Translational Targeted Proteomics Profiling of Mitochondrial Energy

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Translational targeted proteomics profiling of mitochondrial energy metabolic pathways in mouse and human samples Justina C Wolters, Jolita Ciapaite, Karen van Eunen, Klary E Niezen-Koning, Alix Matton, Robert J Porte, Péter Horvatovich, Barbara M Bakker, Rainer Bischoff, and Hjalmar P. Permentier J. Proteome Res., Just Accepted Manuscript • DOI: 10.1021/acs.jproteome.6b00419 • Publication Date (Web): 22 Jul 2016 Downloaded from http://pubs.acs.org on July 31, 2016

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Journal of Proteome Research

Translational targeted proteomics profiling of mitochondrial energy metabolic pathways in mouse and human samples Justina C. Wolters1,2,3, ‡, Jolita Ciapaite2,3, ‡, Karen van Eunen2,3, Klary E. Niezen-Koning4, Alix Matton5,6, Robert J. Porte5,6, Peter Horvatovich1, Barbara M. Bakker2,3, Rainer Bischoff1,3, Hjalmar P. Permentier1* 1

Department of Pharmacy, Analytical Biochemistry, University of Groningen, Groningen, The

Netherlands 2

Department of Pediatrics, Center for Liver, Digestive and Metabolic Diseases, University of

Groningen, University Medical Center Groningen, Groningen, The Netherlands 3

Systems Biology Centre for Energy Metabolism and Ageing, University of Groningen,

University Medical Center Groningen, Groningen, The Netherlands 4

Department of Laboratory Medicine, Center for Liver, Digestive and Metabolic Diseases,

University of Groningen, University Medical Center Groningen, Groningen, The Netherlands 5

Surgical Research Laboratory, Department of Surgery, University of Groningen, University

Medical Center Groningen, The Netherlands

ACS Paragon Plus Environment

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Section Hepato-Pancreato-Biliary Surgery and Liver Transplantation, Department of Surgery,

University Medical Center Groningen, The Netherlands KEYWORDS: targeted proteomics, selected reaction monitoring (SRM), mitochondrial energy metabolic pathways, translational proteomics, concatemer, absolute quantification.

ABSTRACT: Absolute measurements of protein abundance are important in the understanding of biological processes and the precise computational modeling of biological pathways. We developed targeted LC-MS/MS assays in the Selected Reaction Monitoring (SRM) mode to quantify over 50 mitochondrial proteins in a single run. The targeted proteins cover the tricarboxylic acid cycle, fatty acid β-oxidation, oxidative phosphorylation and the detoxification of reactive oxygen species.

Assays used isotopically labelled concatemers as internal standards designed to target murine mitochondrial proteins and their human orthologues. Most assays were also suitable to quantify the corresponding protein orthologues in rats. After exclusion of peptides that did not pass the selection criteria, we arrived at SRM assays for 55 mouse, 52 human and 51 rat proteins. These assays were optimized in isolated mitochondrial fractions from mouse and rat liver, from cultured human fibroblasts and in total liver extracts from mouse, rat and human. The developed proteomics approach is suitable for the quantification of proteins in the mitochondrial energy metabolic pathways in mice, rats and humans as a basis for translational research. Initial data show that the assays have great potential for elucidating the adaptive response of human patients to mutations in mitochondrial proteins in a clinical setting.

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Introduction Studies into the molecular mechanisms of human diseases like metabolic disorders often use both human patient samples and laboratory animals. In this type of translational research, phenotypic changes caused by a molecular defect are first observed in human patients, but detailed molecular characterization of the underlying causes is usually performed in mouse models and/or human cell lines. In these studies, the classical methods to analyze and quantify proteins make use of antibodies (ELISA or immunoblotting (1-3)). Antibody detection is based on a high affinity of the antibody towards the target protein. However, the antibody might also show affinity towards other homologous or non-homologous proteins, decreasing the specificity of the antibody-based assays (1). In addition, production of specific monoclonal antibodies against the target protein is not always successful (1, 4). Over the last years, mass spectrometry-based proteomics methods have been developed into a powerful alternative for these antibody-based quantitative techniques (3). In proteomics, proteins are generally detected as peptides obtained by digestion with a protease (usually trypsin), in a mass spectrometer (MS) coupled to a liquid chromatography (LC) system. In targeted proteomics Selected Reaction Monitoring (SRM) is used to achieve increased selectivity and sensitivity by specifically targeting only a selected subset of peptides (so-called proteotypic or signature peptides) for the target proteins of interest. Proteotypic peptides are unique for the target proteins with respect to the species genome and have favorable LC-MS properties (e.g. ionization, fragmentation) for quantitative detection (5). The targeted proteomics workflow offers the possibility to create quantitative assays for specific protein biomarkers, or even for specific post-translational modifications (PTMs, for example phosphorylation) of proteins, with the added benefit that assays for multiple proteins can be performed within a single LC-MS SRM


