Importance of normalizing both actual to mitochondrial and actual mitochondrial <i>Po</i>₂ () to mitochondrial <i>P</i>₅₀ when and/or <i>P</i>₅₀ may vary within or between muscles.

<p>Panel 1: Example of two muscles (A & B) with the same but different <i>P</i>₅₀ that happen to have the same absolute (closed circles). Although is lower in A than B, normalization of both axes (Panel 2) shows that in this case, relative to <i>P</i>₅₀ is the same, and this means that ROS generation will be the same for A and B. Panel 3: Example of two muscles (A & B) with the same <i>P</i>₅₀ but different that again happen to have the same absolute (closed circles). is again lower in A than B, but normalization of both axes (Panel 4) shows that relative to <i>P</i>₅₀ is lower in A than B, and this means that ROS generation will be high for A and normal for B.</p

Effect of altitude on ROS generation when considering typical values for lung and muscle heterogeneities.

<p>A: Between 0 and 17,000 ft., ROS generation is within the normal range throughout the exercising muscle (open circles). Open triangles indicate that between 17,000 and 22,000 ft., abnormally high levels of ROS are predicted in up to 25% of exercising muscle (in regions with highest metabolic capacity in relation to O₂ transport). The closed triangle (23,000 ft.) indicates high ROS in 25 to 50% of muscle. The open square (24,000 ft.) indicates 50–75% of muscle has high levels of ROS and filled squares (25,000–30,000 ft.) show that 75–100% of muscle regions express high levels of ROS (see text for more details). B: shows in more detail the percentage of exercising muscle that generates abnormally high levels of ROS at each altitude.</p

Dynamics of ROS production (expressed as SQo produced, normalized to total complex III abundance (taken as 0.4 nmol/mg mitochondrial protein)) at four steady state concentrations of oxygen (expressed as mitochondrial <i>Po</i>₂ relative to <i>P</i>₅₀, the oxygen partial pressure at the half-maximal rate of respiration).

<p>Before and after exercise, rest is simulated (no ATP hydrolysis, proton gradient dissipates only due to membrane leak). Between 2 and 6.6 min, exercise is simulated (membrane proton gradient dissipates due to ATP hydrolysis and ATP synthase activity). Overall, ROS production falls into two distinct patterns: one (HR, seen when ) reflects high ROS generation and the other (LR, when ) reflects little or no ROS generation compared to rest. Note post-exercise persistence of high ROS generation, especially at the lowest to <i>P</i>₅₀ ratio.</p

Mitochondrial <i>Po</i>₂ () as a function of regional ratios of metabolic capacity () to blood flow () at four altitudes.

<p>The lower (supply) is in relation to (demand), the lower is at any altitude; also, at any ratio falls with increasing altitude. Vertical dashed lines mark the normal range of . Both panels show the same data, but the lower panel expands the y-axis in its lower range to show when ROS generation is high (i.e., when ). Below 17,000ft, ROS generation remains low, but above this altitude, regions of normal muscle with high ratio generate high ROS levels, until at the Everest summit, almost the entire muscle generates high ROS levels.</p

Reduction of maximal and mitochondrial <i>Po</i>₂ in a <i>homogeneous</i> muscle as a function of altitude, (solid line, computed from the O₂ pathway model) and degree of ROS generation as a function of mitochondrial <i>Po</i>₂ (computed from the mitochondrial respiration model).

<p>Below 24,000 ft, ROS production is low (open circles), but above 24,000 ft, ROS production abruptly increases (closed circles).</p

Additional file 10: Figure S6. of From comorbidities of chronic obstructive pulmonary disease to identification of shared molecular mechanisms by data integration

Ranked based distances between DG and COPD. Each column denotes the ranking of distances (from 1 to 27, larger is closer) between each DG and COPD. JC, T and PHI denote respectively Jaccard-type, Total and phi distance. Genes, KEGG, REAC, BIOC and GO denote respectively KEGG, Reactome, BioCarta and Gene Ontology gene sets. EXT denotes distance computed with extended gene-disease associations by PPI. Φ and RR denote the co-occurrence based distances. (PDF 293 kb

Additional file 15: Tables S4, S5 and S6. of From comorbidities of chronic obstructive pulmonary disease to identification of shared molecular mechanisms by data integration

Text-mining analysis by PolySearch. set1 = (“aging”, “age”), set2 = (“smoking”,”smoke”), set3 = (“training”,”train”,”healthy life style”); the results of the queries are shown in Additional file 14: Tables S4, S5 and S6 respectively. (ZIP 97 kb

Additional file 13: Figure S9. of From comorbidities of chronic obstructive pulmonary disease to identification of shared molecular mechanisms by data integration

PCA from the data displayed in Additional file 9: Figure S8. Both panels are showing the same information with different color-coding to highlight specific results. (a) Color-code to show the different types of measurements: JC, phi or co-occurrence (Φ and RR) based measures. (b) Color-coded to show the different sources of information: genes, gene-sets and co-occurrence based measurements. (PDF 191 kb

Additional file 1: of ChainRank, a chain prioritisation method for contextualisation of biological networks

This file contains supplementary tables, figures and further information on the scores and the search algorithm. (PDF 1.43 MB

MOESM2 of Network modules uncover mechanisms of skeletal muscle dysfunction in COPD patients

Additional file 2: Table S1. Number of differentially expressed genes in the different groups and conditions. Table S2. Previous measurements: List of variables measured. Table S3. Network modules: HotNet2 results. Table S4. Network modules: HotNet2 consensus. Table S5. Network modules: Functional characterization. Table S6. Network modules: Gene differential expression. Table S7. Previous measurements: Differentials. Table S8. Previous measurements: Association with network modules. Table S9. Comparison of microarray results with qPCR validation of external datasets