Proteomic analysis of mitochondria from senescent Podospora anserina casts new light on ROS dependent aging mechanisms

EXG-09359; No of Pages 13 Experimental Gerontology xxx (2014) xxx–xxx

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Proteomic analysis of mitochondria from senescent Podospora anserina casts new light on ROS dependent aging mechanisms Nicole Plohnke a, Andrea Hamann b, Ansgar Poetsch a, Heinz D. Osiewacz b, Matthias Rögner a, Sascha Rexroth a,⁎ a b

Plant Biochemistry, Faculty of Biology & Biotechnology, Ruhr University Bochum, D-44780 Bochum, Germany Institute of Molecular Biosciences, Faculty for Biosciences and Cluster of Excellence Macromolecular Complexes, Johann Wolfgang Goethe University, D-60438 Frankfurt, Germany

a r t i c l e

i n f o

Article history: Received 6 December 2013 Received in revised form 4 February 2014 Accepted 11 February 2014 Available online xxxx Keywords: Podospora anserina Mass spectrometry Selected reaction monitoring Free radical theory of aging Proteome analysis

a b s t r a c t The mitochondrial free radical theory of aging (MFRTA) states that reactive oxygen species (ROS) generated at the respiratory electron transport chain are active in causing age-related damage of biomolecules like lipids, nucleic acids and proteins. Accumulation of this kind of damage results in functional impairments, aging and death of biological systems. Here we report data of an analysis to monitor the age-related quantitative protein composition of the mitochondria of the fungal aging model Podospora anserina. The impact of senescence on mitochondrial protein composition was analyzed by LC–MS. In an untargeted proteomic approach, we identified 795 proteins in samples from juvenile and senescent wild-type cultures and obtained quantitative information for 226 of these proteins by spectral counting. Despite the broad coverage of the proteome, no substantial changes in known age-related pathways could be observed. For a more detailed analysis, a targeted proteome analysis was applied focusing on 15 proteins from respiratory, ROS-scavenging and quality control pathways. Analyzing six distinct age-stages from juvenile to senescent P. anserina cultures revealed low, but statistically significant changes for the mitochondrial respiratory complexes. A P. anserina PaSod3 over-expression mutant with a phenotype of mitochondrial ROS over-production was used for biological evaluation of changes observed during aging. LC–MS analysis of the mutant revealed severe changes to the mitochondrial proteome — substantially larger than observed during senescence. Interestingly the amount of ATP synthase subunit g, involved in cristae formation is significantly decreased in the mutant implicating ROS-induced impairments in ATP synthase dimer and cristae formation. The difference between protein-profiles of aging wild type and ROS stressed mutant suggests that oxidative stress within the mitochondria is not the dominating mechanism for the aging process in P. anserina. Collectively, while our data do not exclude an effect of ROS on specific proteins and in signaling and control of pathways which are governing aging of P. anserina, it contradicts increasing ROS as a cause of a gross general and non-selective accumulation of damaged proteins during senescence. Instead, ROS may be effective by controlling specific regulators of mitochondrial function. © 2014 Elsevier Inc. All rights reserved.

1. Introduction Oxidative stress is a consequence of aerobic life. Oxidative metabolism with oxygen as terminal electron acceptor is inevitably linked to side reactions releasing reactive oxygen species (ROS) and the subsequent

Abbreviations: FRTA, free radical theory of aging; LC, liquid chromatography; MFRTA, mitochondrial free radical theory of aging; MS, mass spectrometry; ROS, reactive oxygen species; SRM, selected reaction monitoring. ⁎ Corresponding author at: Ruhr-University Bochum, Universitätsstraße 150, ND 3/133, 44780 Bochum, Germany. Tel.: +49 234/32 29896; fax: +49 234/32 14322. E-mail addresses: [email protected] (N. Plohnke), [email protected] (A. Hamann), [email protected] (A. Poetsch), [email protected] (H.D. Osiewacz), [email protected] (M. Rögner), [email protected] (S. Rexroth).

potential to damage DNA, proteins and lipids. According to the free radical theory of aging (FRTA) (Harman, 1956), molecular damage accumulates when ROS production exceeds the capacity of the scavenging and repair mechanisms, and constitutes the primary cause for cellular aging. Mitochondria have subsequently been identified as both the most prominent source and primary target of ROS leading to the mitochondrial free radical theory of aging (MFRTA) (Harman, 1972). While aging research has been strongly inspired over decades by the MFRTA, accumulating counterintuitive or even contradictory results are currently challenging this aging theory (for review see: (Lapointe and Hekimi, 2010; Mockett et al., 1999; Scialo et al., 2012)). The filamentous fungus P. anserina is a well-established experimentally tractable aging model with a clear mitochondrial etiology of aging (Osiewacz, 2002b; Osiewacz and Borghouts, 2000b; Osiewacz et al., 2013). It has a defined lifespan and develops a senescence syndrome

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Please cite this article as: Plohnke, N., et al., Proteomic analysis of mitochondria from senescent Podospora anserina casts new light on ROS dependent aging mechanisms, Exp. Gerontol. (2014), http://dx.doi.org/10.1016/j.exger.2014.02.008

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(Rizet, 1953). Several mechanisms underlying this syndrome have been identified including altered mitochondrial function (Osiewacz, 2002a) and morphology (Brust et al., 2010; Scheckhuber et al., 2007) as well as mtDNA-fragmentation (Esser and Tudzynski, 1980; Esser et al., 1983; Kück et al., 1985). In senescent P. anserina cultures the majority of the mitochondria appear to be nonfunctional and therefore less ATP is generated (Osiewacz, 2002a). Juvenile mitochondria are filamentous with a vesicular ultrastructure while senescent ones are punctate with a reticulate ultrastructure (Brust et al., 2010; Scheckhuber et al., 2007). Due to their potentially deleterious effects, the release and abundance of ROS is strongly regulated. In P. anserina different layers of pathways control cellular ROS concentrations and the elimination of ROS-induced damage, including the induction of ROS scavenging, DNA repair, protein quality control and degradation (Fischer et al., 2012; Luce and Osiewacz, 2009; Müller-Ohldach et al., 2011; Soerensen et al., 2009; Weil et al., 2011; Zintel et al., 2010). In P. anserina three different superoxide dismutases (SOD), one of them, PaSOD3, located in the mitochondrial matrix, are involved in ROS scavenging and convert ROS into less harmful compounds (Osiewacz et al., 2010). As ROS can damage proteins, the mitochondria host protein quality control systems composed of chaperones and proteases in all sub-compartments of the mitochondrion (for recent reviews see: (Fischer et al., 2012; Luce et al., 2010; Osiewacz et al., 2010)). In the inner mitochondrial membrane the i-AAA and m-AAA proteases are located and involved in degrading and processing components of the respiratory electron transport chain (Osiewacz et al., 2010; Weil et al., 2011). In the matrix LON and CLPXP proteases preferentially degrade oxidatively damaged proteins (Fischer et al., 2013; Luce and Osiewacz, 2009). Other mechanisms ensuring mitochondrial quality are mitochondrial fission and fusion as well as autophagy (Figge et al., 2012, 2013; Knuppertz et al., in press; Osiewacz et al., 2010; Philipp et al., 2013; Scheckhuber et al., 2007). It is so far only scarcely understood, how the collapse of this fine-tuned system occurs during the aging process. As the ROS scavenging system is tightly adjusted, modifications of single components often yield unexpected or even contradictory results (for a review see: (Lapointe and Hekimi, 2010)). This has been demonstrated for the SOD — the first enzyme in the superoxide anion detoxification mechanism. The over-expression of the different genes coding for the different isoforms results in a reduction of the lifespan in various organisms, such as Drosophila melanogaster (Mockett et al., 1999), yeast (Fabrizio et al., 2004), and P. anserina (Zintel et al., 2010). Recently, a computational model was applied to explain the unexpected lifespan reduction of a PaSod3 over-expressor of P. anserina (Kowald et al., 2012) identifying an increased cellular hydrogen peroxide concentration as the cause of observed damage of mitochondrial peroxiredoxin and CLP protease, which is consistent with earlier experimental observations indicating a severe oxidative stress phenotype for this mutant (Zintel et al., 2010). In the past, a number of studies have focused on changes in proteome level during aging in different biological systems so far without giving conclusive results. A global proteome analysis of different mouse tissues revealed only minor age-related changes (Walther and Mann, 2011). In different rat tissues some age-related changes on the protein level were detected using 2D gel electrophoresis and mass spectrometry but no mitochondrial proteins were affected (Chang et al., 2007). The effects of aging in P. anserina mitochondria at the protein level have already been studied using 2D electrophoresis to some extent with a focus on oxidative protein modifications. In these studies, it was demonstrated that especially the ATP synthase is a target for posttranslational modifications not only in P. anserina but also in rat brain and human cells (Groebe et al., 2007). However, these oxidative modifications are not strictly correlated to the aging process as we were able to demonstrate recently (Rexroth et al., 2012). Here we present data obtained from an untargeted and targeted proteome analysis of mitochondria isolated from six different age-stages of

