Metagenomic Assembly Research Articles

Integrating chromatin conformation information in a self-supervised learning model improves metagenome binning.

Metagenome binning is a key step, downstream of metagenome assembly, to group scaffolds by their genome of origin. Although accurate binning has been achieved on datasets containing multiple samples from the same community, the completeness of binning is often low in datasets with a small number of samples due to a lack of robust species co-abundance information. In this study, we exploited the chromatin conformation information obtained from Hi-C sequencing and developed a new reference-independent algorithm, Metagenome Binning with Abundance and Tetra-nucleotide frequencies-Long Range (metaBAT-LR), to improve the binning completeness of these datasets. This self-supervised algorithm builds a model from a set of high-quality genome bins to predict scaffold pairs that are likely to be derived from the same genome. Then, it applies these predictions to merge incomplete genome bins, as well as recruit unbinned scaffolds. We validated metaBAT-LR's ability to bin-merge and recruit scaffolds on both synthetic and real-world metagenome datasets of varying complexity. Benchmarking against similar software tools suggests that metaBAT-LR uncovers unique bins that were missed by all other methods. MetaBAT-LR is open-source and is available at https://bitbucket.org/project-metabat/metabat-lr.

Phables: from fragmented assemblies to high-quality bacteriophage genomes.

Microbial communities have a profound impact on both human health and various environments. Viruses infecting bacteria, known as bacteriophages or phages, play a key role in modulating bacterial communities within environments. High-quality phage genome sequences are essential for advancing our understanding of phage biology, enabling comparative genomics studies and developing phage-based diagnostic tools. Most available viral identification tools consider individual sequences to determine whether they are of viral origin. As a result of challenges in viral assembly, fragmentation of genomes can occur, and existing tools may recover incomplete genome fragments. Therefore, the identification and characterization of novel phage genomes remain a challenge, leading to the need of improved approaches for phage genome recovery. We introduce Phables, a new computational method to resolve phage genomes from fragmented viral metagenome assemblies. Phables identifies phage-like components in the assembly graph, models each component as a flow network, and uses graph algorithms and flow decomposition techniques to identify genomic paths. Experimental results of viral metagenomic samples obtained from different environments show that Phables recovers on average over 49% more high-quality phage genomes compared to existing viral identification tools. Furthermore, Phables can resolve variant phage genomes with over 99% average nucleotide identity, a distinction that existing tools are unable to make. Phables is available on GitHub at https://github.com/Vini2/phables.

Evaluation of metagenomic assembly methods for the detection and characterization of antimicrobial resistance determinants and associated mobilizable elements

Antimicrobial resistance genes (ARGs) can be transferred between members of a bacterial population by mobile genetic elements (MGE). Understanding the risk of these transfer events is important in monitoring and predicting antimicrobial resistance (AMR), especially in the context of a One Health Continuum. However, there is no universally accepted method for detection of ARGs and MGEs, and especially for determining their linkages. This study used publicly available shotgun metagenomic DNA short-read (Illumina, 100 bp paired-end) sequence data from samples across the One Health Continuum (including beef cattle composite feces from feedlots, catch basin water at feedlots, agricultural soil from feedlot manured surrounding fields, and urban/municipal sewage influent from two municipal wastewater treatment plants) to develop a workflow to identify and associate ARGs and MGEs. ARG- and MGE-based targeted-assemblies with available short-read data were unable to meet this analysis goal. In contrast, de novo assembly of contigs provided enough sequence context to associate ARGs and MGEs, without compromising discovery rate. However, to estimate the relative abundance of these elements, unassembled sequence data must still be used.

VIRify: An integrated detection, annotation and taxonomic classification pipeline using virus-specific protein profile hidden Markov models.

