bioRxivImmunologyPreprint
Permutational immune analysis reveals architectural similarities between inflammaging, Down syndrome and autoimmunity
K. Lambert, K. G. Moo, A. Arnett, G. Goel, K. J. Flynn, C. Speake, A. E. Wiedeman, C. J. Greenbaum, S. A. Long, R. Partridge, J. H. Buckner, B. Khor
Abstract
People with Down syndrome show cellular and clinical features of dysregulated aging of the immune system, including naive-memory shift in the T cell compartment and increased incidence of autoimmunity. However, a quantitative understanding of how various immune compartments change with age in Down syndrome remains lacking. Here we performed deep immunophenotyping of a cohort of individuals with Down syndrome across the lifespan, selecting for individuals not affected by autoimmunity. We simultaneously interrogated age- and sex-matched healthy neurotypical controls and people with type 1 diabetes, as a representative autoimmune disease. We built a new analytical software, IMPACD, that enabled us to rapidly identify many features of immune dysregulation in Down syndrome that are recapitulated in other autoimmune diseases. We found significant quantitative and qualitative dysregulation of naive CD4+ and CD8+ T cells in Down syndrome and identified IL-6 as a candidate driver of some of these changes, thus extending the consideration of immunopathologic cytokines in Down syndrome beyond interferons. Notably, we successfully used immune cellular composition to generate three quantitative models of aging (i.e. immune clocks) trained on control subjects. All three immune clocks demonstrated significantly advanced immune aging in people with Down syndrome. Notably, one of these clocks, informed by Down syndrome-relevant biology, also showed advanced immune aging in people with type 1 diabetes. Together, our findings demonstrate a novel approach to studying immune aging in Down syndrome which may have implications in the context of other autoimmune diseases. One Sentence SummaryPermutational analysis of immune landscape reveals advanced immune aging in people with Down syndrome and in people with type 1 diabetes.
- organism: human
- assay: flow cytometry
- assay: proteomics
- research focus: cell type annotation
- research focus: differential expression
- research focus: gene regulatory network inference
- research focus: drug response / drug discovery
- approach: deep learning
- approach: statistical model
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