Network modules uncover mechanisms of skeletal muscle dysfunction in COPD patients

Abstract

Background: Chronic obstructive pulmonary disease (COPD) patients often show skeletal muscle dysfunction that has a prominent negative impact on prognosis. The study aims to further explore underlying mechanisms of skeletal muscle dysfunction as a characteristic systemic effect of COPD, potentially modifiable with preventive interventions (i.e. muscle training). The research analyzes network module associated pathways and evaluates the findings using independent measurements.

Methods: We characterized the transcriptionally active network modules of interacting proteins in the vastus lateralis of COPD patients (n = 15, FEV1 46 ± 12% pred, age 68 ± 7 years) and healthy sedentary controls (n = 12, age 65 ± 9 years), at rest and after an 8-week endurance training program. Network modules were functionally evaluated using experimental data derived from the same study groups.

Results: At baseline, we identified four COPD specific network modules indicating abnormalities in creatinine metabolism, calcium homeostasis, oxidative stress and inflammatory responses, showing statistically significant associations with exercise capacity (VO2 peak, Watts peak, BODE index and blood lactate levels) (P < 0.05 each), but not with lung function (FEV1). Training-induced network modules displayed marked differences between COPD and controls. Healthy subjects specific training adaptations were significantly associated with cell bioenergetics (P < 0.05) which, in turn, showed strong relationships with training-induced plasma metabolomic changes; whereas, effects of training in COPD were constrained to muscle remodeling.

Conclusion: In summary, altered muscle bioenergetics appears as the most striking finding, potentially driving other abnormal skeletal muscle responses. Trial registration The study was based on a retrospectively registered trial (May 2017), ClinicalTrials.gov identifier: NCT03169270.

Keywords: Chronic obstructive pulmonary disease; Exercise training; Gene modules; Muscular weakness; Systems medicine.

Figures

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]

Fig. 2

Disease effects (COPD-DE) network modules. a The four network modules associated to COPD disease effects and their composing genes. Genes are colored according to their differential regulation, namely: up regulation—red nodes; and down regulation—blue nodes. Significantly differentially expressed genes are indicated by * (FDR ≤ 0.05) (for detailed information see Additional file 2: Table S6). b The significant correlations of independent measurements with any of the network modules’ first three principal components. Blue squares depict exercise related independent variables [19]; red squares show cytokines measured in blood [20]; yellow squares correspond to amino acids measured in serum [19]; and, green squares represents redox biomarkers [20]

Fig. 3

Relationships between genes from COPD specific modules (disease effects) and previous experimental data. a The relationships between S100A1, from the Ca2+ dependent binding module, and VO2max. The two groups, healthy subjects (blue circles) and COPD patients (low and normal FFMI, empty and filled squares, respectively) fell on the same regression line (R = 0.52, P = 0.006, FDR = 0.026). b The relationships between SMURF1 from the TGF-β signaling module and skeletal muscle nitrosative stress. A statistical significant correlation was seen in the COPD group, both normal and low FFMI (R = − 0.67, P = 0.018 and FDR = 0.07), but not in healthy subjects (R = − 0.2, P = 0.55)

Fig. 4

Training effects (TE) network modules. a Active network modules identified in case of COPD-TE, Healthy-TE and in both (shared). Genes are colored according to their differential regulation in COPD-TE (inner color of the nodes) and in Healthy-TE (border color of the nodes): up regulation with training (red circles), down regulation with training (blue circles). Modules are named after significantly enriched GO terms. Training differential expression significance is signed by * for COPD-TE, and § for Healthy-TE (FDR < 0.05) (for detailed information see Additional file 2: Table S6). b The significant correlations of the independent measurements with any of the significantly-changed training modules’ first three principal components in COPD, depicted as purple dashed lines, and in healthy subjects, depicted as blue dotted-dashed lines. Blue squares depict exercise related independent variables; red squares show cytokines measured in blood; and yellow squares correspond to amino acids measured in serum

Fig. 5

Relationships between genes from Healthy-TE specific modules and previous experimental plasma metabolomics data. The figure depicts the relationships between training-induced changes in both SF3A3, from the Amino acid biosynthesis module, and glutamine. A strong correlation was seen in healthy subjects (blue circles) (R = 0.70, P = 0.001), but not in COPD patients (low and normal FFMI, empty and filled red squares, respectively) (R = − 0.14, P = 0.518)