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Joint-specific rheumatoid arthritis fibroblast-like synoviocyte regulation identified by integration of chromatin access and transcriptional activity
Eunice Choi, Camilla R.L. Machado, Takaichi Okano, David Boyle, Wei Wang, Gary S. Firestein
Eunice Choi, Camilla R.L. Machado, Takaichi Okano, David Boyle, Wei Wang, Gary S. Firestein
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Research Article Inflammation

Joint-specific rheumatoid arthritis fibroblast-like synoviocyte regulation identified by integration of chromatin access and transcriptional activity

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Abstract

The mechanisms responsible for the distribution and severity of joint involvement in rheumatoid arthritis (RA) are not known. To explore whether site-specific fibroblast-like synoviocyte (FLS) biology might be associated with location-specific synovitis and explain the predilection for hand (wrist/metacarpal phalangeal joints) involvement in RA, we generated transcriptomic and chromatin accessibility data from FLS to identify the transcription factors and pathways. Networks were constructed by integration of chromatin accessibility and gene expression data. Analysis revealed joint-specific patterns of FLS phenotype, with proliferative, migratory, proinflammatory, and matrix-degrading characteristics observed in resting FLS derived from the hand joints compared with hip or knee. TNF stimulation amplified these differences, with greater enrichment of proinflammatory and proliferative genes in hand FLS compared with hip and knee FLS. Hand FLS also had the greatest expression of markers associated with an “activated” state relative to the “resting” state, with the greatest cytokine and MMP expression in TNF-stimulated hand FLS. Predicted differences in proliferation and migration were biologically validated with hand FLS exhibiting greater migration and cell growth than hip or knee FLS. Distinctive joint-specific FLS biology associated with a more aggressive inflammatory response might contribute to the distribution and severity of joint involvement in RA.

Authors

Eunice Choi, Camilla R.L. Machado, Takaichi Okano, David Boyle, Wei Wang, Gary S. Firestein

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Figure 2

Joint-specific patterns of unstimulated RA FLS transcriptional regulation.

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Joint-specific patterns of unstimulated RA FLS transcriptional regulatio...
(A) Heatmap of PageRanks of the top differential TFs selected from pairwise comparisons between unstimulated FLS from hand, hip, and knee. Pathway enriched TFs are labeled. For example, cAMP-responsive element–binding protein 1 (CREB1) has the greatest importance in hand FLS. Important TFs are reflected by increased accessibility of loci containing the TF motif, increased TF expression, and increased predicted TF binding affinity of the identified motif within the open chromatin regions. (B) Intersection plot of differential regulators (TFs) and regulatees (DARs and DEGs that are regulated by the differential regulator). Top bars indicate all differential features for each pairwise comparison. Heatmap indicates shared differential comparisons. Side bars indicate number of features that are shared between comparisons. For example, hand versus hip has 115 TFs that were identified with differential PageRanks. Fifty-one of those differential TFs were also identified from hip versus knee. Blue, red, and green are used for hand versus knee, hand versus hip, and knee versus hip comparisons, respectively. (C) For each joint, differential TFs with the greatest PageRank score in that joint was visualized. Nodes are scaled by PageRank. For example, PATZ1 has differential PageRanks between hand versus hip and has the greatest PageRank in hand (fold change in PATZ1 PageRank for hand versus knee: 1.2; hand versus hip: 1.3; knee versus hip: 0.8). (D) Pathway enrichment analysis of TFs and regulatee DARs and DEGs between pairwise comparisons. The greatest bias toward FLS aggressiveness was observed in hand versus hip comparisons and least between hip versus knee comparisons. Hand versus hip and hand versus knee comparisons were enriched for proinflammatory, e.g., “MAP kinase activation” (P < 0.003 by hypergeometric distribution test), and proliferative pathways e.g., “Constitutive signaling by AKT1 E17K in cancer” (P < 0.006 by hypergeometric distribution test). (E) Visualization of selected features. Genome browser shows differential open chromatin regulatory loci (left) and box-and-whisker plots are DEGs as representative examples of differential proliferative and proinflammatory features. The box-and-whisker plots display the median (line), the interquartile range (IQR) for the first (Q1) and third (Q3) quartiles (box bounds), and error bars (Q1 – 1.5 × IQR [low] and Q3 + 1.5 × IQR [high]). Values outside of the minimum and maximum are considered outliers (solid circles). **P < 0.01, ***P < 0.005 by Wilcoxon’s test.

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