Evaluating metagenomics tools for genome binning with real metagenomic datasets and CAMI datasets

PCVR: a pre-trained contextualized visual representation for DNA sequence classification

BACKGROUND

The classification of DNA sequences is pivotal in bioinformatics, essentially for genetic information analysis. Traditional alignment-based tools tend to have slow speed and low recall. Machine learning methods learn implicit patterns from data with encoding techniques such as k-mer counting and ordinal encoding, which fail to handle long sequences or sacrifice structural and sequential information. Frequency chaos game representation (FCGR) converts DNA sequences of arbitrary lengths into fixed-size images, breaking free from the constraints of sequence length while preserving more sequential information than other representations. However, existing works merely consider local information, ignoring long-range dependencies and global contextual information within FCGR image.

RESULTS

We propose PCVR, a Pre-trained Contextualized Visual Representation for DNA sequence classification. PCVR encodes FCGR with a vision transformer into contextualized features containing more global information. To meet the substantial data requirements of the training of vision transformer and learn more robust features, we pre-train the encoder with a masked autoencoder. Pre-trained PCVR exhibits impressive performance on three datasets even with only unsupervised learning. After fine-tuning, PCVR outperforms existing methods on superkingdom and phylum levels. Additionally, our ablation studies confirm the contribution of the vision transformer encoder and masked autoencoder pre-training to performance improvement.

CONCLUSIONS

PCVR significantly improves DNA sequence classification accuracy and shows strong potential for new species discovery due to its effective capture of global information and robustness. Codes for PCVR are available at https://github.com/jiaruizhou/PCVR .

Community structure and metabolic potentials of keystone taxa and their associated bacteriophages within rice root endophytic microbiome in response to metal(loid)s contamination

Heavy metal (HM) contamination of agricultural products is of global environmental concern as it directly threatened the food safety. Plant-associated microbiome, particularly endophytic microbiome, hold the potential for mitigating HM stress as well as promoting plant growth. The metabolic potentials of the endophytes, especially those under the HM stresses, have not been well addressed. Rice, a major staple food worldwide, is more vulnerable to HM contamination compared to other crops and therefore requires special attentions. Therefore, this study selected rice as the target plants. Geochemical analysis and amplicon sequencing were combined to characterize the rice root endophytic bacterial communities and identify keystone taxa in two HM-contaminated rice fields. Metagenomic analysis was employed to investigate the metabolic potentials of these keystone taxa. Burkholderiales and Rhizobiales were identified as predominant keystone taxa. The metagenome-assembled genome (MAG)s associated with these keystone populations suggested that they possessed diverse genetic potentials related to metal resistance and transformation (e.g., As resistance and cycling, V reduction, Cr efflux and reduction), and plant growth promotion (nitrogen fixation, phosphate solubilization, oxidative stress resistance, indole-3-acetic acid, and siderophore production). Moreover, bacteriophages encoding auxiliary metabolism genes (AMGs) associated with the HM resistance as well as nitrogen and phosphate acquisition were identified, suggesting that these phages may contribute to these crucial biogeochemical processes within rice roots. The current findings revealed the beneficial roles of rice endophytic keystone taxa and their associated bacteriophages within HM-contaminated rice root endophytic microbiome, which may provide valuable insights on future applications of employing root microbiome for safety management of agriculture productions.

Bioinformatic approaches to blood and tissue microbiome analyses: challenges and perspectives

Advances in next-generation sequencing have resulted in a growing understanding of the microbiome and its role in human health. Unlike traditional microbiome analysis, blood and tissue microbiome analyses focus on the detection and characterization of microbial DNA in blood and tissue, previously considered a sterile environment. In this review, we discuss the challenges and methodologies associated with analyzing these samples, particularly emphasizing blood and tissue microbiome research. Key preprocessing steps-including the removal of ribosomal RNA, host DNA, and other contaminants-are critical to reducing noise and accurately capturing microbial evidence. We also explore how taxonomic profiling tools, machine learning, and advanced normalization techniques address contamination and low microbial biomass, thereby improving reliability. While it offers the potential for identifying microbial involvement in systemic diseases previously undetectable by traditional methods, this methodology also carries risks and lacks universal acceptance due to concerns over reliability and interpretation errors. This paper critically reviews these factors, highlighting both the promise and pitfalls of using blood and tissue microbiome analyses as a tool for biomarker discovery.

Antiviral defense systems in the rumen microbiome

The continuous interaction between phages and their respective hosts has resulted in the evolution of multiple bacterial immune mechanisms. However, the diversity and prevalence of antiviral defense systems in complex communities are still unknown. We therefore investigated the diversity and abundance of viral defense systems in 3,038 high-quality bacterial and archaeal genomes from the rumen. In total, 14,241 defense systems and 31,948 antiviral-related genes were identified. Those genes represented 114 unique system types grouped into 49 families. We observed a high prevalence of defense systems in the genomes. However, the number of defense systems, defense system families, and system density varied widely from genome to genome. Additionally, the number of defense system per genome correlated positively with the number of defense system families and the genome size. Restriction modification, Abi, and cas system families were the most common, but many rare systems were present in only 1% of the genomes. Antiviral defense systems are prevalent and diverse in the rumen, but only a few are dominant, indicating that most systems are rarely present. However, the collection of systems throughout the rumen may represent a pool of mechanisms that can be shared by different members of the community and modulate the phage-host interaction.IMPORTANCEPhages may act antagonistically at the cell level but have a mutualistic interaction at the microbiome level. This interaction shapes the structure of microbial communities and is mainly driven by the defense mechanism. However, the diversity of such mechanism is larger than previously thought. Because of that, we described the abundance and diversity of the antiviral defense system of a collection of genomes, metagenome-assembled genomes (MAGs) and isolates, from the rumen. While defense mechanisms seem to be prevalent among bacteria and archaea, only a few were common. This suggests that most of these defense mechanisms are not present in many rumen microbes but could be shared among different members of the microbial community. This is consistent with the "pan-immune system" model, which appears to be common across different environments.

Evaluating the potential of assembler-binner combinations in recovering low-abundance and strain-resolved genomes from human metagenomes

Human-associated microbial communities are a complex mixture of bacterial species and diverse strains prevalent at varying abundances. Due to the inherent limitations of metagenomic assemblers and genome binning tools in recovering low-abundance species (<1 %) and strains, we lack comprehensive insight into these communities. Although many bioinformatics approaches are available for recovering metagenome-assembled genomes, their effectiveness in recovering low-abundance species and strains is often questioned. Moreover, each tool has its trade-offs, making selecting the right tools challenging. In this study, we investigated the combinatory effect of various assemblers and binning tools on the recovery of low-abundance species and strain-resolved genomes from real and simulated human metagenomes. We evaluated the performance of nine combinations of metagenome assemblers and genome binning tools for their potential to recover genomes of useable quality. Our results revealed that the metaSPAdes-MetaBAT2 combination is highly effective in recovering low-abundance species, while MEGAHIT-MetaBAT2 excels in recovering strain-resolved genomes. These findings highlight the significant variation in the performance of different combinations, even when aiming for the same objective. This suggests the profound impact of selecting the right assembler-binner combination for metagenome analyses. We believe this study will be a cornerstone for the scientific community, guiding the choice of tools by highlighting their complementary effects. Furthermore, it underscores the potential of existing tools to address the current challenges in the field improving the recovery of information from metagenomes.

