Accurate Quantification of More Than 4000 Mouse Tissue Proteins Reveals Minimal Proteome Changes During Aging

The biological process of aging is believed to be the result of an accumulation of cellular damage to biomolecules. Although there are numerous studies addressing mutation frequencies, morphological or transcriptional changes in aging mammalian tissues, few have measured global changes at the protein level. Here, we present an in depth proteomic analysis of three brain regions as well as heart and kidney in mice aged 5 or 26 months, using stable isotope labeling of whole animals (SILAC mouse) and high resolution mass spectrometry. In the frontal cortex and hippocampal regions of the brain, more than 4200 proteins were quantitatively compared between age groups. Proteome differences between individual mice were observable within and between age groups. However, mean protein abundance changes of more than twofold between young and old mice were detected in less than 1% of all proteins and very few of these were statistically significant. Similar outcomes were obtained when comparing cerebellum, heart, and kidney between age groups. Thus, unexpectedly, our results indicate that aging-related effects on the tissue proteome composition at the bulk level are only minor and that protein homeostasis remains functional up to a relatively high age. The biological process of aging is believed to be the result of an accumulation of cellular damage to biomolecules. Although there are numerous studies addressing mutation frequencies, morphological or transcriptional changes in aging mammalian tissues, few have measured global changes at the protein level. Here, we present an in depth proteomic analysis of three brain regions as well as heart and kidney in mice aged 5 or 26 months, using stable isotope labeling of whole animals (SILAC mouse) and high resolution mass spectrometry. In the frontal cortex and hippocampal regions of the brain, more than 4200 proteins were quantitatively compared between age groups. Proteome differences between individual mice were observable within and between age groups. However, mean protein abundance changes of more than twofold between young and old mice were detected in less than 1% of all proteins and very few of these were statistically significant. Similar outcomes were obtained when comparing cerebellum, heart, and kidney between age groups. Thus, unexpectedly, our results indicate that aging-related effects on the tissue proteome composition at the bulk level are only minor and that protein homeostasis remains functional up to a relatively high age. Aging in higher organisms is a multifactorial process. It is commonly believed that lifespan is restricted because of the accumulation of cellular damage, ultimately interfering with crucial biological functions. In mammals, hallmarks of aging tissues include declining rates of self renewal capability and accumulating damages to DNA, proteins, and lipids (1.Finkel T. Holbrook N.J. 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Effects of senescence on the left rat heart ventricle was addressed using two-dimensional gel electrophoresis or iTRAQ labeling and matrix-assisted laser desorption/ionization (MALDI)-based quantitative mass spectrometry in which differential expression of metabolic enzymes, structural and antioxidant proteins were reported (24.Dai Q. Escobar G.P. Hakala K.W. Lambert J.M. Weintraub S.T. Lindsey M.L. The left ventricle proteome differentiates middle-aged and old left ventricles in mice.J. Proteome Res. 2008; 7: 756-765Crossref PubMed Scopus (29) Google Scholar, 25.Richardson M.R. Lai X. Mason S.B. Miller S.J. Witzmann F.A. Differential protein expression during aging in ventricular myocardium of Fischer 344 x Brown Norway hybrid rats.Exp. Gerontol. 2008; 43: 909-918Crossref PubMed Scopus (9) Google Scholar, 26.Grant J.E. Bradshaw A.D. Schwacke J.H. Baicu C.F. Zile M.R. Schey K.L. Quantification of Protein Expression Changes in the Aging Left Ventricle of Rattus norvegicus.J. 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Using this technology, we compared the proteomes of each of four female C57BL/6JN mice aged 5 or 26 months and obtained highly accurate quantification over a broad range of tissues. Female C57BL/6JN mice aged 5 or 26 months, fed ad libitum, were obtained from the Aged Rodent Colonies (National Institute of Aging, Bethesda, MD). Animals were starved overnight and sacrificed by cervical dislocation. Brains were immediately removed and placed in ice cold PBS. The frontal cortex region was isolated by removal of olfactory tracts and cutting 1 mm posterior to the bregma. Subsequently, hippocampus and cerebellum were collected. Remaining corpses were perfused by injection of PBS into the heart before removal of kidneys and heart. All tissues and organs were washed in cold PBS and shock frozen in liquid nitrogen. For SILAC labeled standards, animals were fed for four generations with a diet containing exclusively 13C6 lysine as previously described (23.Krüger M. Moser M. Ussar S. Thievessen I. Luber C.A. Forner F. Schmidt S. Zanivan S. Fässler R. Mann M. SILAC mouse for quantitative proteomics uncovers kindlin-3 as an essential factor for red blood cell function.Cell. 2008; 134: 353-364Abstract Full Text Full Text PDF PubMed Scopus (548) Google Scholar). Corresponding