MétaCan
Menu
Back to cohort
Record W3135438514 · doi:10.1101/2021.03.08.434514

<i>Legionella pneumophila</i> CRISPR-Cas suggests recurrent encounters with <i>Gokushovirinae</i>

2021· preprint· en· W3135438514 on OpenAlexafffund
Shayna R. Deecker, Malene L. Urbanus, Beth Nicholson, Alexander W. Ensminger

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2021
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicLegionella and Acanthamoeba research
Canadian institutionsUniversity of Toronto
FundersCanadian Institutes of Health ResearchUniversity of Toronto
KeywordsLegionella pneumophilaLegionnaires' diseaseLegionellaBiologyMicrobiologyCRISPRPathogenBacteriaVirologyGeneticsGene

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.012
GPT teacher head0.237
Teacher spread0.226 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2021
Admission routes2
Has abstractyes

Explore more

Same venuebioRxiv (Cold Spring Harbor Laboratory)Same topicLegionella and Acanthamoeba researchFrench-language works237,207