Forecasting complex, multivariate time series using machine learning presents significant challenges, particularly when dealing with high-dimensional biological data. One such application is the prediction of microbial community dynamics in wastewater treatment plants (WWTPs), which is directly relevant to public and environmental health. Previously, we addressed this problem by training different model architectures and comparing their performance. In this study, an attempt was made to enhance model performance by adding operational and environmental metadata, such as temperature, inflow rates, and nutrient levels. The parameters were incorporated into the time series, and their influence was systematically evaluated. The findings indicate that while metadata enhances predictive accuracy, the significance of individual features varies across WWTPs. Training a model on one dataset and retraining that model on site-specific data has been demonstrated to enhance performance, underscoring the necessity for adaptive, localized modeling strategies. The findings demonstrate the value of machine learning approaches for extracting predictive insights from complex, high-dimensional biological time series data in wastewater systems. The implementation of such a system as an additional wastewater monitoring technique has the potential to facilitate more timely public health decision-making in the future.

BackgroundWastewater-based epidemiology (WBE) is a promising complement to traditional surveillance systems, yet its practical utility and performance in real-world public health settings remain insufficiently characterized. This study aims to evaluate the feasibility and added value of WBE for monitoring infectious disease dynamics at the regional level, with a particular focus on jointly identifying, together with public health authorities, actionable and scalable methodological strategies based on cost, applicability, and the relevance and timeliness of the information generated.MethodsComposite influent wastewater samples were collected over 6 weeks from a treatment plant serving a defined district in western Germany. Samples were analyzed using quantitative PCR and both targeted and shotgun metagenomic sequencing. WBE findings were compared with routine case-based surveillance data from the corresponding catchment area.ResultsAll pathogens reported through routine public health surveillance during the study period were also detected in wastewater. In addition, WBE identified signals from clinically relevant pathogens not captured by case-based surveillance. Sequencing approaches provided further resolution on pathogen diversity and resistance profiles. The combined use of targeted and untargeted methods revealed differences in sensitivity and resolution, with complementary strengths across approaches, and enabled the definition of a practical, tiered approach to support actionable surveillance at the regional level.ConclusionThis study describes the operational integration of WBE into a regional public health workflow, providing timely, population-level data that complements routine surveillance and can reveal pathogen circulation not captured by reported cases. Building on the established advantages of WBE, our results highlight its practical value when jointly implemented with public health authorities, enabling context-specific, actionable insights that enhance situational awareness, guide targeted local responses and support earlier detection of emerging threats.

IntroductionDiphtheria is re-emerging as a public health concern in Europe, with recent fatalities underlining the need for strengthened surveillance. In highly vaccinated populations, limited clinical suspicion and reduced routine testing can obscure low-level circulation of toxigenic strains, increasing the risk of silent spread. In addition to classical Corynebacterium diphtheriae infections, the potential mobilization of the diphtheria toxin (tox) gene within and possibly beyond different Corynebacterium species represents a growing threat.MethodsWe developed a high-fidelity wastewater-based surveillance approach capable of detecting tox genes as an early warning system to complement clinical and laboratory-based monitoring.Results and DiscussionDuring routine metagenomic surveillance, including weekly monitoring of the local wastewater treatment plant, screening with a tox-specific Hidden Markov Model (HMM) detected tox signals in 18 of 160 environmental samples. Technical and sample-availability constraints allowed PCR confirmation of 15 HMM-positive samples, and 14 of these PCR products were confirmed by amplicon sequencing. One detection coincided spatially and temporally with a tox-positive Corynebacterium clinical case: wastewater from the hospital ward and downstream treatment plant was positive that week and negative the following week. No corresponding clinical isolates were identified for the other wastewater signals. Although not conclusive, this concordance supports alerting clinicians and public-health authorities when tox is detected in wastewater.

SARS-CoV-2 remains a major global health challenge, as infection can lead to potential life-threatening conditions such as COVID-19. Emerging variants of the virus are characterized by higher transmission rates and immune escape mutations, enabling them to evade vaccine-induced immunity. Existing treatment options, including monoclonal antibodies, are often variant-specific and not widely accessible, especially in low- and middle-income countries. Natural compounds derived from medicinal herbs and green tea have demonstrated antiviral activity against various viruses and may offer promising, variant-independent therapeutic potential. In this study, we examined the antiviral activity of four plant-derived compounds: glycyrrhizin, curcumin, harmaline, and (-)-epigallocatechin. The compounds were tested in vitro against SARS-CoV-2 D614G, Omicron BA.5, and Omicron XBB.1. The antiviral efficacy was assessed at subtoxic concentrations to evaluate potential therapeutic applicability. All tested compounds showed effective neutralization of SARS-CoV-2 D614G, Omicron BA.5, and Omicron XBB.1 at subtoxic concentrations. In particular, glycyrrhizin, curcumin, and harmaline exhibited potent antiviral activity across all tested variants. Our findings support the potential of glycyrrhizin, curcumin, and harmaline as variant-independent treatment candidates for COVID-19. However, further clinical studies are necessary to validate their efficacy and safety in vivo.