the P. anserina wild type and compare the observed changes to those revealed by the analysis of a PaSod3 over-expression mutant, which we use as a benchmark for a well-defined ROS stress phenotype. We demonstrate that the observed age-related changes in protein abundance within mitochondria are rather negligible, as compared to the ROS mutant and propose that although aging of P. anserina has a clear mitochondrial etiology, either the ROS dependent non-selective protein damaging is not the primary mechanism of aging or it is compensated by efficient mechanisms ensuring proteostasis even in the senescent stage. 2. Materials and methods 2.1. P. anserina strains and cultivation In this study the P. anserina wild-type strain s (Rizet, 1953) and the PaSod3 over-expression strain PaSod3_OEx3 (Zintel et al., 2010) were first cultivated on solid medium and then transferred to liquid medium for additional two days in order to obtain sufficient material for mitochondria isolation as previously described by (Rexroth et al., 2012). Wild-type mycelia were harvested at six different age-stages (6 d, 9 d, 13 d, 15 d, 16 d, senescent = sen) with six independent cultures per age-stage and mutant mycelia at 6 d with four independent cultures. In contrast to the other age stages, senescent mycelia were incubated in liquid culture medium without shaking. Crude mitochondrial fractions were isolated by differential centrifugation (Rexroth et al., 2012). 2.2. Protein analysis 10 μg of protein were loaded onto a SDS gel. Electrophoresis was terminated when the migrating front reached the interface between stacking and separating gel — resulting in one single, clearly focused band per sample. Subsequently, the gel was stained with Coomassie. The band was cut out and digested with trypsin (Rexroth et al., 2003). Peptides were extracted using 50% (v/v) acetonitrile/1% (v/v) formic acid and then lyophilized. Before MS-analysis peptides were resolubilized in 0.1% (v/v) formic acid. 2.3. LC–MS analysis LC–MS analysis was carried out as described previously with slight modifications (Rexroth et al., 2012). For reversed-phase chromatography a gradient of solvent A (0.1% formic acid) and solvent B (99.9% acetonitrile/0.1% formic acid) was used. For MS-analysis a Thermo LTQ Orbitrap XL mass spectrometer was operated in duty cycle consisting of one 400–2000 m/z FT-MS and four MS/MS LTQ scans. MS and MS/MS data were acquired using Xcalibur (Thermo Scientific). Each sample consisted of three independent biological replicates. Each biological replicate was analyzed 6-fold. All MS-data from the untargeted approach have been deposited to the ProteomeXchange Consortium (http://proteomecentral. proteomexchange.org) via the PRIDE partner repository (Vizcaino et al., 2013) with the dataset identifier PXD000356. 2.4. Data analysis LC–MS/MS-data were analyzed using the Sequest algorithm (Eng et al., 1994) implemented in the Bioworks 3.3.1 software (Thermo scientific) for peptide identification versus a P. anserina protein database (release 6.31) (Espagne et al., 2008) containing 10,614 protein sequences and the same number of reverse sequences. As enzyme specificity trypsin was selected. Two missed cleavages and methionine mono-oxidation as a variable modification were permitted and the precursor and fragment ion mass tolerance were set to 10 ppm and 1 u, respectively. Peptide and protein probability score provided by the Bioworks software b10−3 was used as a threshold for acceptance.

Please cite this article as: Plohnke, N., et al., Proteomic analysis of mitochondria from senescent Podospora anserina casts new light on ROS dependent aging mechanisms, Exp. Gerontol. (2014), http://dx.doi.org/10.1016/j.exger.2014.02.008

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The reverse sequences of the original dataset were included to calculate false discovery rates (Kall et al., 2008). Acceptance criteria and filters were set to achieve a false positive rate of 5%. For juvenile and senescent samples the average and standard deviation over all respective biological and technical replicates were calculated. Spectral counting (Liu et al., 2004) and normalized spectral abundance factors (Usaite et al., 2008; Zybailov et al., 2006) were applied for relative quantification, when at least 20 peptides per protein were identified. The normalized spectral abundance factor (NSAF) considers the length of a protein (L) which influences the number of spectral counts (Spc). The number of spectral counts of a protein is divided by its length and this value (Spc/L) is then divided by the sum of all Spc/L in an experiment (Neilson et al., 2011). In contrast to spectral counting, NSAF take into account that bigger proteins yield more peptides than small ones during proteolytic digest. According to a t-test on a significance level of 95% and an at least 3-fold change in abundance proteins were determined as significantly changed. 2.5. Absolute peptide quantification Absolute quantification was performed using a triple quadrupole mass spectrometer (TSQ Vantage, Thermo Scientific) in SRM mode (Gerber et al., 2003). Custom peptides were synthesized for 15 selected proteins from P. anserina (Thermo Scientific) containing lysine or arginine stable isotopes to induce mass shifts of 8 or 10 Da per peptide (supplemental Table S3). These heavy labeled peptides were spiked into the tryptic digests before MS-analysis. For reversed-phase chromatography a gradient of solvent A (0.1% formic acid) and solvent B (99.9% acetonitrile/0.1% formic acid) was used. The TSQ Vantage was operated in SRM-mode. The ion spray voltage was set to 1800 V, the ion transferring tube had a temperature of 270 °C and the collision-activated dissociation pressure was set to 1.5 mTorr for each acquisition. S-lens voltage was experimentally defined for each peptide and collision energy for each peptide was generated by the skyline software (MacLean et al., 2010). Each sample consisted of six independent biological replicates. Each biological replicate was analyzed 3-times. All SRM-data have been deposited to the PeptidesAtlas SRM Experiment Library (PASSEL) and are accessible via the website http://www. peptideatlas.org/PASS/PASS00274 (Farrah et al., 2012) as well as on the website http://proteomecentral.proteomexchange.org with the dataset identifier PXD000374. 2.6. SRM data analysis Data analysis was performed using the Skyline software (MacLean et al., 2010). All SRM-data were manually inspected to ensure correct peak identification. The ratios between the peak areas of each light and heavy labeled peptide were calculated and for each age-stage median and standard deviation of all corresponding biological and technical replicates were calculated. According to a t-test on a significance level of 95% changes in protein abundance were regarded as significant. All results were normalized on the outer mitochondrial membrane protein porin from P. anserina. 3. Results While previous proteome studies of P. anserina have been performed mainly on the soluble protein complement (Chimi et al., 2013; Groebe et al., 2007), in this work we put our special focus on mitochondrial proteome applying an unbiased, untargeted approach to cover both soluble and membrane-integral proteins. Crude mitochondria from different age stages of P. anserina (6, 9, 13, 15, 16 days and senescent) were analyzed. Further purification of the mitochondria by sucrose density centrifugation was omitted to avoid the preferential selection of morphological mitochondrial subclasses.

