PD-1/PD-L Pathway Potentially Involved in ITP Immunopathogenesis
Bibliographic record
Abstract
Abstract Introduction Immune thrombocytopenia (ITP) is an autoimmune disease in which autoreactive T and B cells are activated by platelet autoantigens resulting in immune-mediated platelet destruction and/or suppression of platelet production. The binding of programmed death 1 (PD-1) to its ligands PD-L1 and PD-L2 on antigen-presenting cells turns off autoreactive T cells and induces peripheral tolerance. Aberrant PD-1/PD-L signalling could result in a breakdown of peripheral tolerance and lead to autoimmune diseases. Methods Thirty-four patients with primary active ITP who were diagnosed and/or followed up and 26 healthy controls were enrolled in this study. Platelet counts in all ITP patients were less than 30×109 /L at sampling. They had not been treated with any immunosuppressive agents for at least one week prior to sampling for this study. To determine the role of the PD-1/PD-L signalling pathway in ITP, we detected PD-1 expression on T cells and PD-L expression on dendritic cells (DCs) in both ITP patients with active disease and healthy controls by flow cytometry. To investigate the effects of PD-L1-Fc fusion protein (PD-L1-Fc) on T cells, PBMCs from ITP patients and healthy controls with autologous platelets were cultured with soluble anti-CD3 monoclonal antibody (mAb) and anti-CD28 mAb in the presence (PD-L1-Fc+ group) or absence (PD-L1-Fc- group) of the PD-L1-Fc (0.5 μg/mL) at 37 ˚C with 5% CO2 for 4 days. The cells were harvested and stained for flow cytometry to detect the apoptosis, activation and proliferation of T cells. IL-2 and IFN-γ levels in the co-culture supernatant were assayed by ELISA. Results Enhanced PD-1 expression on T cells and decreased PD-L1 expression on mDCs in ITP patients We found that PD-1 mean fluorescence intensities (MFIs) increased in CD4+ cells (P < 0.01) and in CD8+ cells (P < 0.01) from ITP patients compared with those from healthy controls. However, PD-L1 expression on monocyte-derived DCs was lower in patients with active ITP than in healthy controls (P < 0.01, Figure 1). PD-L1-Fc promoted T cell apoptosis Annexin V-FITC and propidium iodide (PI) were used to detect T cell apoptosis. The percentage of apoptotic cells and dead cells were analysed to determine T cell apoptosis levels. In ITP patients, the percentage of apoptotic cells and dead cells were higher in PD-L1-Fc+ group than in the PD-L1-Fc- group (P < 0.01 for both CD4+ and CD8+ T cells). However, we found no significant difference in apoptosis between PD-L1-Fc- and PD-L1-Fc+ groups in healthy controls (Figure 2). PD-L1-Fc inhibited T cell activation and proliferation CD25 MFIs were analysed to determine the activation level of cocultured T lymphocytes. Compared with healthy controls, CD25 expression on CD4+ and CD8+ T cells was significantly increased in ITP patients (P < 0.05 for both CD4+ and CD8+ T cells). These results suggest that ITP patients had more activated CD4+ and CD8+ T cells than in healthy controls. PD-L1-Fc significantly inhibited T cell activation in ITP patients (P < 0.01, n=34) but not in healthy controls (P = 0.0834 in CD4+ T cells, P = 0.6834 in CD8+ T cells, n=26, Figure 3). To analyse proliferation, the frequency of divided cells was calculated according to the loss of CFSE fluorescence intensity. PD-L1-Fc inhibited proliferation of T cells from ITP patients (P < 0.01 in both CD4+ T and CD8+ T cells, n=34, Figure 4). We found no significant difference in proliferation in T cells from healthy controls (CD4+ T cells P = 0.9758, CD8+ T cells P = 0.5658, n=26). PD-L1-Fc inhibited IL-2 and IFN-γ production IL-2 secretion was higher in ITP than in healthy controls in the absence of PD-L1-Fc (P = 0.0308, Figure 5). PD-L1-Fc significantly inhibited IL-2 and IFN-γ production in ITP patients. IL-2 and IFN-γ levels were lower in the PD-L1-Fc+ group than in the PD-L1-Fc- group in ITP (P < 0.01 for both IL-2 and IFN-γ, n=34). However, we did not detect a significant difference in secretion of IL-2 (P = 0.2016, n=26) or IFN-γ (P = 0.2989, n=26) in healthy controls. Conclusions In summary, our study suggests that the aberrant PD-1/PD-L negative costimulatory pathway may play a role in ITP. Enhancing PD-1/PD-L signalling might be a promising therapeutic approach for ITP by promoting T cell apoptosis, inhibiting T cell activation and proliferation, and reducing secretion of inflammatory factors. Disclosures No relevant conflicts of interest to declare.
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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) | 0.002 | 0.000 |
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.
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".