Dataset Information

Functional Gene Module-Based Identification of Phillyrin as an Anticardiac Fibrosis Agent.

ABSTRACT: Cardiac fibrosis (CF) greatly influences the therapeutic effects of heart diseases and remains an urgent challenge in clinical therapy. Till now, only a few methods are used to find potential anti-CF drugs effectively. This study aimed to construct a gene functional module to represent the core pathological process of CF and screen antifibrotic agents capable of decreasing the expression of the gene functional module. First, three CF marker genes Postn, Ddr2, and Pdgfra were selected to identify the corresponding highest coexpressed genes in the genome-based transcriptional profiles of human hearts. Both the marker genes and the coexpressed genes formed the CF-related gene functional module. Second, the correlation of the module with the CF process was measured in a collection of gene expression profiles of heart diseases to evaluate the participation of the functional module in heart diseases. Third, the anti-CF effects of phillyrin were predicted by the enrichment analysis of the module in the phillyrin-induced transcriptional profile. Finally, the myocardial infarction animal model was used to validate the cardioprotective and anti-CF effects of phillyrin experimentally. The results showed that phillyrin was a novel antifibrotic agent in heart diseases.

SUBMITTER: Wang L

PROVIDER: S-EPMC7379486 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Functional Gene Module-Based Identification of Phillyrin as an Anticardiac Fibrosis Agent.

Wang Lei L Zhang Wuxia W Lu Ziwen Z Wang Baofu B Li Yang Y Yang Jingjing J Li Peng P Zhao Mingjing M

Frontiers in pharmacology 20200717

Cardiac fibrosis (CF) greatly influences the therapeutic effects of heart diseases and remains an urgent challenge in clinical therapy. Till now, only a few methods are used to find potential anti-CF drugs effectively. This study aimed to construct a gene functional module to represent the core pathological process of CF and screen antifibrotic agents capable of decreasing the expression of the gene functional module. First, three CF marker genes Postn, Ddr2, and Pdgfra were ...[more]

PMID: 32765276

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Project description:Alzheimer's disease (AD) is the most common type of neurological disorder that results from brain cell death; however, not all brain regions are simultaneously affected to the same extent. Despite single biomarkers for AD having been determined on a genome‑wide scale, the differential co‑expression in gene pairs between regions and interactions with other types of cellular molecules, particularly non‑coding (nc)RNAs, are often overlooked in studies investigating the underlying mechanisms associated with AD. In the present study, based on 1,548 samples obtained from a cohort of 90 patients with AD spanning 19 brain regions, a gene‑pair based method was established for the classification of 19 brain regions into seven different groups, including marked disparate groupings of six single regions and a cluster of another 13 regions as revealed by principal component analysis (PCA). To further investigate the different underlying mechanisms associated with each group, five highly interconnected functional modules of the protein‑protein interaction network were demonstrated to characterize the seven region groups containing six single groups and 13 clustered regions based on 4,731 gene‑pairs. Genes in two of the functional modules exhibited a strong association with pathways associated with the nervous system, including cholinergic synapses, circadian entrainment and dopaminergic synapses. Notably, following integration of these two modules with a ncRNA‑mediated network, one module demonstrated a strong association with micro (mi)RNAs, which were revealed to interact with numerous long non‑coding (lnc)RNAs associated with AD, such as metastasis associated lung adenocarcinoma transcript 1 and taurine upregulated 1. This suggested that mRNAs and lncRNAs may represent competing endogenous RNAs for binding with miRNAs. Thus, these results indicated that the ncRNA‑mediated gene regulatory module detected by the established gene pair‑based method may further the understanding of underlying mechanisms associated with AD as well as aid the development of novel therapeutic strategies for the treatment of patients with AD.

Project description:MotivationIdentifying functional modules in protein-protein interaction (PPI) networks may shed light on cellular functional organization and thereafter underlying cellular mechanisms. Many existing module identification algorithms aim to detect densely connected groups of proteins as potential modules. However, based on this simple topological criterion of 'higher than expected connectivity', those algorithms may miss biologically meaningful modules of functional significance, in which proteins have similar interaction patterns to other proteins in networks but may not be densely connected to each other. A few blockmodel module identification algorithms have been proposed to address the problem but the lack of global optimum guarantee and the prohibitive computational complexity have been the bottleneck of their applications in real-world large-scale PPI networks.ResultsIn this article, we propose a novel optimization formulation LCP(2) (low two-hop conductance sets) using the concept of Markov random walk on graphs, which enables simultaneous identification of both dense and sparse modules based on protein interaction patterns in given networks through searching for LCP(2) by random walk. A spectral approximate algorithm SLCP(2) is derived to identify non-overlapping functional modules. Based on a bottom-up greedy strategy, we further extend LCP(2) to a new algorithm (greedy algorithm for LCP(2)) GLCP(2) to identify overlapping functional modules. We compare SLCP(2) and GLCP(2) with a range of state-of-the-art algorithms on synthetic networks and real-world PPI networks. The performance evaluation based on several criteria with respect to protein complex prediction, high level Gene Ontology term prediction and especially sparse module detection, has demonstrated that our algorithms based on searching for LCP(2) outperform all other compared algorithms.Availability and implementationAll data and code are available at http://www.cse.usf.edu/~xqian/fmi/slcp2hop/.

Dataset Information

Functional Gene Module-Based Identification of Phillyrin as an Anticardiac Fibrosis Agent.

Publications

Functional Gene Module-Based Identification of Phillyrin as an Anticardiac Fibrosis Agent.

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