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Fig. 1 | Journal of Translational Medicine

Fig. 1

From: Network modules uncover mechanisms of skeletal muscle dysfunction in COPD patients

Fig. 1

Schematic diagram of the workflow of the study. (a) Study design of the used datasets. COPD patients (n = 15) and healthy controls (n = 12) were studied before (BT) and after (AT) an 8-week endurance training program. Measurements of skeletal gene expression [15] were used for network modules identification. Differential conditions of COPD disease effects (COPD-DE) and training-induced effects in COPD (COPD-TE) and in healthy muscles (Healthy-TE) were analyzed in the study. (b) Network modules were identified for each differential condition with the HotNet2 algorithm [22], using the gene’s false discovery rate (FDR) adjusted differential expression P values and selected protein–protein interaction (PPI) networks [17, 23] as explained in details in Additional file 1: Section 1. Thereafter (c), each module was functionally characterized using gene ontology (GO) term enrichment analysis. (d) Correlation of network modules with independent multilevel measurements was analyzed for evaluation purposes. Specifically, independent measurements were sampled both pre- and post-training and consisted of physiological parameters measured with a constant-work rate exercise at 75% of pre-training maximum peak exercise, inflammatory and redox biomarkers measured in plasma and in skeletal muscle [20], as well as plasma metabolomics measured at rest and after exercise [19]

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