A Novel Deep Proteomic Approach in Human Skeletal Muscle Unveils Distinct Molecular Signatures Affected by Aging and Resistance Training
Aging – Michael Roberts, et al.
Proteomic analysis of skeletal muscle has significantly advanced our understanding of how resistance training influences aging and skeletal muscle disorders. However, while studies have provided valuable insights into related molecular mechanisms and the aging process, they have traditionally been limited in scope due to the muscle tissue’s large dynamic range and dominance of certain key proteins (e.g., contractile and structural proteins).
In this breakthrough research with Auburn University, we implemented a study technique never done before to potentially offer new insights into the molecular intricacies of aging and the effects of aging and resistance training on protein levels.
Not only did the novel approach allow us to identify exercise-related protein targets and uncover undetected isoforms, but it has opened the door to new possibilities for how we research tissue samples in our mission to better understand human health and biology.
Key Insights
- The deep proteomic signatures of the MyoF and non-MyoF fractions in 11 participants — 5 younger adults and 6 middle-aged adults — were analyzed.
- Using the Proteograph™ workflow and Orbitrap Fusion Lumos Tribid Mass Spectrometer, 10,866 proteins were identified — 4421 myofibrillar (MyoF proteins) and 6445 non-myofibrillar (non-MyoF proteins) — which brought a robust >13-fold increase in detection depth compared to the group’s previous work.
- The study showed that cellular stress, mRNA splicing, translation elongation, and ubiquitin-mediated proteolysis predicted to be operative in middle-aged vs. young participants.
- Deep proteomic analysis suggests aging and resistance training (in the middle-aged cohort only) predominantly affects protein abundances in a non-contractile protein pool.
- Metabolic enzymes constituted the top class of proteins in both fractions, potentially countering the notion that the MyoF fraction contains exclusively contractile proteins.
- Given its unbiased nature coupled with LC-MS/MS, the Proteograph allows resolution of alternative protein isoforms to the correlation of age.
The Study Design
Using an existing protocol and non-denaturation protein structure technique through Auburn with Seer’s Proteograph workflow and nanoparticle-based approach and Thermo Fisher’s Orbitrap Fusion Lumos Tribid Mass Spectrometer, we examined skeletal muscle proteomics profiles of MyoF and non-MyoF tissue fractions in younger and middle-aged adults. Additionally, we analyzed these fractions in the middle-aged participants before and after eight weeks of knee extensor resistance training.
To address the concern that bottom-up proteomics in skeletal muscle can typically yield wide protein abundance ranges that mask lowly expressed proteins, the MyoF and non-MyoF fractions were separately subjected to protein corona nanoparticle complex formation before digestion and Liquid Chromatography Mass Spectrometry (LC-MS) analysis.
The Results
Specifically: the resolution of alternative protein isoforms to the correlation of age highlighted that more non-MyoF proteins differed between age cohorts compared to MyoF proteins – 8.4% versus 2.5% of the respective protein pools.
From a broader perspective: the powerful combination of the Proteograph’s high throughput workflow to depict low abundant proteins across a sample type combined with Thermo Fisher’s mass spec technology provides a novel workflow solution for homogenized muscle tissue analysis.
The combination of biochemical techniques to separate MyoF and non-MyoF protein fractions in skeletal muscle along with Seer’s novel Proteograph technology enabled us to interrogate the muscle proteome at an unparalleled level. This study will hopefully provide researchers a template to build upon with the intent of identifying novel proteins in skeletal muscle and other tissues affected by aging, exercise, and disease states.
— Mike Roberts, Ph.D.
Professor, Auburn University
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DOI: 10.18632/aging.205751