Impact of Uremia on Regulatory T Lymphocytes Proliferation and Phenotype
Bibliographic record
Abstract
Background: Chronic kidney disease (CKD) patients have a dysfunctional immune system that is chronically and non-specifically activated, leading to low grade inflammation. Inflammation is now considered both a risk factor and a consequence of reduced kidney function and is highly associated with cardiovascular disease, which is the leading cause of mortality on dialysis. Regulatory T cells (Tregs) are important inhibitors of proinflammatory responses. While Tregs numbers are decreased in patients with CKD, their state and effectiveness remain poorly understood. We aim to investigate the impact of uremia on those cells. Methods: Tregs and conventional CD4+ cells from hemodialysis (HD) patients and healthy donors were fluorescence-activated cell sorting (FACS)-sorted and serum was collected. We analyzed the Treg transcriptome using single cell RNA sequencing (scRNA-seq). Tregs and conventional CD4+ T cells from healthy donors were also expanded invitro with media (IL-2 500U/mL) containing healthy donors' or HD patients' serum. After 7 and 12 days in culture, we used flow cytometry to compare changes in phenotype, viability, apoptosis and assess their function. Results: We identified 11 Treg clusters from the scRNA seq data. From those, one is more present in HD patients, irrespective of donor's sex (FDR <0.05 & abs(Log2FD)>0.26) and 3 less present in HD patients. In vitro cultures show decreased Tregs number in uremic serum after 7 days compared to healthy donors (fold expansion 11.35 vs 19.27; p<0.001) and no significant impact on conventional CD4+ T cells (fold expansion 17.88 vs 19.52; p=0.34). They were no significant differences in Treg expansion or function, as assessed by a suppression assay, after 12 days (p>0.99). Staining for apoptosis with Annexin V/PI indicate no difference in viability or apoptosis between serums after 7 and 12 days. Markers associated with Tregs functions and activation such as CTLA4, GARP, LAP, CD69 and CD71 were expressed similarly in HD and healthy donors' serum after 7 days (p=0.82, 0.77, 0.73, 0.23 and 0.44 respectively). Conclusions: Serum from HD patients selectively delays Tregs proliferation in vitro and not conventional CD4+ T cells, with no impact on cell death, activation and Tregs markers' expression. HD patients also have different Treg cluster composition. Further studies are needed to identify the causes and impacts of those changes.
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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".