Polymorphisms in the P2X7 receptor, and differential expression of Toll-like receptor-mediated cytokines and defensins, in a Canadian Indigenous group
Bibliographic record
Abstract
Canadian Indigenous peoples (First Nations and Inuit) exhibit a high burden of infectious diseases including tuberculosis influenced by societal factors, and biological determinants. Toll-like receptor (TLR)-mediated innate immune responses are the first line of defence against infections. We examined the production of a panel of 30 cytokines in peripheral blood-derived mononuclear cells (PBMC) isolated from Indigenous and non-Indigenous participants, following stimulation with five different TLR ligands. The levels of TLR-induced pro-inflammatory cytokines such as IL-12/23p40, IL-16, and IFN-γ, and chemokines (MCP-4, MDC and eotaxin) were different between Indigenous compared to non-Indigenous participants. Antimicrobial cationic host defence peptides (CHDP) induced by TLR activation are critical for resolution of infections and modulate the TLR-to-NFκB pathway to alter downstream cytokine responses. Therefore, we examined the expression of human CHDP defensins and cathelicidin in PBMC. mRNA expression of genes encoding for def-A1 and def-B1 were significantly higher following stimulation with TLR ligands in Indigenous compared to non-Indigenous participants. The purinergic receptor P2X7 known to be activated by ATP released following TLR stimulation, is a receptor for CHDP. Therefore, we further examined single nucleotide polymorphisms (SNP) in P2X7. Indigenous participants had a significantly higher percentage of a P2X7 SNP which is associated with reduced function and lower ability to clear infections. These results suggest that a higher frequency of non-functional P2X7 receptors may influence the activity of downstream immune mediators required for resolution of infections such as pro-inflammatory cytokines and CHDP defensins, thus contributing to higher burden of infections in Indigenous population.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".