Acta Neuropathologica

, 122:75 | Cite as

Expression analysis of dopaminergic neurons in Parkinson’s disease and aging links transcriptional dysregulation of energy metabolism to cell death

  • Matthias Elstner
  • Christopher M. Morris
  • Katharina Heim
  • Andreas Bender
  • Divya Mehta
  • Evelyn Jaros
  • Thomas Klopstock
  • Thomas Meitinger
  • Douglass M. Turnbull
  • Holger Prokisch
Original Paper

Abstract

Dopaminergic (DA) neuron degeneration is a feature of brain aging but is markedly increased in patients with Parkinson’s disease (PD). Recent data indicate elevated metabolic stress as a possible explanation for DA neuron vulnerability. Using laser capture microdissection, we isolated DA neurons from the substantia nigra pars compacta of PD patients, age-matched and young controls to determine transcriptional changes by expression profiling and pathway analysis. We verified our findings by comparison to a published dataset. Parallel processing of isolated neurons and bulk tissue allowed the discrimination of neuronal and glial transcription signals. Our data show that genes known to be involved in neural plasticity, axon and synaptic function, as well as cell fate are differentially regulated in aging DA neurons. The transcription patterns in aging suggest a largely maintained expression of genes in energy-related pathways in surviving neurons, possibly supported by the mediation of PPAR/RAR and CREB signaling. In contrast, a profound down-regulation of genes coding for mitochondrial and ubiquitin–proteasome system proteins was seen in PD when compared to the age-matched controls. This is in accordance with the established mitochondrial dysfunction in PD and provides evidence for mitochondrial impairment at the transcriptional level. In addition, the PD neurons had disrupted pathways that comprise a network involved in the control of energy metabolism and cell survival in response to growth factors, oxidative stress, and nutrient deprivation (PI3K/Akt, mTOR, eIF4/p70S6K and Hif-1α). PI3K/Akt and mTOR signaling are central hubs of this network which is of relevance to longevity and—together with induction of mitochondrial biogenesis—may constitute potential targets for therapeutic intervention.

Keywords

Parkinson’s disease Aging Dopaminergic neuron Glia Gene expression Pathway analysis Mitochondria Energy metabolism PI3K/Akt mTOR Hif-1α 

Introduction

In the aging brain, dopaminergic (DA) neurons degenerate at an estimated rate of 4–5% per decade [25, 58]. The lifelong cell loss in the substantia nigra zona compacta (SNc) accumulates to 30–40% and is associated with declining motor function in the elderly [52, 53, 54]. A compelling explanation for the vulnerability of DA neurons in the SNc, is their high metabolic rate due to an autonomous pacemaking activity that is driven by voltage-dependent L-type Ca2+ channels and results in altered Ca2+ metabolism and cellular redox imbalance in aging [16, 33, 59]. It is assumed that over a lifetime, lipids, proteins, and DNA are damaged by reactive oxygen species (ROS), which are not sufficiently counteracted by anti-oxidative defense systems [18, 30]. As a possible consequence, high levels of clonally expanded mtDNA deletions are detected in aged DA neurons, which in turn are considered a cause of mitochondrial dysfunction, further compromising cellular energy status [6, 7].

Whole-genome expression studies of post-mortem samples have been proposed for the identification of key genes and regulatory pathways involved in idiopathic PD, as well as for the identification of potential therapeutic targets [3, 44, 51]. Hitherto, most studies were limited to the analysis of brain homogenates of the SNc or other brain areas [10, 32, 34, 38, 43, 45, 66, 68]. In a re-analysis of published datasets, common pathways such as IGF1-, VEGF- and axon guidance signaling were identified [60], highlighting differential regulation of growth and survival pathways in PD. More recently, by applying a comprehensive meta-analysis to an expanded dataset, Zheng et al. [69] identified defects of mitochondrial electron transport, glucose utilization and glucose sensing as early disease events.

To date, three studies on targeted whole-genome expression analysis of DA neurons in PD have been published [15, 23, 56], but due to differences in the study design and statistical methods they are not directly comparable. Our previous study focused on four hits significant after Bonferroni correction, which led to the identification of a new PD risk gene [23]. Cantuti-Castelvetri et al. [15] reported only a few differentially regulated genes specific for PD and highlighted sex-specific differential regulation. Simunovic et al. [56] discovered 1,045 significantly regulated transcripts (up/down = 580/465) including prominent down-regulation of members of the PARK gene family.

Here, we extended our global gene expression analysis of individually isolated DA neurons of the SNc and applied a standardized pathway approach to integrate data of the Simunovic study [56], which is similar in design and statistical methods. For the first time, we included a younger control group to discriminate age- from disease-specific regulation. The pathway-based approach implicates disrupted energy metabolism and adaptation in PD that may explain the impact of aging and other known risk and protective factors.

Materials and methods

Ethics statement/inclusion criteria

Frozen human brain tissue was obtained from individuals who had a history of PD and from age matched and younger control individuals without any neurological disease. Written consent was obtained with verification/assent in writing from next of kin who confirmed the wishes at time of death. All procedures were in line with the UK Human Tissue Authority guidance and approved by the Local Research Ethics Committee. The cases were clinically well documented as having had PD symptoms prior to death. Neuropathological diagnosis demonstrated the presence of Lewy body pathology in the substantia nigra with typical pathological features, including moderate to severe neuronal loss and gliosis. Synuclein immunohistochemistry or ubiquitin immunohistochemistry was used to confirm findings on H&E stained sections and cases were graded according to published criteria for Lewy body disorders (LBD) [11, 42].

RNA quality control

Bulk tissue of cortical grey matter was dissected, weighed, transferred to RNAlater-ICE® (Ambion) and stored at −80°C. RNAlater-ICE® was removed and samples were rapidly homogenized (using an ultra-turrax homogenizer) for 10 s in pre-cooled 4°C TRI reagent (Applied Biosystems, Carlsbad, CA, USA). Total RNA was isolated using a spin column method according to the manufacturer’s instructions (RiboPure®, Applied Biosystems, Carlsbad, CA, USA). After extraction, RNA integrity of the samples was analyzed on an Agilent 2100 Bioanalyzer (Agilent, Santa Clara, CA, USA). Parallel cortical tissue samples were homogenized in distilled water (9× volume/weight) and the pH of the homogenate was measured at room temperature.

Initially, post-mortem brain tissue of 80 PD/LBD and control individuals was assessed for inclusion into the study. Brain samples of 11 PD cases (PD), 11 age-matched controls (old controls; OC), and eight young controls (YC) met the screening criteria of pH ≥ 6.3 and RNA integrity numbers (RIN) > 6 and these were used for laser-capture microdissection (LMD), RNA isolation, in vitro transcription (IVT) and microarray hybridization. Detailed information on patients and samples, as well as pre- and post microarray analysis is given in Online Resource 1.

