Transcriptomic signatures associated with regional cortical thickness changes in Parkinson’s disease

bioRxiv (Cold Spring Harbor Laboratory)

Abstract

Abstract Cortical atrophy is a common manifestation in Parkinson’s disease, particularly in later disease stages. Here, we investigated patterns of cortical thickness using T1-weighted anatomical MRI data of 149 Parkinson’s disease patients and 369 controls. To elucidate the molecular underpinnings of cortical thickness changes in Parkinson’s disease, we performed an integrated analysis of brain-wide healthy transcriptomic data from the Allen Human Brain Atlas and neuroimaging features. For this purpose, we used partial least squares regression to identify gene expression patterns correlated with cortical thickness changes. In addition, we identified gene expression patterns underlying the relationship between cortical thickness and clinical domains of Parkinson’s disease. Our results show that genes whose expression in the healthy brain is associated with cortical thickness changes in Parkinson’s disease are enriched in biological pathways related to sumoylation, regulation of mitotic cell cycle, mitochondrial translation, DNA damage responses, and ER-Golgi traffic. The associated pathways were highly related to each other and all belong to cellular maintenance mechanisms. The expression of genes within most pathways was negatively correlated with cortical thickness changes, showing higher expression in regions associated with decreased cortical thickness (atrophy). On the other hand, sumoylation pathways were positively correlated with cortical thickness changes, showing higher expression in regions with increased cortical thickness (hypertrophy). Our findings suggest that alterations in the balanced interplay of these mechanisms play a role in changes of cortical thickness in Parkinson’s disease and possibly influence motor and cognitive functions.

Authors 6

1. Arlin Keo corresponding Aachen Leiden Computational Biology Center

   Leiden University Medical Center · Delft University of Technology

   Affiliation as printed

   Delft Bioinformatics Lab, Delft University of Technology, Delft, the Netherlands

   Leiden Computational Biology Center, Leiden University Medical Center, Leiden, the Netherlands

2. Oleh Dzyubachyk Aachen Department of Radiology, Leiden University Medical Center

   Leiden University Medical Center

   Affiliation as printed

   Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands

3. Jeroen van der Grond Aachen Department of Radiology, Leiden University Medical Center

   Leiden University Medical Center

   Affiliation as printed

   Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands

4. Jacobus Johannes van Hilten Aachen Department of Neurology, Leiden University Medical Center (LUMC)

   Leiden University Medical Center

   Affiliation as printed

   Department of Neurology, Leiden University Medical Center, Leiden, the Netherlands

5. Marcel J. T. Reinders Aachen Department of Human Genetics Leiden Computational Biology Center

   Leiden University Medical Center · Delft University of Technology

   Affiliation as printed

   Delft Bioinformatics Lab, Delft University of Technology, Delft, the Netherlands

   Department of Human Genetics, Leiden University Medical Center, Leiden, the Netherlands

   Leiden Computational Biology Center, Leiden University Medical Center, Leiden, the Netherlands

6. Ahmed Essam Mahfouz Aachen Department of Human Genetics Leiden Computational Biology Center

   Leiden University Medical Center · Delft University of Technology

   Affiliation as printed

   Delft Bioinformatics Lab, Delft University of Technology, Delft, the Netherlands

   Department of Human Genetics, Leiden University Medical Center, Leiden, the Netherlands

   Leiden Computational Biology Center, Leiden University Medical Center, Leiden, the Netherlands