The study of viral communities has revealed the enormous diversity and impact these biological entities have on various ecosystems. These observations have sparked widespread interest in developing computational strategies that support the comprehensive characterisation of viral communities based on sequencing data. Here we introduce VIRify, a new computational pipeline designed to provide a user-friendly and accurate functional and taxonomic characterisation of viral communities. VIRify identifies viral contigs and prophages from metagenomic assemblies and annotates them using a collection of viral profile hidden Markov models (HMMs). These include our manually-curated profile HMMs, which serve as specific taxonomic markers for a wide range of prokaryotic and eukaryotic viral taxa and are thus used to reliably classify viral contigs. We tested VIRify on assemblies from two microbial mock communities, a large metagenomics study, and a collection of publicly available viral genomic sequences from the human gut. The results showed that VIRify could identify sequences from both prokaryotic and eukaryotic viruses, and provided taxonomic classifications from the genus to the family rank with an average accuracy of 86.6%. In addition, VIRify allowed the detection and taxonomic classification of a range of prokaryotic and eukaryotic viruses present in 243 marine metagenomic assemblies. Finally, the use of VIRify led to a large expansion in the number of taxonomically classified human gut viral sequences and the improvement of outdated and shallow taxonomic classifications. Overall, we demonstrate that VIRify is a novel and powerful resource that offers an enhanced capability to detect a broad range of viral contigs and taxonomically classify them.

BinaRena: a dedicated interactive platform for human-guided exploration and binning of metagenomes

BackgroundExploring metagenomic contigs and “binning” them into metagenome-assembled genomes (MAGs) are essential for the delineation of functional and evolutionary guilds within microbial communities. Despite the advances in automated binning algorithms, their capabilities in recovering MAGs with accuracy and biological relevance are so far limited. Researchers often find that human involvement is necessary to achieve representative binning results. This manual process however is expertise demanding and labor intensive, and it deserves to be supported by software infrastructure.ResultsWe present BinaRena, a comprehensive and versatile graphic interface dedicated to aiding human operators to explore metagenome assemblies via customizable visualization and to associate contigs with bins. Contigs are rendered as an interactive scatter plot based on various data types, including sequence metrics, coverage profiles, taxonomic assignments, and functional annotations. Various contig-level operations are permitted, such as selection, masking, highlighting, focusing, and searching. Binning plans can be conveniently edited, inspected, and compared visually or using metrics including silhouette coefficient and adjusted Rand index. Completeness and contamination of user-selected contigs can be calculated in real time.In demonstration of BinaRena’s usability, we show that it facilitated biological pattern discovery, hypothesis generation, and bin refinement in a complex tropical peatland metagenome. It enabled isolation of pathogenic genomes within closely related populations from the gut microbiota of diarrheal human subjects. It significantly improved overall binning quality after curating results of automated binners using a simulated marine dataset.ConclusionsBinaRena is an installation-free, dependency-free, client-end web application that operates directly in any modern web browser, facilitating ease of deployment and accessibility for researchers of all skill levels. The program is hosted at https://github.com/qiyunlab/binarena, together with documentation, tutorials, example data, and a live demo. It effectively supports human researchers in intuitive interpretation and fine tuning of metagenomic data.85aKgQNhGdzniqM56hYkLtVideo

Complex organic matter degradation by secondary consumers in chemolithoautotrophy-based subsurface geothermal ecosystems.

Microbial communities in terrestrial geothermal systems often contain chemolithoautotrophs with well-characterized distributions and metabolic capabilities. However, the extent to which organic matter produced by these chemolithoautotrophs supports heterotrophs remains largely unknown. Here we compared the abundance and activity of peptidases and carbohydrate active enzymes (CAZymes) that are predicted to be extracellular identified in metagenomic assemblies from 63 springs in the Central American and the Andean convergent margin (Argentinian backarc of the Central Volcanic Zone), as well as the plume-influenced spreading center in Iceland. All assemblies contain two orders of magnitude more peptidases than CAZymes, suggesting that the microorganisms more often use proteins for their carbon and/or nitrogen acquisition instead of complex sugars. The CAZy families in highest abundance are GH23 and CBM50, and the most abundant peptidase families are M23 and C26, all four of which degrade peptidoglycan found in bacterial cells. This implies that the heterotrophic community relies on autochthonous dead cell biomass, rather than allochthonous plant matter, for organic material. Enzymes involved in the degradation of cyanobacterial- and algal-derived compounds are in lower abundance at every site, with volcanic sites having more enzymes degrading cyanobacterial compounds and non-volcanic sites having more enzymes degrading algal compounds. Activity assays showed that many of these enzyme classes are active in these samples. High temperature sites (> 80°C) had similar extracellular carbon-degrading enzymes regardless of their province, suggesting a less well-developed population of secondary consumers at these sites, possibly connected with the limited extent of the subsurface biosphere in these high temperature sites. We conclude that in < 80°C springs, chemolithoautotrophic production supports heterotrophs capable of degrading a wide range of organic compounds that do not vary by geological province, even though the taxonomic and respiratory repertoire of chemolithoautotrophs and heterotrophs differ greatly across these regions.

