Aging in Mouse Brain Is a Cell/Tissue-Level Phenomenon Exacerbated by Proteasome Loss Lei Mao, Irmgard Ro ¨ mer, Grit Nebrich, Oliver Klein, Andrea Koppelsta¨tter, Sascha C. Hin, Daniela Hartl, and Claus Zabel* Institute for Human Genetics, Charite´ - University Medicine Berlin, Augustenburger Platz 1, 13353 Berlin, Germany Received January 21, 2010
Biological aging is often described by its phenotypic effect on individuals. Still, its causes are more likely found on the molecular level. Biological organisms can be considered as reliability-engineered, robust systems and applying reliability theory to their basic nonaging components, proteins, could provide insight into the aging mechanism. Reliability theory suggests that aging is an obligatory tradeoff in a fault-tolerant system such as the cell which is constructed based on redundancy design. Aging is the inevitable redundancy loss of functional system components, that is proteins, over time. In our study, we investigated mouse brain development, adulthood, and aging from embryonic day 10 to 100 weeks. We determined redundancy loss of different protein categories with age using reliability theory. We observed a near-linear decrease of protein redundancy during aging. Aging may therefore be a phenotypic manifestation of redundancy loss caused by nonfunctional protein accumulation. This is supported by a loss of proteasome system components faster than dictated by reliability theory. This loss is highly detrimental to biological self-renewal and seems to be a key contributor to aging and therefore could represent a major target for therapies for aging and age-related diseases. Keywords: aging • mouse brain • proteome • proteasome • redundancy • reliability theory
Introduction Aging is the phenomenon of an increasing mortality (failure rate) of cells and tissue with the passage of time.1 There are over 300 independent mechanistic aging theories.2 Although no consensus exists in this wide variety of aging theories, one common denominator is that aging is predominantly explained by aging of the components of an organism. However, to discriminate simple physiological aging from pathological cellular decay, at least four different aspects need to be addressed by a comprehensive aging theory: (i) Why do only complex but not primitive organisms deteriorate with age, (ii) why do failure rates increase exponentially with age, (iii) why does the age related increase in failure rates decrease/vanish at very old ages, and (iv) how is the compensation law of mortality (failure rate) explained, that is, high failure rates are compensated by a low apparent aging rate at old age. To address these questions in detail, one has to consider the cell, the building block of each organism in its entirety. Aging of an organism is supposed to be caused by aging of organs, cells, and subcellular components. However many building blocks of biological organisms have constant mortality rates within a biological lifespan. Therefore, we will consider cellular proteins as nonaging components. An extensive body of literature3–5 shows that the function of many proteins (e.g., enzymes) exhibits no difference between young and old organ* To whom correspondence should be addressed. Claus Zabel, Institute for Human Genetics, Charite´ - University Medicine Berlin, Augustenburger Platz 1, 13353 Berlin, Germany. E-mail:
[email protected]. Fax: +4930-450-566904. 10.1021/pr100059j
2010 American Chemical Society
isms. The overall decrease of protein (e.g., enzyme) activity is rather caused by a gradual accumulation of dysfunctional proteins (e.g., enzymes) over time due to misfolding and lack of degradation instead of a decrease in protein function. We are now faced with the problem of how to explain aging of a biological entity built by nonaging components applying the criteria mentioned above. Using reliability theory, which was developed from mainstream probability and statistical theories in system engineering for the description and prediction of complex machinery systems aging,6,7 is one possibility. Reliability theory states that redundancy is key for understanding that aging is a trait of a (biological) system. Systems, which are redundant in the number of not-fully maintainable components, do show an increasing failure rate over time, even if they are built with nonaging components.8 In short, the reliability model considers aging as a progressive accumulation of random damage.9 As compared to technical devices which are manufactured from previously tested macroscopic (large) components of high quality by external assembly, biological systems are formed by ontogenesis using self-assembly out of de novo forming externally untested elements (cells). In addition, biological systems achieve an extraordinary degree of miniaturization of their components (small dimensions of cells, DNA, and RNA), permitting the creation of a huge redundancy in the number of elements with a similar purpose. Therefore, the reliability of a biological system is not achieved by a high initial quality of all its elements but by the large number of redundant subcomponents.9 The high redundancy and the lack of external Journal of Proteome Research 2010, 9, 3551–3560 3551 Published on Web 05/17/2010
research articles quality control make it possible that a substantial number of defective elements is present in biological systems, even at their creation. In summary, the large number of components with similar functions, that is the same proteins with identical function, compensates for low quality of some components in biological systems. Proteins, as one of the major components in a biological system, can be misfolded, aberrantly phosphorylated and/or glycosylated and can aggregate and no longer be degraded due to lack of ubiquitinylation. All these modifications render proteins unusable for their normal cellular function. The reliability theory proposes aging as a macroscopic outcome of a progressive accumulation of random damage. Therefore, it could provide a straightforward explanation for aging. To determine the applicability of reliability theory to a biological organism, we investigated mouse brain development and aging for different functional categories of proteins. In our study, redundancy design is reflected by the large number of identical proteins in a cell. Using reliability theory we could explain aging as a gradual loss of this redundancy based on predictions from our model and finding them reflected in our experimental data. Interestingly, a special role of the cellular protein degradation system in aging was highlighted as its excessive redundancy loss exceeds the predictions made by reliability theory. Protein degradation may disproportionally influence aging due to its functional involvement in important cellular self-renewal processes. The implementation of reliability theory in aging research may alter potential antiaging strategies based on its predictions such as the exceptional proteasome involvement found in our study.
