Suzana Stanisavljević*
Institute for Biological Research "Siniša Stanković"
ssuzana [at] ibiss.bg.ac.rs
Abstract
Innate lymphoid cells (ILCs) are emerging regulators of immune responses, yet their role in autoimmune diseases remains insufficiently characterised. In multiple sclerosis (MS), there are controversial findings regarding the role of ILCs in the pathogenesis of the disease. Some studies in an animal model of MS, experimental autoimmune encephalomyelitis (EAE), are showing that the infiltration of ILCs in the central nervous system can exacerbate the disease. Other studies are exploiting modulation of some ILCs, such as gut ILC3, in order to alleviate the EAE. In order to better understand the potential role of ILCs in MS, a computational re-analysis of publicly available transcriptomic datasets is performed. Using signature genes, ILC subsets (ILC1, ILC2 and ILC3) were identified. Relative enrichment of ILC subsets was quantified in peripheral blood samples of healthy controls and patients with different forms of MS, relapsing-remitting and progressive MS. Differences in transcriptional profile across MS disease forms were analysed and used as a potential indicator of the role of specific ILC subsets in the progression of MS.
Our analysis revealed a consistent shift in ILC-associated signatures between disease stages. Progressive MS samples exhibited increased enrichment of ILC1- and ILC3-related transcriptional profiles, accompanied by a relative reduction in ILC2-associated signatures. This pattern suggests a polarisation toward pro-inflammatory innate lymphoid profiles with disease progression. Notably, these trends were reproducible across independent datasets, supporting their strength despite differences in cohort composition and experimental platforms.
These findings highlight the importance of ILC polarisation as a potential contributor to disease progression in MS. Moreover, our study demonstrates that meaningful insights can be obtained from transcriptomic data through targeted computational approaches.
Keywords: transcriptomics, multiple sclerosis, ILC