Novel Approach to Meta-Analysis of Microarray Datasets Reveals Muscle Remodeling-related Drug Targets and Biomarkers in Duchenne Muscular Dystrophy

Ekaterina Kotelnikova,Alessandra Ferlini,Nikolai Daraselia,Maria A Shkrob,Mikhail A Pyatnitskiy,Russ B Altman

doi:10.1371/journal.pcbi.1002365

Ekaterina Kotelnikova, Alessandra Ferlini + Show 4 more

Open Access

https://doi.org/10.1371/journal.pcbi.1002365

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Abstract

Elucidation of new biomarkers and potential drug targets from high-throughput profiling data is a challenging task due to a limited number of available biological samples and questionable reproducibility of differential changes in cross-dataset comparisons. In this paper we propose a novel computational approach for drug and biomarkers discovery using comprehensive analysis of multiple expression profiling datasets.The new method relies on aggregation of individual profiling experiments combined with leave-one-dataset-out validation approach. Aggregated datasets were studied using Sub-Network Enrichment Analysis algorithm (SNEA) to find consistent statistically significant key regulators within the global literature-extracted expression regulation network. These regulators were linked to the consistent differentially expressed genes.We have applied our approach to several publicly available human muscle gene expression profiling datasets related to Duchenne muscular dystrophy (DMD). In order to detect both enhanced and repressed processes we considered up- and down-regulated genes separately. Applying the proposed approach to the regulators search we discovered the disturbance in the activity of several muscle-related transcription factors (e.g. MYOG and MYOD1), regulators of inflammation, regeneration, and fibrosis. Almost all SNEA-derived regulators of down-regulated genes (e.g. AMPK, TORC2, PPARGC1A) correspond to a single common pathway important for fast-to-slow twitch fiber type transition. We hypothesize that this process can affect the severity of DMD symptoms, making corresponding regulators and downstream genes valuable candidates for being potential drug targets and exploratory biomarkers.

Highlights

Microarray-based expression profiling is a widely used, quick and inexpensive method to obtain information about the specific diseases
To get the deeper understanding of the disease mechanisms, the functional analysis of differential genes can be performed using a number of different methods [4]. They rely on Gene Ontology (GO) – based annotation of genes
Comparison of gene expression in diseased and normal tissue is a powerful tool of studying processes involved in pathogenesis and searching for potential drug targets and biomarkers of the disease’s progression and treatment outcome

Summary

Introduction

Microarray-based expression profiling is a widely used, quick and inexpensive method to obtain information about the specific diseases. To get the deeper understanding of the disease mechanisms, the functional analysis of differential genes can be performed using a number of different methods [4]. They rely on Gene Ontology (GO) – based annotation of genes. In this paper we used a proprietary literature-derived gene expression regulation network as a source of functional protein annotation. This global expression network consists of direct or indirect effects of a network node (protein) on expression of other genes [11]. The central idea of SNEA approach is that if the downstream expression targets of a ‘‘seed’’ are enriched with differentially expressed genes, the ‘‘seed’’ is likely to be one of the key regulators of the differential expression changes, e.g. a transcrip-

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Findings

Methods