SNCA overexpression disturbs hippocampal gene expression trajectories in midlife

Results

Hippocampal gene expression changes through SNCA overexpression occurred age-dependently

To better understand gene expression changes underlying early stages of SNCA-related pathogenesis we interrogated hippocampal transcriptome data of 6- and 12-month-old SNCA-overexpressing (TG) and wildtype (WT) mice that were housed in either a standard (SE), enriched (EE), or chronic stress environment (ST) for differentially expressed genes (DEGs) along the genotype (g), time (t), and environment (e) axis (Fig. 1A). With respect to the genotype axis, no DEGs besides SNCA were identified in 6-month-old TG mice in both tested environmental conditions (Fig. 1A). In sharp contrast, the number of DEGs for 12-month-old TG mice housed in SE increased to 495, which were overrepresented for several pathways linked to SNCA pathology before, most significantly neuroinflammation signalling (Fig. 1B, Table S1). These genes were also enriched for disease terms that reflect the pivotal role of SNCA in Parkinson’s disease and other synucleinopathies (Fig 1C).

Figure 1. SNCA overexpressing mice developed hippocampal transcriptome disturbances in an age-dependent manner between 6 and 12 months of age. (A) Schematic diagram of experimental groups along the genotype (wildtype WT, transgenic TG), age (6 and 12 months), and environment (standard environment SE, enriched environment EE, stress ST) axis that were used to determine differential expression in the hippocampal transcriptome of mice. Lower part highlights age-dependent increase of DEGs in TG animals in SE. (B) Overrepresented pathways among 495 DEGs derived for 12-month-old TG mice in SE. Ten most significant terms, their adjusted p values, and overlapping DEG count shown. (C) Disease aspects and biological functions overrepresented among 495 DEGs. Top five significant terms shown. (D) Composition and relative expression levels of murine and human SNCA transcript isoforms.

The age-dependent increase of DEGs between 6 and 12 months with respect to genotype cannot be attributed to altered activity of the transgene itself as its (over-) expression level and compositional shares of murine and human transcript isoforms remained virtually identical (Fig 1D). Computational estimates of the underlying cell type composition neither indicated shifts between neuronal and glial cell populations for any of the experimental groups during that time frame (Fig. S1).

Together, these findings suggest an unfolding of events during 6 and 12 months of age that originate from the interference of persistent SNCA overexpression and dynamics of the system during midlife that resulted in observed disturbances in TG animals under standard conditions. Intriguingly, these disturbances were not observed when TG animals were housed in enriched conditions (Fig. 1A, lower part), highlighting the beneficial capacity and compensatory effects of the EE in context of SNCA overexpression that we have previously described in greater detail [17].

Midlife changes in hippocampal gene expression occurred largely irrespective of environmental condition

To put dysregulated genes that arose age-dependently in context of SNCA overexpression into perspective, we first examined expression changes in WT animals to identify typical changes during midlife. For WT animals, we identified 1940 DEGs that arose between 6 and 12 months of age (Fig. 2A) with consistent and dominant expression changes along the temporal dimension and only modest modulation by environmental condition (Fig. 2B) as reflected by the low count of DEGs (3 and 41 for ST and EE, respectively) along the environment axis (Fig. 2A, Fig. S2).

Figure 2. Midlife aging in WT mice was associated with hippocampal gene expression changes largely irrespective of environmental condition. (A) Schematic diagram showing number of DEGs derived from comparing 6- and 12-month-old WT mice. (B) Heatmap of hierarchically clustered z-scores for 1940 DEGs derived from comparing 6- and 12-month-old WT mice across environmental conditions. (C) Scatter plot of gene expression changes between 6- and 12-month-old WT mice with respect to standard (x-axis) and enriched (y-axis) environment. 1251 DEGs within the 50% window around the ordinary diagonal coloured in green. (D) Enriched pathways for 1251 DEGs from (C). Ten most significant terms, their adjusted p values, and overlapping DEG count shown. (E) Venn diagrams comparing DEGs identified between 6- and 12-month-old WT mice with known age-regulated genes in the hippocampus of 24- and 29-month-old mice [16]. (F) Expression changes of C4b, Pisd-ps1, and Sox11 between 6- and 12-month-old WT mice plotted as individual data points with mean ± SEM.

Genes that changed most consistently along the temporal axis and responded weakest even to the EE were located closest to the standard diagonal when correlating their changes in SE and EE (Fig. 2C, highlighted in green). This subset of genes was enriched for cellular pathways like opiod signaling, sirtuin signaling, and CREB signaling in neurons (Fig. 2D, Table S1). While some of the underlying genes in these pathways have been shown to change expression with age and play a role in age-dependent cellular processes [18–20], collectively these DEGs did not reflect characteristic signatures of aging in the murine hippocampus, indicated by the small overlap with such gene sets (Fig. 2E) [16]. The three DEGs that did overlap were the complement component C4b, the non-coding RNA Pisd-ps1, and Sox11 (Fig. 2F), known for its role in development and neurogenesis [19]. Determining the overlap of these DEGs with a second gene set that defines a cross-species age signature based on mouse, rat, and human and is enriched for mainly inflammatory and immune response genes [14], resulted in similarly few commonalities with 5 out of 73 possible candidates.