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measurement in an precise and consistent way (6). This multiplexing of protein assays makes this method especially suitable for systems biology approaches, since all enzymes of a metabolic pathway, or complexes consisting of dozens or even hundreds of different proteins can be targeted within the same measurement batch. For absolute quantification purposes, isotopically labelled peptides or protein standards are added at known concentrations. The substitution of light with heavy isotopes like 13C or 15N does not affect the quantitative MS response and the LC retention of the peptide, but these peptides can be distinguished via the mass shift introduced by the heavy isotopes. In a powerful and novel strategy to create labelled standards for multiple-protein assays, the selected proteotypic peptides for all protein targets are concatenated into a synthetic protein (7). For these concatemers, exemplified by the QconCAT concatemer proteins introduced by Beynon et al. (8, 9), synthetic cDNA genes are translated in E. coli using growth medium containing isotopically labelled lysine and arginine. Trypsin cleaves proteins after these amino acid residues, and therefore all tryptic peptides will contain either an isotopically labelled lysine or arginine. The purified and quantified concatemer protein is used for the subsequent quantification of the endogenous concentration of the targeted proteins in the LC-MS SRM experiment. The concatemers are spiked into the sample prior to protein digestion in order to correct for the loss of proteins during subsequent sample preparation steps. This is an advantage compared to the more commonly used synthetic labelled peptides (10) which are spiked into the sample after protein digestion and therefore do not correct for protein loss during digestion. The large number of proteins that can be targeted and the possibility to correct for losses during sample preparation provide a clear benefit for the use of these concatemers for systems biology approaches. The cleavage efficiency of the concatemers was verified by untargeted MS analyses (full scan MS and MS/MS analyses,


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data not shown). The assumption is that the endogenous peptides are cleaved with the same efficiency as the peptides in the concatemers. Until now concatemers have only been applied to the quantification of sets of proteins that belong to a single organism (11, 12). Due to the specificity of peptide detection, even single amino acid substitutions will render its utilization as a standard for accurate quantification for other organisms impossible. In this paper, targeted proteomics LC-MS SRM assays were developed in such a way that a single standard was used to quantify mouse and human orthologues, by preferentially selecting peptides that are identical in both organisms. In this way, a single assay was developed that simultaneously detects and precisely quantifies more than 50 protein targets in a single measurement independent of the organism (mouse or human). Additionally, the selection includes a large subset of peptides that can be used for quantification of rat protein orthologues. The targeted proteomics workflow described here is focused on proteins involved in key mitochondrial energy metabolic pathways: the tricarboxylic acid (TCA) cycle, fatty acid β-oxidation, oxidative phosphorylation and the detoxification of reactive oxygen species. Mitochondria are involved in many diseases like cancer, type 2 diabetes, Parkinson, or enzyme deficiencies due to inborn errors of metabolism, as well as in the natural course of biological ageing (13-15). Our method provides a unique tool to identify regulatory adaptations in mitochondrial energy metabolic pathways related to these diseases allowing a better understanding of the molecular mechanism of the disease process for individual patients. Experimental section Preparation of isolated mitochondria and total liver extracts


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Mitochondria were isolated from freshly harvested mouse (C57Bl/6, male, 4 months of age, n=6) and rat (Wistar, male, 7 weeks of age, n=6) livers by differential centrifugation as described previously (16). Human skin fibroblasts were from control subjects (n=2), patients with homozygous c.985A>G (n=3) mutation in the ACADM gene and a patient with homozygous c.140G>C (n=1) mutation in the ETFA gene. The latter mutation has not been described in literature. Fibroblasts were established from skin biopsies and were cultured in Ham’s F10 medium (Gibco) with glutamine, 10% fetal bovine serum (Gibco), 1% mixture of penicillin, streptomycin, fungizone (Gibco) and incubated in a humidified CO2 incubator (5% CO2, 95% air) at 37 °C. Mitochondria were isolated according to (17) with modifications. Briefly, two confluent flasks of 162 cm2 per cell line were washed with 25 mL of medium A containing 100 mM sucrose, 1 mM EGTA, 20 mM MOPS, protease inhibitor cocktail (Sigma-Aldrich), pH 7.4, scraped in 25 mL of medium A on ice, followed by centrifugation at 300 g for 5 min at 4 °C. The cell pellets were suspended in 1 mL of medium B (medium A plus 10 mM triethanolamine, 5% Percoll and 0.1 mg/mL digitonin), incubated 3 min on ice, and homogenized using a PotterElvehjem homogenizer (8 strokes at 500 rpm). The homogenate was centrifuged at 800 g for 10 min at 4 °C. The supernatant was collected and centrifuged at 10000 g for 10 min at 4 °C. Mitochondrial pellets were suspended in 20-25 µL of medium C containing 300 mM sucrose, 1 mM EGTA, 20 mM MOPS, pH 7.4. For preparation of total mouse and rat liver extracts, pieces of liver (100-200 mg, n=6) were ground in liquid nitrogen. Tissue powder was homogenized (~1/10 (w/v)) in a medium containing 250 mM sucrose and 5 mM Tris, protease inhibitor cocktail (Sigma-Aldrich), pH 7.3 at 4 °C using a Potter-Elvehjem homogenizer at 500 rpm for 30 s. Peripheral liver parenchyma biopsies were obtained from human donor liver grafts (n=9, demographics are summarized in


