The immunogenetics of rheumatoid arthritis

“In the Viatte Lab, we study the genetic control of immunological mechanisms involved in the cause and outcome of rheumatoid arthritis.”

6/2019

FHEA, Fellow of the Higher Education Academy, UK

1/2013

CCT (Certificate of Completion of Specialist Training) Internal / General Medicine, Switzerland

4/2005

PhD Human Immunology and Immunogenetics, University of Lausanne, Switzerland

8/2004

MD, Medical Doctorate, University of Basel, Switzerland

12/2001

MBChB, University of Basel, Switzerland

10/1999

BSc Molecular Biology and Biophysics, University of Basel, Switzerland

8/2024 – present

Senior Lecturer in Genetics, Centre for Musculoskeletal Research, University of Manchester, UK

10/2018 – present

Principal investigator, Lydia Becker Institute for Immunology, Immune Tolerance Theme, University of Manchester, UK

12/2015 – present

Associate member, Manchester Collaborative Centre for Inflammation Research (MCCIR) University of Manchester, UK

3/2016 – 7/2024

Lecturer in Genetics

3/2013 – 2/2016

Research Fellow in Genetics

3/2010 – 2/2013

Post-doctoral Research Fellow in Genetics

10/2007 – 2/2010

Resident / Specialist Registrar in Internal Medicine, Biel Hospital, Centre, Switzerland

3/2006 – 9/2007

Resident / Specialist Registrar in Rheumatology, Geneva University Hospital, HUG, Switzerland

4/2005 – 2/2006

Medical Intern / Foundation doctor, Dept. of Internal Medicine, Lausanne University Hospital, CHUV, Switzerland

3/2002 – 3/2005

Ludwig Institute for Cancer Research, Ph.D. student in Tumour Immunology (Prof. J.-C. Cerottini), University of Lausanne, Switzerland

1995 - 2001

Medical School, University of Basel, Switzerland

10/1997 - 5/1999

Medical thesis (Prof. C. Moroni), Institute for Medical Microbiology, Tumour Virology, Basel,Switzerland

1997-1999

Studies of Molecular Biology and Biophysics, University of Basel, Switzerland

The Viatte lab combines experience in immunology using single cell technologies (single cell RNA Seq, mass and flow cytometry, multiplex imaging – wet lab component) with experience in statistical genetics and bioinformatics (data analysis - dry component).

Disease focus

Rheumatoid arthritis (RA) is a disease affecting around 1% of the population in the United Kingdom. Typically, RA onset is in the fifth decade of life, but disease onset at an earlier age is frequent. RA affects mainly the small joints of hands and feet, causing pain, disability and, if not treated, joint destruction. The exact cause of the disease is not known, but it is established that the immune system of the patient is attacking  his own joints - therefore, RA is classified as an autoimmune disease.

Based on the presence or absence of antibodies called anti-citrullinated peptide antibodies (ACPA), RA can be classified as ACPA+ or ACPA- disease. ACPA+ RA is usually more severe than ACPA- RA, though several patients with ACPA+ RA have a milder disease course, while  several patients with ACPA- RA have a more severe disease course. Therefore, ACPA cannot be used predict for sure which patients will develop a disabling disease and should be treated more aggressively at disease onset.

Vision

Our lack of understanding of basic mechanisms underlying autoimmune disease aetiology prevents the development successful management strategies for patients’ benefit. Genetic studies of the Human Leucocyte Antigen (HLA) system suggest a central role of peptide autoantigens in the initiation and maintenance of autoimmune diseases.

Main Research Questions

• Understanding disease mechanisms underlying genetic associations with rheumatoid arthritis

• • Phenotype and function of anti-citrulline CD4⁺ T cells in different anatomical compartments and different disease stages
  • Genetic mapping: identify immune cell states and intracellular pathways under the control of rheumatoid arthritis genetic susceptibility polymorphisms

• Identification of genetic and cellular classifiers for precision medicine in rheumatoid arthritis
  • Genetic pleiotropy: identification of shared and unique genetic markers underlying susceptibility and prognosis/outcome in rheumatoid arthritis and their overlap with other autoimmune diseases.
  • Immunophenotyping programme of myeloid and lymphoid cell subsets by mass cytometry across anatomical compartments to identify immune signatures of clinical relevance

Genetic associations of rheumatoid arthritis susceptibility, severity and response to treatment

We contributed to show that anti-citrullinated peptide antibody negative and positive rheumatoid arthritis are genetically different. Some genetic markers are shared, while others are specific to one serotype only (Viatte; Ann Rheum Dis, 2012; Eyre, Nat Genet, 2012; Viatte, Arthritis Rheumatol, 2016).

Using and further developing longitudinal modelling techniques of radiographic outcome in RA, the Viatte lab identified or replicated several genetic markers of disease severity outside the HLA (CARD9 in Sharma, Ann Rheum Dis, 2025; FOXO3A in Viatte & Lee, Arthritis Rheumatol, 2016 and in Lee, Cell, 2013; TRAF1 in Viatte, J Rheumatol, 2013). We were the first lab to show that the strongest genetic association with radiographic outcome in RA across the entire genome is the carriage of the amino acid valine at position 11 of HLA-DRβ1 (Viatte, JAMA, 2015). We used Generalized Linear Latent And Mixed Models (GLLAMMs) to incorporate multiple records per patients over time in one statistical model to predict non-normally distributed measures of disease outcome. Interestingly, in contrast to genetic associations outside the HLA, or in contrast to other autoimmune diseases, we managed to show that, within the HLA, associations with RA susceptibility are the same as associations , and this with multiple measures of RA severity (or prognosis, outcome – Sharma, Arthritis Res Ther, 2022; Ling & Viatte, Arthritis Rheumatol, 2016; Viatte, JAMA, 2015).

Cellular mechanisms underlying genetic associations with disease

This programme of work is ongoing – in preliminary data analysis of mass cytometry datasets, we have shown the superiority of agnostic approaches (automated clustering) over manual gating to identify cellular signatures of rheumatoid arthritis in peripheral blood in small sample sizes (Mulhearn & Marshall, Front Immunol, 2023).

Clinical translation and precision medicine

The Viatte lab has shown that genetic haplotypes defined by HLA-DRB1 positions 11/13, 71 and 74 classify patients into different prognostic categories (Viatte, JAMA, 2015), however, working with collaborators, we could also show that the predictive capacity of the same position is not sufficient to guide clinical decisions for treatment with with either TNF inhibitors or abatacept (Jiang, Arthritis Res Ther, 2016; Yap, Arthritis Rheumatol, 2025).