Background: Microbiome analysis is becoming a standard component in many scientific studies, but also requires extensive quality control of the 16S rRNA gene sequencing data prior to analysis. In particular, when investigating low-biomass microbial environments such as human skin, contaminants distort the true microbiome sample composition and need to be removed bioinformatically. We introduce MicrobIEM, a novel tool to bioinformatically remove contaminants using negative controls. Results: We benchmarked MicrobIEM against five established decontamination approaches in four 16S rRNA amplicon sequencing datasets: three serially diluted mock communities (10⁸–10³ cells, 0.4–80% contamination) with even or staggered taxon compositions and a skin microbiome dataset. Results depended strongly on user-selected algorithm parameters. Overall, sample-based algorithms separated mock and contaminant sequences best in the even mock, whereas control-based algorithms performed better in the two staggered mocks, particularly in low-biomass samples (≤ 10⁶ cells). We show that a correct decontamination benchmarking requires realistic staggered mock communities and unbiased evaluation measures such as Youden’s index. In the skin dataset, the Decontam prevalence filter and MicrobIEM’s ratio filter effectively reduced common contaminants while keeping skin-associated genera. Conclusions: MicrobIEM’s ratio filter for decontamination performs better or as good as established bioinformatic decontamination tools. In contrast to established tools, MicrobIEM additionally provides interactive plots and supports selecting appropriate filtering parameters via a user-friendly graphical user interface. Therefore, MicrobIEM is the first quality control tool for microbiome experts without coding experience.

During the current severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, healthcare systems worldwide have to prevent nosocomial SARS-CoV-2 transmission while maintaining duty of care. In our study, we characterize the transmission dynamic of SARS-CoV-2 in inpatients and healthcare workers (HCWs) at the University Hospital Münster (UHM) in northwest Germany. We identified 27 cases of healthcare-associated SARS-CoV-2 infections (4 inpatients and 23 HCWs) who had contact with patients and/or HCWs without the use of adequate PPE. The contacts of these index cases were followed up for SARS-CoV-2 infection after unprotected exposure and a quantitative measure of probability of becoming infected, the attack rate, was calculated. In addition, transmission was evaluated in the context of infection control measures established during the pandemic and we compared the epidemiological data of all index cases, including symptoms and C_t values of virology test results. The overall attack rate in the hospital setting was 1.3% (inpatients 0.9%, HCWs 1.6%). However, during an outbreak, the attack rate was 25.5% (inpatients 20.0%, HCWs 29.6%). For both scenarios, HCWs had a higher attack rate illustrating their role in healthcare-associated SARS-CoV-2 transmission. Taken together, our experiences demonstrate how infection control measures can minimize the transmission of SARS-CoV-2 in the healthcare setting.

Plasmid transfer is one important mechanism how antimicrobial resistance can spread between different species, contributing to the rise of multidrug resistant bacteria (MDRB) worldwide. Here were present whole genome sequencing (WGS) data of two MDRB isolates, an Escherichia coli and a Klebsiella quasipneumoniae, which were isolated from a single patient. Detailed analysis of long-read sequencing data identified an identical F2:A-:B- lncFII plasmid containing bla_CTX-M-27 in both isolates, suggesting horizontal plasmid exchange between the two species. As the plasmid of the E. coli strain carried multiple copies of the resistance cassette, the genomic data correlated with the increased antimicrobial resistance (AMR) detected for this isolate. Our case report demonstrates how long-read sequencing data of MDRB can be used to investigate the role of plasmid mediate resistance in the healthcare setting and explain resistance phenotypes.

Since March 2020, the COVID-19 pandemic forced hospitals worldwide to intensify their infection control measures to prevent health care-associated transmission of SARS-CoV-2. The correct use of personal protective equipment, especially the application of masks, was quickly identified as priority to reduce transmission with this pathogen. Here, we report a nosocomial cluster of methicillin-resistant Staphylococcus aureus (MRSA) that occurred during the COVID-19 pandemic in a gynecology/obstetrics department, despite these intensified contact precautions. Five MRSA originating from clinical samples after surgical intervention led to an outbreak investigation. Firstly, this included environmental sampling of the operation theatre (OT) and, secondly, a point prevalence screening of patients and health care workers (HCW). All detected MRSA were subjected to whole genome sequencing (WGS) and isolate relatedness was determined using core genome multilocus sequence typing (cgMLST). WGS revealed one MRSA cluster with genetically closely related five patient and two HCW isolates differing in a single cgMLST allele at maximum. The outbreak was terminated after implementation of infection control bundle strategies. Although contact precaution measures, which are also part of MRSA prevention bundle strategies, were intensified during the COVID-19 pandemic, this MRSA outbreak could take place. This illustrates the importance of adherence to classical infection prevention strategies.

Whole-genome sequencing has transformed microbial genomics since the first bacterial genome was published in 1995. Advances in sequencing technology, together with decreasing costs, now enable high-resolution investigation of bacterial pathogens for epidemiological surveillance, and infection control. A major breakthrough has been the advent of third-generation long-read sequencing (LRS) platforms, such as Pacific Biosciences and Oxford Nanopore Technologies, which overcome the limitations of short-read sequencing by producing long continuous reads. LRS facilitates accurate de novo genome assembly, resolution of repetitive and structurally complex regions, and precise characterization of plasmids and other mobile genetic elements that frequently harbor antimicrobial resistance genes (ARGs). A particular strength of LRS lies in its ability to reveal the complete genomic architecture of ARGs, including their localization, copy number, and surrounding genetic environment. Such contextual information is essential, since e.g. the interpretation of antimicrobial resistance (AMR) depends not only on the presence of specific genes but also on their structural organization, mobility potential, and genomic integration. By contrast, LRS provides a reliable foundation for understanding AMR evolution and dissemination through both clonal expansion and horizontal gene transfer. Recent developments in bioinformatics, including dedicated tools for plasmid reconstruction, typing, and annotation, further enhance the analytical value of LRS and hybrid approaches. Beyond isolate-level analyses, LRS enables plasmid surveillance and the tracing of ARG transmission across strains, hosts, and healthcare settings. This review sets out to give readers a brief overview of LRS technology and its capabilities and outlines current approaches and tools to analyze bacterial plasmids.

Water systems in health care facilities can form reservoirs for Gram-negative bacteria. While planning a new neonatal intensive care unit (NICU), we performed a retrospective evaluation of potential risks from water-diverting systems on the existing NICU of our tertiary care University Hospital. During 2017 to 2023, we recorded nine nosocomial cluster events with bacterial pathogens in our NICU. Of these, three clusters of Gram-negative bacteria were potentially related to sink drains: A Klebsiella oxytoca, a Pseudomonas aeruginosa, and an Enterobacter hormaechei cluster were uncovered by clinical routine screening of patients and breastmilk samples. They were confirmed using whole-genome sequencing and a subsequent core genome multilocus sequence typing (cgMLST) algorithm. Our observations highlight that the implementation of sink drains in a NICU may have negative effects on patients’ safety. Construction planning should concentrate on the avoidance of washbasins in patient rooms when redesigning sensitive areas such as NICUs.