Collectively, these results suggested that while very first signs of aging began to surface during the investigated time period, the typical spectrum of age-related changes was not yet detectable in 12-months-old WT mice. Instead, expression changes in that period likely reflected normal midlife trajectories and adaptations in gene activity that took place largely irrespective of environmental condition.

SNCA overexpression disturbed midlife gene expression trajectories

With a better understanding of gene expression changes occurring during midlife in WT animals, we next examined expression changes along the temporal dimension in TG mice. Equivalent to the WT layer, DEGs from all comparisons between 6- and 12-month-old TG animals were combined, totalling to 1149 genes (Fig. 3A). As subsets of these DEGs were also identified along the temporal axis in WT mice and along the genotype axis at 12 months (Fig. 3B), we further explored the indicated interplay between (normal) age-dependent adaptations during midlife and effects of SNCA overexpression by partitioning the DEGs into three main classes (Fig. 3C), each comprising two nearly mirror-imaged expression profiles based on WT and TG animals at 6 and 12 months of age (Fig. S3). While class 1 represented genes unaffected by SNCA overexpression that had midlife expression trajectories similar to their WT reference, genes in class 2 and 3 clearly showed expression levels for 12-month-old TG mice that seemingly disagreed with the actual age of the animals (Fig. 3C). Specifically, class 3 contained genes that showed adaptations in WT animals but no longer occurred under SNCA overexpression. In contrast, class 2 contained genes that had no changes in WT mice during midlife but showed induction/repression under influence of the overexpressed transgene.

Figure 3. Midlife gene expression trajectories were disturbed through SNCA overexpression in two distinctive modes. (A) Schematic diagram showing number of DEGs derived from comparing 6- and 12-month-old WT (green) and TG (purple) mice. (B) Venn diagram putting midlife expression changes in TG animals (1149 DEGs in (A) in perspective to midlife expression changes in WT animals (1251 DEGs, see Fig. 2C) and SNCA-induced disturbances (495 DEGs, see Fig. 1A), totalling to 2162 age-SNCA-regulated DEGs. (C) Partitioning of 2162 age-SNCA-regulated DEGs based on their gene expression pattern in 6- and 12-month-old WT and TG mice in SE (see Fig. S3 for details). Subplots show longitudinal expression medoids and standard deviation of six primary gene clusters grouped into three classes. Number of DEGs per cluster in brackets. (D) Canonical pathway analysis for 2162 DEGs according to their cluster/class assignment (see C). Two most significant terms per cluster selected, and their significance values across all clusters hierarchically ordered. (E) Hierarchically clustered expression changes (relative to WT⁶_SE) for DEGs attributed to the most significantly overrepresented pathway per cluster in class 2 and 3.

When asking for overrepresented cellular processes in these classes, an intriguing pattern emerged (Fig. 3D, Table S1): Changes evoked by the transgene and not typically occurring during midlife (class 2) were highly enriched for pathways like complement system and neuroinflammation signaling (Fig. 3D), both hallmarks of advanced stages of age [14]. Specifically, all three subunits of the 1q complement component (C1qa, C1qb, C1qc), known to activate the complement system, were prominently upregulated in 12-month-old TG mice (Fig. 3E). GADD45 signaling, in contrast, was the most significant pathway among downregulated genes in class 2 like Atm and Gadd45b that are involved in growth arrest and DNA damage responses (Fig. 3E).

On the other side, class 3 genes, whose trajectories failed to undergo midlife adaptations in context of SNCA overexpression, were identified as highly enriched for pathways such as circadian rhythm signaling and mitochondrial dysfunction (Fig. 3D). Specifically, genes like Per3, Npas2, and Cry2 associated in circadian regulation remained below their WT reference levels, while genes attributed to mitochondrial dysfunction such as Ndufab1, Cox8a, and Maoa were higher in TG animals compared to WT mice of the same age (Fig. 3E).

Together, these results suggest a bimodality among age-dependent disturbances in context of SNCA overexpression: First, seemingly accelerated cellular processes that are not yet activated in same-aged WT mice (mode I) and, second, disrupted or potentially delayed adaptations of cellular processes that WT animals typically undergo in that phase (mode II).

Age-SNCA-dependent expression changes in mouse agreed with age- and PD-dependent expression signatures in human brain

To put both disturbances modes into perspective, we first used a gene set defining a cross-species age signature of expression changes in mouse, rat, and human brain [14] in order to determine commonalities with age-SNCA-dependent DEGs. The overlap between both gene sets, particularly with respect to upregulated reference genes, agreed with the acceleration part of hypothesis that a prominent number of class 2 genes showed expression levels in 12-month-old TG animals that are typically found much later (Fig. 4A, B). Reflected by results on pathway level (Fig. 3D), genes linked to complement system and neuroinflammatory processes such as C1qa, C1qb, C1qc, C4a, and Gfap, or S100a6 showed prominent age-SNCA-dependent disturbances (Fig. 4B).

Figure 4. Age-SNCA-dependent expression changes in mouse resembled age- and PD-dependent expression signatures in human. (A) Venn diagram comparing 2162 age-SNCA-regulated DEGs (see Fig. 3B) with a cross-species age signature gene set [14]. (B) Left panel shows WT¹²_SE/WT⁶_SE and TG¹²_SE/WT¹²_SE expression changes for 29 DEGs grouped by their class assignment that overlap with an age signature gene set (see A). Right heatmap reflects age-related expression changes in human brain as well as in mouse and rat hippocampus [14]. Greyed-out cells: no information. (C) Venn diagram comparing 2162 age-SNCA-regulated DEGs (see Fig. 3B) with genes known to change expression with respect to both age and PD in human [21]. (D) Left panel shows WT¹²_SE/WT⁶_SE and TG¹²_SE/WT¹²_SE expression changes for 19 DEGs grouped by their class assignment that overlap with the age-PD signature gene set (see C) [21]. Right panel indicates whether age- and PD-dependent expression changes in human are similar or opposite in direction [21].