Sectioning, staining, laser capture microdissection, in vitro transcription (IVT) and microarray

Unfixed midbrains stored at −80°C were used for analysis. All procedures were carried out under RNAse-free conditions. Frozen 20-μm midbrain sections were mounted on Leica 2 μm PEN- membrane slides (Leica Microsystems, Wetzlar, Germany), rapidly stained with toluidine blue solution (1%), dehydrated in an ethanol series and processed on a Leica AS LMD microscope (Leica Microsystems, Wetzlar, Germany). Approximately, 100 neurons per case/control were collected and RNA extracted with the Arcturus PicoPure® Kit (Applied Biosystems, Carlsbad, CA, USA) according to the manufacturers protocol. Four OC, four PD, and all YC were collected in biological replicate (one YC was sampled and processed four times) with a total of 48 individually processed samples. For some experiments, the SNc was delineated on membrane slides and a 20 μm cross-section of the SNc was removed entirely for RNA isolation. IVT comprised one round of linear amplification with Ambion MessageAmp™ II (Applied Biosystems, Carlsbad, CA, USA), followed by a second round of IVT with the Illumina® TotalPrep™ RNA Amplification Kit (Applied Biosystems, Carlsbad, CA, USA). Second round IVT yielded >3 μg cRNA with an average length of 800 bp and was used for hybridization on Illumina® WG6v1 expression chips (Illumina, San Diego, CA, USA). A detailed protocol is given in Online Resource 2.

Statistical analysis of microarray data

After hybridization of expression chips, raw data were exported from the Illumina Software “Beadstudio” to R (http://www.cran.r-project.org), log-scale transformed (log, basis 2) and normalized (nonlinear transformation employing the loess smoother). 8,491 transcripts were detected in all samples. For the detection of gene expression changes shared or different in aging and disease, we applied ANOVA with a cutoff at a false discovery rate (FDR) p < 0.05 (Benjamini and Hochberg). Probes were mapped on transcripts and validated using UCSC Genome browser. Mitochondrial localization was verified using the MitoP2 database [22]. Detected transcripts were further classified as changed in aging and/or disease with a p value threshold <0.05.

Microarray cluster analysis

Including biological replicates, 48 microarray expression profiles were obtained from microdissected midbrain DA neurons. Cluster analysis revealed three main clusters (Online Resource 1). Cluster I contained 24 arrays, which were predominantly control samples (13 YC, 8 OC, 3 PD). Cluster II contained 12 arrays, including most PD samples (2 YC, 3 OC, 7 PD). Cluster III was distant from Clusters I and II and contained 12 mixed samples (3 YC, 4 OC, 5 PD). These 12 outliers exceeded the expected inter-individual variability and introduced an extreme level of heterogeneity that largely prohibited data analysis. When analyzing all 48 samples, only 3 transcripts were significant at FDR 5%. We were not able to clarify the cause (no clear relation to groups, pH, RIN, array batch effects, detected genes on array, etc.), although one possibility is IVT performance. Therefore, samples of cluster III were not included in any further analysis. Values of duplicate samples were averaged and after exclusion of cluster III, data from 8 PD cases, 9 OC and 7 YC were used for determination of differentially expressed genes. Post-mortem data for PD/OC/YC were age at death 78.6 ± 6.5/76.8 ± 9.8/52.7 ± 2.4 years, pH 6.5 ± 0.1/6.61 ± 0.21/6.81 ± 0.23 and RIN 7.7 ± 0.8/7.6 ± 1.1/8.2 ± 1.1. Only the pH of PD versus YC showed a significant difference (p = 0.005).

Correlation of gene expression profiles and enrichment of neuron/glial-specific gene expression

To determine the benefit of dissecting individual dopaminergic neurons from the SNc rather than using bulk tissue for gene expression analysis, we calculated the mean expression values from both approaches collected from adjacent microtome sections. The enrichment factor (EF) was determined by dividing the normalized expression values obtained for 100 pooled neurons (N) by the expression values of bulk isolation (B). EF = expression N/B. A factor of 1.0 indicates equal expression in neuronal and non-neuronal cells. Values <1.0 indicate a higher expression in non-neuronal cell types, values >1.0 a higher expression in DA neurons. The EF is provided for all genes in the supplemental gene lists and was used to correct for glial-specific signals.

Ingenuity pathway analysis (IPA®)

Pathways were generated with Ingenuity Pathway Analysis (Ingenuity® Systems, http://www.ingenuity.com). Canonical pathway analysis identifies pathways from the expert curated IPA library of canonical pathways that are most significant to the data set. The p value is determined by the probability that the association between the genes in the dataset and the canonical pathway is explained by chance alone. The significance of the association between the data set and the canonical pathway was calculated using Fischer’s exact test. For multiple testing correction, Benjamini and Hochberg (FDR 5%) was applied. In addition, network analysis was used to identify genes with a functional regulatory relationship that was most significant in the aging dataset. Networks are generated using the IPA Network Generation Algorithm and are scored based on the number of network eligible molecules they contain from the dataset [14].

Results

Single neuron versus bulk nigral tissue

Here we studied the differential gene expression of DA midbrain neurons in aging and PD by whole-genome expression analysis. In isolated DA neuron samples, an average of 13,400 of the 47,321 probes were significantly detected (detection p value p < 0.01, Illumina® BeadStudio software). 8,491 probes were shared in all samples and used for further analysis. Replicates of 100 micro-dissected neurons from the same region of the same midbrain, carried through IVT and applied to microarrays individually, showed a high correlation of expression profiles (ranging from r2 = 0.96 to 0.99; Fig. 1a; Online Resource 3, Fig. S1). When gene expression profiles of 100 isolated neurons were compared to profiles obtained from homogenates of an adjacent SNc cross-section, clear differences were visible (r2 ≈ 0.90). Several genes with large differences in expression between isolated neurons and bulk SNc tissue were well-established glial-specific genes (e.g., glial fibrillary acidic protein, GFAP; oligodendrocyte lineage transcription factor 2, OLIG2; myelin-associated glycoprotein, MAG), or neuron-specific genes (tyrosine hydroxylase, TH; neuron specific enolase, ENO2) (Fig. 1b). The enrichment factor (EF) values ranged from 0.06 to 8, indicating that LMD preparations can be virtually free of glial-specific mRNA transcripts (e.g., EF(MAG) = 0.07; in LMD preparations the expression value of MAG is 7% of that seen in bulk tissue). Conversely, in bulk tissue, typical neuronal genes are only detected at far lower expression values than seen in isolated neurons (e.g., EF(TH) = 3.3; in bulk tissue the expression value of TH is 30% of that measured in isolated neurons). Therefore, the LMD approach offers a substantial increase in the sensitivity of detecting neuronal transcripts, yet still displays a high level of reproducibility.
Fig. 1

Reproducibility of data acquisition and enrichment of neuron-specific transcripts by the LMD approach. a Gene expression patterns were compared between single DA neurons (single cells A vs. B), bulk tissue (bulk A vs. B) and single cells versus bulk tissue. Representative examples were chosen. b Correlation pattern of single cell versus bulk tissue in more detail. Values are log2 transformed (increase by one = doubling of expression values) and span raw values from about 128 [log2(7)] to 65.500 [log2(16)]. Established non-neuronal transcripts are reduced approximately tenfold when using the LMD approach. The enrichment factor (EF) is calculated by dividing values of single cell versus bulk tissue. Genes with an EF < 0.5 (twofold higher expression in bulk tissue compared to isolated neurons) were subtracted from gene lists to eliminate potential glial transcript signals (red arrow)

Differential DA neuron gene expression in aging and PD

To identify gene expression changes in aging and PD, we applied a one-way ANOVA at various levels of stringency (FDR 1% = 409 transcripts; FDR 5% = 1,661 transcripts; FDR 10% = 2,953 transcripts). We chose an FDR of 5%, which includes 1,608 mappable genes significantly changed between any of the three groups for further analysis. Twelve different expression patterns were possible (Fig. 2), but most of the transcripts were (i) specifically changed in PD (1,185 transcripts); followed by (ii) specifically changed in aging (256 transcripts); and (iii) up- or down-regulated in aging and further enhanced in PD (166 transcripts). Remarkably, while all 12 patterns would be expected to be equally distributed by chance alone, only 54 transcripts were distributed over the remaining six patterns (iv). Thus, the vast majority (>96%) of differentially expressed genes fall into the three groups that represent biologically meaningful expression patterns, i.e., PD- and age-specific up- and down-regulated or an incremental/decremental pattern in aging and PD. A full gene list of all patterns is provided in Online Resource 4.
Fig. 2

