to encode chromatin information. We also establish that the most recent technique, based on random walks, is superior. Overall, our results demonstrate that chromatin conformation and gene regulation share a non-linear relationship and that gene topological embeddings encode relevant information, which could be used also for downstream analysis. Availability and implementation The source code for the analysis is available at: https://github.com/marcovarrone/gene-expression-chromatin. Supplementary information Supplementary data are available at Bioinformatics online.

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Protein Physical Interaction Network

Encyclopedia of Systems Biology ◽

10.1007/978-1-4419-9863-7_101229 ◽

2013 ◽

pp. 1778-1778

Keyword(s):

Interaction Network ◽

Physical Interaction

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Incorporating K-mers Highly Correlated to Epigenetic Modifications for Bayesian Inference of Gene Interactions

Current Bioinformatics ◽

10.2174/1574893615999200728193621 ◽

2020 ◽

Vol 15 ◽

Author(s):

Dariush Salimi ◽

Ali Moeini

Keyword(s):

Bayesian Inference ◽

Biological Networks ◽

Interaction Network ◽

Gene Interaction ◽

Epigenetic Modifications ◽

Physical Interaction ◽

Gene Interaction Network ◽

Human Genes ◽

Highly Correlated ◽

Specific Pathway

Objective: A gene interaction network, along with its related biological features, has an important role in computational biology. Bayesian network, as an efficient model, based on probabilistic concepts is able to exploit known and novel biological casual relationships between genes. Success of Bayesian networks in predicting the relationships greatly depends on selecting priors Methods: K-mers have been applied as the prominent features to uncover similarity between genes in a specific pathway, suggesting that this feature can be applied to study genes dependencies. In this study, we propose k-mer (4,5 and 6-mers) highly correlated with epigenetic modifications, including 17 modifications, as a new prior for Bayesian inference in gene interaction network Result: Employing this model on a network of 23 human genes and on a network based on 27 genes related to yeast resulted in F-measure improvements in different biological networks Conclusion: The improvements in the best case are 12%, 36% and 10% in pathway, co-expression, and physical interaction, respectively.

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Developmental stage-dependent differential gene expression of superoxide dismutase isoenzymes and their localization and physical interaction network in rice (Oryza sativa L.)

Genes & Genomics ◽

10.1007/s13258-013-0138-9 ◽

2013 ◽

Vol 36 (1) ◽

pp. 45-55 ◽

Cited By ~ 17

Author(s):

Krishna Nath ◽

Susheel Kumar ◽

Roshan Sharma Poudyal ◽

Young Nam Yang ◽

Rupak Timilsina ◽

...

Keyword(s):

Gene Expression ◽

Superoxide Dismutase ◽

Oryza Sativa ◽

Developmental Stage ◽

Differential Gene Expression ◽

Oryza Sativa L ◽

Interaction Network ◽

Physical Interaction ◽

Differential Gene

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Comprehensive Analysis of Prognostic Value of MEX3A and Its Relationship with Immune Infiltrates in Ovarian Cancer

Journal of Immunology Research ◽

10.1155/2021/5574176 ◽

2021 ◽

Vol 2021 ◽

pp. 1-21

Author(s):

Panpan Zhang ◽

Tong Su ◽

Shu Zhang

Keyword(s):

Ovarian Cancer ◽

Prognostic Value ◽

Rna Binding ◽

Interaction Network ◽

Gene Interaction ◽

Physical Interaction ◽

Biological Functions ◽

Gene Interaction Network ◽

Immune Infiltration

MEX3A is a critical RNA-binding ubiquitin ligase that is upregulated in various types of cancer. However, the correlations of MEX3A with prognosis and its molecular mechanism in ovarian cancer (OC) remain unclear. The expression level, prognostic values, and the genetic variations of MEX3A were analyzed via Gene Expression Profiling Interactive Analysis (GEPIA) Oncomine, Kaplan–Meier plotter, and cBioPortal. We used the LinkedOmics database to investigate the functions of MEX3A coexpressed genes and performed visualizing gene interaction network analysis on the GeneMANIA website. The correlations between MEX3A and cancer immune infiltration were analyzed by the Tumor Immune Estimation Resource (TIMER) site and the TISIDB database. Furthermore, in vitro analysis was performed to evaluate the biological functions of MEX3A in OC cells. Our study showed that the expression of the MEX3A in OC was higher than in normal tissues; it had the greatest prognostic value in OC, and strong physical interaction with PABPC1, LAMTOR2, KHDRBS2, and IGF2BP2, which indicated the association between MEX3A and immune infiltration. We also found that MEX3A was negatively related to infiltrating levels of several types of immune cells, including macrophages, neutrophils, dendritic cells (DCs), B cells, and CD8+ T cells. Additionally, in vitro experiments demonstrated that MEX3A promotes proliferation and migration in OC cells. Taken together, MEX3A might influence the biological functions of OC cells by regulating the immune infiltration in the microenvironment as a prognostic biomarker and a potential therapeutic target.

