Studies on the importance of the microbiome of cooling towers on Legionella spp. ecology
Bibliographic record
Abstract
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
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".