Ученые открыли генетическую программу старения мозга человека

In our lifetime, we go through many changes - physically and also intellectually. At certain ages, we are particularly vulnerable to develop psychiatric disorders, and the majority of mental conditions start to manifest in teenagers and young adults. The symptoms for schizophrenia, for example, a mental health disorder in which patients often experience hallucinations, delusion or changes in behavior, typically start in the mid-twenties.

Schizophrenia tends to run in families and it is likely that different combinations of faulty genes that affect the connections between nerve cells increase the chance of having the disease. Until now, scientists have assumed that certain situations and environmental factors trigger the condition, but it was unknown if genes could influence the age at which the disease will begin.

To explore whether genes in the brain change at certain time points, Skene et al. examined how the genes are turned on and off across the lifespan of healthy mice and humans. The results showed that in both mice and humans, a ‘genetic lifespan calendar’ controlled every cell type in the brain and directed the way they worked at different ages. The timing was so precise that it was possible tell the age of a mouse or a person simply by looking at the way the genes were expressed in a tissue sample.

Skene et al. then studied how the genetic lifespan calendar controlled the genes damaged in schizophrenia, and found that the calendar caused a major reorganization of the genes at the time when the symptoms started. This suggests that the genetic lifespan calendar is a crucial factor that can determine at what age the disease will start.

The next step will be to study how the genetic lifespan calendar programs changes throughout the brain and to explore if it could be manipulated to change how the brain ages. This could help to develop new types of treatments for schizophrenia and other conditions of the brain.

Identifying the genetic mechanisms that underpin brain ageing across the lifespan may provide explanations for the maturation of behaviours and age of onset of diseases. Longitudinal studies show cognition, emotion and personality emerge progressively during childhood and adolescence, with executive functions peaking in early adulthood (Craik and Bialystok, 2006; De Luca et al., 2003). This coincides with the onset of some of the most devastating psychiatric disorders, most of which arise during later stages of brain development in the teenage years and early twenties (Kessler et al., 2007). For example, impulse-control disorders arise in late childhood and early teen years, substance abuse peaks in the early twenties, and schizophrenia in the mid-twenties (with a delay of around two years in females) (Häfner et al., 1993). Some monogenic neurological disorders also have early adult onset, such as Inclusion Body Myopathy associated with Paget disease Frontotemporal Dementia (Watts et al., 2004) and rapid-onset dystonia Parkinsonism (Brashear et al., 2012).

Why some brain disorders with a strong genetic component have a late developmental onset is unknown. The prevailing hypothesis for schizophrenia proposes that an early (fetal) insult or mutation renders the brain vulnerable to a secondary environmental insult that occurs in young adults, which then triggers the onset of psychosis (Bayer et al., 1999). However, the finding that the age of onset for schizophrenia has a heritability estimated at 33% (Hare et al., 2010), suggests that the timing may have a genetic basis. In recent years, there has been major progress in understanding the genetic basis of schizophrenia with the identification of many mutations and variants contributing to disease susceptibility. It is widely accepted that many mutations directly impact on synapse proteins, particularly those involved with postsynaptic signalling mechanisms in excitatory synapses (Kirov et al., 2008; Pocklington et al., 2015; Fromer et al., 2014; Fernández et al., 2009a; Singh et al., 2017). The postsynaptic proteome of excitatory synapses is physically organised into multiprotein complexes of which the supercomplexes assembled by PSD95 (Husi et al., 2000; Frank et al., 2016a; Frank and Grant, 2017; Frank et al., 2017) play a major role in regulating cognitive functions (Migaud et al., 1998; Nithianantharajah et al., 2013) and are disrupted by schizophrenia mutations (Pocklington et al., 2015; Fromer et al., 2014; Fernández et al., 2009a; Singh et al., 2017; Kirov et al., 2012; Purcell et al., 2014). Together these observations suggest there may be genetic mechanisms that account for the convergence between the many schizophrenia susceptibility genes, the postsynaptic proteome and the young adult brain.

