Schematic graphs of over-represented Gene Ontology biological process terms in enriched SFG and PC reference modules.

<p><i>Seed genes</i> were obtained from the union of differentially expressed genes with most significant SNPs and primary drug targets. GO terms are represented as nodes, and the strongest GO term pairwise similarities are designated as edges in the graph. GO terms are grouped to illustrate the main metabolic signature in PC, while both metabolic and synaptic transmission functions characterize SFG.</p

Summary of statistically significant Gene Ontology biological processes functional annotation corresponding to genes in enriched reference modules.

<p>Data refers to reference modules obtained using gene expression only (expression), by integrating this information with SNPs (expression+SNPs), and by merging the mRNA expression data, SNPs and drug targets (expression+SNPs+drug targets), and by combining the mRNA expression data, SNPs, drug targets, and OMIM genes (expression +SNPs+drug targets+ OMIM). In light blue are GO terms associated to synaptic transmission and neuronal signaling, in dark green are metabolism-associated GO terms, in gray remaining relevant terms. Highlighted are the results which have been discussed in detail in the discussion section. Specific GO terms are described in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0078919#pone.0078919.s004" target="_blank">Table S2</a>.</p

Gene lists associated to main classes of Gene Ontology biological process terms.

<p>Comparative gene lists associated to main classes of Gene Ontology biological process terms derived by integrating gene expression, SNPs and drug targets data in SFG and PC. In few cases (Fatty acid and TOR signaling) the gene list are perfectly matching, while in Insulin, Autophagy and Circadian Rhythm, they differed considerably. <i>Seed genes</i> are in bold.</p><p>PRKAA1-2, PRKAB1-2 and PRKAG1-3 are AMPK subunits, while ACACA and ACACB are ACC.</p

Schematic representation of the network analysis workflow.

<p>Significant gene expression signatures associated to AD were extracted from the GSE5281 dataset, while lists of SNPs, drug targets, and OMIM Alzheimer’s genes were obtained from public databases. These <i>seed genes</i> (in yellow and orange) inform about transcriptomic and genetic properties of AD, also providing details on drug targets in the different phases of the drug discovery process and AD associated genes in the OMIM database. Following module structure detection in the protein-protein interaction (PPI) network derived from HPRD data (see groups of nodes in the same white-background circles), we investigated the presence of reference modules where <i>seed genes</i> (obtained through the three simple lists and their union) were over-represented. We characterized the functionality of these enriched modules by testing over-represented Gene Ontology biological process terms, both considering <i>seed genes</i> (in yellow and orange) and non-<i>seed genes</i> (in light blue) that closely interact with them.</p

Simplified schematic graph of AMPK interactions with.

<p>(1) genes included in the enriched reference modules (purple), (2) differentially expressed genes (pink), (3) drug targets (light blue), and (4) SNPs (orange). Ellipses show the biological process terms associated to the genes (color as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0078919#pone-0078919-g002" target="_blank">Figure 2</a>) and altered in AD; rhomboid shapes stand for histological markers of AD.</p

Combined use of protein biomarkers and network analysis unveils deregulated regulatory circuits in Duchenne muscular dystrophy

<div><p>Although the genetic basis of Duchenne muscular dystrophy has been known for almost thirty years, the cellular and molecular mechanisms characterizing the disease are not completely understood and an efficacious treatment remains to be developed. In this study we analyzed proteomics data obtained with the SomaLogic technology from blood serum of a cohort of patients and matched healthy subjects. We developed a workflow based on biomarker identification and network-based pathway analysis that allowed us to describe different deregulated pathways. In addition to muscle-related functions, we identified other biological processes such as apoptosis, signaling in the immune system and neurotrophin signaling as significantly modulated in patients compared with controls. Moreover, our network-based analysis identified the involvement of FoxO transcription factors as putative regulators of different pathways. On the whole, this study provided a global view of the molecular processes involved in Duchenne muscular dystrophy that are decipherable from serum proteome.</p></div

Gene-set enrichment analysis.

<p>Gene-set enrichment analysis.</p

Long biomarker panel.

<p>Long biomarker panel.</p

Comparison of NASFinder results with pathway enrichment analysis of dataset GSE1004.

<p>Comparison of NASFinder results with pathway enrichment analysis of dataset GSE1004.</p

Network-based pathway enrichment analysis.

<p>Network-based pathway enrichment analysis.</p