P180 RNA sequencing of regulatory T cells sorted from Rheumatoid Arthritis peripheral blood
Authors: Kommoju Vallayyachari , Mariaselvam Christinamary , Vembar Shruthi , Bulusu Sree Nethra , Kavadichanda chengappa , Thabah Molly Mary , Singh Vir Negi 
Published: April 2024

Background/Aims

In the presence of an inflammatory environment, regulatory T cells (Tregs) have the ability to adopt an effector T helper(Th)-like phenotype, enabling them to produce pro-inflammatory cytokines, particularly IFN-γ (resembling Th1-like Tregs) and IL-17(resembling Th17-like Tregs). This adaptability of Tregs may be driven by the increased activation of effector pathways. In order to unveil distinct gene expression patterns while simultaneously characterizing Tregs based on their phenotype, we did RNA sequencing of sorted Tregs from the peripheral blood(PB) of individuals with rheumatoid arthritis(RA), osteoarthritis(OA), and healthy controls(HC).

Methods

Phenotyping of Tregs, Tregs expressing CXCR3(Th1-like Treg), CCR6(Th17-like Treg) from PB and synovial fluid(SF) of RA(n = 20 each), OA(n = 20 each) and HC (n = 20) was done using flow cytometry. Cell frequencies between the groups were compared using Student t test or one-way ANOVA (Tukey's post hoc Test) as appropriate; p &lt; 0.05 was considered as statistically significant. RNA was extracted from the sorted PB Tregs of RA, OA and HC(n = 3 each) and was sequenced using Illumina Novaseq 6000 platform. Sample clustering was done using principal component analysis generated using DeSeq2. Genes were categorized as upregulated or downregulated based on their log2 fold change cut off of 2. Significantly upregulated and downregulated differentially expressed genes (DEGs) were shortlisted using a false discovery rate(FDR) corrected p-value cut-off of ≤ 0.05. Heatmaps and volcano plots were generated using R package. Functional pathway enrichment analysis was done using GO and KEGG software.

Results

Proportion of Tregs in the PB of RA was significantly lower than OA and HC. This difference was not observed in the SF. Both RA PB and SF exhibited higher percentages of Th1 and Th17-like Tregs when compared to OA and HC (Table 1A).DEG analysis revealed altered gene regulation in RA Tregs compared to Tregs from HC and OA. Compared to HC, genes related to TCR signaling, cytokine signaling, and chemokine signaling pathways were enriched in RA. In contrast, compared to OA, an upregulation of pathway genes such as NR4A1(PI3K-Akt signaling), and TNFRSF1B(TNF signaling) was noted in RA. Furthermore, genes associated with Th1 differentiation and activation (IFNGR1, IL12RB1, STAT4) and genes associated with Th17 profile(RORC, STAT3, IL6R, IL17B) were upregulated in RA relative to HC and but not in OA compared to to HC.(Table 1B). Additionally, genes involved in glucose metabolism, purine metabolism, oxidative phosphorylation, and pyrimidine metabolism exhibited distinct patterns of regulation in RA.

Conclusion

Our Treg phenotyping data shows that there is higher conversion of Tregs into Th1 and Th17 like Tregs in RA and transcriptome data indicates cytokine(IFNγ, IL-6, IL-12 & IL-17) mediated transcriptional changes likely drove the conversion of Tregs towards Th1 and Th17 phenotype. Our data also suggests a possible metabolic re-programming of Tregs towards an inflammatory phenotype affecting its stability.

Disclosure

V. Kommoju: None. C. mariaselvam: None. S. Vembar: None. S. Bulusu: None. C. Kavadichanda: None. M. Thabah: None. V. Negi: None.