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

How genomics can help unravel the evolution of endophytic fungi

Endophytic fungi (EFs) form intimate associations with plants, residing within their tissues without causing apparent harm. Understanding the evolution of endophytic fungal genomes is essential for uncovering the mechanisms that drive their symbiotic relationships with host plants. This review explores the dynamic interactions between EFs and host plants, focusing on the evolutionary processes that shape their genomes. We highlighted key genomic adaptations promoting their endophytic lifestyle, including genes involved in plant cell wall degradation, secondary metabolite production, and stress tolerance. By combining genomic data with ecological and physiological information, this review provides a comprehensive understanding of the coevolutionary dynamics between EFs and host plants. Moreover, it provides insights that help elucidate the complex interdependencies governing their symbiotic interactions.

Refining microbiome diversity analysis by concatenating and integrating dual 16S rRNA amplicon reads

Understanding the role of human gut microbiota in health and disease requires insights into its taxonomic composition and functional capabilities. This study evaluates whether concatenating paired-end reads enhances data output for gut microbiome analysis compared to the merging approach across various regions of the 16S rRNA gene. We assessed this approach in both mock communities and Korean cohorts with or without ulcerative colitis. Our results indicate that using the direct joining method for the V1-V3 or V6-V8 regions improves taxonomic resolution compared to merging paired-end reads (ME) in post-sequencing data. While predicting microbial function based on 16S rRNA sequencing has inherent limitations, integrating sequencing reads from both the V1-V3 and V6-V8 regions enhanced functional predictions. This was confirmed by whole metagenome sequencing (WMS) of Korean cohorts, where our approach improved taxa detection that was lost using the ME method. Thus, we propose that the integrated dual 16S rRNA sequencing technique serves as a valuable tool for microbiome research by bridging the gap between amplicon sequencing and WMS.

Metagenomics studies in aquaculture systems: Big data analysis, bioinformatics, machine learning and quantum computing

The burgeoning field of aquaculture has become a pivotal contributor to global food security and economic growth, presently surpassing capture fisheries in aquatic animal production as evidenced by recent statistics. However, the dense fish populations inherent in aquaculture systems exacerbate abiotic stressors and promote pathogenic spread, posing a risk to sustainability and yield. This study delves into the transformative potential of metagenomics, a method that directly retrieves genetic material from environmental samples, in elucidating microbial dynamics within aquaculture ecosystems. Our findings affirm that metagenomics, bolstered by tools in big data analytics, bioinformatics, and machine learning, can significantly enhance the precision of microbial assessment and pathogen detection. Furthermore, we explore quantum computing's emergent role, which promises unparalleled efficiency in data processing and model construction, poised to address the limitations of conventional computational techniques. Distinct from metabarcoding, metagenomics offers an expansive, unbiased profile of microbial biodiversity, revolutionizing our capacity to monitor, predict, and manage aquaculture systems with high accuracy and adaptability. Despite the challenges of computational demands and variability in data standardization, this study advocates for continued technological integration, thereby fostering resilient and sustainable aquaculture practices in a climate of escalating global food requirements.

Isolation and characterization of Schleiferilactobacillus harbinensis GX0002947 from naturally fermented sour porridge and its application in cereal fermentation

Sour porridge, a fermented food from the Guangxi Zhuang Autonomous Region of China, contains an abundance of lactic acid bacteria and has high nutritional value. In this study, a strain of Schleiferilactobacillus harbinensis GX0002947 was isolated from naturally fermented sour porridge from Fusui County, Chongzuo City, Guangxi Zhuang Autonomous Region of China. The strain was highly effective in the fermentation of sour porridge. It was found that strain S. harbinensis GX0002947 showed good acid and bile salt resistance at pH 3.5, bile salt concentration of 0.3 g/100 mL, in artificial gastrointestinal fluids, and the bacterial population density was greater than 106 CFU/mL. The fermentation broth and culture supernatant of strain S. harbinensis GX0002947 showed effective antibacterial activity against the foodborne pathogens Escherichia coli, Staphylococcus aureus, and Bacillus cereus. The optimum fermentation process for sour porridge was found to consist of a fermentation temperature of 37°C, inoculation dose of 12.5%, and fermentation time of 96 h, resulting in a total protein content of 397.33 μg/mL and a total amino acid content of 629.63 μmol/mL in the sour porridge. In addition, the community diversity of fermented sour porridge was explored by high-throughput Illumina sequencing. The results showed that fermentation of sour porridge by S. harbinensis GX0002947 resulted in the formation of a unique microbial community. Metabolites were compared between sour porridge fermented by strain S. harbinensis GX0002947 and naturally fermented sour porridge and were analyzed by LC-MS. This identified 24 differential metabolites which primarily included amino acids, carbohydrates, and lipids, suggesting that the associated pathways played a key role in the fermentation of sour porridge by S. harbinensis GX0002947. In conclusion, this study used inoculation of lactic acid bacteria for the fermentation of sour porridge, and assessed differences in microbial community structure and metabolites after inoculation with S. harbinensis GX0002947. These findings provided a theoretical basis and technical support for sour porridge production.