tissues were obtained from two 12-month-old females with 97.9% incorporation rate of heavy lysine. SILAC tissues were pooled from both animals before processing. All tissue samples were stored at −80 °C until use. Tissues were blended with an Ultra-Turrax disperser (IKA, Staufen, Germany) in 150 mm Tris/HCl pH 8, 4 mm EDTA, 1 mm phenylmethylsulfonyl fluoride at 4 °C. Hearts were ground in the frozen state in a mortar before homogenization. Immediately following homogenization, SDS was added to a final concentration of 4% (w/w) and samples were incubated at 95 °C for 3 min. For shearing of DNA, samples were subjected to treatment with a Bioruptor ultra sonication bath (Diagenode, Liège, Belgium) at high energy setting for 10 min. Samples were heated again for 3 min at 95 °C and clarified by centrifugation for 10 min at 20,000 × g. Protein content was determined using the BCA Protein Assay Kit (Thermo, Rockford, IL) according to the manufacturer's instructions. For frontal cortex and hippocampus, 100 μg protein from each animal were mixed with an identical amount of the corresponding SILAC labeled standard. For cerebellum and kidney, lysates were pooled within the young or old animal groups before mixing with SILAC standard. Reduction of disulfide bridges was achieved by addition of dithiotreitol to a final concentration of 0.1 m followed by incubation at 75 °C for 5 min. Further processing for in-solution digestion was performed using the previously described filter-aided sample preparation method (28.Wiśniewski J.R. Zougman A. Nagaraj N. Mann M. Universal sample preparation method for proteome analysis.Nat. Methods. 2009; 6: 359-362Crossref PubMed Scopus (5116) Google Scholar) with Microcon YM-30 devices (Millipore, Billerica, MA), but with the following minor modifications: Alkylation was carried out with 2-chloroacetamide instead of 2-iodoacetamide and proteins were digested exclusively with endoproteinase LysC (Wako Bioproducts, Richmond, VA) in 2 m urea, 25 mm Tris/HCl, pH 8 overnight at room temperature. Obtained peptides were acidified with trifluoroacetic acid and desalted via C18 solid phase extraction cartridges (3M, St. Paul, MN). Peptide mixtures were measured both directly and following fractionation into six fractions via strong anion exchange chromatography according to published procedures (29.Wiśniewski J.R. Zougman A. Mann M. Combination of FASP and StageTip-based fractionation allows in-depth analysis of the hippocampal membrane proteome.J. Proteome Res. 2009; 8: 5674-5678Crossref PubMed Scopus (439) Google Scholar). Liquid chromatography (LC)-MS experiments were essentially performed as described previously (30.Olsen J.V. Ong S.E. Mann M. Trypsin cleaves exclusively C-terminal to arginine and lysine residues.Mol. Cellular Proteomics. 2004; 3: 608-614Abstract Full Text Full Text PDF PubMed Scopus (876) Google Scholar, 31.Forner F. Foster L.J. Campanaro S. Valle G. Mann M. Quantitative proteomic comparison of rat mitochondria from muscle, heart, and liver.Mol. Cell Proteomics. 2006; 5: 608-619Abstract Full Text Full Text PDF PubMed Scopus (247) Google Scholar). Briefly, reversed phase separation of peptides was performed using an Easy nLC nanoflow HPLC system (Proxeon Biosystems, Odense, Denmark now Thermo Fisher Scientific). Peptide mixtures were loaded onto a column with 15 cm length and 75 μm inner diameter, packed in-house with RepoSil-Pur C18-AQ 3 μm resin (Dr. Maisch, Ammerbuch-Entringen, Germany) at 0.7 μm/min. Peptides were then eluted in fraction-optimized nonlinear gradient from 3% to 60% acetonitrile in 0.5% acetic acid over a duration of 200 min. Eluting peptides were electrosprayed online via a nanospray ion source (Proxeon Biosystems) at a voltage of 2.2 kV into an LTQ Orbitrap XL mass spectrometer (Thermo Fisher Scientific, Bremen, Germany), except for unfractionated frontal cortex samples, which were acquired using an LTQ-FT mass spectrometer (Thermo Fisher Scientific). Survey scans were performed in the Orbitrap analyzer at a resolution of 60,000 at target values of 1,000,000 ions and maximum allowed fill times of 1 s over a mass range between m/z 350–1750. The 10 most intense peaks were subjected to fragmentation via collision induced dissociation in the LTQ. For each scan, 5000 ions were accumulated over a maximum allowed fill time of 250 ms and fragmented by wideband activation. Exclusion of precursor ion masses over a time window of 150 s was used to suppress repeated fragmentation of peaks. In all MS experiments except for the measurements of the hippocamupus proteomes, internal lock mass recalibration was disabled and an Active Background Ion Repression Device (ABIRD, ESI Source Solutions, Woburn, MA) was used to increase the signal to noise ratio. Raw data consisting of 154 liquid chromatography-coupled tandem mass spectrometry (LC-MS/MS) files are deposited at Tranche and are freely available upon publication. Hash keys are provided in the Supplemental Materials section. Raw data was analyzed using the MaxQuant software environment, version 1.1.0.25. Retention time dependent mass recalibration was applied and peak lists were searched against a database containing all 56,729 entries from the International Protein Index mouse protein database version 3.68 and 255 frequently observed contaminants as well as reversed sequences of all entries. Searches were