Biology subjects

Aarestrup, F. M.

Publications and source records attributed to Aarestrup, F. M..

At least 19 recordsLinked to original sources

bioRxiv2026

Single-Cell Metagenomics Links Plasmid-Derived Antimicrobial Resistance to Hosts in the Pig Gut Microbiome

Antimicrobial exposure can alter gut resistance reservoirs, but bulk metagenomics alone often cannot distinguish whether observed changes reflect expansion of bacterial hosts, altered abundance of plasmid-derived sequences, or redistribution of mobile elements across host backgrounds. Here, we combined longitudinal bulk short-read metagenomics with selected bulk long-read and single-cell shotgun metagenomic sequencing to analyse faecal samples from six Danish pigs over 11 weeks, including an unplanned tiamulin exposure affecting the three pigs housed on the right side of the stable. We constructed a catalogue of 885 plasmid-derived sequences collapsed into 195 bins. Twenty-eight bins and 212 contigs carried resistance annotations, including ribosomal-target markers relevant to pleuromutilin exposure. Single-cell evidence linked subsets of plasmid-derived bins and contigs to bacterial host taxa, enabling host-resolved inspection of resistance-associated plasmid-derived features in longitudinal bulk metagenomes. The microbiome-wide plasmid-derived-sequence prevalence screen identified two bins with post-event associations, whereas resistance-gene abundance and host-attributed plasmid-derived-sequence abundance screens identified no significant host-resolved associations. Because exposure was unplanned and confounded with pen side and disease signs, treatment-response results are exploratory. The main contribution is a single-cell-informed microbial ecology workflow for linking plasmid-derived resistance features to host backgrounds and longitudinal abundance patterns in complex gut

microbiology↗

bioRxiv2026

Impact of ceftiofur administration and Escherichia coli inoculation on the calf fecal microbiome

The cattle gastrointestinal tract harbors a diverse community of microorganisms, including pathogenic and commensal strains of Escherichia coli. Antimicrobial use in cattle can disrupt the gut microbiome leading to shifts in bacterial diversity and abundance. Here, we combined shotgun metagenomics and single-cell sequencing to assess how ceftiofur antibiotic treatment impacted microbial diversity and structure. At the start of the experiment, ceftiofur was administered intramuscularly in parallel with the inoculation of a cocktail of extended-beta-lactamase-producing E. coli strains, to simulate environmental exposure and acquisition of resistant strains while animals are under antibiotic treatment. Fecal samples were collected from both the antibiotic-treated (ceftiofur and inoculation) and control (inoculation only) calves over the course of 35 days. Read mapping to genome and gene databases showed substantial differences in microbial richness and beta diversity between treatment groups. Treatment group-enriched taxa included Bacteroidaceae and Fibrobacter, which were more abundant in samples that did not receive ceftiofur, and Akkermansia in ceftiofur-treated calves. In ceftiofur-exposed animals, we observed a gradual loss of virulence factors alongside increased abundances of beta-lactam resistance genes, including cfxA5 and cfxA6 likely encoded by CAG-485 (Muribaculaceae). We further profiled individual cells using single-cell sequencing, which revealed a high number of Clostridium carrying macrolide resistance genes lnu(P) and mph(N) in both ceftiofur-treated and control samples. Overall, our complementary approaches reveal distinct remodeling of the calf microbiome following antibiotic and E. coli administration, tied to key functional genes that can be assigned to specific genera or recurrently detected across diverse taxa. IMPORTANCECattle serve as natural reservoirs of zoonotic strains of E. coli, which can cause severe gastrointestinal infections in humans. Antibiotic usage on cattle farms can drive the emergence of antimicrobial resistant bacterial strains and alter the underlying cattle gastrointestinal microbiome. Consequently, there is a need to understand how antibiotic administration impacts population dynamics of cattle rumen and intestinal microbes. In this study, we combined both shotgun metagenomics and single-cell genomics on feces from ruminating calves to determine microbiome changes following administration of both ceftiofur and E. coli cocktails. We observed considerable variation in prevalence and abundance of virulence factors, antimicrobial resistance-related genes, and taxa with key roles in animal nutrition and health between the microbiomes of antibiotic-treated and antibiotic-free calves, with potential implications for their subsequent development and overall well-being.

genomics↗

bioRxiv2025

Acinetobacter enrichment shapes composition and function of the bacterial microbiota of field-grown tomato plants

