Modified interferon-α subtypes production and chemokine networks in the thymus during acute simian immunodeficiency virus infection, impact on thymopoiesis
Bibliographic record
Abstract
OBJECTIVES: Thymus dysfunction characterizes human/simian immunodeficiency virus (SIV) infections and contributes to physiopathology. However, both the mechanisms involved in thymic dysfunction and its precise timing remain unknown. We here analyzed thymic function during acute SIV infection in rhesus macaques. DESIGN AND METHODS: Rhesus macaques were intravenously infected with SIVmac251 and bled every 2/3 days or necropsied at different early time points postinfection. Naive T-cell counts were followed by flow cytometry and their T-cell receptor excision circle content evaluated by qPCR. Thymic chemokines were quantified by reverse transcription-qPCR and localized by in-situ hybridization in thymuses collected at necropsy. Thymic interferon alpha (IFN-α) subtype production was quantified by reverse transcription-qPCR combined to heteroduplex tracking assay. The effect of thymic IFN-α subtypes was tested on sorted triple negative thymocytes cultured on OP9-hDL1 cells. RESULTS: A reduced intrathymic proliferation history characterizes T cells produced during the first weeks of infection. Moreover, we evidenced a profound alteration of both chemokines and IFN-α subtypes transcriptional patterns in SIV-infected thymuses. Finally, we showed that IFN-α subtypes produced in the infected thymuses inhibit thymocyte proliferation, still preserving their differentiation capacity. CONCLUSION: Thymopoiesis is deeply impacted from the first days of SIV infection. Reduced thymocyte proliferation - a time-consuming process - together with modified chemokine networks is consistent with thymocyte differentiation speed-up. This may transiently enhance thymic output, thus increasing naive T-cell counts and diversity and the immune competence of the host. Nonetheless, long-lasting modification of thymic physiology may lead to thymic exhaustion, as observed in late primary HIV 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) | 0.000 | 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) | 0.001 | 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".