performed with the following settings: Precursor and fragment ion peaks were searched with an initial mass tolerance of 7 ppm and 0.5 Th, respectively. Enzyme specificity was set to LysC, additionally allowing cleavage between lysine and proline. Up to two missed cleavages were allowed and only peptides with at least six amino acids in length were considered. Carbamidomethylcysteine was set as a fixed modification whereas oxidation on methionine was set as a variable modification. Up to two missed cleavages were allowed. Precursor masses of already identified peptides were further searched within a 3 min time window in chromatograms derived from corresponding and adjacent peptide fractions (“match between runs” option in MaxQuant). For reliability estimation of peptide identifications, the posterior error probability for each top scoring hit was calculated. This metric is based on the tandem MS (MS/MS) score but additionally takes into account peptide length dependent histograms of forward and reverse hits to assess the probability of a false identification using the Bayes theorem (described in detail in (32.Cox J. Mann M. MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification.Nat. Biotechnol. 2008; 26: 1367-1372Crossref PubMed Scopus (9251) Google Scholar)). Using a decoy database strategy (33.Elias J.E. Gygi S.P. Target-decoy search strategy for increased confidence in large-scale protein identifications by mass spectrometry.Nat. Methods. 2007; 4: 207-214Crossref PubMed Scopus (2842) Google Scholar), peptide identifications were accepted based on their posterior error probability until less than 1% reverse hits were retained in the list. Accepted peptide sequences were subsequently assembled into proteins in ascending order of their posterior error probability up to false discovery rate of 1% at the protein level. For successful protein identifications, at least two peptides and one peptide with a unique peptide sequence were required. If no unique peptide sequence to a single database entry was identified, the resulting protein identification was reported as an indistinguishable “protein group.” Protein quantifications were based on the median SILAC ratios of at least two peptides (two valid “ratio counts”) in each biological sample. Quantification of SILAC pairs was performed by MaxQuant with standard settings (32.Cox J. Mann M. MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification.Nat. Biotechnol. 2008; 26: 1367-1372Crossref PubMed Scopus (9251) Google Scholar). Briefly, centroids of isotope clusters in the intensity-m/z plain were detected over multiple full scans and the median intensity ratios were used for the calculation of SILAC ratios. For histogram representations, only proteins with at least one quantification per age group were considered. Mean normalized protein ratios between sample and SILAC standard were then calculated within age groups and histograms were plotted using the R statistical programming language (34.R Foundation for Statistical Computing (2009) R: A language and environment for statistical computingGoogle Scholar). For calculation of correlation coefficients and principal component analyses, datasets were filtered for entries with valid quantifications in all experiments (see above). Principal component analyses were performed using the Gene Expression Similarity Investigation Suite (Genesis) (35.Sturn A. Quackenbush J. Trajanoski Z. Genesis: cluster analysis of microarray data.Bioinformatics. 2002; 18: 207-208Crossref PubMed Scopus (1475) Google Scholar) following mean centering of expression data. Two-sided t-tests were carried out after filtering for proteins with at least three out of four quantifications under the assumption of unequal variance between age groups. Significance thresholds were then calculated via a permutation-based false discovery rate estimation of 1% (36.Tibshirani R. A simple method for assessing sample sizes in microarray experiments.Bmc Bioinformatics. 2006; 7: 106Crossref PubMed Scopus (78) Google Scholar). For comparison of microarray and proteome studies, median intensities for probes corresponding to Uniprot annotations of identified protein groups were calculated before statistical analyses analogous to the proteome data. To quantify age-related proteome changes in mouse tissue, we used the SILAC technology in vivo and coupled it to high-resolution LC-MS/MS (Fig. 1). We labeled mice with a diet containing exclusively 13C6 lysine over several generations and obtained virtually complete incorporation. To exclude potential effects because of the SILAC diet, we performed all experiments in a “spike-in” format, using the tissue from the SILAC mice as a common internal standard for all experiments. This design also allows comparison between multiple animals within and between each age group. For each experiment, equal amounts of protein sample from biological replicates and the corresponding SILAC standard were mixed, digested in solution with endoproteinase LysC using the filter-aided sample preparation method and peptides were fractionated via strong anion exchange chromatography (28.Wiśniewski J.R. Zougman A. Nagaraj N. Mann M. Universal sample preparation method for proteome analysis.Nat. Methods. 