Genome-reconstruction for eukaryotes from complex natural microbial communities

Combined application of metagenomics and FEAST to trace sources of microbial eukaryotic contamination in the Pasig-Marikina-San Juan (PAMARISAN) river system in Metro Manila, Philippines

Microbial eukaryotes are vital to global microbial diversity, but there is limited information about their composition and sources in contaminated surface waters. This study examined the pathogens and potential sources of microbial eukaryotic communities in polluted sink environments using the 18S rDNA amplicon sequencing combined with the fast expectation-maximization for microbial source tracking (FEAST) program. Six sampling sites were selected along the Pasig-Marikina-San Juan (PAMARISAN) River System, representing different locations within the waterway and classified as sinks (n = 12), whereas animal fecal samples collected from various farms were classified as sources (n = 29). Taxonomic composition revealed Stramenopila, Alveolata, Rhizaria (SAR), Archaeplastida, and Excavata in the rivers, accounting for 85.1%, 13.2%, and 0.36% mean abundance of microbial sink communities, respectively. Clinically relevant human pathogens were also observed in sink environments. The correlation test demonstrated that dissolved oxygen, total suspended solids, pH, temperature, fecal coliform count, and phosphates were important environmental factors driving community variations. Moreover, FEAST results indicated that sewage (19.6%) was the primary source of microbial eukaryotes, followed by duck (0.644%) and cow (0.566%) feces. Spatio-seasonal variations showed higher contributions at downstream stations and during the wet season, highlighting the role of rainfall in enhancing microbial dispersal. Results from community-based microbial source tracking can be used to explore factors shaping microbial eukaryotes in freshwater environments, assess potential pathogen-related hazards, and inform river conservation and management strategies. Furthermore, this also serves as preliminary data for microbial eukaryotic source tracking in the Philippines, laying groundwork for future research.

Metagenomic analysis of pristine oil sheds new light on the global distribution of microbial genetic repertoire in hydrocarbon-associated ecosystems

Oil reservoirs are society's primary source of hydrocarbons. While microbial communities in industrially exploited oil reservoirs have been investigated in the past, pristine microbial communities in untapped oil reservoirs are little explored, as are distribution patterns of respective genetic signatures. Here, we show that a pristine oil sample contains a complex community consisting of bacteria and fungi for the degradation of hydrocarbons. We identified microorganisms and their pathways for the degradation of methane, n-alkanes, mono-aromatic, and polycyclic aromatic compounds in a metagenome retrieved from biodegraded petroleum encountered in a subsurface reservoir in the Barents Sea. Capitalizing on marker genes from metagenomes and public data mining, we compared the prokaryotes, putative viruses, and putative plasmids of the sampled site to those from 10 other hydrocarbon-associated sites, revealing a shared network of species and genetic elements across the globe. To test for the potential dispersal of the microbes and predicted elements via seawater, we compared our findings to the Tara Ocean dataset, resulting in a broad distribution of prokaryotic and viral signatures. Although frequently shared between hydrocarbon-associated sites, putative plasmids, however, showed little coverage in the Tara Oceans dataset, suggesting an undiscovered mode of transfer between hydrocarbon-affected ecosystems. Based on our analyses, genetic information is globally shared between oil reservoirs and hydrocarbon-associated sites, and we propose that currents and other physical occurrences within the ocean along with deep aquifers are major distributors of prokaryotes and viruses into these subsurface ecosystems.

Rumen protozoa and viruses: New insights into their diversity and potential roles through omics lenses-A review

The rumen microbiome is essential for breaking down indigestible plant material, supplying ruminants with most of their metabolizable energy and protein. While research has primarily focused on bacteria and archaea, protozoa and viruses (phages) have only gained attention in recent years. Protozoa contribute to feed digestion and fermentation, but as predators, they regulate microbial populations by lysing large quantities of microbial cells (the primary protein source for ruminants) and influence the amount of microbial protein reaching the small intestines, along with other mechanisms of interactions. While rumen viruses (or phages) are abundant and diverse, they remain the least understood component of the rumen ecosystem. They can profoundly affect the rumen microbiome by directly lysing their hosts and reprogramming host metabolism through multiple mechanisms, including gene transfer and alteration of central carbon metabolism. Recent advances in omics technologies have deepened our understanding of these viruses, revealing their complex roles in rumen function. This review integrates current knowledge and recent discoveries from omics studies, highlighting the transformative impact of omics-based approaches. It also identifies critical knowledge gaps and outlines future research directions, including selective inhibition of rumen protozoa, development of phages as potential intervention tools to manage specific undesirable rumen microbes, and the causal impacts of rumen viruses on microbial dynamics and animal productivity.

Contiguous and complete assemblies of Blastocystis gut microbiome-associated protists reveal evolutionary diversification to host ecology

Blastocystis , an obligate host-associated protist, is the most common microbial eukaryote in the human gut and is widely distributed across vertebrate hosts. The evolutionary transition of Blastocystis from its free-living stramenopile ancestors to a radiation of host-associated organisms is poorly understood. To explore this, we cultured and sequenced eight strains representing the significant phylogenetic diversity of the genus using long-read, short-read, and Hi-C DNA sequencing, alongside gene annotation and RNA sequencing. Comparative genomic analyses revealed significant variation in gene content and genome structure across Blastocystis. Notably, three strains from herbivorous tortoises, phylogenetically distant from human subtypes, have markedly larger genomes with longer introns and intergenic regions, and retain canonical stop codons absent in the human-associated strains. Despite these genetic differences, all eight isolates exhibit gene losses linked to the reduced cellular complexity of Blastocystis, including losses of cilia and flagella genes, microtubule motor genes, and signal transduction genes. Isolates from herbivorous tortoises contained higher numbers of plant carbohydrate-metabolizing enzymes, suggesting that like gut bacteria, these protists ferment plant material in the host gut. We find evidence that some of these carbohydrate-metabolizing enzymes were horizontally acquired from bacteria, indicating that horizontal gene transfer is an ongoing process in Blastocystis that has contributed to host-related adaptation. Together, these results highlight substantial genetic and metabolic diversity within the Blastocystis genus, indicating different lineages of Blastocystis have varied ecological roles in the host gut.

Insights into gut fungi in pigs: A comprehensive review

Fungi in the gut microbiota of mammals play a crucial role in host physiological regulation, including intestinal homeostasis and host immune regulation. However, our understanding of gut fungi in mammals remains limited, especially in economically valuable animals, such as pigs. Therefore, this review first describes the classification and characterisation of fungi, provides insights into the methods used to study gut fungi, and summarises the recent progress on pig gut fungi. Additionally, it discusses the challenges in the study of pig gut fungi and highlights potential perspectives. The aim of this review is to serve as a valuable reference for advancing our knowledge of gut fungi in animals.

Long-read sequencing sheds light on key bacteria contributing to deadwood decomposition processes

BACKGROUND

Deadwood decomposition is an essential ecological process in forest ecosystems, playing a key role in nutrient cycling and carbon sequestration by enriching soils with organic matter. This process is driven by diverse microbial communities encompassing specialized functions in breaking down organic matter, but the specific roles of individual microorganisms in this process are still not fully understood.

RESULTS

Here, we characterized the deadwood microbiome in a natural mixed temperate forest in Central Europe using PacBio HiFi long-read sequencing and a genome-resolved transcriptomics approach in order to uncover key microbial contributors to wood decomposition. We obtained high quality assemblies, which allowed attribution of complex microbial functions such as nitrogen fixation to individual microbial taxa and enabled the recovery of metagenome-assembled genomes (MAGs) from both abundant and rare deadwood bacteria. We successfully assembled 69 MAGs (including 14 high-quality and 7 single-contig genomes) from 4 samples, representing most of the abundant bacterial phyla in deadwood. The MAGs exhibited a rich diversity of carbohydrate-active enzymes (CAZymes), with Myxococcota encoding the highest number of CAZymes and the full complement of enzymes required for cellulose decomposition. For the first time we observed active nitrogen fixation by Steroidobacteraceae, as well as hemicellulose degradation and chitin recycling by Patescibacteria. Furthermore, PacBio HiFi sequencing identified over 1000 biosynthetic gene clusters, highlighting a vast potential for secondary metabolite production in deadwood, particularly in Pseudomonadota and Myxococcota.