Occurrence, fate and potential health risks of antibiotic resistomes in a constructed wetlands-reservoir ecosystem for drinking water source improvement

The development of effective and feasible engineering technologies to control the transmission of antibiotic resistance genes (ARGs) and pathogenic antibiotic-resistant bacteria (PARB) form drinking water sources is urgently needed for ensuring drinking water safety. In this study, metagenomic analysis was applied to systematically explore the full profiles, removal, and potential health risks of antibiotic resistomes in a large constructed wetlands-reservoir ecosystem (CWs-R) for drinking water source improvement. A total of 343 ARG subtypes belonging to 18 ARG types were identified from water and sediment samples in the CWs-R ecosystem, with an average abundance of 0.339 copies/cell, and bacitracin and multidrug resistance genes were the predominant ARG types in the water and sediment, respectively. The CWs-R ecosystem showed an excellent removal efficiency of ARGs and mobile genetic elements (MGEs) in water, with the total removal rate reaching 64.82 % and 77.09 %, respectively, among which the emergent plant zone and ecological storage unit played major roles. The metagenomic assembly tracked many mobile ARGs and opportunistic pathogens in the CWs-R ecosystem and identified 19 contigs as ARG-carrying pathogens, including Staphylococcus aureus, Salmonella enterica, Escherichia coli, and Klebsiella pneumonia. Overall, the CWs-R ecosystem has an important role in reducing the potential public health risks posed by antibiotic resistomes in drinking water sources but still cannot fully eliminate them. Therefore, we further classified water and sediment samples in the CWs-R ecosystem and identified potential ARGs and PARB indicators based on the metagenomic analysis results by considering the potential for horizontal transfer of ARGs to opportunistic pathogens. Taken together, this work demonstrates the CWs-R ecosystem as an economical and feasible engineering technology to reduce the dissemination of antibiotic resistomes in the drinking water source, provides useful information for monitoring and controlling antibiotic resistance in similar water sources, and ensures biosafety of drinking water.

HMSS2: An advanced tool for the analysis of sulphur metabolism, including organosulphur compound transformation, in genome and metagenome assemblies.

The global sulphur cycle has implications for human health, climate change, biogeochemistry and bioremediation. The organosulphur compounds that participate in this cycle not only represent a vast reservoir of sulphur but are also used by prokaryotes as sources of energy and/or carbon. Closely linked to the inorganic sulphur cycle, it involves the interaction of prokaryotes, eukaryotes and chemical processes. However, ecological and evolutionary studies of the conversion of organic sulphur compounds are hampered by the poor conservation of the relevant pathways and their variation even within strains of the same species. In addition, several proteins involved in the conversion of sulphonated compounds are related to proteins involved in sulphur dissimilation or turnover of other compounds. Therefore, the enzymes involved in the metabolism of organic sulphur compounds are usually not correctly annotated in public databases. To address this challenge, we have developed HMSS2, a profiled Hidden Markov Model-based tool for rapid annotation and synteny analysis of organic and inorganic sulphur cycle proteins in prokaryotic genomes. Compared to its previous version (HMS-S-S), HMSS2 includes several new features. HMM-based annotation is now supported by nonhomology criteria and covers the metabolic pathways of important organosulphur compounds, including dimethylsulphoniopropionate, taurine, isethionate, and sulphoquinovose. In addition, the calculation speed has been increased by a factor of four and the available output formats have been extended to include iTol compatible data sets, and customized sequence FASTA files.