Motivation Antimicrobial resistance is a growing global threat, creating a need for rapid and accurate antimicrobial susceptibility testing. Current phenotypic antimicrobial susceptibility testing methods rely on prior isolation and cultivation, making them time-consuming. Whole genome sequencing combined with machine learning offers a faster and cost-effective alternative, but existing approaches are often limited in species coverage, antimicrobial scope, or data availability.Results We developed stackPredAMR, a machine learning framework for predicting resistance to 18 antimicrobial agents in three clinically important bacterial species: Escherichia coli, Klebsiella pneumoniae, and Acinetobacter baumannii. The model uses antimicrobial resistance gene presence as input and incorporates cross-resistance patterns through a stacked architecture with two random forest layers. Benchmarking on more than 2500 publicly available whole genome sequencing datasets with linked phenotypic resistance data showed strong performance, achieving a median accuracy of 0.94, ROC AUC of 0.97, and F1-score of 0.91, outperforming previously published methods. stackPredAMR is freely available and designed to support future extension to additional species and antimicrobial agents.Availability and implementation Source code and datasets (database-driven reference approach, sample lists, and input features) are available at WIN-KID repository (https://github.com/IKIM-Essen/WIN-KID/tree/v1.0.0.0) and the release page (https://github.com/IKIM-Essen/WIN-KID/releases/tag/v1.0.0.0).

Extracorporeal membrane oxygenation (ECMO) cannulas are potential reservoirs for pathogens, yet their role in bacteremia and sepsis following decannulation remains poorly understood. This proof-of-concept study aims to characterize bacterial colonization of ECMO cannulas, identify potential sources of these bacteria, and assess their association with post-decannulation bloodstream infections and sepsis. We conducted a single-center observational study including 10 patients receiving venovenous ECMO support between January 2022 and January 2023. Microbial colonization of cannulas, skin sites, and plasma was analyzed using culture-based methods and 16S rDNA amplicon sequencing. Alpha and beta diversity analyses were performed, and findings were correlated with clinical outcomes, including sepsis and bacteremia. A total of 117 samples yielded 11 million sequencing reads. Bacteria colonizing ECMO cannulas matched pathogens causing prior bacteremia during ECMO support in all affected patients. Bacteria detected on cannulas and insertion sites were frequently recovered in plasma following decannulation. Notably, 16S rDNA analysis detected circulating pathogens that conventional cultures missed, often those from prior infections that were thought to be eradicated by antibiotics. Patients who developed sepsis post-decannulation exhibited higher bacterial diversity on cannulas and a higher overall abundance of Pseudomonas, while non-septic patients had greater Enterococcus abundance. Our results confirm that ECMO cannulas serve as pathogen reservoirs, with decannulation enabling bacterial translocation into the bloodstream and contributing to post-decannulation sepsis. 16S rDNA sequencing exhibited greater sensitivity than cultures for detecting bloodstream pathogens. These findings support re-assessment of prophylactic measures during ECMO decannulation and lay the groundwork for developing early sepsis risk stratification tools.

Differentiating significant microbial community changes from normal fluctuations is vital for understanding microbial dynamics in human and environmental ecosystems. This knowledge could enable early warning systems to monitor critical changes affecting human or environmental health. We applied 16S rRNA gene sequencing and time-series analysis to model bacterial abundance trajectories in human gut and wastewater microbiomes. We evaluated various model architectures using datasets from two human studies and five wastewater settings. Long short-term memory (LSTM) models consistently outperformed other models in predicting bacterial abundances and detecting outliers, as measured by multiple metrics. Prediction intervals for each genus allowed us to identify significant changes and signaling shifts in community states. This study proposes a machine learning model capable of monitoring microbial communities and providing insights into their responses to internal and external factors in medical and environmental settings.

In the original publication [...].

