Abstract
BackgroundThe systemic information enclosed in microarray data encodes relevant clues to overcome the poorly understood combination of genetic and environmental factors in Parkinson’s disease (PD), which represents the major obstacle to understand its pathogenesis and to develop disease-modifying therapeutics. While several gene prioritization approaches have been proposed, none dominate over the rest. Instead, hybrid approaches seem to outperform individual approaches.MethodsA consensus strategy is proposed for PD related gene prioritization from mRNA microarray data based on the combination of three independent prioritization approaches: Limma, machine learning, and weighted gene co-expression networks.ResultsThe consensus strategy outperformed the individual approaches in terms of statistical significance, overall enrichment and early recognition ability. In addition to a significant biological relevance, the set of 50 genes prioritized exhibited an excellent early recognition ability (6 of the top 10 genes are directly associated with PD). 40 % of the prioritized genes were previously associated with PD including well-known PD related genes such as SLC18A2, TH or DRD2. Eight genes (CCNH, DLK1, PCDH8, SLIT1, DLD, PBX1, INSM1, and BMI1) were found to be significantly associated to biological process affected in PD, representing potentially novel PD biomarkers or therapeutic targets. Additionally, several metrics of standard use in chemoinformatics are proposed to evaluate the early recognition ability of gene prioritization tools.ConclusionsThe proposed consensus strategy represents an efficient and biologically relevant approach for gene prioritization tasks providing a valuable decision-making tool for the study of PD pathogenesis and the development of disease-modifying PD therapeutics.Electronic supplementary materialThe online version of this article (doi:10.1186/s12920-016-0173-x) contains supplementary material, which is available to authorized users.
Highlights
The systemic information enclosed in microarray data encodes relevant clues to overcome the poorly understood combination of genetic and environmental factors in Parkinson’s disease (PD), which represents the major obstacle to understand its pathogenesis and to develop disease-modifying therapeutics
linear models for microarray data (Limma) based gene prioritization First, the background of 8477 genes provided by the 102 samples of healthy control (HC) and PD patients was processed with Limma
If we look for those genes in the KEGG Dopaminergic Synapse Pathway (129 genes in the DA Pathway) and in the KEGG Parkinson’s Disease Pathway (142 genes in the PD Pathway) comprised in the set of 246 unique genes coming from the union of machine learning (ML) and Limma prioritizations, it is possible to note that only 4.47 % (11 DA genes out of 246) of this set corresponds to the DA pathway, which indicates an insignificant risk of “dopamine bias” for this set
Summary
The systemic information enclosed in microarray data encodes relevant clues to overcome the poorly understood combination of genetic and environmental factors in Parkinson’s disease (PD), which represents the major obstacle to understand its pathogenesis and to develop disease-modifying therapeutics. Parkinson’s disease (PD) is the second most common neurodegenerative disorder (ND). Dopamine replacement drugs remains the principal and most effective treatment for PD [4]. As the disease progresses, their efficacy diminishes and fails to address the degeneration observed in other brain areas [5,6,7]. Disease-modifying treatments are needed that address both the motor and nonmotor symptoms of PD. The most important diagnostic marker of PD is limited to the presence of motor disturbances. Due to overlap of symptoms with other neurodegenerative disorders, misdiagnosis is common. Cruz-Monteagudo et al BMC Medical Genomics (2016) 9:12 motor deficits allowing clinical diagnosis generally appear when 50–60 % of dopaminergic neurons in the substantia nigra (SN) are already lost, limiting the effectiveness of potential neuroprotective therapies [8]
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