Generating lineage-resolved, complete metagenome-assembled genomes from complex microbial communities

Metagenomic approaches and opportunities in arid soil research

Arid soils present unique challenges and opportunities for studying microbial diversity and bioactive potential due to the extreme environmental conditions they bear. This review article investigates soil metagenomics as an emerging tool to explore complex microbial dynamics and unexplored bioactive potential in harsh environments. Utilizing advanced metagenomic techniques, diverse microbial populations that grow under extreme conditions such as high temperatures, salinity, high pH levels, and exposure to metals and radiation can be studied. The use of extremophiles to discover novel natural products and biocatalysts emphasizes the role of functional metagenomics in identifying enzymes and secondary metabolites for industrial and pharmaceutical purposes. Metagenomic sequencing uncovers a complex network of microbial diversity, offering significant potential for discovering new bioactive compounds. Functional metagenomics, connecting taxonomic diversity to genetic capabilities, provides a pathway to identify microbes' mechanisms to synthesize valuable secondary metabolites and other bioactive substances. Contrary to the common perception of desert soil as barren land, the metagenomic analysis reveals a rich diversity of life forms adept at extreme survival. It provides valuable findings into their resilience and potential applications in biotechnology. Moreover, the challenges associated with metagenomics in arid soils, such as low microbial biomass, high DNA degradation rates, and DNA extraction inhibitors and strategies to overcome these issues, outline the latest advancements in extraction methods, high-throughput sequencing, and bioinformatics. The importance of metagenomics for investigating diverse environments opens the way for future research to develop sustainable solutions in agriculture, industry, and medicine. Extensive studies are necessary to utilize the full potential of these powerful microbial communities. This research will significantly improve our understanding of microbial ecology and biotechnology in arid environments.

Removal of sequencing adapter contamination improves microbial genome databases

Advances in assembling microbial genomes have led to growth of reference genome databases, which have been transformative for applied and basic microbiome research. Here we show that published microbial genome databases from humans, mice, cows, pigs, fish, honeybees, and marine environments contain significant sequencing-adapter contamination that systematically reduces assembly accuracy and contiguousness. By removing the adapter-contaminated ends of contiguous sequences and reassembling MGnify reference genomes, we improve the quality of assemblies in these databases.

Strainy: phasing and assembly of strain haplotypes from long-read metagenome sequencing

Bacterial species in microbial communities are often represented by mixtures of strains, distinguished by small variations in their genomes. Short-read approaches can be used to detect small-scale variation between strains but fail to phase these variants into contiguous haplotypes. Long-read metagenome assemblers can generate contiguous bacterial chromosomes but often suppress strain-level variation in favor of species-level consensus. Here we present Strainy, an algorithm for strain-level metagenome assembly and phasing from Nanopore and PacBio reads. Strainy takes a de novo metagenomic assembly as input and identifies strain variants, which are then phased and assembled into contiguous haplotypes. Using simulated and mock Nanopore and PacBio metagenome data, we show that Strainy assembles accurate and complete strain haplotypes, outperforming current Nanopore-based methods and comparable with PacBio-based algorithms in completeness and accuracy. We then use Strainy to assemble strain haplotypes of a complex environmental metagenome, revealing distinct strain distribution and mutational patterns in bacterial species.

Research on intensive nitrogen removal of municipal sewage by mainstream anaerobic ammonia oxidation process

The anaerobic ammonia oxidation (anammox) process is a pivotal nitrogen removal technique, playing a significant role in the field of wastewater treatment. The paper commences by delineating the merits of the anammox process in comparison to conventional nitrification-denitrification techniques. Subsequently, it delves into the characteristics of different sludge morphologies process of the behavior of anammox bacteria and their reactions to environmental factors. Revising the issues associated with managing urban sewage in mainstream areas., it discusses the issues faced by the anammox process under reduced nitrogen loads, such as restricted activity due to decreased the levels of ammonia nitrogen and nitrite concentrations, as well as the impact of environmental factors like low temperature, organic matter, and sulfur ions. Following this, a comprehensive review of various types of coupled anammox processes is provided, highlighting the advantages and characteristics of partial nitrification (PN), partial denitrification (PD), methane-dependent nitrite/nitrate reduction (DAMO), sulfur-driven autotrophic denitrification (SAD), iron ammonia oxidation (feammox) and algae photoautotrophy coupling techniques, emphasizing their significance in system stability and resource utilization efficiency. Future research directions include exploring the applicability of the anammox process under various temperature conditions and addressing NO3--N issues in effluent. The findings from these studies will offer valuable insights for further enhancing the optimization of the anammox process in mainstream urban wastewater treatment.

Seamless, rapid, and accurate analyses of outbreak genomic data using split k-mer analysis

Sequence variation observed in populations of pathogens can be used for important public health and evolutionary genomic analyses, especially outbreak analysis and transmission reconstruction. Identifying this variation is typically achieved by aligning sequence reads to a reference genome, but this approach is susceptible to reference biases and requires careful filtering of called genotypes. There is a need for tools that can process this growing volume of bacterial genome data, providing rapid results, but that remain simple so they can be used without highly trained bioinformaticians, expensive data analysis, and long-term storage and processing of large files. Here we describe split k-mer analysis (SKA2), a method that supports both reference-free and reference-based mapping to quickly and accurately genotype populations of bacteria using sequencing reads or genome assemblies. SKA2 is highly accurate for closely related samples, and in outbreak simulations, we show superior variant recall compared with reference-based methods, with no false positives. SKA2 can also accurately map variants to a reference and be used with recombination detection methods to rapidly reconstruct vertical evolutionary history. SKA2 is many times faster than comparable methods and can be used to add new genomes to an existing call set, allowing sequential use without the need to reanalyze entire collections. With an inherent absence of reference bias, high accuracy, and a robust implementation, SKA2 has the potential to become the tool of choice for genotyping bacteria. SKA2 is implemented in Rust and is freely available as open-source software.

Metagenomic approach reveals the role of bioagents in the environmental dissemination risk of rhizosphere soil antibiotic resistance genes pollution

Antibiotic resistance genes (ARGs) have been identified as emerging contaminants, raising concerns around the world. As environmentally friendly bioagents (BA), plant growth-promoting rhizobacteria (PGPR) have been used in agricultural systems. The introduction of BA will lead to the turnover of the microbial communities structure. Nevertheless, it is still unclear how the colonization of the invaded microorganisms could affects the rhizosphere resistome. Consequently, 190 ARGs and 25 integrative and conjugative elements (ICEs) were annotated using the metagenomic approach in 18 samples from the Solanaceae crop rhizosphere soil under BA and conventional treatment (CK) groups. Our study found that, after 90 days of treatment, ARG abundance was lower in the CK group than in the BA group. The results showed that aminoglycoside antibiotic resistance (OprZ), phenicol antibiotic resistance (OprN), aminoglycoside antibiotic resistance (ceoA/B), aminocoumarin antibiotic resistance (mdtB) and phenicol antibiotic resistance (MexW) syntenic with ICEs. Moreover, in 11 sequences, OprN (phenicol antibiotic resistance) was observed to have synteny with ICEPaeLESB58-1, indicating that the ICEs could contribute to the spread of ARGs. Additionally, the binning result showed that the potential bacterial hosts of the ARGs were beneficial bacteria which could promote the nutrition cycle, such as Haliangium, Nitrospira, Sideroxydans, Burkholderia, etc, suggesting that bacterial hosts have a great influence on ARG profiles. According to the findings, considering the dissemination of ARGs, BA should be applied with caution, especially the use of beneficial bacteria in BA. In a nutshell, this study offers valuable insights into ARGs pollution control from the perspective of the development and application of BA, to make effective strategies for blocking pollution risk migration in the ecological environment.

