Spatial kinetics and immune control of murine cytomegalovirus infection in the salivary glands
Bibliographic record
Abstract
Abstract Human cytomegalovirus (HCMV) is the most common congenital infection. Several HCMV vaccines are in development, but none have yet been approved. An understanding of the kinetics of CMV replication and transmission may inform the rational design of vaccines to prevent this infection. The salivary glands (SG) are an important site of sustained CMV replication following primary infection and during viral reactivation from latency. As such, the strength of the immune response in the SG likely influences viral dissemination within and between hosts. To study the relationship between the immune response and viral replication in the SG, and viral dissemination from the SG to other tissues, mice were infected with low doses of murine CMV (MCMV). Following intra-SG inoculation, we characterized the viral and immunological dynamics in the SG, blood, and spleen, and identified organ-specific immune correlates of protection. Using these data, we constructed compartmental mathematical models of MCMV infection. Model fitting to data and analysis indicate the importance of cellular immune responses in different organs and point to a threshold of infection within the SG necessary for the establishment and spread of infection. Author Summary Cytomegalovirus (CMV) is the most common congenital infection and causes an enormous burden of childhood disease. To gain insight into the immune requirements for controlling infection, we used a mouse model to reproduce characteristics of natural CMV infection, employing a low viral inoculum, and delivering the virus to the salivary glands (SG), a key site of CMV replication. Our results provide detailed data on the spatial and temporal spread of infection throughout the body and identify key immune correlates of the control of viral replication. By translating these findings into mechanistic mathematical models, we revealed the importance of organ-specific immune responses, particularly the requirement of TNF- α and IFN- γ to control infection within the salivary glands. Furthermore, our mathematical modeling allowed us to compare known characteristics of human CMV infection related to infection establishment and spread to those predicted in mice, underscoring the suitability of the MCMV model to study its human homologue. These insights provide guidance for developing targeted vaccines to prevent CMV infection and disease.
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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".