Conditional independence mapping of DIGE data reveals PDIA3 protein species as key nodes associated with muscle aerobic capacity.
Profiling of protein species is important because gene polymorphisms, splice variations and post-translational modifications may combine and give rise to multiple protein species that have different effects on cellular function. Two-dimensional gel electrophoresis is one of the most robust methods for differential analysis of protein species, but bioinformatic interrogation is challenging because the consequences of changes in the abundance of individual protein species on cell function are unknown and cannot be predicted. We conducted DIGE of soleus muscle from male and female rats artificially selected as either high- or low-capacity runners (HCR and LCR, respectively). In total 696 protein species were resolved and LC-MS/MS identified proteins in 337 spots. Forty protein species were differentially (P<0.05, FDR<10%) expressed between HCR and LCR and conditional independence mapping found distinct networks within these data, which brought insight beyond that achieved by functional annotation. Protein disulphide isomerase A3 emerged as a key node segregating with differences in aerobic capacity and unsupervised bibliometric analysis highlighted further links to signal transducer and activator of transcription 3, which were confirmed by western blotting. Thus, conditional independence mapping is a useful technique for interrogating DIGE data that is capable of highlighting latent features.


CONCLUSIONS
Quantitative proteome profiling revealed that there is little or no sexual dimorphism in the skeletal muscle response to artificial selection on running capacity. Instead we found that noncanonical STAT3 signalling may be associated with low exercise capacity and skeletal muscle insulin resistance. Importantly, this discovery was made using unsupervised multivariate association mapping and bibliometric network analyses. This allowed our interpretation of the findings to be guided by patterns within the data rather than our preconceptions about which proteins or processes are of greatest interest. Moreover, we demonstrate that this novel approach can be applied to 2D gel analysis, which is unsurpassed in its ability to profile protein species but currently has few dedicated bioinformatic tools.
Research Institute for Sport and Exercise Sciences, Liverpool John Moores University, Liverpool L3 3AF, UK. Electronic address: j.burniston@ljmu.ac.uk.
Jatin G
JG
Burniston
Department of Cellular and Molecular Physiology, University of Liverpool, Nuffield Building, Liverpool L69 3BX, UK.
Jenna
J
Kenyani
Department of Obesity and Endocrinology, Clinical Sciences Center, University Hospital Anitree, Liverpool L9 7AL, UK.
Donna
D
Gray
Center for Genetic Medicine Research, Children's National Medical Center, Washington, DC 20010, USA.
Eleonora
E
Guadagnin
Department of Mathematics and Statistics, Liverpool John Moores University, Liverpool L3 3AF, UK.
Ian H
IH
Jarman
Research Institute for Sport and Exercise Sciences, Liverpool John Moores University, Liverpool L3 3AF, UK.
James N
JN
Cobley
Department of Obesity and Endocrinology, Clinical Sciences Center, University Hospital Anitree, Liverpool L9 7AL, UK.
Daniel J
DJ
Cuthbertson
Center for Genetic Medicine Research, Children's National Medical Center, Washington, DC 20010, USA; Department of Integrative Systems Biology, George Washington University, Washington DC, USA.
Yi-Wen
YW
Chen
Department of Infection Biology, Institute of Infection and Global Health, University of Liverpool, Liverpool Science Park IC2, L3 5RF, UK.
Jonathan M
JM
Wastling
Department of Mathematics and Statistics, Liverpool John Moores University, Liverpool L3 3AF, UK.
Paulo J
PJ
Lisboa
Department of Internal Medicine, University of Michigan, Ann Arbor, MI 48109-2200, USA; Department of Anesthesiology, University of Michigan, Ann Arbor, MI 48109-2200, USA.
Lauren G
LG
Koch
Department of Internal Medicine, University of Michigan, Ann Arbor, MI 48109-2200, USA; Department of Anesthesiology, University of Michigan, Ann Arbor, MI 48109-2200, USA.
Steven L
SL
Britton
D000818:Animals; D019295:Computational Biology; D015180:Electrophoresis, Gel, Two-Dimensional; D005260:Female; D020738:Leptin / Q000097:blood; D008297:Male; D018482:Muscle, Skeletal / Q000378:metabolism*; D010085:Oxidative Phosphorylation; D010641:Phenotype; D010766:Phosphorylation; D010807:Physical Endurance; D011110:Polymorphism, Genetic; D019704:Protein Disulfide-Isomerases / Q000378:metabolism*; D020543:Proteome; D040901:Proteomics; D051381:Rats; D012420:Running / Q000502:physiology; D050796:STAT3 Transcription Factor / Q000378:metabolism*; D012737:Sex Factors; D015398:Signal Transduction; D021241:Spectrometry, Mass, Electrospray Ionization; D053719:Tandem Mass Spectrometry
D020738:Leptin; D020543:Proteome; D050796:STAT3 Transcription Factor; C494088:Stat3 protein, rat; C513196:PDIA3 protein, rat; D019704:Protein Disulfide-Isomerases
Animal Selection Model; Bibliometric network analysis; Mass spectrometry; N-myc down-regulated gene 2; Sexual dimorphism; Signal transducer and activator of transcription 3