<i>Legionella pneumophila</i> CRISPR-Cas suggests recurrent encounters with <i>Gokushovirinae</i>
Bibliographic record
Abstract
ABSTRACT Legionella pneumophila is a ubiquitous freshwater pathogen and the causative agent of Legionnaires’ disease. This pathogen and its ability to cause disease is closely tied to its environmental encounters. From phagocytic protists, L. pneumophila has “learned” how to avoid predation and exploit conserved eukaryotic processes to establish an intracellular replicative niche. Legionnaires’ disease is a product of these evolutionary pressures as L. pneumophila uses the same molecular mechanisms to replicate in grazing protists and in macrophages of the human lung. L. pneumophila growth within protists also provides a refuge from desiccation, disinfection, and other remediation strategies. One outstanding question has been whether this protection extends to phages. L. pneumophila isolates are remarkably devoid of prophages and to date no Legionella phages have been identified. Nevertheless, many L. pneumophila isolates maintain active CRISPR-Cas defenses. So far, the only known target of these systems has been an episomal element that we previously named Legionella Mobile Element-1 (LME-1). In this study, we have identified over 150 CRISPR-Cas systems across 600 isolates, to establish the clearest picture yet of L. pneumophila ’s adaptive defenses. By leveraging the sequence of 1,500 unique spacers, we can make two main conclusions: current data argue against CRISPR-Cas targeted integrative elements beyond LME-1 and the heretofore “missing” L. pneumophila phages are most likely lytic gokushoviruses. IMPORTANCE The causative agent of Legionnaires’ disease, an often-fatal pneumonia, is an intracellular bacterium, Legionella pneumophila , that normally grows inside amoebae and other freshwater protists. Unfortunately for us, this has two major consequences: the bacterium can take what it has learned in amoebae and use similar strategies to grow inside our lungs; and these amoebae can protect Legionella from various forms of chemical and physical disinfection regimes. Legionella are ubiquitous in the environment and frequently found in man-made water systems. Understanding the challenges to Legionella survival before it reaches the human lung is critical to preventing disease. We have leveraged our earlier discovery that L. pneumophila CRISPR-Cas systems are active and adaptive – meaning that they respond to contemporary threats encountered in the environment. In this way, CRISPR arrays can be considered genomic diaries of past encounters, with spacer sequences used to identify elements that may impinge on the pathogen’s survival. One outstanding question in the field is whether L. pneumophila is susceptible to phage, given the presumptive protection provided by intracellular replication within its eukaryotic hosts. In this work, we use CRISPR spacer sequences to suggest that the heretofore “missing” L. pneumophila phage are most likely lytic gokushoviruses. Such information is critical to the long-term goal of developing of new strategies for preventing colonization of our water systems by Legionella and subsequent human exposure to the pathogen.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".