Transcriptome profiling of the brain at different ages has shown complex changes in expression levels across the lifespan (Colantuoni et al., 2011). In early onset brain disorders, such as intellectual disability and autism, gene expression levels in fetal and early postnatal development correlate with enrichment in autism susceptibility genes (Willsey et al., 2013; Parikshak et al., 2013). However, correlation based approaches (Willsey et al., 2013; Parikshak et al., 2013; Gulsuner et al., 2013) (e.g. weighted gene coexpression network analysis) are unable to account for the full complexity of transcriptional changes that occur with age. Furthermore, although there has been extensive characterisation of cellular and anatomical maturation in neuronal, glial and synaptic subtypes (Alexander and Goldman, 1978; Shaw et al., 2006; Gogtay et al., 2004, 2006; Zehr et al., 2006; Woo et al., 1997; Huttenlocher, 1979; Anderson et al., 1995; Bourgeois et al., 1994; Kalsbeek et al., 1988; Klingberg et al., 1999), the relevant transcriptome changes remain to be identified.

To further understand the transcriptional events underlying brain development and ageing, we developed new tools that identify age-dependent gene regulatory events. These methods detect when gene expression trajectories change direction or plateau: we refer to these events as Transcriptome Trajectory Turning Points (TTTPs) (Figure 1). The timing of TTTPs has been shown to be an important feature of transcriptome trajectories during maize embryo development and the yeast cell-cycle: in both systems, genes with linked biological functions were found to turn/plateau at similar time points (Lee et al., 2002; Spellman et al., 1998). We have characterised TTTPs in the neocortex of humans and hippocampus of mice across their respective postnatal lifespans. This revealed a previously unknown, species conserved, gene regulatory program. These methods were also used with single-cell transcriptomes to define the age-dependent sequence of changes in neuronal, glial and endothelial cell-types. Our data suggest that the late onset of some psychiatric and neurological disorders is timed by mutations in this genetically programmed developmental sequence. Our findings also indicate that misregulation in the molecular maturation of synapse proteomes during a critical window in young adults is important for the onset of schizophrenia. These methods and findings open new areas of investigation into the genetic regulation of brain age and highlight their importance in the adolescent and young adult.

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We first applied these methods to the Braincloud dataset, which measured mRNA expression levels from 269 prefrontal cortex samples across the human lifespan (14th gestational week to 78 years). Although TTTPs were identified across the lifespan, they were sharply concentrated during early adulthood. Summing the number at each age shows a striking peak and a mean of 26.0 years in males and 27.5 years in females (Wilcoxon signed rank test, p=0, Figure 2a, example trajectories with different turning points shown in Figure 2-figure supplement 1). Prominent peaks in early adulthood were confirmed with three regression methods (cubic splines with three degrees of freedom, four degrees of freedom and Loess regression) (Figure 2b). Removing X-chromosome genes from the analysis had no effect on this sex difference (p=0). To determine whether this TTTP-peak was human specific, we performed an equivalent analysis using transcriptome data from the hippocampus of 186 mice of both sexes between 58 and 600 days of age (Figure 2-figure supplement 2): this also revealed a peak in the frequency of TTTPs with a mean of 156 days for male and 165 days for female mice (p<1×10⁻³²³, Figure 2c). Although there is a lack of previous research on the age equivalence of early adulthood between rodents and humans, our results are concordant with estimations made using the TranslatingTime species comparison model which suggests that p156 (5 months) is equivalent to human early adulthood based on equivalent levels of cortical synaptic maturation (Pinto et al., 2015; Pinto et al., 2013; Workman et al., 2013). Plotting the cumulative distribution of TTTPs across the lifespan further illustrates the TTTP-peak in young adults (Figure 2d,e), and shows that 90% of TTTPs occur by 40 years of age, corresponding to the last stages of human brain development (De Luca et al., 2003; Lebel et al., 2012; Wood et al., 2004) and 7 months of age in mice. These data indicate that despite greatly differing lifespans, these two mammalian species share a lifespan program of brain gene expression with conserved features.

