Deep sequencing defines the transcriptional map of<i>L. pneumophila</i>and identifies growth phase-dependent regulated ncRNAs implicated in virulence
Bibliographic record
Abstract
The bacterium Legionella pneumophila is found ubiquitously in aquatic environments and can cause a severe pneumonia in humans called Legionnaires' disease. How this bacterium switches from intracellular to extracellular life and adapts to different hosts and environmental conditions is only partly understood. Here we used RNA deep sequencing from exponentially (replicative) and post exponentially (virulent) grown L. pneumophila to analyze the transcriptional landscape of its entire genome. We established the complete operon map and defined 2561 primary transcriptional start sites (TSS). Interestingly, 187 of the 1805 TSS of protein-coding genes contained tandem promoters of which 93 show alternative usage dependent on the growth phase. Similarly, over 60% of 713 here identified ncRNAs are phase dependently regulated. Analysis of their conservation among the seven L. pneumophila genomes sequenced revealed many strain specific differences suggesting that L. pneumophila contains a highly dynamic pool of ncRNAs. Analysis of six ncRNAs exhibiting the same expression pattern as virulence genes showed that two, Lppnc0584 and Lppnc0405 are indeed involved in intracellular growth of L. pneumophila in A. castellanii. Furthermore, L. pneumophila encodes a small RNA named RsmX that functions together with RsmY and RsmZ in the LetA-CsrA regulatory pathway, crucial for the switch to the virulent phenotype. Together our data provide new insight into the transcriptional organization of the L. pneumophila genome, identified many new ncRNAs and will provide a framework for the understanding of virulence and adaptation properties of L. pneumophila.
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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".