Differential regulation of mitochondrial uncoupling protein 2 in cancer cells.

The persistent growth of cancer cells is underscored by complex metabolic reprogramming, with mitochondria playing a key role in the transition to aerobic glycolysis and representing new therapeutic targets. Mitochondrial uncoupling protein 2 (UCP2) has attracted interest because of its abundance in rapidly proliferating cells, including cancer cells, and its involvement in cellular metabolism. However, the specific contributions of UCP2 to cancer biology remain poorly defined. Our investigation of UCP2 expression in various human and mouse cancer cell lines aimed to elucidate its links to metabolic states, proliferation, and adaptation to environmental stresses such as hypoxia and nutrient deprivation. We observed significant variability in UCP2 expression across cancer types, with no direct correlation to their metabolic activity or proliferation rates. UCP2 abundance was also differentially affected by nutrient availability in different cancer cells, but UCP2 was generally downregulated under hypoxia. These findings challenge the notion that UCP2 is a marker of malignant potential and suggest its more complex involvement in the metabolic landscape of cancer.

Deletions of conserved extracytoplasmic function sigma factors-encoding genes in Streptomyces have a major impact on secondary metabolism.

BACKGROUND: Ethanol shock significantly affects expression of over 1200 genes in Streptomyces venezuelae NRRL B-65,442, including those involved in secondary metabolite biosynthesis and a cryptic gene pepX, which encodes a 19-amino acid peptide with an unknown function. RESULTS: To establish a possible correlation between the PepX peptide and secondary metabolism in S. venezuelae, its gene was deleted, followed by analyses of the transcriptome and secondary metabolome of the mutant. Although the secondary metabolome of the pepX mutant was not strongly affected, pepX deletion, similar to ethanol shock, mostly resulted in downregulated expression of secondary metabolite biosynthesis gene clusters (BGCs). At the same time, there was a reverse correlation between the expression of certain extracytoplasmic function sigma factors (ECFs) and several BGCs. Individual deletions of three selected ECF-coding genes conserved in Streptomyces that were upregulated upon both pepX deletion and ethanol shock, had a profound positive effect on the expression of BGCs, which also correlated with the overproduction of specific secondary metabolites. Deletion of one such ECF-coding gene in a marine sponge-derived Streptomyces sp. also significantly altered the secondary metabolite profile, suggesting an important role of this ECF in the regulation of secondary metabolism. CONCLUSIONS: These findings pave the way for the activation or upregulation of BGCs in Streptomyces bacteria harboring genes for ECFs homologous to those identified in this study, hereby assisting in the discovery of novel bioactive secondary metabolites.

SoxY gene family expansion underpins adaptation to diverse hosts and environments in symbiotic sulfide oxidizers.

Sulfur-oxidizing bacteria (SOB) have developed distinct ecological strategies to obtain reduced sulfur compounds for growth. These range from specialists that can only use a limited range of reduced sulfur compounds to generalists that can use many different forms as electron donors. Forming intimate symbioses with animal hosts is another highly successful ecological strategy for SOB, as animals, through their behavior and physiology, can enable access to sulfur compounds. Symbioses have evolved multiple times in a range of animal hosts and from several lineages of SOB. They have successfully colonized a wide range of habitats, from seagrass beds to hydrothermal vents, with varying availability of symbiont energy sources. Our extensive analyses of sulfur transformation pathways in 234 genomes of symbiotic and free-living SOB revealed widespread conservation in metabolic pathways for sulfur oxidation in symbionts from different host species and environments, raising the question of how they have adapted to such a wide range of distinct habitats. We discovered a gene family expansion of soxY in these genomes, with up to five distinct copies per genome. Symbionts harboring only the "canonical" soxY were typically ecological "specialists" that are associated with specific host subfamilies or environments (e.g., hydrothermal vents, mangroves). Conversely, symbionts with multiple divergent soxY genes formed versatile associations across diverse hosts in various marine environments. We hypothesize that expansion and diversification of the soxY gene family could be one genomic mechanism supporting the metabolic flexibility of symbiotic SOB enabling them and their hosts to thrive in a range of different and dynamic environments.IMPORTANCESulfur metabolism is thought to be one of the most ancient mechanisms for energy generation in microorganisms. A diverse range of microorganisms today rely on sulfur oxidation for their metabolism. They can be free-living, or they can live in symbiosis with animal hosts, where they power entire ecosystems in the absence of light, such as in the deep sea. In the millions of years since they evolved, sulfur-oxidizing bacteria have adopted several highly successful strategies; some are ecological "specialists," and some are "generalists," but which genetic features underpin these ecological strategies are not well understood. We discovered a gene family that has become expanded in those species that also seem to be "generalists," revealing that duplication, repurposing, and reshuffling existing genes can be a powerful mechanism driving ecological lifestyle shifts.

