Stress signaling and cellular proliferation reverse the effects of mitochondrial mistranslation
The fidelity of translation is crucial in prokaryotes and for the nuclear-encoded proteins of eukaryotes, however little is known about the role of mistranslation in mitochondria and its effects on metabolism. We generated yeast and mouse models with error-prone and hyperaccurate mitochondrial translation fidelity and found that translation rate is more important than translational accuracy for cell function in mammals. We found that mitochondrial mistranslation reduces overall mitochondrial translation and the rate of respiratory complex assembly, however in mammals this is compensated for by increased mitochondrial protein stability and upregulation of the citric acid cycle. Moreover, mitochondrial stress signaling enables the recovery of mitochondrial translation via mitochondrial biogenesis, telomerase expression and cell proliferation, normalizing metabolism. Conversely, we show that increased fidelity of mitochondrial translation reduces the rate of protein synthesis without eliciting the mitochondrial stress response. Consequently, the rate of translation cannot be recovered causing dilated cardiomyopathy. Our findings reveal mammalian specific signaling pathways that can respond to changes in the fidelity of mitochondrial protein synthesis
Sample Processing Protocol
Mitochondrial proteins (100 µg) were resuspended in lysis buffer (6 M guanidinium chloride, 2.5 mM tris(2-carboxyethyl)phosphine hydrochloride, 10 mM chloroacetamide, and 100 mM Tris-HCl). After lysis, samples were diluted 1:10 in 20 mM Tris-HCL pH 8.0 and 100 µg of protein were mixed with 1 µg of Trypsin Gold (Promega) and incubated overnight at 37˚C to achieve complete digestion. Peptides were cleaned with home-made STAGEtips (Empore Octadecyl C18; 3M, Germany) and eluted in 60% acetonitrile/0.1% formic acid buffer. Samples were dried in a SpeedVac apparatus (Eppendorf concentrator plus 5305) at 45˚C and the peptides were suspended with 0.1% formic acid, 1.2 µg of peptides were analyzed by LC- MS/MS. For mass spectrometric analysis, digested peptides were loaded onto a trapping column (PepMap100, C18, 100 mm x 2cm) at a flow rate of 8 mL/min with 0.1% formic acid in water for 8 minutes before being separated on an analytical column (EASY-spray PepMap C18 column 75µm x 50cm, 2mm bead diameter column)Thermo Fisher Scientific) using a Dionex UltiMate 3000 Nano-UHPLC system (Thermo Fisher Scientific). The column was maintained at 50˚C. Buffer A and B were 0.1% formic acid in water and 0.1% formic acid in acetonitrile, respectively. Peptides were separated on a segmented gradient from 3% to 10% buffer B for 8 min, from 10% to 25% buffer B for 44 mins, from 25% to 40% buffer B for 10 min, and from 40% to 95% buffer B for 12 min and equilibration (3% B) for 12 minutes, at 300 nl/min. Eluting peptides were analyzed on an Orbitrap Fusion mass spectrometer (Thermo Fisher Scientific). The instrument was operated in a data dependent, “Top Speed” mode, with cycle times of 2 s. The “Universal Method” template was used with some modifications. Peptide precursor mass to charge ratio (m/z) measurements (MS1) were carried out at 60000 resolution in the 300 to 1500 m/z range. The MS1 AGC target was set to 1e6 and maximum injection time to 300 ms. Precursor priority was set to “Most intense” and precursors with charge state 2 to 7 only were selected for HCD fragmentation. Fragmentation was carried out using 27% collision energy. The m/z of the peptide fragments were measured in the orbitrap using an AGC target of 5e4 and 40 ms maximum injection time.
Data Processing Protocol
Data from the Orbitrap Fusion (v 3.0.2041) was processed using Proteome Discoverer software, version 2.3 (Thermo Scientific). Peptide and protein identification was performed using Sequest HT against the UniProtKB Mus musculus database (UP00000589, release2018_11). Sequest HT parameters included trypsin as a proteolytic enzyme, two missed cleavages allowed, minimum peptide length 6, peptide mass tolerance of 10 ppm, and a fragment mass tolerance of 0.02 Da. Peptide spectral match error rates were determined using the target-decoy strategy coupled to Percolator modelling of positive and false matches. Data was filtered at the peptide spectral match-level to control for false discoveries using a q-value cut off = 0.01, as determined by Percolator. The normalisation parameters in the Proteome Discoverer workflow were selected as follows: (1) the “normalisation mode” was “total peptide amount”; (2) “scaling mode” was “none”; (3) “protein abundance calculation” was the “summed abundances”; (4) “protein ratio calculation” was “protein abundance based”; and (5) “hypothesis test” was “ANOVA (individual proteins)”. Resulting file contained normalised master protein abundance values, where proteins with p-value less than 0.05 were considered significant. In order to categorise the identified proteins, enriched Gene Ontology terms were identified using DAVID v.6.8, and summarised with REVIGO (Huang et al, 2009, Supek et al, 2011). Protein abundance results were visualised with Pheatmap v1.0.12 (R v. 3.5.3 (2019-03-11)), where biological replicate values were averaged (Kolde et al, 2019).
Irina Kuznetsova, UWA
Oliver Rackham, -Harry Perkins Institute of Medical Research, Nedlands, Western Australia 6009, Australia -School of Pharmacy and Biomedical Sciences, Curtin University, Bentley, Western Australia 6102, Australia -Curtin Health Innovation Research Institute, Curtin University, Bentley, WA 6102, Australia ( lab head )