SUPR
Gene expression profiling in ANCA-associated vasculitis
Dnr:

sens2021561

Type:

SNIC SENS

Principal Investigator:

Johanna Dahlqvist

Affiliation:

Uppsala universitet

Start Date:

2021-06-23

End Date:

2025-08-01

Primary Classification:

30210: Rheumatology and Autoimmunity

Allocation

Abstract

ANCA-associated vasculitis (AAV) comprises some of the most severe diseases among the rheumatic disorders, with a potentially fatal disease course. The AAV are characterized by autoimmune inflammation of small vessels which may affect practically any organ, with kidneys, upper airways and lungs being most commonly affected. Current diagnostics sufficiently stratifies between the most common clinical phenotypes of AAV, but additional patient stratification based on cellular or molecular mechanisms is currently not in practice. Hence, available therapeutic options are few with an unspecific immune-suppressive approach. The purpose of this project is to improve diagnostics and treatment strategies for patients, through a systematic exploration of the disease mechanisms. In order to identify the crucial cell types that drive the inflammation in AAV and the transcriptional pathways that are activated in these cells, we will take advantage of a unique collection of AAV patient blood cells, collected during a course of time from active disease to remission. Using single-cell RNA sequencing, cells collected from different time points and from healthy controls will be analyzed for gene expression on a single-gene and a signaling pathway level. Data will be analyzed for expanded and differentiated cell types as well as activated genes, signaling pathways and master regulatory transcription factors. Concurrently, cell samples from the same time points will be analyzed using flow cytometry, for deep characterization of T cell and B cell subsets. This thorough exploration of disease mechanisms will provide us a detailed map of the cellular and transcriptional pathways that drive the inflammatory activity in AAV, enabling identification of novel drug targets. Additionally, integrated analysis of the multi-omics datasets will enable more precise stratification of patients, based on cellular and molecular patterns. Combined, these findings will advance diagnostics and treatment of patients with AAV.