Methods and Materials Animals and Tissue Samples. Animal experiments were carried out in strict accordance to the European Community Council Directive guidelines for the care of laboratory animals and were approved by an ethics committee (Senate of Berlin, Berlin, Germany). Mice (C57BL/6) were housed in a temperature and humidity-controlled animal facility with a 12-h light/ dark cycle. Food and water were provided ad libitum. A total of 15 age stages of mouse brain tissue were investigated from embryonic development to old age, which comprise embryonic day (ED) 10 to 100 weeks of age. In our study, total protein extracts were obtained from entire mouse brains as described previously in detail.10,11 Mouse brain development lasted from ED10 to 4 weeks of age. Adolescence followed by adulthood starts at 8 weeks up to 100 weeks.12 Mice up to 1 week of age were used with mixed gender after that only male mice were included in our study. Since our main predictions for aging were made starting at 8 weeks of age our results may be considered to be obtained from a homogeneous male mouse population. The number of biological repeats for each age group were: n ) 3 for ED10, n ) 7 for ED12, n ) 3 for ED14 and ED16, n ) 6 for ED18 and new born, n ) 3 for 1 week, 2 weeks, 4 weeks, 8 week, 14 week, 22 week and 42 week, n ) 6 for 75 weeks, and n ) 9 for 100 weeks. Thus, a larger number of biological repeats was used for time points where a large number of changes was observed. Two-Dimensional (2-D) Gel Electrophoresis and Protein Identification by Mass Spectrometry. Protein expression profiling with large-gel 2-D electrophoresis was carried out as described previously.10 One-hundred micrograms sample were applied for each 2-D gel. The protein concentration was determined using the Roti Nanoquant kit (Carl Roth GmbH + Co. KG, Karlsruhe, Germany) according to manufacturer in3552
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Mao et al. structions. All necessary safety precautions when handling acryl amide and other toxic substances were taken according to laboratory regulations of the Charite´ - University Medicine (Berlin, Germany). 2-D gels were stained using a silver staining protocol that was described in minute detail.11 For 2-D gel image evaluation, acidic and basic halves of 2-D gels were evaluated in parallel with specialized software (Delta2D version 3.6, Decodon, Greifswald, Germany). The 2-D gels for each time point were imported into separate groups. This group feature is provided by the software. In-group gel matching was carried out using the “exact mode” after initial manual matching employing matching vectors. The automatic vector detection feature of the software assisted in finding additional matching vectors. Afterward the matching of 2-D gels was manually controlled to ensure quality. Since individual 2-D gels created from 100 week samples were very heterogeneous, we created a union image for this group after in-group gel matching. “Union mode” was used for fusion, so that the fusion gel image retains all spots located on individual gels. Subsequently, 2-D gels of adjacent time points were matched. A selected gel image of ED10 was matched, for example, to one selected image of ED12, one selected gel of ED12 was matched to one image of ED14, until 100 weeks were reached. The group union gel image of the 100 weeks group was chosen for matching with a representative gel selected from the 75 weeks group. At the end of this procedure all 15 age groups were linked to each other. Afterward, three further union gel images were generated by “union mode”: Cross-group union gel one contains ED10, ED12, ED14, ED16 and ED18 (22 gel images), cross-group union gel two integrates all images from NB to 14 w (25 gel images) and cross-group union gel three unites all images from 22 weeks to 75 weeks (12 gel images). All four union gels (3 cross-groups union gels and one within-group union gel of 100 weeks) were matched using the exact mode and manual vector setting so that a master-union gel was generated out of the four union gels. Protein spot detection was carried out automatically on the master-union gel. Spots were edited manually after spot detection only when spots were obviously not detected correctly. In cases when spot location and form was ambiguous, we cross referenced to gel images contained in the union gels. After spot detection and editing the master union-gel spot pattern was transferred to each 2-D gel image within the entire project to generate a uniform spot pattern for each 2-D gel image. After the 2-D gel evaluation procedure was finished, a spreadsheet table containing percent spot volume of each spot across all time stages was generated. Finally, the spot information was retrieved using a built-in setting of Delta2D, and spot intensity data were exported from the software for subsequent statistical analyses (with local background extraction and value normalization). For the calculation of percent spot pixel volume data, the total pixel volume of all spots on each parental gel was set as 100%. Protein alterations were consider significant when p < 0.05 and the expression changed more than 20%. Protein identification was done as described in detail previously.10,13,14 Briefly, for protein identification by mass spectrometry, 800 µg protein extract was separated by 2-DE and stained using a mass spectrometry (MS)-compatible silver staining protocol.15 Protein spots of interest were excised from 2-D gels and subjected to in-gel tryptic digestion. Proteins were initially subjected to matrix assisted laser desorption ionization (MALDI) MS and in case proteins could not be identified electrospray ionization (ESI) mass spectrometry was used.
