Microbiome

Horizontal gene transfer (HGT) occurring within microbiomes is linked to complex environmental and ecological dynamics that are challenging to replicate in controlled settings. Consequently, most extant studies of microbiome HGT are either simplistic experimental settings with tenuous relevance to real microbiomes or correlative studies that assume that HGT potential is a function of the relative abundance of mobile genetic elements (MGEs), the vehicles of HGT. Here we introduce Kairos as a bioinformatic tool deployed in nextflow for detecting HGT events "in situ," i.e., within a microbiome, through analysis of time-series metagenomic sequencing data. The in-situ framework proposed here leverages available metagenomic data from a longitudinally sampled microbiome to assess whether the chronological occurrence of potential donors, recipients, and putatively transferred regions could plausibly have arisen due to HGT over a range of defined time periods. The centerpiece of the Kairos workflow is a novel competitive read alignment method that enables discernment of even very similar genomic sequences, such as those produced by MGE-associated recombination. A key advantage of Kairos is its reliance on assemblies rather than metagenome assembled genomes (MAGs), which avoids systematic exclusion of accessory genes associated with the binning process. In an example test-case of real world data, use of assemblies directly produced a 264-fold increase in the number of antibiotic resistance genes included in the analysis of HGT compared to analysis of MAGs with MetaCHIP. Further, in silico evaluation of contig taxonomy was performed to assess the accuracy of classification for both chromosomally- and MGE-derived sequences, indicating a high degree of accuracy even for conjugative plasmids up to the level of class or order. Thus, Kairos enables the analysis of very recent HGT events, making it suitable for studying rapid prokaryotic adaptation in environmental systems without disturbing the ornate ecological dynamics associated with microbiomes. Current versions of the Kairos workflow are available here: https://github.com/clb21565/kairos.

Animals and their associated microbiomes share a long evolutionary history, influenced by a complex interplay between extrinsic environmental and intrinsic host factors. However, we know little about microbiome responses to long-lasting environmental and host-centred processes, which require studying microbiome changes through time. Here, we apply a temporal metagenomics approach to dental calculus, the calcified oral microbial biofilm. We establish dental calculus as a valuable tool for the study of host microbiome evolution by characterising the taxonomic and functional composition of the oral microbiome in a variety of wild mammals. We detect oral pathogens in individuals with evidence of oral disease, assemble near-complete bacterial genomes from historical specimens, characterise antibiotic resistance genes even before the advent of industrial antibiotic production, reconstruct components of the host diet and recover host genetic profiles. Our work demonstrates how dental calculus can be used in the future to study the evolution of oral microbiomes and pathogens, and the impact of anthropogenic changes on wildlife and the environment.

Microbial communities are essential regulators of ecosystem function, with their composition commonly assessed through DNA sequencing. Most current tools focus on detecting changes among individual taxa (e.g., species or genera), however in other omics fields, such as transcriptomics, enrichment analyses like Gene Set Enrichment Analysis (GSEA) are commonly used to uncover patterns not seen with individual features. Here, we introduce TaxSEA, an R package for taxon set enrichment analysis. TaxSEA integrates taxon sets from five public microbiota databases (BugSigDB, MiMeDB, GutMGene, mBodyMap, and GMRepoV2) to assess whether disease signatures, metabolite producers, or previously reported associations are enriched or depleted in a metagenomic dataset of interest. In-silico assessments show TaxSEA is accurate across a range of set sizes. When applied to differential abundance analysis output from Inflammatory Bowel Disease and Type 2 Diabetes metagenomic data, TaxSEA outperforms current tools and can rapidly identify changes in functional groups corresponding to known associations. We also show that TaxSEA is robust to the choice of differential abundance (DA) analysis package. In summary, TaxSEA enables researchers to efficiently contextualize their findings within the broader microbiome literature, facilitating rapid interpretation and advancing understanding of microbiome-host and environmental interactions.