Microbial communities associated with marine sponges from diverse geographic locations harbor biosynthetic novelty

Marine sponges are a prolific source of biologically active small molecules, many of which originate from sponge-associated bacteria. Identifying the producing bacteria is a key step in developing sustainable routes for the production of these metabolites. To facilitate the required computational analyses, we developed MetaSing, a reproducible singularity-based pipeline for assembly, identification of high-quality metagenome-assembled genomes (MAGs), and analysis of biosynthetic gene clusters (BGCs) from metagenomic short-read data. We applied this pipeline to metagenomic sequencing data from 16 marine sponges collected from New Zealand, Tonga, and the Mediterranean Sea. This analysis yielded 643 MAGs representing 510 species. Of the 2,670 BGCs identified across all samples, 70.8% were linked to a MAG. Comparison of BGCs to those identified from previously sequenced bacteria revealed high biosynthetic novelty in variety of underexplored phyla, including Poribacteria, Acidobacteriota, and Dadabacteria. Alongside the observation that each sample contains unique biosynthetic potential, this holds great promise for natural product discovery and for furthering the understanding of different sponge holobionts.IMPORTANCEDiscovery of new chemical compounds such as natural products is a crucial endeavor to combat the increasing resistance to antibiotics and other drugs. This manuscript demonstrates that microbial communities associated with marine sponges investigated in this work encode the potential to produce novel chemistry. Lesser studied bacterial taxa that are often difficult to cultivate are particularly rich in potential.

Visualizing metagenomic and metatranscriptomic data: A comprehensive review

The fields of Metagenomics and Metatranscriptomics involve the examination of complete nucleotide sequences, gene identification, and analysis of potential biological functions within diverse organisms or environmental samples. Despite the vast opportunities for discovery in metagenomics, the sheer volume and complexity of sequence data often present challenges in processing analysis and visualization. This article highlights the critical role of advanced visualization tools in enabling effective exploration, querying, and analysis of these complex datasets. Emphasizing the importance of accessibility, the article categorizes various visualizers based on their intended applications and highlights their utility in empowering bioinformaticians and non-bioinformaticians to interpret and derive insights from meta-omics data effectively.

Microbiota in lymph nodes of cattle harvested in a Canadian meat processing plant

Lymph nodes (LN) harboring bacteria, when being incorporated into ground beef, may impact the microbial safety and quality of such products. We tested two main foodborne pathogens Salmonella and Shiga toxin-producing Escherichia coli (STEC) and profiled the microbiota in LNs (n = 160) of cattle harvested at a Canadian abattoir, by conventional plating methods, PCR, and high throughput sequencing. LNs at two anatomical locations, subiliac and popliteal from 80 cattle were included. All cattle had bacteria detected in popliteal and/or subiliac LNs with the maximum bacterial load of 5.4 and 2.8 log10CFU/g in popliteal and subiliac LNs, respectively. Neither Salmonella nor STEC was found in LNs although STEC was detected in a significant percentage of samples from beef hides (50.6 %) by plating and/or PCR. Both 16S rRNA gene amplicon and metagenome sequencing found the predominance of Escherichia (13-34.6 % among bacterial community), Clostridium (12.6-20.6 %) and Streptococcus (9.7-10 %) in popliteal LNs. Metagenomic sequencing was able to identify the predominant taxa at species level with E. coli (13 %), Clostridium perfringens (11.1 %) and Streptococcus uberis (6 %) predominant in LNs. Low prevalence/abundance of Salmonella was found by metagenomic sequencing. In conclusion, the relatively high bacterial load and diversity in LNs may affect the shelf life of ground beef and high relative abundance of E. coli would warrant further monitoring.

The Role and Applications of Artificial Intelligence in the Treatment of Chronic Pain

PURPOSE OF REVIEW

This review aims to explore the interface between artificial intelligence (AI) and chronic pain, seeking to identify areas of focus for enhancing current treatments and yielding novel therapies.

RECENT FINDINGS

In the United States, the prevalence of chronic pain is estimated to be upwards of 40%. Its impact extends to increased healthcare costs, reduced economic productivity, and strain on healthcare resources. Addressing this condition is particularly challenging due to its complexity and the significant variability in how patients respond to treatment. Current options often struggle to provide long-term relief, with their benefits rarely outweighing the risks, such as dependency or other side effects. Currently, AI has impacted four key areas of chronic pain treatment and research: (1) predicting outcomes based on clinical information; (2) extracting features from text, specifically clinical notes; (3) modeling 'omic data to identify meaningful patient subgroups with potential for personalized treatments and improved understanding of disease processes; and (4) disentangling complex neuronal signals responsible for pain, which current therapies attempt to modulate. As AI advances, leveraging state-of-the-art architectures will be essential for improving chronic pain treatment. Current efforts aim to extract meaningful representations from complex data, paving the way for personalized medicine. The identification of unique patient subgroups should reveal targets for tailored chronic pain treatments. Moreover, enhancing current treatment approaches is achievable by gaining a more profound understanding of patient physiology and responses. This can be realized by leveraging AI on the increasing volume of data linked to chronic pain.

Solving genomic puzzles: computational methods for metagenomic binning

Metagenomics involves the study of genetic material obtained directly from communities of microorganisms living in natural environments. The field of metagenomics has provided valuable insights into the structure, diversity and ecology of microbial communities. Once an environmental sample is sequenced and processed, metagenomic binning clusters the sequences into bins representing different taxonomic groups such as species, genera, or higher levels. Several computational tools have been developed to automate the process of metagenomic binning. These tools have enabled the recovery of novel draft genomes of microorganisms allowing us to study their behaviors and functions within microbial communities. This review classifies and analyzes different approaches of metagenomic binning and different refinement, visualization, and evaluation techniques used by these methods. Furthermore, the review highlights the current challenges and areas of improvement present within the field of research.

Reducing transmission of high-risk antibiotic resistance genes in whole-crop corn silage through lactic acid bacteria inoculation and increasing ensiling temperature

The microbial hosts of antibiotic resistance genes (ARGs) found epiphytically on plant materials could grow and flourish during silage fermentation. This study employed metagenomic analysis and elucidated the occurrence and transmission mechanisms of ARGs and their microbial hosts in whole-crop corn silage inoculated with homofermentative strain Lactiplantibacillus plantarum or heterofermentative strain Lentilactobacillus buchneri ensiled under different temperature (20 and 30 °C). The results revealed that the corn silage was dominated by Lactobacillus, Leuconostoc, Lentilactobacillus, and Latilactobacillus. Both the ensiling temperature and inoculation had greatly modified the silage microbiota. However, regardless of the ensiling temperature, L. buchneri had significantly higher ARGs, while it only exhibited significantly higher mobile genetic elements (MGEs) in low temperature treatments. The microbial community of the corn silage hosted highly diverse form of ARGs, which were primarily MacB, RanA, bcrA, msbA, TetA (58), and TetT and mainly corresponded to macrolides and tetracyclines drug classes. Plasmids were identified as the most abundant MGEs with significant correlation with some high-risk ARGs (tetM, TolC, mdtH, and NorA), and their abundances have been reduced by ensiling process. Furthermore, higher temperature and L. buchneri reduced abundances of high-risk ARGs by modifying their hosts and reduced their transmission in the silage. Therefore, ensiling, L. buchneri inoculation and higher storage temperature could improve the biosafety of corn silage.

