Air-conditioner cooling towers as complex reservoirs and continuous source of Legionella pneumophila infection evidenced by a genomic analysis study in 2017, Switzerland

Small molecule communication of Legionella: the ins and outs of autoinducer and nitric oxide signaling

SUMMARYLegionella pneumophila is a Gram-negative environmental bacterium, which survives in planktonic form, colonizes biofilms, and infects protozoa. Upon inhalation of Legionella-contaminated aerosols, the opportunistic pathogen replicates within and destroys alveolar macrophages, thereby causing a severe pneumonia termed Legionnaires' disease. Gram-negative bacteria employ low molecular weight organic compounds as well as the inorganic gas nitric oxide (NO) for cell-cell communication. L. pneumophila produces, secretes, and detects the α-hydroxyketone compound Legionella autoinducer-1 (LAI-1, 3-hydroxypentadecane-4-one). LAI-1 is secreted by L. pneumophila in outer membrane vesicles and not only promotes communication among bacteria but also triggers responses from eukaryotic cells. L. pneumophila detects NO through three different receptors, and signaling through the volatile molecule translates into fluctuations of the intracellular second messenger cyclic-di-guanylate monophosphate. The LAI-1 and NO signaling pathways are linked via the pleiotropic transcription factor LvbR. In this review, we summarize current knowledge about inter-bacterial and inter-kingdom signaling through LAI-1 and NO by Legionella species.

Can genomics and meteorology predict outbreaks of legionellosis in urban settings?

Legionella pneumophila is ubiquitous and sporadically infects humans causing Legionnaire's disease (LD). Globally, reported cases of LD have risen fourfold from 2000 to 2014. In 2016, Sydney, Australia was the epicenter of an outbreak caused by L. pneumophila serogroup 1 (Lpsg1). Whole-genome sequencing was instrumental in identifying the causal clone which was found in multiple locations across the city. This study examined the epidemiology of Lpsg1 in an urban environment, assessed typing schemes to classify resident clones, and investigated the association between local climate variables and LD outbreaks. Of 223 local Lpsg1 isolates, we identified dominant clones with one clone isolated from patients in high frequency during outbreak investigations. The core genome multi-locus sequence typing scheme was the most reliable in identifying this Lpsg1 clone. While an increase in humidity and rainfall was found to coincide with a rise in LD cases, the incidence of the major L. pneumophila outbreak clone did not link to weather phenomena. These findings demonstrated the role of high-resolution typing and weather context assessment in determining source attribution for LD outbreaks in urban settings, particularly when clinical isolates remain scarce.IMPORTANCEWe investigated the genomic and meteorological influences of infections caused by Legionella pneumophila in Sydney, Australia. Our study contributes to a knowledge gap of factors that drive outbreaks of legionellosis compared to sporadic infections in urban settings. In such cases, clinical isolates can be rare, and thus, other data are needed to inform decision-making around control measures. The study revealed that core genome multi-locus sequence typing is a reliable and adaptable technique when investigating Lpsg1 outbreaks. In Sydney, the genomic profile of Lpsg1 was dominated by a single clone, which was linked to numerous community cases over a period of 40 years. Interestingly, the peak in legionellosis cases during Autumn was not associated with this prevalent outbreak clone. Incorporating meteorological data with Lpsg1 genomics can support risk assessment strategies for legionellosis in urban environments, and this approach may be relevant for other densely populated regions globally.