O_LIPlants accommodate diverse microbial communities (microbiomes), which can change dynamically during plant adaptation to varying environmental conditions. However, the direction of these changes and the underlying mechanisms driving them, particularly in crops adapting to the field conditions, remain poorly understood. C_LIO_LIWe investigate the root-associated microbiome of rice (Oryza sativa L.) using 16S rRNA gene amplicon and metagenome sequencing, across four consecutive cultivation seasons in a high-yield, non-fertilized, and pesticide-free paddy field, compared to a neighboring fertilized and pesticide-treated field. C_LIO_LIOur findings reveal that root microbial community shifts and diverges based on soil fertilization status and plant developmental stages. Notably, nitrogen-fixing bacteria such as Telmatospirillum, Bradyrhizobium and Rhizomicrobium were over-represented in rice grown in the non-fertilized field, implying that the assembly of these microbes supports rice adaptation to nutrient-deficient environments. C_LIO_LIA machine learning model trained on the microbiome data successfully predicted soil fertilization status, highlighting the potential of root microbiome analysis in forecasting soil nutrition levels. Additionally, we observed significant changes in the root microbiome of ccamk mutants, which lack a master regulator of mycorrhizal symbiosis, under laboratory conditions but not in the field, suggesting a condition-dependent role for CCaMK in establishing microbiomes in paddy rice. C_LI

Accurately characterising the human gut virome is critical to understanding virus-microbiome-host interactions. However, widely used methods introduce biases that complicate data interpretation and limit cross-study comparability. For instance, multiple-displacement amplification (MDA) preferentially amplifies single-stranded DNA viruses, while total metagenomes are dominated by non-viral sequences, reducing viral signal. These traditional methods have not been systematically compared to viral size-fraction metagenomes (viromes) prepared without MDA. To address this, we applied four common methods for characterising human gut viral community composition (total metagenomes, viromes with/ without DNase treatment (to remove free DNA), and MDA viromes) to a human stool sample, with technical triplicates for each approach. MDA biased viral community composition to a shocking degree: Microviridae formed [~]90% of MDA viromes compared to just 2% of non-MDA viromes. Removing ssDNA viruses from data analyses substantially reduced, but did not eliminate, MDA bias. Metagenomes were enriched for putative temperate phages and predicted Bacillota-phages, whereas predicted Bacteroidetes-phages dominated all viromes, suggesting that metagenomes and viromes select for different populations within the total viral community. DNase treatment had little-to-no effect on virome richness or community composition. This proof-of-principle experiment demonstrates that preparatory methods for viral community analysis can lead to substantially different conclusions from the same faecal sample, and we provide a comprehensive omic data analysis framework for comparing laboratory methodologies for viral ecology. With sufficient DNA yields now easily achievable from human gut viromes without the use of MDA, our results suggest that this biased amplification method should be avoided in human gut virome studies. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=112 SRC="FIGDIR/small/690293v1_ufig1.gif" ALT="Figure 1"> View larger version (35K): org.highwire.dtl.DTLVardef@36252org.highwire.dtl.DTLVardef@2c06f0org.highwire.dtl.DTLVardef@7b7cceorg.highwire.dtl.DTLVardef@13ec804_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphical AbstractC_FLOATNO C_FIG