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3.1. Untargeted proteome analysis In a first step of the analysis an untargeted mass spectrometric approach was used to provide an overview of age-related alteration in protein abundance to define both metabolism pathways effected by the aging process and housekeeping proteins that do not change in abundance during aging and thus define fixed points in the metabolism. In order to identify changes in protein abundance mitochondrial extracts from three biological replicates of each juvenile and senescent culture of P. anserina were digested with trypsin and analyzed by LC– MS/MS (Fig. 1A) using six technical replicates for each biological replicate. Peptides were identified using Sequest algorithm (Eng et al., 1994) and filter criteria were set to achieve a false positive rate smaller than 5%. Spectral counting (Liu et al., 2004) and normalized spectral abundance factors (Neilson et al., 2011; Usaite et al., 2008; Zybailov et al., 2006) were applied for relative quantification. Spectral counting is a suitable method to detect proteins showing strong changes in abundance, but tends to overestimate regulation factors (Wilm, 2009). In total 795 proteins were identified (supplemental Table S1) and grouped according to their metabolic function (Kanehisa and Goto, 2000) in order to get a better overview of the mitochondrial samples (Fig. 1D). The robustness of the quantification depends strongly on the number of peptides identified for the quantified protein. As shown in Fig. 1B, the coefficient of variation declines exponentially with the number of peptides identified per protein until approximately 20 peptides per quantified protein. Consequently, quantitative analysis was limited to proteins with at least 20 assigned peptides per protein resulting in 226 quantified proteins. The correlation between the p-value and the change between juvenile and senescent mitochondria is depicted in a volcano plot (Fig. 1C) and gives an indication of both statistical and biological significance. Taking into account both variances observed in the biological replicates and the tendency of spectral counting to overestimate regulation factors (Wilm, 2009), proteins were determined as significantly changed with a p-value less than 0.05 and an at least 3-fold change in abundance. Following afore mentioned criteria the amount of 36 proteins was changed in senescent cultures. Table 1 displays a selection of quantified proteins, the whole dataset can be found in supplemental Table S2. During senescence or due to oxidative stress changes in transcription, translation and protein degradation could be expected. However, in our dataset no strong shifts in protein abundance were apparent. In conclusion, age-related changes in protein abundance appear to be connected rather to a number of subtle changes than to huge shifts in the mitochondrial proteome. Due to the crude isolation of mitochondria by differential centrifugation, the samples inevitably contain variable amounts of proteins from the cytosol and other cellular compartments next to proteins of mitochondrial origin. Both juvenile and senescent mitochondrial samples were found to contain significant amounts of the woronin body protein (HEX1), a protein that is involved in sealing septal pores after cell lysis or hyphal wounding (Jedd and Chua, 2000). Also plasma membrane ATPase and smaller amounts of peroxisomal membrane proteins were identified. In total, the proportion of spectral counts assigned to proteins of mitochondrial origin lies between 80 and 90% for the different biological replicates and the observed proportion shows no correlation to the age of the culture. Hyphal fungi are coenocytic organisms consisting of filaments which are not separated by sealed cross-walls but contain septae with pores allowing organelles to pass through. In addition, mitochondria are dynamic organelles undergoing constant cycles of fission and fusion. Consequently, determination of the number of mitochondria per cell is not a suitable measure for quantification. To get a reliable measure for the amount of mitochondrial material in these samples, we screened the dataset for reference proteins with a high abundance to facilitate stable quantification by mass spectrometry and no age-dependent variation. Based on the untargeted proteome data, suitable candidates for this purpose are the ADP–ATP carrier protein, mitochondrial phosphate

Please cite this article as: Plohnke, N., et al., Proteomic analysis of mitochondria from senescent Podospora anserina casts new light on ROS dependent aging mechanisms, Exp. Gerontol. (2014), http://dx.doi.org/10.1016/j.exger.2014.02.008

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Please cite this article as: Plohnke, N., et al., Proteomic analysis of mitochondria from senescent Podospora anserina casts new light on ROS dependent aging mechanisms, Exp. Gerontol. (2014), http://dx.doi.org/10.1016/j.exger.2014.02.008

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Table 1 Protein abundance quantified in juvenile and senescent WT mitochondria by spectral counting. DBIDa)

Name

6 d %b)

sen %b)

Pa_2_7370 Pa_3_7460 Pa_1_17630 Pa_3_8090 Pa_1_8390 Pa_6_5610 Pa_7_2170 Pa_6_4720 Pa_1_9450 Pa_2_3290 Pa_6_4730 Pa_2_12970 Pa_6_240 Pa_1_12550 Pa_mito_cox2 Pa_6_5480 Pa_7_570 Pa_2_11540 Pa_2_7290 Pa_1_8720 Pa_1_2500 Pa_7_5390 Pa_3_1320 Pa_2_7270 Pa_3_7060 Pa_6_5750 Pa_2_1050 Pa_3_4170 Pa_7_2200 Pa_3_9430 Pa_5_9020 Pa_1_6330 Pa_1_20100 Pa_5_1740 Pa_1_10640 Pa_5_12270 Pa_7_8180 Pa_1_22280 Pa_1_24170 Pa_2_12660 Pa_3_4100 Pa_4_7160 Pa_5_6010 Pa_1_8620 Pa_4_7950 Pa_3_4870 Pa_mito_nad5 Pa_2_9780 Pa_2_7800 Pa_2_9700 Pa_7_11410 Pa_3_6820 Pa_2_12760 Pa_2_11990 Pa_3_1110 Pa_2_6490 Pa_6_8420 Pa_2_11810 Pa_1_9290 Pa_1_8920 Pa_1_17000

