- Open Access
Mitochondrial proteomics on human fibroblasts for identification of metabolic imbalance and cellular stress
© Palmfeldt et al; licensee BioMed Central Ltd. 2009
- Received: 09 December 2008
- Accepted: 28 May 2009
- Published: 28 May 2009
Mitochondrial proteins are central to various metabolic activities and are key regulators of apoptosis. Disturbance of mitochondrial proteins is therefore often associated with disease. Large scale protein data are required to capture the mitochondrial protein levels and mass spectrometry based proteomics is suitable for generating such data. To study the relative quantities of mitochondrial proteins in cells from cultivated human skin fibroblasts we applied a proteomic method based on nanoLC-MS/MS analysis of iTRAQ-labeled peptides.
When fibroblast cultures were exposed to mild metabolic stress – by cultivation in galactose medium- the amount of mitochondria appeared to be maintained whereas the levels of individual proteins were altered. Proteins of respiratory chain complex I and IV were increased together with NAD+-dependent isocitrate dehydrogenase of the citric acid cycle illustrating cellular strategies to cope with altered energy metabolism. Furthermore, quantitative protein data, with a median standard error below 6%, were obtained for the following mitochondrial pathways: fatty acid oxidation, citric acid cycle, respiratory chain, antioxidant systems, amino acid metabolism, mitochondrial translation, protein quality control, mitochondrial morphology and apoptosis.
The robust analytical platform in combination with a well-defined compendium of mitochondrial proteins allowed quantification of single proteins as well as mapping of entire pathways. This enabled characterization of the interplay between metabolism and stress response in human cells exposed to mild stress.
- Mitochondrial Protein
- Human Skin Fibroblast
- Mitochondrial Translation
- Galactose Medium
Mitochondrial activity is essential for human health and the number of disorders known to be related to mitochondrial dysfunction is increasing. Defects in mitochondrial functionality cause a wide range of diseases , including respiratory chain defects , fatty acid oxidation deficiencies  and neurodegenerative diseases . Mitochondrial proteins are important regulators of apoptosis and mitochondrial dysfunction is an important factor in aging, diabetes mellitus, cancer [5, 6], cardiovascular disease [7, 8] as well as Alzheimer's and Parkinson's disease . Monogenetically inherited mitochondrial defects are commonly detected indirectly from metabolite signatures in blood or urine, or by sequencing of candidate genes. For more specific assessment of mitochondrial activity, enzymatic activity can be measured on mitochondria isolated from cultivated patient fibroblasts from which selected mitochondrial protein markers also can be quantified using immunological detection. However, a proteomic survey might be advantageous in the investigation of complex defects where several factors may contribute to disease. Relative quantification of proteins from mitochondrial pathways would enable the detection of imbalanced metabolism and stress in mitochondria, serve as a starting point for the selection of disease marker proteins and be used for exploration of disease etiology. The human mitochondrial proteome has been computationally predicted to contain proteins from at least a thousand different genes [10, 11].
Experimental approaches to define the mitochondrial proteome using mitochondria purified with gradient centrifugation have yielded more than six hundred mitochondria-associated proteins from human mitochondria [12, 13] and even higher numbers in studies on mouse mitochondria [11, 14]. These studies have mainly been discovery studies targeted to identify as many proteins as possible from a large amount of purified mitochondria. From a clinical perspective the proteomic methods should be applicable to a low amount of patient material and they should be quantitative. Reliable and fast quantification methods for mass spectrometry (MS) based proteomics are being developed and support the growing applications of proteomic techniques in research and diagnosis of genetic and metabolic disorders . A suitable method for large scale analysis of relative protein quantities is mass spectrometry analysis of peptides chemically labeled by isobaric tags for relative and absolute quantification (iTRAQ) . iTRAQ allows simultaneous determination of both identity and relative abundance of peptides in tandem mass spectra and has gained popularity due to its high sensitivity and robustness, and because it allows simultaneous measurement of 4–8 samples [17–20].