The human gut microbiome has been linked to inflammatory bowel disease (IBD) and colorectal cancer (CRC), yet identifying disease-associated microbial genes across diverse human cohort studies remains challenging due to inconsistent data processing and the high dimensionality of gene-level abundance profiles. Here we present MetaGEAR Explorer, a web platform comprising a user interface and web services for interactive and programmatic gene-centric exploration of >33 million microbial gene families across 9,053 metagenomic samples from 24 IBD, CRC, and healthy cohorts. MetaGEAR Explorer facilitates gene searches against a catalog of non-redundant gene families via nucleotide or amino acid sequence queries (BLAST) and Pfam domain-based searches. For matched gene families, the platform computes disease-stratified prevalence, cross-cohort disease associations, species-level taxonomic stratification, and functional domain annotations. Importantly, users can also explore the genomic context of individual gene families via contig-based co-localization networks derived from metagenomic species pangenome (MSP) assignments and pivot from sequence to domain searches to identify functional homologs. Additionally, the platform features a dedicated catalog to interactively browse 13,795 MSPs and export results programmatically via API endpoints. We demonstrate MetaGEAR Explorers utility using the narG-encoding nitrate reductase gene and a case study of colibactin self-protection genes (clbS and DUF1706 homologs), where the platform revealed a consistent shift from commensals to Gammaproteobacteria carriers in disease. In summary, MetaGEAR Explorer enables rapid cross-cohort functional meta-analyses and is freely available at https://metagear-explorer.schirmerlab.de. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=177 HEIGHT=200 SRC="FIGDIR/small/715271v1_ufig1.gif" ALT="Figure 1"> View larger version (37K): org.highwire.dtl.DTLVardef@ea318dorg.highwire.dtl.DTLVardef@15b497borg.highwire.dtl.DTLVardef@354abcorg.highwire.dtl.DTLVardef@bd7dc5_HPS_FORMAT_FIGEXP M_FIG C_FIG

A variety of marine invertebrates are known to form associations with chemosynthetic bacteria, but to the best of our knowledge this has not been documented in fishes. Here, we apply genome-resolved metagenomics to the hamlets (Hypoplectrus spp), a model system for the study of speciation in the sea. The analysis of 304 gill samples from 12 hamlet species collected at six locations over 13 years revealed a stark contrast between the gill microbiota and ambient water microbial communities. One novel lineage in the Burkholderiaceae-B family was particularly prevalent across host species, sampling locations and years. Its genome encoded highly complete metabolic modules for carbon fixation and sulfur oxidation, indicating chemosynthetic potential. Its pangenome revealed large-scale geographic structure (western Caribbean, eastern Caribbean and Gulf of Mexico), paralleling the phylogenomic pattern observed in the hamlet radiation. Our survey also identified genomes of multiple novel gill-associated lineages related to known fish gill pathogens, fish gut microbes and free-living seawater taxa. These lineages harbor diverse metabolic modules, involved notably in nitrogen cycling, antibiotic production and biofilm formation, revealing a highly dynamic microbial ecosystem. Overall, our findings suggest complex host-microbe and microbe-microbe eco-evolutionary interactions that may influence fish physiology, homeostasis and immune response.

Plant-associated microbiota are composed of hundreds of microbial species. For many of them, little is known about their individual functions and even less is known about their emergent community-level traits. While culture-independent methods provide valuable insights into the composition, diversity, and functional potential of plant-associated microbiota, culture-dependent methods are essential for reductionist lines of inquiry into the roles of individual species and their interactions within a community. Here, we present ZeaMiC, a publicly available culture collection of root-associated bacteria from Zea mays (maize). This resource comprises 88 isolates obtained from diverse soils and several maize genotypes, with live cultures available through DSMZ (German Collection of Microorganisms and Cell Cultures) both as single stocks and as cost-effective bundles (https://www.dsmz.de/collection/catalogue/microorganisms/microbiota/zeamic). To maximize relevance, isolates were selected to be representative of maize root-associated microbiomes in the Corn Belt of the United States, based on abundance-occupancy patterns from previously published root microbiome data, phylogenetic diversity, and literature-based evidence of functional importance. Whole-genome sequencing and annotation revealed genes associated with root colonization, plant growth promotion, and nutrient cycling, including functions such as chemotaxis, biofilm formation, secretion systems, hormone modulation, and phosphate solubilization. This collection serves as a community resource for future mechanistic studies of plant-microbe and microbe-microbe interactions, filling the gap in our understanding of the ecological interactions in plant microbiomes.