Expression patterns in aging and PD. Following ANOVA analysis (FDR 5%), a total of 1,661 transcripts were significantly altered between the three groups: young controls (YC), age-matched (old) controls (OC) and PD. These 1,661 transcripts were distributed among all 12 possible patterns of differential expression indicated by arrows. i 1,185 transcripts were expressed at the same levels in YC and OC, but specifically changed in the PD group (PD-specific, up/down = 435/750). ii 256 transcripts were significantly regulated in aging DA neurons, with no changes in PD compared to the age-matched controls (age-specific, up/down = 109/147); iii The 166 transcripts with changes in both aging and disease were mostly regulated in the same direction and show an increased or decreased expression level (up/down = 85/81). For some transcripts showing this pattern, the difference between groups was too small to yield significance between adjacent groups at p = 0.05 (parenthesis). iv Only 54 transcripts deviated from these three patterns, i.e., displaying changes only in old controls (mixed a), showing an ‘antipodal’ expression in aging and PD (mixed b), or ‘weaker’ expression changes in PD than aging (mixed c)

Determination of quality parameters

RNA quality is crucial for successful microarray data generation, with pH and RIN of post-mortem tissue being the best surrogate markers [4, 12]. In particular, mitochondrial-related expression can be influenced by these factors [65]. In our study, multiple mitochondrial genes were differentially regulated between the age-matched (old) controls (OC) and PD, but these groups did not differ in pH/RIN. An effect of pH could have been expected when comparing young controls (YC) to OC/PD, however, no pH-related effect was seen in the age-related genes (Online Resource 3, Fig. S2). Thus, differential expression in our dataset cannot be explained by agonal factors.

Indication of gliosis/inflammation in PD

Despite careful execution of LMD, ‘contamination’ with non-neuronal transcription signals can occur. 112 of 1,661 significant transcripts showed a twofold higher expression in bulk tissue compared to isolated neurons (EF < 0.5; Fig. 1b). All three major patterns showed a similar proportion of genes with EF < 0.5 (79/1,185 in PD = 6.7%, 18/256 in aging = 7.0%, 12/166 in PD and aging = 7.2%), but in PD most were among the up-regulated genes, e.g., GFAP (Fig. 3). We analyzed the 112 genes for enrichment in canonical pathways using IPA®. Top pathways were ‘Acute Phase Response Signaling’ (p = 6.82E-05) and ‘RhoA-Signaling’ (p = 8.60E-05), indicating detection of transcription signals related to gliosis/inflammation and glia/neuron communication in LMD samples.
Fig. 3

Identification of glial-specific expression differences in aging and PD. This figures shows the fold change and p value distribution of genes with an enrichment factor (EF) > 0.5 (neuron-specific; black dots) and <0.5 (glial-specific; red dots) in aging and PD. The EF for each gene is determined by the gene expression value in isolated DA neurons divided by the expression value of the same gene in bulk SN tissue. Fold changes are plotted against ANOVA p values for age-specific genes, PD-specific genes and genes with either increased or decreased expression changes in aging and PD. The majority of glial-specific genes were up-regulated in PD, indicating gliosis/inflammation directly adjacent to DA neurons (e.g., nuclear factor of kappa light polypeptide gene enhancer in B-cells 1, NFKB1, and glial fibrillary acidic protein, GFAP)

Pathway analysis of gene expression changes in PD

For further analysis, we matched 1,185 differentially regulated transcripts specific for PD with the 1,045 published by Simunovic et al. [56]. Of these, 95 genes (8%) were shared, including the PD genes SNCA, DJ-1, UCHL1, and the PD-associated gene PDXK. Three quarters were regulated in the same direction (Online Resource 5). Interestingly, by increasing the FDR in our dataset, the overlap of shared genes decreased (FDR 0.01: 10%; FDR 0.05: 8%; FDR 0.1: 7%), indicating loss of specificity. Then, we analyzed and compared enrichment of gene lists in canonical pathways using IPA®. 59 of the 364 distinct IPA® canonical pathways were significant (p < 0.05) in our dataset and 95 in the dataset of Simunovic et al. (for a detailed listing on pathways and genes, including significance at different stringencies see Online Resource 6). Intriguingly, a significant fraction of 24 pathways was shared in both studies (p < 0.005; Table 1; Online Resource 3, Fig. S3). Furthermore, of the ten most significant pathways in our study, seven were also significant in the Simunovic dataset, i.e., mitochondrial dysfunction (including oxidative phosphorylation and ubiquinone biosynthesis), the ubiquitin proteasome system (UPS), the translation control pathway eIF4/p70S6K, the microtubule dynamics network Regulation by stathmin 1 and Hypoxia/Hif1α Signaling. Subtraction of glial-specific genes (EF < 0.5) did not influence the significance of the main shared pathways, supporting a predominately neuronal transcription signature. Firstly, this comparison shows that there is considerable overlap of these two independent studies. Secondly, it allows a further focus on the overlapping pathways that have been independently confirmed.
Table 1

Canonical pathways shared between both datasets

Canonical pathway (specific for PD)

Elstner

Simunovic

*

p value

Genes ↑/↓

p value

Genes ↑/↓

Mitochondrial dysfunction

3.98E-19

5/38

4.79E-08

4/22

Oxidative phosphorylation

1.00E-16

7/35

2.01E-08

2/26

Protein ubiquitination pathway

1.07E-08

5/30

2.14E-02

6/11

Ubiquinone biosynthesis

2.88E-08

5/16

2.22E-03

1/11

Regulation of eIF4 and p70S6K signaling

4.79E-05

6/12

1.45E-04

5/11

Regulation of stathmin

8.91E-05

13/12

7.76E-04

8/13

Hypoxia/HIF1α signaling

1.35E-04

3/10

4.17E-02

5/5

EIF2 signaling

1.10E-03

3/10

3.98E-04

4/9

Renal cell carcinoma signaling

2.01E-03

3/8

9.55E-03

5/4

VEGF signaling

2.09E-03

5/8

7.76E-04

5/8

Reelin signaling in neurons

3.63E-03

6/5

1.48E-02

3/6

Clathrin-mediated endocytosis signaling

4.37E-03

3/15

1.35E-03

7/11

Huntington’s disease signaling

5.75E-03

7/16

1.48E-03

7/16

ILK signaling

7.94E-03

9/10

2.40E-02

9/7

Rac signaling

7.94E-03

6/7

2.10E-02

4/7

p70S6K signaling

1.48E-02

9/5

9.12E-05

8/10

mTOR signaling

1.78E-02

8/7

1.58E-02

5/9

CXCR4 signaling

2.14E-02

8/8

1.74E-02

7/8

Axonal guidance signaling

2.45E-02

16/15

8.51E-04

17/17

Cardiac hypertrophy signaling

2.51E-02

9/12

1.86E-03

14/9

PI3K/AKT signaling

2.64E-02

7/6

1.82E-03

4/11

14-3-3-mediated signaling

2.75E-02

5/7

1.62E-05

5/13

Germ cell-sertoli cell junction signaling

2.95E-02

7/8

2.19E-03

10/7

Thrombin signaling

3.16E-02

11/7

2.51E-03

12/8

Virus entry via endocytic pathways

3.89E-02

5/5

2.51E-04

2/12

* Arrows indicate combined predominant (≥60%) direction of gene expression changes in both datasets