The present work establishes an iTRAQ-based proteomic method for relative quantification of mitochondrial proteins in human fibroblasts. In order to obtain comprehensive proteome data we manually assembled and curated a compendium containing proteins from the mitochondrial metabolism and stress response systems. Our method was tested on human skin fibroblasts, which in many cases is the only tissue material readily available from patients. Proteomics of cultivated fibroblasts is suitable for studying the consequences of human diseases at the protein level, and cultivation in controlled environments enables studies conducted under stressful conditions. Characterization of cultivated fibroblasts with respiratory chain deficiencies has previously been performed by cultivation in galactose. Galactose cultivation results in altered energy metabolism – a mild metabolic stress- which enhances the effects of mitochondrial disorders [21, 22]. In the current work we studied the mitochondrial proteome as a function of cultivation in galactose versus glucose medium and describe how wild type cells respond to the metabolic stress.
Primary normal human dermal fibroblasts (NHDF) from newborn males (Camprex # CC-2509 annotated NHDF-1 and ATCC #CRL-2429 annotated NHDF-2), were cultivated at 37°C and 5% (v/v) CO2 in RPMI 1640 medium (BioWhittaker) containing 10% (v/v) fetal calf serum (BioWhittaker). Cells for experiments were used between passage 9 and 13. After preculturing, the cultures were transferred to 150 cm2 flasks and harvested at sub-confluence after approximately 72 h in RPMI 1640 medium (with 2 g/l glucose), or glucose-free RPMI 1640 medium supplemented with 2 g/l galactose, both supplemented with 10% fetal calf serum.
Cells from four 150-cm2 flasks were resuspended in 10 ml MOPS buffer (10 mM, pH 7.2) containing sucrose (200 mM), EDTA (0.1 mM) and protease inhibitor (Complete from Roche). The cells were disrupted on ice by 30 strokes in a Dounce homogenizer. Cell debris was removed by two centrifugation steps at 600 × g for 7 minutes, where the pellets were discarded and the resulting supernatant was centrifuged at 10,000 × g for 15 min. The pellet containing mitochondria was washed in the MOPS buffer (pH 7.2) without protease inhibitor, centrifuged at 10,000 × g for 15 min and stored at -80°C. After adding a sample buffer from the iTRAQ kit (Applied Biosystems, Foster City, California, USA) consisting of 0.5 M triethylammonium bicarbonate buffer (pH 8.5) with 0.1% SDS, the samples were treated with ultrasonication (Branson Sonifier 250, Branson Ultrasonics corp., Danbury, USA) at output control 3 and 30% duty cycle for three rounds of 10 seconds with one minute on ice between each round.
Western blot analysis
The mitochondrial protein samples, 6 μg per well, were separated on a 12% SDS-bis-Tris polyacrylamide gel (BioRad). Each sample was loaded in triplicate, and a standard dilution series with five concentrations in duplicate was loaded on each gel for quantitative purposes. Blotting to a PVDF membrane was performed on a Semi-Dry Transfer Cell (BioRad). The detection procedure was according to instructions from the manufacturer of ECL Plus Western Blotting Detection Reagents (GE Healthcare). The membrane was incubated over night with primary antibodies against NDUFA9 (MitoSciences, Eugene, Oregon, USA) and VDAC1/porin (Abcam, Cambridge, Massachusetts, USA). The blots were scanned on ChemiDoc (UVP, Upland, California, USA) and densitometry was performed in ImageQuant 5.0 software (Molecular Dynamics, Sunnyvale, California, USA). The ratio between the protein amount of NDUFA9 and the loading control VDAC1 was calculated for each lane and the three resulting values from glucose and galactose samples were compared.