Advancing Gut Microbiome Research: The Shift from Metagenomics to Multi-Omics and Future Perspectives

The gut microbiome, a dynamic and integral component of human health, has co-evolved with its host, playing essential roles in metabolism, immunity, and disease prevention. Traditional microbiome studies, primarily focused on microbial composition, have provided limited insights into the functional and mechanistic interactions between microbiota and their host. The advent of multi-omics technologies has transformed microbiome research by integrating genomics, transcriptomics, proteomics, and metabolomics, offering a comprehensive, systems-level understanding of microbial ecology and host-microbiome interactions. These advances have propelled innovations in personalized medicine, enabling more precise diagnostics and targeted therapeutic strategies. This review highlights recent breakthroughs in microbiome research, demonstrating how these approaches have elucidated microbial functions and their implications for health and disease. Additionally, it underscores the necessity of standardizing multi-omics methodologies, conducting large-scale cohort studies, and developing novel platforms for mechanistic studies, which are critical steps toward translating microbiome research into clinical applications and advancing precision medicine.

Sequential co-assembly reduces computational resources and errors in metagenome-assembled genomes

Generating metagenome-assembled genomes from DNA shotgun sequencing datasets can demand considerable computational resources. Here, we describe a sequential co-assembly method that reduces the assembly of duplicate reads through successive application of single-node computing tools for read assembly and mapping. Using a simulated mouse microbiome DNA shotgun sequencing dataset, we demonstrated that this approach shortens assembly time, uses less memory than traditional co-assembly, and produces significantly fewer assembly errors. Applying sequential co-assembly to shotgun sequencing reads from (1) a longitudinal study of gut microbiomes from undernourished Bangladeshi children and (2) a 2.3-terabyte dataset generated from gnotobiotic mice colonized with pooled microbiomes from these children that was too large to be handled by a traditional co-assembly approach also demonstrated significant reductions in assembly time and memory requirements. These results suggest that this approach should be useful in resource-constrained settings, including in low- and middle-income countries.

Augmenting microbial phylogenomic signal with tailored marker gene sets

Phylogenetic marker genes are traditionally selected from a fixed collection of whole genomes evenly distributed across major microbial phyla, covering only a small fraction of gene families. And yet, most microbial diversity is found in metagenome-assembled genomes that are unevenly distributed and harbor gene families that do not fit the criteria of universal orthologous genes. To address these limitations, we systematically evaluate the phylogenetic signal of gene families annotated from KEGG and EggNOG functional databases for deep microbial phylogenomics. We show that markers selected from an expanded pool of gene families and tailored to the input genomes improve the accuracy of phylogenetic trees across simulated and real-world datasets of whole genomes and metagenome-assembled genomes. The improved accuracy of trees compared to previous markers persists even when metagenome-assembled genomes lack a fraction of open reading frames. The selected markers have functional annotations related to metabolism, cellular processes, and environmental information processing, in addition to replication, translation, and transcription. We introduce TMarSel, a software tool for automated, systematic, free-from-expert opinion, and tailored marker selection that provides flexibility in the number of markers and annotation databases while remaining robust against uneven taxon sampling and incomplete genomic data.