In December 2021, an outbreak of the SARS-CoV-2 B.1.640.2 variant, potentially originating from Cameroon, was investigated among schoolchildren in Germany. The index case, an adult who had recently returned from a three-week stay in the Republic of Congo, introduced the variant into a school setting via their children, resulting in subsequent transmission within the school and ultimately to a hospital ward. Whole-genome sequencing of viral samples identified both B.1.640.1 and B.1.640.2 lineages. This outbreak highlights the unpredictable nature of emerging SARS-CoV-2 variants and emphasizes the importance of early detection and containment to mitigate transmission to high-risk populations. Notably, wastewater surveillance detected the variant during the study peri-od, reinforcing the utility of wastewater-based epidemiology as a complementary tool for early warning and containment of novel variants. These findings underline the critical need for timely research and adherence to quarantine measures to enhance outbreak control efforts.

In December 2021, an outbreak of the SARS-CoV-2 B.1.640.2 variant, potentially originating from Cameroon, was investigated among schoolchildren in Germany. The index case, an adult who had recently returned from a three-week stay in the Republic of Congo, introduced the variant into a school setting via their children, resulting in subsequent transmission within the school and ultimately to a hospital ward. Whole-genome sequencing of viral samples identified both B.1.640.1 and B.1.640.2 lineages. This outbreak highlights the unpredictable nature of emerging SARS-CoV-2 variants and emphasizes the importance of early detection and containment to mitigate transmission to high-risk populations. Notably, wastewater surveillance detected the variant during the study peri-od, reinforcing the utility of wastewater-based epidemiology as a complementary tool for the early warning and containment of novel variants. These findings underline the critical need for timely research and adherence to quarantine measures to enhance outbreak control efforts.

Wastewater-based epidemiology (WBE) is a valuable surveillance method for the early detection and monitoring of infectious diseases at the population level. By rapidly sequencing genetic material extracted from wastewater, local and regional virus surveillance can be enhanced, enabling earlier identification of emerging variants than routine clinical testing typically allows. This provides a critical advantage for immune-evasive pathogens. In this study, a cost-effective sequencing and sensitive analysis strategy has been employed to monitor SARS-CoV-2 variants in the metropolitan Ruhr area of Germany. Pathogen-specific tiled amplification was combined with both established analytical tools and a customized method for enhanced sensitivity utilizing unique single nucleotide variants (SNVs) in different lineages. Samples were collected twice weekly over six weeks, resulting in 55 samples from five sampling sites. Consistent with prior knowledge, the findings demonstrated earlier detection of emerging variants of concern (VOCs) in wastewater compared to patient-based genomic surveillance, which, at the national level, sequenced approximately 2.4 % of detected SARS-CoV-2 cases by April 2023. Particularly notable is that XBB.1.5 was identified in local wastewater from intra-urban sewersheds two weeks before its appearance in patient samples and XBB.1.9 several months prior to its local clinical manifestation. Furthermore, this study corroborates own and other prior reports that local sampling sites can extend the early-warning horizon for novel variants when compared to wastewater treatment plants (WWTPs), likely due to lower dilution and closer proximity to infection sources.Overall, these results underscore the utility of sequencing-based WBE in delivering timely, actionable insights to healthcare providers and authorities. Such methodologies represent a promising expansion of current surveillance infrastructures and quantitative frameworks, offering practical benefits for optimizing environmental viral monitoring and supporting improved public health responses.

Science benefits from rapid open data sharing, but current guidelines for data reuse were established two decades ago, when databases were several million times smaller than they are today. These guidelines are largely unfamiliar to the scientific community, and, owing to the rapid increase in biological data generated in the past decade, they are also outdated. As a result, there is a lack of community standards suited to the current landscape and inconsistent implementation of data sharing policies across institutions. Here we discuss current sequence data sharing policies and their benefits and drawbacks, and present a roadmap to establish guidelines for equitable sequence data reuse, developed in consultation with a data consortium of 167 microbiome scientists. We propose the use of a Data Reuse Information (DRI) tag for public sequence data, which will be associated with at least one Open Researcher and Contributor ID (ORCID) account. The machine-readable DRI tag indicates that the data creators prefer to be contacted before data reuse, and simultaneously provides data consumers with a mechanism to get in touch with the data creators. The DRI aims to facilitate and foster collaborations, and serve as a guideline that can be expanded to other data types.