Bold pathways were incorporated in synopsis

Pathway analysis can resolve inter-study variability

The benefit of a pathway analysis can be demonstrated by means of well-defined entities such as ‘oxidative phosphorylation’, i.e., the mitochondrial respiratory chain complexes (RCC). This pathway was highly significant in both studies but overlap of individual genes was minimal: when focusing on differentially expressed genes composing the RCC I, 16/45 genes were dysregulated in our study and 9/45 in the Simunovic study, but only three genes were shared (Fig. 4). Thus, overlap of pathways was complementary rather than identical, as also seen in the other common pathways. Interestingly, whereas multiple nuclear encoded RCC subunits were consistently down-regulated, multiple mtDNA encoded genes were significantly up-regulated: ND1 (p = 4.1E-05; 1.7-fold), ND2 (p = 4.0E-06; 1.8-fold), ND4 (p = 4.8E-05; 1.32-fold), ND4L (p = 2.0E-03; 2.3-fold), ND5 (p = 4.5E-05; 1.74-fold), MTCO2 (p = 2.0E-04; 1.4-fold), and MTATP6 (p = 5.1E-03; 1.66-fold).
Fig. 4

Differential gene-regulation of subunits of the respiratory chain complex I. The current study found 16/45 differentially regulated mitochondrial subunits (upper circle), with Simunovic et al. [56] reporting 9/45 genes as differentially regulated (lower circle). Direct overlap was limited to three genes (bold). All nuclear genes were down-regulated (green), but mtDNA genes were up-regulated in our study (red)

Age-related changes and ‘accelerated’ aging

To achieve a better understanding of expression changes in aging, we next subjected the age-specific gene list to canonical pathway analysis. Surprisingly, genes were not enriched in any of the pathways specific for PD (Table 2a). Fourteen canonical pathways were significantly altered and, in contrast to PD, predominantly included up-regulated genes. Subtraction of glial-specific genes (EF < 0.5) did not influence the overall result of this analysis. Up-regulated pathways included those that mediate nuclear (hormone-) signaling related transcription regulation via cAMP response element-binding (CREB) and retinoic acid receptors (RAR). As a central regulatory gene, CREB1 is up-regulated in a network that might promote neuronal survival in aging DA neurons (Online Resource 3, Fig. S4). Other gene functions of this network include cell assembly/organization, cell-to-cell signaling/adhesion and axonal growth. A significant number of genes in this network are related to neurological disorders. Down-regulated canonical pathways included GABA receptor signaling and biosynthetic pathways.
Table 2

Canonical pathways enriched in aging DA neurons

Canonical pathway

p value

↑/↓

*

Specific for aging

 cAMP-mediated signaling

2.95E-03

5/2

 GABA receptor signaling

3.16E-03

1/3

 Sphingolipid metabolism

9.33E-03

2/3

 Aminophosphonate metabolism

1.26E-02

0/3

 RAR activation

1.66E-02

5/1

 Cardiac β-adrenergic signaling

1.86E-02

3/2

 Methionine metabolism

2.63E-02

0/3

 Selenoamino acid metabolism

2.88E-02

0/3

 Glucocorticoid receptor signaling

3.39E-02

6/1

 G-Protein coupled receptor signaling

4.07E-02

5/1

 IL-22 signaling

4.07E-02

2/0

 Estrogen receptor signaling

4.07E-02

2/2

 TNFR2 signaling

4.37E-02

2/0

 Glycosaminoglycan degradation

4.68E-02

2/1

Aging and PD

 Nicotinate and nicotinamide metabolism

1.62E-02

3/1

 Agrin interactions at neuromuscular junction

1.86E-02

2/1

 Regulation of actin-based motility by rho

3.39E-02

1/2

 PAK signaling

3.55E-02

1/2

 Purine metabolism

3.63E-02

2/5

 Huntington’s disease signaling

3.80E-02

2/3

 Pantothenate and CoA biosynthesis

4.37E-02

0/2

* Arrows indicate predominant (≥60%) direction of gene expression changes

CREP and RAR-related nuclear receptor pathways are in bold type

To identify the changes shared by aging and PD, i.e., the contribution of aging processes to protection/degeneration of DA neurons in PD, we analyzed genes with incremental or decremental changes. These genes were enriched in the canonical pathways for actin regulation, NAD-metabolism and other metabolic/biosynthetic pathways (Table 2b). The primary network generated from these genes was enriched for genes with a regulatory function associated with cellular assembly/organization, cell death and survival (insulin-like growth factor 1 receptor, IGF1R), synaptic transmission (glutamate receptor GRIA1) and gene expression (Online Resource 3, Fig. S5).

Discussion

De novo gene expression, induction, and repression are rarely seen in the mature nervous system. Therefore, the expected magnitude of expression changes found with microarrays is only modest [44]. In PD specifically, the signal associated with DA neurons might be hard to separate from the noise produced by non-neuronal cell populations due to significant neuronal loss and gliosis in the SNc. As shown by our comparison of expression profiles obtained from bulk tissue and isolated DA neurons, an over tenfold reduction of non-neuronal signal can be achieved using LMD and genes with the highest non-neuronal transcription signal can be identified and eliminated from analysis. Most of the glial-specific genes are immune-related and were up-regulated in PD, indicating gliosis/inflammation in PD. In relation to disease pathomechanisms, reactive gliosis is seen in many neurodegenerative disorders and recently a protective effect of non-steroidal anti-inflammatory drugs has been implicated in PD [17, 27].

In LMD, only pg/cell quantities of RNA are obtained and a subsequent second-round IVT might introduce error. Therefore, quality control, e.g. by scatter plot and cluster analysis is mandatory. We demonstrated excellent reproducibility of microarray expression patterns of DA neurons obtained in biological replicates from frozen post-mortem midbrains when stringent quality control was applied. Statistical analysis of differently expressed genes between the three groups YC, OC, and PD revealed non-random directions of gene expression changes that were attributable to meaningful biological patterns, i.e., aging- and PD-specific changes, as well as a stepwise decrease or increase of expression in aging and PD. However, the limited availability of suitable tissue limits the power of our study and comparisons to other studies are necessary. Gene-to-gene comparison with a published dataset revealed only 8% shared transcripts in PD [56]. This might be explained by study differences, such as sampling methods (toluidine blue vs. unstained, 20 vs. 8 μm sections, 100 vs. 300 neurons), IVT (2 vs. 3 rounds) and—most importantly—different probe labeling, microarray platforms (Illumina® vs. Affimetrix®) and statistical methods. Therefore, pathway analysis was chosen to further analyze these gene sets and to provide a better understanding of transcriptional events in PD [46]. A striking overlap was seen at the pathway level.

