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Record W4410715762 · doi:10.3899/jrheum.2025-0390.o003

CHARACTERIZATION OF MEMORY T CELL SUBSETS DURING FLARES AND DISEASE QUIESCENCE IN LUPUS

2025· article· en· W4410715762 on OpenAlexaffvenue
Carol Nassar, Rene Quevedo, M. Teresa Ciudad, Zoha Faheem, Kieran Manion, Carolina Munoz-Grajales, Michael Kim, D. Gladman, Murray B. Urowitz, Zahi Touma, Tracy L. McGaha, Joan Wither

Bibliographic record

VenueThe Journal of Rheumatology · 2025
Typearticle
Languageen
FieldMedicine
TopicSystemic Lupus Erythematosus Research
Canadian institutionsToronto Western HospitalUniversity Health Network
FundersUniversitätsklinikum ErlangenFaculty of Medicine and Health, University of SydneyFondazione IRCCS Ca' Granda Ospedale Maggiore PoliclinicoUniversidad de GranadaPfizerKarolinska InstitutetCentro Pfizer-Universidad de Granada-Junta de Andalucía de Genómica e Investigación OncológicÖrebro UniversitetFriedrich-Alexander-Universität Erlangen-NürnbergNational and Kapodistrian University of Athens
KeywordsMedicineSystemic lupus erythematosusDiseaseLupus erythematosusCharacterization (materials science)ImmunologyPathologyAntibody

Abstract

fetched live from OpenAlex

O003 / #470 Topic:AS01 - Adaptive Immunity SCIENTIFIC HYBRID SESSION: BASIC TRACK PRESENTATIONS - OUTSTANDING ABSTRACT PRESENTATIONS 23-05-2025 9:00 AM - 10:00 AM Background/Purpose Around 70% of Systemic Lupus Erythematosus (SLE) patients follow a relapsing-remitting pattern of disease, with unpredictable flares of disease activity followed by variable periods of disease quiescence. CD4+T cell subsets have been shown to play an important role in driving the autoantibody production which causes flares in SLE, however the precise T cell changes that accompany flares are unknown. Here, we characterized the antigen-experienced memory T cell compartment at various phases of disease to gain insight into this question. Methods CITE-seq was used to assess the transcriptomic profiles of CD4+memory T cells in flaring (change in the clinical SLEDAI-2K > 0 in the last month prompting a change in therapy) and quiescent (clinical SLEDAI = 0 for at least a year, prednisone dose < 10) SLE patients. CD4+memory T cells were isolated from previously archived PBMCs, stained with oligo-conjugated antibodies against surface proteins, and then partitioned, barcoded, and sequenced. Samples from 15 distinct patients at 2 separate clinical visits spaced at least 1 year apart were examined. TCR sequencing was performed to assess clonotype expansion/contraction. The longitudinal nature of our data permitted examination of transcriptional changes both between and within patients. Results Integration of the gene and surface protein expression data led to identification of 23 unique immune populations (Figure 1A). Samples from flaring patients were more enriched for T follicular helper (Tfh), T peripheral helper (Tph), and Th1 cells. Conversely, quiescent patients were more enriched for central memory T cells (TCMs), specifically TCM1/2/6, compared to flaring patients (p < 0.05) (Figure 1B,C). There was no difference in the proportion of exhausted Treg cells between flaring and quiescent patients at baseline suggesting that exhaustion of this subset is a consistent feature of SLE, potentially contributing to an inherent level of immune dysregulation regardless of clinical flare status. Differential gene expression analyses at baseline showed a high prevalence of IFN-induced genes in most identified cell subsets within the genes that were upregulated in flaring compared to quiescent patients, with a few exceptions (eg, Th1). TCR repertoire analyses of samples at baseline revealed a higher proportion of expanded clonotypes in various clusters, such as Th17, in flaring patients (Figure 2A), which was not seen in quiescent patients (Figure 2B). Similarly, clonal overlap among subsets was more pronounced in the flaring patient samples (Figure 2C) than in the quiescent samples (Figure 2D). This overlap indicates that these T cells have shared antigen specificity, suggesting cell plasticity or differentiation from a common precursor – which will be investigated by trajectory pathway analyses. Figure 1: A)UMAP depicting 23 annotated clusters.B)Bar chart of normalized weighted proportions of cells across clusters at baseline based on disease stams: Flaring (F, orange) and Quiescent (Q, blue).C)Boxplots showing distribution of proportional percentages of subsets for each patient at baseline based on disease status. Each dot represents an individual patient’s data point. To assess for statistical difference, Mann-Whitney U test was used, with p-values adjusted for multiple comparisons using the Benjamini-Hochberg method (*, adjusted p < 0.05). Figure 2: Left:Stacked barchart representing the distribution of clonally expanded T cells in each of the identified clusters at baseline (visit 1) in(A)flaring and(B)quiescent patients.Right:Chord diagrams representing clonal overlap between the clusters at baseline in(C)flaring and(D)quiescent patients. The width of each chord indicates the normalized proportion of unique or shared clones between groups which highlights the extent of clonal overlap. Conclusions Although flaring patients demonstrate expansion of relatively few T cell subsets (Tfh, Tph, Th1 cells), analysis of TCR clonotypes suggests that T cells involved in the autoimmune response are found in several additional T cell subsets (Th17, Th2), indicating that there is substantial functional diversification/plasticity in the response. These findings are absent in quiescent patients, which instead show enrichment of TCMs expressing diverse TCRs. Our findings suggest that expanded populations of antigen-specific T cells can be seen in flaring patients, and that further characterization of these cells may provide insight into the specificity of the T cells that support autoantibody production in lupus flares.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.009
GPT teacher head0.264
Teacher spread0.254 · 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 source (direct Gemma or distilled Codex), 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".

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Citations0
Published2025
Admission routes2
Has abstractyes

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