Imputing Metagenomic Hi-C Contacts Facilitates the Integrative Contig Binning Through Constrained Random Walk with Restart

Metagenomic Hi-C (metaHi-C) has shown remarkable potential for retrieving high-quality metagenome-assembled genomes from complex microbial communities. Nevertheless, existing metaHi-C-based contig binning methods solely rely on Hi-C interactions between contigs, disregarding crucial biological information such as the presence of single-copy marker genes. To overcome this limitation, we introduce ImputeCC, an integrative contig binning tool optimized for metaHi-C datasets. ImputeCC integrates both Hi-C interactions and the discriminative power of single-copy marker genes to group marker-gene-containing contigs into preliminary bins. It also introduces a novel constrained random walk with restart algorithm to enhance Hi-C connectivity among contigs. Comprehensive assessments using both mock and real metaHi-C datasets from diverse environments demonstrate that ImputeCC consistently outperforms other Hi-C-based contig binning tools. A genus-level analysis of the sheep gut microbiota reconstructed by ImputeCC underlines its capability to recover key species from dominant genera and identify previously unknown genera.

Efficient and easy-to-use capturing three-dimensional metagenome interactions with GutHi-C

Hi-C can obtain three-dimensional chromatin structure information and is widely used for genome assembly. We constructed the GutHi-C technology. As shown in the graphical abstract, it is a highly efficient and quick-to-operate method and can be widely used for human, livestock, and poultry gut microorganisms. It provides a reference for the Hi-C methodology of the microbial metagenome. DPBS, Dulbecco's phosphate-buffered saline; Hi-C, high-through chromatin conformation capture; LB, Luria-Bertani; NGS, next-generation sequencing; PCR, polymerase chain reaction; QC, quality control.

DeepCheck: multitask learning aids in assessing microbial genome quality

Metagenomic analyses facilitate the exploration of the microbial world, advancing our understanding of microbial roles in ecological and biological processes. A pivotal aspect of metagenomic analysis involves assessing the quality of metagenome-assembled genomes (MAGs), crucial for accurate biological insights. Current machine learning-based methods often treat completeness and contamination prediction as separate tasks, overlooking their inherent relationship and limiting models' generalization. In this study, we present DeepCheck, a multitasking deep learning framework for simultaneous prediction of MAG completeness and contamination. DeepCheck consistently outperforms existing tools in accuracy across various experimental settings and demonstrates comparable speed while maintaining high predictive accuracy even for new lineages. Additionally, we employ interpretable machine learning techniques to identify specific genes and pathways that drive the model's predictions, enabling independent investigation and assessment of these biological elements for deeper insights.

An expanded database and analytical toolkit for identifying bacterial virulence factors and their associations with chronic diseases

Virulence factor genes (VFGs) play pivotal roles in bacterial infections and have been identified within the human gut microbiota. However, their involvement in chronic diseases remains poorly understood. Here, we establish an expanded VFG database (VFDB 2.0) consisting of 62,332 nonredundant orthologues and alleles of VFGs using species-specific average nucleotide identity ( https://github.com/Wanting-Dong/MetaVF_toolkit/tree/main/databases ). We further develop the MetaVF toolkit, facilitating the precise identification of pathobiont-carried VFGs at the species level. A thorough characterization of VFGs for 5452 commensal isolates from healthy individuals reveals that only 11 of 301 species harbour these factors. Further analyses of VFGs within the gut microbiomes of nine chronic diseases reveal both common and disease-specific VFG features. Notably, in type 2 diabetes patients, long HiFi sequencing confirms that shared VF features are carried by pathobiont strains of Escherichia coli and Klebsiella pneumoniae. These findings underscore the critical importance of identifying and understanding VFGs in microbiome-associated diseases.

High-quality metagenome assembly from long accurate reads with metaMDBG

We introduce metaMDBG, a metagenomics assembler for PacBio HiFi reads. MetaMDBG combines a de Bruijn graph assembly in a minimizer space with an iterative assembly over sequences of minimizers to address variations in genome coverage depth and an abundance-based filtering strategy to simplify strain complexity. For complex communities, we obtained up to twice as many high-quality circularized prokaryotic metagenome-assembled genomes as existing methods and had better recovery of viruses and plasmids.

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.

SkinCom, a synthetic skin microbial community, enables reproducible investigations of the human skin microbiome

Existing models of the human skin have aided our understanding of skin health and disease. However, they currently lack a microbial component, despite microbes' demonstrated connections to various skin diseases. Here, we present a robust, standardized model of the skin microbial community (SkinCom) to support in vitro and in vivo investigations. Our methods lead to the formation of an accurate, reproducible, and diverse community of aerobic and anaerobic bacteria. Subsequent testing of SkinCom on the dorsal skin of mice allowed for DNA and RNA recovery from both the applied SkinCom and the dorsal skin, highlighting its practicality for in vivo studies and -omics analyses. Furthermore, 66% of the responses to common cosmetic chemicals in vitro were in agreement with a human trial. Therefore, SkinCom represents a valuable, standardized tool for investigating microbe-metabolite interactions and facilitates the experimental design of in vivo studies targeting host-microbe relationships.

Patterns of genomic variation reveal a single evolutionary origin of the wild allotetraploid Mimulus sookensis

Polyploidy occurs across the tree of life and is especially common in plants. Because newly formed cytotypes are often incompatible with their progenitors, polyploidy is also said to trigger "instantaneous" speciation. If a polyploid can self-fertilize or reproduce asexually, it is even possible for one individual to produce an entirely new lineage, but how often this scenario occurs is unclear. Here, we investigate the evolutionary history of the wild allotetraploid Mimulus sookensis, which was formed through hybridization between self-compatible, diploid species in the Mimulus guttatus complex. We generate a chromosome-scale reference assembly for M. sookensis and define its distinct subgenomes. Despite previous reports suggesting multiple origins of this highly selfing polyploid, we discover patterns of population genomic variation that provide unambiguous support for a single origin. One M. sookensis subgenome is clearly derived from the selfer Mimulus nasutus, which organellar variation suggests is the maternal progenitor. The ancestor of the other subgenome is less certain, but it shares variation with both Mimulus decorus and M. guttatus, two outcrossing diploids with geographic ranges that overlap broadly with M. sookensis. This study establishes M. sookensis as an example of instantaneous speciation, likely facilitated by the polyploid's predisposition to self-fertilize.

