Publications

Genomic antimicrobial susceptibility testing (AST) has been shown to be accurate for many pathogens and antimicrobials. However, these methods have not been systematically evaluated for clinical metagenomic data. We investigate the performance of in-silico AST from clinical metagenomes (MG-AST). Using isolate sequencing data from a multi-center study on antimicrobial resistance (AMR) as well as shotgun-sequenced septic urine samples, we simulate over 2000 complicated urinary tract infection (cUTI) metagenomes with known resistance phenotype to 5 antimicrobials. Applying rule-based and machine learning-based genomic AST classifiers, we explore the impact of sequencing depth and technology, metagenome complexity, and bioinformatics processing approaches on AST accuracy. By using an optimized metagenomics assembly and binning workflow, MG-AST achieved balanced accuracy within 5.1% of isolate-derived genomic AST. For poly-microbial infections, taxonomic sample complexity and relatedness of taxa in the sample is a key factor influencing metagenomic binning and downstream MG-AST accuracy. We show that the reassignment of putative plasmid contigs by their predicted host range and investigation of whole resistome capabilities improved MG-AST performance on poly-microbial samples. We further demonstrate that machine learning-based methods enable MG-AST with superior accuracy compared to rule-based approaches on simulated native patient samples.

Metagenomic assembly enables new organism discovery from microbial communities, but it can only capture few abundant organisms from most metagenomes. Here we present MetaPhlAn 4, which integrates information from metagenome assemblies and microbial isolate genomes for more comprehensive metagenomic taxonomic profiling. From a curated collection of 1.01 M prokaryotic reference and metagenome-assembled genomes, we define unique marker genes for 26,970 species-level genome bins, 4,992 of them taxonomically unidentified at the species level. MetaPhlAn 4 explains ~20% more reads in most international human gut microbiomes and >40% in less-characterized environments such as the rumen microbiome and proves more accurate than available alternatives on synthetic evaluations while also reliably quantifying organisms with no cultured isolates. Application of the method to >24,500 metagenomes highlights previously undetected species to be strong biomarkers for host conditions and lifestyles in human and mouse microbiomes and shows that even previously uncharacterized species can be genetically profiled at the resolution of single microbial strains.

Bacterial endosymbionts of the groups , and are well known for their diverse effects on their arthropod hosts, ranging from mutualistic relationships to reproductive phenotypes. Here, we analysed a unique system in which the dwarf spider is co-infected with up to five different endosymbionts affiliated with , ' Tisiphia' (formerly Torix group ), and . Using short-read genome sequencing data, we show that the endosymbionts are heterogeneously distributed among populations and are frequently found co-infecting spider individuals. To study this intricate host-endosymbiont system on a genome-resolved level, we used long-read sequencing to reconstruct closed genomes of the , '. Tisiphia' and endosymbionts. We provide insights into the ecology and evolution of the endosymbionts and shed light on the interactions with their spider host. We detected high quantities of transposable elements in all endosymbiont genomes and provide evidence that ancestors of the , '. Tisiphia' and endosymbionts have co-infected the same hosts in the past. Our findings contribute to broadening our knowledge about endosymbionts infecting one of the largest animal phyla on Earth and show the usefulness of transposable elements as an evolutionary 'contact-tracing' tool.

Marine and freshwater sponges harbor diverse communities of bacteria with vast potential to produce secondary metabolites that may play an important role in protecting the host from predators and infections. In this work, we initially used cultivation and metagenomics to investigate the microbial community of the freshwater sponge Spongilla lacustris collected in an Austrian lake. Representatives of 41 bacterial genera were isolated from the sponge sample and classified according to their 16S rRNA gene sequences. The genomes of 33 representative isolates and the 20 recovered metagenome-assembled genomes (MAGs) contained in total 306 secondary metabolite biosynthesis gene clusters (BGCs). Comparative 16S rRNA gene and genome analyses showed very little taxon overlap between the recovered isolates and the sponge community as revealed by cultivation-independent methods. Both culture-independent and -dependent analyses suggested high biosynthetic potential of the S. lacustris microbiome, which was confirmed experimentally even at the subspecies level for two isolates. To our knowledge, this is the most thorough description of the secondary metabolite production potential of a freshwater sponge microbiome to date. A large body of research is dedicated to marine sponges, filter-feeding animals harboring rich bacterial microbiomes believed to play an important role in protecting the host from predators and infections. Freshwater sponges have received so far much less attention with respect to their microbiomes, members of which may produce bioactive secondary metabolites with potential to be developed into drugs to treat a variety of diseases. In this work, we investigated the potential of bacteria associated with the freshwater sponge to biosynthesize diverse secondary metabolites. Using culture-dependent and -independent methods, we discovered over 300 biosynthetic gene clusters in sponge-associated bacteria and proved production of several compounds by selected isolates using genome mining. Our results illustrate the importance of a complex approach when dealing with microbiomes of multicellular organisms that may contain producers of medically important secondary metabolites.

Whether the human fetus and the prenatal intrauterine environment (amniotic fluid and placenta) are stably colonized by microbial communities in a healthy pregnancy remains a subject of debate. Here we evaluate recent studies that characterized microbial populations in human fetuses from the perspectives of reproductive biology, microbial ecology, bioinformatics, immunology, clinical microbiology and gnotobiology, and assess possible mechanisms by which the fetus might interact with microorganisms. Our analysis indicates that the detected microbial signals are likely the result of contamination during the clinical procedures to obtain fetal samples or during DNA extraction and DNA sequencing. Furthermore, the existence of live and replicating microbial populations in healthy fetal tissues is not compatible with fundamental concepts of immunology, clinical microbiology and the derivation of germ-free mammals. These conclusions are important to our understanding of human immune development and illustrate common pitfalls in the microbial analyses of many other low-biomass environments. The pursuit of a fetal microbiome serves as a cautionary example of the challenges of sequence-based microbiome studies when biomass is low or absent, and emphasizes the need for a trans-disciplinary approach that goes beyond contamination controls by also incorporating biological, ecological and mechanistic concepts.