Antibiotic resistome associated with inhalable bioaerosols from wastewater to atmosphere: Mobility, bacterial hosts, source contributions and resistome risk

Antibiotic resistome can be carried by the bioaerosols and propagate from wastewater treatment plants (WWTPs) to the atmosphere, but questions remain regarding their mobility, bacterial hosts, source, and resistome risk. Here, fine particulate matter (PM2.5) was collected within and around a large WWTP and analyzed by the metagenomic assembly and binning. PM2.5 was discovered with increasing enrichment of total antibiotic resistance genes (ARGs), potentially mobile ARGs, and antibiotic-resistant bacteria (ARB) along the WWTP-downwind-upwind gradient. Some ARGs were found to be flanked by certain mobile genetic elements and generally mediated by plasmids in WWTP-PM2.5. Totally, 198 metagenome assembled genomes assigning to seven phyla were identified as the ARB, and a contig-based analysis indicated that 32 pathogens were revealed harboring at least two ARGs. Despite disparate aerosolization potentials of ARGs or ARB at different WWTP units, high resistome risks were found, along with the dominant contribution of wastewater for airborne ARGs (44.79–62.82%) and ARB (35.03–40.10%). Among the detected WWTP matrices, the sludge dewatering room was characterized by the highest resistome risk associated with PM2.5. This study underscores the dispersion of ARGs and ARB from WWTPs to the atmosphere and provides a reference for managing risks of antibiotic resistance.

Seasonality Determines the Variations of Biofilm Microbiome and Antibiotic Resistome in a Pilot-Scale Chlorinated Drinking Water Distribution System Deciphered by Metagenome Assembly.

Understanding the biofilm microbiome and antibiotic resistome evolution in drinking water distribution systems (DWDSs) is crucial to ensure the safety of drinking water. We explored the 10 month evolution of the microbial community, antibiotic resistance genes (ARGs), mobile gene elements (MGEs) co-existing with ARGs and pathogenic ARG hosts, and the ARG driving factors in DWDS biofilms using metagenomics assembly. Sampling season was critical in determining the microbial community and antibiotic resistome shift. Pseudomonas was the primary biofilm colonizer, and biofilms diversified more as the formation time increased. Most genera tended to cooperate to adapt to an oligotrophic environment with disinfectant stress. Biofilm microbial community and antibiotic resistome assembly were mainly determined by stochastic processes and changed with season. Metagenome assembly provided the occurrence and fates of MGEs co-existing with ARGs and ARG hosts in DWDS biofilms. The abundance of ARG- and MGE-carrying pathogen Stenotrophomonas maltophilia was high in summer. It primarily harbored the aph(3)-IIb, multidrug transporter, smeD, and metallo-beta-lactamase ARGs, which were transferred via recombination. The microbial community was the most crucial factor driving the antibiotic resistance shift. We provide novel insights about the evolution of pathogens and ARGs and their correlations in DWDS biofilms to ensure the safety of drinking water.

PLASMe: a tool to identify PLASMid contigs from short-read assemblies using transformer.

Plasmids are mobile genetic elements that carry important accessory genes. Cataloging plasmids is a fundamental step to elucidate their roles in promoting horizontal gene transfer between bacteria. Next generation sequencing (NGS) is the main source for discovering new plasmids today. However, NGS assembly programs tend to return contigs, making plasmid detection difficult. This problem is particularly grave for metagenomic assemblies, which contain short contigs of heterogeneous origins. Available tools for plasmid contig detection still suffer from some limitations. In particular, alignment-based tools tend to miss diverged plasmids while learning-based tools often have lower precision. In this work, we develop a plasmid detection tool PLASMe that capitalizes on the strength of alignment and learning-based methods. Closely related plasmids can be easily identified using the alignment component in PLASMe while diverged plasmids can be predicted using order-specific Transformer models. By encoding plasmid sequences as a language defined on the protein cluster-based token set, Transformer can learn the importance of proteins and their correlation through positionally token embedding and the attention mechanism. We compared PLASMe and other tools on detecting complete plasmids, plasmid contigs, and contigs assembled from CAMI2 simulated data. PLASMe achieved the highest F1-score. After validating PLASMe on data with known labels, we also tested it on real metagenomic and plasmidome data. The examination of some commonly used marker genes shows that PLASMe exhibits more reliable performance than other tools.