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The protein interaction network of the inherited central nervous system diseases reveals new gene candidates for molecularly unclassified myelin disorders

Journal of Complex Networks ◽

10.1093/comnet/cnaa040 ◽

2020 ◽

Vol 8 (4) ◽

Author(s):

Luca Paris ◽

Gianluca Como ◽

Ilaria Vecchia ◽

Francesco Pisani ◽

Giovanni Ferrara

Keyword(s):

Central Nervous System ◽

Nervous System ◽

Gene Mutations ◽

Interaction Network ◽

System Level ◽

Physical Interaction ◽

Inherited Disorders ◽

Edge Linking ◽

New Gene ◽

Myelin Disorders

Abstract Mutations in numerous genes cause the inherited disorders of the white matter in the central nervous system. Interestingly, all these mutations ultimately affect myelin, even though the corresponding proteins are involved in dissimilar functions. To address this system-level issue, we assembled the myelin disease network (MDN), in which each node represents a protein (either the mutated protein or one of its interactors), while each edge linking two nodes represents the physical interaction between the two proteins. Compared with control random networks, the MDN contains more pairs of disease proteins, whose members are linked either directly or via one intermediate protein. Then, we surmised that the interactions might not only cluster proteins into functionally homogenous and distinct modules but also link the modules together. This way, even gene mutations arising in functionally distinct modules might propagate their effects to the other modules, thus accounting for a similar pathological outcome. We found, however, that concerning the function the modules are neither homogeneous nor distinct, mostly because many proteins participate in more than one biological process. Rather, our analysis defines a region of the interactome, where different processes intersect. Finally, we propose that many non-disease proteins in the network might be candidates for molecularly unclassified myelin disorders.

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Bud23 promotes the final disassembly of the small subunit Processome in Saccharomyces cerevisiae

PLoS Genetics ◽

10.1371/journal.pgen.1009215 ◽

2020 ◽

Vol 16 (12) ◽

pp. e1009215

Author(s):

Joshua J. Black ◽

Richa Sardana ◽

Ezzeddine W. Elmir ◽

Arlen W. Johnson

Keyword(s):

Binding Site ◽

Ribosomal Rna ◽

Rna Processing ◽

Interaction Network ◽

Small Subunit ◽

Physical Interaction ◽

U3 Snorna ◽

Genes Encoding ◽

Ribosomal Rna Processing ◽

Ssu Processome

The first metastable assembly intermediate of the eukaryotic ribosomal small subunit (SSU) is the SSU Processome, a large complex of RNA and protein factors that is thought to represent an early checkpoint in the assembly pathway. Transition of the SSU Processome towards continued maturation requires the removal of the U3 snoRNA and biogenesis factors as well as ribosomal RNA processing. While the factors that drive these events are largely known, how they do so is not. The methyltransferase Bud23 has a role during this transition, but its function, beyond the nonessential methylation of ribosomal RNA, is not characterized. Here, we have carried out a comprehensive genetic screen to understand Bud23 function. We identified 67 unique extragenic bud23Δ-suppressing mutations that mapped to genes encoding the SSU Processome factors DHR1, IMP4, UTP2 (NOP14), BMS1 and the SSU protein RPS28A. These factors form a physical interaction network that links the binding site of Bud23 to the U3 snoRNA and many of the amino acid substitutions weaken protein-protein and protein-RNA interactions. Importantly, this network links Bud23 to the essential GTPase Bms1, which acts late in the disassembly pathway, and the RNA helicase Dhr1, which catalyzes U3 snoRNA removal. Moreover, particles isolated from cells lacking Bud23 accumulated late SSU Processome factors and ribosomal RNA processing defects. We propose a model in which Bud23 dissociates factors surrounding its binding site to promote SSU Processome progression.

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