High-throughput sequencing has necessitated and facilitated the development of various computational tools for dissecting the taxonomic structure and molecular function of microbial communities residing in diverse ecological niches. A wide variety of tools and protocols have been developed to process amplicon sequencing as well as meta-omic (metagenomic, metatranscriptomic and genomic) data to generate relative abundances of taxonomic groups or functional categories on a per-sample basis. A key output from many of these protocols is a stratified relative abundance table that specifies the taxonomic contribution to each function. However, there are a very few tools which can effectively visualize the different taxa-function relationships from these stratified outputs. Here we introduce an R Shiny application called JarrVis (Just Another stRatified Rpkm VISualizer) which can visualize the taxa-function relationships resulting from different types of microbiome data. JarrVis can visualize relationships between samples (which can be combined based on metadata categories), the taxa that are detected in these samples (at any given taxonomic level) and the functions encoded by these taxa in an interactive interface. We utilized JarrVis to visualize and examine taxa-function relationships in 1) a 16S amplicon time-series spanning 4 years with samples collected weekly, 2) functions associated with microbial cobalamin biosynthesis and uptake in metagenome-assembled genomes from marine metagenomes and 3) a set of metatrascriptomes from gut samples of patients with Crohns Disease, Ulcerative Colitis and non-IBD controls. Our analysis was able recapitulate already published taxa-function relationships as well as discover novel insights from these publicly available datasets. JarrVis and related scripts and data are available at https://github.com/dhwanidesai/JarrVis.

Microbiomes influence diverse ecosystems, and viruses increasingly appear to impose key constraints. While viromics has expanded genomic catalogs, host identification for these viruses remains challenging due to the limitations in scaling cultivation-based approaches and the uncertain reliability and relative low resolution of in silico predictions - particularly for understudied viral taxa. Towards this, Hi-C proximity ligation uses sequenced, cross-linked virus and host genomic fragments to infer virus-host linkages and has now been applied in at least ten studies. However, its accuracy remains unknown. Here we assess Hi-C performance in recovering virus-host interactions using synthetic communities (SynComs) composed of four marine bacterial strains and nine phages with known interactions and then apply optimized bioinformatic protocols to natural soil samples. In SynComs, standard Hi-C sample preparations and analyses showed poor normalized contact score performance (26% specificity, 100% sensitivity, incorrect matches up to class level) that could be dramatically improved by Z-score filtering (Z [≥] 0.5, 99% specificity), though at reduced sensitivity (62% down from 100%). Detection limits were established as reproducibility was poor below minimal phage abundances of 105 PFU/mL. Applying optimized bioinformatic protocols to natural soil samples, we compared virus-host linkages inferred from proximity-ligated Hi-C sequencing with predictions generated by in silico homology-based and machine learning-based bioinformatic approaches. Prior to Z-score thresholding, agreement was relatively high at the phylum to family levels (72%), but not at the genus (43%) or species (15%) levels. Z-score thresholding reduced sensitivity (only 34% of predictions were retained), with only modest improvements in congruence with bioinformatic methods (48% or 18% at genus or species levels, respectively). Regardless, this led to 79 genus-level-congruent virus-host linkages and 293 new ones revealed by Hi-C alone - i.e., providing many new virus-host interactions to explore in already well-studied climate-critical soils. Overall, these findings provide empirical benchmarks and methodological guidelines to improve the accuracy and reliability of Hi-C for virus-host linkage studies in complex microbial communities.

Fungi represent less than 1% of the gut microbiota; However, their importance in host homeostasis and disease is increasingly recognized. Accurate characterization of the gut mycobiome from metagenomic data remains a significant challenge due to the low abundance of fungal DNA, the performance of bacteria-oriented classifiers, and the limited availability of curated fungal reference databases. To overcome these issues, we developed FungiGutDB v1.0, a curated database containing 304 taxa previously identified in culture-dependent human studies, and we integrated the database in a reproducible workflow to ease its application (FungiGut). Benchmarking analyses demonstrated that FungiGut achieved a substantially lower false positive rate in mock communities compared to standard non-gut-specific fungi databases. When applied to real metagenomic datasets, FungiGut successfully characterized the gut mycobiome, identifying Saccharomyces cerevisiae as the predominant species in healthy individuals, along with common dietary fungi found in fermented dairy products (Penicillium camemberti, Debaryomyces hansenii, Kluyveromyces lactis, Pichia kudriavzevii). In contrast, samples from patients with non-responsive celiac disease showed a higher relative abundance of opportunistic pathogens and a lower number of diet-associated taxa, suggesting a trend toward a dysbiotic mycobiome profile. By limiting classification to fungal species previously isolated from the human gut, FungiGut minimizes misclassifications derived from environmental or plant-associated taxa, which often lead to mistaken interpretation of the results. Overall, FungiGut offers a biologically consistent and reproducible approach to gut mycobiome profiling, improving taxonomic accuracy and strengthening confidence in the interpretation of fungal metagenomic data in human microbiome research.