Development of a field-deployable qPCR assay for real-time pest monitoring in algal cultivation systems

Outdoor cultivation is commonly used to produce algal biomass for a variety of bioproducts including food, feed, fuel, pharmaceuticals, and nutraceuticals. Outdoor cultivation ponds are highly susceptible to pest pressures that may lead to periods of low productivity or even entire loss of the algal crop. Therefore, there is a need for rapid, real-time tracking of pests for early intervention to mitigate crop loss. Herein, we describe the development of a field deployable, low-cost qPCR assay for detecting both known and novel pests of a farmed eukaryotic alga species, Nannochloropsis sp. We performed a proximity guided metagenome deconvolution approach (ProxiMeta™) to discover novel pests that temporally correspond to periods of reduced pond productivity. This approach provided high-quality metagenome assemblies that were used to design qPCR probes to detect specific pests of interest. The portable qPCR assay, designed to be deployed at remote field locations, enables low-cost surveillance with a rapid (2 h) turn-around time. Frequent sampling allows for early detection and prompts intervention strategies to remedy infected ponds to minimize crop loss. The qPCR assay was used to successfully detect a known predatory bacterium within the order Bdellovibrionales both in the lab and at a remote field location. Furthermore, we assembled the genome of two novel, site-specific pests in the Saprospiraceae family and successfully designed qPCR probes that differentially detected their presence in two different pond locations. Ultimately, this assay has the potential to monitor multiple pests simultaneously and tailor targets to match likely pest infections that differ across geographical locations, helping to mitigate crop loss on a large scale.

Two-Component System Sensor Kinases from Asgardian Archaea May Be Witnesses to Eukaryotic Cell Evolution

The signal transduction paradigm in bacteria involves two-component systems (TCSs). Asgardarchaeota are archaea that may have originated the current eukaryotic lifeforms. Most research on these archaea has focused on eukaryotic-like features, such as genes involved in phagocytosis, cytoskeleton structure, and vesicle trafficking. However, little attention has been given to specific prokaryotic features. Here, the sequence and predicted structural features of TCS sensor kinases analyzed from two metagenome assemblies and a genomic assembly from cultured Asgardian archaea are presented. The homology of the sensor kinases suggests the grouping of Lokiarchaeum closer to bacterial homologs. In contrast, one group from a Lokiarchaeum and a meta-genome assembly from Candidatus Heimdallarchaeum suggest the presence of a set of kinases separated from the typical bacterial TCS sensor kinases. AtoS and ArcB homologs were found in meta-genome assemblies along with defined domains for other well-characterized sensor kinases, suggesting the close link between these organisms and bacteria that may have resulted in the metabolic link to the establishment of symbiosis. Several kinases are predicted to be cytoplasmic; some contain several PAS domains. The data shown here suggest that TCS kinases in Asgardian bacteria are witnesses to the transition from bacteria to eukaryotic organisms.

Predicting variable gene content in Escherichia coli using conserved genes.