A review of neural networks for metagenomic binning

One of the main goals of metagenomic studies is to describe the taxonomic diversity of microbial communities. A crucial step in metagenomic analysis is metagenomic binning, which involves the (supervised) classification or (unsupervised) clustering of metagenomic sequences. Various machine learning models have been applied to address this task. In this review, the contributions of artificial neural networks (ANN) in the context of metagenomic binning are detailed, addressing both supervised, unsupervised, and semi-supervised approaches. 34 ANN-based binning tools are systematically compared, detailing their architectures, input features, datasets, advantages, disadvantages, and other relevant aspects. The findings reveal that deep learning approaches, such as convolutional neural networks and autoencoders, achieve higher accuracy and scalability than traditional methods. Gaps in benchmarking practices are highlighted, and future directions are proposed, including standardized datasets and optimization of architectures, for third-generation sequencing. This review provides support to researchers in identifying trends and selecting suitable tools for the metagenomic binning problem.

FGeneBERT: function-driven pre-trained gene language model for metagenomics

Metagenomic data, comprising mixed multi-species genomes, are prevalent in diverse environments like oceans and soils, significantly impacting human health and ecological functions. However, current research relies on K-mer, which limits the capture of structurally and functionally relevant gene contexts. Moreover, these approaches struggle with encoding biologically meaningful genes and fail to address the one-to-many and many-to-one relationships inherent in metagenomic data. To overcome these challenges, we introduce FGeneBERT, a novel metagenomic pre-trained model that employs a protein-based gene representation as a context-aware and structure-relevant tokenizer. FGeneBERT incorporates masked gene modeling to enhance the understanding of inter-gene contextual relationships and triplet enhanced metagenomic contrastive learning to elucidate gene sequence-function relationships. Pre-trained on over 100 million metagenomic sequences, FGeneBERT demonstrates superior performance on metagenomic datasets at four levels, spanning gene, functional, bacterial, and environmental levels and ranging from 1 to 213 k input sequences. Case studies of ATP synthase and gene operons highlight FGeneBERT's capability for functional recognition and its biological relevance in metagenomic research.

A genome-scale metabolic reconstruction resource of 247,092 diverse human microbes spanning multiple continents, age groups, and body sites

Genome-scale modeling of microbiome metabolism enables the simulation of diet-host-microbiome-disease interactions. However, current genome-scale reconstruction resources are limited in scope by computational challenges. We developed an optimized and highly parallelized reconstruction and analysis pipeline to build a resource of 247,092 microbial genome-scale metabolic reconstructions, deemed APOLLO. APOLLO spans 19 phyla, contains >60% of uncharacterized strains, and accounts for strains from 34 countries, all age groups, and multiple body sites. Using machine learning, we predicted with high accuracy the taxonomic assignment of strains based on the computed metabolic features. We then built 14,451 metagenomic sample-specific microbiome community models to systematically interrogate their community-level metabolic capabilities. We show that sample-specific metabolic pathways accurately stratify microbiomes by body site, age, and disease state. APOLLO is freely available, enables the systematic interrogation of the metabolic capabilities of largely still uncultured and unclassified species, and provides unprecedented opportunities for systems-level modeling of personalized host-microbiome co-metabolism.

Screening, identification, and application of anaerobic ammonia oxidizing bacteria in activated sludge systems: A comprehensive review

Anaerobic ammonium oxidation (Anammox) has garnered significant attention due to its ability to eliminate the need for aeration and supplementary carbon sources in biological nitrogen removal process, relying on the capacity of anaerobic ammonium oxidizing bacteria (AnAOB) to directly convert ammonium and nitrite nitrogen into nitrogen gas. This review consolidates the latest advancements in AnAOB research, outlining the mechanisms and enzymatic processes of Anammox, and summarizing the molecular biological techniques used for studying AnAOB, such as 16s rRNA sequencing, qPCR, and metagenomic sequencing. Additionally, it also overviews the currently identified AnAOB species and their distinct metabolic traits, while consolidating strategies to improve their performance. It further delineates coupled processes that utilize Anammox technology, offering practical insights for process selection. Eventually, the review concludes by suggesting future research directions and highlighting critical areas for further investigation. This review serves as a theoretical reference for the enrichment and cultivation of AnAOB, environmental impact management, and the selection of effective treatment processes.

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.

