Longitudinal analysis of blood DNA methylation identifies mechanisms of response to tumor necrosis factor inhibitor therapy in rheumatoid arthritis

Abstract

Background: Rheumatoid arthritis (RA) is a chronic, immune-mediated inflammatory disease of the joints that has been associated with variation in the peripheral blood methylome. In this study, we aim to identify epigenetic variation that is associated with the response to tumor necrosis factor inhibitor (TNFi) therapy.

Methods: Peripheral blood genome-wide DNA methylation profiles were analyzed in a discovery cohort of 62 RA patients at baseline and at week 12 of TNFi therapy. DNA methylation of individual CpG sites and enrichment of biological pathways were evaluated for their association with drug response. Using a novel cell deconvolution approach, altered DNA methylation associated with TNFi response was also tested in the six main immune cell types in blood. Validation of the results was performed in an independent longitudinal cohort of 60 RA patients.

Findings: Treatment with TNFi was associated with significant longitudinal peripheral blood methylation changes in biological pathways related to RA (FDR<0.05). 139 biological functions were modified by therapy, with methylation levels changing systematically towards a signature similar to that of healthy controls. Differences in the methylation profile of T cell activation and differentiation, GTPase-mediated signaling, and actin filament organization pathways were associated with the clinical response to therapy. Cell type deconvolution analysis identified CpG sites in CD4+T, NK, neutrophils and monocytes that were significantly associated with the response to TNFi.

Interpretation: Our results show that treatment with TNFi restores homeostatic blood methylation in RA. The clinical response to TNFi is associated to methylation variation in specific biological pathways, and it involves cells from both the innate and adaptive immune systems.

Funding: The Instituto de Salud Carlos III.

Keywords: DNA methylation; Epigenetics; Rheumatoid arthritis; TNF inhibitors; Treatment response.

Figure 1

Principal component analysis (PCA) of the RA and healthy control cohort. PCA revealed a group of individuals showing an outlier methylation profile. (a). Clustering analysis using the Partitioning Around Medoids implemented in M3C software supported the presence of two groups of individuals showing significantly different methylation profile. The x-axis indicates each of the clustering partitions evaluated (k=2 to 10); k=2 (orange dot) showed a highly significant evidence for clustering (P<0.001). (b). Principal components of the RA and control cohort showing the outlier group of individuals in the second PC, and color-coded based on the M3C k=2 class assignments.

Figure 2

Schematic representation of the analytical design used to identify methylation variation associated with TNFi response. (a). Using blood methylation data from a large case-control cohort, the biological pathways associated with RA were identified (n=246). Using these disease-linked pathways, we were able to identify biological processes that are modified by TNFi treatment in longitudinal cohort of RA patients starting therapy (n=62). Also, pathways associated with the response to therapy at week 12 were identified. Using an independent patient cohort (n=59), the findings could be validated. (b). Using a novel cell-deconvolution approach in the discovery and validation cohorts, we were able to identify differentially methylated positions in multiple immune cell types associated with the response to TNFi. Monocytes showed the larger number of validated associations and we conducted TF motif enrichment to characterize the principal differentiation programs associated with response in this innate immune cell type.

Figure 3

Clustering of pathways associated with RA. Comparing the methylation profile of n=301 patients and n=305 healthy individuals, a total of 246 biological processes were found to be associated with RA using gometh. In this heatmap, the associated GO terms (rows and columns) are clustered according to their similarity based on Lin's measure [38]. The terms are then hierarchically clustered using complete linkage, and the tree is cut at the desired threshold (here 0.7). For each resulting cluster, the biological process showing the most significant association with RA was chosen as the representative biological function (right legend). A total of n=20 pathway clusters were identified associated with RA.

Figure 4

RA pathway association with TNFi response. A. Percentage of pathways linked to RA (n=246) that were significantly associated to the discovery cohort (left bar plots). From these, the middle bar plots indicate the percentage of significantly validated pathways in the validation cohort. Finally, the right bar plots show the percentage of validated pathways that have consistent methylation changes between the two patient cohorts using the resampling-based test. wk0: pathways associated with TNFi response at week 0; wk12: pathways associated with TNFi response at week 12; wk12 vs wk0: pathways changing from week 0 to week 12 of TNFi therapy; R vs NR: pathways showing longitudinal methylation differences between responders and non-responders to TNFi therapy. B. Association results in the validation cohort of the RA pathways associated with TNFi response at week 0 in the discovery cohort. Four pathways were found to be significantly replicated (red bars, FDR < 0.05). C. Association results in the validation cohort of the RA pathways associated with TNFi response at week 12 in the discovery cohort. One pathway, regulation of small GTPase signal transduction was found to be significantly replicated (red bar, FDR < 0.05).

Figure 5

Directionality test results for pathway methylation changes induced by TNFi. Profile plot showing the percentage of CpGs having opposite methylation changes between disease development (i.e. RA vs controls) and the response to TNFi (i.e. week12 vs baseline) for the pathways linked with RA. The top 40 pathways showing the strongest significant statistical evidence are included in the plot. These results confirm the effect of blood methylation variation induced by TNFi, clearly reversing it towards that of healthy individuals.

Figure 6

Transcription factors associated with the response to TNFi in monocytes. Using motif-enrichment analysis on the CpG sites associated with TNFi response in monocytes, we identified multiple transcription factors significantly associated with the regulatory changes. A 500 bp region centered around the associated CpG sites in monocytes was used in the analysis. Analysis of differentially methylated positions (DMPs) was performed separately for hypermethylated and hypomethylated CpGs in responders. The motif enrichment score for each transcription factor family (TF) is depicted as a horizontal line; at the end of each line, the significance of the TF association is represented as the diameter of the circle.