Proxi-RIMS-seq2 applied to native microbiomes uncovers hundreds of known and novel m5C methyltransferase specificities

Methylation patterns in bacteria can be used to study Restriction-Modification (RM) or other defense systems with novel properties. While m4C and m6A methylation is well characterized mainly through PacBio sequencing, the landscape of m5C methylation is under-characterized. To bridge this gap, we performed RIMS-seq2 on microbiomes composed of resolved assemblies of distinct genomes through proximity ligation. This high-throughput approach enables the identification of m5C methylated motifs and links them to cognate methyltransferases directly on native microbiomes without the need to isolate bacterial strains. Methylation patterns can also be identified on viral DNA and compared to host DNA, strengthening evidence for virus-host interaction. Applied to three different microbiomes, the method unveils over 1900 motifs that were deposited in REBASE. The motifs include a novel 8-base recognition site (CATm5CGATG) that was experimentally validated by characterizing its cognate methyltransferase. Our findings suggest that microbiomes harbor arrays of untapped m5C methyltransferase specificities, providing insights to bacterial biology and biotechnological applications.

Genomic representativeness and chimerism in large collections of SAGs and MAGs of marine prokaryoplankton

BACKGROUND

Single amplified genomes (SAGs) and metagenome-assembled genomes (MAGs) are the predominant sources of information about the coding potential of uncultured microbial lineages, but their strengths and limitations remain poorly understood. Here, we performed a direct comparison of two previously published collections of thousands of SAGs and MAGs obtained from the same, global environment.

RESULTS

We found that SAGs were less prone to chimerism and more accurately reflected the relative abundance and the pangenome content of microbial lineages inhabiting the epipelagic of the tropical and subtropical ocean, as compared to MAGs. SAGs were also better suited to link genome information with taxa discovered through 16S rRNA amplicon analyses. Meanwhile, MAGs had the advantage of more readily recovering genomes of rare lineages.

CONCLUSIONS

Our analyses revealed the relative strengths and weaknesses of the two most commonly used genome recovery approaches in environmental microbiology. These considerations, as well as the need for better tools for genome quality assessment, should be taken into account when designing studies and interpreting data that involve SAGs or MAGs. Video Abstract.

Deep metagenomic sequencing unveils novel SAR202 lineages and their vertical adaptation in the ocean

SAR202 bacteria in the Chloroflexota phylum are abundant and widely distributed in the ocean. Their genome coding capacities indicate their potential roles in degrading complex and recalcitrant organic compounds in the ocean. However, our understanding of their genomic diversity, vertical distribution, and depth-related metabolisms is still limited by the number of assembled SAR202 genomes. In this study, we apply deep metagenomic sequencing (180 Gb per sample) to investigate microbial communities collected from six representative depths at the Bermuda Atlantic Time Series (BATS) station. We obtain 173 SAR202 metagenome-assembled genomes (MAGs). Intriguingly, 154 new species and 104 new genera are found based on these 173 SAR202 genomes. We add 12 new subgroups to the current SAR202 lineages. The vertical distribution of 20 SAR202 subgroups shows their niche partitioning in the euphotic, mesopelagic, and bathypelagic oceans, respectively. Deep-ocean SAR202 bacteria contain more genes and exhibit more metabolic potential for degrading complex organic substrates than those from the euphotic zone. With deep metagenomic sequencing, we uncover many new lineages of SAR202 bacteria and their potential functions which greatly deepen our understanding of their diversity, vertical profile, and contribution to the ocean's carbon cycling, especially in the deep ocean.

Benchmarking short and long read polishing tools for nanopore assemblies: achieving near-perfect genomes for outbreak isolates

BACKGROUND

Oxford Nanopore provides high throughput sequencing platforms able to reconstruct complete bacterial genomes with 99.95% accuracy. However, even small levels of error can obscure the phylogenetic relationships between closely related isolates. Polishing tools have been developed to correct these errors, but it is uncertain if they obtain the accuracy needed for the high-resolution source tracking of foodborne illness outbreaks.

RESULTS

We tested 132 combinations of assembly and short- and long-read polishing tools to assess their accuracy for reconstructing the genome sequences of 15 highly similar Salmonella enterica serovar Newport isolates from a 2020 onion outbreak. While long-read polishing alone improved accuracy, near perfect accuracy (99.9999% accuracy or ~ 5 nucleotide errors across the 4.8 Mbp genome, excluding low confidence regions) was only obtained by pipelines that combined both long- and short-read polishing tools. Notably, medaka was a more accurate and efficient long-read polisher than Racon. Among short-read polishers, NextPolish showed the highest accuracy, but Pilon, Polypolish, and POLCA performed similarly. Among the 5 best performing pipelines, polishing with medaka followed by NextPolish was the most common combination. Importantly, the order of polishing tools mattered i.e., using less accurate tools after more accurate ones introduced errors. Indels in homopolymers and repetitive regions, where the short reads could not be uniquely mapped, remained the most challenging errors to correct.

CONCLUSIONS

Short reads are still needed to correct errors in nanopore sequenced assemblies to obtain the accuracy required for source tracking investigations. Our granular assessment of the performance of the polishing pipelines allowed us to suggest best practices for tool users and areas for improvement for tool developers.

Microbial diversity and ecological complexity emerging from environmental variation and horizontal gene transfer in a simple mathematical model

BACKGROUND

Microbiomes are generally characterized by high diversity of coexisting microbial species and strains, and microbiome composition typically remains stable across a broad range of conditions. However, under fixed conditions, microbial ecology conforms with the exclusion principle under which two populations competing for the same resource within the same niche cannot coexist because the less fit population inevitably goes extinct. Therefore, the long-term persistence of microbiome diversity calls for an explanation.

RESULTS

To explore the conditions for stabilization of microbial diversity, we developed a simple mathematical model consisting of two competing populations that could exchange a single gene allele via horizontal gene transfer (HGT). We found that, although in a fixed environment, with unbiased HGT, the system obeyed the exclusion principle, in an oscillating environment, within large regions of the phase space bounded by the rates of reproduction and HGT, the two populations coexist. Moreover, depending on the parameter combination, all three major types of symbiosis were obtained, namely, pure competition, host-parasite relationship, and mutualism. In each of these regimes, certain parameter combinations provided for synergy, that is, a greater total abundance of both populations compared to the abundance of the winning population in the fixed environment.

CONCLUSIONS

The results of this modeling study show that basic phenomena that are universal in microbial communities, namely, environmental variation and HGT, provide for stabilization and persistence of microbial diversity, and emergence of ecological complexity.

Unravelling biosynthesis and biodegradation potentials of microbial dark matters in hypersaline lakes

Biosynthesis and biodegradation of microorganisms critically underpin the development of biotechnology, new drugs and therapies, and environmental remediation. However, most uncultured microbial species along with their metabolic capacities in extreme environments, remain obscured. Here we unravel the metabolic potential of microbial dark matters (MDMs) in four deep-inland hypersaline lakes in Xinjiang, China. Utilizing metagenomic binning, we uncovered a rich diversity of 3030 metagenome-assembled genomes (MAGs) across 82 phyla, revealing a substantial portion, 2363 MAGs, as previously unclassified at the genus level. These unknown MAGs displayed unique distribution patterns across different lakes, indicating a strong correlation with varied physicochemical conditions. Our analysis revealed an extensive array of 9635 biosynthesis gene clusters (BGCs), with a remarkable 9403 being novel, suggesting untapped biotechnological potential. Notably, some MAGs from potentially new phyla exhibited a high density of these BGCs. Beyond biosynthesis, our study also identified novel biodegradation pathways, including dehalogenation, anaerobic ammonium oxidation (Anammox), and degradation of polycyclic aromatic hydrocarbons (PAHs) and plastics, in previously unknown microbial clades. These findings significantly enrich our understanding of biosynthesis and biodegradation processes and open new avenues for biotechnological innovation, emphasizing the untapped potential of microbial diversity in hypersaline environments.