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To characterize the features of the human TTTPs in more detail, we focussed on those genes that showed the greatest changes. As shown in Figure 2f, the TTTPs for genes with the greatest expression changes prior to the TTTP were concentrated around the late-twenties, reinforcing the earlier finding that this is a significant period for switching in the trajectories. Next, we separated genes into those with upward or downward trajectories prior to the turning point: overall there were similar numbers in each category, although there was a skew toward upward inflecting genes being more common in young subjects (<25 years) and downward inflecting genes more common in older subjects (>40 years) (Figure 2g). Finally, we examined the direction of the trajectories after the TTTP by dividing genes into those that established a stable plateau (Post-Turn Plateau) or reversed their direction (Post-Turn Reversal) (Figure 2h). The vast majority of probes had plateaued by 30 years of age. While the exact ages at which TTTPs occur is sensitive to both the regression method and the dataset used, it is clear that the TTTP-peak reveals a major molecular reorganisation in young adults towards the end of development. Together these findings show that young adulthood is a crucial time for switching brain gene expression and establishing the set points of most genes for later life.

Even though there are major changes during brain maturation in young adults, the complex trajectories were found throughout the lifespan, suggesting they could be used to predict the biological age of the brain. To test this, we used radial basis support vector machines and demonstrated that classifiers trained on partitioned subsets of the gene expression data (training sets) predicted age in the test sets with an accuracy (defined as mean /Age_Actual−Age_Predicted/) in humans of 5.5 years and 28 days in mice. Remarkably, they showed accurate age predictions across the entire range of ages in both species (human, R² = 0.88, mice, R² = 0.94) (Figure 2i,j) using only 40 probes in humans and 100 in mice (Figure 2-figure supplement 3). Thus, TTTPs and trajectories are highly characteristic features defining brain age across the lifespan in mice and humans. This indicates a ‘genetic lifespan calendar’ of transcriptome events is a conserved feature of mammals.

To ascertain the biological processes affected by the TTTPs, we first sought to identify the cell types affected at each age. We asked if the TTTPs were enriched in the transcriptome of specific cell types using brain single-cell RNA-seq data and the Expression Weighted Cell-type Enrichment (EWCE) method (Skene and Grant, 2016). TTTPs were binned into approximately equal sized age-groups (similar to the DeGeT method) and then tested for cellular enrichments (Figure 3a). We assumed that different biological processes could be associated with up/down-regulated genes and so performed enrichment analyses for each direction separately. To ensure the findings were robust, the analysis was performed on both the Braincloud (Colantuoni et al., 2011) dataset and an independent human prefrontal cortex transcriptome dataset (Somel et al., 2009) (see Materials and methods). Significant enrichments were found for each cell-type tested and these were strongly correlated between the two datasets (Figure 3a, median correlation of enrichments for each cell-type and direction with at least one significant change between the two datasets was 0.37). The majority of significant enrichments were found amongst the Upward (Figure 1a) gene sets relative to the Downward trajectory gene sets.