CONCLUSIONS

PacBio HiFi long-read sequencing offers comprehensive insights into deadwood decomposition processes by advancing the identification of functional features involving multiple genes. It represents a robust tool for unraveling novel microbial genomes in complex ecosystems and allows the identification of key microorganisms contributing to deadwood decomposition.

Gene horizontal transfers and functional diversity negatively correlated with bacterial taxonomic diversity along a nitrogen gradient

Horizontal gene transfer (HGT) mediated diversification is a critical force driving evolutionary and ecological processes. However, how HGT might relate to anthropogenic activity such as nitrogen addition, and its subsequent effect on functional diversity and cooccurrence networks remain unknown. Here we approach this knowledge gap by blending bacterial 16S rRNA gene amplicon and shotgun metagenomes from a platform of cessation of nitrogen additions and continuous nitrogen additions. We found that bacterial HGT events, functional genes, and virus diversities increased whereas bacterial taxonomic diversity decreased by nitrogen additions, resulting in a counterintuitive strong negative association between bacterial taxonomic and functional diversities. Nitrogen additions, especially the ceased one, complexified the cooccurrence network by increasing the contribution of vitamin B12 auxotrophic Acidobacteria, indicating cross-feeding. These findings advance our perceptions of the causes and consequences of the diversification process in community ecology.

Acidic proteomes are linked to microbial alkaline preference in African lakes

Microbial amino acid composition (AA) reflects adaptive strategies of cellular and molecular regulations such as a high proportion of acidic AAs, including glutamic and aspartic acids in alkaliphiles. It remains understudied how microbial AA content is linked to their pH adaptation especially in natural environments. Here we examined prokaryotic communities and their AA composition of genes with metagenomics for 39 water and sediments of East African lakes along a gradient of pH spanning from 7.2 to 10.1. We found that Shannon diversity declined with the increasing pH and that species abundance were either positively or negatively associated with pH, indicating their distinct habitat preference in lakes. Microbial communities showed higher acidic proteomes in alkaline than neutral lakes. Species acidic proteomes were also positively correlated with their pH preference, which was consistent across major bacterial lineages. These results suggest selective pressure associated with high pH likely shape microbial amino acid composition both at the species and community levels. Comparative genome analyses further revealed that alkaliphilic microbes contained more functional genes with higher acidic AAs when compared to those in neutral conditions. These traits included genes encoding diverse classes of cation transmembrane transporters, antiporters, and compatible solute transporters, which are involved in cytoplasmic pH homeostasis and osmotic stress defense under high pH conditions. Our results provide the field evidence for the strong relationship between prokaryotic AA composition and their habitat preference and highlight amino acid optimization as strategies for environmental adaptation.

Missing microbial eukaryotes and misleading meta-omic conclusions

Meta-omics is commonly used for large-scale analyses of microbial eukaryotes, including species or taxonomic group distribution mapping, gene catalog construction, and inference on the functional roles and activities of microbial eukaryotes in situ. Here, we explore the potential pitfalls of common approaches to taxonomic annotation of protistan meta-omic datasets. We re-analyze three environmental datasets at three levels of taxonomic hierarchy in order to illustrate the crucial importance of database completeness and curation in enabling accurate environmental interpretation. We show that taxonomic membership of sequence clusters estimates community composition more accurately than returning exact sequence labels, and overlap between clusters can address database shortcomings. Clustering approaches can be applied to diverse environments while continuing to exploit the wealth of annotation data collated in databases, and selecting and evaluating these databases is a critical part of correctly annotating protistan taxonomy in environmental datasets. We argue that ongoing curation of genetic resources is crucial in accurately annotating protists in in situ meta-omic datasets. Moreover, we propose that precise taxonomic annotation of meta-omic data is a clustering problem rather than a feasible alignment problem.

Metagenome-assembled genome of the glacier alga Ancylonema yields insights into the evolution of streptophyte life on ice and land

Contemporary glaciers are inhabited by streptophyte algae that balance photosynthesis and growth with tolerance of low temperature, desiccation and UV radiation. These same environmental challenges have been hypothesised as the driving force behind the evolution of land plants from streptophyte algal ancestors in the Cryogenian (720-635 million years ago). We sequenced, assembled and analysed the metagenome-assembled genome of the glacier alga Ancylonema nordenskiöldii to investigate its adaptations to life in ice, and whether this represents a vestige of Cryogenian exaptations. Phylogenetic analysis confirms the placement of glacier algae within the sister lineage to land plants, Zygnematophyceae. The metagenome-assembled genome is characterised by an expansion of genes involved in tolerance of high irradiance and UV light, while lineage-specific diversification is linked to the novel screening pigmentation of glacier algae. We found no support for the hypothesis of a common genomic basis for adaptations to ice and to land in streptophytes. Comparative genomics revealed that the reductive morphological evolution in the ancestor of Zygnematophyceae was accompanied by reductive genome evolution. This first genome-scale data for glacier algae suggests an Ancylonema-specific adaptation to the cryosphere, and sheds light on the genome evolution of land plants and Zygnematophyceae.

4CAC: 4-class classifier of metagenome contigs using machine learning and assembly graphs

Microbial communities usually harbor a mix of bacteria, archaea, plasmids, viruses and microeukaryotes. Within these communities, viruses, plasmids, and microeukaryotes coexist in relatively low abundance, yet they engage in intricate interactions with bacteria. Moreover, viruses and plasmids, as mobile genetic elements, play important roles in horizontal gene transfer and the development of antibiotic resistance within microbial populations. However, due to the difficulty of identifying viruses, plasmids, and microeukaryotes in microbial communities, our understanding of these minor classes lags behind that of bacteria and archaea. Recently, several classifiers have been developed to separate one or more minor classes from bacteria and archaea in metagenome assemblies. However, these classifiers often overlook the issue of class imbalance, leading to low precision in identifying the minor classes. Here, we developed a classifier called 4CAC that is able to identify viruses, plasmids, microeukaryotes, and prokaryotes simultaneously from metagenome assemblies. 4CAC generates an initial four-way classification using several sequence length-adjusted XGBoost models and further improves the classification using the assembly graph. Evaluation on simulated and real metagenome datasets demonstrates that 4CAC substantially outperforms existing classifiers and combinations thereof on short reads. On long reads, it also shows an advantage unless the abundance of the minor classes is very low. 4CAC runs 1-2 orders of magnitude faster than the other classifiers. The 4CAC software is available at https://github.com/Shamir-Lab/4CAC.

Metagenomic insights into microalgae-bacterium-virus interactions and viral functions in phycosphere facing environmental fluctuations

Despite the ecological and biotechnological significance of microalgae-bacterium symbionts, the response of host-virus interactions to external environmental fluctuations and the role of viruses in phycosphere remain largely unexplored. Herein, we employed algal-bacterial granular sludge (ABGS) with varying light intensity and organic carbon loading to investigate the mechanisms of microalgae-bacterium-virus symbionts in response to environmental fluctuations. Metagenomics revealed that enhanced light intensity decreased the diversity of microalgae, so did the diversity of symbiotic bacteria and viruses. As carbon sources decreased, bacteria prompted horizontal gene transfer in phycosphere by 12.76 %-157.40 %, increased the proportion of oligotrophs as keystone species (0.00 % vs 14.29 %) as well as viruses using oligotrophs as hosts (18.52 % vs 25.00 %). Furthermore, virus-carried auxiliary metabolic genes (AMGs) and biosynthetic gene clusters (BGCs) encoding vitamin B12 synthesis (e.g., cobS), antioxidation (e.g., queC), and microbial aggregation (e.g., cysE). Additionally, phylogenetic and similarity analysis further revealed the evolutionary origin and potential horizontal transfer of the AMGs and BGCs, which could potentially enhance the adaptability of bacteria and eukaryotic microalgae. Overall, our research demonstrates that environmental fluctuations have cascading effects on the microalgae-bacteria-virus interactions, and emphasizes the important role of viruses in maintaining the stability of the phycosphere symbiotic community.