Hybrid sequence-based analysis reveals the distribution of bacterial species and genes in the oral microbiome at a high resolution

Bacteria in the oral microbiome are poorly identified owing to the lack of established culture methods for them. Thus, this study aimed to use culture-free analysis techniques, including bacterial single-cell genome sequencing, to identify bacterial species and investigate gene distribution in saliva. Saliva samples from the same individual were classified as inactivated or viable and then analyzed using 16S rRNA sequencing, metagenomic shotgun sequencing, and bacterial single-cell sequencing. The results of 16S rRNA sequencing revealed similar microbiota structures in both samples, with Streptococcus being the predominant genus. Metagenomic shotgun sequencing showed that approximately 80 % of the DNA in the samples was of non-bacterial origin, whereas single-cell sequencing showed an average contamination rate of 10.4 % per genome. Single-cell sequencing also yielded genome sequences for 43 out of 48 wells for the inactivated samples and 45 out of 48 wells for the viable samples. With respect to resistance genes, four out of 88 isolates carried cfxA, which encodes a β-lactamase, and four isolates carried erythromycin resistance genes. Tetracycline resistance genes were found in nine bacteria. Metagenomic shotgun sequencing provided complete sequences of cfxA, ermF, and ermX, whereas other resistance genes, such as tetQ and tetM, were detected as fragments. In addition, virulence factors from Streptococcus pneumoniae were the most common, with 13 genes detected. Our average nucleotide identity analysis also suggested five single-cell-isolated bacteria as potential novel species. These data would contribute to expanding the oral microbiome data resource.

Biosynthetic potential of uncultured anammox community bacteria revealed through multi-omics analysis

Microbial secondary metabolites (SMs) and their derivatives have been widely used in medicine, agriculture, and energy. Growing needs for renewable energy and the challenges posed by antibiotic resistance, cancer, and pesticides emphasize the crucial hunt for new SMs. Anaerobic ammonium-oxidation (anammox) systems harbor many uncultured or underexplored bacteria, representing potential resources for discovering novel SMs. Leveraging HiFi long-read metagenomic sequencing, 1,040 biosynthetic gene clusters (BGCs) were unearthed from the anammox microbiome with 58% being complete and showcasing rich diversity. Most of them showed distant relations to known BGCs, implying novelty. Members of the underexplored lineages (Chloroflexota and Planctomycetota) and Proteobacteria contained lots of BGCs, showcasing substantial biosynthetic potential. Metaproteomic results indicated that Planctomycetota members harbored the most active BGCs, particularly those involved in producing potential biofuel-ladderane. Overall, these findings underscore that anammox microbiomes could serve as valuable resources for mining novel BGCs and discovering new SMs for practical application.

Unveiling microbial diversity: harnessing long-read sequencing technology

Long-read sequencing has recently transformed metagenomics, enhancing strain-level pathogen characterization, enabling accurate and complete metagenome-assembled genomes, and improving microbiome taxonomic classification and profiling. These advancements are not only due to improvements in sequencing accuracy, but also happening across rapidly changing analysis methods. In this Review, we explore long-read sequencing's profound impact on metagenomics, focusing on computational pipelines for genome assembly, taxonomic characterization and variant detection, to summarize recent advancements in the field and provide an overview of available analytical methods to fully leverage long reads. We provide insights into the advantages and disadvantages of long reads over short reads and their evolution from the early days of long-read sequencing to their recent impact on metagenomics and clinical diagnostics. We further point out remaining challenges for the field such as the integration of methylation signals in sub-strain analysis and the lack of benchmarks.

New insights into functional divergence and adaptive evolution of uncultured bacteria in anammox community by complete genome-centric analysis

Anaerobic ammonium-oxidation (anammox) bacteria play a crucial role in global nitrogen cycling and wastewater nitrogen removal, but they share symbiotic relationships with various other microorganisms. Functional divergence and adaptive evolution of uncultured bacteria in anammox community remain underexplored. Although shotgun metagenomics based on short reads has been widely used in anammox research, metagenome-assembled genomes (MAGs) are often discontinuous and highly contaminated, which limits in-depth analyses of anammox communities. Here, for the first time, we performed Pacific Biosciences high-fidelity (HiFi) long-read sequencing on the anammox granule sludge sample from a lab-scale bioreactor, and obtained 30 accurate and complete metagenome-assembled genomes (cMAGs). These cMAGs were obtained by selecting high-quality circular contigs from initial assemblies of long reads generated by HiFi sequencing, eliminating the need for Illumina short reads, binning, and reassembly. One new anammox species affiliated with Candidatus Jettenia and three species affiliated with novel families were found in this anammox community. cMAG-centric analysis revealed functional divergence in general and nitrogen metabolism among the anammox community members, and they might adopt a cross-feeding strategy in organic matter, cofactors, and vitamins. Furthermore, we identified 63 mobile genetic elements (MGEs) and 50 putative horizontal gene transfer (HGT) events within these cMAGs. The results suggest that HGT events and MGEs related to phage and integration or excision, particularly transposons containing tnpA in anammox bacteria, might play important roles in the adaptive evolution of this anammox community. The cMAGs generated in the present study could be used to establish of a comprehensive database for anammox bacteria and associated microorganisms. These findings highlight the advantages of HiFi sequencing for the studies of complex mixed cultures and advance the understanding of anammox communities.

Evaluating and improving the representation of bacterial contents in long-read metagenome assemblies

BACKGROUND

In the metagenomic assembly of a microbial community, abundant species are often thought to assemble well given their deeper sequencing coverage. This conjuncture is rarely tested or evaluated in practice. We often do not know how many abundant species are missing and do not have an approach to recover them.

RESULTS

Here, we propose k-mer based and 16S RNA based methods to measure the completeness of metagenome assembly. We show that even with PacBio high-fidelity (HiFi) reads, abundant species are often not assembled, as high strain diversity may lead to fragmented contigs. We develop a novel reference-free algorithm to recover abundant metagenome-assembled genomes (MAGs) by identifying circular assembly subgraphs. Complemented with a reference-free genome binning heuristics based on dimension reduction, the proposed method rescues many abundant species that would be missing with existing methods and produces competitive results compared to those state-of-the-art binners in terms of total number of near-complete genome bins.

CONCLUSIONS

Our work emphasizes the importance of metagenome completeness, which has often been overlooked. Our algorithm generates more circular MAGs and moves a step closer to the complete representation of microbial communities.