Genome-resolved metagenomics identifies novel active microbes in biogeochemical cycling within methanol-enriched soil

Metagenome assembled genomes (MAGs), generated from sequenced 13C-labelled DNA from 13C-methanol enriched soils, were binned using an ensemble approach. This method produced a significantly larger number of higher-quality MAGs compared to direct binning approaches. These MAGs represent both the primary methanol utilizers and the secondary utilizers labelled via cross-feeding and predation on the labelled methylotrophs, including numerous uncultivated taxa. Analysis of these MAGs enabled the identification of multiple metabolic pathways within these active taxa that have climatic relevance relating to nitrogen, sulfur and trace gas metabolism. This includes denitrification, dissimilatory nitrate reduction to ammonium, ammonia oxidation and metabolism of organic sulfur species. The binning of viral sequence data also yielded extensive viral MAGs, identifying active viral replication by both lytic and lysogenic phages within the methanol-enriched soils. These MAGs represent a valuable resource for characterizing biogeochemical cycling within terrestrial environments.

Multi-omics integration strategy in the post-mortem interval of forensic science

Estimates of post-mortem interval (PMI), which often serve as pivotal evidence in forensic contexts, are fundamentally based on assessments of variability among diverse molecular markers (including proteins and metabolites), their correlations, and their temporal changes in post-mortem organisms. Nevertheless, the present approach to estimating the PMI is not comprehensive and exhibits poor performance. We developed an innovative approach that integrates multi-omics and artificial intelligence, using multimolecular, multimarker, and multidimensional information to accurately describe the intricate biological processes that occur after death, ultimately enabling inference of the PMI. Called the multi-omics stacking model (MOSM), it combines metabolomics, protein microarray electrophoresis, and fourier transform-infrared spectroscopy data. It shows improved prediction accuracy of the PMI, which is urgently needed in the forensic field. It achieved an accuracy of 0.93, generalized area under the receiver operating characteristic curve of 0.98, and minimum mean absolute error of 0.07. The MOSM integration framework not only considers multiple markers but also incorporates machine-learning models with distinct algorithmic principles. The diversity of biological mechanisms and algorithmic models further ensures the generalizability and robustness of PMI estimation.

Unraveling metagenomics through long-read sequencing: a comprehensive review

The study of microbial communities has undergone significant advancements, starting from the initial use of 16S rRNA sequencing to the adoption of shotgun metagenomics. However, a new era has emerged with the advent of long-read sequencing (LRS), which offers substantial improvements over its predecessor, short-read sequencing (SRS). LRS produces reads that are several kilobases long, enabling researchers to obtain more complete and contiguous genomic information, characterize structural variations, and study epigenetic modifications. The current leaders in LRS technologies are Pacific Biotechnologies (PacBio) and Oxford Nanopore Technologies (ONT), each offering a distinct set of advantages. This review covers the workflow of long-read metagenomics sequencing, including sample preparation (sample collection, sample extraction, and library preparation), sequencing, processing (quality control, assembly, and binning), and analysis (taxonomic annotation and functional annotation). Each section provides a concise outline of the key concept of the methodology, presenting the original concept as well as how it is challenged or modified in the context of LRS. Additionally, the section introduces a range of tools that are compatible with LRS and can be utilized to execute the LRS process. This review aims to present the workflow of metagenomics, highlight the transformative impact of LRS, and provide researchers with a selection of tools suitable for this task.

How to Obtain and Compare Metagenome-Assembled Genomes

Metagenome-assembled genomes, or MAGs, are genomes retrieved from metagenome datasets. In the vast majority of cases, MAGs are genomes from prokaryotic species that have not been isolated or cultivated in the lab. They, therefore, provide us with information on these species that are impossible to obtain otherwise, at least until new cultivation methods are devised. Thanks to improvements and cost reductions of DNA sequencing technologies and growing interest in microbial ecology, the rise in number of MAGs in genome repositories has been exponential. This chapter covers the basics of MAG retrieval and processing and provides a practical step-by-step guide using a real dataset and state-of-the-art tools for MAG analysis and comparison.

Taxonomic Re-Classification and Expansion of the Phylum Chloroflexota Based on over 5000 Genomes and Metagenome-Assembled Genomes

The phylum Chloroflexota (formerly Chloroflexi) encompasses metabolically diverse bacteria that often have high prevalence in terrestrial and aquatic habitats, some even with biotechnological application. However, there is substantial disagreement in public databases which lineage should be considered a member of the phylum and at what taxonomic level. Here, we addressed these issues through extensive phylogenomic analyses. The analyses were based on a collection of >5000 Chloroflexota genomes and metagenome-assembled genomes (MAGs) from public databases, novel environmental sites, as well as newly generated MAGs from publicly available sequence reads via an improved binning approach incorporating covariance information. Based on calculated relative evolutionary divergence, we propose that Candidatus Dormibacterota should be listed as a class (i.e., Ca. Dormibacteria) within Chloroflexota together with the classes Anaerolineae, Chloroflexia, Dehalococcoidia, Ktedonobacteria, Ca. Limnocylindria, Thermomicrobia, and two other classes containing only uncultured members. All other Chloroflexota lineages previously listed at the class rank appear to be rather orders or families in the Anaerolineae and Dehalococcoidia, which contain the vast majority of genomes and exhibited the strongest phylogenetic radiation within the phylum. Furthermore, the study suggests that a common ecophysiological capability of members of the phylum is to successfully cope with low energy fluxes.

Uncovering Microbiome Adaptations in a Full-Scale Biogas Plant: Insights from MAG-Centric Metagenomics and Metaproteomics

The current focus on renewable energy in global policy highlights the importance of methane production from biomass through anaerobic digestion (AD). To improve biomass digestion while ensuring overall process stability, microbiome-based management strategies become more important. In this study, metagenomes and metaproteomes were used for metagenomically assembled genome (MAG)-centric analyses to investigate a full-scale biogas plant consisting of three differentially operated digesters. Microbial communities were analyzed regarding their taxonomic composition, functional potential, as well as functions expressed on the proteome level. Different abundances of genes and enzymes related to the biogas process could be mostly attributed to different process parameters. Individual MAGs exhibiting different abundances in the digesters were studied in detail, and their roles in the hydrolysis, acidogenesis and acetogenesis steps of anaerobic digestion could be assigned. Methanoculleus thermohydrogenotrophicum was an active hydrogenotrophic methanogen in all three digesters, whereas Methanothermobacter wolfeii was more prevalent at higher process temperatures. Further analysis focused on MAGs, which were abundant in all digesters, indicating their potential to ensure biogas process stability. The most prevalent MAG belonged to the class Limnochordia; this MAG was ubiquitous in all three digesters and exhibited activity in numerous pathways related to different steps of AD.

