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. 2013 Apr;34(4):1199-209.
doi: 10.1016/j.neurobiolaging.2012.10.021. Epub 2012 Nov 21.

Age-associated changes in gene expression in human brain and isolated neurons

Azad Kumar  1 , J Raphael Gibbs, Alexandra Beilina, Allissa Dillman, Ravindran Kumaran, Daniah Trabzuni, Mina Ryten, Robert Walker, Colin Smith, Bryan J Traynor, John Hardy, Andrew B Singleton, Mark R Cookson
Affiliations

Age-associated changes in gene expression in human brain and isolated neurons

Azad Kumar et al. Neurobiol Aging. 2013 Apr.

Abstract

Previous studies have suggested that there are genes whose expression levels are associated with chronological age. However, which genes show consistent age association across studies, and which are specific to a given organism or tissue remains unresolved. Here, we reassessed this question using 2 independently ascertained series of human brain samples from 2 anatomic regions, the frontal lobe of the cerebral cortex and cerebellum. Using microarrays to estimate gene expression, we found 60 associations between expression and chronological age that were statistically significant and were replicated in both series in at least 1 tissue. There were a greater number of significant associations in the frontal cortex compared with the cerebellum. We then repeated the analysis in a subset of samples using laser capture microdissection to isolate Purkinje neurons from the cerebellum. We were able to replicate 5 gene associations from either frontal cortex or cerebellum in the Purkinje cell dataset, suggesting that there is a subset of genes which have robust changes with aging. Of these, the most consistent and strongest association was with expression of RHBDL3, a rhomboid protease family member. We confirmed several hits using an independent technique (quantitative reverse transcriptase polymerase chain reaction) and in an independent published sample series that used a different array platform. We also interrogated larger patterns of age-related gene expression using weighted gene correlation network analysis. We found several modules that showed significant associations with chronological age and, of these, several that showed negative associations were enriched for genes encoding components of mitochondria. Overall, our results show that there is a distinct and reproducible gene signature for aging in the human brain.

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Figures

Figure 1
Figure 1
Consistency of probes that show association with chronological age across the two datasets in frontal cortex (left) and cerebellum (right). Each point shows the estimated effect size for the association between age and expression of a given probe that was detected in both the discovery (x axis) and replication (y axis) datasets. Points are colored from blue to black by FDR adjusted P value in the discovery datasets and sized by FDR adjusted P value in the replication datasets and thus larger, darker points are those that are more highly significant in both datasets. There is an overall correlation in each dataset between effect sizes that is highly significant (P<2×10−16; see text for details). The red horizontal and vertical lines indicate an effect size of 0 in each dataset and the orange lines indicate the range of effect sizes more than 0.115, which is the least significant effect size in the discovery dataset for frontal cortex. The majority of the probes that were significant outside this threshold were congruent in discovery and replication datasets.
Figure 2
Figure 2
Replicated age associations. Each plot shows the association between age (x axes) and residuals of expression (y axes) for discovery (red) and replication (blue) datasets. Genes that were significant and replicated in either the Frontal Cortex (left hand panels) or the Cerebellum (center panels) were also tested in laser-captured Purkinje cells from a subset of the discovery dataset (right panels). These included the genes RHBDL3 (A), NR3C2 (B), GPX3 (C), VPS18 (D) and SGSH (E). Values of R2 and associated P values are given in table 1.
Figure 3
Figure 3
Technical replication of age-associations by qRT-PCR. (A–C) We performed quantitative reverse-transcriptase PCR (qRT-pCR) for RHDBL3 (A), SGSH (F) and NR3C2 (C) and plotted the residuals of expression after correction for co-variates on the y-axes against age on the x-axes. In each panel, the red lines show a linear regression and the pale red line shows the 95% confidence interval of the fit to the line.(D) Comparing all three genes tested here for their association (estimated as R) between age and residuals of expression for each of the genes usgin qRT-PCR (x axis) or in the array study (y axis). Error bars in both directions indicate the 95% confidence interval for effect size estimate with each technique.
Figure 4
Figure 4
Validation in an independent sample series. (A,B) Comparison of estimated effect sizes for the relationships between age and gene expression. Each point shows the estimated effect size for the relationship between age and expression of a detected gene in the work of Colantuoni et al, 2011 (y axes) compared to the frontal cortex data from this work (x axes) in the discovery (A) or replication (B) datasets. Data points are colored by −log of FDR adjusted P values in the Colantuoni dataset and sized by −log of FDR adjusted P values in our datasets and thus larger, darker datapoints represent more highly significant associations. (C–F) Normalized, covariate adjusted expression of age associated genes were plotted for each individual sample in each of three datasets; the discovery (red) and replication (blue) datasets from this study and the data from Colantuoni et al (green). These included RHDBL3 (C), NR3C2 (D), VPS18 (E) and SGSH (F) (see table 1), all of which showed significant age-associations in all tested datasets. GPX3 (shown in figure 2) was not included, as a probe for this gene was not annotated in the Colantuoni et al dataset.
Figure 5
Figure 5
Weighted gene correlation network analysis. (A) Dendrogram for consensus modules across five tested datasets (discovery and replication datasets in Frontal Cortex and Cerebellum, plus LCM enriched purkinje cells). Each line is a single gene where height (y-axis) indicates dissimilarity. Branches of the dendrogram with similar expression patterns are captured into modules identified with different colors as indicated below the dendrogram. Grey indicates genes that were not assigned to any specific module. (B) For each module (numbered on the left hand side of the table where colors match those in panel A), module membership for individual genes was correlated with gene significance for each of the covariates of post mortem interval (PMI), Gender, Age, principal component 1 (PC1) and 2 (PC2) of the genetic analysis and hybridization batch as indicated along the bottom. Each value shows the correlation coefficient (with P values in parentheses below each), color-coded as shown on the scale on the right of the plot. NA; Not applicable.
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