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Table S1) that were declined for transplantation. Approval for using these livers for research was obtained from the local Medical Ethical Committee and consent for research was given by the family of the donors. Biopsies were snap frozen and stored at -80 °C after which 10-20 mg were cut using a cryostat with subsequent homogenization using the same protocol as for the mouse and liver samples. The isolated mitochondria and total liver homogenates were stored at -80 °C until further use. Protein concentrations in all types of samples were determined using the BCA protein assay kit (Pierce, Thermo Fisher Scientific Inc., Rockford, IL, USA). Concatemer development Protein sequences for all targets were downloaded in FASTA-format from the UniProt website (www.uniprot.org) for mouse and human proteins (canonical reference proteomes, downloaded July 2014) and processed using the CONSeQuence website (http://king.smith.man.ac.uk/CONSeQuence, predicts LC-MS detectability (18)) and MC:Pred algorithms (http://king.smith.man.ac.uk/mcpred, predicts the chance of trypsin miscleavage (19)). All information (including the UniProt codes for the proteins in the different organisms) was combined in a table in Excel with information on whether the peptides were detected previously (presence in the peptide library from the National Institute of Standards and Technology (NIST), http://chemdata.nist.gov). General properties of the peptides were added to the table such as, i) length of the peptide, ii) whether the peptide is located at a protein terminus, iii) possible modification sites which affect the CONSeQuence scores (M-oxidation; DPhydrolysis and subsequent internal fragmentation, NG- deamidation, (18)) and iv) presence of D/E or K/R near the tryptic cleavage sites (19) which influences the MC:Pred score.


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In order to take uniqueness across multiple organisms into account, in silico digests were created for the list of intended protein targets as well as for the complete reference proteomes of the target organisms (canonical proteomes from UniProt, downloaded July 2014), using trypsin as protease, allowing no missed cleavages and screening all peptides within a mass range of 5005000 Da. All peptides from the protein target list were cross-referenced against all peptides from the reference proteomes of the intended target organisms. Proteins were considered unique if the gene name was unique and corresponded to the genes of the target protein within the same species (the protein with lowest protein evidence score (PE) was listed in the initial target list). Uncharacterized proteins and fragments were ignored. Non-standard amino acid residues were given an arbitrary mass of 111 Da in order to be able to calculate peptide masses during comparisons. CONSeQuence and MC:Pred analyses could not be performed with non-standard amino acid residues. In our case only GPX41 contained one U (representing a selenocysteine) in both the mouse and human sequence. Preferably, peptides were selected that were identical and unique in both human and mouse proteomes. In total, 11 human peptides were added to the concatemer for those proteins where no identical peptides could be found in the mouse proteome. The selected peptides are listed in Table S2 with the annotation for the different organisms. Due to initial issues with the selection of unique peptides, a few peptides are not unique in one or multiple organisms. For those peptides, both targeted proteins are listed in Table S2, and the determined concentrations represent those for the protein groups rather than for individual targets, in case no other unique peptides were selected. The peptides were grouped into three concatemers, as indicated in Table S2. Isotopically labelled concatemers (QconCATs) with 13C-lysines and arginines were obtained from Polyquant GmbH (Germany) at a known concentration.