iTRAQ labeling, IEF separation and purification of peptides
Protein concentrations in the samples enriched for mitochondria were measured by the Bradford assay (Bio-Rad Laboratories) and 100 μg of each protein sample was processed according to iTRAQ manufacturer's instruction (Applied Biosystems). Each protein sample was digested with 2 μg trypsin (Trypsin Gold from Promega, Madison, Wisconsin, USA) overnight at 30°C in iTRAQ sample buffer. Different combinations of the 4-plex iTRAQ labels, two labels per LC-MS/MS run, were used in the four different experiments, to minimize risks of systematic errors. After iTRAQ-labeling the peptide samples were combined and subsequently purified using a strong cation exchange (SCX)-cartridge; Strata from Phenomenex (Torrence, California, USA). Before loading, the samples were adjusted to pH 3.0 by dilution at least a factor ten in 10 mM phosphoric acid with 25% acetonitrile (AcN) and pH 3.0, which also served as washing buffer. The peptides were eluted with a mixture of 5% of ammonia and 30% methanol and subsequently vacuum-dried. The peptides were separated by isoelectric focusing (IEF) on a Multiphor II unit (Pharmacia Biotech AB, Uppsala, Sweden) using an Immobiline Drystrip Gradient (IPG) pH 3.5–4.5 gel (GE Healthcare, Uppsala, Sweden), a pH range previously shown to give high proteome coverage . The sample was dissolved in rehydration solution, containing 8 M urea, 0.5% IPG buffer 3.5–5 (GE Healthcare) and 0.002% bromophenol blue, and the 18 cm Drystrip was rehydrated overnight. IEF was run for 59 kVh with the following program: 1 min gradient from 0–500 V, 1.5 h gradient from 500–3500 V followed by 16 h at 3500 V. The gel strip was wiped with filter paper to remove excess cover oil from IEF and cut in ten pieces of equal size. Peptides were extracted from the gel in two steps, of one hour each, with 100 μl 5% AcN, 0.5% trifluoracetic acid (TFA), and purified on PepClean C-18 Spin Columns (Pierce, Rockford, Illinois, USA) according to manufacturer's protocol.
Nano-liquid chromatography and mass spectrometry (MS) analysis
The peptide mixtures were separated by liquid chromatography (Easy nLC from Proxeon, Odense, Denmark) coupled to mass spectrometry (LTQ-Orbitrap, Thermo Fisher Scientific, Waltham, USA) through a nano-electrospray source with stainless steel emitter (Proxeon). The peptides were separated on a reverse phase column, 75 μm in diameter and 100 mm long, packed with 3.5 μm Kromasil C18 particles (Eka Chemicals, Bohus, Sweden) at a flow of 300 nL/minute using a 100 minutes gradient of AcN in 0.4% acetic acid; starting with 5% and ending with 35% AcN. The mass spectrometry detection constituted of full scan (m/z 400–2000) with Orbitrap detection at resolution R = 60,000 (at m/z 400) followed by up to four data dependent MS/MS scans, with linear ion trap (LTQ) detection of the most intense ions. Dynamic exclusion of 25 s was employed as well as rejection of charge state +1 and real time recalibration  by lock mass on m/z 445.120025. Pulsed Q dissociation (PQD) fragmentation was performed with activation time of 0.1 s and activation Q of 0.7. For efficient fragmentation and detection of iTRAQ reporter ions, normalized collision energy of 33 was used since optimization experiments showed that it gave the highest number of identified peptides with iTRAQ signal. Selected ion monitoring (SIM) was designed as data dependent scanning targeting m/z values of proteotypic peptides (typically two peptides per protein), which had been identified in the previous experimental runs. SIM analyses were performed using full scan in LTQ, followed by SIM in Orbitrap (with a mass width window of ± 3 m/z units) and MS/MS in LTQ. Thus the fragmentation scans and acquisition of iTRAQ signal in the SIM analyses were performed in the same way as in the previous standard experimental runs. Approximately 35 peptides were on the inclusion list per run, with retention time limit of ± 5 minutes.
