Legionella Pneumophila Transcriptome during Intracellular Multiplication in Human Macrophages
Bibliographic record
Abstract
Legionella pneumophila is the causative agent of Legionnaires' disease, an acute pulmonary infection. L. pneumophila is able to infect and multiply in both phagocytic protozoa, such as Acanthamoeba castellanii, and mammalian professional phagocytes. The best-known L. pneumophila virulence determinant is the Icm/Dot type IVB secretion system, which is used to translocate more than 150 effector proteins into host cells. While the transcriptional response of Legionella to the intracellular environment of A. castellanii has been investigated, much less is known about the Legionella transcriptional response inside human macrophages. In this study, the transcriptome of L. pneumophila was monitored during exponential and post-exponential phase in rich AYE broth as well as during infection of human cultured macrophages. This was accomplished with microarrays and an RNA amplification procedure called selective capture of transcribed sequences to detect small amounts of mRNA from low numbers of intracellular bacteria. Among the genes induced intracellularly are those involved in amino acid biosynthetic pathways leading to l-arginine, l-histidine, and l-proline as well as many transport systems involved in amino acid and iron uptake. Genes involved in catabolism of glycerol are also induced during intracellular growth, suggesting that glycerol could be used as a carbon source. The genes encoding the Icm/Dot system are not differentially expressed inside cells compared to control bacteria grown in rich broth, but the genes encoding several translocated effectors are strongly induced. Moreover, we used the transcriptome data to predict previously unrecognized Icm/Dot effector genes based on their expression pattern and confirmed translocation for three candidates. This study provides a comprehensive view of how L. pneumophila responds to the human macrophage intracellular environment.
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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 | 0.001 |
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".