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. 2023 Sep 28;12(19):2376.
doi: 10.3390/cells12192376.

DNA Hypomethylation in the TNF-Alpha Gene Predicts Rheumatoid Arthritis Classification in Patients with Early Inflammatory Symptoms

Rujiraporn Pitaksalee  1 , Rekha Parmar  1 , Richard Hodgett  2 , Paul Emery  1   3 , Frederique Ponchel  1
Affiliations

DNA Hypomethylation in the TNF-Alpha Gene Predicts Rheumatoid Arthritis Classification in Patients with Early Inflammatory Symptoms

Rujiraporn Pitaksalee et al. Cells. .

Abstract

Biomarkers for the classification of rheumatoid arthritis (RA), and particularly for anti-citrullinated peptide antibody (ACPA)-negative patients, remain an important hurdle for the early initiation of treatment. Taking advantage of DNA-methylation patterns specific to early RA, quantitative methylation-specific qPCR (qMSP) offers a robust technology for the development of biomarkers. We developed assays and established their value as RA classification biomarkers.

Methods: DNA-methylation data were screened to select candidate CpGs to design qMSP assays. Eight assays were developed and tested on two early inflammatory arthritis cohorts. Logistic regression and bootstrapping were used to demonstrate the added value of the qMSP assays.

Result: Differentially methylated CpG data were screened for candidate CpG, thereby meeting the qMSP assay requirements. The top CpG candidate was in the TNF gene, for which we successfully developed a qMSP assay. Significantly lower DNA-methylation levels were observed in RA (p < 4 × 10-9), with a high predictive value (OR < 0.54/AUC < 0.198) in both cohorts (n = 127/n = 157). Regression using both datasets showed improved accuracy = 87.7% and AUC = 0.944 over the model using only clinical variables (accuracy = 85.2%, AUC = 0.917). Similar data were obtained in ACPA-negative patients (n = 167, accuracy = 82.6%, AUC = 0.930) compared to the clinical variable model (accuracy = 79.5%, AUC = 0.892). Bootstrapping using 2000 datasets confirmed that the AUCs for the clinical+TNF-qMSP model had significant added value in both analyses.

Conclusion: The qMSP technology is robust and can successfully be developed with a high specificity of the TNF qMSP assay for RA in patients with early inflammatory arthritis. It should assist classification in ACPA-negative patients, providing a means of reducing time to diagnosis and treatment.

Keywords: DNA-methylation; qMSP biomarker; rheumatoid arthritis.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
qMSP assay optimisation and verification. (A) Amplification plot and (B) CT values of TNF, GAPDH, and IRF8 qMSP assays using forward and reverse primers at different concentrations (50, 300 or 900 nM). The primer concentrations at F300/R50 and F900/R900 and F50/R300 nM were chosen for TNF, GAPDH, and IRF8 assays, respectively (black arrow). (C) CT values of TNF, GAPDH, and IRF8 assays using 100% methylated control DNA and 100% unmethylated control DNA as templates. (D) Dilution curve and best-fit line of 2 qMSP; GAPDH assay, for methylated and unmethylated control DNA and the TNF assay, the 100% methylated control DNA only showing amplification. Both assays show good efficiency and a good fit of the regression model to the data. (E) Boxplot of the % of methylation of the TNF qMSP in CD4+ T-cells, PBMC, or WB in early RA (Grey, n = 6) and healthy control (black, n = 6). MWU test: ** p < 0.001, * p < 0.05, ns: not significant.
Figure 2
Figure 2
RA classification using the TNF-qMSP assay. (A) Box plot representation of the levels of DNA-methylation (%) for the TNF assay in IACON samples (n = 127) for 4 diagnostic groups and HCs. Kruskal-Wallis test p-values, followed by Dunn’s multiple comparison test: **** p < 0.0001, *** p < 0.0001, ** p < 0.001 were shown. (B) Box plot representation of the levels of DNA methylation (%) in IACON (left panel, RA n = 64 versus non-RA n = 63, MWU p = 4.0 × 10−9), and RADAR (right panel, RA n = 126 versus non-RA n = 31, p = 8.2 × 10−9). (C) Predictive value (AUROC analysis) of the TNF DNA-methylation levels (black line) and TNF DNA-methylation risk category (grey line) in cohorts combined (left panel, n = 284) and in the ACPA-negative patients (right panel, n = 167). (D) Predictive value (AUROC analysis) of the clinical model without (grey line) and with the TNF qMSP data (black line) or with the TNF-qMSP risk category (dotted line) in the combined dataset (n = 284) and in the ACPA-negative patients (n = 167). AUC values are indicated on the graph.
Figure 2
Figure 2
RA classification using the TNF-qMSP assay. (A) Box plot representation of the levels of DNA-methylation (%) for the TNF assay in IACON samples (n = 127) for 4 diagnostic groups and HCs. Kruskal-Wallis test p-values, followed by Dunn’s multiple comparison test: **** p < 0.0001, *** p < 0.0001, ** p < 0.001 were shown. (B) Box plot representation of the levels of DNA methylation (%) in IACON (left panel, RA n = 64 versus non-RA n = 63, MWU p = 4.0 × 10−9), and RADAR (right panel, RA n = 126 versus non-RA n = 31, p = 8.2 × 10−9). (C) Predictive value (AUROC analysis) of the TNF DNA-methylation levels (black line) and TNF DNA-methylation risk category (grey line) in cohorts combined (left panel, n = 284) and in the ACPA-negative patients (right panel, n = 167). (D) Predictive value (AUROC analysis) of the clinical model without (grey line) and with the TNF qMSP data (black line) or with the TNF-qMSP risk category (dotted line) in the combined dataset (n = 284) and in the ACPA-negative patients (n = 167). AUC values are indicated on the graph.
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Grants and funding

This research was directly funded by a Royal Thai Government Scholarship to RP and partly supported by the IMI-funded project BeTheCure No 115142-2.