Having the ability to predict the protein-encoding gene content of an incomplete genome or metagenome-assembled genome is important for a variety of bioinformatic tasks. In this study, as a proof of concept, we built machine learning classifiers for predicting variable gene content in Escherichia coli genomes using only the nucleotide k-mers from a set of 100 conserved genes as features. Protein families were used to define orthologs, and a single classifier was built for predicting the presence or absence of each protein family occurring in 10%-90% of all E. coli genomes. The resulting set of 3,259 extreme gradient boosting classifiers had a per-genome average macro F1 score of 0.944 [0.943-0.945, 95% CI]. We show that the F1 scores are stable across multi-locus sequence types and that the trend can be recapitulated by sampling a smaller number of core genes or diverse input genomes. Surprisingly, the presence or absence of poorly annotated proteins, including "hypothetical proteins" was accurately predicted (F1 = 0.902 [0.898-0.906, 95% CI]). Models for proteins with horizontal gene transfer-related functions had slightly lower F1 scores but were still accurate (F1s = 0.895, 0.872, 0.824, and 0.841 for transposon, phage, plasmid, and antimicrobial resistance-related functions, respectively). Finally, using a holdout set of 419 diverse E. coli genomes that were isolated from freshwater environmental sources, we observed an average per-genome F1 score of 0.880 [0.876-0.883, 95% CI], demonstrating the extensibility of the models. Overall, this study provides a framework for predicting variable gene content using a limited amount of input sequence data. IMPORTANCE Having the ability to predict the protein-encoding gene content of a genome is important for assessing genome quality, binning genomes from shotgun metagenomic assemblies, and assessing risk due to the presence of antimicrobial resistance and other virulence genes. In this study, we built a set of binary classifiers for predicting the presence or absence of variable genes occurring in 10%-90% of all publicly available E. coli genomes. Overall, the results show that a large portion of the E. coli variable gene content can be predicted with high accuracy, including genes with functions relating to horizontal gene transfer. This study offers a strategy for predicting gene content using limited input sequence data.

Terabase-Scale Coassembly of a Tropical Soil Microbiome.

Petabases of environmental metagenomic data are publicly available, presenting an opportunity to characterize complex environments and discover novel lineages of life. Metagenome coassembly, in which many metagenomic samples from an environment are simultaneously analyzed to infer the underlying genomes' sequences, is an essential tool for achieving this goal. We applied MetaHipMer2, a distributed metagenome assembler that runs on supercomputing clusters, to coassemble 3.4 terabases (Tbp) of metagenome data from a tropical soil in the Luquillo Experimental Forest (LEF), Puerto Rico. The resulting coassembly yielded 39 high-quality (>90% complete, <5% contaminated, with predicted 23S, 16S, and 5S rRNA genes and ≥18 tRNAs) metagenome-assembled genomes (MAGs), including two from the candidate phylum Eremiobacterota. Another 268 medium-quality (≥50% complete, <10% contaminated) MAGs were extracted, including the candidate phyla Dependentiae, Dormibacterota, and Methylomirabilota. In total, 307 medium- or higher-quality MAGs were assigned to 23 phyla, compared to 294 MAGs assigned to nine phyla in the same samples individually assembled. The low-quality (<50% complete, <10% contaminated) MAGs from the coassembly revealed a 49% complete rare biosphere microbe from the candidate phylum FCPU426 among other low-abundance microbes, an 81% complete fungal genome from the phylum Ascomycota, and 30 partial eukaryotic MAGs with ≥10% completeness, possibly representing protist lineages. A total of 22,254 viruses, many of them low abundance, were identified. Estimation of metagenome coverage and diversity indicates that we may have characterized ≥87.5% of the sequence diversity in this humid tropical soil and indicates the value of future terabase-scale sequencing and coassembly of complex environments. IMPORTANCE Petabases of reads are being produced by environmental metagenome sequencing. An essential step in analyzing these data is metagenome assembly, the computational reconstruction of genome sequences from microbial communities. "Coassembly" of metagenomic sequence data, in which multiple samples are assembled together, enables more complete detection of microbial genomes in an environment than "multiassembly," in which samples are assembled individually. To demonstrate the potential for coassembling terabases of metagenome data to drive biological discovery, we applied MetaHipMer2, a distributed metagenome assembler that runs on supercomputing clusters, to coassemble 3.4 Tbp of reads from a humid tropical soil environment. The resulting coassembly, its functional annotation, and analysis are presented here. The coassembly yielded more, and phylogenetically more diverse, microbial, eukaryotic, and viral genomes than the multiassembly of the same data. Our resource may facilitate the discovery of novel microbial biology in tropical soils and demonstrates the value of terabase-scale metagenome sequencing.