Aging in Mouse Brain Mass spectra from peptide mixtures were generated by a Bruker Reflex IV MALDI-Time of Flight (TOF) mass spectrometer (Bruker Daltonik GmbH, Bremen, Germany) using reflector mode. Signals corresponding to mass-to-charge (m/z) ratios from 0 to 3500 were monitored. XMASS/NT 5.1.16 software package (Bruker Daltonik, Bremen, Germany) was used for subsequent data processing. Internal calibration was performed using m/z peaks 842.509 [M+H]+ and 2211.104 [M+H]+, which were created by autoproteolytic trypsin digestion. Peaks were selected automatically based on the following settings: m/z range was 800 to 3500 Da, a maximum of 200 peaks per sample were analyzed, the SNAP algorithm was used and peak sensitivity was at least 3. Masses from an exclusion list, containing known background peaks and trypsin specific, autoproteolytic peptide masses, were automatically deleted from a generated mass list. For database searches these mass lists were compared to entries in the NCBInr protein database (version NCBInr_ 20061206, 107,853 sequences) using Mascot Daemon 2.1.0. A maximum of one missed cleavage was allowed. Peptide mass tolerance was set to 100 ppm and methionine oxidation and acrylamide derived cysteine alkylation (cysteinyl- S-propionamide) were considered as possible modifications. Searches were restricted to Mus/Mus musculus. Proteins identifications were evaluated according to their MOWSE Score (Mascot) (corresponding to p < 0.05), the number of matched peptides and the sequence coverage. Six single peptide identifications were marked in red within Supplementary Table 1 (Supporting Information) and should be treated with caution. If MALDIMS did not allow for an exact identification of a protein (spot), nano LC ESI MS was used. For this purpose, tryptic fragments were analyzed by nanoflow high-performance liquid chromatography (nanoHPLC; Dionex/LC Packings, Amsterdam, Netherlands)/ (ESI) -MS and -MS/MS with a LCQ Deca XP ion trap MS (Thermo Finnigan, Waltham, MA, USA). Nano-HPLC was directly coupled to ESI-MS analysis. Protein spot eluates of 15 µL were desalted by a PepMap100 C18 precolumn (5 µm, 100 Å, 300 µm I.D. × 5 mm; Dionex/LC Packings) using 0.1% v/v trifluoroacetic acid at a flow rate of 20 µL/min. Peptides were separated with a PepMap100 C18 100 column (3 µm, 100 A, 75 µm I.D. × 15 cm; Dionex/LC Packings). The elution gradient was established by mixing 0.1% v/v formic acid in water (solvent A) and 0.1% v/v formic acid in acetonitrile (solvent B) at a flow rate of 200 nL/min. The gradient was started at 5% v/v solvent B and increased linearly up to 50% v/v solvent B after 40 min. ESI-MS data acquisition was performed throughout the LC run. Three scan events, (i) full scan, (ii) zoom scan of the most intense ion in full scan and (iii) MS/MS scan of the most intense ion in full scan were carried out sequentially. No MS/MS scan on single charged ions was performed. Raw data were extracted by the TurboSEQUEST algorithm, and trypsin autolytic fragments and known keratin peptides were subsequently filtered. All DTA files generated by BioWorks version 3.2 (Thermo Scientific, Waltham, MA) were merged and converted to MASCOT generic format files (MGF). Mass spectra were analyzed using our in-house MASCOT software package license version 2.1 automatically searching the NCBInr database for Mus musculus (house mouse) (NCBInr_20061206, 107,853 sequences). The Mus musculus subset of the NCBInr database was used since only mouse samples were investigated. In rare cases, hits were researched using the “mammalian” subset of the NCBInr database. All non Mus musculus proteins are indicated in Supplementary Table 1 (Supporting Information) by addition of either “Homo sapiens” or “Rattus norvegicus”
research articles after their protein name. In order to reduce the length of the protein names for the large majority of Mus musculus identifications due to space constraints the species label was omitted in many cases. A MS/MS ion search was performed using the following set of parameters: (i) taxonomy: Mus musculus (house mouse), (ii) proteolytic enzyme: trypsin, (iii) maximum of accepted missed cleavages: 1, (iv) mass value: monoisotopic, (v) peptide mass tolerance 0.8 Da, (vi) fragment mass tolerance: 0.8 Da, and (vii) variable modifications: oxidation of methionine and acrylamide adducts (propionamide) on cysteine. No fixed modifications were considered. Only proteins with scores corresponding to p < 0.05, with at least two independent peptides identified were considered. The cut off score for individual peptides using ESI identification was equivalent to p < 0.05 for each peptide and usually reflected in a MOWSE score range from 32 to 37. MOWSE scores were calculated by the MASCOT software. Furthermore, theoretical and practical molecular weight and pI for each protein identified by database search were compared to experimental values from a 2-D gel to exclude proteins with deviating masses and pI values. Statistical Data Analysis. To determine brain protein expression differences during the mouse lifespan, an unpaired ANOVA analysis (p < 0.05) was first carried out for each protein spot (SPSS 14.0, Chicago, IL). We obtained information whether there is a between-group difference for the detected spots across all age stages investigated. For each statistically altered data point an Anderson-Darling test to determine normal distribution was performed. In our study all data points were normally distributed (p > 0.05). p > 0.05 in an Anderson-Darling test indicates normal distribution. Therefore an unpaired Student’s t test was used in all cases to access statistical significant differences between two stages. The individual p-values are supplied in Supplementary Table S2 (Supporting Information). Subsequently, a Turkey’s honest significance test using an overall significance level of p < 0.05 was used to ensure that the difference between two group means was greater than the standard error (SPSS 14.0). This post hoc was used to greatly reduce false positives due to experimental design. In addition a false discovery rate (FDR) correction was performed using Cyber-t (http://cybert.microarray.ics.uci.edu/). The threshold of FDR < 5% was selected. A total of 105 t tests were conducted for each spot. Subsequently, the mean and standard deviation of each spot volume was calculated to generate a time course profile for protein expression. Spots that were not statistically significantly altered (p > 0.05) were considered to be constitutively expressed proteins during development and aging, and therefore excluded from further analyses. Origin6 software (Origin Lab Corporation, Northampton, MA) was used to visualize the protein expression time course. This analysis was performed separately for the developmental stages ED10 to 4-week using log regression and adult/aging stages (8 weeks -100 weeks) using linear regression. The slopes and intercepts of the line fits, as well as the regression quality (R2) were determined for each regression analysis. A Pearson correlation analysis was used to determine the correlation between the degree of redundancy and aging rates of different protein categories. For this purpose, relative redundancy and aging rate data were utilized, where the highest value of each parameter across all protein categories was set to 100%. Protein functional categorization was carried out using the keywords of the biological process subset category in the UniProtKB/Swiss-Prot database (www.expasy.ch/sprot/). When the functional clasJournal of Proteome Research • Vol. 9, No. 7, 2010 3553