PyCoMo: a python package for community metabolic model creation and analysis.

SUMMARY: PyCoMo is a python package for quick and easy generation of genome-scale compartmentalized community metabolic models that are compliant with current openCOBRA file formats. The resulting models can be used to predict (i) the maximum growth rate at a given abundance profile, (ii) the feasible community compositions at a given growth rate, and (iii) all exchange metabolites and cross-feeding interactions in a community metabolic model independent of the abundance profile; we demonstrate PyCoMo's capability by analysing methane production in a previously published simplified biogas community metabolic model. AVAILABILITY AND IMPLEMENTATION: PyCoMo is freely available under an MIT licence at http://github.com/univieCUBE/PyCoMo, the Python Package Index, and Zenodo.

The Fish Pathogen "Candidatus Clavichlamydia salmonicola"-A Missing Link in the Evolution of Chlamydial Pathogens of Humans.

Chlamydiae like Chlamydia trachomatis and Chlamydia psittaci are well-known human and animal pathogens. Yet, the chlamydiae are a much larger group of evolutionary ancient obligate intracellular bacteria that includes predominantly symbionts of protists and diverse animals. This makes them ideal model organisms to study evolutionary transitions from symbionts in microbial eukaryotes to pathogens of humans. To this end, comparative genome analysis has served as an important tool. Genome sequence data for many chlamydial lineages are, however, still lacking, hampering our understanding of their evolutionary history. Here, we determined the first high-quality draft genome sequence of the fish pathogen "Candidatus Clavichlamydia salmonicola", representing a separate genus within the human and animal pathogenic Chlamydiaceae. The "Ca. Clavichlamydia salmonicola" genome harbors genes that so far have been exclusively found in Chlamydia species suggesting that basic mechanisms important for the interaction with chordate hosts have evolved stepwise in the history of chlamydiae. Thus, the genome sequence of "Ca. Clavichlamydia salmonicola" allows to constrain candidate genes to further understand the evolution of chlamydial virulence mechanisms required to infect mammals.

Genome Dynamics and Temperature Adaptation During Experimental Evolution of Obligate Intracellular Bacteria.

Evolution experiments with free-living microbes have radically improved our understanding of genome evolution and how microorganisms adapt. Yet there is a paucity of such research focusing on strictly host-associated bacteria, even though they are widespread in nature. Here, we used the Acanthamoeba symbiont Protochlamydia amoebophila, a distant relative of the human pathogen Chlamydia trachomatis and representative of a large group of protist-associated environmental chlamydiae, as a model to study how obligate intracellular symbionts evolve and adapt to elevated temperature, a prerequisite for the pivotal evolutionary leap from protist to endothermic animal hosts. We established 12 replicate populations under two temperatures (20 °C, 30 °C) for 510 bacterial generations (38 months). We then used infectivity assays and pooled whole-genome resequencing to identify any evolved phenotypes and the molecular basis of adaptation in these bacteria. We observed an overall reduction in infectivity of the symbionts evolved at 30 °C, and we identified numerous nonsynonymous mutations and small indels in these symbiont populations, with several variants persisting throughout multiple time points and reaching high frequencies. This suggests that many mutations may have been beneficial and played an adaptive role. Mutated genes within the same temperature regime were more similar than those between temperature regimes. Our results provide insights into the molecular evolution of intracellular bacteria under the constraints of strict host dependance and highly structured populations and suggest that for chlamydial symbionts of protists, temperature adaptation was facilitated through attenuation of symbiont infectivity as a tradeoff to reduce host cell burden.