Tomato is a staple crop and an excellent model to study host-microbiota interactions in the plant food chain. In this study we describe a lab-in-the-field approach to investigate the microbiota of field-grown tomato plants. High-throughput amplicon sequencing revealed a three-microhabitat partition, phyllosphere, rhizosphere and root interior, differentiating host-associated communities from the environmental microbiota. An individual bacterium, classified as Acinetobacter sp., emerged as a dominant member of the microbiota at the plant-soil continuum. To gain insights into the functional significance of this enrichment, we subjected rhizosphere specimens to shotgun metagenomics. Similar to the amplicon sequencing survey, a microhabitat effect defined by a set of rhizosphere-enriched functions was identified. Mobilisation of mineral nutrients, as well as adaptation to salinity and polymicrobial communities, including antimicrobial resistance genes (ARGs), emerged as a functional requirement sustaining metagenomic diversification. A metagenome-assembled genome (MAG) representative of Acinetobacter calcoaceticus was retrieved and metagenomic reads associated to this species identified a functional specialisation for plant-growth promotion traits (PGPTs), such as phosphate solubilization, siderophores production and reactive oxygen species detoxification, which were similarly represented in across tomato genotype-independent fashion. Our results revealed that the enrichment of a beneficial bacterium capable of alleviating plants abiotic stresses appears decoupled from ARGs facilitating microbiota persistence at the root-soil interface. IMPORTANCETomatoes are at centre-stage in global food security due to their high nutritional value, widespread cultivation, and versatility. Tomatoes provide essential vitamins and minerals, contribute to diverse diets, and support farmer livelihoods, making them a cornerstone of sustainable food systems. Beyond direct dietary benefits, the intricate relationship between tomatoes, their associated microbiota, and ARG is increasingly recognised. Tomato plants host diverse microbial communities in association with their organs, which influence plant health and productivity. Crop management impacts on composition and function of these communities, contributing to the prevalence of ARG in the soil and on the plants themselves. These genes can potentially transfer to human pathogens, posing a food safety and public health risk. Understanding these complex interactions is critical for developing sustainable agricultural practices capable of mitigating the impact of climatic modifications and the global threat of antimicrobial resistance.

plant biology↗

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

Time-series sewage metagenomics can separate the seasonal, human-derived and environmental microbial communities, holding promise for source-attributed surveillance

Sewage metagenomics has risen to prominence in urban population surveillance of pathogens and antimicrobial resistance (AMR). Unknown species with similarity to known genomes cause database bias in reference-based metagenomics. To improve surveillance, we designed this study to recover sewage genomes and develop a quantification and correlation workflow for these genomes and AMR over time. We used longitudinal sewage sampling in seven treatment plants from five major European cities to explore the utility of catch-all sequencing of these population-level samples. Using metagenomic assembly methods, we recovered 2,332 metagenome-assembled genomes (MAGs) from prokaryotic species, 1,334 of which were previously undescribed. These genomes account for [~]69% of sequenced DNA and provide insight into sewage microbial dynamics. Rotterdam (Netherlands) and Copenhagen (Denmark) showed strong seasonal microbial community shifts, while Bologna, Rome, (Italy) and Budapest (Hungary) had occasional blooms of Pseudomonas-dominated communities, accounting for up to [~]95% of sample DNA. Seasonal shifts and blooms present challenges for effective sewage surveillance. We find that bacteria of known shared origin, like human gut microbiota, form communities, suggesting the potential for source-attributing novel species and their ARGs through network community analysis. This could significantly improve AMR tracking in urban environments.

microbiology↗

bioRxiv2023

Large-scale phenotypic and genomic characterization of Listeria monocytogenes susceptibility to quaternary ammonium compounds