In contrast, the overlap of age signature genes with class 3 genes was marginal (Fig. 4B), agreeing with the idea of disrupted and potentially delayed adaptations along midlife trajectories rather than accelerated developments. Evidence for this fundamentally different principle has also been found in human by comparing age- and PD-dependent expression changes [21]. There, too, PD- and age-dependent changes sometimes disagree or even possess opposing directionalities, suggesting that not all PD-dependent expression changes reflect accelerated aging. By comparing age-SNCA- with age-PD-dependent DEGs (Fig. 4C), we identified several candidates for which such opposing directionalities between age- and SNCA/PD-related changes were consistent across species (Fig. 4D). Among them were Npas2, linked to circadian rhythms, and Ndufb2, associated with mitochondrial dysfunction (Fig. 4D), both highly enriched pathways in class 3 (Fig. 3C).

These findings indicate that one part of the disturbances through SNCA overexpression agree with the shared concept of accelerated aging for some synucleinopathies like DLB and tauopathies, but also derive further evidence for existence of a second disturbance mode in which age- and SNCA-related expression trajectories diverge.

Environmental enrichment largely prevented age-dependent disturbances in the context of SNCA overexpression and restored midlife gene expression trajectories.

While we have previously shown that environmental enrichment exhibits beneficial impact and is capable of maintaining a near-normal transcriptome state despite persistent SNCA overexpression [17], we here revisited these results to put them into temporal perspective. When comparing all 2162 age-SNCA-regulated DEGs (see Fig. 3B) with 347 genes responding to the EE in 12-month-old TG mice (Fig. 5A), EE-responsive genes were distributed evenly between class 2 and 3 (Fig. 5B), suggesting the EE to be capable of preventing both modes of disturbances as reflected by the underlying expression profiles (Fig. 5C).

Figure 5. Environmental enrichment largely prevented age-SNCA-dependent disturbances and activated topologically relevant genes in the SNCA interactome with respect to both disturbance modes. (A) DEGs identified in response to EE for 12-month-old TG animals compared to 2162 age-SNCA-regulated DEGs (see Fig. 3B). (B) Distribution of 265 overlapping DEGs (see A) with respect to their cluster/class assignments (see Fig 3C). (C) Gene expression modulation of 265 DEGs overlapping with class 2 or 3 (254 total) that significantly responded to the EE in TG animals. Expression medoids and standard deviation as well as cluster cardinalities shown. (D) Interaction network of DEGs attributed to the most significantly overrepresented pathway per cluster in class 2 and 3 (see Fig. 3E) and their relations to DEGs identified in TG¹²_EE/TG¹²_SE. Node colour indicates expression change of a gene in TG¹²_SE/WT¹²_SE and TG¹²_EE/WT¹²_SE. Node stroke reflects origin of a DEG, and node size its topological relevance in the network. Edges according to IPA knowledgebase (see Methods for details). (E) Characterisations of network genes (see D) with respect to their topological relevance and class 2 / 3 neighbourhood ratio. Node colour indicates relative compensation of gene expression change observed in TG animals through provision of EE. DEGs not significantly responding to the EE in TG animals shown in dark purple.

To better understand the EE-response with respect to both disturbance modes, we built an interaction network based on all age-SNCA-induced DEGs from the most enriched up- and down-regulated pathways in class 2 and 3 (see Fig. 3E) and their EE modulations (Fig. 5D). Given a gene’s topological relevance in the network, its relative EE response magnitude, and its neighbour count with respect to class 2 and 3, we derived a mapping of network characteristics that allowed identifying topologically relevant and EE-responsive genes (Fig. 5F). Candidates such as Egr1 and Nr4a2/Nurr1 that have already been suggested in our previous work [17] to play key roles in transducing beneficial environmental cues were found in the top ranks here again. Their location in a class 2-dominated neighbourhood (Fig. 5F) suggested a preferential unfolding of preventive capacity on expression changes that occurred too early in TG animals. On the other hand, candidates such as Nr1d1 and Ndrg1 appeared in the class 3-dominated neighbourhood (Fig. 5F), hinting at potential capacities to restore midlife adaptations that failed or were possibly delayed in TG animals. Additionally, candidates like Bhle40 were connected to equal numbers of class 2 and class 3 DEGs, suggesting them to be capable of transducing beneficial EE effects into both classes.

Taken together, these results point at topologically relevant genes in the age-SNCA-dependent interactome that prominently responded to environmental enrichment, suggesting them as pivotal hubs for the unfolding of protection and restoring adaptation trajectories to a near-normal transcriptome state for midlife despite continuous SNCA overexpression.

Discussion

In this study, we examined expression changes in hippocampal transcriptome data of 6- and 12-month-old wildtype and SNCA-overexpressing mice to better understand interactions between the genetic, temporal, and environmental dimension in the unfolding of SNCA-related pathology. We identified expression disturbances previously linked to early stages of SNCA-related pathogenesis that emerged during midlife and originated from two distinctive interference modes (Fig. 6). Intriguingly, environmental enrichment rendered both modes ineffective, resulting in a near-normal transcriptome state despite persistent SNCA overexpression (Fig. 6).

