| 41. |
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| 42. |
- Yoosuf, N, et al.
(author)
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Early prediction of clinical response to anti-TNF treatment using multi-omics and machine learning in rheumatoid arthritis
- 2022
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In: Rheumatology (Oxford, England). - : Oxford University Press (OUP). - 1462-0332 .- 1462-0324. ; 61:4, s. 1680-1689
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Journal article (peer-reviewed)abstract
- ObjectivesAdvances in immunotherapy by blocking TNF have remarkably improved treatment outcomes for Rheumatoid arthritis (RA) patients. Although treatment specifically targets TNF, the downstream mechanisms of immune suppression are not completely understood. The aim of this study was to detect biomarkers and expression signatures of treatment response to TNF inhibition.MethodsPeripheral blood mononuclear cells (PBMCs) from 39 female patients were collected before anti-TNF treatment initiation (day 0) and after 3 months. The study cohort included patients previously treated with MTX who failed to respond adequately. Response to treatment was defined based on the EULAR criteria and classified 23 patients as responders and 16 as non-responders. We investigated differences in gene expression in PBMCs, the proportion of cell types and cell phenotypes in peripheral blood using flow cytometry and the level of proteins in plasma. Finally, we used machine learning models to predict non-response to anti-TNF treatment.ResultsThe gene expression analysis in baseline samples revealed notably higher expression of the gene EPPK1 in future responders. We detected the suppression of genes and proteins following treatment, including suppressed expression of the T cell inhibitor gene CHI3L1 and its protein YKL-40. The gene expression results were replicated in an independent cohort. Finally, machine learning models mainly based on transcriptomic data showed high predictive utility in classifying non-response to anti-TNF treatment in RA.ConclusionsOur integrative multi-omics analyses identified new biomarkers for the prediction of response, found pathways influenced by treatment and suggested new predictive models of anti-TNF treatment in RA patients.
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| 44. |
- Zhao, J. H., et al.
(author)
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Genetics of circulating inflammatory proteins identifies drivers of immune-mediated disease risk and therapeutic targets
- 2023
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In: Nature Immunology. - : Springer Nature. - 1529-2908 .- 1529-2916. ; 24:9, s. 1540-1551
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Journal article (peer-reviewed)abstract
- Circulating proteins have important functions in inflammation and a broad range of diseases. To identify genetic influences on inflammation-related proteins, we conducted a genome-wide protein quantitative trait locus (pQTL) study of 91 plasma proteins measured using the Olink Target platform in 14,824 participants. We identified 180 pQTLs (59 cis, 121 trans). Integration of pQTL data with eQTL and disease genome-wide association studies provided insight into pathogenesis, implicating lymphotoxin-alpha in multiple sclerosis. Using Mendelian randomization (MR) to assess causality in disease etiology, we identified both shared and distinct effects of specific proteins across immune-mediated diseases, including directionally discordant effects of CD40 on risk of rheumatoid arthritis versus multiple sclerosis and inflammatory bowel disease. MR implicated CXCL5 in the etiology of ulcerative colitis (UC) and we show elevated gut CXCL5 transcript expression in patients with UC. These results identify targets of existing drugs and provide a powerful resource to facilitate future drug target prioritization. Here the authors identify genetic effectors of the level of inflammation-related plasma proteins and use Mendelian randomization to identify proteins that contribute to immune-mediated disease risk.
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| 48. |
- Broen, JCA, et al.
(author)
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The functional polymorphism 844 A>G in FcαRI (CD89) does not contribute to systemic sclerosis or rheumatoid arthritis susceptibility
- 2011
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In: The Journal of rheumatology. - : The Journal of Rheumatology. - 0315-162X .- 1499-2752. ; 38:3, s. 446-449
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Journal article (peer-reviewed)abstract
- To investigate the role of the FcαRI 844 A>G functional polymorphism in the genetic predisposition to rheumatoid arthritis (RA) and systemic sclerosis (SSc) susceptibility.Methods.The study population was composed of 1401 patients with SSc, 642 patients with RA, and 1317 healthy controls. The FcαRI (CD89) single-nucleotide polymorphism rs16986050 was genotyped by pyrosequencing.Results.We observed no significant deviation of the genotype and allele frequencies in RA and SSc compared to controls. A metaanalysis and a recessive and dominant model yielded similar negative results.Conclusion.Our data show that the FcαRI 844 A>G polymorphism is not associated with SSc or RA susceptibility.
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