Listeria monocytogenes is a significant concern for the food industry due to its ability to persist in the food processing environment. Decreased susceptibility to disinfectants is one of the factors that contribute to the persistence of L. monocytogenes. The objective of this study was to explore the diversity of L. monocytogenes susceptibility to quaternary ammonium compounds (QACs) using 1,671 L. monocytogenes isolates. This was used to determine the phenotype-genotype concordance and characterize genomes of the QAC sensitive and tolerant isolates for stress resistance, virulence and plasmid replicon genes. Distribution of QAC tolerance genes among 37,897 publicly available L. monocytogenes genomes were also examined. The minimum inhibitory concentration to QACs was determined by the broth microdilution method and non-sequenced isolates (n=1,244) were whole genome sequenced. Genotype-phenotype concordance was 99% for benzalkonium chloride, DDAC and a commercial QAC based sanitizer. Prevalence of QAC tolerance genes was 23% and 28% in our L. monocytogenes collection and in the global dataset, respectively. qacH was the most prevalent gene in our collection (61%), with 19% prevalence in the global dataset. Notably, bcrABC was most common (72%) globally, while 25% in our collection. Prevalence of emrC and emrE was comparable in both datasets, 7% and 2%, respectively. Replicon genes, indicative of plasmid harborage, were detected in 44% of the isolates and associated with the QAC tolerant phenotype. The presented analysis is based on the biggest L. monocytogenes collection in diversity and quantity for characterization of the L. monocytogenes QAC tolerance at both phenotypic and genomic levels. IMPORTANCEContamination of Listeria monocytogenes within the food processing environment is of concern to the food industry due to challenges in eradicating the pathogen once it becomes persistent in the environment. Genetic markers associated with increased tolerance to disinfectants have been identified, which alongside factors favor the persistence of L. monocytogenes in the production environment. By employing a comprehensive large-scale phenotypic testing and genomic analysis our study significantly enhances the understanding of the prevalence of quaternary ammonium compound (QAC) tolerant L. monocytogenes and the genetic determinants associated with the increased tolerance. Furthermore, we report on the prevalence of QAC tolerance genes among 37,897 publicly available L. monocytogenes sequences and their distribution within clonal complexes, isolation sources and geographical locations. As the propagation of QAC tolerance showed not be evenly distributed globally this highlights that understanding the development of L. monocytogenes disinfectant tolerance can be monitored using publicly available WGS data.

microbiology↗

bioRxiv2023

Mobilisation and analyses of publicly available SARS-CoV-2 data for pandemic responses

The COVID-19 pandemic has seen large-scale pathogen genomic sequencing efforts, becoming part of the toolbox for surveillance and epidemic research. This resulted in an unprecedented level of data sharing to open repositories, which has actively supported the identification of SARS-CoV-2 structure, molecular interactions, mutations and variants, and facilitated vaccine development and drug reuse studies and design. The European COVID-19 Data Platform was launched to support this data sharing, and has resulted in the deposition of several million SARS-CoV-2 raw reads. In this paper we describe (1) open data sharing, (2) tools for submission, analysis, visualisation and data claiming (e.g. ORCiD), (3) the systematic analysis of these datasets, at scale via the SARS-CoV-2 Data Hubs as well as (4) lessons learned. As a component of the Platform, the SARS-CoV-2 Data Hubs enabled the extension and set up of infrastructure that we intend to use more widely in the future for pathogen surveillance and pandemic preparedness.

bioinformatics↗

bioRxiv2022

Utilizing co-abundances of antimicrobial resistance genes to identify potential co-selection in the resistome

The rapid spread of antimicrobial resistance (AMR) is a threat to global health, and the nature of co-occurring antimicrobial resistance genes (ARGs) may cause collateral AMR effects once antimicrobial agents are used. Therefore, it is essential to identify which pairs of ARGs co-occur. Given the wealth of NGS data available in public repositories, we have investigated the correlation between ARG abundances in a collection of 214,095 metagenomic datasets. Using more than 6.76{middle dot}108 read fragments aligned to acquired ARGs to infer pairwise correlation coefficients, we found that more ARGs correlated with each other in human and animal sampling origins than in soil and water environments. Furthermore, we argued that the correlations could serve as risk profiles of resistance co-occurring to critically important antimicrobials. Using these profiles, we found evidence of several ARGs conferring resistance for critically important antimicrobials (CIA) being co-abundant, such as tetracycline ARGs correlating with most other forms of resistance. In conclusion, this study highlights the important ARG players indirectly involved in shaping the resistomes of various environments that can serve as monitoring targets in AMR surveillance programs.

bioinformatics↗

bioRxiv2022

West Nile Virus spread in Europe - phylogeographic pattern analysis and key drivers

Spread and emergence of West Nile virus (WNV) in Europe have been very different from those observed in North America. Here, we describe key drivers by combining viral genome sequences with epidemiological data and possible factors of spread into phylodynamic models. WNV in Europe has greater lineage diversity than other regions of the world, suggesting repeated introductions and local amplification. Among the six lineages found in Europe, WNV-2a is predominant, has spread to at least 14 countries and evolved into two major co-circulating clusters (A and B). Both of these seem to originate from regions of Central Europe. Viruses of Cluster A emerged earlier and have spread towards the west of Europe with higher genetic diversity. Amongst multiple drivers, high agriculture activities were associated with both spread direction and velocity. Our study suggests future surveillance activities should be strengthened in Central Europe and Southeast European countries, and enhanced monitoring should be targeted to areas with high agriculture activities.

