The proteomic and metabolomic characterization of exercise-induced sweat for human performance monitoring: A pilot investigation
Author(s) -
Sean W. Harshman,
Rhonda L. Pitsch,
Zachary K Smith,
Maegan L. O’Connor,
Brian Geier,
Anthony Qualley,
Nicole M. Schaeublin,
Molly V. Fischer,
Jason J. Eckerle,
Adam J. Strang,
Jennifer A. Martin
Publication year - 2018
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0203133
Subject(s) - metabolomics , sweat , biomarker discovery , biomarker , metabolite , proteomics , computational biology , biology , bioinformatics , medicine , biochemistry , gene
Sweat is a biofluid with several attractive attributes. However, investigation into sweat for biomarker discovery applications is still in its infancy. To add support for the use of sweat as a non-invasive media for human performance monitoring, volunteer participants were subjected to a physical exertion model using a treadmill. Following exercise, sweat was collected, aliquotted, and analyzed for metabolite and protein content via high-resolution mass spectrometry. Overall, the proteomic analysis illustrates significant enrichment steps will be required for proteomic biomarker discovery from single sweat samples as protein abundance is low in this medium. Furthermore, the results indicate a potential for protein degradation, or a large number of low molecular weight protein/peptides, in these samples. Metabolomic analysis shows a strong correlation in the overall abundance among sweat metabolites. Finally, hierarchical clustering of participant metabolite abundances show trends emerging, although no significant trends were observed (alpha = 0.8, lambda = 1 standard error via cross validation). However, these data suggest with a greater number of biological replicates, stronger, statistically significant results, can be obtained. Collectively, this study represents the first to simultaneously use both proteomic and metabolomic analysis to investigate sweat. These data highlight several pitfalls of sweat analysis for biomarker discovery applications.
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