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Monocyte-derived transcriptomes explain the ineffectiveness of abatacept in rheumatoid arthritis
Takeshi Iwasaki1,2, Ryu Watanabe3, Hiromu Ito4
1Department of Rheumatology and Clinical Immunology, Kyoto University Graduate School of Medicine, Kyoto, Japan.
Background:
The biological mechanisms underlying the differential response to abatacept in patients with rheumatoid arthritis (RA) are unknown. Here, we aimed to identify cellular, transcriptomic, and proteomic features that predict resistance to abatacept in patients with RA.
Methods:
Blood samples were collected from 22 RA patients treated with abatacept at baseline and after 3 months of treatment. Response to treatment was defined by the European League Against Rheumatism (EULAR) response criteria at 3 months, and seven patients were classified as responders and the others as non-responders. We quantified gene expression levels by RNA sequencing, 67 plasma protein levels, and the expression of surface molecules (CD3, 19, and 56) by flow cytometry. In addition, three gene expression data sets, comprising a total of 27 responders and 50 non-responders, were used to replicate the results.
Results:
Among the clinical characteristics, the number of monocytes was significantly higher in the non-responders before treatment. Cell type enrichment analysis showed that differentially expressed genes (DEGs) between responders and non-responders were enriched in monocytes. Gene set enrichment analysis, together with single-cell analysis and deconvolution analysis, identified that Toll-like receptor 5 (TLR5) and interleukin-17 receptor A (IL17RA) pathway in monocytes was upregulated in non-responders. Hepatocyte growth factor (HGF) correlated with this signature showed higher concentrations in non-responders before treatment. The DEGs in the replication set were also enriched for the genes expressed in monocytes, not for the TLR5 and IL17RA pathway but for the oxidative phosphorylation (OXPHOS) pathway.
Conclusions:
Monocyte-derived transcriptomic features before treatment underlie the differences in abatacept efficacy in patients with RA. The pathway activated in monocytes was the TLR5 and IL17RA-HGF signature in the current study, while it was the OXPHOS pathway in the replication set. Elevated levels of HGF before treatment may serve as a potential biomarker for predicting poor responses to abatacept. These findings provide insights into the biological mechanisms of abatacept resistance, contributing valuable evidence for stratifying patients with RA.
Insights
Monocyte gene expression profiles before treatment predict rheumatoid arthritis (RA) response to abatacept. Elevated hepatocyte growth factor (HGF) may indicate poor treatment outcomes, aiding patient stratification.
Area of Science:
- Immunology
- Genomics
- Proteomics
Background:
- The biological basis for varied responses to abatacept in rheumatoid arthritis (RA) patients remains unclear.
- Identifying predictors of abatacept resistance is crucial for personalized RA treatment.
Purpose of the Study:
- To uncover cellular, transcriptomic, and proteomic markers that predict resistance to abatacept in RA patients.
- To elucidate the mechanisms behind differential responses to abatacept therapy.
Main Methods:
- Analyzed blood samples from 22 RA patients (7 responders, 15 non-responders) treated with abatacept.
- Utilized RNA sequencing, plasma protein quantification, and flow cytometry.
- Replicated findings using three independent gene expression datasets.
Main Results:
- Non-responders had higher monocyte counts and upregulated Toll-like receptor 5 (TLR5) and interleukin-17 receptor A (IL17RA) pathways in monocytes.
- Hepatocyte growth factor (HGF) levels were elevated in non-responders.
- Replication analysis identified oxidative phosphorylation (OXPHOS) pathway enrichment in monocytes of non-responders.
Conclusions:
- Monocyte transcriptomic features at baseline predict abatacept efficacy in RA.
- The TLR5/IL17RA-HGF pathway (primary) and OXPHOS pathway (replication) in monocytes are implicated in non-response.
- HGF may serve as a biomarker for predicting poor abatacept response, facilitating patient stratification.
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