Metagenomic discovery of microbial eukaryotes in stool microbiomes

Host-associated microbiota form complex microbial communities that are increasingly associated with host behavior and disease. While these microbes include bacterial, archaeal, viral, and eukaryotic constituents, most studies have focused on bacteria due to their dominance in the human host and available tools for investigation. Accumulating evidence suggests microbial eukaryotes in the microbiome play pivotal roles in host health, but our understandings of these interactions are limited to a few readily identifiable taxa because of technical limitations in unbiased eukaryote exploration. Here, we combined cell sorting, optimized eukaryotic cell lysis, and shotgun sequencing to accelerate metagenomic discovery and analysis of host-associated microbial eukaryotes. Using synthetic communities with a 1% microbial eukaryote representation, the eukaryote-optimized cell lysis and DNA recovery method alone yielded a 38-fold increase in eukaryotic DNA. Automated sorting of eukaryotic cells from stool samples of healthy adults increased the number of microbial eukaryote reads in metagenomic pools by up to 28-fold compared to commercial kits. Read frequencies for identified fungi increased by 10,000× on average compared to the Human Microbiome Project and allowed for the identification of novel taxa, de novo assembly of contigs from previously unknown microbial eukaryotes, and gene prediction from recovered genomic segments. These advances pave the way for the unbiased inclusion of microbial eukaryotes in deciphering determinants of health and disease in the host-associated microbiome.IMPORTANCEMicrobial eukaryotes are common constituents of the human gut where they can contribute to local ecology and host health, but they are often overlooked in microbiome studies. The lack of attention is due to current technical limitations that are heavily biased or poorly recovered DNA from microbial eukaryotes. We developed a method to increase the representation of these eukaryotes in metagenomic sequencing of microbiome samples that allows to improve their detection compared to prior methods and allows for the identification of new species. Application of the technique to gut microbiome samples improved detection of fungi, protists, and helminths. New eukaryotic taxa and their encoded genes could be identified by sequencing a small number of samples. This approach can improve the inclusion of eukaryotes into microbiome research.

Cold-water coral mortality under ocean warming is associated with pathogenic bacteria

Cold-water corals form vast reefs that are highly valuable habitats for diverse deep-sea communities. However, as the deep ocean is warming, it is essential to assess the resilience of cold-water corals to future conditions. The effects of elevated temperatures on the cold-water coral Lophelia pertusa (now named Desmophyllum pertusum) from the north-east Atlantic Ocean were experimentally investigated at the holobiont level, the coral host, and its microbiome. We show that at temperature increases of + 3 and + 5 °C, L. pertusa exhibits significant mortality concomitant with changes in its microbiome composition. In addition, a metagenomic approach revealed the presence of gene markers for bacterial virulence factors suggesting that coral death was due to infection by pathogenic bacteria. Interestingly, different coral colonies had different survival rates and, colony-specific microbiome signatures, indicating strong colony-specific variability in their response to warming waters. These results suggest that L. pertusa can only survive a long-term temperature increase of < 3 °C. Therefore, regional variations in deep-sea temperature increase should be considered in future estimates of the global distribution of cold-water corals.

Simulation of 69 microbial communities indicates sequencing depth and false positives are major drivers of bias in prokaryotic metagenome-assembled genome recovery

We hypothesize that sample species abundance, sequencing depth, and taxonomic relatedness influence the recovery of metagenome-assembled genomes (MAGs). To test this hypothesis, we assessed MAG recovery in three in silico microbial communities composed of 42 species with the same richness but different sample species abundance, sequencing depth, and taxonomic distribution profiles using three different pipelines for MAG recovery. The pipeline developed by Parks and colleagues (8K) generated the highest number of MAGs and the lowest number of true positives per community profile. The pipeline by Karst and colleagues (DT) showed the most accurate results (~ 92%), outperforming the 8K and Multi-Metagenome pipeline (MM) developed by Albertsen and collaborators. Sequencing depth influenced the accurate recovery of genomes when using the 8K and MM, even with contrasting patterns: the MM pipeline recovered more MAGs found in the original communities when employing sequencing depths up to 60 million reads, while the 8K recovered more true positives in communities sequenced above 60 million reads. DT showed the best species recovery from the same genus, even though close-related species have a low recovery rate in all pipelines. Our results highlight that more bins do not translate to the actual community composition and that sequencing depth plays a role in MAG recovery and increased community resolution. Even low MAG recovery error rates can significantly impact biological inferences. Our data indicates that the scientific community should curate their findings from MAG recovery, especially when asserting novel species or metabolic traits.

Integrated analysis of facial microbiome and skin physio-optical properties unveils cutotype-dependent aging effects

BACKGROUND

Our facial skin hosts millions of microorganisms, primarily bacteria, crucial for skin health by maintaining the physical barrier, modulating immune response, and metabolizing bioactive materials. Aging significantly influences the composition and function of the facial microbiome, impacting skin immunity, hydration, and inflammation, highlighting potential avenues for interventions targeting aging-related facial microbes amidst changes in skin physiological properties.

RESULTS

We conducted a multi-center and deep sequencing survey to investigate the intricate interplay of aging, skin physio-optical conditions, and facial microbiome. Leveraging a newly-generated dataset of 2737 species-level metagenome-assembled genomes (MAGs), our integrative analysis highlighted aging as the primary driver, influencing both facial microbiome composition and key skin characteristics, including moisture, sebum production, gloss, pH, elasticity, and sensitivity. Further mediation analysis revealed that skin characteristics significantly impacted the microbiome, mostly as a mediator of aging. Utilizing this dataset, we uncovered two consistent cutotypes across sampling cities and identified aging-related microbial MAGs. Additionally, a Facial Aging Index (FAI) was formulated based on the microbiome, uncovering the cutotype-dependent effects of unhealthy lifestyles on skin aging. Finally, we distinguished aging related microbial pathways influenced by lifestyles with cutotype-dependent effect.

CONCLUSIONS

Together, our findings emphasize aging's central role in facial microbiome dynamics, and support personalized skin microbiome interventions by targeting lifestyle, skin properties, and aging-related microbial factors. Video Abstract.

Prokaryotic-virus-encoded auxiliary metabolic genes throughout the global oceans

BACKGROUND

Prokaryotic microbes have impacted marine biogeochemical cycles for billions of years. Viruses also impact these cycles, through lysis, horizontal gene transfer, and encoding and expressing genes that contribute to metabolic reprogramming of prokaryotic cells. While this impact is difficult to quantify in nature, we hypothesized that it can be examined by surveying virus-encoded auxiliary metabolic genes (AMGs) and assessing their ecological context.

RESULTS

We systematically developed a global ocean AMG catalog by integrating previously described and newly identified AMGs and then placed this catalog into ecological and metabolic contexts relevant to ocean biogeochemistry. From 7.6 terabases of Tara Oceans paired prokaryote- and virus-enriched metagenomic sequence data, we increased known ocean virus populations to 579,904 (up 16%). From these virus populations, we then conservatively identified 86,913 AMGs that grouped into 22,779 sequence-based gene clusters, 7248 (~ 32%) of which were not previously reported. Using our catalog and modeled data from mock communities, we estimate that ~ 19% of ocean virus populations carry at least one AMG. To understand AMGs in their metabolic context, we identified 340 metabolic pathways encoded by ocean microbes and showed that AMGs map to 128 of them. Furthermore, we identified metabolic "hot spots" targeted by virus AMGs, including nine pathways where most steps (≥ 0.75) were AMG-targeted (involved in carbohydrate, amino acid, fatty acid, and nucleotide metabolism), as well as other pathways where virus-encoded AMGs outnumbered cellular homologs (involved in lipid A phosphates, phosphatidylethanolamine, creatine biosynthesis, phosphoribosylamine-glycine ligase, and carbamoyl-phosphate synthase pathways).

CONCLUSIONS

Together, this systematically curated, global ocean AMG catalog and analyses provide a valuable resource and foundational observations to understand the role of viruses in modulating global ocean metabolisms and their biogeochemical implications. Video Abstract.

Melon: metagenomic long-read-based taxonomic identification and quantification using marker genes

Long-read sequencing holds great potential for characterizing complex microbial communities, yet taxonomic profiling tools designed specifically for long reads remain lacking. We introduce Melon, a novel marker-based taxonomic profiler that capitalizes on the unique attributes of long reads. Melon employs a two-stage classification scheme to reduce computational time and is equipped with an expectation-maximization-based post-correction module to handle ambiguous reads. Melon achieves superior performance compared to existing tools in both mock and simulated samples. Using wastewater metagenomic samples, we demonstrate the applicability of Melon by showing it provides reliable estimates of overall genome copies, and species-level taxonomic profiles.