The human microbiome is an integral component of the human body and a co-determinant of several health conditions. However, the extent to which interpersonal relations shape the individual genetic makeup of the microbiome and its transmission within and across populations remains largely unknown. Here, capitalizing on more than 9,700 human metagenomes and computational strain-level profiling, we detected extensive bacterial strain sharing across individuals (more than 10 million instances) with distinct mother-to-infant, intra-household and intra-population transmission patterns. Mother-to-infant gut microbiome transmission was considerable and stable during infancy (around 50% of the same strains among shared species (strain-sharing rate)) and remained detectable at older ages. By contrast, the transmission of the oral microbiome occurred largely horizontally and was enhanced by the duration of cohabitation. There was substantial strain sharing among cohabiting individuals, with 12% and 32% median strain-sharing rates for the gut and oral microbiomes, and time since cohabitation affected strain sharing more than age or genetics did. Bacterial strain sharing additionally recapitulated host population structures better than species-level profiles did. Finally, distinct taxa appeared as efficient spreaders across transmission modes and were associated with different predicted bacterial phenotypes linked with out-of-host survival capabilities. The extent of microorganism transmission that we describe underscores its relevance in human microbiome studies, especially those on non-infectious, microbiome-associated diseases.

Methanotrophs oxidize most of the methane (CH) produced in natural and anthropogenic ecosystems. Often living close to soil surfaces, these microorganisms must frequently adjust to temperature change. While many environmental studies have addressed temperature effects on CH oxidation and methanotrophic communities, there is little knowledge about the physiological adjustments that underlie these effects. We have studied thermal acclimation in Methylobacter, a widespread, abundant, and environmentally important methanotrophic genus. Comparisons of growth and CH oxidation kinetics at different temperatures in three members of the genus demonstrate that temperature has a strong influence on how much CH is consumed to support growth at different CH concentrations. However, the temperature effect varies considerably between species, suggesting that how a methanotrophic community is composed influences the temperature effect on CH uptake. To understand thermal acclimation mechanisms widely we carried out a transcriptomics experiment with Methylobacter tundripaludum SV96. We observed, at different temperatures, how varying abundances of transcripts for glycogen and protein biosynthesis relate to cellular glycogen and ribosome concentrations. Our data also demonstrated transcriptional adjustment of CH oxidation, oxidative phosphorylation, membrane fatty acid saturation, cell wall composition, and exopolysaccharides between temperatures. In addition, we observed differences in M. tundripaludum SV96 cell sizes at different temperatures. We conclude that thermal acclimation in Methylobacter results from transcriptional adjustment of central metabolism, protein biosynthesis, cell walls and storage. Acclimation leads to large shifts in CH consumption and growth efficiency, but with major differences between species. Thus, our study demonstrates that physiological adjustments to temperature change can substantially influence environmental CH uptake rates and that consideration of methanotroph physiology might be vital for accurate predictions of warming effects on CH emissions.

Biological networks are often used to represent complex biological systems, which can contain several types of entities. Analysis and visualization of such networks is supported by the Cytoscape software tool and its many apps. While earlier versions of stringApp focused on providing intraspecies protein-protein interactions from the STRING database, the new stringApp 2.0 greatly improves the support for heterogeneous networks. Here, we highlight new functionality that makes it possible to create networks that contain proteins and interactions from STRING as well as other biological entities and associations from other sources. We exemplify this by complementing a published SARS-CoV-2 interactome with interactions from STRING. We have also extended stringApp with new data and query functionality for protein-protein interactions between eukaryotic parasites and their hosts. We show how this can be used to retrieve and visualize a cross-species network for a malaria parasite, its host, and its vector. Finally, the latest stringApp version has an improved user interface, allows retrieval of both functional associations and physical interactions, and supports group-wise enrichment analysis of different parts of a network to aid biological interpretation. stringApp is freely available at https://apps.cytoscape.org/apps/stringapp.

The eggNOG (evolutionary gene genealogy Non-supervised Orthologous Groups) database is a bioinformatics resource providing orthology data and comprehensive functional information for organisms from all domains of life. Here, we present a major update of the database and website (version 6.0), which increases the number of covered organisms to 12 535 reference species, expands functional annotations, and implements new functionality. In total, eggNOG 6.0 provides a hierarchy of over 17M orthologous groups (OGs) computed at 1601 taxonomic levels, spanning 10 756 bacterial, 457 archaeal and 1322 eukaryotic organisms. OGs have been thoroughly annotated using recent knowledge from functional databases, including KEGG, Gene Ontology, UniProtKB, BiGG, CAZy, CARD, PFAM and SMART. eggNOG also offers phylogenetic trees for all OGs, maximising utility and versatility for end users while allowing researchers to investigate the evolutionary history of speciation and duplication events as well as the phylogenetic distribution of functional terms within each OG. Furthermore, the eggNOG 6.0 website contains new functionality to mine orthology and functional data with ease, including the possibility of generating phylogenetic profiles for multiple OGs across species or identifying single-copy OGs at custom taxonomic levels. eggNOG 6.0 is available at http://eggnog6.embl.de.