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Identification of a noncoding RNA?mediated gene pair?based regulatory module in Alzheimer's disease.

ABSTRACT: 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.

SUBMITTER: Yu L

PROVIDER: S-EPMC6072230 | biostudies-literature | 2018 Aug

REPOSITORIES: biostudies-literature

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Identification of a noncoding RNA‑mediated gene pair‑based regulatory module in Alzheimer's disease.

Yu Lin L Qian Shi S Wei Sun S

Molecular medicine reports 20180619 2

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 associa ...[more]

PMID: 29956760

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Project description:BackgroundGenes work coordinately as gene modules or gene networks. Various computational approaches have been proposed to find gene modules based on gene expression data; for example, gene clustering is a popular method for grouping genes with similar gene expression patterns. However, traditional gene clustering often yields unsatisfactory results for regulatory module identification because the resulting gene clusters are co-expressed but not necessarily co-regulated.ResultsWe propose a novel approach, motif-guided sparse decomposition (mSD), to identify gene regulatory modules by integrating gene expression data and DNA sequence motif information. The mSD approach is implemented as a two-step algorithm comprising estimates of (1) transcription factor activity and (2) the strength of the predicted gene regulation event(s). Specifically, a motif-guided clustering method is first developed to estimate the transcription factor activity of a gene module; sparse component analysis is then applied to estimate the regulation strength, and so predict the target genes of the transcription factors. The mSD approach was first tested for its improved performance in finding regulatory modules using simulated and real yeast data, revealing functionally distinct gene modules enriched with biologically validated transcription factors. We then demonstrated the efficacy of the mSD approach on breast cancer cell line data and uncovered several important gene regulatory modules related to endocrine therapy of breast cancer.ConclusionWe have developed a new integrated strategy, namely motif-guided sparse decomposition (mSD) of gene expression data, for regulatory module identification. The mSD method features a novel motif-guided clustering method for transcription factor activity estimation by finding a balance between co-regulation and co-expression. The mSD method further utilizes a sparse decomposition method for regulation strength estimation. The experimental results show that such a motif-guided strategy can provide context-specific regulatory modules in both yeast and breast cancer studies.

Dataset Information

Identification of a noncoding RNA?mediated gene pair?based regulatory module in Alzheimer's disease.

Publications

Identification of a noncoding RNA‑mediated gene pair‑based regulatory module in Alzheimer's disease.

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