A computational approach to identify blood cell-expressed Parkinson's disease biomarkers that are coordinately expressed in brain tissue

• Published in: Computers in biology and medicine 2019-10, Vol.113, p.103385-103385, Article 103385
• Main Authors: Moni, Mohammad Ali, Rana, Humayan Kabir, Islam, M. Babul, Ahmed, Mohammad Boshir, Xu, Haoming, Hasan, Md Al Mehedi, Lei, Yiming, Quinn, Julian M.W.
• Format: Article
• Language: English
• Subjects: Benchmarks; Biomarkers; Blood; Blood cells; Brain; cis-eQTL; Computational neuroscience; Datasets; DNA microarrays; Gene expression; Gene mapping; Gene regulation; Genes; Genetic factors; GWAS; Identification methods; Mathematical analysis; Movement disorders; Multilayers; Network topologies; Neurodegenerative diseases; Neurological complications; Neurological diseases; Ontology; Parkinson's disease; Quantitative trait loci; Ribonucleic acid; RNA; Single-nucleotide polymorphism; Tissues; Topology; Transcription
• ISSN: 0010-4825; 1879-0534
• DOI: 10.1016/j.compbiomed.2019.103385

Identification of genes whose regulation of expression is functionally similar in both brain tissue and blood cells could in principle enable monitoring of significant neurological traits and disorders by analysis of blood samples. We thus employed transcriptional analysis of pathologically affected tissues, using agnostic approaches to identify overlapping gene functions and integrating this transcriptomic information with expression quantitative trait loci (eQTL) data. Here, we estimate the correlation of gene expression in the top-associated cis-eQTLs of brain tissue and blood cells in Parkinson's Disease (PD). We introduced quantitative frameworks to reveal the complex relationship of various biasing genetic factors in PD, a neurodegenerative disease. We examined gene expression microarray and RNA-Seq datasets from human brain and blood tissues from PD-affected and control individuals. Differentially expressed genes (DEG) were identified for both brain and blood cells to determine common DEG overlaps. Based on neighborhood-based benchmarking and multilayer network topology approaches we then developed genetic associations of factors with PD. Overlapping DEG sets underwent gene enrichment using pathway analysis and gene ontology methods, which identified candidate common genes and pathways. We identified 12 significantly dysregulated genes shared by brain and blood cells, which were validated using dbGaP (gene SNP-disease linkage) database for gold-standard benchmarking of their significance in disease processes. Ontological and pathway analyses identified significant gene ontology and molecular pathways that indicate PD progression. In sum, we found possible novel links between pathological processes in brain tissue and blood cells by examining cell pathway commonalities, corroborating these associations using well validated datasets. This demonstrates that for brain-related pathologies combining gene expression analysis and blood cell cis-eQTL is a potentially powerful analytical approach. Thus, our methodologies facilitate data-driven approaches that can advance knowledge of disease mechanisms and may, with clinical validation, enable prediction of neurological dysfunction using blood cell transcript profiling. [Display omitted] •A blood based test would be ideal to assess Parkinson's Disease (PD) progression.•We identified transcriptional changes evident in PD brain tissues.•Some transcripts altered in PD are under similar control in blood cells and