Contribution of the patient microbiome to surgical site infection and antibiotic prophylaxis failure in spine surgery

Despite modern antiseptic techniques, surgical site infection (SSI) remains a leading complication of surgery. However, the origins of SSI and the high rates of antimicrobial resistance observed in these infections are poorly understood. Using instrumented spine surgery as a model of clean (class I) skin incision, we prospectively sampled preoperative microbiomes and postoperative SSI isolates in a cohort of 204 patients. Combining multiple forms of genomic analysis, we correlated the identity, anatomic distribution, and antimicrobial resistance profiles of SSI pathogens with those of preoperative strains obtained from the patient skin microbiome. We found that 86% of SSIs, comprising a broad range of bacterial species, originated endogenously from preoperative strains, with no evidence of common source infection among a superset of 1610 patients. Most SSI isolates (59%) were resistant to the prophylactic antibiotic administered during surgery, and their resistance phenotypes correlated with the patient's preoperative resistome (P = 0.0002). These findings indicate the need for SSI prevention strategies tailored to the preoperative microbiome and resistome present in individual patients.

A comparison of short-read, HiFi long-read, and hybrid strategies for genome-resolved metagenomics

Shotgun metagenomics enables the reconstruction of complex microbial communities at a high level of detail. Such an approach can be conducted using both short-read and long-read sequencing data, as well as a combination of both. To assess the pros and cons of these different approaches, we used 22 fecal DNA extracts collected weekly for 11 weeks from two respective lab mice to study seven performance metrics over four combinations of sequencing depth and technology: (i) 20 Gbp of Illumina short-read data, (ii) 40 Gbp of short-read data, (iii) 20 Gbp of PacBio HiFi long-read data, and (iv) 40 Gbp of hybrid (20 Gbp of short-read +20 Gbp of long-read) data. No strategy was best for all metrics; instead, each one excelled across different metrics. The long-read approach yielded the best assembly statistics, with the highest N50 and lowest number of contigs. The 40 Gbp short-read approach yielded the highest number of refined bins. Finally, the hybrid approach yielded the longest assemblies and the highest mapping rate to the bacterial genomes. Our results suggest that while long-read sequencing significantly improves the quality of reconstructed bacterial genomes, it is more expensive and requires deeper sequencing than short-read approaches to recover a comparable amount of reconstructed genomes. The most optimal strategy is study-specific and depends on how researchers assess the trade-off between the quantity and quality of recovered genomes.IMPORTANCEMice are an important model organism for understanding the gut microbiome. When studying these gut microbiomes using DNA techniques, researchers can choose from technologies that use short or long DNA reads. In this study, we perform an extensive benchmark between short- and long-read DNA sequencing for studying mice gut microbiomes. We find that no one approach was best for all metrics and provide information that can help guide researchers in planning their experiments.

Nanopore and Illumina sequencing reveal different viral populations from human gut samples

The advent of viral metagenomics, or viromics, has improved our knowledge and understanding of global viral diversity. High-throughput sequencing technologies enable explorations of the ecological roles, contributions to host metabolism, and the influence of viruses in various environments, including the human intestinal microbiome. However, bacterial metagenomic studies frequently have the advantage. The adoption of advanced technologies like long-read sequencing has the potential to be transformative in refining viromics and metagenomics. Here, we examined the effectiveness of long-read and hybrid sequencing by comparing Illumina short-read and Oxford Nanopore Technology (ONT) long-read sequencing technologies and different assembly strategies on recovering viral genomes from human faecal samples. Our findings showed that if a single sequencing technology is to be chosen for virome analysis, Illumina is preferable due to its superior ability to recover fully resolved viral genomes and minimise erroneous genomes. While ONT assemblies were effective in recovering viral diversity, the challenges related to input requirements and the necessity for amplification made it less ideal as a standalone solution. However, using a combined, hybrid approach enabled a more authentic representation of viral diversity to be obtained within samples.

Comprehensive assessment of 11 de novo HiFi assemblers on complex eukaryotic genomes and metagenomes

Pacific Biosciences (PacBio) HiFi sequencing technology generates long reads (>10 kbp) with very high accuracy (<0.01% sequencing error). Although several de novo assembly tools are available for HiFi reads, there are no comprehensive studies on the evaluation of these assemblers. We evaluated the performance of 11 de novo HiFi assemblers on (1) real data for three eukaryotic genomes; (2) 34 synthetic data sets with different ploidy, sequencing coverage levels, heterozygosity rates, and sequencing error rates; (3) one real metagenomic data set; and (4) five synthetic metagenomic data sets with different composition abundance and heterozygosity rates. The 11 assemblers were evaluated using quality assessment tool (QUAST) and benchmarking universal single-copy ortholog (BUSCO). We also used several additional criteria, namely, completion rate, single-copy completion rate, duplicated completion rate, average proportion of largest category, average distance difference, quality value, run-time, and memory utilization. Results show that hifiasm and hifiasm-meta should be the first choice for assembling eukaryotic genomes and metagenomes with HiFi data. We performed a comprehensive benchmarking study of commonly used assemblers on complex eukaryotic genomes and metagenomes. Our study will help the research community to choose the most appropriate assembler for their data and identify possible improvements in assembly algorithms.

BASALT refines binning from metagenomic data and increases resolution of genome-resolved metagenomic analysis

Metagenomic binning is an essential technique for genome-resolved characterization of uncultured microorganisms in various ecosystems but hampered by the low efficiency of binning tools in adequately recovering metagenome-assembled genomes (MAGs). Here, we introduce BASALT (Binning Across a Series of Assemblies Toolkit) for binning and refinement of short- and long-read sequencing data. BASALT employs multiple binners with multiple thresholds to produce initial bins, then utilizes neural networks to identify core sequences to remove redundant bins and refine non-redundant bins. Using the same assemblies generated from Critical Assessment of Metagenome Interpretation (CAMI) datasets, BASALT produces up to twice as many MAGs as VAMB, DASTool, or metaWRAP. Processing assemblies from a lake sediment dataset, BASALT produces ~30% more MAGs than metaWRAP, including 21 unique class-level prokaryotic lineages. Functional annotations reveal that BASALT can retrieve 47.6% more non-redundant opening-reading frames than metaWRAP. These results highlight the robust handling of metagenomic sequencing data of BASALT.

MCSS: microbial community simulator based on structure

De novo assembly plays a pivotal role in metagenomic analysis, and the incorporation of third-generation sequencing technology can significantly improve the integrity and accuracy of assembly results. Recently, with advancements in sequencing technology (Hi-Fi, ultra-long), several long-read-based bioinformatic tools have been developed. However, the validation of the performance and reliability of these tools is a crucial concern. To address this gap, we present MCSS (microbial community simulator based on structure), which has the capability to generate simulated microbial community and sequencing datasets based on the structure attributes of real microbiome communities. The evaluation results indicate that it can generate simulated communities that exhibit both diversity and similarity to actual community structures. Additionally, MCSS generates synthetic PacBio Hi-Fi and Oxford Nanopore Technologies (ONT) long reads for the species within the simulated community. This innovative tool provides a valuable resource for benchmarking and refining metagenomic analysis methods. Code available at: https://github.com/panlab-bio/mcss.