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sification was ambiguous, proteins were categorized according to additional data from the literature. Assessment of the Degree of Protein Redundancy. To determine the degree of protein redundancy within different protein categories, the average copy number of protein molecules present in a given protein functional category was estimated as the ratio of total protein concentration of a given protein category to the average protein molecular weight of the given protein category by the following equation:
n)
∑ [Pr] ) %spot volume × 250 g/L MW
(average MW)/NA
(1)
The total protein concentration of the cell was set to 250 µg/mL according to previous estimates for the total protein concentration in a cell.16 The following abbreviations were used in eq 1: Pr, protein; MW, average molecular weight of a given protein category; NA, Avogadro constant. Estimation of Aging Rates for Different Protein Categories. We adopted the Gompertz-Makeham law for the calculation of protein aging rates. We used it based on the assumption that diverse proteins and their isofoms within the same protein functional category are considered as different individuals of the same population. Based on the time-dependent protein concentration profile data obtained, the protein turn over rate was calculated as decrease of protein concentration per day. Subsequently, the aging rates of different protein categories were estimated using the Gompertz-Makeham law.17 The Gompertz-Makeham law states that the propensity to die (also known as hazard rate or failure rate) increases exponentially with age in many biological species. Accordingly, the increase in failure rate with time of a given population can be approximated by an exponential function. The extended form of the Gompertz equation, the Gompertz-Makeham equation reads:
m(t) ) I · eGt + E(t)
(2)
Here, “m” represents failure rate, “I” intrinsic vulnerability of a given population, which can be considered as a measure of basic protection of an organism/protein against failure and damage (the property of error-prone/fault-resistant design). “G” determines how fast this protection deteriorates with time, and thus can be regarded as a measure (marker) for the aging rate of a given population. “E”, also denoted as “Makeham factor”, represents the age-independent external failure rate. Applying the natural logarithm to both sides of the eq 2, we obtain: ln m ) ln I + Gt
(3)
When we plot the ln(failure rate) vs time, an aging rate can be estimated from the slope of the linear fit of the failure rate data.1 For the estimation of aging rates of different protein categories, we consider the mean protein concentration decrease of a given protein functional category as “protein failure rate”. For this purpose, we averaged the protein concentration decrease rates of all protein isoforms of each protein category. 3554
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Results Proteomic Analysis Reveals a High Heterogeneity of Aging Patterns among Individual Mice. In our proteomics study, we investigated mouse brain protein changes in embryonic development until old age using 15 time points. We compared time points starting at ED10 to 100 weeks of age (see Supplementary Tables S1-S3 for details, Supporting Information). A total of 3990 protein spots (2027 on the acidic and 1964 on the basic gel side) were detected on each 2-D gel. Six representative 2-D gels are shown in Figure 1. Overall we found that the 2-D protein pattern of 100 week old mice was very heterogeneous within the group, that is, among different individuals. To carry out a comparison with 75 weeks, we created a fusion image of all gels in the group to account for all spots present at this age. In addition, this apparent heterogeneity prompted us to conduct an in-depth investigation of the entire time course to determine if this heterogeneity is present although to a lesser degree during the entire mouse brain development and aging. As can be seen in Figure 2, the normalized standard deviation (SD to 4 weeks set as 1) of the relative protein concentrations of all spots (%Vol) was quite similar from ED10 to 42 weeks of age. However, starting at 75 weeks, the standard deviation of the protein concentration increases drastically with age (p ) 3.5 × 10-7, Student’s t test, two tailed, unpaired). This suggests that each mouse develops a very distinct protein expression pattern at old age, which becomes most pronounced at the oldest stage (100 weeks) investigated. After evaluation of about 3990 different protein spots, we observed a total of 217 spots that were altered significantly (p < 0.05, >20% up- or downregulation) between at least two age stages of the 15 investigated. A total of 105 stage comparisons were conducted starting at ED10 vs ED12 and ending at 75 vs 100 weeks (see Supplementary Tables S2 and S3, Supporting Information). If protein changes occurred during aging they were present in at least nine stages compared. Protein changes in single stages were not observed (see Supplementary Tables S2 and S3, Supporting Information). Of these 217 protein spots, 199 were successfully identified (Supplementary Table S1, Supporting Information). Most of the altered proteins were present in 2 to 9 isoforms, which means that several protein spots could be assigned to a single gene symbol. The average number of protein isoforms per gene was three. Next, the 199 altered proteins were grouped into 16 categories based on their cellular function (Table 1). The functional categories with the most proteins were proteasome (47), mitochondrial respiratory chain (32), chaperones and heat shock proteins (30), as well as cytoskeleton proteins (19) (Table 1). Aging Rates of Different Protein Categories. To determine the expression profile of different protein categories in the context of reliability theory, we estimated turnover rates for each protein category based on the average protein concentration alterations over time. We assumed that mouse brain tissue is a complex system guided by a modulating design principle, where each functional protein category was considered as a distinct subsystem of the entire system (mouse brain) according to eq 1 (see Methods section). We assumed that proteins which belong to the same functional category do not influence their own degradation and are thus not considered separately. The average protein concentration of different protein categories showed an overall accelerating turnover rate with time (Figure 3A). However, there is a shift in turnover rate at about 50 days where the change in the protein turnover rate decreases (Figure 3B). The time span
Aging in Mouse Brain
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Figure 1. Large 2-D gels of six representative time points for the study of development and aging. In our 2-D proteomics approach, 15 time points were compared to each other. Six representative time points of very early embryonic development (ED 10), later embryonic development (ED 16), newborn (NB), early adolescence (2 weeks), adulthood (14 weeks), and old age (100 weeks) are shown. Note the macroscopic changes in protein amount especially during embryonic development (compare ED10, ED 16 and NB). On the contrary, protein patterns of 2, 14, and 100 weeks look rather similar macroscopically.