Database searches and statistics
The raw data files were processed using extract_msn.exe (Thermo Fischer Scientific) to generate peak lists of the tandem spectra. The processed data was searched with Mascot http://www.matrixscience.com version 2.2.04 (Matrix Science, London, UK), which was used for protein identification and iTRAQ reporter quantification. Full scan tolerance was 5 ppm, MS/MS tolerance was 0.9 Da, and up to two missed cleavages were accepted. Fixed modifications were those originating from iTRAQ protocol: iTRAQ-4plex of lysine and N-terminal and methylthio modification of cysteines, whereas oxidation of methionine and iTRAQ-4plex of tyrosine were set as variable modifications. The threshold of significance was set to 0.001, which resulted in a false discovery frequency of less than 0.003 when searched in Mascot against the decoy database of random sequences. In each study, all generated peak lists, from standard analyses as well as from SIM analyses, of the ten different fractions of peptides were merged together. The merged files were searched against the IPI human database version 3.45 (71,983 sequences, released 6/10/2008) using the MudPIT scoring algorithm of Mascot. Protein identification data can be found in additional files [see Additional files 1, 2, 3 and 4]. Throughout the manuscript the HGNC symbol http://www.genenames.org/ obtained from the IPI-database was used to refer to protein hits. iTRAQ values were reported for proteins with three or more measured iTRAQ values, where each peptide should have an expectation value of 0.02 or below. iTRAQ quantitation was performed in Mascot, were normalization to summed intensities was applied to compensate for possible variation in starting material. For details see http://www.matrixscience.com/help/quant_config_help.html. Three iTRAQ-studies were performed comparing galactose and glucose cultivation of the fibroblast NHDF-1. The three studies were performed at different times and on independent cultivations. The iTRAQ-ratio of galactose to glucose values were calculated for each protein from the three independent studies giving independent triplicate values. Average galactose to glucose ratios for each protein was reported as significantly different from 1.0 if they passed two tests 1) a threshold test of two times the global standard error (2 × 0.055 = 0.11) and 2) a two-tailed student's T-test for equal variance data.
We have performed proteomic analyses on mitochondria from cultivated human skin fibroblasts to obtain an overview of the mitochondrial protein levels and thus detect stress response and unbalanced metabolism. The method, including all steps from cultivation to data analysis, was developed and standardized to obtain robust and easily interpretable data.
Amount of mitochondrial proteins in galactose and glucose cultivations
The fibroblasts exhibited decreased growth rate in galactose medium; with approximately fifty percent longer generation time (data not shown). Since the mitochondria from galactose and glucose cultivation had similar enrichment ratios, it indicates that the slow growth rate in the cultures with galactose was not due to altered mitochondrial protein amount. Subsequent studies were conducted to explore the protein profiles of individual proteins sorted into mitochondrial pathways.
Definition of mitochondrial pathways
Many mitochondrial pathways have been thoroughly studied and the gene products to a large extent are known. In order to facilitate interpretation of the proteome data we divided the main mitochondrial activities into nine different categories and defined the proteins belonging to the respective categories [see Additional file 6]. In addition to four metabolic categories there are three categories related to stress response, one to mitochondrial translation and one contains miscellaneous proteins. A criterion for inclusion into the sorted lists was that the proteins had previously been demonstrated or predicted to be localized to mitochondria . For energy metabolism the pathways of the KEGG database [26, 27] were adopted as an initial framework and manually curated using data from Gene Ontology  and literature data. The metabolic pathways are fatty acid oxidation (FAO), tricarboxylic (or citric) acid cycle (TCA) merged with pyruvate dehydrogenase (PDH), respiratory chain (RESP) and amino acid metabolism (AA). The other categories were primarily built bottom-up from literature data. The antioxidant systems (ANTIOX) category is composed of proteins that protect against oxidative stress; the translation (TRANS) category contains proteins of the mitochondrial translation machinery and protein quality control (PQC) category lists molecular chaperones and proteases. The apoptosis and mitochondrial morphology (APOP) category is composed of proteins known to play an important role for mitochondrial stress response and for influencing apoptosis regulation.
Robust protein profile data
Galactose versus glucose
To assess the reproducibility an additional fibroblast cell line (referred to as NHDF-2) was cultivated and analyzed using the same proteomic method. All of the 136 previously detected proteins were successfully detected, and only three of them had insufficient signal for quantification. Moreover, 13 of the 16 proteins with statistically significant change in the first study exhibited change in the same direction in the second study. Two of the three proteins that could not be confirmed (NDUFC2 and ATP5D) deviated in the opposite direction only weakly (less than six percent deviation from 1.0). For the third protein (OXCT1) no data was obtained due to insufficient quantification signal.
Diseases related to mitochondrial function
Many mitochondrial proteins are involved in human diseases and their corresponding genes have been listed in the OMIM database of human diseases . Additional file 8 shows the 38 gene products identified in the present study also described in OMIM. The detected proteins are involved in a wide range of diseases; spastic paraplegia (HSPD1), cancer (BAX, PHB), optic atrophy (OPA1), ethylmalonic encephalopathy (ETHE1) and Parkinson's disease (NDUFV2). The majority of the proteins are involved in metabolic diseases [see Additional file 8]. Moreover, a couple of the proteins linked to disease, NDUSF3 and NDUFS1 of respiratory complex I, were found to be galactose regulated.
