RT Journal Article
SR Electronic
T1 T cell receptor mining of RNA-Seq data to gain insight into inflammatory responses: correlative studies compare the αβ to the γδ T cell infiltrate in psoriasis
JF The Journal of Immunology
JO J. Immunol.
FD American Association of Immunologists
SP 45.47
OP 45.47
VO 200
IS 1 Supplement
A1 Marusina, Alina I.
A1 Merleev, Alexander A.
A1 Shimoda, Michiko
A1 Maverakis, Emanual
YR 2018
UL http://www.jimmunol.org/content/200/1_Supplement/45.47.abstract
AB γδ T cells have been proposed to play a major role in the pathophysiology of psoriasis and other immune-mediated skin diseases. T cell repertoire analysis is one method of characterizing the T cell component of an immune response. Although traditionally conducted on T cell receptor (TCR)-dedicated PCR products, with the appropriate bioinformatics pipeline and sequencing depth, T cell repertoire analysis can also be performed directly on RNA-Seq data sets, allowing for informative correlative studies to be performed in parallel. To compare the role of γδ versus αβ T cells in psoriasis, we have chosen to mine psoriasis and healthy control skin RNA-Seq data sets for TCR receptor sequences. Unlike prior studies, our results are validated in four independently collected datasets.We demonstrate that in the setting of psoriasis, TCRγ and TCR δ-mapped reads are only a small fraction of their TCR α and TCR β counterparts. Although we identified several TCR sequences that were found in multiple skin samples, none were associated with HLA-Cw6. We also observed an increase in TCR repertoire diversity in psoriatic biopsies, demonstrating the polyclonal nature of the T cell infiltrate in psoriasis. Analysis of CDR3 sequences were also conducted revealing no psoriasis-associated public CDR3 sequences. A meta-analysis revealed a statistically significant link between the of TRAJ23 usage in psoriasis and the psoriasis-associated cytokine IL17A. TRGV5, a TCR γ segment, was also associated with psoriasis but correlated instead with IL36A.This work highlights novel strategies for characterizing tissue-infiltrating T cells from RNA-Seq data sets, an approach with broad applications in the fields of autoimmunity and cancer immunotherapy.