10 research outputs found
Divergent Gene Activation in Peripheral Blood and Tissues of Patients with Rheumatoid Arthritis, Psoriatic Arthritis and Psoriasis following Infliximab Therapy
<div><p>Objective</p><p>The immune inflammatory disorders rheumatoid arthritis (RA), psoriatic arthritis (PsA) and psoriasis (Ps) share common pathologic features and show responsiveness to anti-tumor necrosis factor (TNF) agents yet they are phenotypically distinct. The aim of this study was to examine if anti-TNF therapy is associated with divergent gene expression profiles in circulating cells and target tissues of patients with these diseases.</p><p>Methods</p><p>Peripheral blood CD14<sup>+</sup> and CD14<sup>â</sup> cells were isolated from 9 RA, 12 PsA and 10 Ps patients before and after infliximab (IFX) treatment. Paired synovial (nâ=â3, RA, PsA) and skin biopsies (nâ=â5, Ps) were also collected. Gene expression was analyzed by microarrays.</p><p>Results</p><p>26 out of 31 subjects responded to IFX. The transcriptional response of CD14<sup>+</sup> cells to IFX was unique for the three diseases, with little overlap (<25%) in significantly changed gene lists (with PsA having the largest number of changed genes). In Ps, altered gene expression was more pronounced in lesional skin (relative to paired, healthy skin) compared to blood (relative to healthy controls). Marked suppression of up-regulated genes in affected skin was noted 2 weeks after therapy but the expression patterns differed from uninvolved skin. Divergent patterns of expression were noted between the blood cells and skin or synovial tissues in individual patients. Functions that promote cell differentiation, proliferation and apoptosis in all three diseases were enriched. RA was enriched in functions in CD14<sup>â</sup> cells, PsA in CD14<sup>+</sup> cells and Ps in both CD14<sup>+</sup> and CD14<sup>â</sup> cells, however, the specific functions showed little overlap in the 3 disorders.</p><p>Conclusion</p><p>Divergent patterns of altered gene expression are observed in RA, PsA and Ps patients in blood cells and target organs in IFX responders. Differential gene expression profiles in the blood do not correlate with those in target organs.</p></div
Venn/hive plots showing genes that changed following IFX treatment in three diseases in CD14<sup>+</sup> cells.
<p>The three shaded bars emanating from the center each represent a list of significant probe sets where their members are distributed along the bar sorted by fold-change (indicated by color). The numbers of probe sets up- and down-regulated are shown on the red and blue portions of the bars, respectively. For example, in PsA,148 genes were up-regulated and 91 down-regulated following IFX therapy. Arcs that connect pairs of shaded bars represent genes that are common between two comparisons (in the same fashion, the intersect of two circles in a Venn diagram); the gray numbers indicate the numbers of arcs. Probe sets that are common to all three lists are represented by green arcs (15 probe sets representing eight genes). Probe set lists were obtained using a general linear model with p<.05, with the requirement that the log<sub>2</sub> signal was required to be >5 for at least one of the sample groups and the absolute fold change was required to be greater than 1.5. Only annotated probe sets are depicted. For this figure, the larger fold change was used for the two time points after baseline.</p
Numbers of significant genes in PBMC comparisons.
<p>Numbers reflect total number of genes as well as the number up- and down-regulated. These numbers were derived from a slightly larger number of probe sets. Unannotated probe sets are not included in this tabulation. BLâ=âbaseline, HCâ=âhealthy control (nâ=â19), Dxâ=âdisease. Numbers are based on general linear model with p<.05 and fold change at least 1.5.</p><p>Numbers of significant genes in PBMC comparisons.</p
Numbers of significant genes in biopsy comparisons.
<p>Numbers reflect total number of genes as well as the number up- and down-regulated. These numbers were derived from a slightly larger number of probe sets. Unannotated probe sets are not included in this tabulation. BLâ=âbaseline, Lâ=âlesional, NLâ=ânon-lesional. Numbers are based on paired t-test with p<.01 and fold change at least 1.5.</p><p>Numbers of significant genes in biopsy comparisons.</p
Upstream regulator analysis using Ingenuity Pathway Analysis.
<p>Upstream regulators predicted based on genes that were significantly different for lesional skin before and after treatment with IFX (nâ=â379). Subset of âsignificantâ transcription factors are shown that had an overlap p-value <.0001 (negative log of 4) and a reported activation z-score, indicating that many of the targets were enriched in the gene list. Except for STAT3 which was down-regulated, none of these were in the gene list. The further the activation z-score is from zero, the more likely that the direction of change of the target genes are consistent with the regulator being in either an âactivatedâ or âinhibitedâ state. Bias terms over 25 are shown, indicating that the regulation of the targets in the data set as well as all of those for the regulator are skewed towards a particular direction <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0110657#pone.0110657-Systems1" target="_blank">[26]</a>. Six additional upstream regulators, E2F4, E2F1, PBRM1, NFYB, MYBL2 and NFkB, had low overlap p-values, but there wasnât sufficient data about known relationships with targets to predict an activation state. Although no upstream regulator was predicted to be activated or inhibited for the IFX-response in CD14<sup>+</sup> or CD14<sup>â</sup> cells, some still had low overlap p-values.</p><p>Upstream regulator analysis using Ingenuity Pathway Analysis.</p
Heat map of IFX treatment effect for CD14<sup>+</sup> cells in different diseases.
<p>In each heat map, columns correspond to patients and rows to probe sets. Separate heat maps are shown for three diseases and two cell types as indicated. Color is the log<sub>2</sub> difference in expression at 2 or 10 weeks relative to baseline as specified by the blue and gold bars, respectively (patients are in same order for each set of columns for each disease). Probe set clustering was accomplished using Euclidean distance and complete linkage. Only genes for which p<.005 for one of the time points are shown. BLâ=âbaseline. Font color for gene names indicate whether the probe set was significant at p<.05 at 2 weeks (purple), 10 weeks (green) or both (gray). For all heat maps, the columns (patients) are sorted first by responders/non-responders, and then by change in disease activity at 10 weeks relative to baseline (DAS28 for RA and PsA, PASI for Ps), which is shown beneath each heat map (responders are black bars, non-responders are gray bars).</p
Summary of enriched functions for IFX responses for different tissues and diseases.
<p>For each of seven pre/post IFX gene lists, results from Ingenuity Pathway Analysis functional enrichment were retained if the Benjamini-Hochberg-corrected p value was <0.001 and if there were more than four probe sets associated with the function. The unions of these functions are listed alongside of the heat map. Color maps to âlog<sub>10</sub> of the corrected p-value, with blue being very significant. Functions (rows) were clustered based on their associated broader, category labels using the kappa statistic in order to group similar functions together to aid in visualization. Up and down triangles indicate functions that were increased or decreased, respectively, based on activation z-score.</p
Additional file 3: of A computational framework for complex disease stratification from multiple large-scale datasets
Table S7. Estimated accuracy and standard deviation of the RFE procedure. Table S8. Accuracy and Kappa values of the Random Forest models in the training set. Table S9. Performances values for the Random Forest model in the testing set. Figure S11. Relative importance of the top 20 predictors building the final model of the RF. The importance axis is scaled, with the mRNA expression of CD3D scaled to 100% and the methylation state of POLA2 to 0% (not shown). (DOCX 18Â kb
Additional file 4: of A computational framework for complex disease stratification from multiple large-scale datasets
DIABLO sPLSDA model results. (DOCX 18966Â kb