Cultivated fibroblasts are commonly used for analyzing mitochondrial enzymatic activity and for detection of respiratory chain defects. In this study we applied proteomics for relative quantification of mitochondrial proteins and managed to cover the main pathways of mitochondrial activity obtaining in-depth data on energy metabolism and stress response. We analyzed the response of fibroblast cells to galactose cultivation and found that galactose resulted in increased levels of respiratory chain proteins. Respiration is crucial for growth in the slowly metabolized galactose  and fibroblast cells have previously been shown to be unable to grow in galactose when complex I or IV are defective [21, 22]. Proteins of respiratory chain complex I, and to some extent complex IV, were in the present study found to be up-regulated in response to galactose. This might be a way for the cell to cope with the energy imbalance caused by galactose. Interestingly, a few proteins of complex I (e.g. NDUFA9, NDUFS3 and NDUFB9) were elevated to a higher degree than the rest, indicating sub-stoichiometric regulation of these proteins. Recently, it was shown that NDUFS3 was present in a matrix-soluble assembly and in several membrane-bound assemblies, including the holo-enzyme . Further studies will have to elucidate whether the various complex I subunits are present in different subassemblies, which would explain the sub-stoichimetric relationship observed in the present study.
Protein profiles from the other functional categories displayed less difference between galactose and glucose cultivated cells. However, IDH3A, a part of the NAD+ dependent isocitrate dehydrogenase (NADH-IDH) of the tricarboxylic acid cycle (TCA) was clearly increased when galactose was used as carbon source. NADH-IDH catalyzes the first NADH-yielding reaction of the TCA cycle, described to have a high impact of the overall rate of the TCA cycle and to be allosterically regulated, so that its activity can be increased in response to, for example, a low ATP to ADP ratio . During galactose cultivation it seems that the allosteric activation was insufficient so that the protein amount of IDH3A enzyme also had to be increased, whereas the levels of the other TCA enzymes were close to unaffected. Diseases related to IDH3 are not described in the OMIM database. This could be related to the existence of the parallel and compensatory activity of NADP+ -dependent IDH , which is able to compensate for the NADH-dependent activity of IDH3A. Recently, a loss-of-function of IDH3B, the β-subunit of NADH-IDH, was found in patients with retinitis pigmentosa. It is thus likely that deficient NADH-IDH activity is a contributory factor in energy deficiency disorders, causing severe symptoms only in certain tissues .
Single mitochondrial proteins might result in disease when they are present at an insufficient level, often caused by genetic variations resulting in protein misfolding and/or degradation . Several of the proteins in Additional file 8 are associated with metabolic diseases, a group of diseases in which synergistic heterozygosity has been described, i.e. diseases resulting from multiple partial defects in one or more metabolic pathways [35, 36]. For these diseases, the simultaneous quantification of several metabolic proteins is highly valuable for identification of the components of the synergistic effects. The present study detected 38 proteins related to disease according to the OMIM database. Since these disease-related proteins were detected in all of our experiments, their relative amounts are likely to be detectable in future, similar studies. Furthermore, there is growing evidence of links between metabolic defects, protein misfolding, oxidative stress, and disease . It is therefore highly relevant to obtain protein profiles from multiple, well-defined pathways of metabolism and stress response to be able to study diseases involving multiple components.
Quantitative mitochondrial proteomics of cultivated patient fibroblasts show promising results for exploring the consequences of genetic diseases at the protein level. Moreover, this type of method is suitable for environmental stress studies on cultivated human cells, as exemplified here by metabolic stress. Mapping of the interplay between various proteins and pathways might serve as a powerful tool for elucidation of the effects of disease and cellular stress. It was shown that when the cells had limited access to energy sources through cultivation in galactose, the amount of mitochondria did not seem to change; instead, the cells up-regulated parts of their respiratory pathway and specific metabolic proteins to compensate for the compromised energy state.
