From Genomics to Omics Landscapes of Parkinson's Disease: Revealing the Molecular Mechanisms

Abstract

Molecular mechanisms of Parkinson's disease (PD) have already been investigated in various different omics landscapes. We reviewed the literature about different omics approaches between November 2005 and November 2017 to depict the main pathological pathways for PD development. In total, 107 articles exploring different layers of omics data associated with PD were retrieved. The studies were grouped into 13 omics layers: genomics–DNA level, transcriptomics, epigenomics, proteomics, ncRNomics, interactomics, metabolomics, glycomics, lipidomics, phenomics, environmental omics, pharmacogenomics, and integromics. We discussed characteristics of studies from different landscapes, such as main findings, number of participants, sample type, methodology, and outcome. We also performed curation and preliminary synthesis of multiple omics data, and identified overlapping results, which could lead toward selection of biomarkers for further validation of PD risk loci. Biomarkers could support the development of targeted prognostic/diagnostic panels as a tool for early diagnosis and prediction of progression rate and prognosis. This review presents an example of a comprehensive approach to revealing the underlying processes and risk factors of a complex disease. It urges scientists to structure the already known data and integrate it into a meaningful context.

Personalized medicine—concepts, technologies, and applications in inflammatory skin diseases

“…For example, a five-layer approach may include genomics, epigenomics (three sublayers: DNA methylation, histone code, miRNA), transcriptomics, proteomics, and metabolomics, which coupled to phenotype (phenomics) data appears as an 'obvious' integrative omics approach, and one that we are currently exploring. However, so far, most published studies are limited to three layers, namely genomics-transcriptomics-proteomics, which will capture post-transcriptional regulatory mechanisms, whenever there is discrepancy between gene and protein expression (52), but which do not take advantage of the orthogonal information that e.g. metabolomics adds to the (almost) full picture (52,53).…”

“…However, so far, most published studies are limited to three layers, namely genomics-transcriptomics-proteomics, which will capture post-transcriptional regulatory mechanisms, whenever there is discrepancy between gene and protein expression (52), but which do not take advantage of the orthogonal information that e.g. metabolomics adds to the (almost) full picture (52,53). A major concern about integromics analysis and sharing of such big medical data is the difficult question regarding privacy and security (120), which needs to be solved before a massive open online medical (MOOM) repository can become a reality (22).…”

Rise of Systems Glycobiology and Personalized Glycomedicine: Why and How to Integrate Glycomics with Multiomics Science?

“…The first step toward classification of these complex genomics information according to omics levels was enabled by proposed taxonomy of multiomics science (Pirih and Kunej, 2017) and extended in previous studies (Pirih and Kunej, 2018;Redenšek et al, 2018) (Fig. 2).…”

“…The number of included omics levels into integrative analyses is expected to increase as well. Data from 13 omics layers including glycomics were synthesized with the aim to enable a step toward a systems view on pathological mechanisms and for prioritization of novel potential biomarkers in Parkinson's disease and asthma (Pecak et al, 2018;Redenšek et al, 2018).…”

Harnessing Omics Big Data in Nine Vertebrate Species by Genome-Wide Prioritization of Sequence Variants with the Highest Predicted Deleterious Effect on Protein Function

“…Our catalog of missense pDelVars is of great importance since it can be applied in numerous fields, for example, to research of candidate disease markers associated with polygenic diseases, such as Parkinson's disease (Redenšek et al, 2018), personalized medicine, iatromics (Hekim and Ö zdemir, 2017), pharmacogenomics, nutrigenomics, ecogenomics (Ozdemir et al, 2009), and other omics types (Pirih and Kunej, 2017). Furthermore, the catalog may be applied in identification of pDelVars, located within miRNA-target interaction sites (Piletič and Kunej, 2017) or other types of molecular interactions.…”