Disrupted energy metabolism and UPS in PD

Environmental and genetic factors are believed to predispose to the development of PD and the study of hereditary forms has greatly improved our understanding of PD pathogenesis [28]. Localization and biological function of PD proteins have highlighted key pathogenic mechanisms, such as mitochondrial dysfunction and oxidative stress (DJ1, parkin, PINK1), vesicle and cytoskeletal dynamics (α-synuclein, LRRK2), MAPK signaling (LRRK2) as well as decreased microtubule stability (tau) [24]. There is an intriguing overlap of these genetic pathways with the transcriptional changes found in our study. The most significantly altered pathways in our study include mitochondrial, in particular, oxidative phosphorylation (OXPHOS) genes, and the UPS. Since toxic and genetic dysfunction of mitochondria is well established in PD [1], this additionally supports the feasibility of the LMD and pathway-based approach. While the role of the UPS in PD remains somewhat controversial, mitochondria and the UPS are highly co-affected in experimental systems, with mitochondrial dysregulation leading to increased oxidative stress and proteasomal deregulation [8, 19, 20]. In a recent meta-analysis by Zheng et al. [69], the integration of all currently available PD transcriptome datasets also indicated downregulation of OXPHOS genes, as well as genes of the pyruvate metabolism and tricarboxylic acid cycle (TCA) early in the disease process. Our data support these findings and provide additional information on the impact of aging on gene expression of mitochondrial, glycolytic and TCA genes (Online Resource 3, Figs. S6 and S7). Some glycolytic and TCA genes display a decreased expression pattern in aging and PD, but the expression of most genes is stable during aging and decreases specifically in PD. This is complemented by the upregulation of pyruvate dehydrogenase kinase, indicating the use of alternative carbon sources in PD, i.e., of acetate and fatty acids (ketogenic) and amino acids (glutaminolysis) [5, 26, 55].

Dysregulation of energy-sensing/signaling pathways in PD

Another key finding of this study is that several significant pathways are connected in an evolutionary conserved network that relays metabolic signals to adaptive changes in protein translation and gene expression [9, 35], i.e., PI3K/Akt, mTOR, eIF4/P70S6K and Hif1α signaling (Fig. 5). Since genes in these pathways are not redundant in the mitochondrial pathways, their detection seems to independently support a bioenergetic disturbance of DA neurons in PD at the transcriptional level. Through the various branches of this network, its signaling inputs include insulin, growth factors and cytokines, AMP/ATP ratio, amino acid levels, ROS and Ca2+ levels. Depending on the specific cue, each branch can function independently or in synergy. As a starvation response, mTOR inhibits translation via eIF4/P70S6K signaling, mediates autophagy and facilitates degradation of cytoplasmic components and organelles (including mitochondria) to maintain amino acid and nutrient levels. Autophagy and the UPS are the two major intracellular degradation systems and appear to collaboratively protect against neurodegeneration, and as such, are receiving increasing attention in relation to PD [49, 50]. Evidence for a role of autophagy in PD comes from recent studies in model systems; Geisler et al. [29] and Narendra et al. [48] showed that PINK1 and Parkin are responsible for the targeting of damaged mitochondria to autophagy (mitophagy) in non-neuronal and neuronal cells, suggesting a link to compromised mitochondrial function. Several other recent studies also provide independent evidence for a possible pharmacological prevention of dopaminergic cell death through Akt and mTOR [2, 41, 62]. Pharmacological inhibition of mTOR using rapamycin has been shown to suppress the pathological effects of PINK1 and Parkin through activation of the translation inhibitor 4E-BP, which in turn is suppressed by the PD protein LRRK2 [61]. Hypoxia/HIF-1α signaling is another contributing pathway to this network and is enriched in both our and the Simunovic study [56]. Intriguingly, DJ-1 (PARK 7) expression has been shown to allow Akt and mTOR to sustain HIF-1α stability [64] and up-regulation of HIF-1α seems to protect against MPTP-induced nigral dopaminergic cell loss [37].
Fig. 5

Synopsis of significant canonical pathways. The figure shows the main canonical pathways dysregulated in PD (smooth thin lines) and aging (dotted lines). PD-specific pathways mainly include down-regulated genes (green), age-related pathways show mainly up-regulated genes (red). PD-specific dysregulation prominently includes genes involved in mitochondrial structure and function, e.g., OXPHOS and mitochondrial import carriers. Additional energy-related pathways down-regulated in PD are glycolysis and the tricarboxylic acid cycle (TCA). Conversely, the mitochondrial glutamate carrierSLC25A18, carnitine-acylcarnitine translocator SLC25A29, Acyl-CoA synthetase (ACSS1) and pyruvate dehydrogenase kinase (PDK) are up-regulated (squares), possibly in an attempt to increase supply of alternative reducing equivalents to mitochondria. Several other significantly dysregulated pathways in PD comprise a network involved in the control of energy metabolism and cell survival in response to growth factors, oxidative stress and nutrient deprivation (PI3K/Akt, mTOR, eIF2, eIF4/p70S6K, AMPK and NRF2/Hif-1α). Under anabolic conditions, this network promotes cell and tissue growth, while starvation arrests it. In the nucleus, adaptive regulation requires transcription factors such as CREB, PPAR/RAR, FOXO and estrogen receptors (ER), some of which might be up-regulated in response to Ca2+/cAMP-signaling in aging. Other significantly dysregulated pathways in PD and aging are involved in cell adhesion, axon guidance and cytoskeletal maintenance, e.g., Rac signaling, integrin linked kinase (ILK) and RhoGTPase signaling (RhoA). Regulation of Stathmin comprises a pathway involved in the regulation of microtubule dynamics

Genetic factors may principally determine the neuronal ability to adapt to age-related energetic and toxic stress in this regulatory network. Several other PD risk and protective factors, including environmental toxins, sex hormones, diabetes and the dietary stimulants coffee and nicotine might have an additional influence [21]. For example, PD is more prevalent in males than in females and expression profiles are gender-specific, with an emphasis on genes related to OXPHOS, apoptosis and synaptic transmission [57]. As outlined above, insulin has a prominent role in the adaption of energy metabolism and a disrupted metabolic control in diabetic patients might predispose DA neurons to metabolic failure. Furthermore, caffeine treatment stimulates mitogenesis in a (CAMK/p38)-dependent manner and the PI3K pathway might mediate the protective effect of caffeine [47, 67]. In conclusion, in addition to significant mitochondrial dysfunction we identified dysregulation of energy-sensing/signaling pathways in PD, which is also supported by recent functional studies.

Gene expression changes in aging

Aging is considered the most unequivocal risk factor for idiopathic PD, where loss of DA neurons in the SNc can exceed 80% at clinical onset [63]. Nevertheless, the accentuated cell loss of ventrolateral portions of the SNc in PD as opposed to the dorsomedial SNc loss that occurs in aging indicates that PD is not simply explained by pronounced age-related processes [25, 53]. Stereological studies showed hypertrophy of the remaining DA neurons in aging, which may be considered a compensatory mechanism of surviving neurons rather than an indication of cell degeneration or necrosis, whereas atrophy of neurons is seen in PD [13, 54] (for review see Stark et al. [58]). In our study, analysis of genes specifically changed in aging revealed fewer significant hits compared to PD. While 1,185 transcripts were changed in PD, only 256 genes were significantly changed when comparing DA neurons of humans with an average age of 53 and 78 years. This implies that expression profiles are more dramatically and specifically changed in surviving DA neurons of individuals with PD compared to 25 years of aging. Moreover, most genes that are enriched in pathways significant for PD, show maintained expression in aging, emphasizing the specificity of PD-associated gene dysregulation. Pathway analysis in aging revealed enrichment of signaling pathways that may have an impact on healthy aging [35, 36, 39]. The cAMP/CREB signaling pathway is involved in the regulation of a wide range of biological functions such as growth factor-dependent cell proliferation and survival, glucose homeostasis and synaptic plasticity. In agreement with published data on cortical gene expression changes in brain aging, genes in inflammatory pathways (IL-22, TNFR2 signaling) are also up-regulated, whereas biosynthetic pathways and GABA receptor signaling predominately contain down-regulated genes [9, 40].