Genetic Diversity of Legionella pneumophila Isolates from Artificial Water Sources in Brazil

Legionella pneumophila (Lp) is a Gram-negative bacterium found in natural and artificial aquatic environments and inhalation of contaminated aerosols can cause severe pneumonia known as Legionnaires' Disease (LD). In Brazil there is hardly any information about this pathogen, so we studied the genetic variation of forty Legionella spp. isolates obtained from hotels, malls, laboratories, retail centers, and companies after culturing in BCYE medium. These isolates were collected from various sources in nine Brazilian states. Molecular identification of the samples was carried out using Sequence-Based Typing (SBT), which consists of sequencing and analysis of seven genes (flaA, pilE, asd, mip, mompS, proA, and neuA) to define a Sequence Type (ST). Eleven STs were identified among 34/40 isolates, of which eight have been previously described (ST1, ST80, ST152, ST242, ST664, ST1185, ST1464, ST1642) and three were new STs (ST2960, ST2962, and ST2963), the former identified in five different cooling towers in the city of São Paulo. The ST1 that is widely distributed in many countries was also the most prevalent in this study. In addition, other STs that we observed have also been associated with legionellosis in other countries, reinforcing the potential of these isolates to cause LD in Brazil. Unfortunately, no human isolates could be characterized until presently, but our observations strongly suggest the need of surveillance implementation system and control measures of Legionella spp. in Brazil, including the use of more sensitive genotyping procedures besides ST.

High performance Legionella pneumophila source attribution using genomics-based machine learning classification

Fundamental to effective Legionnaires' disease outbreak control is the ability to rapidly identify the environmental source(s) of the causative agent, Legionella pneumophila. Genomics has revolutionized pathogen surveillance, but L. pneumophila has a complex ecology and population structure that can limit source inference based on standard core genome phylogenetics. Here, we present a powerful machine learning approach that assigns the geographical source of Legionnaires' disease outbreaks more accurately than current core genome comparisons. Models were developed upon 534 L. pneumophila genome sequences, including 149 genomes linked to 20 previously reported Legionnaires' disease outbreaks through detailed case investigations. Our classification models were developed in a cross-validation framework using only environmental L. pneumophila genomes. Assignments of clinical isolate geographic origins demonstrated high predictive sensitivity and specificity of the models, with no false positives or false negatives for 13 out of 20 outbreak groups, despite the presence of within-outbreak polyclonal population structure. Analysis of the same 534-genome panel with a conventional phylogenomic tree and a core genome multi-locus sequence type allelic distance-based classification approach revealed that our machine learning method had the highest overall classification performance-agreement with epidemiological information. Our multivariate statistical learning approach maximizes the use of genomic variation data and is thus well-suited for supporting Legionnaires' disease outbreak investigations.IMPORTANCEIdentifying the sources of Legionnaires' disease outbreaks is crucial for effective control. Current genomic methods, while useful, often fall short due to the complex ecology and population structure of Legionella pneumophila, the causative agent. Our study introduces a high-performing machine learning approach for more accurate geographical source attribution of Legionnaires' disease outbreaks. Developed using cross-validation on environmental L. pneumophila genomes, our models demonstrate excellent predictive sensitivity and specificity. Importantly, this new approach outperforms traditional methods like phylogenomic trees and core genome multi-locus sequence typing, proving more efficient at leveraging genomic variation data to infer outbreak sources. Our machine learning algorithms, harnessing both core and accessory genomic variation, offer significant promise in public health settings. By enabling rapid and precise source identification in Legionnaires' disease outbreaks, such approaches have the potential to expedite intervention efforts and curtail disease transmission.

Legionnaires' disease in Switzerland: rationale and study protocol of a prospective national case-control and molecular source attribution study (SwissLEGIO)

Switzerland has one of the highest annual Legionnaires' disease (LD) notification rates in Europe (7.8 cases/100,000 population in 2021). The main sources of infection and the cause for this high rate remain largely unknown. This hampers the implementation of targeted Legionella spp. control efforts. The SwissLEGIO national case-control and molecular source attribution study investigates risk factors and infection sources for community-acquired LD in Switzerland. Over the duration of one year, the study is recruiting 205 newly diagnosed LD patients through a network of 20 university and cantonal hospitals. Healthy controls matched for age, sex, and residence at district level are recruited from the general population. Risk factors for LD are assessed in questionnaire-based interviews. Clinical and environmental Legionella spp. isolates are compared using whole genome sequencing (WGS). Direct comparison of sero- and sequence types (ST), core genome multilocus sequencing types (cgMLST), and single nucleotide polymorphisms (SNPs) between clinical and environmental isolates are used to investigate the infection sources and the prevalence and virulence of different Legionella spp. strains detected across Switzerland. The SwissLEGIO study innovates in combining case-control and molecular typing approaches for source attribution on a national level outside an outbreak setting. The study provides a unique platform for national Legionellosis and Legionella research and is conducted in an inter- and transdisciplinary, co-production approach involving various national governmental and national research stakeholders.

