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Background The disruption of healthcare systems during the COVID-19 pandemic had widespread effects on patient care, including interruption of scheduled visits and diagnostic testing. Many diseases were under-investigated due to the focus on the SARS-CoV-2 virus and the redeployment of resources to the pandemic response. This study aimed to assess Legionella trends in Ontario during the COVID-19 pandemic years, by comparing the demographics of individuals tested for Legionella prior to pandemic (2018 and 2019) to those during the pandemic (2020, 2021 and 2022). Additionally, for individuals who underwent Legionella testing, testing for additional respiratory pathogens was examined in the context of Legionella co-detection. Methods Two Poisson regression models were constructed to compare testing rate and positivity rate during the pre-pandemic years with the pandemic years, adjusted for age, sex, year, and Ontario population. Results Relative to the pre-pandemic years, the testing rates significantly decreased by 8% in 2020, decreased by 8% in 2021 and increased by 14% in 2022. The positivity rate for Legionella decreased by 13% only in 2020 but did not reach significance for the other two years. Individuals older than 50 years of age and males remained the population with highest positivity rate of Legionella infection across all years. Co-detection of Legionella with SARS-CoV-2 or seasonal respiratory viruses was rare but present during the pandemic. Conclusions Legionella  testing rates decreased by 8% in 2020 and 8% in 2021 and increased by 14% in 2022, which was associated with a decrease in positivity rate only in 2020, at 13%, but not in the other two years. Maintaining vigilance for Legionella  testing in future pandemics may support timely diagnosis and treatment, leading to improved patient outcomes. Co-detection of Legionella with SARS-CoV-2 or seasonal respiratory viruses was rare but present during the pandemic. Accordingly, Legionella testing remains essential among high-risk groups, such as the elderly with co-morbidities, critically ill patients, or those with severe or unresponsive pneumonia. Such an approach can aid in differential diagnosis, prompt appropriate treatment, and improve patient outcomes.

Legionellosis is a resvpiratory disease of public health concern. Identification and quantification from environmental sources are crucial for identifying outbreak origins and providing information for risk assessment and disease prevention. Legionella pneumophila is typically detected and quantified using the culture method, which is considered the gold standard, but it has some critical limitations. The Legioler/Quanti-Tray test can be used as an alternative method to simplify the testing process and reduce the time required to obtain the result. In this study, we compare the new liquid culture method Legiolert™ and real-time PCR with traditional plate culture, assessing the performance of PCR and culture methods for detecting L. pneumophila in potable water samples. We analyzed 75 environmental water samples in parallel using the Standard method (ISO 11731:1998), Legiolert, and real-time PCR for the detection of L. pneumophila. The McNemar test was used to assess the difference in accuracy between the Legiolert and real-time PCR methods, showing that the culture test was more accurate than the molecular biology method. The study confirmed that the Legiolert test is specific, easy to use, and may serve as an alternative to standardized procedures for the quantification of L. pneumophila in water. However, due to its high sensitivity and rapid result acquisition, we believe it could be used as a screening tool to quickly ascertain the absence of the microorganism.

Binary diagnostic assays, such as urinary antigen (uAg) tests, provide rapid results but lack quantitative output for conventional quality control. This study evaluated whether long-term monitoring of test positivity patterns can serve as a practical tool for quality assurance (QA) of pneumococcal and Legionella uAg assays. All pneumococcal and Legionella uAg tests performed in Uppsala County, Sweden (01/01/2007-31/12/2024), were retrospectively analyzed. Positivity trends were assessed over time, including seasonal variation and demographic subgroups. Outlier detection was performed using interquartile range (IQR) thresholds to identify potential analytical drift. Bayesian predictive values were additionally estimated as a theoretical comparison to illustrate the influence of prevalence on predictive performance. A total of 17,356 pneumococcal and 15,280 Legionella uAg tests were included. Pneumococcal positivity displayed significant seasonal variation, lowest in August (OR 0.49; 95% CI 0.35-0.68), whereas Legionella positivity varied mainly by year, with peaks in 2007, 2012, and 2021-2022. Pneumococcal positivity was highest in children and female patients, while Legionella showed no demographic trends. QA intervals derived from IQR thresholds captured expected long-term stability (7.4% for pneumococcus; 0.7% for Legionella), with outliers corresponding to known epidemiological events. Bayesian estimates highlighted the large discrepancy between incidence-based and test-based predictive values but were secondary to the trend-based framework. Rapid uAg tests pose challenges for QA. Positivity trends provide a feasible strategy for long-term, population-based monitoring of binary diagnostic tests and can complement conventional controls and detect analytical drift.