Aflatoxin biosynthesis ketoreductase nor-1 Alcohol dehydrogenase ATP synthase, subunit 4 ATP synthase, subunit 5 ATP synthase, subunit alpha ATP synthase, subunit beta ATP synthase, subunit delta ATP synthase, subunit epsilon ATP synthase, subunit gamma Calcium-binding mitochondrial carrier protein Aralar1 Cytochrome b–c1 complex, subunit 2 Cytochrome b–c1 complex, subunit 7 Cytochrome b–c1 complex, subunit Rieske Cytochrome c Cytochrome c oxidase, subunit 2 Cytochrome c oxidase, subunit 5 Cytochrome c oxidase, subunit IV Cytochrome c oxidase, subunit VI Cytochrome P450 monooxygenase Dolichyl-diphosphooligosaccharide–protein glycosyltransferase 67 kDa subunit Endoplasmic reticulum transmembrane protein External mitochondrial NADH:ubiquinone oxidoreductase 1 precursor Fungal-specific cytosolic translation elongation factor 3. eEF3. GMC oxidoreductase GTP-binding protein rhoA precursor Heat shock protein 60 Ketol-acid reductoisomerase. mitochondrial recursor LON protease Mitochondrial 5-aminolevulinate synthase precursor Mitochondrial acetylornithine aminotransferase precursor Mitochondrial carrier protein Mitochondrial elongation factor G Mitochondrial isovaleryl-CoA dehydrogenase precursor Mitochondrial SOD Molybdenum cofactor sulfurase Monooxygenase NADH:ubiquinone oxidoreductase, 14.8 kDa subunit NADH:ubiquinone oxidoreductase, 20.8 kDa subunit NADH:ubiquinone oxidoreductase, 21 kDa subunit NADH:ubiquinone oxidoreductase, 21.3 kDa subunit NADH:ubiquinone oxidoreductase, 29.9 kDa subunit NADH:ubiquinone oxidoreductase, 30.4 kDa subunit NADH:ubiquinone oxidoreductase, 40 kDa subunit NADH:ubiquinone oxidoreductase, 49 kDa subunit NADH:ubiquinone oxidoreductase, 51 kDa subunit NADH:ubiquinone oxidoreductase, 78 kDa subunit NADH:ubiquinone oxidoreductase, ND5 Outer mitochondrial membrane porin OXA1 TOM70 Peroxisomal membrane protein Plasma membrane ATPase Prohibitin-1 Protein disulfide-isomerase precursor Protein similar to zinc alcohol dehydrogenase of Neurospora crassa Protein transport similar to SEC61 of Neurospora crassa SCDA mitochondrial precursor short/branched chain specific acyl-CoA dehydrogenase Septin Succinate dehydrogenase, flavoprotein subunit Succinate dehydrogenase, iron–sulfur subunit Woronin body major protein HEX1

0.10 ± 0.07 0.02 ± 0.03 0.64 ± 0.10 0.74 ± 0.22 2.34 ± 1.04 4.06 ± 1.01 0.34 ± 0.28 0.28 ± 0.30 0.32 ± 0.28 0.03 ± 0.04 0.72 ± 0.23 1.27 ± 0.68 0.22 ± 0.13 0.53 ± 0.18 0.27 ± 0.20 1.12 ± 0.65 0.08 ± 0.13 0.39 ± 0.41 0.05 ± 0.03 0.01 ± 0.02 0.02 ± 0.04 0.02 ± 0.03 0.03 ± 0.04 0.12 ± 0.11 0.03 ± 0.06 0.22 ± 0.17 0.12 ± 0.15 0.02 ± 0.03 0.01 ± 0.01 0.01 ± 0.02 0.01 ± 0.02 0.02 ± 0.03 0.08 ± 0.08 0.10 ± 0.13 0.01 ± 0.02 0.01 ± 0.02 0.06 ± 0.11 0.05 ± 0.07 0.07 ± 0.11 0.16 ± 0.12 0.21 ± 0.31 0.16 ± 0.08 0.06 ± 0.08 0.11 ± 0.09 0.08 ± 0.04 0.16 ± 0.15 0.02 ± 0.02 1.93 ± 0.20 0.03 ± 0.05 0.01 ± 0.02 0.82 ± 0.41 0.52 ± 0.24 0.23 ± 0.15 0.03 ± 0.04 0.02 ± 0.05 0.01 ± 0.03 0.07 ± 0.1 0.03 ± 0.05 0.14 ± 0.12 0.15 ± 0.12 17.49 ± 6.95

0.03 ± 0.06 0.09 ± 0.07 0.70 ± 0.22 0.70 ± 0.21 5.06 ± 3.09 5.87 ± 2.34 0.12 ± 0.15 0.15 ± 0.21 0.46 ± 0.17 0.11 ± 0.06 0.99 ± 0.28 0.70 ± 0.27 0.18 ± 0.14 0.67 ± 0.28 0.29 ± 0.20 0.64 ± 0.54 0.07 ± 0.10 0.30 ± 0.39 0.01 ± 0.02 0.05 ± 0.05 0.12 ± 0.03 0.09 ± 0.05 0.09 ± 0.04 0.01 ± 0.02 0.1 ± 0.11 0.42 ± 0.13 0.50 ± 0.2 0.03 ± 0.02 0.05 ± 0.04 0.08 ± 0.07 0.22 ± 0.09 0.08 ± 0.05 0.02 ± 0.03 0.01 ± 0.03 0.06 ± 0.05 0.06 ± 0.06 0.26 ± 0.23 0.13 ± 0.13 0.11 ± 0.14 0.31 ± 0.18 0.27 ± 0.19 0.3 ± 0.15 0.34 ± 0.19 0.18 ± 0.09 0.14 ± 0.07 0.46 ± 0.14 0.05 ± 0.04 2.33 ± 0.63 0.12 ± 0.1 0.07 ± 0.05 0.68 ± 0.32 0.89 ± 0.27 0.48 ± 0.24 0.13 ± 0.11 0.10 ± 0.06 0.1 ± 0.09 0.01 ± 0.03 0.14 ± 0.09 0.33 ± 0.17 0.26 ± 0.18 9.96 ± 8.94

a) Identification number assigned during genome sequencing (Espagne et al., 2008). b) Values represent normalized spectral abundance factors (Zybailov et al., 2006) averaged over 3 biological replicates per age-stage and corresponding standard deviation.

Fig. 1. Experimental approach and overview about proteins identified in the untargeted approach. (A) Workflow of the untargeted analysis of 6 d and senescent P. anserina cultures in order to select proteins for the targeted approach. First mycelia were cultivated on solid medium and then transferred to liquid medium. Juvenile cultures were cultivated under shaking, senescent cultures without shaking. After isolation of mitochondria by differential centrifugation the samples were digested with trypsin and analyzed using LC–MS/MS. Based on the spectral counting results interesting proteins for the targeted approach were selected. (B) The number of peptides was plotted against the coefficient of variation of the corresponding proteins. White points: juvenile wild-type mitochondria, dark gray points: senescent wild-type mitochondria. (C) Volcano plot of all proteins quantified in the untargeted approach. The negative log of the p-value from a t-test was plotted against the log2 of the fold change between juvenile and senescent mitochondria. Light gray points represent proteins with a statistical significance less than 95% and less than 3-fold change in abundance. Dark gray points represent proteins which are statistically or biologically significant and black points represent proteins with biological and statistical significance. (D) Functional overview about the proteins identified in juvenile and senescent mitochondria in the untargeted approach. Gray bars: senescent mitochondria, white bars: juvenile mitochondria. Proteins were grouped according to their metabolic function (Kanehisa and Goto, 2000). Error bars display the standard deviation over three biological replicates per age-stage.

