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Internetwork connectivity of molecular networks across species of life.

ABSTRACT: Molecular interactions are studied as independent networks in systems biology. However, molecular networks do not exist independently of each other. In a network of networks approach (called multiplex), we study the joint organization of transcriptional regulatory network (TRN) and protein-protein interaction (PPI) network. We find that TRN and PPI are non-randomly coupled across five different eukaryotic species. Gene degrees in TRN (number of downstream genes) are positively correlated with protein degrees in PPI (number of interacting protein partners). Gene-gene and protein-protein interactions in TRN and PPI, respectively, also non-randomly overlap. These design principles are conserved across the five eukaryotic species. Robustness of the TRN-PPI multiplex is dependent on this coupling. Functionally important genes and proteins, such as essential, disease-related and those interacting with pathogen proteins, are preferentially situated in important parts of the human multiplex with highly overlapping interactions. We unveil the multiplex architecture of TRN and PPI. Multiplex architecture may thus define a general framework for studying molecular networks. This approach may uncover the building blocks of the hierarchical organization of molecular interactions.

SUBMITTER: Mahajan T

PROVIDER: S-EPMC7806680 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature

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Internetwork connectivity of molecular networks across species of life.

Mahajan Tarun T Dar Roy D RD

Scientific reports 20210113 1

Molecular interactions are studied as independent networks in systems biology. However, molecular networks do not exist independently of each other. In a network of networks approach (called multiplex), we study the joint organization of transcriptional regulatory network (TRN) and protein-protein interaction (PPI) network. We find that TRN and PPI are non-randomly coupled across five different eukaryotic species. Gene degrees in TRN (number of downstream genes) are positively correlated with pr ...[more]

PMID: 33441907

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Project description:BackgroundChronic unilateral vestibulopathy (CUVP) is often accompanied by dizziness and postural instability, which restrict patients' daily activities. It is important to understand central compensation mechanisms underlying these symptoms in patients with CUVP by evaluating their brain functional status.PurposeTo analyze the changes in resting-state intranetwork and internetwork functional connectivity (FC) and explore the state of central vestibular compensation in patients with CUVP.Study typeRetrospective.PopulationEighteen patients with right-sided CUVP and 18 age- and sex-matched healthy controls.Field strength/sequenceA 3.0 T, three-dimensional magnetization-prepared rapid gradient-echo (MP-RAGE) and resting-state echo-planar imaging (EPI) functional MRI sequences.AssessmentFC alterations were explored using independent component analysis (ICA). Twelve independent components were identified via ICA. Dizziness Handicap Inventory (DHI) score for all patients was determined.Statistical testsTwo-sample t test, family-wise error (FWE) correction, Pearson correlation coefficient (r). A P value <0.05 was considered statistically significant.ResultsCompared with healthy controls, patients with CUVP showed significantly decreased FC in the right middle occipital gyrus within the lateral visual network, and significantly increased FC in the right supplementary motor area within the sensorimotor network. The FC was decreased between the medial visual and auditory networks, the right frontoparietal and posterior default networks, as well as the sensorimotor and auditory networks. There was a significant negative correlation between the FC changes in the visual, auditory networks and the DHI score in patients with CUVP (r = -0.583).Data conclusionCompared to healthy controls, the FC was significantly decreased in the right visual cortex and significantly enhanced in the right sensorimotor network in patients with CUVP. Patients with CUVP showed decreased FC between multiple whole-brain networks, suggesting that abnormal integration of multisensory information may be involved in the occurrence of chronic dizziness and instability in patients with CUVP.Level of evidence4 TECHNICAL EFFICACY: Stage 2.

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Project description:The recently proposed concept of molecular connectivity maps enables researchers to integrate experimental measurements of genes, proteins, metabolites, and drug compounds under similar biological conditions. The study of these maps provides opportunities for future toxicogenomics and drug discovery applications. We developed a computational framework to build disease-specific drug-protein connectivity maps. We integrated gene/protein and drug connectivity information based on protein interaction networks and literature mining, without requiring gene expression profile information derived from drug perturbation experiments on disease samples. We described the development and application of this computational framework using Alzheimer's Disease (AD) as a primary example in three steps. First, molecular interaction networks were incorporated to reduce bias and improve relevance of AD seed proteins. Second, PubMed abstracts were used to retrieve enriched drug terms that are indirectly associated with AD through molecular mechanistic studies. Third and lastly, a comprehensive AD connectivity map was created by relating enriched drugs and related proteins in literature. We showed that this molecular connectivity map development approach outperformed both curated drug target databases and conventional information retrieval systems. Our initial explorations of the AD connectivity map yielded a new hypothesis that diltiazem and quinidine may be investigated as candidate drugs for AD treatment. Molecular connectivity maps derived computationally can help study molecular signature differences between different classes of drugs in specific disease contexts. To achieve overall good data coverage and quality, a series of statistical methods have been developed to overcome high levels of data noise in biological networks and literature mining results. Further development of computational molecular connectivity maps to cover major disease areas will likely set up a new model for drug development, in which therapeutic/toxicological profiles of candidate drugs can be checked computationally before costly clinical trials begin.

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Internetwork connectivity of molecular networks across species of life.

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Internetwork connectivity of molecular networks across species of life.

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