Community Legionella outbreak linked to a cooling tower, 2022

Background

Thirty-five laboratory-confirmed legionellosis cases were reported to the Simcoe Muskoka District Health Unit (Ontario, Canada) between September 27, 2022, and October 15, 2022, resulting in one death and 29 hospitalizations. This article describes the Legionella outbreak and highlights activities for managing the outbreak, including various environmental and infrastructural controls associated with the public health response and some of the unique challenges and potential solutions to mitigate future outbreaks.

Methods

All cases of legionellosis were reported to and investigated by the local provincial health unit. Within a 6 km radius around the community, 27 cooling towers (CTs) were identified as potential sources of Legionella. Environmental samples were collected from 19 CTs and a long-term care home.

Outcome

Of the 35 cases, 29 (83%) were hospitalized (including three long-term care residents) with two requiring intubation/ventilation. Of the five sputa (clinical isolates) collected from confirmed cases, four tested positive for Legionella pneumophila (one was positive for L. pneumophila serogroup 1-with the same sequence type as one of the CT isolates). Education and recommendations were provided by the local provincial health unit to operators to improve CT operation.

Conclusion

Detection and management of community legionellosis outbreaks associated with CTs involve resources and time to properly identify and control risks. Measures for community risk mitigation included coordinating with provincial and community partners, developing methods to rapidly identify CTs as a likely source of infection and applying operational/maintenance/testing standards for CTs to control bacterial growth and minimize the dispersion of contaminated aerosols.

Genomic characterization and assessment of pathogenic potential of Legionella spp. isolates from environmental monitoring

Several species in the genus Legionella are known to cause an acute pneumonia when the aerosols containing the bacteria from man-made water systems are inhaled. The disease is usually caused by Legionella pneumophila, but other species have been implicated in the infection. The disease is frequently manifested as an outbreak, which means several people are affected when exposed to the common source of Legionella contamination. Therefor environmental surveillance which includes isolation and identification of Legionella is performed routinely. However, usually no molecular or genome-based methods are employed in further characterization of the isolates during routine environmental monitoring. During several years of such monitoring, isolates from different geographical locations were collected and 39 of them were sequenced by hybrid de novo approach utilizing short and long sequencing reads. In addition, the isolates were typed by standard culture and MALDI-TOF method. The sequencing reads were assembled and annotated to produce high-quality genomes. By employing discriminatory genome typing, four potential new species in the Legionella genus were identified, which are yet to be biochemically and morphologically characterized. Moreover, functional annotations concerning virulence and antimicrobial resistance were performed on the sequenced genomes. The study contributes to the knowledge on little-known non-pneumophila species present in man-made water systems and establishes support for future genetic relatedness studies as well as understanding of their pathogenic potential.

Polymer Brushes on GaAs and GaAs/AlGaAs Nanoheterostructures: A Promising Platform for Attractive Detection of Legionella pneumophila

This work reports on the potential of polymer brushes (PBs) grown on GaAs substrates (PB-GaAs) as a promising platform for the detection of Legionella pneumophila (Lp). Three functionalization approaches of the GaAs surface were used, and their compatibility with antibodies against Lp was evaluated using Fourier transform infrared spectroscopy and fluorescence microscopy. The incorporation of PBs on GaAs has allowed a significant improvement of the antibody immobilization by increased surface coverage. Bacterial capture experiments demonstrated the promising potential for enhanced immobilization of Lp in comparison with the conventional alkanethiol self-assembled monolayer-based biosensing architectures. Consistent with an eightfold improved capture of bacteria on the surface of a PB-functionalized GaAs/AlGaAs digital photocorrosion biosensor, we report the attractive detection of Lp at 500 CFU/mL.

