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Multilayer network construction and analysis pipeline for gene expression data

ABSTRACT: Molecular networks are key to understanding the complexity of biological systems. Despite the advances in network construction and analysis techniques, challenges remain in incorporating multidimensional interactions into a hybrid network model and reducing the complexity of large networks into functional modules or influential nodes. Here, we introduce CREAM, a user-friendly application that empowers biologists to construct multilayer networks by integrating gene expression data with various known interactions predicted by experimental and computational techniques, and to identify functional modules and prognostic biomarkers within these networks. When applied to multiple cancer types, CREAM outperforms state-of-the-art module detection methods in identifying biologically relevant modules. Further investigation of colon adenocarcinoma revealed numerous well-known cancer regulators and a promising new therapeutic target, miR-8485. Our in vitro and in vivo experiments demonstrated its inhibitory effects on colon cancer cell proliferation and migration. Overall, CREAM is expected to significantly advance network-based precision medicine research.

ORGANISM(S): Homo sapiens

PROVIDER: GSE248963 | GEO | 2024/04/01

REPOSITORIES: GEO

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Project description:Previous systems-based proteomic approaches have characterized alterations in protein co-expression networks of unfractionated asymptomatic (AsymAD) and symptomatic Alzheimer’s disease (AD) brains. However, it remains unclear how sample fractionation and sub-proteomic analysis influences the organization of these protein networks and their relationship to clinicopathological traits of disease. In this proof-of-concept study, we performed a systems-based sub-proteomic analysis of membrane-enriched post-mortem brain samples from pathology-free control, AsymAD, and AD brains (n=6 per group). Label-free mass spectrometry based on peptide ion intensity was used to quantify the 18 membrane-enriched fractions. Differential expression and weighted protein co-expression network analysis (WPCNA) were then used to identify and characterize modules of co-expressed proteins most significantly altered between the groups. We identified a total of 27 modules of co-expressed membrane-associated proteins. In contrast to the unfractionated proteome, these networks did not map strongly to cell-type specific markers. Instead, these modules were principally organized by their associations with a wide variety of membrane-bound compartments and organelles. Of these, the mitochondrion was associated with the greatest number of modules, followed by modules linked to the cell surface compartment. In addition, we resolved networks with strong associations to the endoplasmic reticulum, Golgi apparatus, nucleus, and other membrane-bound organelles. Fourteen of the 27 modules demonstrated significant correlations with clinical and pathological AD phenotypes. These results revealed that the proteins within individual compartments feature a heterogeneous array of AD-associated expression patterns, particularly during preclinical stages of disease. In conclusion, this systems-based analysis of the membrane-associated AsymAD brain proteome yielded a unique network organization highly linked to cellular compartmentalization. Further study of this membrane-associated proteome may reveal novel insight into the complex pathways governing the earliest stages of disease.

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Multilayer network construction and analysis pipeline for gene expression data

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