Back

Proteins and transcriptional dysregulation of the brain extracellular matrix in Parkinson's disease

Rike, W. A.; Stern, S.

2023-03-01 neurology
10.1101/2023.03.01.23286630 medRxiv
Show abstract

The extracellular matrix (ECM) of the brain is a dynamic structure made up of a vast network of bioactive macromolecules that modulate cellular events. Structural, organizational and functional changes in these macromolecules due to genetic variation or environmental stressors are thought to affect the cellular functions, and may result in disease. Most mechanistic studies to date usually focus on the cellular aspects of diseases and pay less attention to the relevance of the processes governing the dynamic nature of the extracellular matrix on disease pathogenesis. Here in this review, we gathered postmortem brain tissue and induced pluripotent stem cell (iPSC)-related studies from PubMed and Google scholar to identify, summarize and describe common macromolecular alterations in the expression of brain ECM components in Parkinsons disease (PD). According to proteomic studies, proteins such as collagens, fibronectin, annexins and tenascins were recognized to be differentially expressed in Parkinsons disease. Transcriptomic studies displayed dysregulated pathways including ECM-receptor interaction, focal adhesion, and cell adhesion molecules in Parkinsons disease. Limited number of relevant studies were accessed from our search indicating that much work still remains to be done to better understand the roles of the ECM in neurodegeneration and Parkinsons disease. However, we believe that our review will elicit focused primary studies and thus, support the ongoing efforts of the discovery and development of diagnostic biomarkers as well as therapeutic agents for Parkinsons disease.

Matching journals

The top 11 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.