Combination of Whole Genome Sequencing and Metagenomics for Microbiological Diagnostics

Whole genome sequencing (WGS) provides the highest resolution for genome-based species identification and can provide insight into the antimicrobial resistance and virulence potential of a single microbiological isolate during the diagnostic process. In contrast, metagenomic sequencing allows the analysis of DNA segments from multiple microorganisms within a community, either using an amplicon- or shotgun-based approach. However, WGS and shotgun metagenomic data are rarely combined, although such an approach may generate additive or synergistic information, critical for, e.g., patient management, infection control, and pathogen surveillance. To produce a combined workflow with actionable outputs, we need to understand the pre-to-post analytical process of both technologies. This will require specific databases storing interlinked sequencing and metadata, and also involves customized bioinformatic analytical pipelines. This review article will provide an overview of the critical steps and potential clinical application of combining WGS and metagenomics together for microbiological diagnosis.

Modeling the Removal of Water-Soluble Trace Gases from Indoor Air via Air Conditioner Condensate

Water-soluble trace gas (WSTG) loss from indoor air via air conditioning (AC) units has been observed in several studies, but these results have been difficult to generalize. In the present study, we designed a box model that can be used to investigate and estimate WSTG removal due to partitioning to AC coil condensate. We compared the model output to measurements of a suite of organic acids cycling in an indoor environment and tested the model by varying the input AC parameters. These tests showed that WSTG loss via AC cycling is influenced by Henry's law constant of the compound in question, which is controlled by air and water temperatures and the condensate pH. Air conditioning unit specifications also impact WSTG loss through variations in the sensible heat ratio, the effective recirculation rate of air through the unit, and the timing of coil and fan operation. These findings have significant implications for indoor modeling. To accurately model the fate of indoor WSTGs, researchers must either measure or otherwise account for these unique environmental and operational characteristics.

Are pathogenic Legionella non-pneumophila a common bacteria in Water Distribution Networks?

The present study analyzes at the national level, the presence of circulating Legionella in the artificial aquatic systems of different facilities of all of them state-owned centers throughout Spain for 12 months. 1754 water samples from various state-owned centers were collected from January to December 2014. Samples were collected from the cooling towers and evaporative condensers (CTC), and water distribution networks such as domestic hot water (DHW), cold water for human consumption (CW), sprinkler irrigation systems (SIS), fire sprinkler systems (FSS), and water from decorative fountains (DF). All these facilities are considered, according to current regulations, as potential amplifying systems for bacteria and possible sources of infection by the generation of droplets and aerosols. The isolation and counting of Legionella in water samples was carried out using microbiological culture following the international normative UNE-EN-ISO 11,731:2007 (ISO 11,731:1998) and UNE-EN ISO 8199:2008 (ISO 8199:2005).The quantification of Legionella colonization, the annual distribution, and the geographical distribution of the Legionella isolates recovered in the water were analyzed. Besides, molecular techniques were used for the characterization of the Legionella non-pneumophila isolates. Legionella was recovered from 15.79% of the analyzed water samples. High colonization was more frequently detected in water samples from CTC, DHW, CW, and DF. Regarding the geographic distribution, positive samples of Legionella were obtained in 14 of the 18 Spanish locations analyzed. Legionella non-pneumophila was the most prevalent and was isolated from water samples from 13 different geographical locations (72%). Legionella anisa and Legionella jordanis were the most frequently non-pneumophila species isolated. Legionella donaldsonii was isolated for the first time in the water distribution networks in Spain. Legionella pneumophila sg 2-14 was detected in 13 locations and Legionella pneumophila sg 1 in 11 locations. Therefore, our study concludes that the presence of Legionella pneumophila and Legionella non-pneumophila species in these systems can be a potential threat to public health and should be examined thoroughly with complementary techniques, such as molecular techniques as a screen for routine diagnosis.

