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

Julià, Antonio, Gómez, Antonio, López-Lasanta, María, Blanco, Francisco, Erra, Alba, Fernández-Nebro, Antonio, Mas, Antonio Juan, Pérez-García, Carolina, Vivar, Ma Luz García, Sánchez-Fernández, Simón, Alperi-López, Mercedes, Sanmartí, Raimon, Ortiz, Ana María, Fernandez-Cid, Carlos Marras, Díaz-Torné, César, Moreno, Estefania, Li, Tianlu, Martínez-Mateu, Sergio H., Absher, Devin M., Myers, Richard M., Molina, Jesús Tornero, Marsal, Sara·2022·eBioMedicine
DOI 10.1016/j.ebiom.2022.104053PMC PMC9118662
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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 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.CC BY-NC-ND · PubMed Central
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 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.CC BY-NC-ND · PubMed Central
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 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.CC BY-NC-ND · PubMed Central
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 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).CC BY-NC-ND · PubMed Central
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 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.CC BY-NC-ND · PubMed Central
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.
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.CC BY-NC-ND · PubMed Central

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Research works printed but not linked: 43

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Metadata

Authors
Julià, A., Gómez, A., López-Lasanta, M., Blanco, F., Erra, A., Fernández-Nebro, A., Mas, A. J., Pérez-García, C., Vivar, M. L. G., Sánchez-Fernández, S., Alperi-López, M., Sanmartí, R., Ortiz, A. M., Fernandez-Cid, C. M., Díaz-Torné, C., Moreno, E., Li, T., Martínez-Mateu, S. H., Absher, D. M., Myers, R. M., Molina, J. T., & Marsal, S.
ORCID
Venue
eBioMedicine
Publisher
Elsevier BV
Published
2022-06
Volume
80
Pages
104053
ISSN
2352-3964
DOI
10.1016/j.ebiom.2022.104053
PMC
PMC9118662
Cited by
24
References
1
Language
en
Licence
Funders
Instituto de Salud Carlos III

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