Please cite this article as: Plohnke, N., et al., Proteomic analysis of mitochondria from senescent Podospora anserina casts new light on ROS dependent aging mechanisms, Exp. Gerontol. (2014), http://dx.doi.org/10.1016/j.exger.2014.02.008

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carrier protein and the outer mitochondrial membrane protein porin, as well as a number of proteins involved in oxidative phosphorylation like ATP synthase subunit beta and cytochrome c. The outer mitochondrial membrane protein porin was considered as a best candidate for normalization, as it shows little variability between the different biological and technical replicates and it has been identified and used in prior studies for quantification of mitochondrial material. Although regulation factors estimated by spectral counting have to be treated with caution (Wilm, 2009), for a number of proteins a significant increase in abundance is observed in senescent samples. The putative mitochondrial carrier protein (Pa_5_9020) shows the strongest increase with a 60-fold increase in senescent mitochondria. Also significantly increased are the mitochondrial acetylornithine aminotransferase (Pa_3_9430), TOM70 (Pa_2_9700) and mitochondrial 5-aminolevulinate synthase (Pa_7_2200) showing an 8- to 10-fold increase in protein amount. Among the proteins significantly reduced in abundance in senescent mitochondria is the cytochrome P450 monooxygenase (Pa_2_7290) with a 10-fold decrease, the mitochondrial SOD (PaSOD3) and the GMC oxidoreductase (Pa_2_7270) are decreased about 8-fold (Table 1). Moreover, the mitochondrial isovaleryl-CoA dehydrogenase precursor (Pa_1_20100) is decreased 4-fold. While these detected changes are both statistically and biologically significant, apart from PaSOD3 and cytochrome P450 monooxygenase, no direct connection of pronounced changes in protein abundance and aging is evident and no clear induction of a prominent metabolic pathway is observed. The MFRTA puts the complexes of the respiratory electron transport chain into the focus of the analysis. For complex I, one mitochondrial encoded protein subunit, ND5, and ten nuclear encoded subunits (78 kDa, 51 kDa, 49 kDa, 40 kDa, 30.4 kDa, 29.9 kDa, 21.3 kDa, 21 kDa, 20.8 kDa, 14.8 kDa) were identified (Table 1). Only for the 40 kDa subunit a change in abundance could be observed. For complex II (iron–sulfur and flavoprotein subunit) cytochrome b-c1 complex (Rieske, core protein 2, subunit 7) and cytochrome c oxidase (subunits IV, V, VI, COX II) as well as for eight different subunits of mitochondrial ATP synthase no significant change in abundance could be observed and also a constant amount of the soluble electron carrier cytochrome c was detected in the analyzed mitochondrial samples. While the untargeted analysis covers a vast range of protein subunits of all five respiratory complexes, due to the limited sensitivity of the untargeted approach only for single subunits a change in abundance can be detected. 3.2. Targeted protein quantification Although sensibility of spectral counting is sufficient to exclude major changes in protein abundance, a representative subunit for each complex was selected for the targeted analysis to detect even minor changes with statistical confidence. For the selection process, preferentially protein subunits involved in the catalytic reaction of the respective respiratory chain complex with at least one membrane spanning transmembrane helix were selected, to achieve a good correlation between the concentration of the quantified subunit and the represented active protein complex. For the ATP synthase subunit alpha was selected, due to the lack of hydrophilic domains in the proton transporting subunits. A second objective of the targeted approach is the analysis of low abundant proteins involved in the aging mechanism as LON and CLPP protease and factors of mitochondrial dynamics as MGM1 and mitofilin (supplemental Table S3). These selected proteins were analyzed by SRM, a sensitive technique based on the use of heavy labeled peptides as internal standard in the sample facilitating the detection of low abundant proteins and enabling absolute protein quantification (Fig. 2A). Due to the difficulties mentioned above to define the number of cells and mitochondria as representative biological units, the outer mitochondrial membrane porin was used as the basis for normalization. Although it is not possible to determine protein numbers per cells or mitochondria, SRM provides a high sensitivity and reproducibility. Its

high sample throughput facilitates analysis of six independent P. anserina cultures with three technical replicates at six different agestages. The amounts of all quantified proteins can be found in Table 2. In the targeted approach the biological and technical variances are significantly smaller than in the untargeted approach. The average coefficient of variation for the technical replicates is 30% while the biological variance is slightly higher with 41%. The protein abundances detected in the course of the analysis in a sample with 2 μg of crude mitochondrial proteins – containing on average 11.5 fmol of outer mitochondrial membrane protein porin – range from 57.9 fmol for COX II (complex IV) down to 0.5 fmol for the mitochondrial fusion factor MGM1. During aging the mitochondrial SOD (PaSOD3) shows the highest relative change in abundance and increases 5.9-fold from day six to day nine (Fig. 4A). The highest absolute change in abundance during aging is observed for cytochrome c which increases by 38.4 fmol (Fig. 3A). Significantly, in the later age stages cytochrome c is found to increase in mitochondria 3.0-fold. This increase appears to reflect a compensatory up-regulation expression of the cytochrome c gene to counteract the release of cytochrome c as a result of the induction of apoptosis late in life (Brust et al., 2010). LON protease is increased by 0.8 fmol resembling the lowest absolute change in abundance. In general proteins involved in oxidative phosphorylation show only slight changes in abundance during aging (Fig. 3A). The amount of ND5 (complex I) is 1.5 fmol on day six then increases 2-fold to a peak on day fifteen with 3.1 fmol and then drops back to 1.5 fmol at senescent stage. The amount of the iron sulfur subunit (complex II) increases from 20.8 fmol on day six to 29.6 fmol on day nine and decreases subsequently to 18.6 fmol in senescent mitochondria. Similar tendencies can be seen for cytochrome b (complex III) and COX II (complex IV). A reverse trend shows the mitochondrial ATP synthase subunit alpha. The amount is increased 2-fold from 22.5 fmol on day six till day sixteen and drops afterwards to 42.8 fmol in senescent mitochondria (Fig. 3A). In conclusion, the amount of the complexes I to IV of the respiratory electron transport chain alter during aging, showing a maximum at approximately nine days and a subsequent reduction of abundance in senescent mitochondria. Compared to juvenile samples, only the amount of subunit alpha of complex V appears to be increased in senescent mitochondria (Fig. 3A). The results for the proteins involved in oxidative phosphorylation correlate between the two approaches. The three analyzed mitochondria encoded proteins, ND 5, cytochrome b and COX II show the same behavior as nuclear encoded proteins. Proteins involved in ROS scavenging or general stress response were found to show no significant or only slight changes in abundance during aging. Mitochondrial SOD (PaSOD3) displays the same trend as observed for the respiratory chain complexes (Fig. 4A). The amount of mitochondrial HSP60 (Fig. 4A), CLPP and LON protease (supplemental Fig. 1A), as well as proteins involved in mitochondrial dynamics, PaFCJ1 (mitofilin), MGM1 and ATP synthase subunit g (Fig. 5A) show only small changes and remain rather constant during aging. Overall, protein abundance of the analyzed proteins does not show a strong age-related trend as it would be expected from the general increase in protein damage. 3.3. Comparison of wild type and PaSod3_OEx3 mutant Finally, analysis was performed with PaSod3 over-expression mutant of P. anserina (Zintel et al., 2010) to establish a proteome benchmark for a ROS stress phenotype (Fig. 2B). Although PaSod3 is overexpressed, in the mutant ROS scavenging is not increased. In contrast, the mutant is more sensitive against paraquat stress, shows indications of mitochondrial protein damage (i.e., CLPP and LON protease, peroxiredoxin), and is characterized by a shortened lifespan (Zintel et al., 2010). This unexpected phenotype has been explained by computational modeling to result from an increase in hydrogen peroxide leading to damage of the mitochondrial CLPP protease, and peroxiredoxin