Figure 6. Graphical summary. In standard environmental conditions, SNCA overexpression in TG mice (TG_SE) imbalances the hippocampal transcriptome and interferes with gene expression adaptations in midlife so that some cellular processes become accelerated, while others fail to adapt age-adequately in comparison to WT animals (WT_SE). Provision of environmental enrichment to TG animals (TG_EE) prevents and counter-balances these disturbances so that a near-normal system state can be maintained despite persistent SNCA overexpression.

Genes disturbed through the first mode had a prominent neuroinflammatory expression signature, which in healthy individuals and WT mice arises much later in life [14], suggesting SNCA overexpression accelerated aging-related processes. This agrees with previous findings that transcriptional disturbances of neurodegenerative diseases resemble aging-associated changes in gene activity [22]. With respect to neuron demise, the PD pathogenesis has been described as a form of accelerated aging [23]. Nevertheless, despite an array of molecular hallmarks shared between aging and PD, including regression of dopamine synthesis [24,25], reduced cerebral density of the type 2 vesicular monoamine transporter (VMAT2) [26], elevated levels of deleted mitochondrial DNA [27], and inflammation [28], it is still of active debate whether PD represents as a form of accelerated aging [28]. While the passage of time is required for PD, aging is not PD [28] as even in elderly of 80+ years, prevalence of the disease is in the 1–2% realm [29].

In contrast, genes suffering from mode II disturbances did no longer show expression changes observed in WT animals during midlife with respect to sirtuin signalling, circadian rhythm, mitochondrial dysfunction, and others. From the perspective of aging as time-dependent progressive decline that renders a living system increasingly vulnerable for diseases and eventually death [30,31], disrupting these trajectories and thereby delaying or halting deteriorating developments might seem advantageous at first glance. Indeed, overexpression of human SNCA in Caenorhabditis elegans harbours seemingly beneficial capacity as it extends its lifespan [32]. From the perspective of healthy aging, changes mode II genes undergo in WT may not necessarily reflect deviations from a healthy norm but, instead, represent essential systemic adaptions to, for example, bioenergetic or metabolic constraints [33]. In fact, few of these trajectories were influenced by environmental enrichment in WT animals, advocating the idea that associated cellular functions underwent required adaptions of this life span irrespective of environmental condition. From this perspective, disrupting typical midlife adaptation trajectories in the context of SNCA overexpression causes failure of the system to adjust adequately.

The circadian rhythm, besides other pathways affected through mode II disturbances, is known to undergo such adaptions also in midlife in human and rodents [34] and is linked to the sleep-wake cycle altered in PD patients [35]. Among clock genes that adapted in WT but failed to do so in TG animals, we found key players like Per2, Per3, Cry1, Cry2, and Npas2. Per2 controls Maoa that encodes monoamine oxidase and modulates dopamine levels [36]. In agreement, Maoa showed mode II disturbances in TG animals, too. Similarly, Npas2 adaptations were disrupted, in line with altered expression [21] and decreased DNA methylation levels in the NPAS2 promoter found in PD patients [37]. In fact, hypomethylation of the NPAS2 promoter has been proposed as an early biomarker for PD as it is also found in peripheral blood [38].

The central role of circadian principles in the cell also extends to mitochondrial biology [39]. In line with mitochondrial dysfunction surfacing early on in idiopathic PD cases [40,41], we observed disruption of several mitochondrial gene trajectories in TG mice. Candidates like Ndufb2 are reported also for human to have opposite expression directionalities in aging and PD [21], supporting the disruption hypothesis as brain mitochondria normally adapt gene expression and decrease membrane potential as well as electron transfers in complex I and IV with advancing age [42].

Together, both disturbance modes underlying age-SNCA-dependent gene expression changes seemingly share the principle of temporal confusions in the unfolding of cellular developments because of SNCA overexpression. The resulting disequilibrium of the system, reflected in altered transcriptome state, was largely prevented when SNCA-overexpressing mice were housed in enriched conditions. Both aspects allowed further characterisations of potential mediator genes in the EE-induced protection that we have previously put forward [17]. Specifically, we saw evidence for a gradient among potential mediators to preferentially transduce protective environmental cues onto genes from one or the other disturbance mode. Along that gradient, candidates like Egr1 and Nr4a2/Nurr1 that we have already examined in greater detail [17] were found in an interactome neighbourhood dominated by disturbances through accelerated processes like complement system and neuroinflammation signalling. In line, Nr4a2/Nurr1 has anti-inflammatory capacity [43], and its elevated expression is known to protect dopaminergic neurons in the midbrain [44]. On the other side of the gradient, in an interactome neighbourhood dominated by midlife adaptation failures, topologically important candidates like Nr1d1, also known as Rev-erb alpha, could play key roles in preventing SNCA-dependent disturbances under EE conditions. Interestingly, its role as a potential therapeutic target has already been suggested before, as improvement of Nr1d1 expression increases mitochondrial count and content and decreases autophagy flux [45–48]. Near the centre of the hypothesised gradient, genes like Bhlhe40 were suggested to protectively impact on both disturbances modes, agreeing with its role as an important transcriptional regulator in circadian processes [49] as well as its tight connection to immune cell function [50] and modulating capacities of neuronal excitability and synaptic plasticity in the hippocampus [51].

