Brown bear skeletal muscle 2D-DIGE-LC-MSMS
Muscle atrophy is one of the main deleterious consequences of ageing and physical inactivity. Although basic knowledge regarding the underlying mechanisms of muscle atrophy is continuously growing, there are still no efficient therapeutic strategies for its prevention and treatment. Hibernating bears exhibit a strong and unique ability to preserve muscle mass in conditions where muscle atrophy is observed in humans. However, underlying mechanisms have not been understood yet. To fill this gap, the aim of this study was to characterize changes in the bear muscle proteome during hibernation versus the active period. Muscle biopsies were obtained from Ursus arctos bears.
Sample Processing Protocol
Seven brown bears from Dalarna and Gävleborg counties (Sweden) were captured twice in 2013, during hibernation (February) and then during their active period (June). Biopsies of Vastus lateralis muscle were collected and immediately frozen on dry ice until storage at -80°C. After extraction, protein samples were labelled using a CyDye DIGE Fluor Minimal Dye Labeling Kit (GE HealthCare, Uppsala, Sweden). Prior to 2D gel electrophoresis, the multiplexing of samples from hibernating and active bears was randomized to avoid any bias. Following 2D gel electrophoresis, gels were washed with water and gel images were acquired using an Ettan DIGE Imager (GE HealthCare) at 100 μm resolution. Gel images were analysed using the Progenesis Samespots software (v4.5; Nonlinear Dynamics). Protein spots exhibiting normalized volumes significantly different between seasons were then excised, and proteins were in-gel digested with trypsin. The resulting peptides were extracted from the gels, and analyzed on a UPLC-system (nanoAcquity, Waters) coupled to a quadrupole-Tof hybrid mass spectrometer (maXis 4G; Bruker Daltonik GmbH). The instrument was controlled by Bruker compass Hystar (v3.2) and OtofControl (Rev3.2). The solvent system consisted of 0.1% formic acid in water (solvent A) and 0.1% formic acid in acetonitrile (solvent B). Each sample was first concentrated/desalted on a trap column (Symmetry C18, 180 µm x 20 mm, 5 µm; Waters) at 1% B at a flow rate of 5 µl/min for 3 min. Afterwards, peptides were eluted from the separation column (BEH130 C18, 75 µm x 250 mm, 1.7 µm; Waters) maintained at 60°C using the following elution gradient : t = 0 min 1% B, t = 9 min 35% B, t = 10 min, 90% B. The mass spectrometer was operated in positive mode, with automatic switching between MS and MS/MS scans. The source temperature was set to 160°C with a spray voltage of -4.5kV and dry gas flow rate of 5 l/min. External mass calibration of the Tof (MaXis 4G) was achieved before each set of analyses using Tuning Mix (Agilent Technologies, Paolo Alto, USA) in the mass range of 322-2722m/z, and mass correction was achieved by recalibration of acquired spectra to the applied lock mass using hexakis (2,2,3,3,-tetrafluoropropoxy)phosphazine ([M+H]+ 922.0098m/z). The MS acquisition time was set to 0.4 sec, and MS/MS acquisition time to a range from 0.05 sec (intensity > 250000) to 1.25 sec (intensity < 5000), and ions were excluded after acquisition of one MS/MS spectrum with release of exclusion after 0.2 min. Up to 10 most intense multiply charged precursors per MS scan were isolated, using an isolation window adapted to the isolated m/z (2-5m/z), then fragmented using energy collisional dissociation.
Data Processing Protocol
MS/MS data were analysed using the MascotTM search engine (v2.5.1, Matrix Science, London, UK) installed on local server. Spectra were searched against a target-decoy version of the Ursidae protein database downloaded from UniprotKB (September 2017, 73862 target + decoy entries) to which common contaminants (e.g. trypsin and keratins) were automatically added using the MSDA software suite. Mascot search parameters included a mass tolerance of 10 ppm in MS and 0.05 Da for MS/MS modes, a maximum of one trypsin missed cleavage allowed, carbamidomethylation of cysteine residues set as fixed modification, and oxidation of methionine residues and acetylation of protein N-termini set as variable modifications. Stringent filtering criteria were applied using Proline software (v2.5.1; http://proline.profiproteomics.fr/) to obtain high confidence identifications (FDR < 1% at both protein set and PSM level; and a minimal PSM score of 25). Single-peptide-based protein identifications, as well as the identification of common contaminants such as keratin and trypsin, were not considered.
Chazarin B, Storey KB, Ziemianin A, Chanon S, Plumel M, Chery I, Durand C, Evans AL, Arnemo JM, Zedrosser A, Swenson JE, Gauquelin-Koch G, Simon C, Blanc S, Lefai E, Bertile F. Metabolic reprogramming involving glycolysis in the hibernating brown bear skeletal muscle. Front Zool. 2019 16:12 PubMed: 31080489