Next generation sequencing approaches to evaluate water and wastewater quality

The emergence of next generation sequencing (NGS) is revolutionizing the potential to address complex microbiological challenges in the water industry. NGS technologies can provide holistic insight into microbial communities and their functional capacities in water and wastewater systems, thus eliminating the need to develop a new assay for each target organism or gene. However, several barriers have hampered wide-scale adoption of NGS by the water industry, including cost, need for specialized expertise and equipment, challenges with data analysis and interpretation, lack of standardized methods, and the rapid pace of development of new technologies. In this critical review, we provide an overview of the current state of the science of NGS technologies as they apply to water, wastewater, and recycled water. In addition, a systematic literature review was conducted in which we identified over 600 peer-reviewed journal articles on this topic and summarized their contributions to six key areas relevant to the water and wastewater fields: taxonomic classification and pathogen detection, functional and catabolic gene characterization, antimicrobial resistance (AMR) profiling, bacterial toxicity characterization, Cyanobacteria and harmful algal bloom identification, and virus characterization. For each application, we have presented key trends, noteworthy advancements, and proposed future directions. Finally, key needs to advance NGS technologies for broader application in water and wastewater fields are assessed.

NGS-Based S. aureus Typing and Outbreak Analysis in Clinical Microbiology Laboratories: Lessons Learned From a Swiss-Wide Proficiency Test

Whole genome sequencing (WGS) enables high resolution typing of bacteria up to the single nucleotide polymorphism (SNP) level. WGS is used in clinical microbiology laboratories for infection control, molecular surveillance and outbreak analyses. Given the large palette of WGS reagents and bioinformatics tools, the Swiss clinical bacteriology community decided to conduct a ring trial (RT) to foster harmonization of NGS-based bacterial typing. The RT aimed at assessing methicillin-susceptible Staphylococcus aureus strain relatedness from WGS and epidemiological data. The RT was designed to disentangle the variability arising from differences in sample preparation, SNP calling and phylogenetic methods. Nine laboratories participated. The resulting phylogenetic tree and cluster identification were highly reproducible across the laboratories. Cluster interpretation was, however, more laboratory dependent, suggesting that an increased sharing of expertise across laboratories would contribute to further harmonization of practices. More detailed bioinformatic analyses unveiled that while similar clusters were found across laboratories, these were actually based on different sets of SNPs, differentially retained after sample preparation and SNP calling procedures. Despite this, the observed number of SNP differences between pairs of strains, an important criterion to determine strain relatedness given epidemiological information, was similar across pipelines for closely related strains when restricting SNP calls to a common core genome defined by S. aureus cgMLST schema. The lessons learned from this pilot study will serve the implementation of larger-scale RT, as a mean to have regular external quality assessments for laboratories performing WGS analyses in a clinical setting.

Persistence of Legionella in Routinely Disinfected Heater-Cooler Units and Heater Units assessed by Propidium Monoazide qPCR

BACKGROUND

Evidence to date indicates that heater-cooler units (HCUs) and heater units (HUs) can generate potentially infectious aerosols containing a range of opportunistic pathogens such as Mycobacterium chimaera, other non-tuberculous mycobacterial (NTM) species, Pseudomonas aeruginosa and Legionella spp. Our purpose was to determine the extent of Legionella contamination and total viable count (TVC) in HCUs and HUs and to analyze the relationship by water system design of devices of two different brands (LivaNova vs. Maquet).

METHODS

Legionella spp. were detected and quantified by our optimized PMA-qPCR protocol; TVCs were assessed according to ISO protocol 6222. Analyses were performed in the first sampling round and after six months of surveillance.