In summary, the evidence we present for a bimodal partitioning of transcriptomic disturbances that arise in an age-SNCA-dependent manner cautions the view that early stages of SNCA-related pathology solely reflect accelerated aging. Instead, disruption of healthy adaptation trajectories in midlife seemed to be a second disturbance principle contributing to pathogenic initiations. If further support for this hypothesis can be derived through integrating protein abundancies, physiological parameters, and behavioural phenotypes, candidate genes, their products, or epigenetic makeup could aid in serving as risk biomarkers for PD. In light of inter- and intraneuronal transmission principles that have been proposed as a potential mechanism for pathology spreading induced by overexpressed alpha-synuclein [52], it could be interesting to put our hippocampal observations into perspective with anatomically connected brain regions distant from the site of overexpression. In addition, the validated two-fold disturbance principle could inform efforts towards much-needed therapeutic means for PD to distinguish between accelerated cellular events and disrupted healthy adaptations. Regarding the former, underlying expression changes should probably be approached from a prevention point of view, while the latter might rather be amenable to restorative interventions. For either principle, the plasticity of the midlife transcriptome that we observed in response to environmental enrichment despite stable SNCA overload promotes the idea that activable cellular mechanisms have remained at this point and can still counteract initiations of SNCA-related pathogenesis.

Abbreviations

PD: Parkinson’s disease; WT: wildtype; TG: transgenic; SE: standard environment; EE: enriched environment; ST: chronic stress environment; DEG: Differentially expressed gene.

Author Contributions

JMSH and OR conceived and designed the research. TH analysed the RNA-seq data. ZW provided ideas for the data analysis. TH and JMSH wrote the paper. All authors read and approved the manuscript.

Acknowledgements

We thank the core facility c.ATG for preparing the libraries and sequencing the samples (www.c-atg.de). Furthermore, we thank Philipp Kahle and Nicolas Casadei for helpful discussions. We acknowledge support by the Deutsche Forschungsgemeinschaft and the Open Access Publishing Fund of the University of Tübingen. We acknowledge support by the Deutsche Forschungsgemeinschaft and the Open Access Publishing Fund of the University of Tübingen.

Conflicts of Interest

The authors declare that they have no competing interests.

Funding

This work was supported by the decipher PD transnational consortium on Epigenomics of Complex Diseases (BMBF grant number 01KU1503) (TH, JMSH) and the Interdisciplinary Center of Clinical Research Tübingen (IZKF, 2262-0-0) (TH, JMSH). JMSH was funded by a Margarete-von-Wrangell fellowship supported by the Ministry of Science, Research and the Arts Baden-Württemberg, and ZW by a scholarship of the German Academic Exchange Service (DAAD).

