Genotypic and phenotypic profiling of 127 Legionella pneumophila strains: Insights into regional spread.

Given the recent global surge in Legionnaires' disease cases, the monitoring of Legionella pneumophila becomes increasingly crucial. Epidemiological cases often stem from local outbreaks rather than widespread dissemination, emphasizing the need to study the characteristics of this pathogen at a local level. This study focuses on isolates of L. pneumophila in the Italian region of Friuli Venezia Giulia to assess specific genotype and phenotype distribution over time and space. To this end, a total of 127 L. pneumophila strains isolated between 2005 and 2017 within national surveillance programs were analysed. Rep-PCR, RAPD, and Sau-PCR were used for genotypic characterization, while phenotypic characterization was conducted through fatty acids analysis. RAPD and Sau-PCR effectively assessed genetic characteristics, identifying different profiles for the isolates and excluding the presence of clones. Although Sau-PCR is rarely used to analyse this pathogen, it emerged as the most discriminatory technique. Phenotypically, hierarchical cluster analysis categorized strains into three groups based on varying membrane fatty acid percentages. However, both phenotypic and genotypic analyses revealed a ubiquitous profile distribution at a regional level. These results suggest an absence of correlations between strain profiles, geographical location, and isolation time, indicating instead high variability and strain dissemination within this region.

Draft genome sequences from 127 Legionella spp. strains isolated in water systems linked to legionellosis outbreaks.

Legionnaires' disease is a severe form of pneumonia caused by Legionella spp. bacteria. According to the European Centre for Disease Prevention and Control, problems related to this pathogen showed a significant surge in recent years, making its monitoring critical.

Self-Organizing Maps: An AI Tool for Identifying Unexpected Source Signatures in Non-Target Screening Analysis of Urban Wastewater by HPLC-HRMS.

(1) Background: Monitoring effluent in water treatment plants has a key role in identifying potential pollutants that might be released into the environment. A non-target analysis approach can be used for identifying unknown substances and source-specific multipollutant signatures. (2) Methods: Urban and industrial wastewater effluent were analyzed by HPLC-HRMS for non-target analysis. The anomalous infiltration of industrial wastewater into urban wastewater was investigated by analyzing the mass spectra data of "unknown common" compounds using principal component analysis (PCA) and the Self-Organizing Map (SOM) AI tool. The outcomes of the models were compared. (3) Results: The outlier detection was more straightforward in the SOM model than in the PCA one. The differences among the samples could not be completely perceived in the PCA model. Moreover, since PCA involves the calculation of new variables based on the original experimental ones, it is not possible to reconstruct a chromatogram that displays the recurring patterns in the urban WTP samples. This can be achieved using the SOM outcomes. (4) Conclusions: When comparing a large number of samples, the SOM AI tool is highly efficient in terms of calculation, visualization, and identifying outliers. Interpreting PCA visualization and outlier detection becomes challenging when dealing with a large sample size.

Summary recommendations on "Analytical methods for substances in the Watch List under the Water Framework Directive".

The Watch List (WL) is a monitoring program under the European Water Framework Directive (WFD) to obtain high-quality Union-wide monitoring data on potential water pollutants for which scarce monitoring data or data of insufficient quality are available. The main purpose of the WL data collection is to determine if the substances pose a risk to the aquatic environment at EU level and subsequently to decide whether a threshold, the Environmental Quality Standards (EQS) should be set for them and, potentially to be listed as priority substance in the WFD. The first WL was established in 2015 and contained 10 individual or groups of substances while the 4th WL was launched in 2022. The results of monitoring the substances of the first WL showed that some countries had difficulties to reach an analytical Limit of Quantification (LOQ) below or equal to the Predicted No-Effect Concentrations (PNEC) or EQS. The Joint Research Centre (JRC) of the European Commission (EC) organised a series of workshops to support the EU Member States (MS) and their activities under the WFD. Sharing the knowledge among the Member States on the analytical methods is important to deliver good data quality. The outcome and the discussion engaged with the experts are described in this paper, and in addition a literature review of the most important publications on the analysis of 17-alpha-ethinylestradiol (EE2), amoxicillin, ciprofloxacin, metaflumizone, fipronil, metformin, and guanylurea from the last years is presented.

Environmental surveillance and spatio-temporal analysis of Legionella spp. in a region of northeastern Italy (2002-2017).

Legionella spp. are considered an important cause of potentially preventable morbidity and mortality, making environmental surveillance a crucial component of risk assessment plans. In this work, 20,319 water samples were collected in 3,983 environmental surveys during a 16-year period by ARPA, the Regional Agency for Environmental Protection, Friuli Venezia Giulia, and the results were studied to better understand the diffusion mechanisms of Legionella. The data showed a strong seasonal signal, a prevalence of L. pneumophila serogroup 2-15 in most environments (63% of positive samples), a prevalence of L. pneumophila serogroup 1 in swimming pool-associated environments (82% of positive samples), a persistent presence of Legionella in hospitals and a recurrent presence of Legionella in other facilities such as hotels, possibly years after interventions, highlighting the difficulty of eradicating the bacteria. Retrospective spatio-temporal analyses on geocoded historical data were carried out with SaTScan using an ordinal model with risk as a covariate to identify potential clusters with an excess of cases in the higher-risk categories. Although no outbreaks occurred during the period of study, such analyses identified spatially restricted zones with unusual contamination, which sometimes were also areas in which several surveys triggered by notifications of clinical cases were performed. Simulations of periodic prospective analyses permitted the assessment of the efficacy of the method in early detection of such clusters. The proposed method may be a useful tool in environmental surveillance, prevention and control of Legionella.