CD8+ T CELLS AND THEIR ANTIGENS IN END-ORGAN DAMAGE IN LUPUS NEPHRITIS
Bibliographic record
Abstract
PV006 / #242 Poster Topic: AS01 - Adaptive Immunity Background/Purpose Lupus Nephritis (LN) is a severe and frequent complication of systemic lupus erythematosus (SLE). It is increasingly clear that the LN kidney hosts pathogenic mechanisms contributing to disease severity, with CD8+ T lymphocytes coming to the fore as relevant players. Their pathogenicity may be due to their recognition of renal (neo/modified/cryptic) antigens and consequent tissue damage. Methods We performed scRNASeq on flow-sorted CD8+ T cells from kidney, urine and blood samples at diagnosis, from 2 patients with active LN, using a T cell adapted SmartSeq2 method. Three to 5 additional patients will be included in the future. T cell receptor (TCR) repertoire analyses were performed on the scRNASeq data. To identify antigen(s) that may be recognized by tissue-enriched TCRs, we use a functional in vitro screening assay: Reporter T cell lines (expressing an NFAT-responsive GFP element and a TCR of interest) are co-cultured with modified HEK293T target cells (expressing a patient HLA, and a cDNA library from the autologous kidney biopsy). Autologous EBV-transformed B cells are used as controls for TCR recognition of virally infected cells. This allows for live-cell screening for T cells recognizing antigens presented by target cells, by virtue of their expression of GFP. Results We identified a restricted TCR repertoire, enriched in kidney and urine as compared to blood, in both patients. This may suggest local, antigen-driven expansion. Moreover, the most repeated TCRs in kidney largely overlapped with those from paired urine (but not blood), suggesting that urine can mirror kidney CD8+ T cell populations. We have begun by screening for antigens recognized by the 5 most highly repeated TCRs from kidney and urine from 1 of the 2 patients (Figure 1): a poor responder with high renal CD8+ T cell infiltration and renal damage (histology and clinical tests). Four of the 5 TCRs showed robust recognition of autologous EBV-B cells. Intriguingly, the fifth TCR (that did not show reactivity to B-EBV cells) was identified in T cells expressing a dual TCR α-chain. We hypothesize that this T cell clone may have been positively selected thanks to the reactivity of 1 of its TCRs to EBV-infected cells, but that its second TCR (with the same β-chain but a different α-chain) could be reactive to kidney autoantigens. Screening of the cDNA library with this (non EBV-B cell-reactive) TCR has shown promising results in the first step of the screening process; this will be repeated with further subcloning in order to identify the antigenic peptide responsible for the activation. Figure 1. Clonal expansion of CD8+ T cells as reflected by TCR repertoire in 1 renal biopsy, from a patient with poor outcome. Frequency of cells with identical TCR-β CDR3, by scRNA-Seq of CD8+ cells, or (bottom-right) deep-sequencing of blood (Adaptive Biotechnologies). Colors: specific to each clonotype, across tissues. n: Number of single-cells Conclusions Identifying the antigen(s) responsible for local CD8+ T cell expansion may be key in addressing kidney-based pathogenic mechanisms in LN. These antigen(s) may be expressed in the case of some but not all patients (or, for eg, differ in terms of abundance or spatio-temporal distribution), and may be associated with outcome. The nature of the antigen(s) may also provide information on disease-promoting cellular/molecular processes that occur in the LN kidney.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".