RESULTS

Overall, Legionella spp. was detected in 65.7% of devices. In the second sampling round, Legionella positivity rates were significantly lower in water samples from the Maquet devices compared to the LivaNova ones (27.3% vs. 61.5%). LivaNova HCUs also yielded more Legionella, and aquatic bacteria counts than Maquet in both first and second-round samples.

CONCLUSIONS

We recommend that all surgical patients and staff exposed to aerosols from thermoregulatory devices should be followed up for Legionella infection and that microbiological surveillance on such devices should be conducted regularly as precautionary principle.

Performance of Legiolert Test vs. ISO 11731 to Confirm Legionella pneumophila Contamination in Potable Water Samples

Detection and enumeration of Legionella in water samples is of great importance for risk assessment analysis. The plate culture method is the gold standard, but has received several well-known criticisms, which have induced researchers to develop alternative methods. The purpose of this study was to compare Legionella counts obtained by the analysis of potable water samples through the plate culture method and through the IDEXX liquid culture Legiolert method. Legionella plate culture, according to ISO 11731:1998, was performed using 1 L of water. Legiolert was performed using both the 10 mL and 100 mL Legiolert protocols. Overall, 123 potable water samples were analyzed. Thirty-seven (30%) of them, positive for L. pneumophila, serogroups 1 or 2–14 by plate culture, were used for comparison with the Legiolert results. The Legiolert 10 mL test detected 34 positive samples (27.6%) and the Legiolert 100 mL test detected 37 positive samples, 27.6% and 30% respectively, out of the total samples analyzed. No significant difference was found between either the Legiolert 10 mL and Legiolert 100 mL vs. the plate culture (p = 0.9 and p = 0.3, respectively) or between the Legiolert 10 mL and Legiolert 100 mL tests (p = 0.83). This study confirms the reliability of the IDEXX Legiolert test for Legionella pneumophila detection and enumeration, as already shown in similar studies. Like the plate culture method, the Legiolert assay is also suitable for obtaining isolates for typing purposes, relevant for epidemiological investigations.

A Snapshot of the Prevalence and Molecular Diversity of Legionella pneumophila in the Water Systems of Israeli Hotels

Exposure to Legionella spp. contaminated aerosols in hotel settings confers risk for travel-associated Legionnaire’s disease (TALD). In this study, we investigated the prevalence of Legionella contamination and its molecular diversity in hotels and resorts across Israel. The study was comprised of a convenience sample of water systems from 168 hotels and resorts countrywide, routinely inspected between March 2015 and February 2017. Isolation and quantitation of Legionella were performed in a water laboratory using the ISO 11731 method. The distribution of Legionella isolates was analyzed according to geography and source. The genetic diversity of a subset of isolates was analyzed by sequence-based typing (SBT) at the National Reference Laboratory for Legionella and compared to the national database. Out of 2830 samples tested, 470 (17%) obtained from 102 different premises (60% of hotels) were positive for Legionella spp. In 230 samples (49% of all positive, 8% of total samples), accounting for 37% of hotels, Legionella spp. counts exceeded the regulatory threshold of 1000 CFU/L. The most frequently contaminated water sources were cooling towers (38%), followed by faucets, hot tubs, water lines, and storage tanks (14–17% each). Furthermore, 32% and 17% of samples obtained from cooling towers and hot tubs, respectively, exceeded the regulatory thresholds. SBT was performed on 78 strains and revealed 27 different sequence types (STs), including two novel STs. The most prevalent STs found were ST1 (26%), ST87 (10%), ST93 (6%), and ST461 and ST1516 (5% each). Several L. pneumophila STs were found to be limited to certain geographical regions. This is the first study to investigate the prevalence and diversity of Legionella in hotels and resorts in Israel during non-outbreak environmental inspections. These findings will inform risk assessment, surveillance, and control measures of TALD.