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A striking sequence of events was observed where each cell type was regulated within distinct age-windows (Figure 3a and summarised in Figure 7). Early postnatal life was marked by TTTPs in endothelial cells (pbraincloudBonferroni'>pBonferronibraincloud$p_{braincloud}^{Bonferroni}$=0.025, psomelBonferonni'>pBonferonnisomel$p_{somel}^{Bonferonni}$<0.00001 where ‘ Bonferroni’  indicates a Bonferroni-adjusted p-value) and oligodendrocytes (psomelBonferroni'>pBonferronisomel$p_{somel}^{Bonferroni}$=0.015); followed by microglial genes (pbraincloudBonferroni'>pBonferronibraincloud$p_{braincloud}^{Bonferroni}$<0.00001, psomelBonferroni'>pBonferronisomel$p_{somel}^{Bonferroni}$<0.00001) throughout adolescence; then pyramidal neuron genes (pbraincloudBonferroni'>pBonferronibraincloud$p_{braincloud}^{Bonferroni}$=0.0216, psomelBonferroni'>pBonferronisomel$p_{somel}^{Bonferroni}$<0.00001) in early adulthood (corresponding to the peak in turning points); then interneuron (psomelBonferroni'>pBonferronisomel$p_{somel}^{Bonferroni}$<0.00001) and oligodendrocyte genes through the late twenties and thirties (pbraincloudBonferroni'>pBonferronibraincloud$p_{braincloud}^{Bonferroni}$<0.00001, psomelBonferroni'>pBonferronisomel$p_{somel}^{Bonferroni}$<0.00001). Astrocytes were found to have two periods of enrichment, the first during early adulthood (psomelBonferroni'>pBonferronisomel$p_{somel}^{Bonferroni}$=0.0028) and a second very late in life (pbraincloudBonferroni'>pBonferronibraincloud$p_{braincloud}^{Bonferroni}$=0.00675, psomelBonferroni'>pBonferronisomel$p_{somel}^{Bonferroni}$<0.00001). These enrichments reveal the sequential maturation of cellular processes in the brain across the human lifespan. Moreover, most of these changes occurred prior to 35 years of age with prominent neuronal changes in young adults.

We also performed this analysis on the mouse hippocampal dataset (Figure 3b). As in the human neocortex datasets we found an early adulthood enrichment for upward-turns in pyramidal neuron genes (pmouseBonferroni'>pBonferronimouse$p_{mouse}^{Bonferroni}$=0.008), followed by a later enrichment for downward-turns in genes associated with both pyramidal and interneurons (pmouseBonferroni'>pBonferronimouse$p_{mouse}^{Bonferroni}$=0.006 and, pmouseBonferroni'>pBonferronimouse$p_{mouse}^{Bonferroni}$=0.0086 respectively). Unlike in the human datasets, upward-turns in interneuron genes were found to be enriched at the same ages as those in pyramidal neurons (pmouseBonferroni'>pBonferronimouse$p_{mouse}^{Bonferroni}$=0.025). This may be because the earliest samples in the mouse ageing dataset were 56 days whereas the human datasets included fetal samples: correspondingly the early life enrichments for upward-turns in microglia and endothelial cells were not seen in mice. A later enrichment for upward-turns was seen in endothelial cells in mice (pmouseBonferroni'>pBonferronimouse$p_{mouse}^{Bonferroni}$=0.048). These findings show that the enrichment of neuronal genes in the early adult peak is conserved across species and brain regions.

The young adult peak in TTTPs is a prominent landmark and we next sought to identify the key molecular mechanisms involved. We used ALiGeT to identify the top 10% of genes in both human and mouse in the year in which the largest number of TTTPs occurred (and refer to this as the Peak Gene Turning (PeGeT) score) (Supplementary files 1a and 1b). The relevant biological processes in the genes with high PeGeT scores, conserved between mouse and human (Supplementary file 1c), were first examined for enrichment in Gene Ontology terms: this revealed their role in  synaptic transmission  (pFDR−adj'>pFDR−adj$p^{FDR-adj}$=0.00024 where ‘FDR-adj’ indicates the p-value was adjusted for False Discovery Rate using the Benjamini-Hochberg method). Mammalian Phenotype Ontology annotations indicated their roles in  behavioural  (p=7.5×10⁻⁰⁵) and  nervous system  (p=0.0002) phenotypes. To probe the synaptic role in more detail, we examined genes coding for proteins in the human postsynaptic density (hPSD) (Bayés et al., 2011) and the postsynaptic PSD95 supercomplexes (Husi et al., 2000; Frank et al., 2017; Fernández et al., 2009b; Frank et al., 2016b), which are crucial in controlling synaptic plasticity and behaviour: both sets showed significantly higher PeGeT scores than expected by chance (p<0.0009 and p=0.0019 respectively, Figure 4a,b, Supplementary file 1d).