A large number of highly pathogenic bacteria utilize secretion systems to translocate effector proteins into host cells. Using these effectors, the bacteria subvert host cell processes during infection. Legionella pneumophila translocates effectors via the Icm/Dot type-IV secretion system and to date, approximately 100 effectors have been identified by various experimental and computational techniques. Effector identification is a critical first step towards the understanding of the pathogenesis system in L. pneumophila as well as in other bacterial pathogens. Here, we formulate the task of effector identification as a classification problem: each L. pneumophila open reading frame (ORF) was classified as either effector or not. We computationally defined a set of features that best distinguish effectors from non-effectors. These features cover a wide range of characteristics including taxonomical dispersion, regulatory data, genomic organization, similarity to eukaryotic proteomes and more. Machine learning algorithms utilizing these features were then applied to classify all the ORFs within the L. pneumophila genome. Using this approach we were able to predict and experimentally validate 40 new effectors, reaching a success rate of above 90%. Increasing the number of validated effectors to around 140, we were able to gain novel insights into their characteristics. Effectors were found to have low G+C content, supporting the hypothesis that a large number of effectors originate via horizontal gene transfer, probably from their protozoan host. In addition, effectors were found to cluster in specific genomic regions. Finally, we were able to provide a novel description of the C-terminal translocation signal required for effector translocation by the Icm/Dot secretion system. To conclude, we have discovered 40 novel L. pneumophila effectors, predicted over a hundred additional highly probable effectors, and shown the applicability of machine learning algorithms for the identification and characterization of bacterial pathogenesis determinants.

Interactions between hosts and pathogens are complex, so understanding the events that govern these interactions requires the analysis of molecular mechanisms operating in both organisms. Many pathogens use multiple strategies to target a single event in the disease process, confounding the identification of the important determinants of virulence. We developed a genetic screening strategy called insertional mutagenesis and depletion (iMAD) that combines bacterial mutagenesis and RNA interference, to systematically dissect the interplay between a pathogen and its host. We used this technique to resolve the network of proteins secreted by the bacterium Legionella pneumophila to promote intracellular growth, a critical determinant of pathogenicity of this organism. This strategy is broadly applicable, allowing the dissection of any interface between two organisms involving numerous interactions.

In June 2014 Public Health England confirmed a case of Legionnaires' disease (LD) in a neonate following birth at home in a hired birthing pool incorporating a heater and a recirculation pump which had been filled in advance of labour. The case triggered a public health investigation and a microbiological survey of an additional ten heated birthing pools hired or recently hired to the general public across England. The birthing pool used by the parent of the confirmed case was identified as the source of the neonate's infection following detection of Legionella pneumophila ST48 in both patient and environmental samples. Legionella species were detected by quantitative polymerase chain reaction but not culture in a further three pools together with other opportunistic pathogens identified by culture and matrix-assisted laser desorption ionization–time of flight (MALDI–ToF) mass spectrometry. A Patient Safety Alert from NHS England and Public Health England was issued stating that heated birthing pools filled in advance of labour should not be used for home births. This recommendation remains in place. This investigation in conjunction with other recent reports has highlighted a lack of awareness regarding the microbiological safety of heated birthing pools and their potential to be a source of LD and other opportunistic infections. Furthermore, the investigation raised important considerations with regards to microbiological sampling and testing in such incidents. Public health authorities and clinicians should consider LD in the differential diagnosis of severe respiratory infection in neonates within 14 days of a water birth.

