Nigel Michki [1] Benjamin Singer [2] Javier Perez [1] Aaron Thomas [1] Valerie Natale [3] Kathryn A. Helmin [2] Jennifer Wright [4] Leon Cheng [4] Lisa Young [1] Howard M. Lederman [4] Sharon A. McGrath-Morrow [1] [1] Children's Hospital of Philadelphia Division of Pulmonary Medicine and Sleep and Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA [2] Division of Pulmonary and Critical Care Medicine, Department of Medicine Northwestern University Feinberg School of Medicine Chicago, IL USA [3] Forgotten Diseases Research Foundation, Santa Clara, CA, USA [4] Department of Immunology, Johns Hopkins University School of Medicine, Baltimore, MD,USA
Poster # 65
Ataxia telangiectasia (A-T) is an autosomal recessive neurodegenerative disease with widespread systemic manifestations and marked variability in clinical phenotypes. In this study we sought to determine if molecular phenotyping could be used to identify subsets of individuals with A-T, beyond that of the mild and classic phenotypes, and if transcriptomics could be used to identify novel peripheral biomarkers for disease classification and treatment response to therapy.Methods: Participants with stable A-T were recruited and compared to unaffected controls. PBMCs were isolated and bulk RNAseq and analysis was performed. Plasma was also isolated in a subset of individuals. Affected individuals were designated mild or classic based on ATM mutations and/or clinical and laboratory features.Results: Classic A-T subjects were more likely to be younger and IgA deficient, and to have higher alpha-fetoprotein (AFP) levels and lower % forced vital capacity (% FVC), compared to mild A-T subjects. Using molecular phenotyping, we assigned inflammatory scores to study participants. Higher inflammatory scores were associated with lower ATM expression. Moderate or high inflammatory scores were found in 79% of classic A-T subjects, 46% of mild A-T subjects and 6.7% of unaffected subjects. Classic A-T subjects also had higher SERPINE1 mRNA and protein levels, higher FLT4 protein levels and markedly reduced mRNA neuronal cell adhesion molecule (NrCAM) expression.Conclusion: Using an unbiased transcriptomic approach, we have identified several novel biomarkers and developed an inflammatory score to identify subsets of individuals with different inflammatory phenotypes. Findings from this study could be used to help direct treatment and to track treatment response to therapy.
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