A multi-kingdom collection of 33,804 reference genomes for the human vaginal microbiome

The human vagina harbours diverse microorganisms-bacteria, viruses and fungi-with profound implications for women's health. Genome-level analysis of the vaginal microbiome across multiple kingdoms remains limited. Here we utilize metagenomic sequencing data and fungal cultivation to establish the Vaginal Microbial Genome Collection (VMGC), comprising 33,804 microbial genomes spanning 786 prokaryotic species, 11 fungal species and 4,263 viral operational taxonomic units. Notably, over 25% of prokaryotic species and 85% of viral operational taxonomic units remain uncultured. This collection significantly enriches genomic diversity, especially for prevalent vaginal pathogens such as BVAB1 (an uncultured bacterial vaginosis-associated bacterium) and Amygdalobacter spp. (BVAB2 and related species). Leveraging VMGC, we characterize functional traits of prokaryotes, notably Saccharofermentanales (an underexplored yet prevalent order), along with prokaryotic and eukaryotic viruses, offering insights into their niche adaptation and potential roles in the vagina. VMGC serves as a valuable resource for studying vaginal microbiota and its impact on vaginal health.

Time-series metagenomics reveals changing protistan ecology of a temperate dimictic lake

BACKGROUND

Protists, single-celled eukaryotic organisms, are critical to food web ecology, contributing to primary productivity and connecting small bacteria and archaea to higher trophic levels. Lake Mendota is a large, eutrophic natural lake that is a Long-Term Ecological Research site and among the world's best-studied freshwater systems. Metagenomic samples have been collected and shotgun sequenced from Lake Mendota for the last 20 years. Here, we analyze this comprehensive time series to infer changes to the structure and function of the protistan community and to hypothesize about their interactions with bacteria.

RESULTS

Based on small subunit rRNA genes extracted from the metagenomes and metagenome-assembled genomes of microeukaryotes, we identify shifts in the eukaryotic phytoplankton community over time, which we predict to be a consequence of reduced zooplankton grazing pressures after the invasion of a invasive predator (the spiny water flea) to the lake. The metagenomic data also reveal the presence of the spiny water flea and the zebra mussel, a second invasive species to Lake Mendota, prior to their visual identification during routine monitoring. Furthermore, we use species co-occurrence and co-abundance analysis to connect the protistan community with bacterial taxa. Correlation analysis suggests that protists and bacteria may interact or respond similarly to environmental conditions. Cryptophytes declined in the second decade of the timeseries, while many alveolate groups (e.g., ciliates and dinoflagellates) and diatoms increased in abundance, changes that have implications for food web efficiency in Lake Mendota.

CONCLUSIONS

We demonstrate that metagenomic sequence-based community analysis can complement existing efforts to monitor protists in Lake Mendota based on microscopy-based count surveys. We observed patterns of seasonal abundance in microeukaryotes in Lake Mendota that corroborated expectations from other systems, including high abundance of cryptophytes in winter and diatoms in fall and spring, but with much higher resolution than previous surveys. Our study identified long-term changes in the abundance of eukaryotic microbes and provided context for the known establishment of an invasive species that catalyzes a trophic cascade involving protists. Our findings are important for decoding potential long-term consequences of human interventions, including invasive species introduction. Video Abstract.

DeepMicroClass sorts metagenomic contigs into prokaryotes, eukaryotes and viruses

Sequence classification facilitates a fundamental understanding of the structure of microbial communities. Binary metagenomic sequence classifiers are insufficient because environmental metagenomes are typically derived from multiple sequence sources. Here we introduce a deep-learning based sequence classifier, DeepMicroClass, that classifies metagenomic contigs into five sequence classes, i.e. viruses infecting prokaryotic or eukaryotic hosts, eukaryotic or prokaryotic chromosomes, and prokaryotic plasmids. DeepMicroClass achieved high performance for all sequence classes at various tested sequence lengths ranging from 500 bp to 100 kbps. By benchmarking on a synthetic dataset with variable sequence class composition, we showed that DeepMicroClass obtained better performance for eukaryotic, plasmid and viral contig classification than other state-of-the-art predictors. DeepMicroClass achieved comparable performance on viral sequence classification with geNomad and VirSorter2 when benchmarked on the CAMI II marine dataset. Using a coastal daily time-series metagenomic dataset as a case study, we showed that microbial eukaryotes and prokaryotic viruses are integral to microbial communities. By analyzing monthly metagenomes collected at HOT and BATS, we found relatively higher viral read proportions in the subsurface layer in late summer, consistent with the seasonal viral infection patterns prevalent in these areas. We expect DeepMicroClass will promote metagenomic studies of under-appreciated sequence types.

Critical steps in an environmental metaproteomics workflow

Environmental metaproteomics is a rapidly advancing field that provides insights into the structure, dynamics, and metabolic activity of microbial communities. As the field is still maturing, it lacks consistent workflows, making it challenging for non-expert researchers to navigate. This review aims to introduce the workflow of environmental metaproteomics. It outlines the standard practices for sample collection, processing, and analysis, and offers strategies to overcome the unique challenges presented by common environmental matrices such as soil, freshwater, marine environments, biofilms, sludge, and symbionts. The review also highlights the bottlenecks in data analysis that are specific to metaproteomics samples and provides suggestions for researchers to obtain high-quality datasets. It includes recent benchmarking studies and descriptions of software packages specifically built for metaproteomics analysis. The article is written without assuming the reader's familiarity with single-organism proteomic workflows, making it accessible to those new to proteomics or mass spectrometry in general. This primer for environmental metaproteomics aims to improve accessibility to this exciting technology and empower researchers to tackle challenging and ambitious research questions. While it is primarily a resource for those new to the field, it should also be useful for established researchers looking to streamline or troubleshoot their metaproteomics experiments.

Biofilm colonization and succession in a full-scale partial nitritation-anammox moving bed biofilm reactor

BACKGROUND

Partial nitritation-anammox (PNA) is a biological nitrogen removal process commonly used in wastewater treatment plants for the treatment of warm and nitrogen-rich sludge liquor from anaerobic digestion, often referred to as sidestream wastewater. In these systems, biofilms are frequently used to retain biomass with aerobic ammonia-oxidizing bacteria (AOB) and anammox bacteria, which together convert ammonium to nitrogen gas. Little is known about how these biofilm communities develop, and whether knowledge about the assembly of biofilms in natural communities can be applied to PNA biofilms.

RESULTS

We followed the start-up of a full-scale PNA moving bed biofilm reactor for 175 days using shotgun metagenomics. Environmental filtering likely restricted initial biofilm colonization, resulting in low phylogenetic diversity, with the initial microbial community comprised mainly of Proteobacteria. Facilitative priority effects allowed further biofilm colonization, with the growth of initial aerobic colonizers promoting the arrival and growth of anaerobic taxa like methanogens and anammox bacteria. Among the early colonizers were known 'oligotrophic' ammonia oxidizers including comammox Nitrospira and Nitrosomonas cluster 6a AOB. Increasing the nitrogen load in the bioreactor allowed colonization by 'copiotrophic' Nitrosomonas cluster 7 AOB and resulted in the exclusion of the initial ammonia- and nitrite oxidizers.

CONCLUSIONS

We show that complex dynamic processes occur in PNA microbial communities before a stable bioreactor process is achieved. The results of this study not only contribute to our knowledge about biofilm assembly and PNA bioreactor start-up but could also help guide strategies for the successful implementation of PNA bioreactors. Video Abstract.

