Literature DB >> 35020411

Deep immune phenotyping reveals similarities between aging, Down syndrome, and autoimmunity.

Katharina Lambert1, Keagan G Moo1, Azlann Arnett1, Gautam Goel2, Alex Hu2, Kaitlin J Flynn2, Cate Speake3, Alice E Wiedeman1, Vivian H Gersuk2, Peter S Linsley2, Carla J Greenbaum3, S Alice Long1, Rebecca Partridge1,4, Jane H Buckner1, Bernard Khor1.   

Abstract

Individuals with Down syndrome show cellular and clinical features of dysregulated aging of the immune system, including a shift from naïve to memory T cells 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 life span, selecting for autoimmunity-free individuals. We simultaneously interrogated age- and sex-matched healthy controls and people with type 1 diabetes as a representative autoimmune disease. We built an analytical software, IMPACD (Iterative Machine-assisted Permutational Analysis of Cytometry Data), that enabled us to rapidly identify many features of immune dysregulation in Down syndrome shared with other autoimmune diseases. We found quantitative and qualitative dysregulation of naïve CD4+ and CD8+ T cells in individuals with Down syndrome and identified interleukin-6 as a candidate driver of some of these changes, thus extending the consideration of immunopathologic cytokines in Down syndrome beyond interferons. We used immune cellular composition to generate three linear models of aging (immune clocks) trained on control participants. All three immune clocks demonstrated advanced immune aging in individuals with Down syndrome. One of these clocks, informed by Down syndrome–relevant biology, also showed advanced immune aging in individuals with type 1 diabetes. Orthologous RNA sequencing–derived immune clocks also demonstrated advanced immune aging in individuals with Down syndrome. Together, our findings demonstrate an approach to studying immune aging in Down syndrome that may have implications in other autoimmune diseases.

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Year:  2022        PMID: 35020411      PMCID: PMC9312285          DOI: 10.1126/scitranslmed.abi4888

Source DB:  PubMed          Journal:  Sci Transl Med        ISSN: 1946-6234            Impact factor:   19.319


  84 in total

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Journal:  Vaccine       Date:  2015-10-28       Impact factor: 3.641

2.  Decreased thymic output accounts for decreased naive T cell numbers in children with Down syndrome.

Authors:  Beatrijs L P Bloemers; Louis Bont; Roel A de Weger; Sigrid A Otto; Jose A Borghans; Kiki Tesselaar
Journal:  J Immunol       Date:  2011-02-23       Impact factor: 5.422

3.  Excessive production of interleukin 6/B cell stimulatory factor-2 in rheumatoid arthritis.

Authors:  T Hirano; T Matsuda; M Turner; N Miyasaka; G Buchan; B Tang; K Sato; M Shimizu; R Maini; M Feldmann
Journal:  Eur J Immunol       Date:  1988-11       Impact factor: 5.532

4.  Mass Cytometry Reveals Global Immune Remodeling with Multi-lineage Hypersensitivity to Type I Interferon in Down Syndrome.

Authors:  Katherine A Waugh; Paula Araya; Ahwan Pandey; Kimberly R Jordan; Keith P Smith; Ross E Granrath; Santosh Khanal; Eric T Butcher; Belinda Enriquez Estrada; Angela L Rachubinski; Jennifer A McWilliams; Ross Minter; Tiana Dimasi; Kelley L Colvin; Dmitry Baturin; Andrew T Pham; Matthew D Galbraith; Kyle W Bartsch; Michael E Yeager; Christopher C Porter; Kelly D Sullivan; Elena W Hsieh; Joaquin M Espinosa
Journal:  Cell Rep       Date:  2019-11-12       Impact factor: 9.423

Review 5.  What people with Down Syndrome can teach us about cardiopulmonary disease.

Authors:  Kelley L Colvin; Michael E Yeager
Journal:  Eur Respir Rev       Date:  2017-02-21

6.  DiffCoEx: a simple and sensitive method to find differentially coexpressed gene modules.

Authors:  Bruno M Tesson; Rainer Breitling; Ritsert C Jansen
Journal:  BMC Bioinformatics       Date:  2010-10-06       Impact factor: 3.169

7.  Diminished CXCR5 expression in peripheral blood of patients with Sjögren's syndrome may relate to both genotype and salivary gland homing.

Authors:  L A Aqrawi; M Ivanchenko; A Björk; J I Ramírez Sepúlveda; J Imgenberg-Kreuz; M Kvarnström; P Haselmayer; J L Jensen; G Nordmark; K Chemin; K Skarstein; M Wahren-Herlenius
Journal:  Clin Exp Immunol       Date:  2018-03-24       Impact factor: 4.330

8.  T-helper 17 cells expand in multiple sclerosis and are inhibited by interferon-beta.

Authors:  Luca Durelli; Laura Conti; Marinella Clerico; Daniela Boselli; Giulia Contessa; Paolo Ripellino; Bruno Ferrero; Pierre Eid; Francesco Novelli
Journal:  Ann Neurol       Date:  2009-05       Impact factor: 10.422

9.  HTSeq--a Python framework to work with high-throughput sequencing data.

Authors:  Simon Anders; Paul Theodor Pyl; Wolfgang Huber
Journal:  Bioinformatics       Date:  2014-09-25       Impact factor: 6.937

10.  New interpretable machine-learning method for single-cell data reveals correlates of clinical response to cancer immunotherapy.

Authors:  Evan Greene; Greg Finak; Leonard A D'Amico; Nina Bhardwaj; Candice D Church; Chihiro Morishima; Nirasha Ramchurren; Janis M Taube; Paul T Nghiem; Martin A Cheever; Steven P Fling; Raphael Gottardo
Journal:  Patterns (N Y)       Date:  2021-10-27
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  1 in total

1.  COVID-19 hospitalization with later long COVID in a person with Down syndrome.

Authors:  Mohammad Ashraful Amin; Ishtiakul Islam Khan; Sabrina Nahin; Atia Sharmin Bonna; Sadia Afrin; Mohammad Delwer Hossain Hawlader
Journal:  Clin Case Rep       Date:  2022-10-07
  1 in total

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