Discriminating between harmless and pathogenic bacteria is a key challenge faced by the immune system. Ivanov and Roy demonstrate that virulent bacteria disrupt mTOR signaling, which then skews responses towards inflammatory cytokine production. The mammalian immune system has the ability to discriminate between pathogenic microbes and nonpathogenic microbes to control inflammation. Here we investigated the ubiquitination profiles of host proteins after infection of macrophages with a virulent strain of the intracellular bacterium Legionella pneumophila or a nonpathogenic mutant of L. pneumophila . Only infection with pathogenic L. pneumophila resulted in ubiquitination of positive regulators of the metabolic checkpoint kinase mTOR and led to diminished mTOR activity. Detection of pathogen signatures resulted in translational biasing toward proinflammatory cytokines through mTOR-mediated regulation of cap-dependent translation. Thus, there is a pathogen-detection program in macrophages that stimulates protein ubiquitination and the degradation of regulators of mTOR, which suppresses mTOR function and directs a proinflammatory cytokine program.

Conditions in dental offices are conducive to Legionella pneumophila infections. This is mainly related to the use of a dental unit in the daily clinical work, which is the basic equipment of the office. Water discharged from the dental unit waterlines (DUWLs) and the working tips of the dental unit generates splatter/spatter and bioaerosol, constituting the main sources of potential infection and posing a health threat to both patients and professional dental staff. This article presents a narrative review on the presence and risk associated with Legionella spp., particularly the species L. pneumophila, in the dental office. This paper summarizes current knowledge and offers readers practical references, especially useful in everyday clinical dental practice.

Antimicrobial resistance is an emerging problem in hospitals and long-term healthcare facilities. Early detection of susceptibility pattern changes in pathogenic bacteria can prevent treatment failures. Therefore, this study chose to investigate the antibiotic susceptibility situation of Legionella pneumophila isolates from hospitals and long-term healthcare facilities in Southern Germany. Serogroups and minimal inhibitory concentrations (MICs) of nine antibiotics were determined from 41 L. pneumophila strains. In total, 28% of the collected strains belonged to the more pathogenic serogroup 1, whereas 72% belonged to serogroups 2–14. Among the tested antibiotics, rifampicin had the lowest MIC90 value. The MIC90 values can be summarized in the following order: rifampicin < levofloxacin < moxifloxacin < ciprofloxacin < clarithromycin < azithromycin < erythromycin < doxycycline < tigecycline.

Legionella bacteria are commonly found in natural aquatic environments such as rivers, lakes, ponds and hot springs. Legionella infection occurs through the inhalation of water-air aerosol generated, for example, by showers or hot tubs. The most common species responsible for infection is Legionella pneumophila, which can cause Pontiac fever, and Legionnaires' disease, as well as a rare extrapulmonary form. The aim of the study's is to assess the susceptibility of Legionella pneumophila bacteria isolated from water systems of public buildings in Poland to antibiotics and chemotherapeutic agents used in the treatment of Legionellosis pneumonia. A total of 100 L. pneumophila strains isolated from public buildings, such as hospitals and water recreation facilities, were used for the study. The drug sensitivity of the following antibiotics was determined: erythromycin, azithromycin, ciprofloxacin, levofloxacin, rifampicin, trimethoprim-sulfamethoxazole and tetracycline. Mean MIC50 and MIC90 values were read using accepted standards. The highest mean MIC value was obtained for tetracycline 6,130+/-0,353 μg/ml (with a range from 1,500 μg/ml to 16,000 μg/ml. In contrast, the lowest MIC was recorded with rifampicin: 0.020+/-0.037 μg/ml (with a range from 0.016 μg/ml to 0.380 μg/ml). The lowest biocidal concentration was found for levofloxacin, the highest for tetracycline. The highest MIC50 and MIC90 values were found for tetracycline and the lowest for rifampicin. The highest biocidal values were found for azithromycin and the lowest for tetracycline.