1
|
Baxter RM, Wang CS, Garcia-Perez JE, Kong DS, Coleman BM, Larchenko V, Schuyler RP, Jackson C, Ghosh T, Rudra P, Paul D, Claassen M, Rochford R, Cambier JC, Ghosh D, Cooper JC, Smith MJ, Hsieh EWY. Expansion of extrafollicular B and T cell subsets in childhood-onset systemic lupus erythematosus. Front Immunol 2023; 14:1208282. [PMID: 37965329 PMCID: PMC10641733 DOI: 10.3389/fimmu.2023.1208282] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Accepted: 10/06/2023] [Indexed: 11/16/2023] Open
Abstract
Introduction Most childhood-onset SLE patients (cSLE) develop lupus nephritis (cLN), but only a small proportion achieve complete response to current therapies. The prognosis of children with LN and end-stage renal disease is particularly dire. Mortality rates within the first five years of renal replacement therapy may reach 22%. Thus, there is urgent need to decipher and target immune mechanisms that drive cLN. Despite the clear role of autoantibody production in SLE, targeted B cell therapies such as rituximab (anti-CD20) and belimumab (anti-BAFF) have shown only modest efficacy in cLN. While many studies have linked dysregulation of germinal center formation to SLE pathogenesis, other work supports a role for extrafollicular B cell activation in generation of pathogenic antibody secreting cells. However, whether extrafollicular B cell subsets and their T cell collaborators play a role in specific organ involvement in cLN and/or track with disease activity remains unknown. Methods We analyzed high-dimensional mass cytometry and gene expression data from 24 treatment naïve cSLE patients at the time of diagnosis and longitudinally, applying novel computational tools to identify abnormalities associated with clinical manifestations (cLN) and disease activity (SLEDAI). Results cSLE patients have an extrafollicular B cell expansion signature, with increased frequency of i) DN2, ii) Bnd2, iii) plasmablasts, and iv) peripheral T helper cells. Most importantly, we discovered that this extrafollicular signature correlates with disease activity in cLN, supporting extrafollicular T/B interactions as a mechanism underlying pediatric renal pathogenesis. Discussion This study integrates established and emerging themes of extrafollicular B cell involvement in SLE by providing evidence for extrafollicular B and peripheral T helper cell expansion, along with elevated type 1 IFN activation, in a homogeneous cohort of treatment-naïve cSLE patients, a point at which they should display the most extreme state of their immune dysregulation.
Collapse
Affiliation(s)
- Ryan M. Baxter
- Department of Immunology and Microbiology, School of Medicine, University of Colorado, Aurora, CO, United States
| | - Christine S. Wang
- Department of Pediatrics, Section of Rheumatology, School of Medicine, University of Colorado, Children’s Hospital Colorado, Aurora, CO, United States
| | - Josselyn E. Garcia-Perez
- Department of Immunology and Microbiology, School of Medicine, University of Colorado, Aurora, CO, United States
| | - Daniel S. Kong
- Department of Immunology and Microbiology, School of Medicine, University of Colorado, Aurora, CO, United States
| | - Brianne M. Coleman
- Department of Immunology and Microbiology, School of Medicine, University of Colorado, Aurora, CO, United States
| | - Valentyna Larchenko
- Department of Immunology and Microbiology, School of Medicine, University of Colorado, Aurora, CO, United States
| | - Ronald P. Schuyler
- Department of Immunology and Microbiology, School of Medicine, University of Colorado, Aurora, CO, United States
| | - Conner Jackson
- Center for Innovative Design and Analysis, School of Public Health, University of Colorado, Aurora, CO, United States
| | - Tusharkanti Ghosh
- Department of Biostatistics and Informatics, School of Public Health, University of Colorado, Aurora, CO, United States
| | - Pratyaydipta Rudra
- Department of Statistics, Oklahoma State University, Stillwater, OK, United States
| | - Debdas Paul
- Clinical Bioinformatics & Machine Learning in Translational Single-Cell Biology, University of Tuebingen, Tuebingen, Germany
| | - Manfred Claassen
- Clinical Bioinformatics & Machine Learning in Translational Single-Cell Biology, University of Tuebingen, Tuebingen, Germany
| | - Rosemary Rochford
- Department of Immunology and Microbiology, School of Medicine, University of Colorado, Aurora, CO, United States