Assigning the unassigned: A signature-based classification of rDNA metabarcodes reveals new deep-sea diversity

Environmental DNA metabarcoding reveals a vast genetic diversity of marine eukaryotes. Yet, most of the metabarcoding data remain unassigned due to the paucity of reference databases. This is particularly true for the deep-sea meiofauna and eukaryotic microbiota, whose hidden diversity is largely unexplored. Here, we tackle this issue by using unique DNA signatures to classify unknown metabarcodes assigned to deep-sea foraminifera. We analyzed metabarcoding data obtained from 311 deep-sea sediment samples collected in the Clarion-Clipperton Fracture Zone, an area of potential polymetallic nodule exploitation in the Eastern Pacific Ocean. Using the signatures designed in the 37F hypervariable region of the 18S rRNA gene, we were able to classify 802 unassigned metabarcodes into 61 novel lineages, which have been placed in 27 phylogenetic clades. The comparison of new lineages with other foraminiferal datasets shows that most novel lineages are widely distributed in the deep sea. Five lineages are also present in the shallow-water datasets; however, phylogenetic analysis of these lineages separates deep-sea and shallow-water metabarcodes except in one case. While the signature-based classification does not solve the problem of gaps in reference databases, this taxonomy-free approach provides insight into the distribution and ecology of deep-sea species represented by unassigned metabarcodes, which could be useful in future applications of metabarcoding for environmental monitoring.

Screening putative polyester polyurethane degrading enzymes with semi-automated cell-free expression and nitrophenyl probes

Cell-free expression (CFE) has shown recent utility in prototyping enzymes for discovery efforts. In this work, CFE is demonstrated as an effective tool to screen putative polyester polyurethane degrading enzyme sequences sourced from metagenomic analysis of biofilms prospected on aircraft and vehicles. An automated fluid handler with a controlled temperature block is used to assemble the numerous 30 µL CFE reactions to provide more consistent results over human assembly. In sum, 13 putative hydrolase enzymes from the biofilm organisms as well as a previously verified, polyester-degrading cutinase were expressed using in-house E. coli extract and minimal linear templates. The enzymes were then tested for esterase activity directly in extract using nitrophenyl conjugated substrates, showing highest sensitivity to shorter substrates (4-nitrophenyl hexanoate and 4-nNitrophenyl valerate). This screen identified 10 enzymes with statistically significant activities against these substrates; however, all were lower in measured relative activity, on a CFE volume basis, to the established cutinase control. This approach portends the use of CFE and reporter probes to rapidly prototype, screen and design for synthetic polymer degrading enzymes from environmental consortia. Graphical Abstract.

MuDoGeR: Multi-Domain Genome recovery from metagenomes made easy

Several computational frameworks and workflows that recover genomes from prokaryotes, eukaryotes and viruses from metagenomes exist. Yet, it is difficult for scientists with little bioinformatics experience to evaluate quality, annotate genes, dereplicate, assign taxonomy and calculate relative abundance and coverage of genomes belonging to different domains. MuDoGeR is a user-friendly tool tailored for those familiar with Unix command-line environment that makes it easy to recover genomes of prokaryotes, eukaryotes and viruses from metagenomes, either alone or in combination. We tested MuDoGeR using 24 individual-isolated genomes and 574 metagenomes, demonstrating the applicability for a few samples and high throughput. While MuDoGeR can recover eukaryotic viral sequences, its characterization is predominantly skewed towards bacterial and archaeal viruses, reflecting the field's current state. However, acting as a dynamic wrapper, the MuDoGeR is designed to constantly incorporate updates and integrate new tools, ensuring its ongoing relevance in the rapidly evolving field. MuDoGeR is open-source software available at https://github.com/mdsufz/MuDoGeR. Additionally, MuDoGeR is also available as a Singularity container.

NMPFamsDB: a database of novel protein families from microbial metagenomes and metatranscriptomes

The Novel Metagenome Protein Families Database (NMPFamsDB) is a database of metagenome- and metatranscriptome-derived protein families, whose members have no hits to proteins of reference genomes or Pfam domains. Each protein family is accompanied by multiple sequence alignments, Hidden Markov Models, taxonomic information, ecosystem and geolocation metadata, sequence and structure predictions, as well as 3D structure models predicted with AlphaFold2. In its current version, NMPFamsDB hosts over 100 000 protein families, each with at least 100 members. The reported protein families significantly expand (more than double) the number of known protein sequence clusters from reference genomes and reveal new insights into their habitat distribution, origins, functions and taxonomy. We expect NMPFamsDB to be a valuable resource for microbial proteome-wide analyses and for further discovery and characterization of novel functions. NMPFamsDB is publicly available in http://www.nmpfamsdb.org/ or https://bib.fleming.gr/NMPFamsDB.

gNOMO2: a comprehensive and modular pipeline for integrated multi-omics analyses of microbiomes

BACKGROUND

In recent years, omics technologies have offered an exceptional chance to gain a deeper insight into the structural and functional characteristics of microbial communities. As a result, there is a growing demand for user-friendly, reproducible, and versatile bioinformatic tools that can effectively harness multi-omics data to provide a holistic understanding of microbiomes. Previously, we introduced gNOMO, a bioinformatic pipeline tailored to analyze microbiome multi-omics data in an integrative manner. In response to the evolving demands within the microbiome field and the growing necessity for integrated multi-omics data analysis, we have implemented substantial enhancements to the gNOMO pipeline.

RESULTS

Here, we present gNOMO2, a comprehensive and modular pipeline that can seamlessly manage various omics combinations, ranging from 2 to 4 distinct omics data types, including 16S ribosomal RNA (rRNA) gene amplicon sequencing, metagenomics, metatranscriptomics, and metaproteomics. Furthermore, gNOMO2 features a specialized module for processing 16S rRNA gene amplicon sequencing data to create a protein database suitable for metaproteomics investigations. Moreover, it incorporates new differential abundance, integration, and visualization approaches, enhancing the toolkit for a more insightful analysis of microbiomes. The functionality of these new features is showcased through the use of 4 microbiome multi-omics datasets encompassing various ecosystems and omics combinations. gNOMO2 not only replicated most of the primary findings from these studies but also offered further valuable perspectives.

CONCLUSIONS

gNOMO2 enables the thorough integration of taxonomic and functional analyses in microbiome multi-omics data, offering novel insights in both host-associated and free-living microbiome research. gNOMO2 is available freely at https://github.com/muzafferarikan/gNOMO2.

Forecasting the dynamics of a complex microbial community using integrated meta-omics

Predicting the behaviour of complex microbial communities is challenging. However, this is essential for complex biotechnological processes such as those in biological wastewater treatment plants (BWWTPs), which require sustainable operation. Here we summarize 14 months of longitudinal meta-omics data from a BWWTP anaerobic tank into 17 temporal signals, explaining 91.1% of the temporal variance, and link those signals to ecological events within the community. We forecast the signals over the subsequent five years and use 21 extra samples collected at defined time intervals for testing and validation. Our forecasts are correct for six signals and hint on phenomena such as predation cycles. Using all the 17 forecasts and the environmental variables, we predict gene abundance and expression, with a coefficient of determination ≥0.87 for the subsequent three years. Our study demonstrates the ability to forecast the dynamics of open microbial ecosystems using interactions between community cycles and environmental parameters.

Oxic methane production from methylphosphonate in a large oligotrophic lake: limitation by substrate and organic carbon supply

IMPORTANCE

Methane is an important greenhouse gas that is typically produced under anoxic conditions. We show that methane is supersaturated in a large oligotrophic lake despite the presence of oxygen. Metagenomic sequencing indicates that diverse, widespread microorganisms may contribute to the oxic production of methane through the cleavage of methylphosphonate. We experimentally demonstrate that these organisms, especially members of the genus Acidovorax, can produce methane through this process. However, appreciable rates of methane production only occurred when both methylphosphonate and labile sources of carbon were added, indicating that this process may be limited to specific niches and may not be completely responsible for methane concentrations in Flathead Lake. This work adds to our understanding of methane dynamics by describing the organisms and the rates at which they can produce methane through an oxic pathway in a representative oligotrophic lake.

Eukaryotic genomes from a global metagenomic data set illuminate trophic modes and biogeography of ocean plankton

IMPORTANCE

Single-celled eukaryotes play ecologically significant roles in the marine environment, yet fundamental questions about their biodiversity, ecological function, and interactions remain. Environmental sequencing enables researchers to document naturally occurring protistan communities, without culturing bias, yet metagenomic and metatranscriptomic sequencing approaches cannot separate individual species from communities. To more completely capture the genomic content of mixed protistan populations, we can create bins of sequences that represent the same organism (metagenome-assembled genomes [MAGs]). We developed the EukHeist pipeline, which automates the binning of population-level eukaryotic and prokaryotic genomes from metagenomic reads. We show exciting insight into what protistan communities are present and their trophic roles in the ocean. Scalable computational tools, like EukHeist, may accelerate the identification of meaningful genetic signatures from large data sets and complement researchers' efforts to leverage MAG databases for addressing ecological questions, resolving evolutionary relationships, and discovering potentially novel biodiversity.