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To establish the age window when synaptic genes showed TTTPs, we used a bootstrapping approach to test whether synaptic genes have higher ALiGeT scores in particular years in the Braincloud dataset. Each year between 22 and 33 years of age showed a significant increase in synapse-associated ALiGeT scores and we refer to this as the ALiGeT_hPSD window. A replication study (Somel) using a smaller human transcriptome dataset (Somel et al., 2009) gave an overlapping estimate for the synaptic ALiGeT_hPSD window at 17-22 years (Figure 4c). We next applied the DeGeT method and found all five consecutive age sets from 24 to 36 years were significantly enriched in hPSD genes (Figure 4d). This result was confirmed in two independent human frontal cortex datasets (Somel et al., 2009; Kang et al., 2011) (see Figure 4-figure supplement 1). Similarly, we identified the ALiGeT_mPSD window in mice at 126 to 151 days (Figure 4e). These data show that transcripts encoding postsynaptic proteins were significantly enriched in TTTPs during early adulthood in all four datasets tested, spanning two species and two brain regions.

Since these transcriptome results suggest that specific changes occur in the composition of components of the synapse proteome, we performed relative quantitation by mass spectrometry on 38 forebrain synaptosome samples between 1-5 months in mice. A total of 900 proteins were detected, of which 99 were found to show significant changes with age (Supplementary file 1e, pFDR−adj'>pFDR−adj$p^{FDR-adj}$<0.005). We explored which functional classes of synapse proteins were affected by ageing during this period, and found that ion channel proteins and receptors showed increased likelihood of being differentially expressed (pFDR−adj'>pFDR−adj$p^{FDR-adj}$=0.00034, expected = 7, actual = 17, Figure 4f). Amongst the affected channels were the majority of Ca²⁺- and Na⁺/K⁺-ATPases detected in our dataset, including three of the subunits of Atp2b (often referred to as PMCA). This finding recapitulates and extends the previous reports that aged rodents have decreased Ca²⁺- and Na⁺/K⁺- ATPase activity (Zaidi et al., 1998; Tanaka and Ando, 1990). These proteomic findings support the transcriptome findings that changes in synapse proteome composition occurs within the young adult age window.

Prompted by previous results showing that postsynaptic proteins have been linked to the genetic susceptibility of schizophrenia (Fromer et al., 2014; Fernández et al., 2009a; Nithianantharajah et al., 2013; Kirov et al., 2012; Purcell et al., 2014), we hypothesized that TTTPs may be relevant to the age of onset of schizophrenia. For these analyses we examined multiple sets of publically available genetic data including de novo and GWAS data (see Materials and methods ‘Genes Lists’, Supplementary file 1f) and applied multiple analytical approaches.

As a first step, we applied the same bootstrapping approach used earlier for the PSD genes on the pooled set of genes which have been associated with Schizophrenia using either GWAS or de novo approaches. Strikingly, this analysis showed the TTTPs in susceptibility genes predicted the known age windows for the onset of schizophrenia (Figure 5). The ALiGeT_scz enrichment window spanned 22-26 years (Figure 5a) corresponding to the clinically reported age of onset defined by the first episode of psychotic symptoms and window of maximum vulnerability (Kessler et al., 2007; Häfner et al., 1993). Since males are reported to have an earlier disease onset than females (Kessler et al., 2007), we tested males and females separately and found the ALiGeT_sczenrichment window was significantly earlier in males than females (males 20-26 years, females 26-28 years; wilcoxon p=4.7×10⁻¹⁴, Figure 5b).