A survey on computational strategies for genome-resolved gut metagenomics

Recovering high-quality metagenome-assembled genomes (HQ-MAGs) is critical for exploring microbial compositions and microbe-phenotype associations. However, multiple sequencing platforms and computational tools for this purpose may confuse researchers and thus call for extensive evaluation. Here, we systematically evaluated a total of 40 combinations of popular computational tools and sequencing platforms (i.e. strategies), involving eight assemblers, eight metagenomic binners and four sequencing technologies, including short-, long-read and metaHiC sequencing. We identified the best tools for the individual tasks (e.g. the assembly and binning) and combinations (e.g. generating more HQ-MAGs) depending on the availability of the sequencing data. We found that the combination of the hybrid assemblies and metaHiC-based binning performed best, followed by the hybrid and long-read assemblies. More importantly, both long-read and metaHiC sequencings link more mobile elements and antibiotic resistance genes to bacterial hosts and improve the quality of public human gut reference genomes with 32% (34/105) HQ-MAGs that were either of better quality than those in the Unified Human Gastrointestinal Genome catalog version 2 or novel.

Identifying Eukaryotes and Factors Influencing Their Biogeography in Drinking Water Metagenomes

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

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

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

Recovery and Analysis of Long-Read Metagenome-Assembled Genomes

The development of long-read nucleic acid sequencing is beginning to make very substantive impact on the conduct of metagenome analysis, particularly in relation to the problem of recovering the genomes of member species of complex microbial communities. Here we outline bioinformatics workflows for the recovery and characterization of complete genomes from long-read metagenome data and some complementary procedures for comparison of cognate draft genomes and gene quality obtained from short-read sequencing and long-read sequencing.

Metagenome-assembled genome extraction and analysis from microbiomes using KBase

Uncultivated Bacteria and Archaea account for the vast majority of species on Earth, but obtaining their genomes directly from the environment, using shotgun sequencing, has only become possible recently. To realize the hope of capturing Earth's microbial genetic complement and to facilitate the investigation of the functional roles of specific lineages in a given ecosystem, technologies that accelerate the recovery of high-quality genomes are necessary. We present a series of analysis steps and data products for the extraction of high-quality metagenome-assembled genomes (MAGs) from microbiomes using the U.S. Department of Energy Systems Biology Knowledgebase (KBase) platform ( http://www.kbase.us/ ). Overall, these steps take about a day to obtain extracted genomes when starting from smaller environmental shotgun read libraries, or up to about a week from larger libraries. In KBase, the process is end-to-end, allowing a user to go from the initial sequencing reads all the way through to MAGs, which can then be analyzed with other KBase capabilities such as phylogenetic placement, functional assignment, metabolic modeling, pangenome functional profiling, RNA-Seq and others. While portions of such capabilities are available individually from other resources, the combination of the intuitive usability, data interoperability and integration of tools in a freely available computational resource makes KBase a powerful platform for obtaining MAGs from microbiomes. While this workflow offers tools for each of the key steps in the genome extraction process, it also provides a scaffold that can be easily extended with additional MAG recovery and analysis tools, via the KBase software development kit (SDK).

Whole-Genome Sequencing of Bacterial Endophytes From Fresh and Preserved Plant Specimens

Many plants harbor symbiotic bacteria in their leaves, sometimes within structures visible with the naked eye. These bacteria play critical roles for host development and defense, but are often not amenable to culture. Gaining insight into the functions of these obligate endophytic bacteria hinges on culture-independent omics approaches, which have seen tremendous development in recent years. We describe in this chapter a set of protocols for the extraction and bioinformatic analysis of bacterial genomic DNA from leaf samples of various origins, including fresh, silica-preserved, or herbarium specimens.

Targeted single-cell genomics reveals novel host adaptation strategies of the symbiotic bacteria Endozoicomonas in Acropora tenuis coral

BACKGROUND

Endozoicomonas bacteria symbiosis with various marine organisms is hypothesized as a potential indicator of health in corals. Although many amplicon analyses using 16S rRNA gene have suggested the diversity of Endozoicomonas species, genome analysis has been limited due to contamination of host-derived sequences and difficulties in culture and metagenomic analysis. Therefore, the evolutionary and functional potential of individual Endozoicomonas species symbiotic with the same coral species remains unresolved.

RESULTS

In this study, we applied a novel single-cell genomics technique using droplet microfluidics to obtain single-cell amplified genomes (SAGs) for uncultured coral-associated Endozoicomonas spp. We obtained seven novel Endozoicomonas genomes and quantitative bacterial composition from Acropora tenuis corals at four sites in Japan. Our quantitative 16S rRNA gene and comparative genomic analysis revealed that these Endozoicomonas spp. belong to different lineages (Clade A and Clade B), with widely varying abundance among individual corals. Furthermore, each Endozoicomonas species possessed various eukaryotic-like genes in clade-specific genes. It was suggested that these eukaryotic-like genes might have a potential ability of different functions in each clade, such as infection of the host coral or suppression of host immune pathways. These Endozoicomonas species may have adopted different host adaptation strategies despite living symbiotically on the same coral.

CONCLUSIONS

This study suggests that coral-associated Endozoicomonas spp. on the same species of coral have different evolutional strategies and functional potentials in each species and emphasizes the need to analyze the genome of each uncultured strain in future coral-Endozoicomonas relationships studies. Video Abstract.

Community-integrated multi-omics facilitates screening and isolation of the organohalide dehalogenation microorganism

A variety of anthropogenic organohalide contaminants generated from industry are released into the environment and thus cause serious pollution that endangers human health. In the present study, we investigated the microbial community composition of industrial saponification wastewater using 16S rRNA sequencing, providing genomic insights of potential organohalide dehalogenation bacteria (OHDBs) by metagenomic sequencing. We also explored yet-to-culture OHDBs involved in the microbial community. Microbial diversity analysis reveals that Proteobacteria and Patescibacteria phyla dominate microbiome abundance of the wastewater. In addition, a total of six bacterial groups (Rhizobiales, Rhodobacteraceae, Rhodospirillales, Flavob a cteriales, Micrococcales, and Saccharimonadales) were found as biomarkers in the key organohalide removal module. Ninety-four metagenome-assembled genomes were reconstructed from the microbial community, and 105 hydrolytic dehalogenase genes within 42 metagenome-assembled genomes were identified, suggesting that the potential for organohalide hydrolytic dehalogenation is present in the microbial community. Subsequently, we characterized the organohalide dehalogenation of an isolated OHDB, Microbacterium sp. J1-1, which shows the dehalogenation activities of chloropropanol, dichloropropanol, and epichlorohydrin. This study provides a community-integrated multi-omics approach to gain functional OHDBs for industrial organohalide dehalogenation.

Improved microbial genomes and gene catalog of the chicken gut from metagenomic sequencing of high-fidelity long reads

BACKGROUND

Due to the importance of chicken production and the remarkable influence of the gut microbiota on host health and growth, tens of thousands of metagenome-assembled genomes (MAGs) have been constructed for the chicken gut microbiome. However, due to the limitations of short-read sequencing and assembly technologies, most of these MAGs are far from complete, are of lower quality, and include contaminant reads.

