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Record W2914727366 · doi:10.1093/brain/awz017

Abnormal effector and regulatory T cell subsets in paediatric-onset multiple sclerosis

2019· article· en· W2914727366 on OpenAlexafffundabout
Ina Mexhitaj, Rui Li, Julia O’Mahony, Ayman Rezk, Ayal Rozenberg, Craig S. Moore, Trina Johnson, A. Dessa Sadovnick, D. Louis Collins, Douglas L. Arnold, Bruno Gran, E. Ann Yeh, Ruth Ann Marrie, Brenda Banwell, Amit Bar‐Or

Bibliographic record

VenueBrain · 2019
Typearticle
Languageen
FieldMedicine
TopicMultiple Sclerosis Research Studies
Canadian institutionsUniversity of ManitobaMontreal Neurological Institute and HospitalSickKids FoundationUniversity of TorontoUniversity of British ColumbiaMcGill University
FundersCanadian Institutes of Health ResearchNational Institutes of HealthNational Multiple Sclerosis Society
KeywordsMultiple sclerosisImmune systemMedicineEffectorImmunologyDiseaseAbnormalityT cellClinically isolated syndromePhenotypeNeuroscienceBiologyPathologyGenetics

Abstract

fetched live from OpenAlex

Elucidation of distinct T-cell subsets involved in multiple sclerosis immune-pathophysiology continues to be of considerable interest since an ultimate goal is to more selectively target the aberrant immune response operating in individual patients. While abnormalities of both effector (Teff) and regulatory (Treg) T cells have been reported in patients with multiple sclerosis, prior studies have mostly assessed average abnormalities in either limb of the immune response, rather than both at the same time, which limits the ability to evaluate the balance between effectors and regulators operating in the same patient. Assessing both phenotypic and functional responses of Teffs and Tregs has also proven important. In studies of adults with multiple sclerosis, in whom biological disease onset likely started many years prior to the immune assessments, an added challenge for any reported abnormality is whether the abnormality indeed contributes to the disease (and hence of interest to target therapeutically) or merely develops consequent to inflammatory injury (in which case efforts to develop targeted therapies are unlikely to be beneficial). Paediatric-onset multiple sclerosis, though rare, offers a unique window into early disease mechanisms. Here, we carried out a comprehensive integrated study, simultaneously assessing phenotype and functional responses of both effector and regulatory T cells in the same children with multiple sclerosis, monophasic inflammatory CNS disorders, and healthy controls, recruited as part of the multicentre prospective Canadian Pediatric Demyelinating Disease Study (CPDDS). Stringent standard operating procedures were developed and uniformly applied to procure, process and subsequently analyse peripheral blood cells using rigorously applied multi-parametric flow cytometry panels and miniaturized functional assays validated for use with cryopreserved cells. We found abnormally increased frequencies and exaggerated pro-inflammatory responses of CD8+CD161highTCR-Vα7.2+ MAIT T cells and CD4+CCR2+CCR5+ Teffs in paediatric-onset multiple sclerosis, compared to both control groups. CD4+CD25hiCD127lowFOXP3+ Tregs of children with multiple sclerosis exhibited deficient suppressive capacity, including diminished capacity to suppress disease-implicated Teffs. In turn, the implicated Teffs of multiple sclerosis patients were relatively resistant to suppression by normal Tregs. An abnormal Teff/Treg ratio at the individual child level best distinguished multiple sclerosis children from controls. We implicate abnormalities in both frequencies and functional responses of distinct pro-inflammatory CD4 and CD8 T cell subsets, as well as Treg function, in paediatric-onset multiple sclerosis, and suggest that mechanisms contributing to early multiple sclerosis development differ across individuals, reflecting an excess abnormality in either Teff or Treg limbs of the T cell response, or a combination of lesser abnormalities in both limbs.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.080
Threshold uncertainty score0.683

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.027
GPT teacher head0.259
Teacher spread0.232 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations86
Published2019
Admission routes3
Has abstractyes

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