Transient Oral Human Cytomegalovirus Infections Indicate Inefficient Viral Spread from Very Few Initially Infected Cells
Bibliographic record
Abstract
ABSTRACT Cytomegalovirus (CMV) is acquired by the oral route in children, and primary infection is associated with abundant mucosal replication, as well as the establishment of latency in myeloid cells that results in lifelong infection. The efficiency of primary CMV infection in humans following oral exposure, however, is unknown. We consistently detected self-limited, low-level oral CMV shedding events, which we termed transient CMV infections, in a prospective birth cohort of 30 highly exposed CMV-uninfected infants. We estimated the likelihood of transient oral CMV infections by comparing their observed frequency to that of established primary infections, characterized by persistent high-level shedding, viremia, and seroconversion. We developed mathematical models of viral dynamics upon initial oral CMV infection and validated them using clinical shedding data. Transient infections comprised 76 to 88% of oral CMV shedding events. For this high percentage of transient infections to occur, we identified two mathematical prerequisites: a very small number of initially infected oral cells (1 to 4) and low viral infectivity (<1.5 new cells infected/cell). These observations indicate that oral CMV infection in infants typically begins with a single virus that spreads inefficiently to neighboring cells. Thus, although the incidence of CMV infection is high during infancy, our data provide a mechanistic framework to explain why multiple CMV exposures are typically required before infection is successfully established. These findings imply that a sufficiently primed immune response could prevent CMV from establishing latent infection in humans and support the achievability of a prophylactic CMV vaccine. IMPORTANCE CMV infects the majority of the world's population and is a major cause of birth defects. Developing a vaccine to prevent CMV infection would be extremely valuable but would be facilitated by a better understanding of how natural human CMV infection is acquired. We studied CMV acquisition in infants and found that infections are usually brief and self-limited and are successfully established relatively rarely. Thus, although most people eventually acquire CMV infection, it usually requires numerous exposures. Our analyses indicate that this is because the virus is surprisingly inefficient, barely replicating well enough to spread to neighboring cells in the mouth. Greater knowledge of why CMV infection usually fails may provide insight into how to prevent it from succeeding.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".