Dopamine plays a critical role in animal physiology and interactions with gut microbes. In plants, dopamine is known to function in plant defense and abiotic stress tolerance; however, its role in mediating plant-microbiome interactions remains unexplored. In this study, we observed that dopamine is one of the most abundant exometabolites with natural variation in root exudates across diverse Brachypodium distachyon lines, suggesting a potential role in rhizosphere microbial assembly. To further investigate this, we colonized ten natural B. distachyon lines with a 16-member bacterial synthetic community (SynCom), collected paired metabolomic and 16S rRNA sequencing data, and performed an association analysis. Our results revealed that dopamine levels in root exudates were significantly associated with the abundance of six SynCom members in a hydroponic system. In vitro growth studies demonstrated that dopamine had a significant effect on the growth of the same six bacterial isolates. Additionally, treating soil directly with dopamine enriched Actinobacteria, consistent with both the SynCom-dopamine correlations and the isolate growth results. Collectively, our study underscores the selective influence of dopamine on rhizosphere microbial communities, with implications for precision microbiome management.

Gut microbiome analysis using 16S rRNA frequently focuses on summary statistics (e.g. diversity) or single taxonomic scales (e.g. Operational Taxonomic units, OTUs). This approach risks misinterpreting the phylogenetic or abundance scales of community differences (e.g. over-emphasising the role of single strains). We therefore constructed a 16S phylogenetic tree from mouse stool and colonic mucus communities. Random forest models, of all 428,234 clades, tested community differences among niches (stool versus mucus), host ages (6 versus 18 weeks), genotypes (wildtype versus colitis prone-mdr1a-/-) and social groups (co-housed siblings). Models discriminated all criteria except host genotype, where no community differences were found. Host social groups differed in abundant, low-level, taxa whereas intermediate phylogenetic and abundance scales distinguished ages and niches. Thus, treating evolutionary clades of microbes equivalently without reference to OTUs or taxonomy, clearly identifies whether and how gut microbial communities are distinct and provides a novel way to define functionally important bacteria.

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.

With an estimated 1031 particles on earth, bacteriophages are the most abundant genomic entities across all habitats and important drivers of microbial communities. Growing evidence suggest that they play roles in intestinal human microbiota homeostasis, and recent metagenomics studies on the viral fraction of this ecosystem have provided crucial information about their diversity and specificity. However, the bacterial hosts of this viral fraction, a necessary information to characterize further the balance of these ecosystems, remain poorly characterized. Here we unveil, using an enhanced metagenomic Hi-C approach, a large network of 6,651 host-phage relationships in the healthy human gut allowing to study in situ phage-host ratio. We notably found that half of these contigs appear to be sleeping prophages whereas [1/4] exhibit a higher coverage than their associated MAG representing potentially active phages impacting the ecosystem. We also detect different candidate members of the crAss-like phage family as well as their bacterial hosts showing that these elusive phages infect different genus of Bacteroidetes. This work opens the door to single sample analysis and concomitant study of phages and bacteria in complex communities.

