Dataset Information

Multiplex-Heterogeneous Network-Based Capturing Potential SNP "Switches" of Pathways Associating With Diverse Disease Characteristics of Asthma.

ABSTRACT: Asthma is a complex heterogeneous respiratory disorder. In recent years nubbly regions of the role of genetic variants and transcriptome including mRNAs, microRNAs, and long non-coding RNAs in the pathogenesis of asthma have been separately excavated and reported. However, how to systematically integrate and decode this scattered information remains unclear. Further exploration would improve understanding of the internal communication of asthma. To excavate new insights into the pathogenesis of asthma, we ascertained three asthma characteristics according to reviews, airway inflammation, airway hyperresponsiveness, and airway remodeling. We manually created a contemporary catalog of corresponding risk transcriptome, including mRNAs, miRNAs, and lncRNAs. MIMP is a multiplex-heterogeneous networks-based approach, measuring the relevance of disease characteristics to the pathway by examining the similarity between the determined vectors of risk transcriptome and pathways in the same low-dimensional vector space. It was developed to enable a more concentrated and in-depth exploration of potential pathways. We integrated experimentally validated competing endogenous RNA regulatory information and the SNPs with significant pathways into the ceRNA-mediated SNP switching pathway network (CSSPN) to analyze ceRNA regulation of pathways and the role of SNP in these dysfunctions. We discovered 11 crucial ceRNA regulations concerning asthma disease feature pathway and propose a potential mechanism of ceRNA regulatory SNP → gene → pathway → disease feature effecting asthma pathogenesis, especially for MALAT1 (rs765499057/rs764699354/rs189435941) → hsa-miR-155 → IL13 (rs201185816/rs1000978586/rs202101165) → Interleukin-4 and Interleukin-13 signaling → inflammation/airway remodeling and MALAT1 (rs765499057/rs764699354/rs189435941) → hsa-miR-155 → IL17RB (rs948046241) → Interleukin-17 signaling (airway remodeling)/Cytokine-cytokine receptor interaction (inflammation). This study showed a systematic and propagable workflow for capturing the potential SNP "switch" of asthma through text and database mining and provides further information on the pathogenesis of asthma.

SUBMITTER: Ran MY

PROVIDER: S-EPMC8712737 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:RationaleSevere asthma (SA) is a heterogeneous disease with multiple molecular mechanisms. Gene expression studies of bronchial epithelial cells in individuals with asthma have provided biological insight and underscored possible mechanistic differences between individuals.ObjectivesIdentify networks of genes reflective of underlying biological processes that define SA.MethodsAirway epithelial cell gene expression from 155 subjects with asthma and healthy control subjects in the Severe Asthma Research Program was analyzed by weighted gene coexpression network analysis to identify gene networks and profiles associated with SA and its specific characteristics (i.e., pulmonary function tests, quality of life scores, urgent healthcare use, and steroid use), which potentially identified underlying biological processes. A linear model analysis confirmed these findings while adjusting for potential confounders.Measurements and main resultsWeighted gene coexpression network analysis constructed 64 gene network modules, including modules corresponding to T1 and T2 inflammation, neuronal function, cilia, epithelial growth, and repair mechanisms. Although no network selectively identified SA, genes in modules linked to epithelial growth and repair and neuronal function were markedly decreased in SA. Several hub genes of the epithelial growth and repair module were found located at the 17q12-21 locus, near a well-known asthma susceptibility locus. T2 genes increased with severity in those treated with corticosteroids but were also elevated in untreated, mild-to-moderate disease compared with healthy control subjects. T1 inflammation, especially when associated with increased T2 gene expression, was elevated in a subgroup of younger patients with SA.ConclusionsIn this hypothesis-generating analysis, gene expression networks in relation to asthma severity provided potentially new insight into biological mechanisms associated with the development of SA and its phenotypes.

Project description:In the field of deep learning, the processing of large network models on billions or even tens of billions of nodes and numerous edge types is still flawed, and the accuracy of recommendations is greatly compromised when large network embeddings are applied to recommendation systems. To solve the problem of inaccurate recommendations caused by processing deficiencies in large networks, this paper combines the attributed multiplex heterogeneous network with the attention mechanism that introduces the softsign and sigmoid function characteristics and derives a new framework SSN_GATNE-T (S represents the softsign function, SN represents the attention mechanism introduced by the Softsign function, and GATNE-T represents the transductive embeddings learning for attribute multiple heterogeneous networks). The attributed multiplex heterogeneous network can help obtain more user-item information with more attributes. No matter how many nodes and types are included in the model, our model can handle it well, and the improved attention mechanism can help annotations to obtain more useful information via a combination of the two. This can help to mine more potential information to improve the recommendation effect; in addition, the application of the softsign function in the fully connected layer of the model can better reduce the loss of potential user information, which can be used for accurate recommendation by the model. Using the Adam optimizer to optimize the model can not only make our model converge faster, but it is also very helpful for model tuning. The proposed framework SSN_GATNE-T was tested for two different types of datasets, Amazon and YouTube, using three evaluation indices, ROC-AUC (receiver operating characteristic-area under curve), PR-AUC (precision recall-area under curve) and F1 (F1-score), and found that SSN_GATNE-T improved on all three evaluation indices compared to the mainstream recommendation models currently in existence. This not only demonstrates that the framework can deal well with the shortcomings of obtaining accurate interaction information due to the presence of a large number of nodes and edge types of the embedding of large network models, but also demonstrates the effectiveness of addressing the shortcomings of large networks to improve recommendation performance. In addition, the model is also a good solution to the cold start problem.

Dataset Information

Multiplex-Heterogeneous Network-Based Capturing Potential SNP "Switches" of Pathways Associating With Diverse Disease Characteristics of Asthma.

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