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To validate these results, we performed a series of technical control studies and biological replications. First, we used the DeGeT scoring system that showed the enrichment in schizophrenia peaked at 24-26 years (Figure 5c). Secondly, we performed the DeGeT enrichment test in the two independent human frontal cortex datasets (Somel et al., 2009; Kang et al., 2011) and confirmed that schizophrenia was significantly enriched (confirming the Braincloud result) (Figure 5-figure supplement 1). Thirdly, to confirm the results were not specific to a particular spline regression method, we demonstrate that enrichment window for schizophrenia was found using two alternate approaches to model fitting (Figure 5-figure supplement 2). While validating the occurrence of the disease enrichments, this analysis also revealed that the exact age at which the turning points/windows of enrichments occur depends on the regression model used (Figure 5-figure supplement 2). Fourth, we performed a down-sampling based sensitivity analysis to determine how the size of the gene set influences the enrichment: this indicated that the DeGeT enrichments are stronger for schizophrenia than for the hPSD (using subsets of 650 genes, 95% of schizophrenia subsets were significant at 24-25 years but only 50% of hPSD subsets, Figure 5-figure supplement 3). Fifth, we performed a variation of the bootstrapping analysis which accounts for transcript length and GC content (both of which are known to affect the rate of de novo mutations) and found no effect on the significance of the results (Figure 5-figure supplement 4). Sixth, we confirmed that varying the parameter in the ALiGeT scoring function, which controls the rate of decay with temporal distance, did not adversely affect the results (Figure 5-figure supplement 5). Finally, we dropped all fetal samples from the analysis, recalculated the TTTPs and confirmed that the major peak of TTTPs occurs in the early twenties, that it is delayed in females, that the later DEGET windows are enriched for PSD genes, and that schizophrenia shows discrete and significant enrichments using DEGET (Figure 5-figure supplement 6).

To further validate our findings, we next examined different genetic datasets that have been used to identify schizophrenia susceptibility genes. The ALiGeT_scz analysis results described above used a combined schizophrenia gene set from three orthogonal genome-wide methods: (1) an integrative analysis of Genome Wide Association Studies (GWAS), expression analysis, copy number variants (CNV) and mouse models (Ayalew et al., 2012); (2) combined results of three exome-sequencing studies (Fromer et al., 2014; Xu et al., 2012; Girard et al., 2011), and (3) the most recent GWAS results from the Schizophrenia Working Group of the Psychiatric Disease Consortium (Schizophrenia Working Group of the Psychiatric Genomics Consortium, 2014) (Supplementary file 1f). We therefore separately tested sets of susceptibility genes discovered with all three methods: all showed significantly increased PeGeT scores (Integrative Analysis, pFDR−adj'>pFDR−adj$p^{FDR-adj}$<0.0009, de novo, pFDR−adj'>pFDR−adj$p^{FDR-adj}$=0.0057, GWAS, pFDR−adj'>pFDR−adj$p^{FDR-adj}$=0.027, Figure 5d-g). Interestingly, the stronger enrichment seen with de novo mutations may reflect that GWAS detects common variants that are assumed to have lower penetrance (Schizophrenia Working Group of the Psychiatric Genomics Consortium, 2014). We also validated the results using an additional set of de novo mutations (Gulsuner et al., 2013): this also confirmed the adult enrichments, either when analysed independently or when combined with the combined sets described above (Figure 5-figure supplement 7).

Much of the heritability for schizophrenia is associated with SNPs that have not reached genome-wide significance with current sample sizes (Loh et al., 2015) (and were not included in our analysis thus far) and the sample sizes for de novo studies are too small to determine whether any genes found are significantly associated with disease status. We therefore adapted our methods to include a greater fraction of the SNPs associated with schizophrenia heritability by using association statistics from all SNPs regardless of whether they are genome wide significant (as defined in GWAS summary statistics files) and explicitly modelling linkage disequilibrium (based on results of the 1000 genomes project), such that disease association scores can be ascertained for each gene. The ALiGeT and DEGET approaches were extended to directly utilise the gene association scores generated by MAGMA (Multi-marker Analysis of GenoMic Annotation) (de Leeuw et al., 2015) based on schizophrenia GWAS summary statistics. Using this approach, schizophrenia showed a DEGET enrichment at 26 years in the Braincloud dataset (pBonferroni'>pBonferroni$p^{Bonferroni}$=0.03135, Figure 5h) and at 15-16 years in the Somel dataset (pBonferroni'>pBonferroni$p^{Bonferroni}$=0.0115, Figure 5-figure supplement 8a) and corresponding enrichment windows were found using ALiGeT (Figure 5i, Figure 5-figure supplement 8b).

