Proteomics technology shows how exercise improves the efficiency of muscle energy production
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Mitochondria are the cell's power plants and produce the majority of a cell's energy needs through an electrochemical process called electron transport chain coupled to another process known as oxidative phosphorylation. A number of different proteins in mitochondria facilitate these processes, but it's not fully understood how these proteins are arranged inside mitochondria and the factors that can influence their arrangement.
Now, scientists at the University of Copenhagen have used state-of-the-art proteomics technology to shine new light on how mitochondrial proteins gather into electron transport chain complexes, and further into so-called supercomplexes.
Traditional methods provide too little detail
It is already well established that exercise training stimulates mitochondrial mass and affects the formation of supercomplexes, which allows mitochondria in skeletal muscle to produce energy more efficiently. But questions remain about which complexes cluster into supercomplexes and how.
To better understand supercomplex formation, particularly in response to exercise, the team of scientists studied two groups of mice. One group was active, and given an exercise wheel for 25 days, and the second group was sedentary, and was not provided the exercise wheel. After 25 days, they measured the mitochondrial proteins in skeletal muscle from both groups to see how the supercomplexes had changed over time. When scientists typically analyze how supercomplexes form, they use antibodies to measure one or two proteins per electron transport chain complex. But as there can be up to 44 proteins in a complex, this method is both time consuming and provides limited information about what happens to the remainder of the proteins in each complex. As a result, there is a lack of detailed knowledge in the field.
Proteomics helps supercomplexes give up their secrets
To generate much more detailed data, the team applied a proteomic technology called mass spectrometry to measure the mitochondrial proteins. By applying proteomics instead of antibodies, the scientists were able to measure nearly all of the proteins in each complex. This provided unprecedented detail of mitochondrial supercomplexes in skeletal muscle and how exercise training influences their formation. Their approach demonstrated that not all of the proteins in each complex or a supercomplex respond to exercise in the same manner.
"Mitochondrial protein content is known to increase with exercise, thus understanding how these proteins assemble into supercomplexes is crucial to decipher how they work. Our research represents a valuable and precious resource for the scientific community, especially for those studying how the mitochondrial proteins organize to be better at what they do best: produce energy under demand,", explains Postdoc Alba Gonzalez-Franquesa.
The Journal of Proteomics & Bioinformatics is an academic journal providing an opportunity to researchers and scientist to explore the advanced and latest research developments in the field of proteomics and bioinformatics in animal, plant and microbial world. Journal of Proteomics & Bioinformatics publishes the highest quality scientific articles amalgamating broad range of fields related to proteomics and bioinformatics.
Proteomics Journals is an open access journal that aims to publish the most complete and reliable source of information on the advanced and very latest research topics related to the recent advancements in Omics studies. Proteomics journals have been showing tremendous citations and articles focusing the most advanced research trends in the field of Bioinformatics tools, Protein Biochemistry, Big Data in Genomics and Proteomics. Proteomics journals impact factors is mainly calculated based on the number of articles that undergo single blind peer review process by competent Editorial Board so as to ensure excellence, essence of the work and number of citations received for the same published articles.
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Journal of Proteomics & Bioinformatics
ISSN: 0974-276X