We would like to thank Christian Knudsen for sharing his expertise with us and for performing cell cultivations. We acknowledge financial support from The John and Birthe Meyer Foundation, Institute of Clinical Medicine at Aarhus University, Denmark, The Lundbeck Foundation and The Danish Medical Research Council.
- Chan DC: Mitochondria: dynamic organelles in disease, aging, and development. Cell 2006, 125: 1241–1252. 10.1016/j.cell.2006.06.010PubMedView ArticleGoogle Scholar
- Janssen RJ, Heuvel LP, Smeitink JA: Genetic defects in the oxidative phosphorylation (OXPHOS) system. Expert Rev Mol Diagn 2004, 4: 143–156. 10.1586/14737184.108.40.206PubMedView ArticleGoogle Scholar
- Gregersen N, Bross P, Andresen BS: Genetic defects in fatty acid beta-oxidation and acyl-CoA dehydrogenases. Molecular pathogenesis and genotype-phenotype relationships. Eur J Biochem 2004, 271: 470–482. 10.1046/j.1432-1033.2003.03949.xPubMedView ArticleGoogle Scholar
- Kwong JQ, Beal MF, Manfredi G: The role of mitochondria in inherited neurodegenerative diseases. J Neurochem 2006, 97: 1659–1675. 10.1111/j.1471-4159.2006.03990.xPubMedView ArticleGoogle Scholar
- Del Poeta G, Bruno A, Del Principe MI, Venditti A, Maurillo L, Buccisano F, Stasi R, Neri B, Luciano F, Siniscalchi A, de Fabritiis P, Amadori S: Deregulation of the mitochondrial apoptotic machinery and development of molecular targeted drugs in acute myeloid leukemia. Curr Cancer Drug Targets 2008, 8: 207–222. 10.2174/156800908784293640PubMedView ArticleGoogle Scholar
- Kroemer G, Pouyssegur J: Tumor cell metabolism: cancer's Achilles' heel. Cancer Cell 2008, 13: 472–482. 10.1016/j.ccr.2008.05.005PubMedView ArticleGoogle Scholar
- White MY, Edwards AVG, SJ C, Van Eyk JE: Mitochondria: A mirror into cellular dysfunction in heart disease. Proteomics Clin Appl 2008, 2: 845–861. 10.1002/prca.200780135PubMedView ArticleGoogle Scholar
- Jüllig M, Hickey AJ, Middleditch MJ, Crossman DJ, Lee SC, Cooper GJS: Characterization of proteomic changes in cardiac mitochondria in streptozotocin-diabetic rats using iTRAQ™ isobaric tags. Proteomics Clin Appl 2007, 1: 565–576. 10.1002/prca.200600831PubMedView ArticleGoogle Scholar
- Lin MT, Beal MF: Mitochondrial dysfunction and oxidative stress in neurodegenerative diseases. Nature 2006, 443: 787–795. 10.1038/nature05292PubMedView ArticleGoogle Scholar
- Calvo S, Jain M, Xie X, Sheth SA, Chang B, Goldberger OA, Spinazzola A, Zeviani M, Carr SA, Mootha VK: Systematic identification of human mitochondrial disease genes through integrative genomics. Nat Genet 2006, 38: 576–582. 10.1038/ng1776PubMedView ArticleGoogle Scholar
- Pagliarini DJ, Calvo SE, Chang B, Sheth SA, Vafai SB, Ong SE, Walford GA, Sugiana C, Boneh A, Chen WK, Hill DE, Vidal M, Evans JG, Thorburn DR, Carr SA, Mootha VK: A mitochondrial protein compendium elucidates complex I disease biology. Cell 2008, 134: 112–123. 10.1016/j.cell.2008.06.016PubMed CentralPubMedView ArticleGoogle Scholar
- Taylor SW, Fahy E, Zhang B, Glenn GM, Warnock DE, Wiley S, Murphy AN, Gaucher SP, Capaldi RA, Gibson BW, Ghosh SS: Characterization of the human heart mitochondrial proteome. Nat Biotechnol 2003, 21: 281–286. 10.1038/nbt793PubMedView ArticleGoogle Scholar