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The final peptide selection was cross-referenced against all peptides from the rat reference proteome, to determine which of the peptides were present and unique for the rat orthologues. In-gel digestion and sample cleanup For the mitochondrial samples, 50 µg of total protein was resuspended in SDS loading buffer (0.01% bromophenol blue, 2% SDS, 4% glycerol, 1% β-mercaptoethanol in 150 mM Tris buffer (pH 6.8)) plus 1.5 ng concatemer per µg of total mitochondrial protein. The sample was run briefly into a precast 4-12% Bis-Tris gels (Novex, run for maximally 5 min at 100 V). The gel was stained with Biosafe Coomassie G-250 stain (Biorad) and after destaining with milliQ, the band containing all proteins was excised from gel. The gel band was sliced into small pieces, washed subsequently with 30% and 50% v/v acetonitrile with 100 mM ammonium bicarbonate, each incubated at room temperature (RT) for 30 min while mixing (500 rpm) and lastly with 100% acetonitrile for 5 min, before drying the gel pieces in an oven at 37 °C. The proteins were reduced with 20 µL 10 mM dithiothreitol (30 min, 55 °C) and alkylated with 20 µL 55 mM iodoacetamide (30 min, in the dark at RT). The gel pieces were washed with 50% acetonitrile with 100 mM ammonium bicarbonate for 30 min while mixing (500 rpm) and dried in an oven at 37 °C before overnight digestion with 20 µL trypsin (1:100 g/g, sequencing grade modified trypsin V5111, Promega) at 37 °C. The next day, the residual liquid was collected before elution of the peptides from the gel pieces with 20 µL 75% v/v acetonitrile plus 5% v/v formic acid (incubation 20 min at RT, mixing 500 rpm). The elution fraction was combined with the residual liquid and diluted to 1 mL with 0.1% v/v formic acid for cleanup with a C18-SPE cartridge (SPE C18-Aq 50 mg/1 mL, Gracepure). The cartridge was conditioned with 3x1 mL acetonitrile plus 0.1% v/v formic acid, and re-equilibrated with 3x1 mL 0.1% v/v formic acid before application of the samples. Bound peptides were washed with 2x1 mL 0.1% v/v formic acid and eluted with


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30.4 mL 50% v/v acetonitrile plus 0.1% v/v formic acid. The fractions were combined and dried under vacuum and resuspended in 0.1% v/v formic acid to a final concentration of around 1 µg per µL total mitochondrial protein (based on the original protein concentration) plus 1.5 ng per µL concatemer. LC-MS analysis Selected reaction monitoring (SRM) analyses were performed on a triple quadrupole mass spectrometer with a nano-electrospray ion source (TSQ Vantage, Thermo Scientific). Chromatographic separation of the peptides was performed by liquid chromatography on a nanoUHPLC system (Ultimate 3000 RSLC, Dionex) using a nano-LC column (Acclaim PepMap100 C18, column i.d. 75 µm, column length 150 mm, particle size 3 µm, pore size 100 Å, Dionex). Samples were injected from a cooled autosampler (5 °C ) and loaded onto a trap column (µPrecolumn cartridge, Acclaim PepMap100 C18, column i.d. 300 µm, column length 5 mm, particle size 5 µm, pore size 100 Å, Dionex) at 1 µg total protein digest using the µL-pickup method with 0.1% v/v formic acid as a transport liquid. Peptides were separated on the nano-LC column using a linear gradient from 3-60 % v/v acetonitrile plus 0.1% v/v formic acid in 100 min at a flowrate of 300 nL/min. The mass spectrometer was operated in the positive mode at a spray voltage of 1500 V, a capillary temperature of 270 °C, a half maximum peak width of 0.7 for Q1 and Q3, a collision gas pressure of 1.2 mTorr and a cycle time of 1.2 ms. Optimal collision energies (CE) were predicted using the following linear equations: CE = 0.03 × m/z of precursor ion + 2.905 for doubly charged precursor ions, and CE=0.03 × m/z of precursor ion + 2.467 for triply charged precursor ions. For each of the peptides, the optimal precursor charge and three optimal transitions were selected after screening with the concatemers. The measurements were scheduled in windows of 4 min around the pre-determined retention time.


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Data analysis LC-MS peak assignments were manually curated using the Skyline software (20). The sum of peak areas from all transitions for the endogenous and standard (isotopically labelled concatemer-derived) peptides was used to calculate the ratio between the endogenous and standard peptides. The known concentration of the concatemer-derived peptides was used to calculate the concentration of the endogenous peptides. All determined protein levels are listed in the supplementary material (Excel sheet tab 9-11). The plots for Figure S2 and S3 were prepared using Matlab version 8.3.0.532 (R2014a). Experimental design and statistical rationale The number of samples analyzed per condition varied, mainly dependent on availability of the different materials and the number of biological replicates is listed in the figure legends for each condition (in the MS files they are labelled with different numbers for each biological replicate). Each biological replicate was measured at least twice (labelled as technical replicate (TechRepl) in the MS files) and the average of the technical replicates was used for further calculations. The variation in the average protein levels determined for the biological replicates was given by the standard error of the mean (SEM). Ethical statement The animal treatment conformed to the guidelines of The Institutional Animal Care and Use Committee of the University of Groningen and was in accordance with EC Directive 86/609/EEC for animal experiments. Approval for the use of human tissue was obtained from the


