Efficient and biologically relevant consensus strategy for Parkinson’s disease gene prioritization

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.

Consensus strategy in genes prioritization and combined bioinformatics analysis for preeclampsia pathogenesis

“…In this work and similar to [33], we used some of the most extended metrics to estimate the enrichment ability in order to compare different gene prioritization strategies. The overall enrichment metrics include the area under the accumulation curve ( AUAC ); the area under the ROC curve ( ROC ); and the enrichment factor ( EF ) evaluated at the top 1, 5, 10 and 20% of the ranked list.…”

A review of genome‐wide transcriptomics studies in Parkinson's disease

“…When the levels of SNCA transcripts (which codes for α‐synuclein) were specifically investigated, differential gene expression between patients with PD and controls in both blood and brain were observed (Moran et al., ; Soreq et al., ; Simunovic et al., , ; Shehadeh et al., ; Soreq et al., ; Karlsson et al., ; Alieva et al., ; Infante et al., ; Nkiliza et al., ; Wang et al., ). Interestingly, these studies found decreased SNCA expression in patients with PD; whereas a few studies found no change (Kedmi et al., ; Durrenberger et al., ; Zhang et al., ; Cruz‐Monteagudo et al., ; Dumitriu et al., ; Kobo et al., ). This finding is contrary to what is expected from the literature about increased α‐synuclein protein expression in patients with PD and therefore warrants further investigation.…”

“…When considering studies performed on the SN, the most prominent pathways which were consistently found to be altered in PD include dopamine metabolism, mitochondrial function, oxidative stress, protein degradation, neuroinflammation, vesicular transport and synaptic transmission (Table ; Table S1). Several studies reported a substantial downregulation of dopamine metabolism pathways, coupled with significantly decreased expression of key dopamine metabolism genes including the dopamine transporters; solute carrier family 6 member 3 ( SLC6A3) and solute carrier family 18 member A2 (SLC18A2) and dopamine decarboxylase (Grünblatt et al., ; Miller et al., ; Cruz‐Monteagudo et al., ). These findings have been interpreted to provide support for the involvement of dysregulated dopamine metabolism in PD pathogenesis; however, reports of decreased expression of dopamine metabolism genes are not surprising given the severe reduction in the number of surviving DA neurons in the SN of patients with PD.…”

Concordance analysis of microarray studies identifies representative gene expression changes in Parkinson’s disease: a comparison of 33 human and animal studies

“…Parkinson’s disease (PD) - a neurodegenerative disorder which causes the death of dopaminergic neurons in the substantia nigra, causing tremors and postural instability - has been well-studied at the level of gene expression, with numerous microarray studies available in public repositories. Several meta-analyses of PD gene expression in human patients have been carried out [2–4] on datasets of up to 14 unique studies; however, concordance between these studies has been reported to be low even when standardized analysis is applied [3–6]. It has been proposed that discordance could result from different progression of the disease at time of post-mortem [6] and differing amounts of neuronal loss between the substantia nigra (SN) and other regions of the brain - indeed, an analysis of 11 human PD microarray studies demonstrated increased convergence within the five studies using samples from the SN [3].…”