Carbohydrate flow through agricultural ecosystems: Implications for synthesis and microbial conversion of carbohydrates

Carbohydrates are chemically and structurally diverse biomolecules, serving numerous and varied roles in agricultural ecosystems. Crops and horticulture products are inherent sources of carbohydrates that are consumed by humans and non-human animals alike; however carbohydrates are also present in other agricultural materials, such as soil and compost, human and animal tissues, milk and dairy products, and honey. The biosynthesis, modification, and flow of carbohydrates within and between agricultural ecosystems is intimately related with microbial communities that colonize and thrive within these environments. Recent advances in -omics techniques have ushered in a new era for microbial ecology by illuminating the functional potential for carbohydrate metabolism encoded within microbial genomes, while agricultural glycomics is providing fresh perspective on carbohydrate-microbe interactions and how they influence the flow of functionalized carbon. Indeed, carbohydrates and carbohydrate-active enzymes are interventions with unrealized potential for improving carbon sequestration, soil fertility and stability, developing alternatives to antimicrobials, and circular production systems. In this manner, glycomics represents a new frontier for carbohydrate-based biotechnological solutions for agricultural systems facing escalating challenges, such as the changing climate.

Bacterial genome size and gene functional diversity negatively correlate with taxonomic diversity along a pH gradient

Bacterial gene repertoires reflect adaptive strategies, contribute to ecosystem functioning and are limited by genome size. However, gene functional diversity does not necessarily correlate with taxonomic diversity because average genome size may vary by community. Here, we analyse gene functional diversity (by shotgun metagenomics) and taxonomic diversity (by 16S rRNA gene amplicon sequencing) to investigate soil bacterial communities along a natural pH gradient in 12 tropical, subtropical, and temperate forests. We find that bacterial average genome size and gene functional diversity decrease, whereas taxonomic diversity increases, as soil pH rises from acid to neutral; as a result, bacterial taxonomic and functional diversity are negatively correlated. The gene repertoire of acid-adapted oligotrophs is enriched in functions of signal transduction, cell motility, secretion system, and degradation of complex compounds, while that of neutral pH-adapted copiotrophs is enriched in functions of energy metabolism and membrane transport. Our results indicate that a mismatch between taxonomic and functional diversity can arise when environmental factors (such as pH) select for adaptive strategies that affect genome size distributions.

Unraveling the functional dark matter through global metagenomics

Metagenomes encode an enormous diversity of proteins, reflecting a multiplicity of functions and activities1,2. Exploration of this vast sequence space has been limited to a comparative analysis against reference microbial genomes and protein families derived from those genomes. Here, to examine the scale of yet untapped functional diversity beyond what is currently possible through the lens of reference genomes, we develop a computational approach to generate reference-free protein families from the sequence space in metagenomes. We analyse 26,931 metagenomes and identify 1.17 billion protein sequences longer than 35 amino acids with no similarity to any sequences from 102,491 reference genomes or the Pfam database3. Using massively parallel graph-based clustering, we group these proteins into 106,198 novel sequence clusters with more than 100 members, doubling the number of protein families obtained from the reference genomes clustered using the same approach. We annotate these families on the basis of their taxonomic, habitat, geographical and gene neighbourhood distributions and, where sufficient sequence diversity is available, predict protein three-dimensional models, revealing novel structures. Overall, our results uncover an enormously diverse functional space, highlighting the importance of further exploring the microbial functional dark matter.

ACR: metagenome-assembled prokaryotic and eukaryotic genome refinement tool

Microbial genome recovery from metagenomes can further explain microbial ecosystem structures, functions and dynamics. Thus, this study developed the Additional Clustering Refiner (ACR) to enhance high-purity prokaryotic and eukaryotic metagenome-assembled genome (MAGs) recovery. ACR refines low-quality MAGs by subjecting them to iterative k-means clustering predicated on contig abundance and increasing bin purity through validated universal marker genes. Synthetic and real-world metagenomic datasets, including short- and long-read sequences, evaluated ACR's effectiveness. The results demonstrated improved MAG purity and a significant increase in high- and medium-quality MAG recovery rates. In addition, ACR seamlessly integrates with various binning algorithms, augmenting their strengths without modifying core features. Furthermore, its multiple sequencing technology compatibilities expand its applicability. By efficiently recovering high-quality prokaryotic and eukaryotic genomes, ACR is a promising tool for deepening our understanding of microbial communities through genome-centric metagenomics.

Long-read assembled metagenomic approaches improve our understanding on metabolic potentials of microbial community in mangrove sediments

BACKGROUND

Mangrove wetlands are coastal ecosystems with important ecological features and provide habitats for diverse microorganisms with key roles in nutrient and biogeochemical cycling. However, the overall metabolic potentials and ecological roles of microbial community in mangrove sediment are remained unanswered. In current study, the microbial and metabolic profiles of prokaryotic and fungal communities in mangrove sediments were investigated using metagenomic analysis based on PacBio single-molecule real time (SMRT) and Illumina sequencing techniques.

RESULTS

Comparing to Illumina short reads, the incorporation of PacBio long reads significantly contributed to more contiguous assemblies, yielded more than doubled high-quality metagenome-assembled genomes (MAGs), and improved the novelty of the MAGs. Further metabolic reconstruction for recovered MAGs showed that prokaryotes potentially played an essential role in carbon cycling in mangrove sediment, displaying versatile metabolic potential for degrading organic carbons, fermentation, autotrophy, and carbon fixation. Mangrove fungi also functioned as a player in carbon cycling, potentially involved in the degradation of various carbohydrate and peptide substrates. Notably, a new candidate bacterial phylum named as Candidatus Cosmopoliota with a ubiquitous distribution is proposed. Genomic analysis revealed that this new phylum is capable of utilizing various types of organic substrates, anaerobic fermentation, and carbon fixation with the Wood-Ljungdahl (WL) pathway and the reverse tricarboxylic acid (rTCA) cycle.

CONCLUSIONS

The study not only highlights the advantages of HiSeq-PacBio Hybrid assembly for a more complete profiling of environmental microbiomes but also expands our understanding of the microbial diversity and potential roles of distinct microbial groups in biogeochemical cycling in mangrove sediment. Video Abstract.

Ultra-deep sequencing of Hadza hunter-gatherers recovers vanishing gut microbes

The gut microbiome modulates immune and metabolic health. Human microbiome data are biased toward industrialized populations, limiting our understanding of non-industrialized microbiomes. Here, we performed ultra-deep metagenomic sequencing on 351 fecal samples from the Hadza hunter-gatherers of Tanzania and comparative populations in Nepal and California. We recovered 91,662 genomes of bacteria, archaea, bacteriophages, and eukaryotes, 44% of which are absent from existing unified datasets. We identified 124 gut-resident species vanishing in industrialized populations and highlighted distinct aspects of the Hadza gut microbiome related to in situ replication rates, signatures of selection, and strain sharing. Industrialized gut microbes were found to be enriched in genes associated with oxidative stress, possibly a result of microbiome adaptation to inflammatory processes. This unparalleled view of the Hadza gut microbiome provides a valuable resource, expands our understanding of microbes capable of colonizing the human gut, and clarifies the extensive perturbation induced by the industrialized lifestyle.

Patterns and determinants of the global herbivorous mycobiome

Despite their role in host nutrition, the anaerobic gut fungal (AGF) component of the herbivorous gut microbiome remains poorly characterized. Here, to examine global patterns and determinants of AGF diversity, we generate and analyze an amplicon dataset from 661 fecal samples from 34 mammalian species, 9 families, and 6 continents. We identify 56 novel genera, greatly expanding AGF diversity beyond current estimates (31 genera and candidate genera). Community structure analysis indicates that host phylogenetic affiliation, not domestication status and biogeography, shapes the community rather than. Fungal-host associations are stronger and more specific in hindgut fermenters than in foregut fermenters. Transcriptomics-enabled phylogenomic and molecular clock analyses of 52 strains from 14 genera indicate that most genera with preferences for hindgut hosts evolved earlier (44-58 Mya) than those with preferences for foregut hosts (22-32 Mya). Our results greatly expand the documented scope of AGF diversity and provide an ecologically and evolutionary-grounded model to explain the observed patterns of AGF diversity in extant animal hosts.

