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Clinical Trial
. 2013;8(1):e52242.
doi: 10.1371/journal.pone.0052242. Epub 2013 Jan 4.

Cross-study homogeneity of psoriasis gene expression in skin across a large expression range

Jeannette Bigler  1 , Hugh A Rand, Keith Kerkof, Martin Timour, Christopher B Russell
Affiliations
Clinical Trial

Cross-study homogeneity of psoriasis gene expression in skin across a large expression range

Jeannette Bigler et al. PLoS One. 2013.

Abstract

Background: In psoriasis, only limited overlap between sets of genes identified as differentially expressed (psoriatic lesional vs. psoriatic non-lesional) was found using statistical and fold-change cut-offs. To provide a framework for utilizing prior psoriasis data sets we sought to understand the consistency of those sets.

Methodology/principal findings: Microarray expression profiling and qRT-PCR were used to characterize gene expression in PP and PN skin from psoriasis patients. cDNA (three new data sets) and cRNA hybridization (four existing data sets) data were compared using a common analysis pipeline. Agreement between data sets was assessed using varying qualitative and quantitative cut-offs to generate a DEG list in a source data set and then using other data sets to validate the list. Concordance increased from 67% across all probe sets to over 99% across more than 10,000 probe sets when statistical filters were employed. The fold-change behavior of individual genes tended to be consistent across the multiple data sets. We found that genes with <2-fold change values were quantitatively reproducible between pairs of data-sets. In a subset of transcripts with a role in inflammation changes detected by microarray were confirmed by qRT-PCR with high concordance. For transcripts with both PN and PP levels within the microarray dynamic range, microarray and qRT-PCR were quantitatively reproducible, including minimal fold-changes in IL13, TNFSF11, and TNFRSF11B and genes with >10-fold changes in either direction such as CHRM3, IL12B and IFNG.

Conclusions/significance: Gene expression changes in psoriatic lesions were consistent across different studies, despite differences in patient selection, sample handling, and microarray platforms but between-study comparisons showed stronger agreement within than between platforms. We could use cut-offs as low as log10(ratio) = 0.1 (fold-change = 1.26), generating larger gene lists that validate on independent data sets. The reproducibility of PP signatures across data sets suggests that different sample sets can be productively compared.

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Conflict of interest statement

Competing Interests: All the authors are employees of Amgen Inc. and own stock. Amgen funded this research. There are no patents, products in development, or marketed products to declare. This does not alter the authors' adherence to all the PLOS ONE policies on sharing data and materials.

Figures

Figure 1
Figure 1. Comparison of Differential Expression Across Data Sets.
For each data set a list of probe sets with differential expression at p≤0.05 was generated and compared to all the other data sets. The probe sets were then categorized into four different groups according to the extent of agreement between the source data set and the other data sets: i) “consistent” meant that there was at least one other data set in which the probe set showed differential expression in the same direction with p≤0.05 and no data sets with differential expression in the opposite direction with p≤0.05; ii) “inconsistent between platforms” indicated that there was at least one data set from the other platform with differential expression at p≤0.05 in the opposite direction; iii) the “inconsistent within platform” group contained probe sets with differential expression at p≤0.05 in different directions within the same platform; and iv) the “p>0.05 in all other” group contained probe sets where the source set was the only one with significant differential expression. The number of probe sets with differential expression in the Zaba (GSE11903) and the Reischl sets were smaller because samples were run on U133A arrays, which contain only 22,215 probe sets.
Figure 2
Figure 2. Effect of log10(ratio) and p-value Cut-offs on Overlap of Differentially Expressed Probe Sets.
Effects are shown with source sets (A,C) NCT00867100 and (B,D) Gudjonsson low. (A, B): Number of probe sets agreeing and disagreeing for log10(ratios) from 0 to 0.5 in increments of 0.05 and p-values up to 0.2 in increments of 0.01 in the source set without cut-offs in the target set, (C, D): Number of probe sets agreeing and disagreeing from an analysis using log10(ratios) from 0 to 0.5 in increments of 0.5 and p≤0.05 in the source set and p-values up to 0.2 in increments of 0.01 in the target set are shown. Note: X-axis scales in (A, C) differ from those in (B, D).
Figure 3
Figure 3. Effect of log10(ratio) on Proportion of “Disagreeing” Probe Sets at a p-value of 0.05 in the Source Set.
The data sets are the same as for the data shown in Figure 1. Either NCT00867100 (A, C) or Gudjonsson Low (B, D) were chosen as the source set. The proportion of probe sets disagreeing (out of all the probe sets) is shown for different log10(ratio) cutoffs. A and B: p-value of 0.05 in the source set and no cut-offs in the target sets; C and D: p-value cut-off of 0.05 in source and target set.
Figure 4
Figure 4. Effect of Implementing Fold-Change and p-value Cut-offs on a Comparison Between Two Experiments.
Panels A-D show a hexbin plot comparison of the average log10(ratio) values between the Gudjonsson Low and the NCT00867100 data sets with (A) no cut-offs, (B) a p-value≤0.05 cut-off in the source set only, (C) p-value≤0.05 and log10(ratio)≥0.1 cut-offs only in the source set, and (D) p-value≤0.05 and log10(ratio)≥0.1 cut-offs in the source set and a p-value≤0.05 cut-off in the target set. The numbers in the panel corners indicate the number of data points in those quadrants. Panel E shows average log10(ratio) distributions in the Gudjonsson Low data set (target set) for sequences with log10(ratio) values of 0.100±0.005 (blue), 0.200±0.005 (pink), and 0.300±0.005 (green) in the source set (NCT00867100).
Figure 5
Figure 5. Comparison of Fold-changes in Psoriasis PP/PN Pairs by Microarray and qRT-PCR.
Fold-changes for a selection of mostly immune system transcripts were assessed by qRT-PCR and microarray in a subset of eight psoriasis PP/PN skin biopsies from the Asterand set. Transcripts were selected based on relevance to psoriasis, range of expression level and range of fold-changes; patient biopsies were selected based on microarray data so that the range of differential expression was large. The black line indicates complete concordance.
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