Regional differences in gene expression and promoter usage in aged human brains

Abstract

To characterize the promoterome of caudate and putamen regions (striatum), frontal and temporal cortices, and hippocampi from aged human brains, we used high-throughput cap analysis of gene expression to profile the transcription start sites and to quantify the differences in gene expression across the 5 brain regions. We also analyzed the extent to which methylation influenced the observed expression profiles. We sequenced more than 71 million cap analysis of gene expression tags corresponding to 70,202 promoter regions and 16,888 genes. More than 7000 transcripts were differentially expressed, mainly because of differential alternative promoter usage. Unexpectedly, 7% of differentially expressed genes were neurodevelopmental transcription factors. Functional pathway analysis on the differentially expressed genes revealed an overrepresentation of several signaling pathways (e.g., fibroblast growth factor and wnt signaling) in hippocampus and striatum. We also found that although 73% of methylation signals mapped within genes, the influence of methylation on the expression profile was small. Our study underscores alternative promoter usage as an important mechanism for determining the regional differences in gene expression at old age.

Insights into the Transcriptional Architecture of Behavioral Plasticity in the Honey Bee Apis mellifera

“…While they can arise from distinct promoter regions clearly separated by long stretches of sequence, alternative TSSs can also occur close to each other within the same promoter region; even subtle alterations in a gene’s TSS have been associated with changes in the expression of downstream genes in Drosophila melanogaster 1920 and mammals21. CAGE-based techniques have already made valuable contributions to our understanding of transcription in model organisms such as, the fruit fly1920, zebrafish22, and human and mouse152324, and specifically in the nervous system, where alternative TSSs appear to play a role in establishing developmental25 and region-specific26 gene expression patterns. However, the potential relevance of alternative TSSs in organizing behavior has, to our knowledge, not been addressed in any organism.…”

recount-brain: a curated repository of human brain RNA-seq datasets metadata

“…We present recount-brain , a freely available human brain sample metadata database that pairs with the uniformly-processed RNA-seq data from recount2 enabling researchers to study transcriptomic changes in neurological disease. Our metadata database and reproducible curation approach shows that freely-available data can be cleaned and curated to encourage and facilitate data reuse and increase the value of small studies, such as SRP017933 (Pardo et al, 2013) , and large studies, such as GTEx and TCGA (Hutter and Zenklusen, 2018) , alike.…”

“…Ferreira et al investigated the impact of post-mortem interval (PMI) on gene expression using data from multiple tissues of post-mortem healthy donors obtained from the GTEx project (Ferreira et al, 2018) . To exemplify how recount-brain can be used to replicate findings, we identified 10 SRA studies with variable PMI data for a total of 252 samples from 9 publications (Boudreau et al, 2014;Dumitriu et al, 2016;Khrameeva et al, 2014;Labadorf et al, 2015;Magistri et al, 2015;Pardo et al, 2013;Voineagu et al, 2011;Wu et al, 2012) . In their analysis of GTEx and PMI data, Ferreira et al identified genes with significant temporal changes across tissues (Ferreira et al, 2018) .…”

“…These sets could not be explained by disease status or PMI ( Figures 14 and 15 from Supplementary File 2 ). The two sets of studies are set 1: SRP017933, SRP032539, SRP032540, SRP048683, SRP056604 (Boudreau et al, 2014;Li et al, 2014;Magistri et al, 2015;Pardo et al, 2013) ; set 2: ERP001304, SRP051844, SRP007483, SRP058181, SRP019762 (Dumitriu et al, 2016;Khrameeva et al, 2014;Labadorf et al, 2015;Voineagu et al, 2011;Wu et al, 2012) . Once we considered the study set membership, we found no significant association between PMI and mitochondrial transcription as well as no significant difference in the change in mitochondrial transcription by a unit increase in PMI (the interaction effect): only a significant difference for the intercept ( Figure 4 C ).…”

“…Using recount-brain-v1 we identified 252 samples from 10 SRA studies described in nine publications (Boudreau et al, 2014;Dumitriu et al, 2016;Khrameeva et al, 2014;Labadorf et al, 2015;Li et al, 2014;Magistri et al, 2015;Pardo et al, 2013;Voineagu et al, 2011;Wu et al, 2012) with post-mortem interval (PMI) information that have variable measurements so that we could replicate some of the analyses performed by Ferreira et al (Ferreira et al, 2018) .…”