Immunomics analysis of rheumatoid arthritis identified precursor dendritic cells as a key cell subset of treatment resistance.
Immunomics analysis of rheumatoid arthritis identified precursor dendritic cells as a key cell subset of treatment resistance.
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DOI:
10.1136/ard-2022-223645
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发表时间:
2023-06
影响因子:
27.4
通讯作者:
中科院分区:
文献类型:
--
作者:
Little is known about the immunology underlying variable treatment response in rheumatoid arthritis (RA). We performed large-scale transcriptome analyses of peripheral blood immune cell subsets to identify immune cells that predict treatment resistance. We isolated 18 peripheral blood immune cell subsets of 55 patients with RA requiring addition of new treatment and 39 healthy controls, and performed RNA sequencing. Transcriptome changes in RA and treatment effects were systematically characterised. Association between immune cell gene modules and treatment resistance was evaluated. We validated predictive value of identified parameters for treatment resistance using quantitative PCR (qPCR) and mass cytometric analysis cohorts. We also characterised the identified population by synovial single cell RNA-sequencing analysis. Immune cells of patients with RA were characterised by enhanced interferon and IL6-JAK-STAT3 signalling that demonstrate partial normalisation after treatment. A gene expression module of plasmacytoid dendritic cells (pDC) reflecting the expansion of dendritic cell precursors (pre-DC) exhibited strongest association with treatment resistance. Type I interferon signalling was negatively correlated to pre-DC gene expression. qPCR and mass cytometric analysis in independent cohorts validated that the pre-DC associated gene expression and the proportion of pre-DC were significantly higher before treatment in treatment-resistant patients. A cluster of synovial DCs showed both features of pre-DC and pro-inflammatory conventional DC2s. An increase in pre-DC in peripheral blood predicted RA treatment resistance. Pre-DC could have pathophysiological relevance to RA treatment response.
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影响因子:
27.4
作者:
Figueiredo, Camille P.;Bang, Holger;Schett, Georg
通讯作者:
Schett, Georg
影响因子:
7.3
作者:
Cren, Mailys;Nziza, Nadege;Louis-Plence, Pascale
通讯作者:
Louis-Plence, Pascale
影响因子:
16.6
作者:
通讯作者:
--
影响因子:
32.4
作者:
Dutertre, Charles-Antoine;Becht, Etienne;Ginhoux, Florent
通讯作者:
Ginhoux, Florent
影响因子:
64.8
作者:
Maier B;Leader AM;Chen ST;Tung N;Chang C;LeBerichel J;Chudnovskiy A;Maskey S;Walker L;Finnigan JP;Kirkling ME;Reizis B;Ghosh S;D'Amore NR;Bhardwaj N;Rothlin CV;Wolf A;Flores R;Marron T;Rahman AH;Kenigsberg E;Brown BD;Merad M
通讯作者:
Merad M