Genes that show stepwise expression changes in aging and PD may represent common pathophysiological or adaptive mechanisms. Pathway analysis yielded only a few canonical pathways with low significance and no overlap with PD-specific pathways. Nevertheless, networks generated from these genes are involved in regulation of cellular assembly/organization, cell death and survival. Expression changes of genes such as the DNA-damage-inducible transcript 4 (DDIT4, p = 1.75E-05, YC = 1.3 × OC = 1.3 × PD), Human Fission 1 (TTC11, p = 5.14E-06, YC = −1.2 × OC = −1.2 × PD) or Insulin-like growth factor 1 receptor (IGF1R) may reflect an increase of certain age-related degenerative or protective mechanisms.

Together with the aforementioned neuropathological findings, our data imply that DA neuron degeneration in PD cannot be explained by a simple acceleration of the aging processes, but that specific patterns of gene dysregulation may fundamentally contribute to the disease mechanism. As a cautionary note, the selective DA neuron vulnerability in PD and aging poses a problem in the interpretation of gene expression changes in the remaining neurons, since these changes may be pathogenic or protective. More LMD studies incorporating age-dependent and anatomical aspects would be needed to resolve this question.

Conclusions

We provide a full gene expression dataset that can be mined by researchers with an interest in transcriptome regulation of human DA neurons in PD and aging. This study underscores the value of human post-mortem transcriptome studies, which to date are hampered by the scarcity of suitable tissue resources [31]. Pathway analysis provides an intriguing overlap with disease models deduced from genetic cases of PD as well as inter-study congruence. We propose a mechanistic cellular model that explains the vulnerability of DA neurons largely by their exceptional energy demand. Many genetic and environmental risk factors as well as protective factors for PD may act in part via their propensity to affect energy metabolism, stress and energy sensor pathways as well as pathways adapting to such stress. Further studies are needed to elucidate the detailed roles of the genes and pathways presented in this study.

Notes

Acknowledgments

The European Neurological Society funded M.E.; T.K., T.M., and H.P. are members of the German Network for Mitochondrial Disorders (mitoNET, 01GM0862 and 01GM0867); T.M. and H.P. were supported by the Impulse and Networking Fund of the Helmholtz Association in the framework of the Helmholtz Alliance for Mental Health in an Ageing Society (HA-215), the German Federal Ministry of Education and Research (BMBF) funded German National Research Network (NGFNplus #01GS08134) and Systems Biology of Metabotypes (SysMBo #0315494A). CMM gratefully acknowledges funding from the Health Protection Agency UK. Tissue for this study was provided by the Newcastle Brain Tissue Resource, which is funded in part by a grant from the UK Medical Research Council (G0400074), by the Newcastle NIHR Biomedical Research Centre in Ageing and Age Related Diseases awarded to the Newcastle upon Tyne Hospitals NHS Foundation Trust, and by a grant as part of the Brains for Dementia Research initiative funded by the Alzheimer’s Research Trust and the Alzheimer’s Society.

Supplementary material

401_2011_828_MOESM1_ESM.doc (382 kb)
Supplementary material 1 (DOC 382 kb)
401_2011_828_MOESM2_ESM.doc (1 mb)
Supplementary material 2 (DOC 1073 kb)
401_2011_828_MOESM3_ESM.doc (5.2 mb)
Supplementary material 3 (DOC 5324 kb)
401_2011_828_MOESM4_ESM.xls (2 mb)
Supplementary material 4 (XLS 2041 kb)
401_2011_828_MOESM5_ESM.xls (62 kb)
Supplementary material 5 (XLS 61 kb)
401_2011_828_MOESM6_ESM.xls (114 kb)
Supplementary material 6 (XLS 114 kb)