References

• 1. Spillantini MG, Schmidt ML, Lee VM, Trojanowski JQ, Jakes R, Goedert M. α-synuclein in Lewy bodies. Nature. 1997; 388:839–40. https://doi.org/10.1038/42166 [PubMed]
• 2. Chartier-Harlin MC, Kachergus J, Roumier C, Mouroux V, Douay X, Lincoln S, Levecque C, Larvor L, Andrieux J, Hulihan M, Waucquier N, Defebvre L, Amouyel P, et al. Alpha-synuclein locus duplication as a cause of familial Parkinson’s disease. Lancet. 2004; 364:1167–69. https://doi.org/10.1016/S0140-6736(04)17103-1 [PubMed]
• 3. Eriksen JL, Dawson TM, Dickson DW, Petrucelli L. Caught in the act: alpha-synuclein is the culprit in Parkinson’s disease. Neuron. 2003; 40:453–56. https://doi.org/10.1016/S0896-6273(03)00684-6 [PubMed]
• 4. Ross OA, Braithwaite AT, Skipper LM, Kachergus J, Hulihan MM, Middleton FA, Nishioka K, Fuchs J, Gasser T, Maraganore DM, Adler CH, Larvor L, Chartier-Harlin MC, et al. Genomic investigation of alpha-synuclein multiplication and parkinsonism. Ann Neurol. 2008; 63:743–50. https://doi.org/10.1002/ana.21380 [PubMed]
• 5. Singleton AB, Farrer M, Johnson J, Singleton A, Hague S, Kachergus J, Hulihan M, Peuralinna T, Dutra A, Nussbaum R, Lincoln S, Crawley A, Hanson M, et al. alpha-Synuclein locus triplication causes Parkinson’s disease. Science. 2003; 302:841. https://doi.org/10.1126/science.1090278 [PubMed]
• 6. Siderowf A, Lang AE. Premotor Parkinson’s disease: concepts and definitions. Mov Disord. 2012; 27:608–16. https://doi.org/10.1002/mds.24954 [PubMed]
• 7. Thacker EL, Chen H, Patel AV, McCullough ML, Calle EE, Thun MJ, Schwarzschild MA, Ascherio A. Recreational physical activity and risk of Parkinson’s disease. Mov Disord. 2008; 23:69–74. https://doi.org/10.1002/mds.21772 [PubMed]
• 8. Xu Q, Park Y, Huang X, Hollenbeck A, Blair A, Schatzkin A, Chen H. Physical activities and future risk of Parkinson disease. Neurology. 2010; 75:341–48. https://doi.org/10.1212/WNL.0b013e3181ea1597 [PubMed]
• 9. Andel R, Crowe M, Pedersen NL, Fratiglioni L, Johansson B, Gatz M. Physical exercise at midlife and risk of dementia three decades later: a population-based study of Swedish twins. J Gerontol A Biol Sci Med Sci. 2008; 63:62–66. https://doi.org/10.1093/gerona/63.1.62 [PubMed]
• 10. Geda YE, Roberts RO, Knopman DS, Christianson TJ, Pankratz VS, Ivnik RJ, Boeve BF, Tangalos EG, Petersen RC, Rocca WA. Physical exercise, aging, and mild cognitive impairment: a population-based study. Arch Neurol. 2010; 67:80–86. https://doi.org/10.1001/archneurol.2009.297 [PubMed]
• 11. Chaudhuri KR, Healy DG, Schapira AH, and National Institute for Clinical Excellence. Non-motor symptoms of Parkinson’s disease: diagnosis and management. Lancet Neurol. 2006; 5:235–45. https://doi.org/10.1016/S1474-4422(06)70373-8 [PubMed]
• 12. Leal SL, Yassa MA. Neurocognitive Aging and the Hippocampus across Species. Trends Neurosci. 2015; 38:800–12. https://doi.org/10.1016/j.tins.2015.10.003 [PubMed]
• 13. Kempermann G, Kuhn HG, Gage FH. More hippocampal neurons in adult mice living in an enriched environment. Nature. 1997; 386:493–95. https://doi.org/10.1038/386493a0 [PubMed]
• 14. de Magalhães JP, Curado J, Church GM. Meta-analysis of age-related gene expression profiles identifies common signatures of aging. Bioinformatics. 2009; 25:875–81. https://doi.org/10.1093/bioinformatics/btp073 [PubMed]
• 15. Hargis KE, Blalock EM. Transcriptional signatures of brain aging and Alzheimer’s disease: what are our rodent models telling us? Behav Brain Res. 2017; 322:311–28. https://doi.org/10.1016/j.bbr.2016.05.007 [PubMed]
• 16. Stilling RM, Benito E, Gertig M, Barth J, Capece V, Burkhardt S, Bonn S, Fischer A. De-regulation of gene expression and alternative splicing affects distinct cellular pathways in the aging hippocampus. Front Cell Neurosci. 2014; 8:373. https://doi.org/10.3389/fncel.2014.00373 [PubMed]
• 17. Wassouf Z, Hentrich T, Samer S, Rotermund C, Kahle PJ, Ehrlich I, Riess O, Casadei N, Schulze-Hentrich JM. Environmental Enrichment Prevents Transcriptional Disturbances Induced by Alpha-Synuclein Overexpression. Front Cell Neurosci. 2018; 12:112. https://doi.org/10.3389/fncel.2018.00112 [PubMed]
• 18. Paramanik V, Thakur MK. Role of CREB signaling in aging brain. Arch Ital Biol. 2013; 151:33–42. https://doi.org/10.4449/aib.v151i1.1461 [PubMed]
• 19. Wegner M. SOX after SOX: SOXession regulates neurogenesis. Genes Dev. 2011; 25:2423–28. https://doi.org/10.1101/gad.181487.111 [PubMed]
• 20. Zhao J, Xin X, Xie GX, Palmer PP, Huang YG. Molecular and cellular mechanisms of the age-dependency of opioid analgesia and tolerance. Mol Pain. 2012; 8:38. https://doi.org/10.1186/1744-8069-8-38 [PubMed]
• 21. Glaab E, Schneider R. Comparative pathway and network analysis of brain transcriptome changes during adult aging and in Parkinson’s disease. Neurobiol Dis. 2015; 74:1–13. https://doi.org/10.1016/j.nbd.2014.11.002 [PubMed]