RESULTS

We generated 332 Gb of high-fidelity (HiFi) long reads from the 5 chicken intestinal compartments and assembled 461 and 337 microbial genomes, of which 53% and 55% are circular, at the species and strain levels, respectively. For the assembled microbial genomes, approximately 95% were regarded as complete according to the "RNA complete" criteria, which requires at least 1 full-length ribosomal RNA (rRNA) operon encoding all 3 types of rRNA (16S, 23S, and 5S) and at least 18 copies of full-length transfer RNA genes. In comparison with the short-read-derived chicken MAGs, 384 (83% of 461) and 89 (26% of 337) strain-level and species-level genomes in this study are novel, with no matches to previously reported sequences. At the gene level, one-third of the 2.5 million genes in the HiFi-derived gene catalog are novel and cannot be matched to the short-read-derived gene catalog. Moreover, the HiFi-derived genomes have much higher continuity and completeness, as well as lower contamination; the HiFi-derived gene catalog has a much higher ratio of complete gene structures. The dominant phylum in our HiFi-assembled genomes was Firmicutes (82.5%), and the foregut was highly enriched in 5 genera: Ligilactobacillus, Limosilactobacillus, Lactobacillus, Weissella, and Enterococcus, all of which belong to the order Lactobacillales. Using GTDB-Tk, all 337 species-level genomes were successfully classified at the order level; however, 2, 35, and 189 genomes could not be classified into any known family, genus, and species, respectively. Among these incompletely classified genomes, 9 and 49 may belong to novel genera and species, respectively, because their 16S rRNA genes have identities lower than 95% and 97% to any known 16S rRNA genes.

CONCLUSIONS

HiFi sequencing not only produced metagenome assemblies and gene structures with markedly improved quality but also recovered a substantial portion of novel genomes and genes that were missed in previous short-read-based metagenome studies. The novel genomes and species obtained in this study will facilitate gut microbiome and host-microbiota interaction studies, thereby contributing to the sustainable development of poultry resources.

Mycobacteriaceae Mineralizes Micropolyethylene in Riverine Ecosystems

Microplastic (MP) contamination is a serious global environmental problem. Plastic contamination has attracted extensive attention during the past decades. While physiochemical weathering may influence the properties of MPs, biodegradation by microorganisms could ultimately mineralize plastics into CO₂. Compared to the well-studied marine ecosystems, the MP biodegradation process in riverine ecosystems, however, is less understood. The current study focuses on the MP biodegradation in one of the world's most plastic contaminated rivers, Pearl River, using micropolyethylene (mPE) as a model substrate. Mineralization of ¹³C-labeled mPE into ¹³CO₂ provided direct evidence of mPE biodegradation by indigenous microorganisms. Several Actinobacteriota genera were identified as putative mPE degraders. Furthermore, two Mycobacteriaceae isolates related to the putative mPE degraders, Mycobacterium sp. mPE3 and Nocardia sp. mPE12, were retrieved, and their ability to mineralize ¹³C-mPE into ¹³CO₂ was confirmed. Pangenomic analysis reveals that the genes related to the proposed mPE biodegradation pathway are shared by members of Mycobacteriaceae. While both Mycobacterium and Nocardia are known for their pathogenicity, these populations on the plastisphere in this study were likely nonpathogenic as they lacked virulence factors. The current study provided direct evidence for MP mineralization by indigenous biodegraders and predicted their biodegradation pathway, which may be harnessed to improve bioremediation of MPs in urban rivers.

binny: an automated binning algorithm to recover high-quality genomes from complex metagenomic datasets

The reconstruction of genomes is a critical step in genome-resolved metagenomics and for multi-omic data integration from microbial communities. Here, we present binny, a binning tool that produces high-quality metagenome-assembled genomes (MAG) from both contiguous and highly fragmented genomes. Based on established metrics, binny outperforms or is highly competitive with commonly used and state-of-the-art binning methods and finds unique genomes that could not be detected by other methods. binny uses k-mer-composition and coverage by metagenomic reads for iterative, nonlinear dimension reduction of genomic signatures as well as subsequent automated contig clustering with cluster assessment using lineage-specific marker gene sets. When compared with seven widely used binning algorithms, binny provides substantial amounts of uniquely identified MAGs and almost always recovers the most near-complete ($\gt 95\%$ pure, $\gt 90\%$ complete) and high-quality ($\gt 90\%$ pure, $\gt 70\%$ complete) genomes from simulated datasets from the Critical Assessment of Metagenome Interpretation initiative, as well as substantially more high-quality draft genomes, as defined by the Minimum Information about a Metagenome-Assembled Genome standard, from a real-world benchmark comprised of metagenomes from various environments than any other tested method.

Metagenomic binning with assembly graph embeddings

MOTIVATION

Despite recent advancements in sequencing technologies and assembly methods, obtaining high-quality microbial genomes from metagenomic samples is still not a trivial task. Current metagenomic binners do not take full advantage of assembly graphs and are not optimized for long-read assemblies. Deep graph learning algorithms have been proposed in other fields to deal with complex graph data structures. The graph structure generated during the assembly process could be integrated with contig features to obtain better bins with deep learning.

RESULTS

We propose GraphMB, which uses graph neural networks to incorporate the assembly graph into the binning process. We test GraphMB on long-read datasets of different complexities, and compare the performance with other binners in terms of the number of High Quality (HQ) genome bins obtained. With our approach, we were able to obtain unique bins on all real datasets, and obtain more bins on most datasets. In particular, we obtained on average 17.5% more HQ bins when compared with state-of-the-art binners and 13.7% when aggregating the results of our binner with the others. These results indicate that a deep learning model can integrate contig-specific and graph-structure information to improve metagenomic binning.

AVAILABILITY AND IMPLEMENTATION

GraphMB is available from https://github.com/MicrobialDarkMatter/GraphMB.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Coexistence of antibiotic resistance genes, fecal bacteria, and potential pathogens in anthropogenically impacted water

Microbial indicators are often used to monitor microbial safety of aquatic environments. However, information regarding the correlation between microbial indicators and ecotoxicological factors such as potential pathogens and antibiotic resistance genes (ARGs) in anthropogenically impacted waters remains highly limited. Here, we investigated the bacterial community composition, potential pathogens, ARGs diversity, ARG hosts, and horizontal gene transfer (HGT) potential in urban river and wastewater samples from Chaohu Lake Basin using 16S rRNA and metagenomic sequencing. The composition of the microbial community and potential pathogens differed significantly in wastewater and river water samples, and the total relative abundance of fecal indicator bacteria was positively correlated with the total relative abundance of potential pathogens (p < 0.001 and Pearson's r = 0.758). Network analysis indicated that partial ARG subtypes such as dfrE, sul2, and PmrE were significantly correlated with indicator bacteria (p < 0.05 and Pearson's r > 0.6). Notably, Klebsiella was the indicator bacteria significantly correlated with 4 potential pathogens and 14 ARG subtypes. ARGs coexisting with mobile gene elements were mainly found in Thauera, Pseudomonas, Escherichia, and Acinetobacter. Next-generation sequencing (NGS) can be used to conduct preliminary surveys of environmental samples to access potential health risks, thereby facilitating water resources management.

