Comparison of clustering of 12 diseases of human muscle.

<p>A) Dendrogram obtained using proposed approach based on analysis of regulators activity. B) Dendrogram obtained using Ward's method for clustering gene expression data.</p

Identified significant clusters of regulators discriminating between adenoma, carcinoma and inflammation.

<p>Identified significant clusters of regulators discriminating between adenoma, carcinoma and inflammation.</p

Overall pipeline of the proposed approach for disease subtyping.

<p>See corresponding section for detailed description.</p

Clustering Gene Expression Regulators: New Approach to Disease Subtyping

<div><p>One of the main challenges in modern medicine is to stratify different patient groups in terms of underlying disease molecular mechanisms as to develop more personalized approach to therapy. Here we propose novel method for disease subtyping based on analysis of activated expression regulators on a sample-by-sample basis. Our approach relies on Sub-Network Enrichment Analysis algorithm (SNEA) which identifies gene subnetworks with significant concordant changes in expression between two conditions. Subnetwork consists of central regulator and downstream genes connected by relations extracted from global literature-extracted regulation database. Regulators found in each patient separately are clustered together and assigned activity scores which are used for final patients grouping. We show that our approach performs well compared to other related methods and at the same time provides researchers with complementary level of understanding of pathway-level biology behind a disease by identification of significant expression regulators. We have observed the reasonable grouping of neuromuscular disorders (triggered by structural damage vs triggered by unknown mechanisms), that was not revealed using standard expression profile clustering. For another experiment we were able to suggest the clusters of regulators, responsible for colorectal carcinoma vs adenoma discrimination and identify frequently genetically changed regulators that could be of specific importance for the individual characteristics of cancer development. Proposed approach can be regarded as biologically meaningful feature selection, reducing tens of thousands of genes down to dozens of clusters of regulators. Obtained clusters of regulators make possible to generate valuable biological hypotheses about molecular mechanisms related to a clinical outcome for individual patient.</p></div

Heatmap of activity scores (k-values) for clusters of regulators identified in GSE4183 dataset.

<p>Samples are in columns, clusters of regulators are in rows. Horizontal side bar color encodes true class labels.</p

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

<div><p>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.</p> <p>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.</p> <p>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.</p> </div

Consistent regulators of differentially expressed genes plus regulators from SNEA of reference dataset.

<p>Consistent regulators of differentially expressed genes plus regulators from SNEA of reference dataset.</p

Regulators of down-regulated genes.

<p>Most of SNEA-derived regulators of down-regulated genes regulate the processes related to myotube formation, fast-to-slow fiber type switch (including changes in myofiber composition, mitochondria content and insulin sensitivity) and metabolic changes in DMD affected muscles. Relations are described in text. Catalytic subunit of AMPK, PRKAA2, is shown next to AMPK. Functional class - class of proteins, such as enzyme families. Complex - a group of two or more proteins linked by non-covalent protein-protein interactions. Expression - protein members of one class regulate expression of proteins in another class. DirectRegulation - protein members of one class bind and regulate proteins in another class. Regulation - protein members of one class indirectly regulate proteins in another class. ProteinModification - protein members of the regulator class phosphorylate or otherwise modify proteins in the target class. PromoterBinding - protein members of one class bind promoters of genes encoding proteins in another class.</p

Gene Ontology groups enriched by consistent differentially expressed genes.

<p>Biological processes from Gene Ontology associated with consistently differentially expressed genes were found by applying “Find groups enriched with selected entities” tool embedded in Ariadne Pathway Studio to the list of 431 genes. Resulting significant (p-value<0.05) biological processes were sorted by number of genes involved in a process. Top 10 processes are shown.</p

GEO datasets used for the meta-analysis.

<p>GEO datasets used for the meta-analysis.</p