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Record W7133034254

Leveraging CRISPR-Cas to Examine the Interplay Between Legionella pneumophila and Mobile Genetic Elements

2022· dissertation· W7133034254 on OpenAlexafffund
Shayna Raylen Deecker

Bibliographic record

VenueTSpace · 2022
Typedissertation
Language
FieldBiochemistry, Genetics and Molecular Biology
TopicLegionella and Acanthamoeba research
Canadian institutionsUniversity of Toronto
FundersCanadian Institutes of Health ResearchConnaught FundUniversity of Toronto
KeywordsLegionella pneumophilaMobile genetic elementsLytic cycleAxenicLegionellaGenomeBacteriaCRISPR
DOInot available

Abstract

fetched live from OpenAlex

Legionella pneumophila is a Gram-negative, intracellular bacterium and an accidental human pathogen. Much of the research to date has focused on L. pneumophila's ability to cause disease, and while this is important for developing treatments, studying the factors that influence its environmental persistence is critical to mitigating future outbreaks of disease. To this end, I focused on leveraging the CRISPR-Cas adaptive immune system to examine the impact that mobile genetic elements could have on L. pneumophila. I provided bioinformatic and experimental evidence to support the hypothesis that plasmid-based CRISPR-Cas systems are likely circulated by horizontal gene transfer, then leveraged by the bacterium to strengthen and restore its defences against environmental threats. Next, I expanded on our lab's previous work to generate a comprehensive, bioinformatic catalogue of the mobile genetic elements that L. pneumophila encounters in its environment. Using an expanded catalogue of CRISPR spacer sequences and a custom bioinformatics workflow, I showed that one or more lytic gokushoviruses and the prophage-like Legionella mobile element-1 (LME-1) are frequently encountered by L. pneumophila, providing the first evidence that the bacterium is challenged by lytic phages in the environment. In light of my findings, I re-visited the hypothesis that LME-1 could form phage-like particles under one or more experimental conditions, as opposed to being a phage remnant. I used transmission electron microscopy to show that under axenic growth conditions in rich media, LME-1 could form podophage-like particles, and that these particles were abolished in LME-1 mutants. I also showed that the LME-1 genome was protected from DNase treatment in extracellular fractions, and that a Xer recombinase, XerL, was implicated in LME-1 excision. Finally, I designed and validated a new assay to assess if the LME-1 phage-like particle is required for transfer between Legionella strains. Taken together, the data presented in my graduate research thesis highlight the role that mobile genetic elements could have on L. pneumophila's environmental persistence, and provide avenues for future research to mitigate outbreaks of Legionnaires' disease.

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)0.001
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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.001

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 head0.020
GPT teacher head0.371
Teacher spread0.351 · 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

Citations0
Published2022
Admission routes2
Has abstractyes

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