2009; 6: 359-362Crossref PubMed Scopus (5116) Google Scholar, 29.Wiśniewski J.R. Zougman A. Mann M. Combination of FASP and StageTip-based fractionation allows in-depth analysis of the hippocampal membrane proteome.J. Proteome Res. 2009; 8: 5674-5678Crossref PubMed Scopus (439) Google Scholar). These peptide fractions as well as unfractionated peptide preparations from each experiment were then analyzed by LC-MS/MS on a hybrid high resolution linear ion trap Orbitrap instrument (Fig. 1). Hippocampal and frontal cortex regions from the brains of four young and four old animals were processed individually, resulting in eight individual quantitative proteomes. We extended the study to cerebellum, kidney, and heart muscle but in contrast to the previous experiments, lysates within age groups were pooled for each of these three tissues. The combined dataset over all five tissues comprises 154 LC-MS/MS experiments with 4 h gradients, during which more than 4.5 million MS/MS scans were acquired. The observed average absolute mass deviation for the corresponding precursor ions was 450 ppb. Following analysis with the MaxQuant software environment (32.Cox J. Mann M. MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification.Nat. Biotechnol. 2008; 26: 1367-1372Crossref PubMed Scopus (9251) Google Scholar), more than 60% of the MS/MS scans were unambiguously identified. This led to 44,737 identified nonredundant peptide sequences and 5619 proteins (or protein groups) at a protein false discovery rate of less than 1%. In frontal cortex and hippocampus, expression data for more than 4200 proteins of each four young and old mice were acquired individually. Of this subset of proteins, approximately two thirds were expressed and detected in at least three out of four biological replicate experiments per age group (77 or 61% in frontal cortex or hippocampus, respectively, Supplemental Tables 1 and 2). For protein quantification, our study employed an internal standard, which was added to all biological samples of a given tissue, to serve as a fixed reference point for all observed peptide ratios. To determine the quantitative reliability of this data set in more detail, we first extracted the typical number of quantification events per protein. In the above data set of a young animal it turned out to be 18 events on average (median of 8). This is because of the high redundancy of peptide based quantifications and compares favorably to two-dimensional gel experiments in which typically only one quantification event per protein is obtained in each individual sample. As a consequence, when comparing proteomes of two technical replicate experiments virtually all proteins were detected in equal quantities, demonstrating the usefulness of the method. This “ratio-of-ratios” distribution was narrow, with more than 99% of the proteins showing an apparent fold change of less than a factor of two (Fig 2A). In contrast, a much broader abundance ratio distribution was observed when comparing two different biological samples (Fig. 2B). The figure also shows that the few outlier proteins, on the most part, have a higher abundance in the aged animals. However, most these changes are not statistically significant (see below). Next, we investigated the mean protein expression changes between the two age groups. Unexpectedly, the vast majority of proteins were found in equal quantities in old and young animals (Fig. 3A). An average expression change of more than twofold was detected in less than 1% of all quantified proteins and less than 3% changed by more than one third in both brain tissues (Supplemental Table 3). These data indicate that age-related changes on the protein level in mice are very minor in the two analyzed brain regions.Fig. 3A, Abundance of the vast majority of proteins is unchanged during aging in mice as indicated by a histogram representation of protein abundance ratios between old and young animals in hippocampus or frontal cortex. B, Pearson correlation between the proteomes of mice within the young or old age groups is not generally higher than between old and young individuals. Correlation coefficients in the matrix are color coded as shown. C, Principal component analysis of the individual proteomes.View Large Image Figure ViewerDownload Hi-res image Download (PPT) We asked whether aging causes specific changes in tissue proteomes. For this purpose we calculated the Pearson correlation coefficients between all pairs of biological samples using the abundance ratios of all quantified proteins. Specific age related proteome changes should be reflected by higher Pearson correlation coefficients between the animals of one age group as compared with the coefficients when comparing old and young animals. However, the correlation between proteomes within young or aged animals was not generally higher than that between old and young mice (Fig. 3B). The differences between individual proteomes can be attributed to biological variability between the mice rather than to differences in sample preparation. This was apparent from the high degree of reproducibility obtained when analyzing each of three technical replicates of two selected hippocampal tissue lysates that were prepared and analyzed using the same protocol (Supplemental Fig. 1). To investigate the potential proteome differences between young and old mice by a different method we performed principal component analysis. The distances between the samples of the same age