Extracorporeal membrane oxygenation (ECMO) is widely used to manage acute respiratory distress syndrome (ARDS); however, biofilm formation on cannulas may contribute to infections. This study investigated the prevalence, timing, and microbial profiles of infectious complications following ECMO decannulation. This prospective, single-center cohort study was conducted in the mixed medical-surgical intensive care unit (ICU) at the University Hospital Essen (01/2022–01/2023). Adults who received ECMO for > 48 h were included. Microbiological sampling (blood cultures and wound swabs) was performed before and after decannulation. Cannulas were assessed for biofilms, and plasma samples were analyzed using next-generation sequencing (NGS) of microbial cell-free DNA (cfDNA). Sepsis was defined according to the most recent Sepsis-3 criteria. The study included 18 patients (56% men); 17 received VV-ECMO and one received VA-ECMO. Post-decannulation sepsis occurred in 10 of 18 patients (56%; 95% CI: 31–79%), and overall infectious complications were observed in 72% of patients. A strong negative correlation was evident between pre-ECMO ventilation duration and biofilm-forming bacteria in the blood during ECMO (r = − 0.7, p = 0.002). Blood cultures obtained within 10 min of decannulation were positive in 6 of 18 (33%) patients. NGS identified pathogens in 9 of 12 patients (75%), with 5 (42%) revealing additional organisms not detected by conventional methods. Viral pathogens were detected in 3 of 12 (25%) patients using NGS. Patients with sepsis demonstrated higher antibiotic consumption (p = 0.012) and more frequently met SOFA-based sepsis criteria (p = 0.007), whereas other parameters were comparable. Sepsis and infection frequently occur after ECMO decannulation and may be associated with biofilm-related pathogens. NGS has improved pathogen detection beyond standard diagnostics, although the findings require clinical correlation. These prospective findings support the need for further investigation of advanced integrated microbiological surveillance post-ECMO in larger multicenter studies. Trial registration: DRKS, DRKS00024842. Registered 12 Apr 2021, https://drks.de/search/de/trial/DRKS00024842.

Introduction:Specific antiretroviral therapy (ART) regimens are associated with weight gain in people living with HIV (PLWH). Gut microbiota is involved in weight gain in humans and animals. Human gut microbiota can be classified into enterotypes with distinct microbial and functional profiles. Methods:In a cohort of 118 PLWH, we analyzed the gut microbiome in relation to weight gain and ART regimen using 16S rRNA gene sequencing, taking enterotype classification into account. Results:The enterotype was strongly associated with sexual orientation. Of the 67 individuals forming a Prevotella-dominated enterotype cluster in principal coordinates analysis, 93% were men who had sex with men (MSM), while 31% of individuals in the Bacteroides-dominated enterotype cluster were MSM and 69% were non-MSM. Forty-nine genera differed significantly between the MSM and non-MSM individuals. When stratified by dominant genus, only six taxa were associated with weight gain. Of these, five were restricted to Bacteroides-dominated individuals. Among them, the class Actinobacteria and genus Bifidobacterium differed between individuals gaining more than 5% weight and less than 5% weight 1 year after ART switch. Additionally, three taxa were significantly different between 15% of individuals with the highest weight gain (≥6.3%) and the highest weight loss (≤3.19%) 1 year after ART switch, including the phyla Firmicutes, Verrucomicrobia, and Synergistetes. Distinct functional properties in Bacteroides, but not Prevotella-dominated enterotype individuals, linked to weight gain were observed, particularly for glycan and lipid metabolism. Additionally, ART regimen-associated differences were observed for the phylum Actinobacteria, although this was limited to Prevotella-dominated enterotype individuals. Discussion:Differences in the composition and functional characteristics of the gut microbiome associated with weight gain and ART regimens were enterotype-specific and relatively small compared with differences linked to sexual orientation. Due to the substantial differences in gut microbiome structure among many MSM, categorization into enterotypes is useful for identifying differences in microbiome composition associated with variables such as weight gain or ART, which may be limited to a single enterotype. This may further advance the identification of microbes that contribute to weight gain or alter the gut microbiome composition in the context of the enterotype.