- Gaucher SP, Taylor SW, Fahy E, Zhang B, Warnock DE, Ghosh SS, Gibson BW: Expanded coverage of the human heart mitochondrial proteome using multidimensional liquid chromatography coupled with tandem mass spectrometry. J Proteome Res 2004, 3: 495–505. 10.1021/pr034102aPubMedView ArticleGoogle Scholar
- Zhang J, Li X, Mueller M, Wang Y, Zong C, Deng N, Vondriska TM, Liem DA, Yang JI, Korge P, Honda H, Weiss JN, Apweiler R, Ping P: Systematic characterization of the murine mitochondrial proteome using functionally validated cardiac mitochondria. Proteomics 2008, 8: 1564–1575. 10.1002/pmic.200700851PubMed CentralPubMedView ArticleGoogle Scholar
- Gloerich J, Wevers RA, Smeitink JA, van Engelen BG, Heuvel LP: Proteomics approaches to study genetic and metabolic disorders. J Proteome Res 2007, 6: 506–512. 10.1021/pr060487wPubMedView ArticleGoogle Scholar
- Ross PL, Huang YN, Marchese JN, Williamson B, Parker K, Hattan S, Khainovski N, Pillai S, Dey S, Daniels S, Purkayastha S, Juhasz P, Martin S, Bartlet-Jones M, He F, Jacobson A, Pappin DJ: Multiplexed protein quantitation in Saccharomyces cerevisiae using amine-reactive isobaric tagging reagents. Mol Cell Proteomics 2004, 3: 1154–1169. 10.1074/mcp.M400129-MCP200PubMedView ArticleGoogle Scholar
- Wu WW, Wang G, Baek SJ, Shen RF: Comparative study of three proteomic quantitative methods, DIGE, cICAT, and iTRAQ, using 2D gel- or LC-MALDI TOF/TOF. J Proteome Res 2006, 5: 651–658. 10.1021/pr050405oPubMedView ArticleGoogle Scholar
- Bantscheff M, Boesche M, Eberhard D, Matthieson T, Sweetman G, Kuster B: Robust and sensitive iTRAQ quantification on an LTQ Orbitrap mass spectrometer. Mol Cell Proteomics 2008, 7: 1702–1713. 10.1074/mcp.M800029-MCP200PubMed CentralPubMedView ArticleGoogle Scholar
- Bantscheff M, Schirle M, Sweetman G, Rick J, Kuster B: Quantitative mass spectrometry in proteomics: a critical review. Anal Bioanal Chem 2007, 389: 1017–1031. 10.1007/s00216-007-1486-6PubMedView ArticleGoogle Scholar
- Ernoult E, Gamelin E, Guette C: Improved proteome coverage by using iTRAQ labelling and peptide OFFGEL fractionation. Proteome Sci 2008, 6: 27. 10.1186/1477-5956-6-27PubMed CentralPubMedView ArticleGoogle Scholar
- Robinson BH, Petrova-Benedict R, Buncic JR, Wallace DC: Nonviability of cells with oxidative defects in galactose medium: a screening test for affected patient fibroblasts. Biochem Med Metab Biol 1992, 48: 122–126. 10.1016/0885-4505(92)90056-5PubMedView ArticleGoogle Scholar
- Hofhaus G, Johns DR, Hurko O, Attardi G, Chomyn A: Respiration and growth defects in transmitochondrial cell lines carrying the 11778 mutation associated with Leber's hereditary optic neuropathy. J Biol Chem 1996, 271: 13155–13161. 10.1074/jbc.271.22.13155PubMedView ArticleGoogle Scholar
- Lengqvist J, Uhlen K, Lehtio J: iTRAQ compatibility of peptide immobilized pH gradient isoelectric focusing. Proteomics 2007, 7: 1746–1752. 10.1002/pmic.200600782PubMedView ArticleGoogle Scholar
- Olsen JV, de Godoy LM, Li G, Macek B, Mortensen P, Pesch R, Makarov A, Lange O, Horning S, Mann M: Parts per million mass accuracy on an Orbitrap mass spectrometer via lock mass injection into a C-trap. Mol Cell Proteomics 2005, 4: 2010–2021. 10.1074/mcp.T500030-MCP200PubMedView ArticleGoogle Scholar