A predicted CRISPR-mediated symbiosis between uncultivated archaea.

CRISPR-Cas systems defend prokaryotic cells from invasive DNA of viruses, plasmids and other mobile genetic elements. Here, we show using metagenomics, metatranscriptomics and single-cell genomics that CRISPR systems of widespread, uncultivated archaea can also target chromosomal DNA of archaeal episymbionts of the DPANN superphylum. Using meta-omics datasets from Crystal Geyser and Horonobe Underground Research Laboratory, we find that CRISPR spacers of the hosts Candidatus Altiarchaeum crystalense and Ca. A. horonobense, respectively, match putative essential genes in their episymbionts' genomes of the genus Ca. Huberiarchaeum and that some of these spacers are expressed in situ. Metabolic interaction modelling also reveals complementation between host-episymbiont systems, on the basis of which we propose that episymbionts are either parasitic or mutualistic depending on the genotype of the host. By expanding our analysis to 7,012 archaeal genomes, we suggest that CRISPR-Cas targeting of genomes associated with symbiotic archaea evolved independently in various archaeal lineages.

Food systems microbiome-related educational needs.

Within the European-funded Coordination and Support Action MicrobiomeSupport (https://www.microbiomesupport.eu/), the Workshop 'Education in Food Systems Microbiome Related Sciences: Needs for Universities, Industry and Public Health Systems' brought together over 70 researchers, public health and industry partners from all over the world to work on elaborating microbiome-related educational needs in food systems. This publication provides a summary of discussions held during and after the workshop and the resulting recommendations.

Antiviral immune response reveals host-specific virus infections in natural ant populations.

Hosts can carry many viruses in their bodies, but not all of them cause disease. We studied ants as a social host to determine both their overall viral repertoire and the subset of actively infecting viruses across natural populations of three subfamilies: the Argentine ant (Linepithema humile, Dolichoderinae), the invasive garden ant (Lasius neglectus, Formicinae) and the red ant (Myrmica rubra, Myrmicinae). We used a dual sequencing strategy to reconstruct complete virus genomes by RNA-seq and to simultaneously determine the small interfering RNAs (siRNAs) by small RNA sequencing (sRNA-seq), which constitute the host antiviral RNAi immune response. This approach led to the discovery of 41 novel viruses in ants and revealed a host ant-specific RNAi response (21 vs. 22 nt siRNAs) in the different ant species. The efficiency of the RNAi response (sRNA/RNA read count ratio) depended on the virus and the respective ant species, but not its population. Overall, we found the highest virus abundance and diversity per population in Li. humile, followed by La. neglectus and M. rubra. Argentine ants also shared a high proportion of viruses between populations, whilst overlap was nearly absent in M. rubra. Only one of the 59 viruses was found to infect two of the ant species as hosts, revealing high host-specificity in active infections. In contrast, six viruses actively infected one ant species, but were found as contaminants only in the others. Disentangling spillover of disease-causing infection from non-infecting contamination across species is providing relevant information for disease ecology and ecosystem management.

Metagenomic Antimicrobial Susceptibility Testing from Simulated Native Patient Samples.

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.

One to host them all: genomics of the diverse bacterial endosymbionts of the spider Oedothorax gibbosus.