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Medical Ethical Committee at the University Medical Centre Groningen (METc 2012.068 for liver tissue and M14.161914 for patient fibroblasts). Results and discussion Design of the concatemer & LC-MS SRM assay development Standard peptide LC-MS SRM assays were developed targeting a total of 57 proteins belonging to the TCA cycle, fatty acid β-oxidation, oxidative phosphorylation and the detoxification of reactive oxygen species (Table S2). For each targeted protein we aimed at selecting two proteotypic peptides. Due to the fact that enzymes involved in oxidative phosphorylation form multi-subunit complexes (complex I-V), which are encoded by both nuclear and mitochondrial genomes (except for complex II, which is exclusively encoded by the nuclear genome), we chose to quantify two proteins involved in each complex: one nuclear- and one mitochondrial-genome encoded, while for complex II both selected proteins were nucleargenome encoded. The selected subunits are either involved in the enzymatic catalysis and/or were core subunits essential for the assembly of the functional complex. General selection criteria for the proteotypic peptides were: i) uniqueness of peptides for the target protein with respect to the target species proteome, ii) optimal LC-MS SRM properties, iii) avoiding N- or C-terminal peptides, iv) avoiding peptides with high risk for chemical modifications, and v) avoiding peptides with non-optimal tryptic digestion properties (miscleaved peptides) (18, 19). Due to the multi-species design of the concatemer, additional selection criteria were applied to check that peptides were unique for the target proteins in each screened organism (for details see material and methods section) and to preferentially select peptides with identical primary amino acid sequences in the mouse and human target proteins. If


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identical peptides were not present or if these peptides were not proteotypic, different peptides were chosen for the mouse and human targets. An overview of the multi-species analyses can be found in the supplementary material (Excel sheet tab 3-5) and the final list of peptides (n=134) is shown in Table S2. The peptides were distributed over three concatemers, grouping proteins of the same biological pathway in the same concatemer, as listed in Table S2. One of the possible issues in these analyses is that leucine and isoleucine are considered as different amino acids, while they are not distinguishable by MS, as they have identical masses. This is usually not taken into account in screening whether peptides are unique for the intended target, while in silico analysis of all tryptic peptides from mouse indicated that a significant number of peptides were not unique taking this into consideration into account (1.6% of all mouse peptides listed in the supplementary material (Excel sheet tab 6)). One of the peptides selected for the concatemers, LVEVIK (from the protein HADH) has an overlap with the peptide LVEVLK from multiple other proteins in mouse. In order to check whether this would affect the assay for this peptide, synthetic LVEVIK and LVEVLK peptides were analyzed. While the MS/MS spectra for both peptides are indeed identical (Figure S1), they were separated by 0.2 min using a 100 min LC gradient for the LC-MS SRM assay (Figure S1), which is sufficient for accurate quantitation of both peptides individually. Tryptic digests of the pure concatemers (10 ng) were used to create LC-MS SRM assays, with 2-3 transitions for each peptide, and screened for both isotopically labelled and unlabelled versions of the peptides. The labelling efficiency was on average 97 % in the three concatemers (see supplementary material (Excel sheet tab 7). Hence, concentrations lower than 3% of the spiked concentration cannot be quantified. In the majority of the experiments 1.5 ng of the concatemers was spiked, which means a detection limit of 0.7-0.9 fmol for the individual


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proteins targeted with the three concatemers (the detection limit in fmol is represented as a range to cover the different molecular weights of the three concatemers). All peptides were detected in measurements of the pure concatemer, but several issues prohibited the use of a few peptides for quantification (listed in the supplementary material (Excel sheet tab 5)). The issues include practical limitations like insufficient sensitivity at the spiking level of 1.5 ng (for 6-10 peptides), non-matching LC peak shape of transitions probably due to interferences (for 4 peptides), lack of retention on the LC column (for 5 peptides), and presence in transit/signal sequences (for 9 peptides). The expected peptide retention time and information about signal sequences in a target protein are important factors to take into account for the development of future assays. Selection of multiple peptides per protein is also preferred as many additional modifications are not taken into account in the current workflow (for example phosphorylation), which could affect quantification. After removing problematic peptides from the list, no peptides remained for ATP5F1 (as all four selected peptides were in the signal sequence), and for CYC1 (two peptides in the signal sequence and for two other peptides the signal of the isotopically labelled standard was too low) for all organisms. For the mouse targets this means, in summary, that we can accurately quantify 55 of the 57 intended targets. For the human targets, in addition, no peptides remained for GSR, CPT1A and MTCO3, yielding a total of 52 out of 56 targets (only 56 proteins in total, as ECI does not have a human orthologue). During assay development the aim was to target proteins from mouse and human sources with the same peptides. Subsequently, the selected sequences were found to be highly orthologous in the rat proteome. For all proteins except MTND5 at least one of the peptides was present in the