Wastewater analysis can serve as a source of public health information. In recent years, wastewater-based epidemiology (WBE) has emerged and proven useful for the detection of infectious diseases. However, insights from the wastewater treatment plant do not allow for the small-scale differentiation within the sewer system that is needed to analyze the target population under study in more detail. Small-scale WBE offers several advantages, but there has been no systematic overview of its application. The aim of this scoping review is to provide a comprehensive overview of the current state of knowledge on small-scale WBE for infectious diseases, including methodological considerations for its application. A systematic database search was conducted, considering only peer-reviewed articles. Data analyses included quantitative summary and qualitative narrative synthesis. Of 2130 articles, we included 278, most of which were published since 2020. The studies analyzed wastewater at the building level (n = 203), especially healthcare (n = 110) and educational facilities (n = 80), and at the neighborhood scale (n = 86). The main analytical parameters were viruses (n = 178), notably SARS-CoV-2 (n = 161), and antibiotic resistance (ABR) biomarkers (n = 99), often analyzed by polymerase chain reaction (PCR), with DNA sequencing techniques being less common. In terms of sampling techniques, active sampling dominated. The frequent lack of detailed information on the specification of selection criteria and the characterization of the small-scale sampling sites was identified as a concern. In conclusion, based on the large number of studies, we identified several methodological considerations and overarching strategic aspects for small-scale WBE. An enabling environment for small-scale WBE requires inter- and transdisciplinary knowledge sharing across countries. Promoting the adoption of small-scale WBE will benefit from a common international conceptualization of the approach, including standardized and internationally accepted terminology. In particular, the development of good WBE practices for different aspects of small-scale WBE is warranted. This includes the establishment of guidelines for a comprehensive characterization of the local sewer system and its sub-sewersheds, and transparent reporting to ensure comparability of small-scale WBE results.

Background Next-generation sequencing for microbial communities has become a standard technique. However, the computational analysis remains resource-intensive. With declining costs and growing adoption of sequencing-based methods in many fields, validated, fully automated, reproducible and flexible pipelines are increasingly essential in various scientific fields. Results We present RiboSnake, a validated, automated, reproducible QIIME2-based pipeline implemented in Snakemake for analysing 16S rRNA gene amplicon sequencing data. RiboSnake includes pre-packaged validated parameter sets optimized for different sample types, from environmental samples to patient data. The configuration packages can be easily adapted and shared, requiring minimal user input. Conclusion RiboSnake is a new alternative for researchers employing 16S rRNA gene amplicon sequencing and looking for a customizable and user-friendly pipeline for microbiome analyses with in vitro validated settings. By automating the analysis with validated parameters for diverse sample types, RiboSnake enhances existing methods significantly. The workflow repository can be found on GitHub (https://github.com/IKIM-Essen/RiboSnake).

BACKGROUND:At a global scale, the SARS-CoV-2 virus did not remain in its initial genotype for a long period of time, with the first global reports of variants of concern (VOCs) in late 2020. Subsequently, genome sequencing has become an indispensable tool for characterizing the ongoing pandemic, particularly for typing SARS-CoV-2 samples obtained from patients or environmental surveillance. For such SARS-CoV-2 typing, various in vitro and in silico workflows exist, yet to date, no systematic cross-platform validation has been reported. RESULTS:In this work, we present the first comprehensive cross-platform evaluation and validation of in silico SARS-CoV-2 typing workflows. The evaluation relies on a dataset of 54 patient-derived samples sequenced with several different in vitro approaches on all relevant state-of-the-art sequencing platforms. Moreover, we present UnCoVar, a robust, production-grade reproducible SARS-CoV-2 typing workflow that outperforms all other tested approaches in terms of precision and recall. CONCLUSIONS:In many ways, the SARS-CoV-2 pandemic has accelerated the development of techniques and analytical approaches. We believe that this can serve as a blueprint for dealing with future pandemics. Accordingly, UnCoVar is easily generalizable towards other viral pathogens and future pandemics. The fully automated workflow assembles virus genomes from patient samples, identifies existing lineages, and provides high-resolution insights into individual mutations. UnCoVar includes extensive quality control and automatically generates interactive visual reports. UnCoVar is implemented as a Snakemake workflow. The open-source code is available under a BSD 2-clause license at github.com/IKIM-Essen/uncovar.

While SARS-CoV-2 has transitioned to an endemic phase, infections caused by newly emerged variants continue to result in severe, and sometimes fatal, outcomes or lead to long-term COVID-19 symptoms. Vulnerable populations, such as PLWH, face an elevated risk of severe illness. Emerging variants of SARS-CoV-2, including numerous Omicron subvariants, are increasingly associated with breakthrough infections. Adapting mRNA vaccines to these new variants may offer improved protection against Omicron for vulnerable individuals. In this study, we examined humoral and cellular immune responses before and after administering adapted booster vaccinations to PLWH, alongside a control group of healthy individuals. Four weeks following booster vaccination, both groups exhibited a significant increase in neutralizing antibodies and cellular immune responses. Notably, there was no significant difference in humoral immune response between PLWH and the healthy controls. Immune responses declined rapidly in both groups three months post vaccination. However, PLWH still showed significantly increased neutralizing antibody titers even after three months. These findings demonstrate the efficacy of the adapted vaccination regimen. The results suggest that regular booster immunizations may be necessary to sustain protective immunity.