Viruses impact microbial diversity, phenotype, and gene flow through virus-host interactions that in turn alter ecology and biogeochemistry. Though metagenomics surveys are rapidly cataloging viral diversity, capturing specific virus-host interactions in situ would identify hosts for novel viruses and reveal influential ecological or environmental factors. We leveraged metagenomics and a high-throughput, cultivation-independent gene fusion technique (epicPCR) to investigate viral diversity and virus-host interactions over time in a critical estuarine environment, the Chesapeake Bay. EpicPCR captured in situ virus-host interactions for viral clades with no closely related database representatives. Abundant freshwater Actinobacteria lineages were the most common hosts for these poorly characterized viruses, and observed viral interactions with one abundant Actinobacterial population (Rhodoluna) were correlated with environmental factors. Tracking virus-host interaction dynamics also revealed ecological differences between multi-host (generalist) and single-host (specialist) viruses. Generalist viruses had significantly longer periods with observed virus-host interactions but specialist viruses were observed interacting with hosts at lower minimum abundances, suggesting more efficient interactions. Together, these observations reveal ecological differences between generalist and specialist viruses that provide insight into evolutionary trade-offs. Capturing in situ interactions with epicPCR revealed environmental and ecological factors that shape virus-host interactions, highlighting epicPCR as a scalable new tool in viral ecology.

Pet dogs are considered part of the family, and understanding their gut microbiomes can provide insights into both animal and household health. Most comprehensive studies, however, relied on short-read sequencing, resulting in fragmented MAGs that miss mobile elements, antimicrobial-resistance genes, and ribosomal genes. Here, we applied deep long-read metagenomics (polished with short-reads) to fecal samples from 51 urban pet dogs in Shanghai, generating 2,676 MAGs--representing 320 bacterial species--, of which [~]72% achieved near-finished quality, often improving on the corresponding reference public genome. Comparisons with external datasets showed that our Shanghai-based MAG catalog is representative of pet dogs worldwide (median read mapping of >90%). Moreover, we recovered circular extrachromosomal elements, including those linked to antimicrobial resistance, which were also detected in external dog gut datasets. In conclusion, we provide a high-quality reference resource and demonstrate the power of deep long-read metagenomics to resolve microbial diversity in complex host-associated microbiomes.

Biological pathways, including individual gut microbiome are potential barriers for maternal nutritional supplementation to improvement in infant growth. We evaluated the impact of balanced energy-protein (BEP) supplementation during pregnancy and the first six months of lactation on the composition and functionality of gut microbiome in mothers and their infants in rural Burkina Faso. Our findings reveal that BEP supplementation led to a significant increase in microbiome diversity during pregnancy. In the second trimester, there was a notable decrease in the abundance of an Oscillospiraceae species, while postpartum, the abundance of Bacteroides fragilis increased. We identified concerted enriched or depleted microbial pathways associated with BEP supplementation, including the phosphotransferase system, a critical mechanism for bacterial carbohydrates uptake, which exhibited enrichment in infants born to BEP-supplemented mothers. Despite these observations, the intricate biological connections with other omics necessitate further analysis to fully elucidate the underlying comprehensive biological pathways.

Characterization of the eukaryotic microbiome is required to understand the role of microbial communities in health and disease. Such investigation relies on sequencing 18S ribosomal RNA genes (rDNA), which serve as taxonomic markers; however, this is compromised by contaminating host rDNA sequences. To overcome this problem, we developed CRISPR-Cas Selective Amplicon Sequencing (CCSAS), a high-resolution and efficient approach for characterizing eukaryotic microbiomes. CCSAS uses taxon-specific single-guide RNA (sgRNA) to direct Cas9 to cut 18S rDNA sequences of the host. Validation shows that >96.5% of rDNA amplicons from ten model organisms were cleaved, while rDNA from protists and fungi were unaffected. In oyster spat, CCSAS resolved [~]8.5-fold more taxa, and several additional major phylogenetic groups when compared to the best available alternative approach. We designed taxon-specific sgRNA for [~]16,000 metazoan and plant taxa, making CCSAS widely available for characterizing eukaryotic microbiomes that have largely been neglected because of methodological challenges.

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.