Horizontal transfer potential of antibiotic resistance genes in wastewater treatment plants unraveled by microfluidic-based mini-metagenomics

Wastewater treatment plants (WWTPs) are known to harbor antibiotic resistance genes (ARGs), which can potentially spread to the environment and human populations. However, the extent and mechanisms of ARG transfer in WWTPs are not well understood due to the high microbial diversity and limitations of molecular techniques. In this study, we used a microfluidic-based mini-metagenomics approach to investigate the transfer potential and mechanisms of ARGs in activated sludge from WWTPs. Our results show that while diverse ARGs are present in activated sludge, only a few highly similar ARGs are observed across different taxa, indicating limited transfer potential. We identified two ARGs, ermF and tla-1, which occur in a variety of bacterial taxa and may have high transfer potential facilitated by mobile genetic elements. Interestingly, genes that are highly similar to the sequences of these two ARGs, as identified in this study, display varying patterns of abundance across geographic regions. Genes similar to ermF found are widely found in Asia and the Americas, while genes resembling tla-1 are primarily detected in Asia. Genes similar to both genes are barely detected in European WWTPs. These findings shed light on the limited horizontal transfer potential of ARGs in WWTPs and highlight the importance of monitoring specific ARGs in different regions to mitigate the spread of antibiotic resistance.

Life at the borderlands: microbiomes of interfaces critical to One Health

Microbiomes are foundational components of the environment that provide essential services relating to food security, carbon sequestration, human health, and the overall well-being of ecosystems. Microbiota exert their effects primarily through complex interactions at interfaces with their plant, animal, and human hosts, as well as within the soil environment. This review aims to explore the ecological, evolutionary, and molecular processes governing the establishment and function of microbiome-host relationships, specifically at interfaces critical to One Health-a transdisciplinary framework that recognizes that the health outcomes of people, animals, plants, and the environment are tightly interconnected. Within the context of One Health, the core principles underpinning microbiome assembly will be discussed in detail, including biofilm formation, microbial recruitment strategies, mechanisms of microbial attachment, community succession, and the effect these processes have on host function and health. Finally, this review will catalogue recent advances in microbiology and microbial ecology methods that can be used to profile microbial interfaces, with particular attention to multi-omic, advanced imaging, and modelling approaches. These technologies are essential for delineating the general and specific principles governing microbiome assembly and functions, mapping microbial interconnectivity across varying spatial and temporal scales, and for the establishment of predictive frameworks that will guide the development of targeted microbiome-interventions to deliver One Health outcomes.

MarkerScan: Separation and assembly of cobionts sequenced alongside target species in biodiversity genomics projects

Contamination of public databases by mislabelled sequences has been highlighted for many years and the avalanche of novel sequencing data now being deposited has the potential to make databases difficult to use effectively. It is therefore crucial that sequencing projects and database curators perform pre-submission checks to remove obvious contamination and avoid propagating erroneous taxonomic relationships. However, it is important also to recognise that biological contamination of a target sample with unexpected species' DNA can also lead to the discovery of fascinating biological phenomena through the identification of environmental organisms or endosymbionts. Here, we present a novel, integrated method for detection and generation of high-quality genomes of all non-target genomes co-sequenced in eukaryotic genome sequencing projects. After performing taxonomic profiling of an assembly from the raw data, and leveraging the identity of small rRNA sequences discovered therein as markers, a targeted classification approach retrieves and assembles high-quality genomes. The genomes of these cobionts are then not only removed from the target species' genome but also available for further interrogation. Source code is available from https://github.com/CobiontID/MarkerScan. MarkerScan is written in Python and is deployed as a Docker container.

Tools for microbial single-cell genomics for obtaining uncultured microbial genomes

The advent of next-generation sequencing technologies has facilitated the acquisition of large amounts of DNA sequence data at a relatively low cost, leading to numerous breakthroughs in decoding microbial genomes. Among the various genome sequencing activities, metagenomic analysis, which entails the direct analysis of uncultured microbial DNA, has had a profound impact on microbiome research and has emerged as an indispensable technology in this field. Despite its valuable contributions, metagenomic analysis is a "bulk analysis" technique that analyzes samples containing a wide diversity of microbes, such as bacteria, yielding information that is averaged across the entire microbial population. In order to gain a deeper understanding of the heterogeneous nature of the microbial world, there is a growing need for single-cell analysis, similar to its use in human cell biology. With this paradigm shift in mind, comprehensive single-cell genomics technology has become a much-anticipated innovation that is now poised to revolutionize microbiome research. It has the potential to enable the discovery of differences at the strain level and to facilitate a more comprehensive examination of microbial ecosystems. In this review, we summarize the current state-of-the-art in microbial single-cell genomics, highlighting the potential impact of this technology on our understanding of the microbial world. The successful implementation of this technology is expected to have a profound impact in the field, leading to new discoveries and insights into the diversity and evolution of microbes.

Microbial diversity and ecological complexity emerging from environmental variation and horizontal gene transfer in a simple mathematical model

Microbiomes are generally characterized by high diversity of coexisting microbial species and strains that remains stable within a broad range of conditions. However, under fixed conditions, microbial ecology conforms with the exclusion principle under which two populations competing for the same resource within the same niche cannot coexist because the less fit population inevitably goes extinct. To explore the conditions for stabilization of microbial diversity, we developed a simple mathematical model consisting of two competing populations that could exchange a single gene allele via horizontal gene transfer (HGT). We found that, although in a fixed environment, with unbiased HGT, the system obeyed the exclusion principle, in an oscillating environment, within large regions of the phase space bounded by the rates of reproduction and HGT, the two populations coexist. Moreover, depending on the parameter combination, all three major types of symbiosis obtained, namely, pure competition, host-parasite relationship and mutualism. In each of these regimes, certain parameter combinations provided for synergy, that is, a greater total abundance of both populations compared to the abundance of the winning population in the fixed environments. These findings show that basic phenomena that are universal in microbial communities, environmental variation and HGT, provide for stabilization of microbial diversity and ecological complexity.

Hidden genomic diversity drives niche partitioning in a cosmopolitan eukaryotic picophytoplankton

Marine eukaryotic phytoplankton are fundamental to the marine food web, yet the lack of reference genomes or just a single genome representing a taxon has led to an underestimation of their taxonomic, adaptive, and functional diversity. Here, we integrated strain isolation with metagenomic binning to recover genomes from the cosmopolitan picophytoplankton genus Bathycoccus, traditionally considered monospecific. Our recovery and analysis of 37 Bathycoccus genomes delineated their global genomic diversity and established four evolutionary clades (BI, BII, BIII, BIV). Our metagenomic abundance survey revealed well-differentiated ecological niches and distinct biogeographic distributions for each clade, predominantly shaped by temperature, salinity, and nutrient availability. Comparative genomics analyses further revealed clade-specific genomic traits that underpin niche adaptation and contribute to the global prevalence of Bathycoccus. Our findings underscore temperature as a major driver of genome diversification in this genus, with clade divergences coinciding with major paleoclimatic events that influenced their contemporary thermal niches. Moreover, the unique enrichment of C2H2 zinc finger and ankyrin repeat gene families in polar-adapted clades suggests previously unrecognized cold-adaptation mechanisms in marine eukaryotic phytoplankton. Our study offers a comprehensive genomic landscape of this crucial eukaryotic picophytoplankton, providing insights into their microdiversity and adaptive evolution in response to changing environments.