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rat proteome and also fulfilled the other general selection criteria, such as uniqueness. After removing problematic peptides as described above, the peptides for GSR, SOD2 and ACADL were discarded, yielding a total of 51 proteins that could be quantified in rat and the subsequent analyses were extended to rat mitochondria and tissues extracts as well. In silico comparison of the selected peptides with other model organisms showed that a majority of the selected peptides are also applicable to organisms like pig (78 unique peptides belonging to 47 of the target proteins), cow (79 unique peptides belonging to 49 of the target proteins) and dog (72 peptides belonging to 48 of the target proteins). The different comparisons are listed in the supplementary material (Excel sheet tab 8). Sample preparation When spiking with concatemers, all labelled peptides produced after digestion are present at the same concentration, while the endogenous protein targets might vary significantly according to their concentration in the sample. In order to determine which concatemer concentration is optimal, five amounts were spiked into three independent liver mitochondrial protein extracts: 15, 7.5, 1.5, 0.75 and 0.15 ng concatemer per µg of total mitochondrial protein. The results are summarized in Figure S2 showing the medians for all proteins that were quantified with different amounts of concatemer as internal standard and the variation of individual proteins around this median. Due to the presence of a small percentage of unlabelled concatemer (around 3%) spiking high levels can affect the lower limit of detection, since the unlabelled standard contributes to the signal of the endogenous peptide and should therefore be avoided. This effect can be observed for low abundant endogenous proteins when spiking the concatemers at high concentration of 15 and 7.5 ng per µg of total mitochondrial protein. For these concentrations a large number of proteins exceed the median protein levels determined with the complete range of spiked


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concatemer amounts. On the other hand, spiking low amounts of concatemers might affect the quantification accuracy especially for peptides with a low response. Indeed, at a concentration at 0.15 ng concatemer per µg of total mitochondrial protein, the distribution of the median was significantly shifted, leading to an underestimation of all protein amounts. Our data show that spiking 1.5 ng concatemer per µg total mitochondrial protein resulted in precise quantitative data for the targeted proteins in the mitochondrial preparations and therefore, most of the subsequent experiments were performed using this amount. The losses introduced during the sample separation using gel electrophoresis were estimated with a 13C15N-arginine labelled synthetic peptide (EQGFLSFWR) unique for adenine nucleotide translocase 1 (SLC25A4). This peptide is also present in the concatemers, but in this case the peptide is labelled with a 13C-arginine. Spiking the synthetic peptide at the end of the sample preparation workflow at a known concentration allows determination of the loss of the concatemer between gel loading and LC-MS SRM injection. The overall loss of SLC25A4 during sample preparation is on average 62 ± 1.1% (based on experiments with 92 mouse liver mitochondria, average ± SEM). The benefit of the concatemers is the correction of quantitative data for the losses during sample processing, which is not taken into account when using synthetic peptides standards for quantification. Finally, five different amounts of mitochondria ranging from 0.5 to 50 µg of total mitochondrial protein with 1.5 ng per µg total mitochondrial protein were injected to determine the optimal sample amount for LC-MS SRM analysis (Figure S3). Clearly, the starting amount hardly affects quantification precision, and the analysis can therefore be done with a small amount of sample material, which is important when limited patient material is available, like for the human liver biopsies described below with a total protein amount as low as 10 µg.


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Application to mouse, rat and human samples Mitochondria isolated from mouse and rat liver as well as mitochondria from human fibroblasts were analyzed, and the targeted mitochondrial proteins were precisely quantified in samples from all three organisms (Figure 1). Most protein concentrations were of the same level in rat and mouse liver samples, while they were lower in fibroblasts. Analysis of purified mitochondria is relatively straightforward as the protein composition is less complex and individual proteins are more concentrated than in whole cell or tissue homogenates. This gives a detailed representation of the proteomic state of the mitochondria. However, mitochondria can only be enriched from fresh samples, which are rarely available for human material. To show the applicability of the method for quantification of the target proteins in more complex samples, we compared mouse liver homogenates to mitochondria isolated from liver tissues of the same mice (Figure 2). The results show a linear correlation, as expected for proteins that localize uniquely to mitochondria. The slope of 0.5 indicates that proteins in the mitochondrial fraction were 2-fold enriched. In agreement with this, a quite comparable estimate of the mitochondrial fraction of the total cellular protein amount of 59% was obtained for another set of mouse livers using a different approach, i.e. by determining the ratio between tissue and mitochondrial citrate synthase activity (data not shown). Only two proteins in the entire dataset strongly deviated from this correlation. First, the tissue concentration of peroxiredoxin-6 (PRDX6/PRDX6B) was substantially higher than that measured in enriched mitochondria. This is in agreement with the reported preferential cytoplasmic localization of this protein under physiological conditions (21), while mitochondrial localization is observed upon increased mitochondrial damage (22), including the damage caused by ischemia/reperfusion injury after liver surgery and/or transplantation (21). In contrast, the tissue concentrations of