Recovery of metagenomic data from the Aedes aegypti microbiome using a reproducible snakemake pipeline: MINUUR.

Background: Ongoing research of the mosquito microbiome aims to uncover novel strategies to reduce pathogen transmission. Sequencing costs, especially for metagenomics, are however still significant. A resource that is increasingly used to gain insights into host-associated microbiomes is the large amount of publicly available genomic data based on whole organisms like mosquitoes, which includes sequencing reads of the host-associated microbes and provides the opportunity to gain additional value from these initially host-focused sequencing projects. Methods: To analyse non-host reads from existing genomic data, we developed a snakemake workflow called MINUUR (Microbial INsights Using Unmapped Reads). Within MINUUR, reads derived from the host-associated microbiome were extracted and characterised using taxonomic classifications and metagenome assembly followed by binning and quality assessment. We applied this pipeline to five publicly available Aedes aegypti genomic datasets, consisting of 62 samples with a broad range of sequencing depths. Results: We demonstrate that MINUUR recovers previously identified phyla and genera and is able to extract bacterial metagenome assembled genomes (MAGs) associated to the microbiome. Of these MAGS, 42 are high-quality representatives with >90% completeness and <5% contamination. These MAGs improve the genomic representation of the mosquito microbiome and can be used to facilitate genomic investigation of key genes of interest. Furthermore, we show that samples with a high number of KRAKEN2 assigned reads produce more MAGs. Conclusions: Our metagenomics workflow, MINUUR, was applied to a range of Aedes aegypti genomic samples to characterise microbiome-associated reads. We confirm the presence of key mosquito-associated symbionts that have previously been identified in other studies and recovered high-quality bacterial MAGs. In addition, MINUUR and its associated documentation are freely available on GitHub and provide researchers with a convenient workflow to investigate microbiome data included in the sequencing data for any applicable host genome of interest.

Metagenomics insights into responses of rhizobacteria and their alleviation role in licorice allelopathy

BackgroundAllelopathy is closely associated with rhizosphere biological processes, and rhizosphere microbial communities are essential for plant development. However, our understanding of rhizobacterial communities under influence of allelochemicals in licorice remains limited. In the present study, the responses and effects of rhizobacterial communities on licorice allelopathy were investigated using a combination of multi-omics sequencing and pot experiments, under allelochemical addition and rhizobacterial inoculation treatments.ResultsHere, we demonstrated that exogenous glycyrrhizin inhibits licorice development, and reshapes and enriches specific rhizobacteria and corresponding functions related to glycyrrhizin degradation. Moreover, the Novosphingobium genus accounted for a relatively high proportion of the enriched taxa and appeared in metagenomic assembly genomes. We further characterized the different capacities of single and synthetic inoculants to degrade glycyrrhizin and elucidated their distinct potency for alleviating licorice allelopathy. Notably, the single replenished N (Novosphingobium resinovorum) inoculant had the greatest allelopathy alleviation effects in licorice seedlings.ConclusionsAltogether, the findings highlight that exogenous glycyrrhizin simulates the allelopathic autotoxicity effects of licorice, and indigenous single rhizobacteria had greater effects than synthetic inoculants in protecting licorice growth from allelopathy. The results of the present study enhance our understanding of rhizobacterial community dynamics during licorice allelopathy, with potential implications for resolving continuous cropping obstacle in medicinal plant agriculture using rhizobacterial biofertilizers.9UbUWYdP6gv58ro7UNXMiqVideo

Multiple lineages of nematode-Wolbachia symbiosis in supergroup F and convergent loss of bacterioferritin in filarial Wolbachia.