RefSeq and the prokaryotic genome annotation pipeline in the age of metagenomes

The Reference Sequence (RefSeq) project at the National Center for Biotechnology Information (NCBI) contains over 315 000 bacterial and archaeal genomes and 236 million proteins with up-to-date and consistent annotation. In the past 3 years, we have expanded the diversity of the RefSeq collection by including the best quality metagenome-assembled genomes (MAGs) submitted to INSDC (DDBJ, ENA and GenBank), while maintaining its quality by adding validation checks. Assemblies are now more stringently evaluated for contamination and for completeness of annotation prior to acceptance into RefSeq. MAGs now account for over 17000 assemblies in RefSeq, split over 165 orders and 362 families. Changes in the Prokaryotic Genome Annotation Pipeline (PGAP), which is used to annotate nearly all RefSeq assemblies include better detection of protein-coding genes. Nearly 83% of RefSeq proteins are now named by a curated Protein Family Model, a 4.7% increase in the past three years ago. In addition to literature citations, Enzyme Commission numbers, and gene symbols, Gene Ontology terms are now assigned to 48% of RefSeq proteins, allowing for easier multi-genome comparison. RefSeq is found at https://www.ncbi.nlm.nih.gov/refseq/. PGAP is available as a stand-alone tool able to produce GenBank-ready files at https://github.com/ncbi/pgap.

Community-scale models of microbiomes: Articulating metabolic modelling and metagenome sequencing

Building models is essential for understanding the functions and dynamics of microbial communities. Metabolic models built on genome-scale metabolic network reconstructions (GENREs) are especially relevant as a means to decipher the complex interactions occurring among species. Model reconstruction increasingly relies on metagenomics, which permits direct characterisation of naturally occurring communities that may contain organisms that cannot be isolated or cultured. In this review, we provide an overview of the field of metabolic modelling and its increasing reliance on and synergy with metagenomics and bioinformatics. We survey the means of assigning functions and reconstructing metabolic networks from (meta-)genomes, and present the variety and mathematical fundamentals of metabolic models that foster the understanding of microbial dynamics. We emphasise the characterisation of interactions and the scaling of model construction to large communities, two important bottlenecks in the applicability of these models. We give an overview of the current state of the art in metagenome sequencing and bioinformatics analysis, focusing on the reconstruction of genomes in microbial communities. Metagenomics benefits tremendously from third-generation sequencing, and we discuss the opportunities of long-read sequencing, strain-level characterisation and eukaryotic metagenomics. We aim at providing algorithmic and mathematical support, together with tool and application resources, that permit bridging the gap between metagenomics and metabolic modelling.

From microbiome composition to functional engineering, one step at a time

SUMMARYCommunities of microorganisms (microbiota) are present in all habitats on Earth and are relevant for agriculture, health, and climate. Deciphering the mechanisms that determine microbiota dynamics and functioning within the context of their respective environments or hosts (the microbiomes) is crucially important. However, the sheer taxonomic, metabolic, functional, and spatial complexity of most microbiomes poses substantial challenges to advancing our knowledge of these mechanisms. While nucleic acid sequencing technologies can chart microbiota composition with high precision, we mostly lack information about the functional roles and interactions of each strain present in a given microbiome. This limits our ability to predict microbiome function in natural habitats and, in the case of dysfunction or dysbiosis, to redirect microbiomes onto stable paths. Here, we will discuss a systematic approach (dubbed the N+1/N-1 concept) to enable step-by-step dissection of microbiome assembly and functioning, as well as intervention procedures to introduce or eliminate one particular microbial strain at a time. The N+1/N-1 concept is informed by natural invasion events and selects culturable, genetically accessible microbes with well-annotated genomes to chart their proliferation or decline within defined synthetic and/or complex natural microbiota. This approach enables harnessing classical microbiological and diversity approaches, as well as omics tools and mathematical modeling to decipher the mechanisms underlying N+1/N-1 microbiota outcomes. Application of this concept further provides stepping stones and benchmarks for microbiome structure and function analyses and more complex microbiome intervention strategies.

De Novo Synthesis of a Conjugative System from Human Gut Metagenomic Data for Targeted Delivery of Cas9 Antimicrobials

Metagenomic sequences represent an untapped source of genetic novelty, particularly for conjugative systems that could be used for plasmid-based delivery of Cas9-derived antimicrobial agents. However, unlocking the functional potential of conjugative systems purely from metagenomic sequences requires the identification of suitable candidate systems as starting scaffolds for de novo DNA synthesis. Here, we developed a bioinformatics approach that searches through the metagenomic "trash bin" for genes associated with conjugative systems present on contigs that are typically excluded from common metagenomic analysis pipelines. Using a human metagenomic gut data set representing 2805 taxonomically distinct units, we identified 1598 contigs containing conjugation genes with a differential distribution in human cohorts. We synthesized de novo an entire Citrobacter spp. conjugative system of 54 kb containing at least 47 genes and assembled it into a plasmid, pCitro. We found that pCitro conjugates from Escherichia coli to Citrobacter rodentium with a 30-fold higher frequency than to E. coli, and is compatible with Citrobacter resident plasmids. Mutations in the traV and traY conjugation components of pCitro inhibited conjugation. We showed that pCitro can be repurposed as an antimicrobial delivery agent by programming it with the TevCas9 nuclease and Citrobacter-specific sgRNAs to kill C. rodentium. Our study reveals a trove of uncharacterized conjugative systems in metagenomic data and describes an experimental framework to animate these large genetic systems as novel target-adapted delivery vectors for Cas9-based editing of bacterial genomes.

Mosaic environment-driven evolution of the deep-sea mussel Gigantidas platifrons bacterial endosymbiont

BACKGROUND

The within-species diversity of symbiotic bacteria represents an important genetic resource for their environmental adaptation, especially for horizontally transmitted endosymbionts. Although strain-level intraspecies variation has recently been detected in many deep-sea endosymbionts, their ecological role in environmental adaptation, their genome evolution pattern under heterogeneous geochemical environments, and the underlying molecular forces remain unclear.

RESULTS

Here, we conducted a fine-scale metagenomic analysis of the deep-sea mussel Gigantidas platifrons bacterial endosymbiont collected from distinct habitats: hydrothermal vent and methane seep. Endosymbiont genomes were assembled using a pipeline that distinguishes within-species variation and revealed highly heterogeneous compositions in mussels from different habitats. Phylogenetic analysis separated the assemblies into three distinct environment-linked clades. Their functional differentiation follows a mosaic evolutionary pattern. Core genes, essential for central metabolic function and symbiosis, were conserved across all clades. Clade-specific genes associated with heavy metal resistance, pH homeostasis, and nitrate utilization exhibited signals of accelerated evolution. Notably, transposable elements and plasmids contributed to the genetic reshuffling of the symbiont genomes and likely accelerated adaptive evolution through pseudogenization and the introduction of new genes.

CONCLUSIONS

The current study uncovers the environment-driven evolution of deep-sea symbionts mediated by mobile genetic elements. Its findings highlight a potentially common and critical role of within-species diversity in animal-microbiome symbioses. Video Abstract.