Anammox bacteria adapt to long-term light irradiation in photogranules

Photogranules composed of algae, nitrifiers, and anammox bacteria are promising for nitrogen removal from wastewater with reduced aeration and carbon emissions. However, it is difficult to be achieved as the potential inhibition of anammox bacteria by light. In this study, a syntrophic algal-partial nitrification/anammox granular sludge process was developed, with a nitrogen removal rate of 294.5 mg N/(L·d). We found the symbiosis in the community promoted the adaptation of anammox bacteria under light, and cross-feeding played an important role. Microalgae in the outer layers of photogranules sheltered most of the light and supplied cofactors and amino acids to promote nitrogen removal. In particular, Myxococcota MYX1 degraded the extracellular proteins produced by microalgae, providing amino acids to the entire bacterial community, which helped anammox bacteria save metabolic energy and adapt to light. Notably, the anammox bacteria Candidatus Brocadia exhibited unique light-sensing potential and adaptations to light irradiation compared with Candidatus Jettenia, including diverse DNA repair, scavenging of reactive oxygen species, cell movement. The phytochrome-like proteins encoded by Candidatus Brocadia further facilitated their spatial positioning and niche partitioning in photogranules. This study provides insights into the response of anammox bacteria in the algae-bacteria symbiosis system and suggests its potential application for carbon-negative nitrogen removal.

Methane-Oxidizing Activity Enhances Sulfamethoxazole Biotransformation in a Benthic Constructed Wetland Biomat

Ammonia monooxygenase and analogous oxygenase enzymes contribute to pharmaceutical biotransformation in activated sludge. In this study, we hypothesized that methane monooxygenase can enhance pharmaceutical biotransformation within the benthic, diffuse periphytic sediments (i.e., "biomat") of a shallow, open-water constructed wetland. To test this hypothesis, we combined field-scale metatranscriptomics, porewater geochemistry, and methane gas fluxes to inform microcosms targeting methane monooxygenase activity and its potential role in pharmaceutical biotransformation. In the field, sulfamethoxazole concentrations decreased within surficial biomat layers where genes encoding for the particulate methane monooxygenase (pMMO) were transcribed by a novel methanotroph classified as Methylotetracoccus. Inhibition microcosms provided independent confirmation that methane oxidation was mediated by the pMMO. In these same incubations, sulfamethoxazole biotransformation was stimulated proportional to aerobic methane-oxidizing activity and exhibited negligible removal in the absence of methane, in the presence of methane and pMMO inhibitors, and under anoxia. Nitrate reduction was similarly enhanced under aerobic methane-oxidizing conditions with rates several times faster than for canonical denitrification. Collectively, our results provide convergent in situ and laboratory evidence that methane-oxidizing activity can enhance sulfamethoxazole biotransformation, with possible implications for the combined removal of nitrogen and trace organic contaminants in wetland sediments.

Artificial Intelligence: A Promising Tool in Exploring the Phytomicrobiome in Managing Disease and Promoting Plant Health

There is increasing interest in harnessing the microbiome to improve cropping systems. With the availability of high-throughput and low-cost sequencing technologies, gathering microbiome data is becoming more routine. However, the analysis of microbiome data is challenged by the size and complexity of the data, and the incomplete nature of many microbiome databases. Further, to bring microbiome data value, it often needs to be analyzed in conjunction with other complex data that impact on crop health and disease management, such as plant genotype and environmental factors. Artificial intelligence (AI), boosted through deep learning (DL), has achieved significant breakthroughs and is a powerful tool for managing large complex datasets such as the interplay between the microbiome, crop plants, and their environment. In this review, we aim to provide readers with a brief introduction to AI techniques, and we introduce how AI has been applied to areas of microbiome sequencing taxonomy, the functional annotation for microbiome sequences, associating the microbiome community with host traits, designing synthetic communities, genomic selection, field phenotyping, and disease forecasting. At the end of this review, we proposed further efforts that are required to fully exploit the power of AI in studying phytomicrobiomes.

The endosymbiont of Epithemia clementina is specialized for nitrogen fixation within a photosynthetic eukaryote

Epithemia spp. diatoms contain obligate, nitrogen-fixing endosymbionts, or "diazoplasts", derived from cyanobacteria. These algae are a rare example of photosynthetic eukaryotes that have successfully coupled oxygenic photosynthesis with oxygen-sensitive nitrogenase activity. Here, we report a newly-isolated species, E. clementina, as a model to investigate endosymbiotic acquisition of nitrogen fixation. To detect the metabolic changes associated with endosymbiotic specialization, we compared nitrogen fixation, associated carbon and nitrogen metabolism, and their regulatory pathways in the Epithemia diazoplast with its close, free-living cyanobacterial relative, Crocosphaera subtropica. Unlike C. subtropica, we show that nitrogenase activity in the diazoplast is concurrent with, and even dependent on, host photosynthesis and no longer associated with cyanobacterial glycogen storage suggesting carbohydrates are imported from the host diatom. Carbohydrate catabolism in the diazoplast indicates that the oxidative pentose pathway and oxidative phosphorylation, in concert, generates reducing equivalents and ATP and consumes oxygen to support nitrogenase activity. In contrast to expanded nitrogenase activity, the diazoplast has diminished ability to utilize alternative nitrogen sources. Upon ammonium repletion, negative feedback regulation of nitrogen fixation was conserved, however ammonia assimilation showed paradoxical responses in the diazoplast compared with C. subtropica. The altered nitrogen regulation likely favors nitrogen transfer to the host. Our results suggest that the diazoplast is specialized for endosymbiotic nitrogen fixation. Altogether, we establish a new model for studying endosymbiosis, perform the first functional characterization of this diazotroph endosymbiosis, and identify metabolic adaptations for endosymbiotic acquisition of a critical biological function.

Dataset of 143 metagenome-assembled genomes from the Arctic and Atlantic Oceans, including 21 for eukaryotic organisms

This article presents metagenome-assembled genomes (MAGs) for both eukaryotic and prokaryotic organisms originating from the Arctic and Atlantic oceans, along with gene prediction and functional annotation for MAGs from both domains. Eleven samples from the chlorophyll-a maximum layer of the surface ocean were collected during two cruises in 2012; six from the Arctic in June-July on ARK-XXVII/1 (PS80), and five from the Atlantic in November on ANT-XXIX/1 (PS81). Sequencing and assembly was carried out by the Joint Genome Institute (JGI), who provide annotation of the assembled sequences, and 122 MAGs for prokaryotic organisms. A subsequent binning process identified 21 MAGs for eukaryotic organisms, mostly identified as Mamiellophyceae or Bacillariophyceae. The data for each MAG includes sequences in FASTA format, and tables of functional annotation of genes. For eukaryotic MAGs, transcript and protein sequences for predicted genes are available. A spreadsheet is provided summarising quality measures and taxonomic classifications for each MAG. These data provide draft genomes for uncultured marine microbes, including some of the first MAGs for polar eukaryotes, and can provide reference genetic data for these environments, or used in genomics-based comparison between environments.

Abiotic selection of microbial genome size in the global ocean

Strong purifying selection is considered a major evolutionary force behind small microbial genomes in the resource-poor photic ocean. However, very little is currently known about how the size of prokaryotic genomes evolves in the global ocean and whether patterns reflect shifts in resource availability in the epipelagic and relatively stable deep-sea environmental conditions. Using 364 marine microbial metagenomes, we investigate how the average genome size of uncultured planktonic prokaryotes varies across the tropical and polar oceans to the hadal realm. We find that genome size is highest in the perennially cold polar ocean, reflecting elongation of coding genes and gene dosage effects due to duplications in the interior ocean microbiome. Moreover, the rate of change in genome size due to temperature is 16-fold higher than with depth up to 200 m. Our results demonstrate how environmental factors can influence marine microbial genome size selection and ecological strategies of the microbiome.