Detection of Ancient Viruses and Long-Term Viral Evolution

The COVID-19 outbreak has reminded us of the importance of viral evolutionary studies as regards comprehending complex viral evolution and preventing future pandemics. A unique approach to understanding viral evolution is the use of ancient viral genomes. Ancient viruses are detectable in various archaeological remains, including ancient people's skeletons and mummified tissues. Those specimens have preserved ancient viral DNA and RNA, which have been vigorously analyzed in the last few decades thanks to the development of sequencing technologies. Reconstructed ancient pathogenic viral genomes have been utilized to estimate the past pandemics of pathogenic viruses within the ancient human population and long-term evolutionary events. Recent studies revealed the existence of non-pathogenic viral genomes in ancient people's bodies. These ancient non-pathogenic viruses might be informative for inferring their relationships with ancient people's diets and lifestyles. Here, we reviewed the past and ongoing studies on ancient pathogenic and non-pathogenic viruses and the usage of ancient viral genomes to understand their long-term viral evolution.

The OceanDNA MAG catalog contains over 50,000 prokaryotic genomes originated from various marine environments

Marine microorganisms are immensely diverse and play fundamental roles in global geochemical cycling. Recent metagenome-assembled genome studies, with particular attention to large-scale projects such as Tara Oceans, have expanded the genomic repertoire of marine microorganisms. However, published marine metagenome data is still underexplored. We collected 2,057 marine metagenomes covering various marine environments and developed a new genome reconstruction pipeline. We reconstructed 52,325 qualified genomes composed of 8,466 prokaryotic species-level clusters spanning 59 phyla, including genomes from the deep-sea characterized as deeper than 1,000 m (n = 3,337), low-oxygen zones of <90 μmol O2 per kg water (n = 7,884), and polar regions (n = 7,752). Novelty evaluation using a genome taxonomy database shows that 6,256 species (73.9%) are novel and include genomes of high taxonomic novelty, such as new class candidates. These genomes collectively expanded the known phylogenetic diversity of marine prokaryotes by 34.2%, and the species representatives cover 26.5-42.0% of prokaryote-enriched metagenomes. Thoroughly leveraging accumulated metagenomic data, this genome resource, named the OceanDNA MAG catalog, illuminates uncharacterized marine microbial 'dark matter' lineages.

Introduction to the principles and methods underlying the recovery of metagenome-assembled genomes from metagenomic data

The rise of metagenomics offers a leap forward for understanding the genetic diversity of microorganisms in many different complex environments by providing a platform that can identify potentially unlimited numbers of known and novel microorganisms. As such, it is impossible to imagine new major initiatives without metagenomics. Nevertheless, it represents a relatively new discipline with various levels of complexity and demands on bioinformatics. The underlying principles and methods used in metagenomics are often seen as common knowledge and often not detailed or fragmented. Therefore, we reviewed these to guide microbiologists in taking the first steps into metagenomics. We specifically focus on a workflow aimed at reconstructing individual genomes, that is, metagenome-assembled genomes, integrating DNA sequencing, assembly, binning, identification and annotation.

Decoding microbial genomes to understand their functional roles in human complex diseases

Complex diseases such as cardiovascular disease (CVD), obesity, inflammatory bowel disease (IBD), kidney disease, type 2 diabetes (T2D), and cancer have become a major burden to public health and affect more than 20% of the population worldwide. The etiology of complex diseases is not yet clear, but they are traditionally thought to be caused by genetics and environmental factors (e.g., dietary habits), and by their interactions. Besides this, increasing pieces of evidence now highlight that the intestinal microbiota may contribute substantially to the health and disease of the human host via their metabolic molecules. Therefore, decoding the microbial genomes has been an important strategy to shed light on their functional potential. In this review, we summarize the roles of the gut microbiome in complex diseases from its functional perspective. We further introduce artificial tools in decoding microbial genomes to profile their functionalities. Finally, state-of-the-art techniques have been highlighted which may contribute to a mechanistic understanding of the gut microbiome in human complex diseases and promote the development of the gut microbiome-based personalized medicine.

Accurate prediction of metagenome-assembled genome completeness by MAGISTA, a random forest model built on alignment-free intra-bin statistics

BACKGROUND

Although the total number of microbial taxa on Earth is under debate, it is clear that only a small fraction of these has been cultivated and validly named. Evidently, the inability to culture most bacteria outside of very specific conditions severely limits their characterization and further studies. In the last decade, a major part of the solution to this problem has been the use of metagenome sequencing, whereby the DNA of an entire microbial community is sequenced, followed by the in silico reconstruction of genomes of its novel component species. The large discrepancy between the number of sequenced type strain genomes (around 12,000) and total microbial diversity (10⁶-10¹² species) directs these efforts to de novo assembly and binning. Unfortunately, these steps are error-prone and as such, the results have to be intensely scrutinized to avoid publishing incomplete and low-quality genomes.

RESULTS

We developed MAGISTA (metagenome-assembled genome intra-bin statistics assessment), a novel approach to assess metagenome-assembled genome quality that tackles some of the often-neglected drawbacks of current reference gene-based methods. MAGISTA is based on alignment-free distance distributions between contig fragments within metagenomic bins, rather than a set of reference genes. For proper training, a highly complex genomic DNA mock community was needed and constructed by pooling genomic DNA of 227 bacterial strains, specifically selected to obtain a wide variety representing the major phylogenetic lineages of cultivable bacteria.

CONCLUSIONS

MAGISTA achieved a 20% reduction in root-mean-square error in comparison to the marker gene approach when tested on publicly available mock metagenomes. Furthermore, our highly complex genomic DNA mock community is a very valuable tool for benchmarking (new) metagenome analysis methods.

Computational analyses of bacterial strains from shotgun reads

Shotgun sequencing is routinely employed to study bacteria in microbial communities. With the vast amount of shotgun sequencing reads generated in a metagenomic project, it is crucial to determine the microbial composition at the strain level. This study investigated 20 computational tools that attempt to infer bacterial strain genomes from shotgun reads. For the first time, we discussed the methodology behind these tools. We also systematically evaluated six novel-strain-targeting tools on the same datasets and found that BHap, mixtureS and StrainFinder performed better than other tools. Because the performance of the best tools is still suboptimal, we discussed future directions that may address the limitations.

Small investments with big returns: environmental genomic bioprospecting of microbial life

Microorganisms and their natural products are major drivers of ecological processes and industrial applications. Microbial bioprospecting has been critical for the advancement in various fields such as pharmaceuticals, sustainable industries, food security and bioremediation. Next generation sequencing has been paramount in the exploration of diverse environmental microbiomes. It presents a culture-independent approach to investigating hitherto uncultured taxa, resulting in the creation of massive sequence databases, which are available in the public domain. Genome mining searches available (meta)genomic data for target biosynthetic genes, and combined with the large-scale public data, this in-silico bioprospecting method presents an efficient and extensive way to uncover microbial bioproducts. Bioinformatic tools have progressed to a stage where we can recover genomes from the environment; these metagenome-assembled genomes present a way to understand the metabolic capacity of microorganisms in a physiological and ecological context. Environmental sampling been extensive across various ecological settings, including microbiomes with unique physicochemical properties that could influence the discovery of novel functions and metabolic pathways. Although in-silico methods cannot completely substitute in-vitro studies, the contextual information it provides is invaluable for understanding the ecological and taxonomic distribution of microbial genotypes and to form effective strategies for future microbial bioprospecting efforts.