A novel culture-enriched metagenomic sequencing strategy effectively guarantee the microbial safety of drinking water by uncovering the low abundance pathogens

Assessing the presence of waterborne pathogens and antibiotic resistance genes (ARGs) is crucial for managing the environmental quality of drinking water sources. However, detecting low abundance pathogens in such settings is challenging. In this study, a workflow was developed to enrich for broad spectrum pathogens from drinking water samples. A mock community was used to evaluate the effectiveness of various enrichment broths in detecting low-abundance pathogens. Monthly metagenomic surveillance was conducted in a drinking water source from May to September 2021, and water samples were subjected to five enrichment procedures for 6 h to recover the majority of waterborne bacterial pathogens. Oxford Nanopore Technology (ONT) was used for metagenomic sequencing of enriched samples to obtain high-quality pathogen genomes. The results showed that selective enrichment significantly increased the proportions of targeted bacterial pathogens. Compared to direct metagenomic sequencing of untreated water samples, targeted enrichment followed by ONT sequencing significantly improved the detection of waterborne pathogens and the quality of metagenome-assembled genomes (MAGs). Eighty-six high-quality MAGs, including 70 pathogen MAGs, were obtained from ONT sequencing, while only 12 MAGs representing 10 species were obtained from direct metagenomic sequencing of untreated water samples. In addition, ONT sequencing improved the recovery of mobile genetic elements and the accuracy of phylogenetic analysis. This study highlights the urgent need for efficient methodologies to detect and manage microbial risks in drinking water sources. The developed workflow provides a cost-effective approach for environmental management of drinking water sources with microbial risks. The study also uncovered pathogens that were not detected by traditional methods, thereby advancing microbial risk management of drinking water sources.

Recovery of 52 bacterial genomes from the fecal microbiome of the domestic cat (Felis catus) using Hi-C proximity ligation and shotgun metagenomics

We used Hi-C proximity ligation with shotgun sequencing to retrieve metagenome-assembled genomes (MAGs) from the fecal microbiomes of two domestic cats (Felis catus). The genomes were assessed for completeness and contamination, classified taxonomically, and annotated for putative antimicrobial resistance (AMR) genes.

Identification of errors in draft genome assemblies at single-nucleotide resolution for quality assessment and improvement

Assembly of a high-quality genome is important for downstream comparative and functional genomic studies. However, most tools for genome assembly assessment only give qualitative reports, which do not pinpoint assembly errors at specific regions. Here, we develop a new reference-free tool, Clipping information for Revealing Assembly Quality (CRAQ), which maps raw reads back to assembled sequences to identify regional and structural assembly errors based on effective clipped alignment information. Error counts are transformed into corresponding assembly evaluation indexes to reflect the assembly quality at single-nucleotide resolution. Notably, CRAQ distinguishes assembly errors from heterozygous sites or structural differences between haplotypes. This tool can clearly indicate low-quality regions and potential structural error breakpoints; thus, it can identify misjoined regions that should be split for further scaffold building and improvement of the assembly. We have benchmarked CRAQ on multiple genomes assembled using different strategies, and demonstrated the misjoin correction for improving the constructed pseudomolecules.

MetaCC allows scalable and integrative analyses of both long-read and short-read metagenomic Hi-C data

Metagenomic Hi-C (metaHi-C) can identify contig-to-contig relationships with respect to their proximity within the same physical cell. Shotgun libraries in metaHi-C experiments can be constructed by next-generation sequencing (short-read metaHi-C) or more recent third-generation sequencing (long-read metaHi-C). However, all existing metaHi-C analysis methods are developed and benchmarked on short-read metaHi-C datasets and there exists much room for improvement in terms of more scalable and stable analyses, especially for long-read metaHi-C data. Here we report MetaCC, an efficient and integrative framework for analyzing both short-read and long-read metaHi-C datasets. MetaCC outperforms existing methods on normalization and binning. In particular, the MetaCC normalization module, named NormCC, is more than 3000 times faster than the current state-of-the-art method HiCzin on a complex wastewater dataset. When applied to one sheep gut long-read metaHi-C dataset, MetaCC binning module can retrieve 709 high-quality genomes with the largest species diversity using one single sample, including an expansion of five uncultured members from the order Erysipelotrichales, and is the only binner that can recover the genome of one important species Bacteroides vulgatus. Further plasmid analyses reveal that MetaCC binning is able to capture multi-copy plasmids.

Biosynthetic gene cluster synteny: Orthologous polyketide synthases in Hypogymnia physodes, Hypogymnia tubulosa, and Parmelia sulcata

Lichens are symbiotic associations consisting of a photobiont (algae or cyanobacteria) and a mycobiont (fungus), which together generate a variety of unique secondary metabolites. To access this biosynthetic potential for biotechnological applications, deeper insights into the biosynthetic pathways and corresponding gene clusters are necessary. Here, we provide a comparative view of the biosynthetic gene clusters of three lichen mycobionts derived from Hypogymnia physodes, Hypogymnia tubulosa, and Parmelia sulcata. In addition, we present a high-quality PacBio metagenome of Parmelia sulcata, from which we extracted the mycobiont bin containing 214 biosynthetic gene clusters. Most biosynthetic gene clusters in these genomes were associated with T1PKSs, followed by NRPSs and terpenes. This study focused on biosynthetic gene clusters related to polyketide synthesis. Based on ketosynthase homology, we identified nine highly syntenic clusters present in all three species. Among the four clusters belonging to nonreducing PKSs, two are putatively linked to lichen substances derived from orsellinic acid (orcinol depsides and depsidones, e.g., lecanoric acid, physodic acid, lobaric acid), one to compounds derived from methylated forms of orsellinic acid (beta orcinol depsides, e.g., atranorin), and one to melanins. Five clusters with orthologs in all three species are linked to reducing PKSs. Our study contributes to sorting and dereplicating the vast PKS diversity found in lichenized fungi. High-quality sequences of biosynthetic gene clusters of these three common species provide a foundation for further exploration into biotechnological applications and the molecular evolution of lichen substances.

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.

Capturing the microbial dark matter in desert soils using culturomics-based metagenomics and high-resolution analysis

Deserts occupy one-third of the Earth's terrestrial surface and represent a potentially significant reservoir of microbial biodiversity, yet the majority of desert microorganisms remain uncharacterized and are seen as "microbial dark matter". Here, we introduce a multi-omics strategy, culturomics-based metagenomics (CBM) that integrates large-scale cultivation, full-length 16S rRNA gene amplicon, and shotgun metagenomic sequencing. The results showed that CBM captured a significant amount of taxonomic and functional diversity missed in direct sequencing by increasing the recovery of amplicon sequence variants (ASVs) and high/medium-quality metagenome-assembled genomes (MAGs). Importantly, CBM allowed the post hoc recovery of microbes of interest (e.g., novel or specific taxa), even those with extremely low abundance in the culture. Furthermore, strain-level analyses based on CBM and direct sequencing revealed that the desert soils harbored a considerable number of novel bacterial candidates (1941, 51.4%), of which 1095 (from CBM) were culturable. However, CBM would not exactly reflect the relative abundance of true microbial composition and functional pathways in the in situ environment, and its use coupled with direct metagenomic sequencing could provide greater insight into desert microbiomes. Overall, this study exemplifies the CBM strategy with high-resolution is an ideal way to deeply explore the untapped novel bacterial resources in desert soils, and substantially expands our knowledge on the microbial dark matter hidden in the vast expanse of deserts.

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.