References

  1. 1.
    Abou-Sleiman PM, Muqit MM, Wood NW (2006) Expanding insights of mitochondrial dysfunction in Parkinson’s disease. Nat Rev Neurosci 7:207–219PubMedCrossRefGoogle Scholar
  2. 2.
    Aleyasin H, Rousseaux MW, Marcogliese PC et al (2010) DJ-1 protects the nigrostriatal axis from the neurotoxin MPTP by modulation of the AKT pathway. Proc Natl Acad Sci USA 107:3186–3191PubMedCrossRefGoogle Scholar
  3. 3.
    Altar CA, Vawter MP, Ginsberg SD (2009) Target identification for CNS diseases by transcriptional profiling. Neuropsychopharmacology 34:18–54PubMedCrossRefGoogle Scholar
  4. 4.
    Atz M, Walsh D, Cartagena P et al (2007) Methodological considerations for gene expression profiling of human brain. J Neurosci Methods 163:295–309PubMedCrossRefGoogle Scholar
  5. 5.
    Benard G, Bellance N, Jose C, Melser S, Nouette-Gaulain K, Rossignol R (2010) Multi-site control and regulation of mitochondrial energy production. Biochim Biophys Acta 1797:698–709PubMedCrossRefGoogle Scholar
  6. 6.
    Bender A, Krishnan KJ, Morris CM et al (2006) High levels of mitochondrial DNA deletions in substantia nigra neurons in aging and Parkinson disease. Nat Genet 38:515–517PubMedCrossRefGoogle Scholar
  7. 7.
    Bender A, Schwarzkopf RM, McMillan A et al (2008) Dopaminergic midbrain neurons are the prime target for mitochondrial DNA deletions. J Neurol 255:1231–1235PubMedCrossRefGoogle Scholar
  8. 8.
    Betarbet R, Canet-Aviles RM, Sherer TB et al (2006) Intersecting pathways to neurodegeneration in Parkinson’s disease: effects of the pesticide rotenone on DJ-1, alpha-synuclein, and the ubiquitin-proteasome system. Neurobiol Dis 22:404–420PubMedCrossRefGoogle Scholar
  9. 9.
    Bishop NA, Lu T, Yankner BA (2010) Neural mechanisms of ageing and cognitive decline. Nature 464:529–535PubMedCrossRefGoogle Scholar
  10. 10.
    Bossers K, Meerhoff G, Balesar R et al (2009) Analysis of gene expression in Parkinson’s disease: possible involvement of neurotrophic support and axon guidance in dopaminergic cell death. Brain Pathol 19:91–107PubMedCrossRefGoogle Scholar
  11. 11.
    Braak H, Del Tredici K, Rüb U, de Vos RA, Jansen Steur EN, Braak E (2003) Staging of brain pathology related to sporadic Parkinson’s disease. Neurobiol Aging 24:197–211PubMedCrossRefGoogle Scholar
  12. 12.
    Buesa C, Maes T, Subirada F, Barrachina M, Ferrer I (2004) DNA chip technology in brain banks: confronting a degrading world. J Neuropathol Exp Neurol 63:1003–1014PubMedGoogle Scholar
  13. 13.
    Cabello CR, Thune JJ, Pakkenberg H, Pakkenberg B (2002) Ageing of substantia nigra in humans: cell loss may be compensated by hypertrophy. Neuropathol Appl Neurobiol 28:283–291PubMedCrossRefGoogle Scholar
  14. 14.
    Calvano SE, Xiao W, Richards DR et al (2005) A network-based analysis of systemic inflammation in humans. Nature 437:1032–1037PubMedCrossRefGoogle Scholar
  15. 15.
    Cantuti-Castelvetri I, Keller-McGandy C, Bouzou B et al (2007) Effects of gender on nigral gene expression and Parkinson disease. Neurobiol Dis 26:606–614PubMedCrossRefGoogle Scholar
  16. 16.
    Chan CS, Guzman JN, Ilijic E et al (2007) ‘Rejuvenation’ protects neurons in mouse models of Parkinson’s disease. Nature 447:1081–1086PubMedCrossRefGoogle Scholar
  17. 17.
    Croisier E, Graeber MB (2006) Glial degeneration and reactive gliosis in alpha-synucleinopathies: the emerging concept of primary gliodegeneration. Acta Neuropathol 112:517–530PubMedCrossRefGoogle Scholar
  18. 18.
    Dauer W, Przedborski S (2003) Parkinson’s disease: mechanisms and models. Neuron 39:889–909PubMedCrossRefGoogle Scholar
  19. 19.
    Domingues AF, Arduíno DM, Esteves AR, Swerdlow RH, Oliveira CR, Cardoso SM (2008) Mitochondria and ubiquitin-proteasomal system interplay: relevance to Parkinson’s disease. Free Radic Biol Med 45:820–825PubMedCrossRefGoogle Scholar
  20. 20.
    Duke DC, Moran LB, Kalaitzakis ME et al (2006) Transcriptome analysis reveals link between proteasomal and mitochondrial pathways in Parkinson’s disease. Neurogenetics 7:139–148PubMedCrossRefGoogle Scholar
  21. 21.
    Elbaz A, Moisan F (2008) Update in the epidemiology of Parkinson’s disease. Curr Opin Neurol 21:454–460PubMedCrossRefGoogle Scholar
  22. 22.
    Elstner M, Andreoli C, Klopstock T, Meitinger T, Prokisch H (2009) The mitochondrial proteome database: MitoP2. Methods Enzymol 457:3–20PubMedCrossRefGoogle Scholar
  23. 23.
    Elstner M, Morris CM, Heim K et al (2009) Single-cell expression profiling of dopaminergic neurons combined with association analysis identifies pyridoxal kinase as Parkinson’s disease gene. Ann Neurol 66:792–798PubMedCrossRefGoogle Scholar
  24. 24.
    Farrer MJ (2006) Genetics of Parkinson disease: paradigm shifts and future prospects. Nat Rev Genet 7:306–318PubMedCrossRefGoogle Scholar
  25. 25.
    Fearnley JM, Lees AJ (1991) Ageing and Parkinson’s disease: substantia nigra regional selectivity. Brain 114:2283–2301PubMedCrossRefGoogle Scholar
  26. 26.
    Fujino T, Kondo J, Ishikawa M, Morikawa K, Yamamoto TT (2001) Acetyl-CoA synthetase 2, a mitochondrial matrix enzyme involved in the oxidation of acetate. J Biol Chem 276:11420–11426PubMedCrossRefGoogle Scholar
  27. 27.
    Gagne JJ, Power MC (2010) Anti-inflammatory drugs and risk of Parkinson disease: a meta-analysis. Neurology 74:995–1002PubMedCrossRefGoogle Scholar
  28. 28.
    Gasser T (2009) Molecular pathogenesis of Parkinson disease: insights from genetic studies. Expert Rev Mol Med 11:e22PubMedCrossRefGoogle Scholar
  29. 29.
    Geisler S, Holmström KM, Skujat D et al (2010) PINK1/Parkin-mediated mitophagy is dependent on VDAC1 and p62/SQSTM1. Nat Cell Biol 12:119–131PubMedCrossRefGoogle Scholar
  30. 30.
    Gerlach M, Double KL, Youdim MB, Riederer P (2000) Strategies for the protection of dopaminergic neurons against neurotoxicity. Neurotox Res 2:99–114PubMedCrossRefGoogle Scholar
  31. 31.
    Graeber MB (2008) Twenty-first century brain banking: at the crossroads. Acta Neuropathol 115:493–496PubMedCrossRefGoogle Scholar
  32. 32.
    Grunblatt E, Mandel S, Jacob-Hirsch J et al (2004) Gene expression profiling of parkinsonian substantia nigra pars compacta; alterations in ubiquitin-proteasome, heat shock protein, iron and oxidative stress regulated proteins, cell adhesion/cellular matrix and vesicle trafficking genes. J Neural Transm 111:1543–1573PubMedCrossRefGoogle Scholar
  33. 33.
    Guzman JN, Sanchez-Padilla J, Wokosin D et al (2010) Oxidant stress evoked by pacemaking in dopaminergic neurons is attenuated by DJ-1. Nature 468:696–700PubMedCrossRefGoogle Scholar
  34. 34.
    Hauser MA, Li YJ, Xu H et al (2005) Expression profiling of substantia nigra in Parkinson disease, progressive supranuclear palsy, and frontotemporal dementia with parkinsonism. Arch Neurol 62:917–921PubMedCrossRefGoogle Scholar
  35. 35.
    Hock MB, Kralli A (2009) Transcriptional control of mitochondrial biogenesis and function. Annu Rev Physiol 71:177–203PubMedCrossRefGoogle Scholar
  36. 36.
    Klinge CM (2008) Estrogenic control of mitochondrial function and biogenesis. J Cell Biochem 105:1342–1351PubMedCrossRefGoogle Scholar
  37. 37.
    Lee DW, Rajagopalan S, Siddiq A et al (2009) Inhibition of prolyl hydroxylase protects against 1-methyl-4-phenyl-1, 2, 3, 6-tetrahydropyridine-induced neurotoxicity: model for the potential involvement of the hypoxia-inducible factor pathway in Parkinson disease. J Biol Chem 284:29065–29076PubMedCrossRefGoogle Scholar
  38. 38.
    Lesnick TG, Papapetropoulos S, Mash DC et al (2007) A genomic pathway approach to a complex disease: axon guidance and Parkinson disease. PLoS Genet 3:e98PubMedCrossRefGoogle Scholar
  39. 39.
    López-Lluch G, Irusta PM, Navas P, de Cabo R (2008) Mitochondrial biogenesis and healthy aging. Exp Gerontol 43:813–819PubMedCrossRefGoogle Scholar
  40. 40.
    Lu T, Pan Y, Kao SY et al (2004) Gene regulation and DNA damage in the ageing human brain. Nature 429:883–891PubMedCrossRefGoogle Scholar
  41. 41.
    Malagelada C, Jin ZH, Jackson-Lewis V, Przedborski S, Greene LA (2010) Rapamycin protects against neuron death in in vitro and in vivo models of Parkinson’s disease. J Neurosci 30:1166–1175PubMedCrossRefGoogle Scholar
  42. 42.
    McKeith IG, Dickson DW, Lowe J et al (2005) Diagnosis and management of dementia with Lewy bodies: third report of the DLB Consortium. Neurology 65:1863–1872PubMedCrossRefGoogle Scholar
  43. 43.
    Miller RM, Kiser GL, Kaysser-Kranich TM, Lockner RJ, Palaniappan C, Federoff HJ (2006) Robust dysregulation of gene expression in substantia nigra and striatum in Parkinson’s disease. Neurobiol Dis 21:305–313PubMedCrossRefGoogle Scholar
  44. 44.
    Mirnics K, Pevsner J (2004) Progress in the use of microarray technology to study the neurobiology of disease. Nat Neurosci 7:434–439PubMedCrossRefGoogle Scholar
  45. 45.
    Moran LB, Duke DC, Deprez M, Dexter DT, Pearce RK, Graeber MB (2006) Whole genome expression profiling of the medial and lateral substantia nigra in Parkinson’s disease. Neurogenetics 7:1–11PubMedCrossRefGoogle Scholar
  46. 46.
    Moran LB, Graeber MB (2008) Towards a pathway definition of Parkinson’s disease: a complex disorder with links to cancer, diabetes and inflammation. Neurogenetics 9:1–13PubMedCrossRefGoogle Scholar
  47. 47.
    Nakaso K, Ito S, Nakashima K (2008) Caffeine activates the PI3K/Akt pathway and prevents apoptotic cell death in a Parkinson’s disease model of SH-SY5Y cells. Neurosci Lett 432:146–150PubMedCrossRefGoogle Scholar
  48. 48.
    Narendra DP, Jin SM, Tanaka A et al (2010) PINK1 is selectively stabilized on impaired mitochondria to activate Parkin. PLoS Biol 8:e1000298PubMedCrossRefGoogle Scholar
  49. 49.
    Nedelsky NB, Todd PK, Taylor JP (2008) Autophagy and the ubiquitin-proteasome system: collaborators in neuroprotection. Biochim Biophys Acta 1782:691–699PubMedGoogle Scholar
  50. 50.
    Pan T, Kondo S, Zhu W, Xie W, Jankovic J, Le W (2008) Neuroprotection of rapamycin in lactacystin-induced neurodegeneration via autophagy enhancement. Neurobiol Dis 32:16–25PubMedCrossRefGoogle Scholar
  51. 51.
    Riederer P, Youdim MB, Mandel S, Gerlach M, Grünblatt E (2008) Genomic aspects of sporadic Parkinson’s disease. Parkinsonism Relat Disord 14:S88–S91PubMedCrossRefGoogle Scholar
  52. 52.
    Rollo CD (2009) Dopamine and aging: intersecting facets. Neurochem Res 34:601–629PubMedCrossRefGoogle Scholar
  53. 53.
    Ross GW, Petrovitch H, Abbott RD et al (2004) Parkinsonian signs and substantia nigra neuron density in decendents elders without PD. Ann Neurol 56:532–539PubMedCrossRefGoogle Scholar
  54. 54.
    Rudow G, O’Brien R, Savonenko AV et al (2008) Morphometry of the human substantia nigra in ageing and Parkinson’s disease. Acta Neuropathol 115:461–470PubMedCrossRefGoogle Scholar
  55. 55.
    Sekoguchi E, Sato N, Yasui A et al (2003) A novel mitochondrial carnitine-acylcarnitine translocase induced by partial hepatectomy and fasting. J Biol Chem 278:38796–38802PubMedCrossRefGoogle Scholar
  56. 56.
    Simunovic F, Yi M, Wang Y et al (2008) Gene expression profiling of substantia nigra dopamine neurons: further insights into Parkinson’s disease pathology. Brain 132:1795–1809PubMedCrossRefGoogle Scholar
  57. 57.
    Simunovic F, Yi M, Wang Y, Stephens R, Sonntag KC (2010) Evidence for gender-specific transcriptional profiles of nigral dopamine neurons in Parkinson disease. PLoS One 5:e8856PubMedCrossRefGoogle Scholar
  58. 58.
    Stark AK, Pakkenberg B (2004) Histological changes of the dopaminergic nigrostriatal system in aging. Cell Tissue Res 318:81–92PubMedCrossRefGoogle Scholar
  59. 59.
    Surmeier DJ (2007) Calcium, ageing, and neuronal vulnerability in Parkinson’s disease. Lancet Neurol 6:933–938PubMedCrossRefGoogle Scholar
  60. 60.
    Sutherland GT, Matigian NA, Chalk AM et al (2009) A cross-study transcriptional analysis of Parkinson’s disease. PLoS One 4:e4955PubMedCrossRefGoogle Scholar
  61. 61.
    Tain LS, Mortiboys H, Tao RN, Ziviani E, Bandmann O, Whitworth AJ (2009) Rapamycin activation of 4E-BP prevents parkinsonian dopaminergic neuron loss. Nat Neurosci 12:1129–1135PubMedCrossRefGoogle Scholar
  62. 62.
    Timmons S, Coakley MF, Moloney AM, O’ Neill C (2009) Akt signal transduction dysfunction in Parkinson’s disease. Neurosci Lett 467:30–35PubMedCrossRefGoogle Scholar
  63. 63.
    Vanitallie TB (2008) Parkinson disease: primacy of age as a risk factor for mitochondrial dysfunction. Metabolism 57(Suppl 2):S50–S55PubMedCrossRefGoogle Scholar
  64. 64.
    Vasseur S, Afzal S, Tardivel-Lacombe J, Park DS, Iovanna JL, Mak TW (2009) DJ-1/PARK7 is an important mediator of hypoxia-induced cellular responses. Proc Natl Acad Sci USA 106:1111–1116PubMedCrossRefGoogle Scholar
  65. 65.
    Vawter MP, Tomita H, Meng F et al (2006) Mitochondrial-related gene expression changes are sensitive to agonal-pH state: implications for brain disorders. Mol Psychiatry 11:663–679CrossRefGoogle Scholar
  66. 66.
    Vogt IR, Lees AJ, Evert BO, Klockgether T, Bonin M, Wüllner U (2006) Transcriptional changes in multiple system atrophy and Parkinson’s disease putamen. Exp Neurol 199:465–478PubMedCrossRefGoogle Scholar
  67. 67.
    Wright DC, Geiger PC, Han DH, Jones TE, Holloszy JO (2007) Calcium induces increases in peroxisome proliferator-activated receptor gamma coactivator-1 alpha and mitochondrial biogenesis by a pathway leading to p38 mitogen-activated protein kinase activation. J Biol Chem 282:18793–18799PubMedCrossRefGoogle Scholar
  68. 68.
    Zhang Y, James M, Middleton FA, Davis RL (2005) Transcriptional analysis of multiple brain regions in Parkinson’s disease supports the involvement of specific protein processing, energy metabolism, and signaling pathways, and suggests novel disease mechanisms. Am J Med Genet B Neuropsychiatr Genet 137:5–16Google Scholar
  69. 69.
    Zheng B, Liao Z, Locascio JJ, et al (2010) PGC-1α, a potential therapeutic target for early intervention in Parkinson’s disease. Sci Transl Med 2:52ra73Google Scholar