Bacterial endosymbionts of the groups Wolbachia, Cardinium and Rickettsiaceae 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 Oedothorax gibbosus is co-infected with up to five different endosymbionts affiliated with Wolbachia, 'Candidatus Tisiphia' (formerly Torix group Rickettsia), Cardinium and Rhabdochlamydia. Using short-read genome sequencing data, we show that the endosymbionts are heterogeneously distributed among O. gibbosus 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 Wolbachia, 'Ca. Tisiphia' and Cardinium 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 Cardinium, 'Ca. Tisiphia' and Wolbachia 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.

Secondary Metabolite Production Potential in a Microbiome of the Freshwater Sponge Spongilla lacustris.

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 Streptomyces isolates. To our knowledge, this is the most thorough description of the secondary metabolite production potential of a freshwater sponge microbiome to date. IMPORTANCE 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 Spongilla lacustris 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.

Questioning the fetal microbiome illustrates pitfalls of low-biomass microbial studies.

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.

Thermal acclimation of methanotrophs from the genus Methylobacter.

Methanotrophs oxidize most of the methane (CH4) 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 CH4 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 CH4 oxidation kinetics at different temperatures in three members of the genus demonstrate that temperature has a strong influence on how much CH4 is consumed to support growth at different CH4 concentrations. However, the temperature effect varies considerably between species, suggesting that how a methanotrophic community is composed influences the temperature effect on CH4 uptake. To understand thermal acclimation mechanisms widely we carried out a transcriptomics experiment with Methylobacter tundripaludum SV96T. 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 CH4 oxidation, oxidative phosphorylation, membrane fatty acid saturation, cell wall composition, and exopolysaccharides between temperatures. In addition, we observed differences in M. tundripaludum SV96T 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 CH4 consumption and growth efficiency, but with major differences between species. Thus, our study demonstrates that physiological adjustments to temperature change can substantially influence environmental CH4 uptake rates and that consideration of methanotroph physiology might be vital for accurate predictions of warming effects on CH4 emissions.

Cytoscape stringApp 2.0: Analysis and Visualization of Heterogeneous Biological Networks.

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.

eggNOG 6.0: enabling comparative genomics across 12 535 organisms.

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.

Targeted Metabolomics and High-Throughput RNA Sequencing-Based Transcriptomics Reveal Massive Changes in the Streptomyces venezuelae NRRL B-65442 Metabolism Caused by Ethanol Shock.

The species Streptomyces venezuelae is represented by several distinct strains with variable abilities to biosynthesize structurally diverse secondary metabolites. In this work, we examined the effect of ethanol shock on the transcriptome and metabolome of Streptomyces venezuelae NRRL B-65442 using high-throughput RNA sequencing (RNA-seq) and high-resolution liquid chromatography-tandem mass spectrometry (LC-MS/MS). Ethanol shock caused massive changes in the gene expression profile, differentially affecting genes for secondary metabolite biosynthesis and central metabolic pathways. Most of the data from the transcriptome analysis correlated well with the metabolome changes, including the overproduction of jadomycin congeners and a downshift in the production of desferrioxamines, legonoxamine, foroxymithin, and a small cryptic ribosomally synthesized peptide. Some of the metabolome changes, such as the overproduction of chloramphenicol, could not be explained by overexpression of the cognate biosynthetic genes but correlated with the expression profiles of genes for precursor biosynthesis. Changes in the transcriptome were also observed for several genes known to play a role in stress response in other bacteria and included at least 10 extracytoplasmic function σ factors. This study provides important new insights into the stress response in antibiotic-producing bacteria and will help to understand the complex mechanisms behind the environmental factor-induced regulation of secondary metabolite biosynthesis. IMPORTANCE Streptomyces spp. are filamentous Gram-positive bacteria known as versatile producers of secondary metabolites, of which some have been developed into human medicines against infections and cancer. The genomes of these bacteria harbor dozens of gene clusters governing the biosynthesis of secondary metabolites (BGCs), of which most are not expressed under laboratory conditions. Detailed knowledge of the complex regulation of BGC expression is still lacking, although certain growth conditions are known to trigger the production of previously undetected secondary metabolites. In this work, we investigated the effect of ethanol shock on the production of secondary metabolites by Streptomyces venezuelae and correlated these findings with the expression of cognate BGCs and primary metabolic pathways involved in the generation of cofactors and precursors. The findings of this study set the stage for the rational manipulation of bacterial genomes aimed at enhanced production of industrially important bioactive natural products.