Finally, we performed two additional sets of analyses where we examined mouse proteome datasets and single-cell transcriptome data. Consistent with the transcriptome results, the mouse synapse proteomic dataset showed that schizophrenia-associated proteins were enriched in the synapse proteins that changed prior to the 5 month peak (p=0.018, expected = 6, actual = 11), and 91% of those that change were found to be down-regulated. Next, we examined the role of specific cell types in schizophrenia by restricting the ALiGeT analysis to the subsets of genes defined by single cell transcriptome data (see Materials and methods) (Zeisel et al., 2015). Schizophrenia associated genes showed a significant (pBonferroni'>pBonferroni$p^{Bonferroni}$<0.05) window of enrichment in neuronal genes (Figure 6a, Figure 6-figure supplement 1). We were however unable to confirm this result using the summary statistics based method (Figure 6b). We then examined the types of trajectories associated with schizophrenia and found genes were predominantly down-regulated prior to the TTTP (Figure 6c). This result was confirmed using the summary statistics based method (Figure 6d).

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Together these analyses show robust replication across multiple datasets, including different types of genetic variants, transcriptomes and proteomes, and several complementary analytical approaches. These data strongly suggest that the window of onset and sex difference in schizophrenia is timed by the regulation of susceptibility genes in young adults.

In the mouse synapse proteome dataset, we noticed several proteins that cause adult onset monogenic disorders. We used the Human Phenotype Ontology age-of-onset annotations to identify these neurological disorders (excluding neoplasms, and peripheral/autonomic disorders) and from a total of 39 genes, six were detected in our samples, and we confirmed these were significantly enriched amongst the synapse proteins whose expression levels changed during maturation (p=0.00008, expected = 1, actual = 4, Figure 6-figure supplement 2, Supplementary file 1e). The affected proteins/disorders included Atp2a2 (Darier’s disease), Eef2 and Itpr1 (Spinocerebellar ataxia), Atp1a3 (Dystonia Parkinsonism). No equivalent enrichment was found for congenital/neonatal onset disorders (p=0.45, expected = 4, actual = 4). Further inspection (because HPO annotations were incomplete) found three additional, adult-onset haploinsufficiency disorders encoding by genes showing a 50-80% reduction in the maturing mouse synapse proteome (Figure 6-figure supplement 2): Vcp (Frontotemporal Dementia) (fold change, FC = 0.48; p=0.002); Atl1 (hereditary spastic paraplegia) (FC = 0.19, p=0.00004); Dmxl2, (Polyendocrine-polyneuropathy syndrome) (fold change: 0.28, p=0.00004). These age-dependent reductions in levels of haploinsufficient disease genes is consistent with the model that their respective lifespan trajectories are relevant to their age of onset.

The possibility exists that the lifespan trajectories are also relevant to the age of onset of other polygenic diseases with onset at different ages. To address this we compiled gene lists for six other major brain disorders with different age-windows of onset: onset during infancy (autism and intellectual disabilities); early adulthood (multiple sclerosis); and late adulthood (Amyotrophic Lateral Sclerosis, Parkinson’s and Alzheimer’s) (corresponding gene sets shown in Supplementary file 1f). We applied the same bootstrapping approach used earlier for the PSD genes and none of the disorders showed significant results (Figure 6-figure supplement 3). Because these gene lists are not comparable (different size, population sample size, obtained using different technical approaches etc.) the relative importance of the genetic calendar to schizophrenia cannot be directly compared with these disorders (see Discussion). In addition, because the transcriptome data is from prefrontal cortex and the primary pathology of several of these diseases is in other parts of the nervous system it cannot be assumed that the transcriptome trajectories in one part of the brain are the same as in others. Hence, the lack of any detectable age window does not preclude a role for gene regulation in the onset of these diseases.