| | - John C. Cambier
- Department of Immunology and Microbiology, School of Medicine, University of Colorado, Aurora, CO, United States
| | - Debashis Ghosh
- Department of Biostatistics and Informatics, School of Public Health, University of Colorado, Aurora, CO, United States
| | - Jennifer C. Cooper
- Department of Pediatrics, Section of Rheumatology, School of Medicine, University of Colorado, Children’s Hospital Colorado, Aurora, CO, United States
| | - Mia J. Smith
- Department of Immunology and Microbiology, School of Medicine, University of Colorado, Aurora, CO, United States
- Department of Pediatrics, Barbara Davis Center for Diabetes, School of Medicine, University of Colorado, Aurora, CO, United States
| | - Elena W. Y. Hsieh
- Department of Immunology and Microbiology, School of Medicine, University of Colorado, Aurora, CO, United States
- Department of Pediatrics, Section of Allergy and Immunology, School of Medicine, University of Colorado, Children’s Hospital Colorado, Aurora, CO, United States
| |
Collapse
|
2
|
Libertin CR, Kempaiah P, Gupta Y, Fair JM, van Regenmortel MHV, Antoniades A, Rivas AL, Hoogesteijn AL. Data structuring may prevent ambiguity and improve personalized medical prognosis. Mol Aspects Med 2022; 91:101142. [PMID: 36116999 DOI: 10.1016/j.mam.2022.101142] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2022] [Revised: 08/27/2022] [Accepted: 08/29/2022] [Indexed: 01/17/2023]
Abstract
Topics expected to influence personalized medicine (PM), where medical decisions, practices, and treatments are tailored to the individual patient, are reviewed. Lack of discrimination due to different biological conditions that express similar values of numerical variables (ambiguity) is regarded to be a major potential barrier for PM. This material explores possible causes and sources of ambiguity and offers suggestions for mitigating the impacts of uncertainties. Three causes of ambiguity are identified: (1) delayed adoption of innovations, (2) inadequate emphases, and (3) inadequate processes used when new medical practices are developed and validated. One example of the first problem is the relative lack of medical research on "compositional data" -the type that characterizes leukocyte data. This omission results in erroneous use of data abundantly utilized in medicine, such as the blood cell differential. Emphasis on data output ‒not biomedical interpretation that facilitates the use of clinical data‒ exemplifies the second type of problems. Reliance on tools generated in other fields (but not validated within biomedical contexts) describes the last limitation. Because reductionism is associated with these problems, non-reductionist alternatives are reviewed as potential remedies. Data structuring (converting data into information) is considered a key element that may promote PM. To illustrate a process that includes data-information-knowledge and decision-making, previously published data on COVID-19 are utilized. It is suggested that ambiguity may be prevented or ameliorated. Provided that validations are grounded on biomedical knowledge, approaches that describe certain criteria - such as non-overlapping data intervals of patients that experience different outcomes, immunologically interpretable data, and distinct graphic patterns - can inform, at personalized bases, earlier and/or with fewer observations.
Collapse
Affiliation(s)
- Claudia R Libertin
- Department of Medicine, Division of Infectious Diseases, Mayo Clinic, Jacksonville, FL, 32224, USA
| | - Prakasha Kempaiah
- Department of Medicine, Division of Infectious Diseases, Mayo Clinic, Jacksonville, FL, 32224, USA
| | - Yash Gupta
- Department of Medicine, Division of Infectious Diseases, Mayo Clinic, Jacksonville, FL, 32224, USA
| | - Jeanne M Fair
- Bioscience Division, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA
| | - Marc H V van Regenmortel
- School of Biotechnology, Centre National de la Recherche Scientifique (CNRS), University of Strasbourg, France
| | | | - Ariel L Rivas
- Center for Global Health-Division of Infectious Diseases, School of Medicine, University of New Mexico, Albuquerque, NM, 87131, USA.
| | - Almira L Hoogesteijn
- Human Ecology, Centro de Investigación y de Estudios Avanzados (CINVESTAV), Mérida, Yucatán, Mexico
| |
Collapse
|