Please cite this article as: Plohnke, N., et al., Proteomic analysis of mitochondria from senescent Podospora anserina casts new light on ROS dependent aging mechanisms, Exp. Gerontol. (2014), http://dx.doi.org/10.1016/j.exger.2014.02.008

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Fig. 2. Experimental approach and overview about proteins identified in the targeted approach. (A) Workflow of the targeted analysis to reveal age-related changes in protein abundance of P. anserina cultures from six different age-stages. First mycelia were cultivated on solid medium and then transferred to liquid medium. All cultures except the senescent ones were cultivated under shaking, senescent cultures were cultivated without shaking. After isolation of mitochondria by differential centrifugation the samples were digested with trypsin and reference peptides were added. Samples were then analyzed using SRM. (B) Workflow of the targeted approach to reveal ROS-induced changes in protein abundance of P. anserina wild type (6 d) and PaSod3_OEx3 mutant (6 d). First mycelia were cultivated on solid medium and then transferred to liquid medium. Wild-type and mutant cultures were cultivated under shaking. After isolation of mitochondria by differential centrifugation the samples were digested with trypsin and reference peptides were added. Samples were then analyzed using SRM.

and increased oxidative stress (Kowald et al., 2012). In contrast to the artificial generation of oxidative stress by exogenous application of hydrogen peroxide in this mutant hydrogen peroxide is generated directly in the mitochondria, resulting in a situation much closer to the natural situation of oxidative stress in the cell. So if the cells suffer from enhanced oxidative stress during aging, consequences on the protein level in senescent wild-type mitochondria should correlate with the

effects observed in mitochondria of the mutant. This comparative analysis enables a better classification of the biological significance of the results obtained from aging wild-type mitochondria. To address this possibility, we used mitochondrial extracts from the mutant applying four biological replicates (Fig. 2B) and three technical replicates per biological replicate for SRM analysis. In our analysis, the over-expression of PaSod3 could be clearly confirmed (Fig. 4B). In

Table 2 Protein amount quantified by SRM in different age stages of the wild type and PaSod3_OEx3. DBIDa)

Name

WT 6 d, fmol b)

WT 9 d, fmol b)

WT 13 d, fmol b)

WT 15 d, fmolb)

WT 16 d, fmolb)

WT sen, fmolb)

Pa_Sod3_OEx3 6 d, fmolb)

Pa_mito_nad5 Pa_1_8920 Pa_mito_cob. Pa_mito_cox2 Pa_1_8390 Pa_1_12550 Pa_5_1740 Pa_5_8240 Pa_6_5750 Pa_2_3900 Pa_3_4170 Pa_1_1530 Pa_1_2480 Pa_2_2290 Pa_2_9780

ND5 (complex I) Iron sulfur subunit (complex II) Cytochrome b (complex III) COX II (complex IV) Subunit alpha (complex V) Cytochrome c Mitochondrial SOD Peroxiredoxin Mitochondrial HSP60 CLPP LON protease PaFCJ1 (Mitofilin) ATP synthase, subunit g PaMGM1 Outer mitochondrial membrane porin

1.5 20.8 2.2 52.9 22.5 18.2 8.0 3.6 6.8 3.7 1.2 4.5 7.0 0.5 11.5

2.3 1.6 29.7 ± 6.4 7.2 ± 3.0 58.0 ± 12.7 42.8 ± 12.2 24.3 ± 18.4 15.6 ± 9.4 3.6 ± 0.7 10.8 ± 2.6 5.7±2.4 1.6 ± 0.4 7.9 ± 2.5 6.8 ± 2.2 1.8 ± 1.0 11.5 ± 1.55

1.0 23.6 4.3 51.1 41.9 15.5 6.9 3.0 7.2 4.0 1.5 5.1 5.1 1.4 11.5

3.1 28.8 6.3 37.8 26.9 21.2 10.5 5.2 8.4 5.2 2.0 6.3 6.3 0.9 11.5

1.3 14.4 3.0 33.8 45.3 53.9 6.5 3.2 10.8 4.8 1.7 5.4 5.1 1.0 11.5

1.5 18.6 2.1 30.7 42.8 46.5 2.6 4.8 8.5 4.2 1.6 5.3 7.3 0.7 11.5

0.1 ± 4.6 ± 0.6 ± 7.9 ± 71.4 ± 19.7 ± 1042.4 ± 6.8 ± 80.3 ± 2.9 ± 1.5 ± 6.0 ± 4.0 ± 0.8 ± 11.5 ±

± ± ± ± ± ± ± ± ± ± ± ± ± ± ±

0.9 2.3 1.4 13.0 10.0 14.5 6.1 1.1 1.2 2.2 1.0 1.8 2.1 0.3 1.39

± ± ± ± ± ± ± ± ± ± ± ± ± ± ±

0.1 3.7 3.0 8.6 8.5 5.6 6.1 1.4 1.9 1.7 0.8 2.3 1.3 0.8 1.41

± ± ± ± ± ± ± ± ± ± ± ± ± ± ±

1.2 6.5 2.1 14.7 13.9 7.1 8.5 1.9 2.2 1.8 1.7 2.6 2.6 0.7 2.24

± ± ± ± ± ± ± ± ± ± ± ± ± ± ±

1.0 4.4 1.3 12.3 19.2 15.4 5.3 1.4 4.1 2.5 1.1 2.4 2.6 0.6 2.97

± ± ± ± ± ± ± ± ± ± ± ± ± ± ±

0.7 2.8 1.0 4.6 10.9 8.1 2.6 1.2 1.4 2.1 0.8 1.5 1.8 0.5 1.94

0.1 2.4 0.2 5.4 4.9 10.7 445.2 1.6 54.0 1.0 0.2 1.7 0.3 0.3 4.4

a) Identification number assigned during genome sequencing (Espagne et al., 2008). b) Values represent the amount of substance in fmol averaged over 6 biological replicates for the wild type, 4 biological replicates for the mutant and three technical replicates for each biological replicate and corresponding standard deviation.

Please cite this article as: Plohnke, N., et al., Proteomic analysis of mitochondria from senescent Podospora anserina casts new light on ROS dependent aging mechanisms, Exp. Gerontol. (2014), http://dx.doi.org/10.1016/j.exger.2014.02.008

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Please cite this article as: Plohnke, N., et al., Proteomic analysis of mitochondria from senescent Podospora anserina casts new light on ROS dependent aging mechanisms, Exp. Gerontol. (2014), http://dx.doi.org/10.1016/j.exger.2014.02.008

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Fig. 4. PaSOD3 and HSP60 quantified by SRM in wild type and PaSod3_OEx3 mutant. PaSOD3 and HSP60 quantified by SRM using heavy labeled reference peptides in wild-type mitochondria from six different age-stages (A) and juvenile wild-type and PaSod3_OEx3 mutant mitochondria (B). Gray bars: wild-type mitochondria from different age stages (6 d, 9 d, 13 d, 15 d, 16 d, sen), white bars: mitochondria from PaSod3_OEx3 mutant (6 d). Error bars display the standard deviation over six biological replicates per age-stage for the wild type and four biological replicates for the PaSod3_OEx3 mutant.

