Biology subjects

Kaas, R. S.

Publications and source records attributed to Kaas, R. S..

5 recordsLinked to original sources

bioRxiv2026

PanRes: A database of latent and acquired antimicrobial resistance allowing 3D-based protein homology search

Antimicrobial resistance databases are central to genomic surveillance, but resistance determinants remain distributed across resources with different scopes, structures, and annotations. We developed PanRes, a curated resistance database of 11,717 genes integrating acquired and latent determinants of antibiotic, biocide, and metal resistance within a unified ontology. We predicted representative protein structures and clustered them by structural similarity, grouping proteins into 598 structurally conserved clusters coherent despite sequence divergence. Their structure-guided alignments were used to build Hidden Markov Models (HMMs) for remote homology search. In wastewater metagenomes from seven European cities, PanRes 3D-based HMMs expanded detection beyond high-confidence BLAST, with 35.2% of retained hits identified only by the HMMs and generally showing greater divergence from known proteins. For beta-lactamases, several proteins retained beta-lactamase-like folds and catalytic geometry despite weak sequence similarity. PanRes is available through an interactive web platform (https://panres.rambio.dk/), a structure-informed resource for exploring the whole resistome.

bioinformatics↗

bioRxiv2025

Whole-genome prediction of bacterial pathogenic capacity on novel bacteria using protein language models, with PathogenFinder2

Infectious diseases continue to be a leading cause of mortality and pose a significant global health threat. Thus the development of tools for surveillance and early detection of emerging pathogens is needed. In this study, we introduce PathogenFinder2, a novel predictor of bacterial pathogenic capacity in humans, available through an online server (http://genepi.food.dtu.dk/pathogenfinder2), or as a standalone program (https://github.com/genomicepidemiology/PathogenFinder2). The model, using protein language models for whole-genome phenotype prediction, surpasses the performance of previous methods, especially for novel bacterial taxa, while being taxonomy-agnostic and alignment-free. At the same time, it predicts the importance of each protein for the pathogenic capacity. This output might aid in characterizing potential pathogens, it readily identifies new candidates for virulence factors and vaccine targets, and offers insights into infection metabolic pathways. Furthermore, we introduce the Bacterial Pathogenic Landscape, revealing distributions related to the host conditions, antagonist bacteria, infection site, or habitat.

bioinformatics↗

bioRxiv2024

ListPred: A predictive ML tool for virulence potential and disinfectant tolerance in Listeria monocytogenes

Despite current surveillance and sanitation strategies, foodborne pathogens continue to threaten the food industry and public health. Whole genome sequencing (WGS) has reached an unprecedented resolution to analyse and compare pathogenic bacterial isolates. The increased resolution significantly enhances the possibility of tracing transmission routes and contamination sources of foodborne pathogens. In addition, machine learning (ML) on WGS data has shown promising applications for predicting important microbial traits such as virulence, growth potential, and resistance to antimicrobials. Many regulatory agencies have already adapted WGS and ML methods. However, the food industry hasnt followed a similarly enthusiastic implementation. Some possible reasons for this might be the lack of computational resources and limited expertise to analyse WGS and ML data and interpret the results. Here, we present ListPred, a ML tool to analyse WGS data of Listeria monocytogenes, a very concerning foodborne pathogen. ListPred is able to predict two important bacterial traits, namely virulence potential and disinfectant tolerance, and only requires limited computational resources and practically no bioinformatic expertise, which is essential for a broad application in the food industry. AUTHOR SUMMARYThe contamination of food products with microbes such as pathogenic bacteria is a big concern for the food industry. The rapid detection, characterisation and eradication of microbial contaminants are of utmost importance to ensure safe food products. Fortunately, strict food safety regulations and stringent cleaning protocols prevent the transmission of harmful bacteria to humans. Individual bacteria of the same species can have varying abilities to resist cleaning agents or infect a host, meaning that some pathogen isolates might be more dangerous than others. Novel techniques such as genome sequencing and machine learning can help to determine such differences in individual bacteria. Unfortunately, these techniques require a lot of computational power and expertise that is limited in the food industry. This is why we developed an easy-to-use software tool called ListPred that can be used with few computational requirements and little expertise. ListPred helps food companies to answer two essential questions: how dangerous are Listeria monocytogenes pathogens, and how to get rid of them most efficiently?

bioinformatics↗

bioRxiv2021

Standard Sample Storage Conditions Impact on Inferred Microbiome Composition and Antimicrobial Resistance Patterns

Storage of biological specimens is crucial in the life and medical sciences. The storage conditions for samples can be different for a number of reasons, and it is unclear which effect this can have on the inferred microbiome composition in metagenomics analyses. Here, we assess the effect of common storage temperatures (deep freezer: -80{degrees}C, freezer: -20{degrees}C, fridge: 5{degrees}C, room temperature: 22{degrees}C) and storage times, (immediate sample processing: 0h, next day: 16h, over weekend: 64h, and longer term: 4, 8, 12 months), as well as repeated sample freezing and thawing (2-4 freeze-thaw cycles). We examine two different pig feces and sewage samples, unspiked and spiked with a mock community, and in triplicates, respectively, amounting to a total of 438 samples (777 Gbp; 5.1 billion reads). Storage conditions had a significant and systematic effect on the taxonomic and functional composition of microbiomes. Distinct microbial taxa and antimicrobial resistance classes were in some situations similarly effected across samples, while others were not, suggesting an impact of individual inherent sample characteristics. With an increasing number of freeze-thaw cycles, an increasing abundance of Firmicutes, Actinobacteria, and eukaryotic microorganisms was observed. We include recommendations for sample storage, and strongly suggest including more detailed information in the metadata together with the DNA sequencing data in public repositories to better facilitate meta-analyses and reproducibility of findings. IMPORTANCEPrevious research has reported effects of DNA isolation, library preparation, and sequencing technology on metagenomics-based microbiome composition; however, the effect of biospecimen storage conditions has not been thoroughly assessed. We examined the effect of common sample storage conditions on metagenomics-based microbiome composition and find significant and, in part, systematic effects. Repeated freeze-thaw cycles could be used to improve the detection of microorganisms with more rigid cell walls, including parasites. We provide a dataset that could also be used for benchmarking algorithms to identify and correct for batch effects. Overall, the findings suggest that all samples of a microbiome study should be stored in the same way. Furthermore, there is a need to mandate more detailed information about sample storage and processing published together with the DNA sequencing data at INSDC (ENA/EBI, NCBI, DDBJ) or other repositories.

microbiology↗

bioRxiv2019

The COMPARE Data Hubs

Data sharing enables research communities to exchange findings and build upon the knowledge that arises from their discoveries. Areas of public and animal health as well as food safety would benefit from rapid data sharing when it comes to emergencies. However, ethical, regulatory, and institutional challenges, as well as lack of suitable platforms which provide an infrastructure for data sharing in structured formats often lead to data not being shared, or at most shared in form of supplementary materials in journal publications. Here, we describe an informatics platform that includes workflows for structured data storage, managing and pre-publication sharing of pathogen sequencing data and its analysis interpretations with relevant stakeholders.

bioinformatics↗