Copyright information

© Springer-Verlag 2011

Authors and Affiliations

  • Matthias Elstner
    • 1
    • 2
  • Christopher M. Morris
    • 3
    • 4
  • Katharina Heim
    • 2
  • Andreas Bender
    • 1
  • Divya Mehta
    • 2
  • Evelyn Jaros
    • 4
  • Thomas Klopstock
    • 1
  • Thomas Meitinger
    • 2
    • 5
  • Douglass M. Turnbull
    • 6
  • Holger Prokisch
    • 2
    • 5
  1. 1.Department of Neurology with Friedrich-Baur-Institute, Klinikum GroßhadernLudwig-Maximilians-UniversityMunichGermany
  2. 2.Institute of Human Genetics, Helmholtz Zentrum MünchenGerman Research Center for Environmental HealthNeuherbergGermany
  3. 3.Medical Toxicology Centre, Wolfson Unit of Clinical Pharmacology, Institute of NeuroscienceNewcastle UniversityNewcastle upon TyneUK
  4. 4.Institute for Ageing and HealthNewcastle UniversityNewcastle upon TyneUK
  5. 5.Institute of Human GeneticsTechnical University MunichMunichGermany
  6. 6.Mitochondrial Research Group, Institute of Ageing and Health, Newcastle University Centre for Brain Ageing and VitalityNewcastle UniversityNewcastle upon TyneUK

Personalised recommendations