Production and Differentiation of Myeloid Cells Driven by Proinflammatory Cytokines in Response to Acute Pneumovirus Infection in Mice
Bibliographic record
Abstract
Respiratory virus infections are often pathogenic, driving severe inflammatory responses. Most research has focused on localized effects of virus infection and inflammation. However, infection can induce broad-reaching, systemic changes that are only beginning to be characterized. In this study, we assessed the impact of acute pneumovirus infection in C57BL/6 mice on bone marrow hematopoiesis. We hypothesized that inflammatory cytokine production in the lung upregulates myeloid cell production in response to infection. We demonstrate a dramatic increase in the percentages of circulating myeloid cells, which is associated with pronounced elevations in inflammatory cytokines in serum (IFN-γ, IL-6, CCL2), bone (TNF-α), and lung tissue (TNF-α, IFN-γ, IL-6, CCL2, CCL3, G-CSF, osteopontin). Increased hematopoietic stem/progenitor cell percentages (Lineage(-)Sca-I(+)c-kit(+)) were also detected in the bone marrow. This increase was accompanied by an increase in the proportions of committed myeloid progenitors, as determined by colony-forming unit assays. However, no functional changes in hematopoietic stem cells occurred, as assessed by competitive bone marrow reconstitution. Systemic administration of neutralizing Abs to either TNF-α or IFN-γ blocked expansion of myeloid progenitors in the bone marrow and also limited virus clearance from the lung. These findings suggest that acute inflammatory cytokines drive production and differentiation of myeloid cells in the bone marrow by inducing differentiation of committed myeloid progenitors. Our findings provide insight into the mechanisms via which innate immune responses regulate myeloid cell progenitor numbers in response to acute respiratory virus infection.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".