wild-type mitochondria 7.9 fmol PaSOD3 were detected and in the mutant 1042.4 fmol. Apart from the PaSOD3 the HSP60 shows the highest relative change with an 11.9-fold increase in the mutant (Fig. 4B). In the mutant, the increased ROS levels have a severe impact on the protein complexes involved in oxidative phosphorylation. The strongest effect is observed for ND5 (complex I). It displays a 15-fold reduction to 0.1 fmol in the mutant (Fig. 3B). For the iron sulfur subunit of complex II, cytochrome b (complex III) and COX II (complex IV) a reduction to 22%, 29% and 15% of the wild-type level is observed in the mutant. The amount of cytochrome c does not show significant differences in wild type and mutant. This might be a consequence of an up-regulated cytochrome c transcription in the mutant as it is observed in a PaCypD over-expression strain (Brust et al., 2010). The amount of mitochondrial ATP synthase subunit alpha (complex V) shows a 3-fold increase in the mutant (Fig. 3B). However, this observed increase is in conflict with the changes observed for ATP synthase subunit g (see below) and it is not clear, if the increase reflects an increase in the active ATP synthase complex or represents unbound F1 as an assembly/disassembly intermediate (Li et al., 2012). The CLPP and LON protease do not show significant changes in the mutant (supplemental Fig. 1B). Of the proteins involved in mitochondrial morphology subunit g of the ATP synthase involved in dimerization and cristae formation displays the most prominent effect with a reduction to 57% of the wild-type amount (Fig. 5B). Strikingly, recently an age-dependent dissociation of ATP synthase dimers and loss of inner-membrane cristae was described in aging P. anserina wild type (Daum et al., 2013). It is thus tempting to speculate that the decrease in lifespan of the PaSod3 over-expressing P. anserina results from accelerated reorganization in

cristae architecture due to ROS induced damage of subunit g of the ATP synthase. Overall, ROS released in mitochondria have a significant effect on the protein complexes of oxidative phosphorylation – especially complexes I and IV. These effects are much more pronounced in the PaSod3 overexpressing mutant than the changes observed in the wild type during aging allowing the detection of processes which in the wild type can only hardly be detected. 4. Discussion 4.1. Oxidative stress, radicals and aging Aging, despite its eminent effects and ultimately lethal consequences for each known organism, is a multifactorial process so far only insufficiently explained by existing models. As one of the prominent theories, the MFRTA links aging to the accumulation of damage induced by oxidative stress and furthermore identifies mitochondria as the main source and target of ROS (Harman, 1972). Although MFRTA has been successfully employed to explain many observations, it has become evident that the role of ROS is more complex and models depicting the function of ROS as secondary messengers (Hekimi et al., 2011) as well as the stimulation of lifespan extending mechanisms by ROS (Ristow and Schmeisser, 2011; Scialo et al., 2012) are evolving. While approaches decreasing ROS production – in general agreement with MFRTA – lead to an increased lifespan, as observed for P. anserina mutants grisea and ex1 (Scheckhuber et al., 2011), mutations targeting ROS scavenging often lead to controversial results (Fabrizio et al., 2004; Mockett et al., 1999; Zintel et al., 2010) revealing the complex

Fig. 3. Proteins involved in oxidative phosphorylation quantified by SRM in wild type and PaSod3_OEx3 mutant. Subunits of the five complexes of the respiratory chain quantified by SRM using heavy labeled reference peptides in wild-type mitochondria from six different age-stages (A) and juvenile wild-type and PaSod3_OEx3 mutant mitochondria (B). Gray bars: wildtype mitochondria from different age stages (6 d, 9 d, 13 d, 15 d, 16 d, sen), white bars: mitochondria from PaSod3_OEx3 mutant (6 d). Error bars display the average over six biological replicates per age-stage for the wild type and four biological replicates for the PaSod3_OEx3 mutant.

Please cite this article as: Plohnke, N., et al., Proteomic analysis of mitochondria from senescent Podospora anserina casts new light on ROS dependent aging mechanisms, Exp. Gerontol. (2014), http://dx.doi.org/10.1016/j.exger.2014.02.008

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Fig. 5. Proteins involved in mitochondrial dynamics quantified by SRM in wild type and PaSod3_OEx3 mutant. PaFCJ1 mitofilin, PaMGM1 and ATP synthase subunit g quantified by SRM using heavy labeled reference peptides in wild-type mitochondria from six different age-stages (A) and juvenile wild-type and PaSod3_OEx3 mutant mitochondria (B). Gray bars: wildtype mitochondria from different age stages (6 d, 9 d, 13 d, 15 d, 16 d, sen), white bars: mitochondria from PaSod3_OEx3 mutant (6 d). Error bars display the standard deviation over six biological replicates per age-stage for the wild type and four biological replicates for the PaSod3_OEx3 mutant.

detoxification and repair network. These problems certainly result from the dual effects caused by ROS: molecular damage and affecting gene expression via signaling. P. anserina is a model organism with a well-defined mitochondrial etiology of aging. Effects of age on mtDNA instability (Kück et al., 1985; Osiewacz and Borghouts, 2000a) as the amplification of a circular DNA molecule, termed plDNA or senDNA, and the linear mitochondrial plasmid pAL-2 (van Diepeningen et al., 2008) are apparent and have been linked to the activity of ROS (Soerensen et al., 2009). The direct impact of ROS on the protein level was demonstrated using 2D electrophoresis (Groebe et al., 2007), several proteins as the ATP synthase and aconitase were identified as hot spots of oxidative damage; however, correlation with aging is weak (Rexroth et al., 2012). 4.2. Proteomic changes during aging Ultimately, age-related changes have to affect the proteome: Either as consequence of reduced translation efficiency, mutations and regulation of protein expression, when primary events were targeting DNA (Groebe et al., 2007; Osiewacz and Borghouts, 2000a), or due to accumulation of misfolded, cross-linked or otherwise damaged proteins, when proteins are the primary target. In our study, we combined an untargeted proteomic approach, which provides a broad coverage of the proteome, with a targeted approach focusing on quantification of a small selection of proteins with a high sensitivity and accuracy. To account for stochastic aging

process and to reflect the biological variance for the untargeted approach three independent and for the targeted SRM approach, which allows a higher sample throughput, six independent cultures were used for the analysis. As it has been shown that aging strongly influences mitochondrial morphology, special emphasis has to be put on the isolation of mitochondrial material to avoid a bias introduced by methods selectively enriching healthy mitochondria. As a consequence, separation of mitochondria to high purity, as it can be achieved by sucrose density gradient, was omitted and isolation of mitochondrial material was performed by simple differential centrifugation (Scheckhuber et al., 2009). By the untargeted mass spectrometric approach it is evident, that the mitochondrial fraction is still relatively pure with 80 to 90% of the assigned peptides originating from mitochondrial proteins. In addition, as a selection of proteins from all mitochondrial sub-compartments suited as reference proteins, i.e. showing no age-related changes and only a small variance between biological replicates, the majority of isolated mitochondria have to be intact. Otherwise the standard deviations for proteins originating from mitochondrial matrix or inter-membrane space would have been significantly higher. By the untargeted approach, an overview about the proteins present in juvenile and senescent mitochondria was provided. Relative changes in protein abundance were determined by spectral counting (Liu et al., 2004) and normalized spectral abundance factors (Usaite et al., 2008; Zybailov et al., 2006). Spectral counting is a label-free method which requires no special sample preparation. It is based on the observation that an increase in protein abundance leads to an increase in MS/MS-spectra.