evolutionary biology↗

bioRxiv2022

A curated data resource of 214K metagenomes for characterization of the global resistome

The growing threat of antimicrobial resistance (AMR) calls for new epidemiological surveillance methods, as well as a deeper understanding of how antimicrobial resistance genes (ARGs) have transmitted around the world. The large pool of sequencing data available in public repositories provides an excellent resource for monitoring the temporal and spatial dissemination of AMR in different ecological settings. However, only a limited number of research groups globally have the computational resources allowing analyses of such data. We retrieved 442 Tbp of sequencing reads from 214,095 metagenomic samples from the European Nucleotide Archive (ENA) and aligned them using a uniform approach against ARGs and 16S/18S rRNA genes. Here, we present the results of this extensive computational analysis and share the counts of reads aligned. Over 6.76 {middle dot} 108 read fragments were assigned to ARGs and 3.21 {middle dot} 109 to rRNA genes, where we observed distinct differences in both the abundance of ARGs and the link between microbiome and resistome compositions across various sampling types. This collection is another step towards establishing a global surveillance of AMR and can serve as a resource for further research into the environmental spread and dynamic changes of ARGs.

bioinformatics↗

bioRxiv2022

Detection of Parasites in Microbiomes using Metagenomics

Despite a yearly death toll of up to one million people due to parasite-related infections, parasites are still neglected in genomics research. While there is progress in the detection of bacteria and viruses using metagenomics in the context of infectious diseases, there are still challenges in metagenomics-based detection of parasites. Here, we implement a workflow for the detection of parasites from metagenomics data. We employ stringent cut off criteria to limit false positive detections. We analysed a total of 7.120 metagenomics samples of which 359 originated from gut microbiomes of livestock (pigs and chicken) from nine countries, and 6.761 from gut microbiomes of humans (adults and infants) from 25 countries. Five parasite-related genera were detected in livestock, of which Blastocystis sp. was detected in 71% of all pig herds and Eimeria in 83% of all chicken flocks. Distinct gut bacterial taxa were associated with Blastocystis sp. abundance in pigs. Nine parasite-related genera were detected in humans. Blastocystis sp. subtypes ST1, ST2, and ST3 were detected in all countries, and ST3 was most predominant. A higher overall prevalence of Blastocystis sp. was observed in low-income countries as compared to high-income countries, and a higher diversity of Blastocystis subtypes (ST1, ST2, ST3, ST4, ST6, ST7, ST8) was detected in high-income countries as compared to low-income countries. The prevalence of Blastocystis sp. in infant gut microbiome samples was lower as compared to adults. Overall, metagenomics-based analysis may be a promising tool for parasite detection from complex microbiome samples in clinical and veterinary medicine. Metagenomics could become the preferred method for parasite detection for a wide range of biological samples. Current parasite detection methods often rely on microscopic examination of the sample or using specific PCR. Metagenomics-based analyses may allow for a faster and more convenient way of detecting parasites in humans and animals, as this approach could serve as a one-for-all untargeted approach for pathogen detection, including bacteria, viruses, and parasites.

microbiology↗

bioRxiv2022

Plasmid characterization in bacterial isolates of public health relevance in a tertiary healthcare facility in Kilimanjaro, Tanzania

Plasmids are infectious double stranded DNA molecules that are found within bacteria. Horizontal gene transfer promotes successful spread of different types of plasmids within or among bacteria species, making their detection an important task for guiding clinical treatment. We used whole genome sequenced data to determine the prevalence of plasmid replicons in clinical bacterial isolates, the presence of resistance and virulence genes in plasmids, and the relationship between resistance and virulence genes within each plasmid. All bacterial sequences were de novo assembled using Unicycler before extraction of plasmids. Assembly graphs were submitted to Gplas+plasflow for plasmid prediction. The predicted plasmid components were validated using PlasmidFinder. A total of 159 (56.2%) out of 283 bacterial isolates were found to carry plasmids, with E. coli, K. pneumoniae and S. aureus being the most prevalent plasmid carriers. A total of 27 (87.1%) combined plasmids were found to carry both resistance and virulence genes compared to 4 (12.9%) single plasmids. No statistically significant correlation was found between the number of antimicrobial resistance and virulence genes in plasmids (r =-0.25, p > 0.05). Our findings show a relatively high proportion of plasmid-carrying isolates suggesting selection pressure due to antibiotic use in the hospital. Co-occurrence of antibiotic resistance and virulence genes in clinical isolates is a public health relevant problem needing attention.

