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Record W7161961812 · doi:10.82308/22568

Studies on the importance of the microbiome of cooling towers on Legionella spp. ecology

2020· dissertation· en· W7161961812 on OpenAlexaboutno aff
Kiran Manu Paranjape

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicLegionella and Acanthamoeba research
Canadian institutionsnot available
Fundersnot available
KeywordsLegionella pneumophilaMicrobiomeLegionellaOutbreakColonizationLegionnaires' diseaseHost (biology)Cooling tower

Abstract

fetched live from OpenAlex

Legionella pneumophila is one of the most important cause of waterborne disease in developed countries. It is the causative agent of Legionnaires’ disease, a severe pneumonia. L. pneumophila is a natural bacterial inhabitant of water systems where it parasitizes and grows in protozoan host species, such as amoeba and ciliates. This adaptation to grow intracellularly has also allowed it to grow within human macrophages. In this way, Legionnaires’ disease is contracted by the inhalation of aerosols contaminated with L. pneumophila, which leads to infections of the lung macrophages and subsequent pneumonia. Cooling towers, used for air conditioning and ventilation systems, are a major source of large outbreaks of Legionnaires’ disease, however the reasons for this are not well understood. Indeed, colonization of cooling towers by L. pneumophila is variable, as some cooling towers are perpetually colonized, and others are almost never colonized. As L. pneumophila is an intracellular parasite, it is believed that the microbiome of cooling towers may play a significant role in the colonization, survival, and proliferation of L. pneumophila in these systems. Consequently, our main objective was to characterize the bacterial and eukaryotic communities of cooling towers and understand their role in L. pneumophila ecology. Another objective was to isolate and characterize bacteria from cooling towers that could stimulate or inhibit L. pneumophila and to study their role in the ecology of L. pneumophila in cooling towers. In order to do this, we characterized the bacterial community of 18 different cooling towers in southern Québec, Canada, using a 16S rRNA amplicon sequencing approach. The findings revealed that the bacterial community of the towers was moulded by several physicochemical factors such as water source and application of chlorine. The continuous application of chlorine was associated with the establishment of a Pseudomonas population. The Pseudomonas counts were negatively correlated with most other taxa identified in the cooling towers, including Legionella. As a result, we concluded that continuous application of chlorine could be an effective way to reduce levels of L. pneumophila in cooling towers. As a second step, we characterized the eukaryotic community using an 18S rRNA sequencing approach and examined its interplay with the bacterial community and the Legionella community. The results revealed that cooling towers contain a diverse community of eukaryotes. Several eukaryotic and bacterial taxa formed a complex network based on co-occurrence. The network revealed that several taxa could potentially affect L. pneumophila ecology. This was demonstrated through the study of the interaction of a Brevundimonas sp. isolate and the ciliate community. These two microbial groups could promote the growth of L. pneumophila through direct and indirect mechanisms, such as nutritional supplementation or promoting host population growth. Finally, our last experiment isolated, identified, and characterized through whole genome sequencing several bacterial isolates that could inhibit L. pneumophila on plate. The analysis of the genomes of these isolates revealed a number of potential antimicrobial gene clusters that could explain the inhibition. Overall, the results suggest that the permissiveness of L. pneumophila colonization, survival, proliferation in cooling towers is dependent on the presence of positive and negative interacting species composing the microbiomes of these environments. Manipulating these microbiomes so that they are not permissive to L. pneumophila could be a potential method to control Legionnaires’ disease outbreaks

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.028
GPT teacher head0.310
Teacher spread0.282 · 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 designObservational
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".

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Citations0
Published2020
Admission routes1
Has abstractyes

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