Figure 2. High heterogeneity in brain protein concentration after 42 weeks of age. Standard deviations for each functional group for all significantly altered proteins at each time point were calculated. These standard deviations were averaged for each time point and the standard deviations were calculated. Bar heights represent average fold change of standard deviation of total protein spot volume (% vol) at each time point as compared to the standard deviation at 1 month of age. Error bars indicate the fluctuations of relative standard deviations.
from fertilization to sexual maturation of the house mouse is about 6-9 weeks.18,19 Therefore this shift is consistent with beginning adulthood. A clear-cut shift can be seen after 8 weeks where the slope of the ln(protein concentration decrease rate) decreases. Therefore we only included the time span from 8 weeks to 100 weeks in our consideration of aging. Table 1 shows the concentration decrease rates (aging rates) for different protein categories. Note that
applying the natural logarithm to the protein degradation rate (G) results in no biologically meaningful unit. Protein Redundancy Was Established Exponentially in Embryonic Development and Decreased Linearly during Aging. To explore the influence of protein category redundancy on aging, we constructed a mathematical model based on reliability theory. We consider protein redundancy as a combination of a redundant number of single proteins in a cell such Journal of Proteome Research • Vol. 9, No. 7, 2010 3555
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Pdcd5 Rbbp9, erh (2) Ahsa1, Calr, Cct5, Hsp110, Hspa4 (4), Hspa5 (5), Hspa8 (4), Hspd1 (5), Cct5, Hspa9 (3), Naca (2), Pdia3, Pfdn5, Phb Actb (2), Actl6b (2), Arpc5, Capza1, Capza2, Cnn3, Cotl1, Dstn, Dync1i2, Marcks, Pfn2, Tpm1, Tpm3, Tpm4 (2), Tuba1, Tubb5 Hoxa5 Erp29 (2), Txndc12 Ak1, Cbr1, Cmpk, Ddah1, Ddah2, Dpysl3, Fabp7, Glo1 (2), Idh3a (2), Mdh2, Pdxp (2), Pebp1, Sephs1 Atp5a1, Atp5d (4), Atp6v1e1 (6), Atp6v1f, Cox5a, Mrpl39, Ndufb10, Ndufb11, Ndufv, Tom22, Vdac1 (6), Vdac3 (7), Vdac4 Asmt, Snca, Sncb (2) Pilic-1 (6), Psma3, Psma5, Psmb1 (3), Psmb6 (3), Psmc3 (2), Psmc4, Psmd4 (7), Sumo1, Sumo2, Ubc, Ube2d3 (2); Ube2n, Ube2v1, Ubqln2 (2), Uchl1 (9), Ulip4 (2), Uqcrc1, Uqcrc2 Txn1, Gstm1, Prdx2 (3), Prdx5 (2), Prdx6, Serpinh1, Txn1 (2), Txndc5, Txnl2 (2) Cops8, Gnb2 (2), Hyou1(3), Sh3bgrl, Stmn1(2), Strap Grcc10, Hist1h2bp, Hnrph1, Hnrph2, Lzic, Snrpf Eef1b, Eef1d (2), Eif3s2, Rplp2 (2), Rpsa Apoa4, Atp6v1f, Nutf2 Cbx1, Mtpn, Tpt1
gene symbolsb (number of isoforms)
3
7
6
10
14
4 47
32
1 3 16
19
1 3 30
total number of protein isoforms
2
5
6
6
9
3 19
13
1 2 13
16
1 2 14
number of nonredundant gene symbols
0.0008 0.0038
0.004
0.0019
0.0052
0.0038
0.0044 0.0924
0.0116
0.0022 0.0047 0.0064
0.0094
0.0037 0.0056 0.0209
aging rate
0.87% 4.11%
4.33%
2.06%
5.62%
4.11%
12.55% 100.00%
4.76%
2.38% 5.09% 6.93%
10.17%
4.00% 6.06% 22.62%
relative aging rate
relative system redundancy
27.32% 45.40% 100.00%
71.23%
19.71% 49.01% 51.12%
29.34%
74.35% 50.60%
42.89%
25.91% 37.49% 19.90% 16.66% 35.34%
average system Redundancy (n) at 8 weeks (mL-1)
3.28 × 108 ( 1.50 × 103 5.46 × 108 ( 1.77 × 103 1.20 × 109 ( 4.24 × 102
8.56 × 108 ( 1.13 × 103
2.37 × 108 ( 4.30 × 102 5.89 × 108 ( 1.92 × 103 6.15 × 108 ( 1.55 × 103
3.53 × 108 ( 5.43 × 102
8.94 × 108 ( 1.13 × 103 6.08 × 108 ( 1.25 × 103
5.16 × 108 ( 7.21 × 102 3.11 × 108 ( 1.41 × 103 4.51 × 108 ( 1.68 × 103 2.39 × 108 ( 2.73 × 102 2.00 × 108 ( 2.14 × 102 4.25 × 108 ( 1.19 × 102
0.553 0.485
0.309
0.241
0.716
0.122
0.967 0.439
0.359
0.956 0.707 0.782
0.99
0.83 0.96 0.455
regression quality (R2)
a For protein categories of the 199 differentially expressed proteins (110 nonredundant proteins, p < 0.05; >20% difference), the keywords of the biological process subset category in the UniProtKB/Swiss-Prot database were used. The expression changes from 8 weeks to 100 weeks were used for all analysis performed. b Number in parentheses indicates the number of isoforms of the given protein.