genomics↗

bioRxiv2022

Global distribution and diversity of prevalent sewage water plasmidomes

Sewage water from around the world contains an abundance of short plasmids, a number of which harbor antimicrobial resistance genes (ARGs). The global dynamics of plasmid-derived antimicrobial resistance and functions is only starting to be unveiled. Here, we utilized a previously created dataset of 159,332 assumed small plasmids from 24 different globally collected sewage samples. We investigated the detailed phylogeny as well as the interplay between their protein domains, ARGs, and predicted bacterial host genera to help understand the global sewage plasmidome dynamics. A total of 58,429 circular elements carried genes encoding for plasmid-related features, and MASH distance analyses showed a very high degree of diversity. A single very diverse cluster of 520 predicted Acinetobacter plasmids was predominant among the European sewage water. Based on functional domain network analysis, we identified three groups of plasmids, mainly replication and mobilization domains. However, these backbone domains were not exclusive to any given group. Acinetobacter was the dominant host genus among theta-replicating plasmids at these size ranges. They contained a reservoir of the macrolide resistance gene pair msr(E) and mph(E). Macrolide resistance genes were the most common resistance genes in sewage plasmidomes and found in the largest number of unique plasmids. While msr(E) and mph(E) were limited to Acinetobacter, erm(B) was disseminated among a range of Firmicutes plasmids, including Staphylococcus and Streptococcus, highlighting a potential reservoir of antibiotics resistance for these pathogens from around the globe. ImportanceAntimicrobial resistance is a global threat to human health inhibiting our ability to treat infectious diseases. This study utilizes sewage water plasmidomes to identify plasmid-derived features, and highlights antimicrobial resistance genes, particularly macrolide resistance to be abundant in sewage water plasmidomes in Firmicutes and Acinetobacter hosts. The emergence of macrolide resistance in these bacteria hints that macrolide selective pressure exists in sewage water, and that the resident bacteria readily can acquire macrolide resistance via small plasmids.

microbiology↗

bioRxiv2022

Detection of specific uncultured bacteriophages by fluorescence in situ hybridisation in pig microbiome

Microbial communities have huge impacts on their ecosystems and local environments spanning from marine and soil communities to the mammalian gut. Bacteriophages (phages) are important drivers of population control and diversity in the community, but our understanding of complex microbial communities is halted by biased detection techniques. Metagenomics have provided a method of novel phage discovery independent of in vitro culturing techniques and have revealed a large proportion of understudied phages. Here, five large phage genomes, that were previously assembled in silico from pig faecal metagenomes, are detected and observed directly in their natural environment using a modified phageFISH approach, and combined with methods to decrease bias against large phages. These phages are uncultured with unknown hosts. The specific phages were detected by PCR and fluorescent in situ hybridisation in their original faecal samples as well as across other faecal samples. Co-localisation of bacterial signals and phage signals allowed detection of the different stages of phage life cycle. All phages displayed examples of early infection, advanced infection, burst, and free phages. To our knowledge, this is the first detection of jumbophages in faeces, which were investigated independently of culture, host identification, and size, and based solely on the genome sequence. This approach opens up opportunities for characterisation of novel in silico phages in vivo from a broad range of gut microbiomes.