• 22. Aramillo Irizar P, Schäuble S, Esser D, Groth M, Frahm C, Priebe S, Baumgart M, Hartmann N, Marthandan S, Menzel U, Müller J, Schmidt S, Ast V, et al. Transcriptomic alterations during ageing reflect the shift from cancer to degenerative diseases in the elderly. Nat Commun. 2018; 9:327. https://doi.org/10.1038/s41467-017-02395-2 [PubMed]
• 23. Collier TJ, Kanaan NM, Kordower JH. Ageing as a primary risk factor for Parkinson’s disease: evidence from studies of non-human primates. Nat Rev Neurosci. 2011; 12:359–66. https://doi.org/10.1038/nrn3039 [PubMed]
• 24. Ota M, Yasuno F, Ito H, Seki C, Nozaki S, Asada T, Suhara T. Age-related decline of dopamine synthesis in the living human brain measured by positron emission tomography with L-[beta-11C]DOPA. Life Sci. 2006; 79:730–36. https://doi.org/10.1016/j.lfs.2006.02.017 [PubMed]
• 25. Scatton B, Javoy-Agid F, Rouquier L, Dubois B, Agid Y. Reduction of cortical dopamine, noradrenaline, serotonin and their metabolites in Parkinson’s disease. Brain Res. 1983; 275:321–28. https://doi.org/10.1016/0006-8993(83)90993-9 [PubMed]
• 26. Frey KA, Koeppe RA, Kilbourn MR, Vander Borght TM, Albin RL, Gilman S, Kuhl DE. Presynaptic monoaminergic vesicles in Parkinson’s disease and normal aging. Ann Neurol. 1996; 40:873–84. https://doi.org/10.1002/ana.410400609 [PubMed]
• 27. Bender A, Krishnan KJ, Morris CM, Taylor GA, Reeve AK, Perry RH, Jaros E, Hersheson JS, Betts J, Klopstock T, Taylor RW, Turnbull DM. High levels of mitochondrial DNA deletions in substantia nigra neurons in aging and Parkinson disease. Nat Genet. 2006; 38:515–17. https://doi.org/10.1038/ng1769 [PubMed]
• 28. Collier TJ, Kanaan NM, Kordower JH. Aging and Parkinson’s disease: different sides of the same coin? Mov Disord. 2017; 32:983–90. https://doi.org/10.1002/mds.27037 [PubMed]
• 29. Pringsheim T, Jette N, Frolkis A, Steeves TD. The prevalence of Parkinson’s disease: a systematic review and meta-analysis. Mov Disord. 2014; 29:1583–90. https://doi.org/10.1002/mds.25945 [PubMed]
• 30. Kennedy BK, Berger SL, Brunet A, Campisi J, Cuervo AM, Epel ES, Franceschi C, Lithgow GJ, Morimoto RI, Pessin JE, Rando TA, Richardson A, Schadt EE, et al. Geroscience: linking aging to chronic disease. Cell. 2014; 159:709–13. https://doi.org/10.1016/j.cell.2014.10.039 [PubMed]
• 31. López-Otín C, Blasco MA, Partridge L, Serrano M, Kroemer G. The hallmarks of aging. Cell. 2013; 153:1194–217. https://doi.org/10.1016/j.cell.2013.05.039 [PubMed]
• 32. Vartiainen S, Aarnio V, Lakso M, Wong G. Increased lifespan in transgenic Caenorhabditis elegans overexpressing human alpha-synuclein. Exp Gerontol. 2006; 41:871–76. https://doi.org/10.1016/j.exger.2006.05.005 [PubMed]
• 33. Camandola S, Mattson MP. Brain metabolism in health, aging, and neurodegeneration. EMBO J. 2017; 36:1474–92. https://doi.org/10.15252/embj.201695810 [PubMed]
• 34. Hood S, Amir S. The aging clock: circadian rhythms and later life. J Clin Invest. 2017; 127:437–46. https://doi.org/10.1172/JCI90328 [PubMed]
• 35. Videnovic A, Golombek D. Circadian Dysregulation in Parkinson’s Disease. Neurobiol Sleep Circadian Rhythms. 2017; 2:53–58. https://doi.org/10.1016/j.nbscr.2016.11.001 [PubMed]
• 36. Hampp G, Ripperger JA, Houben T, Schmutz I, Blex C, Perreau-Lenz S, Brunk I, Spanagel R, Ahnert-Hilger G, Meijer JH, Albrecht U. Regulation of monoamine oxidase A by circadian-clock components implies clock influence on mood. Curr Biol. 2008; 18:678–83. https://doi.org/10.1016/j.cub.2008.04.012 [PubMed]
• 37. Lin Q, Ding H, Zheng Z, Gu Z, Ma J, Chen L, Chan P, Cai Y. Promoter methylation analysis of seven clock genes in Parkinson’s disease. Neurosci Lett. 2012; 507:147–50. https://doi.org/10.1016/j.neulet.2011.12.007 [PubMed]
• 38. Mao W, Zhao C, Ding H, Liang K, Xue J, Chan P, Cai Y. Pyrosequencing analysis of methylation levels of clock genes in leukocytes from Parkinson’s disease patients. Neurosci Lett. 2018; 668:115–19. https://doi.org/10.1016/j.neulet.2018.01.027 [PubMed]
• 39. Manella G, Asher G. The Circadian Nature of Mitochondrial Biology. Front Endocrinol (Lausanne). 2016; 7:162. https://doi.org/10.3389/fendo.2016.00162 [PubMed]
• 40. Abou-Sleiman PM, Muqit MM, Wood NW. Expanding insights of mitochondrial dysfunction in Parkinson’s disease. Nat Rev Neurosci. 2006; 7:207–19. https://doi.org/10.1038/nrn1868 [PubMed]
• 41. Büeler H. Impaired mitochondrial dynamics and function in the pathogenesis of Parkinson’s disease. Exp Neurol. 2009; 218:235–46. https://doi.org/10.1016/j.expneurol.2009.03.006 [PubMed]
• 42. Navarro A, Boveris A. Brain mitochondrial dysfunction in aging, neurodegeneration, and Parkinson’s disease. Front Aging Neurosci. 2010; 2. https://doi.org/10.3389/fnagi.2010.00034 [PubMed]