Please cite this article as: Plohnke, N., et al., Proteomic analysis of mitochondria from senescent Podospora anserina casts new light on ROS dependent aging mechanisms, Exp. Gerontol. (2014), http://dx.doi.org/10.1016/j.exger.2014.02.008

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With this method a large number of proteins in a sample can be identified; however, the dynamic range is small, observed changes tend to be overestimated and the relative standard deviation is usually higher compared to other methods (Xie et al., 2011). Due to the complex samples low abundant proteins are always underrepresented in spectral counting analysis. These issues can be efficiently addressed using targeted SRM approach providing a very sensitive detection with a dynamic range over four to five orders of magnitude for a selection of proteins (Wilm, 2009). The combination of both approaches, SRM technique provides sensitivity and accuracy essential for a hypothesis driven analysis, while spectral counting gives a survey over a significant part of the mitochondrial proteome and provides means to detect unexpected changes and novel influence factors. For the analysis of mitochondrial material from six days old and senescent P. anserina cultures, the untargeted approach yielded quantitative information for 226 proteins. However, only a minority of these proteins showed a significant change in abundance. Especially for the proteins involved in oxidative phosphorylation a good coverage for both membrane intrinsic and extrinsic proteins was obtained, but within the experimental and biological variance of spectral counting no significant change could be observed. The few proteins showing significant changes in abundance in the untargeted approach as a putative mitochondrial carrier protein, mitochondrial acetylornithine aminotransferase, and mitochondrial 5aminolevulinate synthase were scattered over a range of biological pathways with no clear correlation to common aging theories. A detailed analysis of these proteins, e.g. knock-out or over-expression approaches of the corresponding genes, may uncover novel pathways relevant to aging. For the targeted mass spectrometric approach 15 proteins linked to MFRTA were selected. These proteins included subunits of all oxidative phosphorylation complexes, as the main source and primary target of ROS, enzymes of ROS scavenging and protein quality control mechanism, as well as factors involved in the dynamics of mitochondrial morphology. In both approaches, the age-related changes in abundance in general appear to be rather small. By applying SRM, statistically significant changes in protein abundance were observed for the respiratory complexes with a rise in abundance on day nine and a decline after fifteen days, very similar to the estimations by 2D DIGE, published recently (Chimi et al., 2013). However, changes – especially when juvenile cultures at six days and sixteen days or senescent cultures are compared – are less than ± 30% and appear small in comparison to the drastic changes in biological fitness of the cultures. Such small changes on proteome level have been reported during aging in different model organisms, as in different mouse tissues (Walther and Mann, 2011) and in rat liver (Chang et al., 2007). Also in our analysis, the amounts of proteins involved in ROS scavenging (mitochondrial SOD, peroxiredoxin), protein folding (mHSP60) and quality control (CLPP, LON) show only very small or even no age-related changes. This is in conflict with expectation, as old mitochondria were shown to produce more ROS than young ones (Scheckhuber et al., 2007) and a response to elevated levels of oxidative stress and subsequent molecular damage could be expected. Such damage could be removed by the different cellular quality control pathways. While previous Western blot analysis did not demonstrate a change in abundance of LON and CLPP proteases in P. anserina during aging, recent work revealed an age-related increase in autophagy (Knuppertz et al., in press; Philipp et al., 2013) counteracting accumulation of misfolded and damaged proteins and allowing de novo synthesis of proteins. While these results rule out a gross general and non-selective accumulation of damaged and dysfunctional proteins, subtle ROS induced changes to susceptible residues (Rexroth et al., 2012) might avoid triggering protein quality control mechanisms and escape detection by proteomics approaches targeting protein abundance, but may nonetheless affect protein function and induce senescence in accordance with MFRTA.

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4.3. Benchmarking of ROS stress induced changes It seems unlikely that the small changes in the abundance of protein complexes of the respiratory chain have significant biological consequences. In tobacco chloroplasts, when the abundance of the protein complexes of the photosynthetic electron transport chain was downregulated by an RNAi approach, lethal effects were only observed, when the amount of individual complexes was reduced to 10% or less of the original amount (Rott et al., 2011). Although it is questionable, if these result can be transferred in a meaningful way to the situation in the mitochondria, it appeared essential to generate a benchmark for the effects of ROS on mitochondrial protein abundance. For this purpose we selected the P. anserina PaSod3 over-expression mutant with a well-established phenotype of mitochondrial ROS over-production (Zintel et al., 2010). In different model organisms similar SOD overexpression mutants have been generated. In P. anserina the mutant over-expressing the gene encoding PaSOD3 has a decreased lifespan of 9 days and suffers from enhanced oxidative stress (Zintel et al., 2010). Similar effects were observed for Sod over-expression in yeast (Fabrizio et al., 2004) and D. melanogaster (Mockett et al., 1999). The mechanism for the lifespan reduction in the PaSod3 over-producing P. anserina mutant was explained recently in detail by a computational model (Kowald et al., 2012). In general, the observed changes in abundance in the mutant are much more prominent than in aging wild-type mitochondria indicating more extensive effects of ROS in the mutant than in the senescent wild type. These high levels of ROS in the mutant do not only induce protein damage, but may also enhance protein degradation resulting in an overall reduction of proteins. In aging wild-type mitochondria the situation might be different as there are only a few proteins affected. Also a significant increase of autophagy has been shown recently in aging mitochondria (Knuppertz et al., in press). Which role this mechanism plays in the mutant is not revealed yet. Very interestingly, we found that in this mutant subunit g of mitochondrial ATP synthase is reduced in abundance by 57% when compared to the wild type. This ATP synthase subunit is involved in cristae architecture since it controls the dimerization of ATP synthase and these dimers are involved in the generation of the convex curvature on the cristae tips (Davies et al., 2011). In the wild type of P. anserina the ATP synthase dimers dissociate during aging and cristae recede. In old age mitochondria have a vesicular structure and vesicles are released giving rise to the induction of cell death (Daum et al., 2013). It is thus well possible that in the PaSod3 over-expressor increased oxidative stress leads to impairments of the generation and/or stabilization of ATP synthase dimers as the result of subunit g damage. It will now be interesting to see whether or not in the wild type in which no significant reduction of ATP synthase subunit g was observed other components related to ATP synthase dimer generation, stability, and dissociation can be linked to the observed changes of mitochondrial ultrastructure during aging. 5. Conclusion In conclusion, we show that the changes in the abundance of components involved in oxidative phosphorylation, ROS scavenging, protein quality control and mitochondrial dynamics on the protein level during aging in P. anserina mitochondrial extracts are only minor and significantly smaller than observed under constitutive ROS over-production in mitochondria. These results imply, that either the accumulation of non-selective protein damage under enhanced oxidative stress within the mitochondria is not the dominating mechanism for the aging process in P. anserina or the effects of ROS on the proteome are overlaid by other pathways which are effective in ensuring mitochondrial protein homeostasis even in later age-stages. For example, increased autophagy during aging, as shown recently (Knuppertz et al., in press; Philipp et al., 2013) could prevent accumulation of damaged proteins. However, effects of ROS on specific proteins may be still relevant for aging but remain to be identified. The analysis of particular mutants in

Please cite this article as: Plohnke, N., et al., Proteomic analysis of mitochondria from senescent Podospora anserina casts new light on ROS dependent aging mechanisms, Exp. Gerontol. (2014), http://dx.doi.org/10.1016/j.exger.2014.02.008

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Please cite this article as: Plohnke, N., et al., Proteomic analysis of mitochondria from senescent Podospora anserina casts new light on ROS dependent aging mechanisms, Exp. Gerontol. (2014), http://dx.doi.org/10.1016/j.exger.2014.02.008