microbiology↗

bioRxiv2021

PlasmidHostFinder: Prediction of plasmid hosts using random forest

Plasmids play a major role facilitating the spread of antimicrobial resistance between bacteria. Understanding the host range and dissemination trajectories of plasmids is critical for surveillance and prevention of antimicrobial resistance. Identification of plasmid host ranges could be improved using automated pattern detection methods, compared to homology-based methods due to the diversity and genetic plasticity of plasmids. In this study, we developed a method for predicting the host range of plasmids based on the random forest machine learning method. We trained the models with 8,519 plasmids from 359 different bacterial species per taxonomic level, where the models achieved 0.662 and 0.867 Matthews correlation coefficients at the species and order levels, respectively. Our results suggest that despite the diverse nature and genetic plasticity of plasmids, our random forest model can accurately distinguish between plasmid hosts. This tool can be used online through Center for Genomic Epidemiology (https://cge.cbs.dtu.dk/services/PlasmidHostFinder/). ImportanceAntimicrobial resistance is a global health threat to humans and animals causing high mortality and morbidity, and effectively ending decades of success in fighting against bacterial infections. Plasmids confer extra genetic capabilities to the host organisms through accessory genes, which can encode antimicrobial resistance and virulence factors. In addition to lateral inheritance, plasmids can be transferred horizontally between bacterial taxa. Therefore, detecting the host range of plasmids is crucial for understanding and predicting the dissemination trajectories of extrachromosomal genes and bacterial evolution, as well as for taking effective counter measures against antimicrobial resistance.

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↗

bioRxiv2021

A Peek into the Plasmidome of Global Sewage

Plasmids can provide a selective advantage for microorganisms to survive and adapt to new environmental conditions. Plasmid-encoded traits, such as antimicrobial resistance (AMR) or virulence, impact on the ecology and evolution of bacteria and can significantly influence the burden of infectious diseases. Insight about the identity and functions encoded on plasmids on the global scale are largely lacking. Here we investigate the plasmidome of 24 samples (22 countries, 5 continents) from the global sewage surveillance project. We obtained 105 Gbp Oxford Nanopore and 167 Gbp Illumina DNA sequences from plasmid DNA preparations and assembled 165,302 contigs (159,322 circular). Of these, 58,429 encoded for genes with plasmid-related and 11,222 with virus/phage-related proteins. About 90% of the circular DNA elements did not have any similarity to known plasmids. Those that exhibited similarity, had similarity to plasmids whose hosts were previously detected in these sewage samples (e.g. Acinetobacter, Escherichia, Moraxella, Enterobacter, Bacteroides, and Klebsiella). Some AMR classes were detected at a higher abundance in plasmidomes (e.g. macrolide-lincosamide-streptogramin B, macrolide, and quinolone), as compared to the respective complex sewage samples. In addition to AMR genes, a range of functions were encoded on the candidate plasmids, including plasmid replication and maintenance, mobilization, and conjugation. In summary, we describe a laboratory and bioinformatics workflow for the recovery of plasmids and other potential extrachromosomal DNA elements from complex microbiomes. Moreover, the obtained data could provide further valuable insight into the ecology and evolution of microbiomes, knowledge about AMR transmission, and the discovery of novel functions. ImportanceThis is, to the best of our knowledge, the first study to investigate plasmidomes at a global scale using long read sequencing from complex untreated domestic sewage. Previous metagenomic surveys have detected AMR genes in a variety of environments, including sewage. However, it is unknown whether the AMR genes were encoded on the microbial chromosome or are located on extrachromosomal elements, such as plasmids. Using our approach, we recovered a large number of plasmids, of which most appear novel. We identified distinct AMR genes that were preferentially located on plasmids, potentially contributing to their transmissibility. Overall, plasmids are of great importance for the biology of microorganisms in their natural environments (free-living and host-associated), as well as molecular biology, and biotechnology. Plasmidome collections may therefore be valuable resources for the discovery of fundamental biological mechanisms and novel functions useful in a variety of contexts.

microbiology↗

bioRxiv2019

Feature matrix normalization, transformation and calculation of beta-diversity in metagenomics: Theoretical and applied perspectives on your decisions

Microbial metagenomics utilising next generation sequencing is a powerful experimental approach enabling detailed and potentially complete descriptions of the microbial world around and within us. Selecting how to perform feature data normalization, transformation and calculate {beta}-diversity is a critical step in the analysis of metagenomic data, but also a step for which a multitude of methods are available. Researchers need to have a broad overview and understand the many methods that exist in the field and the consequences from applying them. In this perspectives article, some of the most widely used metagenomic feature data normalizations, transformations and {beta}-diversity metrics are discussed in the context of multivariate visualizations. We provide a framework that other researchers can utilize to evaluate how robust their test data are when applying different normalizations, transformations and {beta}-diversity metrics, and visually compare the results of the methods. We constructed an in silico test dataset to evaluate the setup and clarify how the theoretical discussion is transferable to this data. We urge other researchers to implement their own test data, normalization, transformation, {beta}-diversity metric and visualization methods, in the hope that it will advance better decision making both in study design and analysis strategy.

microbiology↗