Evaluation of Rapid Library Preparation Protocols for Whole Genome Sequencing Based Outbreak Investigation

Whole genome sequencing (WGS) has become the new gold standard for bacterial outbreak investigation, due to the high resolution available for typing. While sequencing is currently predominantly performed on Illumina devices, the preceding library preparation can be performed using various protocols. Enzymatic fragmentation library preparation protocols are fast, have minimal hands-on time, and work with small quantities of DNA. The aim of our study was to compare three library preparation protocols for molecular typing: Nextera XT (Illumina); Nextera Flex (Illumina); and QIAseq FX (Qiagen). We selected 12 ATCC strains from human Gram-positive and Gram-negative pathogens with %G+C-content ranging from 27% (Fusobacterium nucleatum) to 73% (Micrococcus luteus), each having a high quality complete genome assembly available, to allow in-depth analysis of the resulting Illumina sequence data quality. Additionally, we selected isolates from previously analyzed cases of vancomycin-resistant Enterococcus faecium (VRE) (n = 7) and a local outbreak of Klebsiella aerogenes (n = 5). The number of protocol steps and time required were compared, in order to test the suitability for routine laboratory work. Data analyses were performed with standard tools commonly used in outbreak situations: Ridom SeqSphere+ for cgMLST; CLC genomics workbench for SNP analysis; and open source programs. Nextera Flex and QIAseq FX were found to be less sensitive than Nextera XT to variable %G+C-content, resulting in an almost uniform distribution of read-depth. Therefore, low coverage regions are reduced to a minimum resulting in a more complete representation of the genome. Thus, with these two protocols, more alleles were detected in the cgMLST analysis, producing a higher resolution of closely related isolates. Furthermore, they result in a more complete representation of accessory genes. In particular, the high data quality and relative simplicity of the workflow of Nextera Flex stood out in this comparison. This thorough comparison within an ISO/IEC 17025 accredited environment will be of interest to those aiming to optimize their clinical microbiological genome sequencing.

Whole Genome Sequencing for Surveillance of Diphtheria in Low Incidence Settings

Corynebacterium diphtheriae (C. diphtheriae) is a relatively rare pathogen in most Western countries. While toxin producing strains can cause pharyngeal diphtheria with potentially fatal outcomes, the more common presentation is wound infections. The diphtheria toxin is encoded on a prophage and can also be carried by Corynebacterium ulcerans and Corynebacterium pseudotuberculosis. Currently, across Europe, infections are mainly diagnosed in travelers and refugees from regions where diphtheria is more endemic, patients from urban areas with poor hygiene, and intravenous drug users. About half of the cases are non-toxin producing isolates. Rapid identification of the bacterial pathogen and toxin production is a critical element of patient and outbreak management. Beside the immediate clinical management of the patient, public health agencies should be informed of toxigenic C. diphtheriae diagnoses as soon as possible. The collection of case-related epidemiological data from the patient is often challenging due to language barriers and social circumstances. However, information on patient contacts, vaccine status and travel/refugee route, where appropriate, is critical, and should be documented. In addition, isolates should be characterized using high resolution typing, in order to identify transmissions and outbreaks. In recent years, whole genome sequencing (WGS) has become the gold standard of high-resolution typing methods, allowing detailed investigations of pathogen transmissions. De-centralized sequencing strategies with redundancy in sequencing capacities, followed by data exchange may be a valuable future option, especially since WGS becomes more available and portable. In this context, the sharing of sequence data, using public available platforms, is essential. A close interaction between microbiology laboratories, treating physicians, refugee centers, social workers, and public health officials is a key element in successful management of suspected outbreaks. Analyzing bacterial isolates at reference centers may further help to provide more specialized microbiological techniques and to standardize information, but this is also more time consuming during an outbreak. Centralized communication strategies between public health agencies and laboratories helps considerably in establishing and coordinating effective surveillance and infection control. We review the current literature on high-resolution typing of C. diphtheriae and share our own experience with the coordination of a Swiss-German outbreak.