- Westhuizen FH, Heuvel LP, Smeets R, Veltman JA, Pfundt R, van Kessel AG, Ursing BM, Smeitink JA: Human mitochondrial complex I deficiency: investigating transcriptional responses by microarray. Neuropediatrics 2003, 34: 14–22. 10.1055/s-2003-38618PubMedView ArticleGoogle Scholar
- Kanehisa M, Goto S: KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res 2000, 28: 27–30. 10.1093/nar/28.1.27PubMed CentralPubMedView ArticleGoogle Scholar
- Kanehisa M, Araki M, Goto S, Hattori M, Hirakawa M, Itoh M, Katayama T, Kawashima S, Okuda S, Tokimatsu T, Yamanishi Y: KEGG for linking genomes to life and the environment. Nucleic Acids Res 2008, 36: D480–484. 10.1093/nar/gkm882PubMed CentralPubMedView ArticleGoogle Scholar
- Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT, Harris MA, Hill DP, Issel-Tarver L, Kasarskis A, Lewis S, Matese JC, Richardson JE, Ringwald M, Rubin GM, Sherlock G: Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet 2000, 25: 25–29. 10.1038/75556PubMed CentralPubMedView ArticleGoogle Scholar
- McKusick-Nathans Institute of Genetic Medicine JHUB, MD) and National Center for Biotechnology Information, National Library of Medicine (Bethesda, MD): Online Mendelian Inheritance in Man, OMIM (TM). 2008. [http://www.ncbi.nlm.nih.gov/sites/entrez?db=OMIM]Google Scholar
- Rossignol R, Gilkerson R, Aggeler R, Yamagata K, Remington SJ, Capaldi RA: Energy substrate modulates mitochondrial structure and oxidative capacity in cancer cells. Cancer Res 2004, 64: 985–993. 10.1158/0008-5472.CAN-03-1101PubMedView ArticleGoogle Scholar
- Dieteren CE, Willems PH, Vogel RO, Swarts HG, Fransen J, Roepman R, Crienen G, Smeitink JA, Nijtmans LG, Koopman WJ: Subunits of mitochondrial complex I exist as part of matrix- and membrane-associated subcomplexes in living cells. J Biol Chem 2008, 283: 34753–34761. 10.1074/jbc.M807323200PubMed CentralPubMedView ArticleGoogle Scholar
- Gabriel JL, Zervos PR, Plaut GW: Activity of purified NAD-specific isocitrate dehydrogenase at modulator and substrate concentrations approximating conditions in mitochondria. Metabolism 1986, 35: 661–667. 10.1016/0026-0495(86)90175-7PubMedView ArticleGoogle Scholar
- Hartong DT, Dange M, McGee TL, Berson EL, Dryja TP, Colman RF: Insights from retinitis pigmentosa into the roles of isocitrate dehydrogenases in the Krebs cycle. Nat Genet 2008, 40: 1230–1234. 10.1038/ng.223PubMed CentralPubMedView ArticleGoogle Scholar
- Gregersen N, Bolund L, Bross P: Protein misfolding, aggregation, and degradation in disease. Mol Biotechnol 2005, 31: 141–150. 10.1385/MB:31:2:141PubMedView ArticleGoogle Scholar
- Vockley J, Rinaldo P, Bennett MJ, Matern D, Vladutiu GD: Synergistic heterozygosity: disease resulting from multiple partial defects in one or more metabolic pathways. Mol Genet Metab 2000, 71: 10–18. 10.1006/mgme.2000.3066PubMedView ArticleGoogle Scholar
- Schuler AM, Gower BA, Matern D, Rinaldo P, Vockley J, Wood PA: Synergistic heterozygosity in mice with inherited enzyme deficiencies of mitochondrial fatty acid beta-oxidation. Mol Genet Metab 2005, 85: 7–11. 10.1016/j.ymgme.2004.09.006PubMedView ArticleGoogle Scholar
- Gregersen N, Bross P, Vang S, Christensen JH: Protein Misfolding and Human Disease. Annu Rev Genomics Hum Genet 2006, 7: 103–124. 10.1146/annurev.genom.7.080505.115737PubMedView ArticleGoogle Scholar
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.