A time-resolved multi-omics atlas of Acanthamoeba castellanii encystment.

Encystment is a common stress response of most protists, including free-living amoebae. Cyst formation protects the amoebae from eradication and can increase virulence of the bacteria they harbor. Here, we mapped the global molecular changes that occur in the facultatively pathogenic amoeba Acanthamoeba castellanii during the early steps of the poorly understood process of encystment. By performing transcriptomic, proteomic, and phosphoproteomic experiments during encystment, we identified more than 150,000 previously undescribed transcripts and thousands of protein sequences absent from the reference genome. These results provide molecular details to the regulation of expected biological processes, such as cell proliferation shutdown, and reveal new insights such as a rapid phospho-regulation of sites involved in cytoskeleton remodeling and translation regulation. This work constitutes the first time-resolved molecular atlas of an encysting organism and a useful resource for further investigation of amoebae encystment to allow for a better control of pathogenic amoebae.

Genome-Wide Mutation Scoring for Machine-Learning-Based Antimicrobial Resistance Prediction.

The prediction of antimicrobial resistance (AMR) based on genomic information can improve patient outcomes. Genetic mechanisms have been shown to explain AMR with accuracies in line with standard microbiology laboratory testing. To translate genetic mechanisms into phenotypic AMR, machine learning has been successfully applied. AMR machine learning models typically use nucleotide k-mer counts to represent genomic sequences. While k-mer representation efficiently captures sequence variation, it also results in high-dimensional and sparse data. With limited training data available, achieving acceptable model performance or model interpretability is challenging. In this study, we explore the utility of feature engineering with several biologically relevant signals. We propose to predict the functional impact of observed mutations with PROVEAN to use the predicted impact as a new feature for each protein in an organism's proteome. The addition of the new features was tested on a total of 19,521 isolates across nine clinically relevant pathogens and 30 different antibiotics. The new features significantly improved the predictive performance of trained AMR models for Pseudomonas aeruginosa, Citrobacter freundii, and Escherichia coli. The balanced accuracy of the respective models of those three pathogens improved by 6.0% on average.

Hallstatt miners consumed blue cheese and beer during the Iron Age and retained a non-Westernized gut microbiome until the Baroque period.

We subjected human paleofeces dating from the Bronze Age to the Baroque period (18th century AD) to in-depth microscopic, metagenomic, and proteomic analyses. The paleofeces were preserved in the underground salt mines of the UNESCO World Heritage site of Hallstatt in Austria. This allowed us to reconstruct the diet of the former population and gain insights into their ancient gut microbiome composition. Our dietary survey identified bran and glumes of different cereals as some of the most prevalent plant fragments. This highly fibrous, carbohydrate-rich diet was supplemented with proteins from broad beans and occasionally with fruits, nuts, or animal food products. Due to these traditional dietary habits, all ancient miners up to the Baroque period have gut microbiome structures akin to modern non-Westernized individuals whose diets are also mainly composed of unprocessed foods and fresh fruits and vegetables. This may indicate a shift in the gut community composition of modern Westernized populations due to quite recent dietary and lifestyle changes. When we extended our microbial survey to fungi present in the paleofeces, in one of the Iron Age samples, we observed a high abundance of Penicillium roqueforti and Saccharomyces cerevisiae DNA. Genome-wide analysis indicates that both fungi were involved in food fermentation and provides the first molecular evidence for blue cheese and beer consumption in Iron Age Europe.