Poly(I:C) and Lipopolysaccharide Innate Sensing Functions of Circulating Human Myeloid Dendritic Cells Are Affected In Vivo in Hepatitis C Virus-Infected Patients
Bibliographic record
Abstract
The role of peripheral dendritic cells (DCs) in hepatitis C virus (HCV) infection is unclear. To determine if persistent infection exerts an inhibitory pressure on HCV-specific innate responses, we analyzed DC function in blood through quantification of cell-associated HCV RNA levels in conjunction with multiparametric flow cytometry analysis of pathogen recognition receptor-induced cytokine expression. Independently of the serum viral load, fluorescence-activated cell sorter-purified total DCs had a wide range of cell-associated HCV genomic RNA copy numbers (mean log(10), 5.0 per 10(6) cells; range, 4.3 to 5.8). Here we report that for viremic patients with high viral loads in their total DCs, the myeloid DC (MDC) subset displayed impaired expression of interleukin-12 (IL-12) and tumor necrosis factor alpha (TNF-alpha) but normal IL-6 or chemokine CCL3 expression in response to poly(I:C) and lipopolysaccharide (LPS). IL-6-expressing cells from this subgroup of viremic patients demonstrated a significant increase (sixfold more) in TNF-alpha(-) IL-12(-) cell frequency compared to healthy donors (mean, 38.8% versus 6.5%; P < 0.0001), indicating a functional defect in a subpopulation of cytokine-producing MDCs ( approximately 6% of MDCs). Attenuation of poly(I:C) and LPS innate sensing was HCV RNA density dependent and did not correlate with viremia or deficits in circulating MDC frequencies in HCV-infected patients. Monocytes from these patients were functionally intact, responding normally on a per-cell basis following stimulation, independent of cell-associated HCV RNA levels. Taken together, these data indicate that detection of HCV genomic RNA in DCs and loss of function in the danger signal responsiveness of a small proportion of DCs in vivo are interrelated rather than independent phenomena.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".