The intracellular endosymbiotic proteobacteria Wolbachia have evolved across the phyla nematoda and arthropoda. In Wolbachia phylogeny, supergroup F is the only clade known so far with members from both arthropod and filarial nematode hosts and therefore can provide unique insights into their evolution and biology. In this study, 4 new supergroup F Wolbachia genomes have been assembled using a metagenomic assembly and binning approach, wMoz and wMpe from the human filarial parasites Mansonella ozzardi and Mansonella perstans, and wOcae and wMoviF from the blue mason bee Osmia caerulescens and the sheep ked Melophagus ovinus respectively. A comprehensive phylogenomic analysis revealed two distinct lineages of filarial Wolbachia in supergroup F, indicating multiple horizontal transfer events between arthropod and nematode hosts. The analysis also reveals that the evolution of Wolbachia-filaria symbioses is accompanied by a convergent pseudogenization and loss of the bacterioferritin gene, a phenomenon found to be shared by all filarial Wolbachia, even those outside supergroup F. These observations indicate that differences in heme metabolism might be a key feature distinguishing filarial and arthropod Wolbachia. The new genomes provide a valuable resource for further studies on symbiosis, evolution, and the discovery of new antibiotics to treat mansonellosis.

Herbivorous Fish Microbiome Adaptations to Sulfated Dietary Polysaccharides.

Marine herbivorous fish that feed primarily on macroalgae, such as those from the genus Kyphosus, are essential for maintaining coral health and abundance on tropical reefs. Here, deep metagenomic sequencing and assembly of gut compartment-specific samples from three sympatric, macroalgivorous Hawaiian kyphosid species have been used to connect host gut microbial taxa with predicted protein functional capacities likely to contribute to efficient macroalgal digestion. Bacterial community compositions, algal dietary sources, and predicted enzyme functionalities were analyzed in parallel for 16 metagenomes spanning the mid- and hindgut digestive regions of wild-caught fishes. Gene colocalization patterns of expanded carbohydrate (CAZy) and sulfatase (SulfAtlas) digestive enzyme families on assembled contigs were used to identify likely polysaccharide utilization locus associations and to visualize potential cooperative networks of extracellularly exported proteins targeting complex sulfated polysaccharides. These insights into the gut microbiota of herbivorous marine fish and their functional capabilities improve our understanding of the enzymes and microorganisms involved in digesting complex macroalgal sulfated polysaccharides. IMPORTANCE This work connects specific uncultured bacterial taxa with distinct polysaccharide digestion capabilities lacking in their marine vertebrate hosts, providing fresh insights into poorly understood processes for deconstructing complex sulfated polysaccharides and potential evolutionary mechanisms for microbial acquisition of expanded macroalgal utilization gene functions. Several thousand new marine-specific candidate enzyme sequences for polysaccharide utilization have been identified. These data provide foundational resources for future investigations into suppression of coral reef macroalgal overgrowth, fish host physiology, the use of macroalgal feedstocks in terrestrial and aquaculture animal feeds, and the bioconversion of macroalgae biomass into value-added commercial fuel and chemical products.

ResMiCo: Increasing the quality of metagenome-assembled genomes with deep learning

The number of published metagenome assemblies is rapidly growing due to advances in sequencing technologies. However, sequencing errors, variable coverage, repetitive genomic regions, and other factors can produce misassemblies, which are challenging to detect for taxonomically novel genomic data. Assembly errors can affect all downstream analyses of the assemblies. Accuracy for the state of the art in reference-free misassembly prediction does not exceed an AUPRC of 0.57, and it is not clear how well these models generalize to real-world data. Here, we present the Residual neural network for Misassembled Contig identification (ResMiCo), a deep learning approach for reference-free identification of misassembled contigs. To develop ResMiCo, we first generated a training dataset of unprecedented size and complexity that can be used for further benchmarking and developments in the field. Through rigorous validation, we show that ResMiCo is substantially more accurate than the state of the art, and the model is robust to novel taxonomic diversity and varying assembly methods. ResMiCo estimated 7% misassembled contigs per metagenome across multiple real-world datasets. We demonstrate how ResMiCo can be used to optimize metagenome assembly hyperparameters to improve accuracy, instead of optimizing solely for contiguity. The accuracy, robustness, and ease-of-use of ResMiCo make the tool suitable for general quality control of metagenome assemblies and assembly methodology optimization.