A review of computational tools for generating metagenome-assembled genomes from metagenomic sequencing data

Metagenomic sequencing provides a culture-independent avenue to investigate the complex microbial communities by constructing metagenome-assembled genomes (MAGs). A MAG represents a microbial genome by a group of sequences from genome assembly with similar characteristics. It enables us to identify novel species and understand their potential functions in a dynamic ecosystem. Many computational tools have been developed to construct and annotate MAGs from metagenomic sequencing, however, there is a prominent gap to comprehensively introduce their background and practical performance. In this paper, we have thoroughly investigated the computational tools designed for both upstream and downstream analyses, including metagenome assembly, metagenome binning, gene prediction, functional annotation, taxonomic classification, and profiling. We have categorized the commonly used tools into unique groups based on their functional background and introduced the underlying core algorithms and associated information to demonstrate a comparative outlook. Furthermore, we have emphasized the computational requisition and offered guidance to the users to select the most efficient tools. Finally, we have indicated current limitations, potential solutions, and future perspectives for further improving the tools of MAG construction and annotation. We believe that our work provides a consolidated resource for the current stage of MAG studies and shed light on the future development of more effective MAG analysis tools on metagenomic sequencing.

A review of the resistome within the digestive tract of livestock

Antimicrobials have been widely used to prevent and treat infectious diseases and promote growth in food-production animals. However, the occurrence of antimicrobial resistance poses a huge threat to public and animal health, especially in less developed countries where food-producing animals often intermingle with humans. To limit the spread of antimicrobial resistance from food-production animals to humans and the environment, it is essential to have a comprehensive knowledge of the role of the resistome in antimicrobial resistance (AMR), The resistome refers to the collection of all antimicrobial resistance genes associated with microbiota in a given environment. The dense microbiota in the digestive tract is known to harbour one of the most diverse resistomes in nature. Studies of the resistome in the digestive tract of humans and animals are increasing exponentially as a result of advancements in next-generation sequencing and the expansion of bioinformatic resources/tools to identify and describe the resistome. In this review, we outline the various tools/bioinformatic pipelines currently available to characterize and understand the nature of the intestinal resistome of swine, poultry, and ruminants. We then propose future research directions including analysis of resistome using long-read sequencing, investigation in the role of mobile genetic elements in the expression, function and transmission of AMR. This review outlines the current knowledge and approaches to studying the resistome in food-producing animals and sheds light on future strategies to reduce antimicrobial usage and control the spread of AMR both within and from livestock production systems.

Metagenome-Assembled Genomes Contribute to Unraveling of the Microbiome of Cocoa Fermentation

Metagenomic studies about cocoa fermentation have mainly reported on the analysis of short reads for determination of operational taxonomic units. However, it is also important to determine metagenome-assembled genomes (MAGs), which are genomes deriving from the assembly of metagenomics. For this research, all the cocoa metagenomes from public databases were downloaded, resulting in five data sets: one from Ghana and four from Brazil. In addition, in silico approaches were used to describe putative phenotypes and the metabolic potential of MAGs. A total of 17 high-quality MAGs were recovered from these microbiomes, as follows: (i) for fungi, Yamadazyma tenuis (n = 1); (ii) lactic acid bacteria, Limosilactobacillus fermentum (n = 5), Liquorilactobacillus cacaonum (n = 1), Liquorilactobacillus nagelli (n = 1), Leuconostoc pseudomesenteroides (n = 1), and Lactiplantibacillus plantarum subsp. plantarum (n = 1); (iii) acetic acid bacteria, Acetobacter senegalensis (n = 2) and Kozakia baliensis (n = 1); and (iv) Bacillus subtilis (n = 1), Brevundimonas sp. (n = 2), and Pseudomonas sp. (n = 1). Medium-quality MAGs were also recovered from cocoa microbiomes, including some that, to our knowledge, were not previously detected in this environment (Liquorilactobacillus vini, Komagataeibacter saccharivorans, and Komagataeibacter maltaceti) and others previously described (Fructobacillus pseudoficulneus and Acetobacter pasteurianus). Taken together, the MAGs were useful for providing an additional description of the microbiome of cocoa fermentation, revealing previously overlooked microorganisms, with prediction of key phenotypes and biochemical pathways. IMPORTANCE The production of chocolate starts with the harvesting of cocoa fruits and the spontaneous fermentation of the seeds in a microbial succession that depends on yeasts, lactic acid bacteria, and acetic acid bacteria in order to eliminate bitter and astringent compounds present in the raw material, which will be further roasted and grinded to originate the cocoa powder that will enter the food processing industry. The microbiota of cocoa fermentation is not completely known, and yet it advanced from culture-based studies to the advent of next-generation DNA sequencing, with the generation of a myriad of data that need bioinformatic approaches to be properly analyzed. Although the majority of metagenomic studies have been based on short reads (operational taxonomic units), it is also important to analyze entire genomes to determine more precisely possible ecological roles of different species. Metagenome-assembled genomes (MAGs) are very useful for this purpose; here, MAGs from cocoa fermentation microbiomes are described, and the possible implications of their phenotypic and metabolic potentials are discussed.

Challenges, Strategies, and Perspectives for Reference-Independent Longitudinal Multi-Omic Microbiome Studies

In recent years, multi-omic studies have enabled resolving community structure and interrogating community function of microbial communities. Simultaneous generation of metagenomic, metatranscriptomic, metaproteomic, and (meta) metabolomic data is more feasible than ever before, thus enabling in-depth assessment of community structure, function, and phenotype, thus resulting in a multitude of multi-omic microbiome datasets and the development of innovative methods to integrate and interrogate those multi-omic datasets. Specifically, the application of reference-independent approaches provides opportunities in identifying novel organisms and functions. At present, most of these large-scale multi-omic datasets stem from spatial sampling (e.g., water/soil microbiomes at several depths, microbiomes in/on different parts of the human anatomy) or case-control studies (e.g., cohorts of human microbiomes). We believe that longitudinal multi-omic microbiome datasets are the logical next step in microbiome studies due to their characteristic advantages in providing a better understanding of community dynamics, including: observation of trends, inference of causality, and ultimately, prediction of community behavior. Furthermore, the acquisition of complementary host-derived omics, environmental measurements, and suitable metadata will further enhance the aforementioned advantages of longitudinal data, which will serve as the basis to resolve drivers of community structure and function to understand the biotic and abiotic factors governing communities and specific populations. Carefully setup future experiments hold great potential to further unveil ecological mechanisms to evolution, microbe-microbe interactions, or microbe-host interactions. In this article, we discuss the challenges, emerging strategies, and best-practices applicable to longitudinal microbiome studies ranging from sampling, biomolecular extraction, systematic multi-omic measurements, reference-independent data integration, modeling, and validation.

Identifying biases and their potential solutions in human microbiome studies

Advances in DNA sequencing technology have vastly improved the ability of researchers to explore the microbial inhabitants of the human body. Unfortunately, while these studies have uncovered the importance of these microbial communities to our health, they often do not result in similar findings. One possible reason for the disagreement in these results is due to the multitude of systemic biases that are introduced during sequence-based microbiome studies. These biases begin with sample collection and continue to be introduced throughout the entire experiment leading to an observed community that is significantly altered from the true underlying microbial composition. In this review, we will highlight the various steps in typical sequence-based human microbiome studies where significant bias can be introduced, and we will review the current efforts within the field that aim to reduce the impact of these biases. Video abstract.

