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Meta-Analysis
. 2017 Mar 23;17(1):58.
doi: 10.1186/s12883-017-0838-x.

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

Erin Oerton  1 , Andreas Bender  2
Affiliations
Meta-Analysis

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

Erin Oerton et al. BMC Neurol. .

Erratum in

Abstract

Background: As the popularity of transcriptomic analysis has grown, the reported lack of concordance between different studies of the same condition has become a growing concern, raising questions as to the representativeness of different study types, such as non-human disease models or studies of surrogate tissues, to gene expression in the human condition.

Methods: In a comparison of 33 microarray studies of Parkinson's disease, correlation and clustering analyses were used to determine the factors influencing concordance between studies, including agreement between different tissue types, different microarray platforms, and between neurotoxic and genetic disease models and human Parkinson's disease.

Results: Concordance over all studies is low, with correlation of only 0.05 between differential gene expression signatures on average, but increases within human patients and studies of the same tissue type, rising to 0.38 for studies of human substantia nigra. Agreement of animal models, however, is dependent on model type. Studies of brain tissue from Parkinson's disease patients (specifically the substantia nigra) form a distinct group, showing patterns of differential gene expression noticeably different from that in non-brain tissues and animal models of Parkinson's disease; while comparison with other brain diseases (Alzheimer's disease and brain cancer) suggests that the mixed study types display a general signal of neurodegenerative disease. A meta-analysis of these 33 microarray studies demonstrates the greater ability of studies in humans and highly-affected tissues to identify genes previously known to be associated with Parkinson's disease.

Conclusions: The observed clustering and concordance results suggest the existence of a 'characteristic' signal of Parkinson's disease found in significantly affected human tissues in humans. These results help to account for the consistency (or lack thereof) so far observed in microarray studies of Parkinson's disease, and act as a guide to the selection of transcriptomic studies most representative of the underlying gene expression changes in the human disease.

Keywords: Concordance; Gene expression; Meta-analysis; Microarray; Parkinson’s disease.

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Figures

Fig. 1
Fig. 1
Average concordance of differential gene expression within subsets of shared factors. Average concordance over all studies is low, but increases within human patients and studies of the substantia nigra
Fig. 2
Fig. 2
Average concordance within subgroups of human studies of PD. Concordance increases in studies of human patients (i.e., excluding human cell line studies), and within tissue subgroups. Concordance of pathways compares regulation at the level of biological processes rather than individual genes, and accordingly concordance at the pathway level is generally higher than at the level of differential gene expression
Fig. 3
Fig. 3
Principal component analysis of PD studies based on differential expression signatures. PCA of the 1,008 genes in the union of the top 50 genes by absolute log-fold change across all 33 studies reveals a distinct group of studies composed mainly of human studies (centre, right) of the substantia nigra and frontal cortex (left). There appears to be little separation between different disease model types (right); although the two studies using other neurotoxins (rotenone and Maneb-Paraquat) appear very distinct from the other studies. This is most clearly visualised in the second and third principal components; a similar separation is seen in the first two principal components (see Additional file 5)
Fig. 4
Fig. 4
Hierarchical clustering of studies based on the most highly differentially expressed genes in each PD study. Clustering was performed based on the union of the top 10 genes by absolute log-fold change across the 33 studies. The highlighted cluster contains all but one of the human studies of the substantia nigra, as well as both human frontal cortex studies. This indicates a distinct differential gene expression pattern that is shared by these study types. This cluster also contains one rat study, however, indicating that it is possible for animal models to capture the expression patterns observed here. Aside from this outgroup, there is no apparent clustering of other factors such as platform, disease model, or treatment (e.g., with L-DOPA), reflecting the low concordance seen in these groups
Fig. 5
Fig. 5
Principal component analysis of differential gene expression in Parkinson’s disease, Alzheimer’s disease and brain tumor studies. The tumor studies are mostly distant in principal component space from PD or AD studies, suggesting different patterns of gene expression in the two diseases; whilst the AD studies look very similar to those of Parkinson’s disease, suggesting that gene expression patterns in these neurodegenerative diseases could be related to some extent. This is most clearly visualised in the second and third principal components; a similar separation is seen in the first two principal components (see Additional file 13)
Fig. 6
Fig. 6
Percentage (bar) and number (number above bar) of genes previously associated with PD amongst genes identified by a meta-analysis in each grouping. Gene lists from human studies and studies using tissue from the basal ganglia (here including studies of the striatum and substantia nigra) are more enriched for genes and proteins that have been associated with PD through genetic mutations, drugs, or literature-mining than those from animal models or studies using other tissues
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