• 43. Saijo K, Winner B, Carson CT, Collier JG, Boyer L, Rosenfeld MG, Gage FH, Glass CK. A Nurr1/CoREST pathway in microglia and astrocytes protects dopaminergic neurons from inflammation-induced death. Cell. 2009; 137:47–59. https://doi.org/10.1016/j.cell.2009.01.038 [PubMed]
• 44. Decressac M, Volakakis N, Björklund A, Perlmann T. NURR1 in Parkinson disease--from pathogenesis to therapeutic potential. Nat Rev Neurol. 2013; 9:629–36. https://doi.org/10.1038/nrneurol.2013.209 [PubMed]
• 45. Solt LA, Wang Y, Banerjee S, Hughes T, Kojetin DJ, Lundasen T, Shin Y, Liu J, Cameron MD, Noel R, Yoo SH, Takahashi JS, Butler AA, et al. Regulation of circadian behaviour and metabolism by synthetic REV-ERB agonists. Nature. 2012; 485:62–68. https://doi.org/10.1038/nature11030 [PubMed]
• 46. Duez H, Staels B. Nuclear receptors linking circadian rhythms and cardiometabolic control. Arterioscler Thromb Vasc Biol. 2010; 30:1529–34. https://doi.org/10.1161/ATVBAHA.110.209098 [PubMed]
• 47. Bugge A, Feng D, Everett LJ, Briggs ER, Mullican SE, Wang F, Jager J, Lazar MA. Rev-erbα and Rev-erbβ coordinately protect the circadian clock and normal metabolic function. Genes Dev. 2012; 26:657–67. https://doi.org/10.1101/gad.186858.112 [PubMed]
• 48. Cho H, Zhao X, Hatori M, Yu RT, Barish GD, Lam MT, Chong LW, DiTacchio L, Atkins AR, Glass CK, Liddle C, Auwerx J, Downes M, et al. Regulation of circadian behaviour and metabolism by REV-ERB-α and REV-ERB-β. Nature. 2012; 485:123–27. https://doi.org/10.1038/nature11048 [PubMed]
• 49. St-Pierre B, Flock G, Zacksenhaus E, Egan SE. Stra13 homodimers repress transcription through class B E-box elements. J Biol Chem. 2002; 277:46544–51. https://doi.org/10.1074/jbc.M111652200 [PubMed]
• 50. Ow JR, Tan YH, Jin Y, Bahirvani AG, Taneja R. Stra13 and Sharp-1, the non-grouchy regulators of development and disease. Curr Top Dev Biol. 2014; 110:317–38. https://doi.org/10.1016/B978-0-12-405943-6.00009-9 [PubMed]
• 51. Hamilton KA, Wang Y, Raefsky SM, Berkowitz S, Spangler R, Suire CN, Camandola S, Lipsky RH, Mattson MP. Mice lacking the transcriptional regulator Bhlhe40 have enhanced neuronal excitability and impaired synaptic plasticity in the hippocampus. PLoS One. 2018; 13:e0196223. https://doi.org/10.1371/journal.pone.0196223 [PubMed]
• 52. Rusconi R, Ulusoy A, Aboutalebi H, Di Monte DA. Long-lasting pathological consequences of overexpression-induced α-synuclein spreading in the rat brain. Aging Cell. 2018; 17:e12727. https://doi.org/10.1111/acel.12727 [PubMed]
• 53. Andrews S. FastQC: a quality control tool for high throughput sequence data. 2010. http://www.bioinformatics.babraham.ac.uk/projects/fastqc.
• 54. Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, Batut P, Chaisson M, Gingeras TR. STAR: ultrafast universal RNA-seq aligner. Bioinformatics. 2013; 29:15–21. https://doi.org/10.1093/bioinformatics/bts635 [PubMed]
• 55. Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, Marth G, Abecasis G, Durbin R, and 1000 Genome Project Data Processing Subgroup. The Sequence Alignment/Map format and SAMtools. Bioinformatics. 2009; 25:2078–79. https://doi.org/10.1093/bioinformatics/btp352 [PubMed]
• 56. Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014; 15:550. https://doi.org/10.1186/s13059-014-0550-8 [PubMed]
• 57. Leek JT, Johnson WE, Parker HS, Jaffe AE, Storey JD. The sva package for removing batch effects and other unwanted variation in high-throughput experiments. Bioinformatics. 2012; 28:882–83. https://doi.org/10.1093/bioinformatics/bts034 [PubMed]
• 58. Maze I, Shen L, Zhang B, Garcia BA, Shao N, Mitchell A, Sun H, Akbarian S, Allis CD, Nestler EJ. Analytical tools and current challenges in the modern era of neuroepigenomics. Nat Neurosci. 2014; 17:1476–90. https://doi.org/10.1038/nn.3816 [PubMed]
• 59. Srinivasan K, Friedman BA, Larson JL, Lauffer BE, Goldstein LD, Appling LL, Borneo J, Poon C, Ho T, Cai F, Steiner P, van der Brug MP, Modrusan Z, et al. Untangling the brain’s neuroinflammatory and neurodegenerative transcriptional responses. Nat Commun. 2016; 7:11295. https://doi.org/10.1038/ncomms11295 [PubMed]
• 60. Patro R, Duggal G, Love MI, Irizarry RA, Kingsford C. Salmon provides fast and bias-aware quantification of transcript expression. Nat Methods. 2017; 14:417–19. https://doi.org/10.1038/nmeth.4197 [PubMed]
• 61. Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B, Ideker T. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003; 13:2498–504. https://doi.org/10.1101/gr.1239303 [PubMed]
• 62. Zeisel A, Muñoz-Manchado AB, Codeluppi S, Lönnerberg P, La Manno G, Juréus A, Marques S, Munguba H, He L, Betsholtz C, Rolny C, Castelo-Branco G, Hjerling-Leffler J, Linnarsson S. Brain structure. Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq. Science. 2015; 347:1138–42. https://doi.org/10.1126/science.aaa1934 [PubMed]