An improved direct metamobilome approach increases the detection of larger-sized circular elements across kingdoms

Mobile genetic elements (MGEs) are instrumental in natural prokaryotic genome editing, permitting genome plasticity and allowing microbes to accumulate genetic diversity. MGEs serve as a vast communal gene pool and include DNA elements such as plasmids and bacteriophages (phages) among others. These mobile DNA elements represent a human health risk as they can introduce new traits, such as antibiotic resistance or virulence, to a bacterial strain. Sequencing libraries targeting environmental circular MGEs, referred to as metamobilomes, may broaden our current understanding of the mechanisms behind the mobility, prevalence and content of these elements. However, metamobilomics is affected by a severe bias towards small circular elements, introduced by multiple displacement amplification (MDA). MDA is typically used to overcome limiting DNA quantities after the removal of non-circular DNA during library preparations. By examining the relationship between sequencing coverage and the size of circular MGEs in paired metamobilome datasets with and without MDA, we show that larger circular elements are lost when using MDA. This study is the first to systematically demonstrate that MDA is detrimental to detecting larger-sized plasmids if small plasmids are present. It is also the first to show that MDA can be omitted when using enzyme-based DNA fragmentation and PCR in library preparation kits such as Nextera XT® from Illumina.

A total of 219 metagenome-assembled genomes of microorganisms from Icelandic marine waters

Marine microorganisms contribute to the health of the global ocean by supporting the marine food web and regulating biogeochemical cycles. Assessing marine microbial diversity is a crucial step towards understanding the global ocean. The waters surrounding Iceland are a complex environment where relatively warm salty waters from the Atlantic cool down and sink down to the deep. Microbial studies in this area have focused on photosynthetic micro- and nanoplankton mainly using microscopy and chlorophyll measurements. However, the diversity and function of the bacterial and archaeal picoplankton remains unknown. Here, we used a co-assembly approach supported by a marine mock community to reconstruct metagenome-assembled genomes (MAGs) from 31 metagenomes from the sea surface and seafloor of four oceanographic sampling stations sampled between 2015 and 2018. The resulting 219 MAGs include 191 bacterial, 26 archaeal and two eukaryotic MAGs to bridge the gap in our current knowledge of the global marine microbiome.

Comparative genomics of the ADA clade within the Nostocales

The ADA clade of Nostocales cyanobacteria, a group that is prominent in current harmful algal bloom events, now includes over 40 genome sequences with the recent addition of sixteen novel sequenced genomes (Dreher et al., Harmful Algae, 2021). Fourteen genomes are complete (closed), enabling highly detailed assessments of gene content and genome architecture. ADA genomes contain 5 rRNA operons, genes expected to support a photoautotrophic and diazotrophic lifestyle, and a varied array of genes for the synthesis of bioactive secondary metabolites. Genes for the production of the taste-and-odor compound geosmin and the four major classes of cyanotoxins - anatoxin-a, cylindrospermopsin, microcystin and saxitoxin - are represented in members of the ADA clade. Notably, the gene array for the synthesis of cylindrospermopsin by Dolichospermum sp. DET69 was located on a plasmid, raising the possibility of facile horizontal transmission. However, genes supporting independent conjugative transfer of this plasmid are lacking. Further, analysis of genomic loci containing this and other cyanotoxin gene arrays shows evidence that these arrays have long-term stability and do not appear to be genomic islands easily capable of horizontal transmission to other cells. There is considerable diversity in the gene complements of individual ADA genomes, including the variable presence of physiologically important genes: genomes in three species-level subclades lack the gas vesicle genes that facilitate a planktonic lifestyle, and, surprisingly, the genome of Cuspidothrix issatschenkoi CHARLIE-1, a reported diazotroph, lacks the genes for nitrogen fixation. Notably, phylogenetically related genomes possess limited synteny, indicating a prominent role for chromosome rearrangements during ADA strain evolution. The genomes contain abundant insertion sequences and repetitive transposase genes, which could be the main drivers of genome rearrangement through active transposition and homologous recombination. No prophages were found, and no evidence of viral infection was observed in the bloom population samples from which the genomes discussed here were derived. Phages thus seem to have a limited influence on ADA evolution.

Metabolic and molecular modelling of zebrafish gut biome to unravel antimicrobial peptides through metagenomics

Recently efforts have been taken for unravelling mysteries between host-microbe interactions in gut microbiome studies of model organisms through metagenomics. Co-existence and the co-evolution of the microorganisms is the significant cause of the growing antimicrobial menace. There needs a novel approach to develop potential antimicrobials with capabilities to act directly on the resistant microbes with reduced side effects. One such is to tap them from the natural resources, preferably the gut of the most closely related animal model. In this study, we employed metagenomics approaches to identify the large taxonomic genomes of the zebra fish gut. About 256 antimicrobial peptides were identified using gene ontology predictions from Macrel and Pubseed servers. Upon the property predictions, the top 10 antimicrobial peptides were screened based on their action against many resistant bacterial species, including Klebsiella pneumoniae, Pseudomonas aeruginosa, Staphylococcus aureus, E. coli, and Bacillus cereus. Metabolic modelling and flux balance analysis (FBA) were computed to conclude the antibiotic such as tetracycline, cephalosporins, puromycin, neomycin biosynthesis pathways were adopted by the microbiome as protection strategies. Molecular modelling strategies, including molecular docking and dynamics, were performed to estimate the antimicrobial peptides' binding against the target-putative nucleic acid binding lipoprotein and confirm stable binding. One specific antimicrobial peptide with the sequence "MPPYLHEIQPHTASNCQTELVIKL" showed promising results with 53% hydrophobic residues and a net charge +2.5, significant for the development of antimicrobial peptides. The said peptide also showed promising interactions with the target protein and expressed stable binding with docking energy of -429.34 kcal/mol and the average root mean square deviation of 1 A⁰. The study is a novel approach focusing on tapping out potential antimicrobial peptides to be developed against most resistant bacterial species.

Metagenome-assembled genome binning methods with short reads disproportionately fail for plasmids and genomic Islands

Metagenomic methods enable the simultaneous characterization of microbial communities without time-consuming and bias-inducing culturing. Metagenome-assembled genome (MAG) binning methods aim to reassemble individual genomes from this data. However, the recovery of mobile genetic elements (MGEs), such as plasmids and genomic islands (GIs), by binning has not been well characterized. Given the association of antimicrobial resistance (AMR) genes and virulence factor (VF) genes with MGEs, studying their transmission is a public-health priority. The variable copy number and sequence composition of MGEs makes them potentially problematic for MAG binning methods. To systematically investigate this issue, we simulated a low-complexity metagenome comprising 30 GI-rich and plasmid-containing bacterial genomes. MAGs were then recovered using 12 current prediction pipelines and evaluated. While 82-94 % of chromosomes could be correctly recovered and binned, only 38-44 % of GIs and 1-29 % of plasmid sequences were found. Strikingly, no plasmid-borne VF nor AMR genes were recovered, and only 0-45 % of AMR or VF genes within GIs. We conclude that short-read MAG approaches, without further optimization, are largely ineffective for the analysis of mobile genes, including those of public-health importance, such as AMR and VF genes. We propose that researchers should explore developing methods that optimize for this issue and consider also using unassembled short reads and/or long-read approaches to more fully characterize metagenomic data.