Transportation Others
Translation
Transcription
Signaling
ROS defense
Neuro-specific proteins Proteasome subunits
Mitochondrial
Embryonic development endoplasmic reticulum Metabolism enzymes
Cytoskeleton
Apoptosis Cell Cycle Chaperone and heat shock proteins
protein functional categorya
Table 1. Aging Rates Are Largest for Proteasomal, Mitochondrial, Heat Shock Proteins
research articles Mao et al.
Aging in Mouse Brain
Figure 3. Average aging rates of all protein categories investigated. A time-dependent protein degradation profile was obtained by proteome analysis. The profile was calculated as the average decrease of protein concentration per day across all protein categories investigated (see Table 1) for each time point. (A) Average protein expression change profile for all protein categories across the entire mouse lifespan shows an initial drastic acceleration period, followed by less pronounced changes after the transition between development and adulthood (aging). (B) Schematic illustration of the estimation of aging rates for different protein categories according to the Gompertz-Makeham law. The slope of the linear fit, based on natural logarithm values of the protein concentration decrease rates, represents the aging rate of a given time span (8-100 weeks).
as GAPDH and the ability of proteins to functionally compensate for each other. Although the exact nature of the compensation mechanism is not clear so far, it describes adequately the general behavior of the system “cell” on a functional level. For this purpose, we considered each protein category as a separate module (subsystem) inside the biological system, assigning the same average orientation of expression for different protein subunits. This corresponds to a “simple parallel” design (Figure 4A), which represents the most rudimentary redundancy design for a fault-tolerant system. Now we consider a reliability model having a simple parallel design with n initial redundant elements. The degree of redundancy for this system thus equals n. Applied to our experimental data, the degree of redundancy for each protein category was estimated as the ratio of the total protein concentration of a given protein category to the average protein molecular weight of the given protein category (eq 1). The total protein concentration of a cell was assumed to be 250 g/L16 to convert protein concentration data (%vol) into absolute values corrected for molecular mass differences. Using this approach, the degree of redundancy is supplied as “protein copy number per ml of cell biomass”. For all protein categories (see Table 1), we
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Figure 4. (A) Schematic illustration of a simple parallel system consisting of six sequentially connected blocks, each of which contains parallel connected elements (spheres) of varying degrees of redundancy (1-5 spheres). Each component (sphere) may fail with a constant failure rate, but the loss of function of a given block is reached only when all redundant components inside fail. The damage accumulation of components generates the apparent aging rate of the whole system. The reliability of a single component (to exhibit the correct expression pattern) was set to 50% (see Figure 5B). (B) Illustration of the relative average protein category redundancy time profile of 16 different protein functional categories during the course of mouse brain development and aging. Relative protein concentrations (% vol) were corrected for average molecular mass for all protein categories. The time point with highest average redundancy was set to 100% (between 14 and 22 weeks). All protein categories (see Table 1) showed a drastic increase of protein redundancy during development that can be approximated by a logarithmic increase and an approximately linear redundancy reduction during aging. All categories were averaged for each time point and plotted over time. Subsequently a representative curve was fitted.
calculated the category redundancy time curve (Figure 4B). For simplicity, the relative degree of redundancy was used, where the time with the highest redundancy was set to 100%. We observed a drastic increase in protein redundancy during development which can be modeled using log regression analysis of all protein functional categories. Maximum protein redundancy was usually reached shortly after reaching adulthood at 14 and 22 weeks of age (Figure 4B). This initial phase of a steep increase in redundancy was followed by an approximately linear decrease during the entire adult and aging phase, This shows that protein redundancy is swiftly established during embryonic development and adolescence stages (ED10 to 8 weeks). After reaching sexual maturity, protein redundancy decreases gradually with time at an approximately constant rate throughout the residual lifespan. Redundancy alterations after 8 weeks will now be considered as mouse brain aging. Proteasome Defies the Positive Correlation between the Degree of System Redundancy and Aging Rate. We assumed that in our reliability model all elements were nonaging during the time investigated, but fail randomly and independent of each other with a constant failure rate. The reliability of each single system component was denoted as p. System reliability Journal of Proteome Research • Vol. 9, No. 7, 2010 3557
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Figure 6. Correlation of aging rate and redundancy across different protein categories. The aging rate of most protein categories correlates with their degree of redundancy. A discrepancy was observed for proteasomal proteins, which show a much higher aging rate as would be predicted by their degree of redundancy. Linear fitting is shown without including the proteasome functional category.
Figure 5. Redundancy design results in high system reliability and an aging phenotype. (A) System reliability increases rapidly with increasing system redundancy. (B) Aging is only present in system with (high) redundancy. The aging rate is small early in a system with high redundancy. The aging rate increases with time and approaches nonredundancy levels late in live.
R(t) denotes the probability of a system not to fail during a specific time period [0, t]. Since a simple, parallel system will lose its reliability only when all its redundant subcomponents fail, the system reliability R(t) ) f(p, n) can be calculated as: R ) 1 - (1 - p)n
(4)
This means that for p ) 0.5 and n ) 3, R ) 1 - (1 - 0.5)3 ) 87.5%
(5)
Figure 5 illustrates that system redundancy generates system reliability. Simultaneously, the degree of redundancy is positively correlated with a decreasing system failure rate, otherwise known as aging. When considering our current experimental data, we observed that all protein categories have an extremely high degree of redundancy. The system reliability for all protein categories is therefore always approximately 100%. To compare system redundancies and aging rates of different protein categories, we plotted the aging rate as a function of the degree of protein redundancy using their relative values (percentages). Hereby, the highest value of each parameter across all protein categories was set to 100% (Table 1, Figure 6). All protein categories except for “proteasome” display a high correlation between the degree of redundancy and aging rate. However, although the proteasome system has a degree of system redundancy within range of the other protein categories, it shows an exceptionally high aging rate (Figure 6). Indeed, the Pearson correlation factor of the degree of redundancy and aging rates including the proteasome functional category is 0.078, while without the proteasome category it increases more than 10-fold to 0.84.
