Common gene expression signatures in Parkinson’s disease are driven by changes in cell composition

Abstract

<jats:title>Abstract</jats:title><jats:sec><jats:title>Background</jats:title><jats:p>The etiology of Parkinson’s disease (PD) is largely unknown. Genome-wide transcriptomic studies in bulk brain tissue have identified several molecular signatures associated with the disease. While these studies have the potential to shed light into the pathogenesis of PD, they are also limited by two major confounders: RNA post mortem degradation and heterogeneous cell type composition of bulk tissue samples. We performed RNA sequencing following ribosomal RNA depletion in the prefrontal cortex of 49 individuals from two independent case-control cohorts. Using cell-type specific markers, we estimated the cell-type composition for each sample and included this in our analysis models to compensate for the variation in cell-type proportions.</jats:p></jats:sec><jats:sec><jats:title>Results</jats:title><jats:p>Ribosomal RNA depletion results in substantially more even transcript coverage, compared to poly(A) capture, in post mortem tissue. Moreover, we show that cell-type composition is a major confounder of differential gene expression analysis in the PD brain. Correcting for cell-type proportions attenuates numerous transcriptomic signatures that have been previously associated with PD, including vesicle trafficking, synaptic transmission, immune and mitochondrial function. Conversely, pathways related to endoplasmic reticulum, lipid oxidation and unfolded protein response are strengthened and surface as the top differential gene expression signatures in the PD prefrontal cortex.</jats:p></jats:sec><jats:sec><jats:title>Conclusions</jats:title><jats:p>Differential gene expression signatures in PD bulk brain tissue are significantly confounded by underlying differences in cell-type composition. Modeling cell-type heterogeneity is crucial in order to unveil transcriptomic signatures that represent regulatory changes in the PD brain and are, therefore, more likely to be associated with underlying disease mechanisms.</jats:p></jats:sec>

Reduced ribosomal DNA transcription in the prefrontal cortex of suicide victims: consistence of new molecular RT-qPCR findings with previous morphometric data from AgNOR-stained pyramidal neurons

“…Other variables which may confound present results, among them postmortem interval, did not influence our current results. The observed accentuated intra-group differences in rDNA expression profiles in bulk cortical tissue may be a consequence of differences in cellular composition related to the variation in gray/white matter ratios in the extracted tissue samples and/or inter-subject variability [44]. Our method does not allow to explain these differences.…”

Altered transcriptome-proteome coupling indicates aberrant proteostasis in Parkinson’s disease

“…This leads to a steady state in which the transcript resides in the soma, whereas most of the protein is either under transport in the axon or at the synapsis [38]. In these cases, since brain tissue samples vary in relative grey/white matter content and therefore also in relative soma/axonal content [39], readouts of transcript and protein levels will be anticorrelated across-samples. Specifically, samples enriched in somas will show a high relative transcript/protein ratio, whereas samples enriched in axons will show a low relative transcript/protein ratio.…”

“…This would decrease the spatial separation between transcript and protein, thereby blunting the negative correlation across samples. Second, the PD brain, including the prefrontal cortex, is characterized by neuronal and synaptic loss and a relative increase in glial populations [39]. It is therefore conceivable that, if the anticorrelation signal originates from neurons, it may be diluted as a result of these changes in cellular composition.…”

Genome-wide dysregulation of histone acetylation in the Parkinson’s disease brain

“…To address this question, we analyzed RNA-seq data from prefrontal cortex of the same individuals included in the ChIP-seq analysis ( Supplementary Table 1). A detailed description of the RNA-seq data analysis was recently published 40 . For each gene, we calculated Pearson's correlation between H3K27ac state (ChIP-seq) and transcript levels (RNA-seq) across control or PD individuals.…”