Identifying Eukaryotes and Factors Influencing Their Biogeography in Drinking Water Metagenomes

The biogeography of eukaryotes in drinking water systems is poorly understood relative to that of prokaryotes or viruses, limiting the understanding of their role and management. A challenge with studying complex eukaryotic communities is that metagenomic analysis workflows are currently not as mature as those that focus on prokaryotes or viruses. In this study, we benchmarked different strategies to recover eukaryotic sequences and genomes from metagenomic data and applied the best-performing workflow to explore the factors affecting the relative abundance and diversity of eukaryotic communities in drinking water distribution systems (DWDSs). We developed an ensemble approach exploiting k-mer- and reference-based strategies to improve eukaryotic sequence identification and identified MetaBAT2 as the best-performing binning approach for their clustering. Applying this workflow to the DWDS metagenomes showed that eukaryotic sequences typically constituted small proportions (i.e., <1%) of the overall metagenomic data with higher relative abundances in surface water-fed or chlorinated systems with high residuals. The α and β diversities of eukaryotes were correlated with those of prokaryotic and viral communities, highlighting the common role of environmental/management factors. Finally, a co-occurrence analysis highlighted clusters of eukaryotes whose members' presence and abundance in DWDSs were affected by disinfection strategies, climate conditions, and source water types.

Exploring microbial functional biodiversity at the protein family level-From metagenomic sequence reads to annotated protein clusters

Metagenomics has enabled accessing the genetic repertoire of natural microbial communities. Metagenome shotgun sequencing has become the method of choice for studying and classifying microorganisms from various environments. To this end, several methods have been developed to process and analyze the sequence data from raw reads to end-products such as predicted protein sequences or families. In this article, we provide a thorough review to simplify such processes and discuss the alternative methodologies that can be followed in order to explore biodiversity at the protein family level. We provide details for analysis tools and we comment on their scalability as well as their advantages and disadvantages. Finally, we report the available data repositories and recommend various approaches for protein family annotation related to phylogenetic distribution, structure prediction and metadata enrichment.

Recovery of 197 eukaryotic bins reveals major challenges for eukaryote genome reconstruction from terrestrial metagenomes

As most eukaryotic genomes are yet to be sequenced, the mechanisms underlying their contribution to different ecosystem processes remain untapped. Although approaches to recovering Prokaryotic genomes have become common in genome biology, few studies have tackled the recovery of eukaryotic genomes from metagenomes. This study assessed the reconstruction of microbial eukaryotic genomes using 6000 metagenomes from terrestrial and some transition environments using the EukRep pipeline. Only 215 metagenomic libraries yielded eukaryotic bins. From a total of 447 eukaryotic bins recovered 197 were classified at the phylum level. Streptophytes and fungi were the most represented clades with 83 and 73 bins, respectively. More than 78% of the obtained eukaryotic bins were recovered from samples whose biomes were classified as host-associated, aquatic, and anthropogenic terrestrial. However, only 93 bins were taxonomically assigned at the genus level and 17 bins at the species level. Completeness and contamination estimates were obtained for a total of 193 bins and consisted of 44.64% (σ = 27.41%) and 3.97% (σ = 6.53%), respectively. Micromonas commoda was the most frequent taxon found while Saccharomyces cerevisiae presented the highest completeness, probably because more reference genomes are available. Current measures of completeness are based on the presence of single-copy genes. However, mapping of the contigs from the recovered eukaryotic bins to the chromosomes of the reference genomes showed many gaps, suggesting that completeness measures should also include chromosome coverage. Recovering eukaryotic genomes will benefit significantly from long-read sequencing, development of tools for dealing with repeat-rich genomes, and improved reference genomes databases.

Diversity of protist genera in periphyton of tufa-depositing karstic river

Purpose

In aquatic ecosystems, protists play a crucial role and cover numerous ecological functions. The karstic Krka River (Croatia) is a unique hotspot for high diversity of aquatic organisms, especially protists. The main objective of the present study was to obtain a detailed overview of the protist community structure in the periphyton of the Krka River and to determine the differences in protist diversity along the river.

Methods

Protist diversity was detected by amplicon sequencing of the hypervariable region V9 of the 18S rRNA gene, using the universal eukaryotic primer pair.

Results

The three main groups of protists were as follows: Ciliophora, Cercozoa, and Bacillariophyta. In terms of abundance of protist OTUs, the shade plot revealed an evident difference from the upstream to downstream river section, which increased between locations from Krka spring to Skradinski buk. Diversity was explored using measures of alpha and beta diversity. Alpha diversity showed an increasing trend in the downstream direction of the river. The location effect, or clustering/grouping of samples by location, was confirmed by the PERMANOVA permutation test of beta diversity.

Conclusion

The combination of alpha and beta diversity can help provide deeper insight into the study of diversity patterns, but also point out to decline in species diversity and allow for effective ways to protect aquatic karst habitats in future management.

Genome-centric view of the microbiome in a new deep-sea glass sponge species Bathydorus sp

Sponges are widely distributed in the global ocean and harbor diverse symbiotic microbes with mutualistic relationships. However, sponge symbionts in the deep sea remain poorly studied at the genome level. Here, we report a new glass sponge species of the genus Bathydorus and provide a genome-centric view of its microbiome. We obtained 14 high-quality prokaryotic metagenome-assembled genomes (MAGs) affiliated with the phyla Nitrososphaerota, Pseudomonadota, Nitrospirota, Bdellovibrionota, SAR324, Bacteroidota, and Patescibacteria. In total, 13 of these MAGs probably represent new species, suggesting the high novelty of the deep-sea glass sponge microbiome. An ammonia-oxidizing Nitrososphaerota MAG B01, which accounted for up to 70% of the metagenome reads, dominated the sponge microbiomes. The B01 genome had a highly complex CRISPR array, which likely represents an advantageous evolution toward a symbiotic lifestyle and forceful ability to defend against phages. A sulfur-oxidizing Gammaproteobacteria species was the second most dominant symbiont, and a nitrite-oxidizing Nitrospirota species could also be detected, but with lower relative abundance. Bdellovibrio species represented by two MAGs, B11 and B12, were first reported as potential predatory symbionts in deep-sea glass sponges and have undergone dramatic genome reduction. Comprehensive functional analysis indicated that most of the sponge symbionts encoded CRISPR-Cas systems and eukaryotic-like proteins for symbiotic interactions with the host. Metabolic reconstruction further illustrated their essential roles in carbon, nitrogen, and sulfur cycles. In addition, diverse putative phages were identified from the sponge metagenomes. Our study expands the knowledge of microbial diversity, evolutionary adaption, and metabolic complementarity in deep-sea glass sponges.

Metagenomics Shines Light on the Evolution of "Sunscreen" Pigment Metabolism in the Teloschistales (Lichen-Forming Ascomycota)

Fungi produce a vast number of secondary metabolites that shape their interactions with other organisms and the environment. Characterizing the genes underpinning metabolite synthesis is therefore key to understanding fungal evolution and adaptation. Lichenized fungi represent almost one-third of Ascomycota diversity and boast impressive secondary metabolites repertoires. However, most lichen biosynthetic genes have not been linked to their metabolite products. Here we used metagenomic sequencing to survey gene families associated with production of anthraquinones, UV-protectant secondary metabolites present in various fungi, but especially abundant in a diverse order of lichens, the Teloschistales (class Lecanoromycetes, phylum Ascomycota). We successfully assembled 24 new, high-quality lichenized-fungal genomes de novo and combined them with publicly available Lecanoromycetes genomes from taxa with diverse secondary chemistry to produce a whole-genome tree. Secondary metabolite biosynthetic gene cluster (BGC) analysis showed that whilst lichen BGCs are numerous and highly dissimilar, core enzyme genes are generally conserved across taxa. This suggests metabolite diversification occurs via re-shuffling existing enzyme genes with novel accessory genes rather than BGC gains/losses or de novo gene evolution. We identified putative anthraquinone BGCs in our lichen dataset that appear homologous to anthraquinone clusters from non-lichenized fungi, suggesting these genes were present in the common ancestor of the subphylum Pezizomycotina. Finally, we identified unique transporter genes in Teloschistales anthraquinone BGCs that may explain why these metabolites are so abundant and ubiquitous in these lichens. Our results support the importance of metagenomics for understanding the secondary metabolism of non-model fungi such as lichens.