Discussion We studied mouse brain to determine protein expression alterations associated with development and aging. The brain 3558
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is a typical postmitotic tissue. Nerve cells do not divide extensively during adulthood. Thus it provided us with the unique opportunity to study age-related protein alterations in the absence of extensive tissue renewal after brain development is completed after 8 weeks of age. Due to the amount of information stably stored by the brain in an adverse environment, it can be considered a robust, fault-tolerant system. Moreover, a large number of genetic manipulations (even disease-related genes) do not necessarily lead to a phenotype.20 This suggests a redundancy design and provides the basis for the application of reliability theory to mouse brain aging. We believe that when determining the applicability of reliability theory, protein function has to be considered. We used general protein functional categories as a simplified approach to reduce the number of components in our experimental system that need to be considered. We determined the average number of the protein molecules within a given protein functional category to represent its redundancy. Provided they belong to the same overall protein functional category, we considered protein isoforms as variable but still redundant system subcomponents. We are aware of the simplification but believe that this facilitates an initial approach to consider redundancy and reliability in a biological system such as the cell and in our case brain tissue. Figure 5 illustrates that according the reliability theory, although the reliability of a single component is very modest (p ) 0.5), system redundancy can lead to an overall system reliability of over 85%. Reliability increases rapidly with the degree of redundancy in a system. This demonstrates that high system reliability can be effectively achieved by increasing system redundancy. However, the cost of stability achieved by system redundancy is an increasing failure rate (aging) over time: As shown in Figure 5B, if no redundancy exists (n ) 1), the system has a constant turn over (failure) rate, thus exhibiting nonaging properties. In biological systems, it is known that when a single component system (viruses or microbial spores) is exposed to a hostile environment such as heat deactivation, their survival curve obeys virtually nonaging turn over kinetics.21 As redundancy increases, a system constructed of nonaging elements starts to behave like an aging one, with its failure rate (aging rate) increasing over time. The time it takes to reach nonredundancy levels is positively correlated with the degree of system redundancy. High redundancy generates genuine fault-tolerance, but also supports
Aging in Mouse Brain extensive damage accumulation and taxes the system with a high cost of maintenance in terms of energy consumption and cellular components consumed. This may constitute one of the reasons why systems have finite system redundancy. Reliability theory states that the apparent aging rate is positively correlated with a systems degree of redundancy. Thus, aging may be regarded as a direct consequence of a system’s loss of redundancy over time due to failing components. In our current study, we observed that the relative cellular concentration of different cellular protein categories increased first drastically during mouse development and displayed a linear decrease during aging. This constant decrease in protein category concentration during aging suggests that due to the nonaging properties of cellular proteins in the time span considered in our study, aging is a system-level phenomenon which is consistent with reliability theory. Furthermore, old mouse brains (75 to 100 weeks of age) showed a very heterogeneous protein expression pattern. This heterogeneity, predominantly seen of 2-D gels by the emergence of additional protein spots specific for each biological replicate, suggests an individualized pattern of nonfunctional protein accumulation. The gradual accumulation of damage to individual proteins, may therefore lead to an ever increasing heterogeneity at old age. Presumably, each individual has a very individual damage pattern in early life. The effects of the early damage are amplified with time and influence the late-life aging rate and disease pattern.22 As compared to a homogeneous situation in early life, aged or very old mice may have a very heterogeneous number of functional proteins at their disposal which may influence cellular and tissue functions leading to heterogeneous protein isoform patterns at old age. This loss in functional proteins is equivalent to a drastic reduction in system redundancy at advanced age and the hazard factor becomes the chief factor contributing to failure rate (turn over) (“E” in the Gompertz-Makeham equation), which is very heterogeneous. In our study using the same genetic background, the uniqueness of individuals may partially be caused by a heterogeneous exposure to damage, thus creating a unique individual damage pattern. Applying our reliability model to our experimental data, we showed that as predicted, the reliability of diverse protein categories in the cellular system is generally positively correlated with the aging rates of most protein categories. This justifies the application of the reliability theory on living systems. Unexpectedly, the proteasome category showed the highest aging rate among all protein categories investigated, despite its modest degree of system redundancy when compared to other protein categories investigated. A decrease in proteasome function was also found on the mRNA level when comparing adult (5 months) to very old (30 months) mice23 confirming our results. Due to this obvious deviation from the predictions by reliability theory we hypothesized that biological organisms have special properties which are not found in complex technical gadgets where reliability theory is normally applied. Presumably, the high aging rate of the proteasome category could be due to its position in biological self-renewal. Two major processes influence the halflife of cellular proteins: protein production by ribosomes and protein degradation by proteasomal24 and lysosomal25 systems. Ribosomal errors are less influential because error-prone ribosomes produce mainly inactive polypeptides which are not further processed but degraded.26,27 On the contrary, loss of lysosome and proteasome function results in a reduced degradation of proteins. In addition to proteasomes, lysosomes