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cytochrome c (CYCS) were unexpectedly low, while it was detected at high concentrations in the extracts of enriched mitochondria. We currently have no explanation for this phenomenon, but it might be due to different modification states not determined with our targeted method. Another unexpected result is that the observed amount of some proteins which are part multisubunit complexes (e.g. complex I and II) is not equal. A possible explanation is that not all synthesized subunit proteins will be present in a complex. In addition, it should be kept in mind that the protein quantification method does not take into account differences in the efficiency of the lysis and extraction method for each subunit. In summary, the overall linearity of the data sets is a clear indication that the assay is suitable for the quantification of mitochondrial proteins in whole tissue extracts. Human and rat liver tissue extracts were prepared using the same methodology as was used for mouse liver extracts. In Figure 3 protein concentrations are compared between human, rat and mouse liver, showing that in many instances protein concentrations in all three species are comparable, with a few exceptions. For example, the level of long-chain acyl-CoA dehydrogenase (ACADL, an enzyme involved in mitochondrial fatty acid β-oxidation) is much lower in human than in mouse and rat tissue, which is in agreement with a previous report that ACADL is hardly expressed in human fibroblasts, in contrast to mouse (23). Potential for patient-specific screening A number of inborn errors of metabolism caused by a single-gene mutation are known to affect mitochondrial pathways targeted by our approach. An important feature of these diseases is the considerable patient-to-patient variation in the severity of the symptoms and the outcome of the disease. Patient-specific adaptations in the affected metabolic pathways may explain this


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variation. Therefore, quantifying protein levels of the entire pathway may offer a more in-depth understanding of patient-specific adaptation mechanisms and aid individual treatment options. An interesting example relates to mutations in the ACADM gene coding for medium-chain acyl-CoA dehydrogenase (ACADM), a protein involved in the mitochondrial fatty acid βoxidation and important for oxidation of medium-chain fatty acids (24, 25). The severity of the disease varies widely ranging from asymptomatic to life-threatening episodes of hypoglycemia (26). Currently it is not known what underlies these differences, besides the observation that the manifestation of symptoms is often triggered by additional factors such as fever or prolonged fasting (27). To validate our approach we used mitochondria isolated from cultured human fibroblasts of healthy individuals and patients with the most common mutation (alanine to glycine mutation in position 985 (c.985A>G)) in the ACADM gene. ACADM protein levels were comparable in mitochondria from patient and healthy subject fibroblasts (Figure 4C, the part carrying the protein modification is not targeted). This indicates that in this case protein function, which was described with a residual enzyme activity of C), which is a mutation that has not been described in literature. The pathogenicity of this new mutation has been confirmed by a disturbed overall palmitate loading test of cultured skin fibroblasts (29) which is seen in most mitochondrial fatty acid oxidation defects (unpublished results NiezenKoning). A deficiency in ETF, which consists of subunits α and β, and/or ETF dehydrogenase (ETFDH) results in a severe inherited metabolic disorder called multiple acyl-CoA dehydrogenase deficiency (MADD) (30). Partial deficiency in either ETF or ETFDH limits electron transfer from fatty acid β-oxidation to the mitochondrial electron transfer chain resulting in a lower capacity to use fatty acids as an energy source (27). Our data show that the ETFA c.140G>C mutation does not affect the protein levels of the ETF subunit α itself (ETFA, Figure 4C), but rather results in strongly decreased levels of the β subunit (ETFB, Figure 4C). While one of the two peptides used for the quantification of the ETFB levels is susceptible to phosphorylation, both peptides show the same decrease. Interestingly, this defect results in increased protein levels of the same enzymes as in MCAD deficiency, but to a larger extent (Figure 4). In addition, the entire TCA cycle and the related substrate transporters (Figure 4B) along with subunits of the electron transfer chain (complex III subunit UQCRC2, cytochrome c (CYCS), complex IV subunit COX5A) and enzymes and transporters involved in ATP synthesis (ATP synthase subunit ATP5B, phosphate carrier protein (SLC25A3) and adenine nucleotide translocases 1 (SLC25A4) and 2 (SLC25A5)) were upregulated compared to the healthy controls (Figure 4A). Proteins involved in mitochondrial acyl-CoA uptake (CPT1B and CPT2) were not increased, as expected, since this would lead to an accumulation of intermediates when the downstream oxidation of acyl-CoA is compromised. Enzymes involved in the oxidation of


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unsaturated acyl-CoA (DECR1 and ECHS1) were not altered either. The fact that the adaptation of the ETF α -deficient fibroblasts was similar but stronger than that of the ACADM-deficient fibroblasts is compatible with the severity of the deficiency: the ETF system is required for all acyl-CoA dehydrogenases, while ACADM is specific for medium-chain-length substrates and its role can be taken over by ACADS, ACADVL and possibly ACADL. This initial analysis shows the potential of the targeted proteomics method to study the effect of mutations on metabolic pathways in what are possibly patient-specific effects. Although the number of samples used in the present study is small, the data suggests that a number of adaptations of compensatory nature in response to the ACADM gene mutation c.985A>G occur in mitochondrial pathways involved in substrate oxidation and ATP synthesis, which was used to illustrate the possibilities of the methods for possible screening of patient-specific effects. Conclusions We have developed a quantitative targeted LC-MS-SRM method for the quantification of proteins in three key mitochondrial metabolic pathways, i.e. the TCA cycle, fatty acid βoxidation, oxidative phosphorylation and the detoxification of reactive oxygen species. Quantification is based on the addition of stable isotope-labelled concatemers that together contain proteotypic peptides for more than 50 proteins. This approach can be transferred to other protein sets following the described design strategy. The method proved to be applicable to mouse, rat and human samples of various complexities. We measured precise protein concentrations for the aforementioned pathways in both mitochondria isolated from liver or fibroblasts as well as from liver tissue. We applied this profiling method to study compensatory mechanisms in patients with inborn errors of energy metabolism by providing an overview of


