IFN-γ Induces the Erosion of Preexisting CD8 T Cell Memory during Infection with a Heterologous Intracellular Bacterium
Bibliographic record
Abstract
Memory T cells are critical for the control of intracellular pathogens and require few signals for maintenance; however, erosion of established preexisting memory CD8(+) T cells has been shown to occur during infection with heterologous viral infections. We evaluated whether this also occurs during infection with various intracellular bacteria and what mechanisms may be involved. We demonstrate that erosion of established memory is also induced during infection of mice with various intracellular bacteria, such as Listeria monocytogenes, Salmonella typhimurium, and Mycobacterium bovis (bacillus Calmette-Guérin). The extent of erosion of established CD8(+) T cell memory was dependent on the virulence of the heterologous pathogen, not persistence. Furthermore, when antibiotics were used to comprehensively eliminate the heterologous pathogen, the numbers of memory CD8(+) T cells were not restored, indicating that erosion of preexisting memory CD8(+) T cells was irreversible. Irrespective of the initial numbers of memory CD8(+) T cells, challenge with the heterologous pathogen resulted in a similar extent of erosion of memory CD8(+) T cells, suggesting that cellular competition was not responsible for erosion. After challenge with the heterologous pathogen, effector memory CD8(+) T cells were rapidly eliminated. More importantly, erosion of preexisting memory CD8(+) T cells was abrogated in the absence of IFN-gamma. These studies help reveal the paradoxical role of IFN-gamma. Although IFN-gamma promotes the control of intracellular bacterial replication during primary infection, this comes at the expense of erosion of preexisting memory CD8(+) T cells in the wake of infection with heterologous pathogens.
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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 | 0.000 |
| 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 | 0.001 |
| 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 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".