research articles facilitate mostly nonselective protein degradation by autophagy. During autophagy a volume of cytoplasm is enclosed by a double-membrane vesicle and these vesicles are delivered to a lysosome where proteins are degraded by proteases.25 The rates of lysosomal degradation can vary greatly for different cell types and conditions, from less than 1% of total cell protein per hour to 5-10% per hour. For degradation by the proteasome, proteins are selectively tagged by ubiquitin28 and then transferred to the 26S proteasome complex (mammals).24 In our study we found a decrease in expression of proteasomal subunits which we associated with decreased proteasome function since no up-regulation of alternative proteasomal subunits occurred during aging. Therefore, in addition to damaged proteins nonfunctional proteasome subunits have to be recycled by the remaining functional proteasomes contributing to a reduction in the capacity to deal with aberrant proteins.29–32 As a consequence of this damage accumulation in the proteolytic system, even a slight rise in the fraction of erroneous proteasomal proteins may lead to a significant increase of damaged protein half-life.29 This explains the amplified aging effect we observed after the age-related removal of proteasomal proteins in our current study. Considering aging as a system-level phenomenon could have profound consequences on antiaging strategies. The reliability theory states that aging is primarily a phenotypic expression for different kinds of error accumulation. Therefore, research on aging should not be limited to the level of qualitative changes (such as searching for “aging genes” or “anti-aging genes”), because changes in quantity may represent a much more fundamental driving force of the aging process. Accordingly, antiaging could be achieved by better body maintenance and advanced regenerative medicine approaches. In addition, interventions aiming at minimizing the exposure of an organism to early life damage could slow down the general pace of aging. Moreover, based on our current findings, aging could be attenuated by an enhancement of the biological self-renewal system aiming at reduced error accumulation. Here, the proteasome and immune system could represent potential targets for the selective detection and elimination of defective proteins and cells. There is no increase in the gliosis or astrocytosis during aging as the marker GFAP (astrocytes) is not changed during the entire study. The protein spots representing GFAP on our 2-D gels are known to us and they remained unchanged between stages. We already observed changes in GFAP in the study of neurodegenerative diseases such as Alzheimer33 and Huntington disease10 (see supplementary tables to both studies for details10,33). Therefore the contribution of gliosis and astrocytosis seems rather small or not present at all. The mRNA alterations published earlier by Lee et al. were obtained with mice aged 135 weeks which is about 33% older than our oldest stage investigated (100 weeks).23 In order to test reliability theory for predicting aging related changes, our current work investigates the age-related protein expression changes by considering mouse brain as a complex system with redundancy design. However, we are aware that there could be serious limitations in our approach to consider only the average decrease of concentration in protein categories as redundancy reduction of functional system subcomponents. Individual proteins occurring at a very low concentration could precisely regulate protein expression and have a larger importance to the system than is reflected by their cellular concentration. In addition, it is important to remember that our observed overrepresentation of certain Journal of Proteome Research • Vol. 9, No. 7, 2010 3559
research articles protein functional categories may be influenced by our experimental approach which may create artifacts, as membrane proteins, extremely acidic or basic proteins are commonly not detected by 2DE.
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Conclusions In summary, we could show by applying reliability theory that a high system redundancy is present early in life by establishing a high degree of redundancy during early development in a biological organism such as mouse brain. This constitutes the foundation for fault-tolerance in mouse brain. At the same time, a redundancy reduction after 8 weeks in mouse brain results in a progressive error accumulation that culminates in mouse brain aging. Due to the special features of the biological self-renewal system, the proteasome system represents the most vulnerable part of the cell contributing the largest share to biological aging but simultaneously offers us a potential target for antiaging strategies. The heterogonous aging pattern found in old mouse brain (75-100 weeks) suggests that any effective antiaging therapy should be personalized to be most efficient. Abbreviations: 2-D, two-dimensional; 2-DE, 2-D gel electrophoresis; ED, embryonic day; NB, newborn; SD, standard deviation.
Acknowledgment. We acknowledge the excellent technical support of Marion Herrmann and Janine Stuwe. This work was supported by grants from the ministry for education and research (NGFN 2) and the European Union grant 37627 “AnEUploidy” (M.L.) and the German Research Society (DFG) grant KL 237/12-1 (D.H.) and ZA 579/2-1 (C.Z.).
Supporting Information Available: Supplementary Tables S1-S3. This material is available free of charge via the Internet at http://pubs.acs.org. References (1) Kowald, A. Lifespan does not measure ageing. Biogerontology 2002, 3 (3), 187–90. (2) Medvedev, Z. A. An attempt at a rational classification of theories of ageing. Biol. Rev. Camb. Philos. Soc. 1990, 65 (3), 375–98. (3) Reiss, U.; Gershon, D. Rat-liver superoxide dismutase. Purification and age-related modifications. Eur. J. Biochem. 1976, 63 (2), 617– 23. (4) Sohal, R. S.; Farmer, K. J.; Allen, R. G.; Cohen, N. R. Effect of age on oxygen consumption, superoxide dismutase, catalase, glutathione, inorganic peroxides and chloroform-soluble antioxidants in the adult male housefly, Musca domestica. Mech Ageing Dev 1984, 24 (2), 185–95. (5) Vanella, A.; Geremia, E.; D’Urso, G.; Tiriolo, P.; Di Silvestro, I.; Grimaldi, R.; Pinturo, R. Superoxide dismutase activities in aging rat brain. Gerontology 1982, 28 (2), 108–13. (6) Barlow, R. E.; Proschan, F. Mathematical Theory of Reliability; SIAM: Philadelphia, 1996; Vol. 17. (7) Gnedenko, B.; Pavlov, I.; Ushakov, I. Statistical reliability engineering; Wiley: New York, 1999. (8) Gavrilov, L. A.; Gavrilova, N. S. The reliability-engineering approach to the problem of biological aging. Ann. N.Y. Acad. Sci. 2004, 1019, 509–12. (9) Gavrilov, L. A.; Gavrilova, N. S. The reliability theory of aging and longevity. J. Theor. Biol. 2001, 213 (4), 527–45. (10) Zabel, C.; Mao, L.; Woodman, B.; Rohe, M.; Wacker, M. A.; Klare, Y.; Koppelstatter, A.; Nebrich, G.; Klein, O.; Grams, S.; Strand, A.; Luthi-Carter, R.; Hartl, D.; Klose, J.; Bates, G. P. A large number of
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