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adaptations of enzyme levels to (single) mutations. This improves our understanding of the (individual) phenotype and may facilitate the development of personalized treatment strategies. Future work will focus on applying this methodology to follow the differentiation of various cell types derived from patient-derived pluripotent stem cells to study metabolic pathways in hepatocytes and other cell types. FIGURES


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Figure 1. Levels of individual proteins in various mitochondrial samples. Results are plotted for proteins grouped per concatemer (QcC) for mitochondria isolated from mouse liver (n=6 ± SEM), rat liver (n=6 ± SEM) and from human fibroblasts (n=2 ± SEM). Protein levels are expressed in fmol per µg total mitochondrial protein. Proteins that were not quantified in a specific organism are annotated with an X.

Figure 2. Comparison of the levels of individual proteins in mouse liver mitochondria and liver tissue. Mitochondria were isolated from the same liver tissue that served for the comparison (n=6 ± SEM). Protein levels are expressed in fmol per µg total mitochondrial protein for mitochondria and in fmol per µg total extracted protein for liver tissue.


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Figure 3. Levels of individual proteins in mouse and human liver tissue samples. Results are plotted for proteins grouped per concatemer (QcC) for mouse liver tissue (n=6 ± SEM), rat liver tissue (n=6 ± SEM) and for human tissue (n=9 ± SEM). Protein levels are expressed in fmol per µg total extracted protein. Proteins that were not quantified in a specific organism are annotated with an X.


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Figure 4. Levels of individual proteins in mitochondria isolated from fibroblasts from patients with MCAD deficiency (mutation c.985A>G, n=3 ± SEM), from a patient with a mutation in the


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electron transfer flavoprotein subunit α (c.1-40G>C, n=1) and controls (n=2 ± SEM). Results are plotted for proteins grouped per concatemer (QcC). Protein levels are expressed in fmol per µg total mitochondrial protein. ASSOCIATED CONTENT Supporting Information. Table S1

Demographics of human donor livers

Table S2

Peptide targets for LC-MS analyses

Figure S1

Uniqueness of leucine or isoleucine containing peptides

Figure S2

Protein variability to the median after spiking with different concentrations of

concatemer Figure S3

Protein variability to the median starting with different amounts of liver

mitochondria Excel sheet

Design and results of the concatemers

This material is available free of charge via the Internet at http://pubs.acs.org AUTHOR INFORMATION Corresponding Author * Correspondence: tel: +31 50 363 3262, email: [email protected]. Author Contributions


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The manuscript was written through contributions of all authors. All authors have given approval to the final version of the manuscript. ‡These authors contributed equally. Notes SRM data were uploaded to PeptideAtlas http://www.peptideatlas.org/PASS/PASS00783 (username: PASS00783; password: EW8379v).

ACKNOWLEDGMENT We thank dr. Robert Bandsma (Toronto, Canada) for providing rat liver samples. This study was supported by the Netherlands Organization for Scientific Research (NWO) through a Centre for Systems Biology Research (CSBR) grant to the Systems Biology Centre for Energy Metabolism and Aging (NWO project number 853.00.110). Karen van Eunen received an unrestricted grant from the Top Institute for Food and Nutrition (GH003). ABBREVIATIONS BCA, Bicinchoninic acid; CE, collision energy; EGTA, ethylene glycol tetraacetic acid; ELISA, enzyme-linked immunosorbent assay; LC, liquid chromatography; MOPS, 3-(Nmorpholino)propanesulfonic acid; MS, mass spectrometry; m/z, mass-to-charge; NIST, National Institute of Standards and Technology; QcC, QconCAT; SDS, sodium dodecyl sulfate; SEM, standard error of the mean; SPE, solid phase extraction; SRM, selected reaction monitoring; TCA, tricarboxylic acid; UHPLC, ultra high performance liquid chromatography. REFERENCES 1. Baker, M. (2015) Reproducibility crisis: Blame it on the antibodies. Nature 521, 274-276


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170x66mm (300 x 300 DPI)


Translational Targeted Proteomics Profiling of Mitochondrial Energy

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