Advances in genotypic antimicrobialresistance testing: a comprehensive review

Antimicrobial resistance (AMR) represents a substantial threat to global public health, complicating the treatment of common infections and leading to prolonged illness and escalated healthcare expenses. To effectively combat AMR, timely and accurate detection is crucial for AMR surveillance and individual-based therapy. Phenotypic antibiotic resistance testing (AST) has long been considered the gold standard in clinical applications, serving as the foundation for clinical AMR diagnosis and optimized therapy. It has significantly contributed to ensuring patients' health and the development of novel antimicrobials. Despite advancements in automated culture-based AST technologies, inherent limitations impede the widespread use of phenotypic AST in AMR surveillance. Genotypic AST technologies offer a promising alternative option, exhibiting advantages of rapidity, high sensitivity, and specificity. With the continuous advancement and expanding applications of genotypic AST technologies, such as microfluidics, mass spectrometry, and high-resolution melting curve analysis, new vigor has been injected into the development and clinical implementation of genotypic AST technologies. In this narrative review, we discuss the principles, applications, and advancements of emerging genotypic AST methods in clinical settings. The comprehensive review aims to highlight the significant scientific potential of emerging genotypic AST technologies in clinical AMR diagnosis, providing insights to enhance existing methods and explore novel approaches.

Shallow shotgun sequencing of healthcare waste reveals plastic-eating bacteria with broad-spectrum antibiotic resistance genes

The burgeoning crises of antimicrobial resistance and plastic pollution are converging in healthcare settings, presenting a complex challenge to global health. This study investigates the microbial populations in healthcare waste to understand the extent of antimicrobial resistance and the potential for plastic degradation by bacteria. Our metagenomic analysis, using both amplicon and shallow shotgun sequencing, provided a comprehensive view of the taxonomic diversity and functional capacity of the microbial consortia. The viable bacteria in healthcare waste samples were analyzed employing full-length 16S rRNA sequencing, revealing a diverse bacterial community dominated by Firmicutes and Proteobacteria phyla. Notably, Proteus mirabilis VFC3/3 and Pseudomonas sp. VFA2/3 were detected, while Stenotrophomonas maltophilia VFV3/2 surfaced as the predominant species, holding implications for the spread of hospital-acquired infections and antimicrobial resistance. Antibiotic susceptibility testing identified multidrug-resistant strains conferring antimicrobial genes, including the broad-spectrum antibiotic carbapenem, underscoring the critical need for improved waste management and infection control measures. Remarkably, we found genes linked to the breakdown of plastic that encoded for enzymes of the esterase, depolymerase, and oxidoreductase classes. This suggests that specific bacteria found in medical waste may be able to reduce the amount of plastic pollution that comes from biological and medical waste. The information is helpful in formulating strategies to counter the combined problems of environmental pollution and antibiotic resistance. This study emphasises the importance of monitoring microbial communities in hospital waste in order to influence waste management procedures and public health policy. The findings highlight the need for a multidisciplinary approach to mitigate the risks associated with antimicrobial resistance and plastic waste, especially in hospital settings where they intersect most acutely.

Insights on Wet and Dry Workflows for Human Gut Metaproteomics

The human gut microbiota (GM) is a community of microorganisms that resides in the gastrointestinal (GI) tract. Recognized as a critical element of human health, the functions of the GM extend beyond GI well-being to influence overall systemic health and susceptibility to disease. Among the other omic sciences, metaproteomics highlights additional facets that make it a highly valuable discipline in the study of GM. Indeed, it allows the protein inventory of complex microbial communities. Proteins with associated taxonomic membership and function are identified and quantified from their constituent peptides by liquid chromatography coupled to mass spectrometry analyses and by querying specific databases (DBs). The aim of this review was to compile comprehensive information on metaproteomic studies of the human GM, with a focus on the bacterial component, to assist newcomers in understanding the methods and types of research conducted in this field. The review outlines key steps in a metaproteomic-based study, such as protein extraction, DB selection, and bioinformatic workflow. The importance of standardization is emphasized. In addition, a list of previously published studies is provided as hints for researchers interested in investigating the role of GM in health and disease states.

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.

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.

Large-scale metagenomic assembly provide new insights into the genetic evolution of gut microbiomes in plateau ungulates

Trillions of microbes colonize the ungulate gastrointestinal tract, playing a pivotal role in enhancing host nutrient utilization by breaking down cellulose and hemicellulose present in plants. Here, through large-scale metagenomic assembly, we established a catalog of 131,416 metagenome-assembled genomes (MAGs) and 11,175 high-quality species-level genome bins (SGBs) from 17 species of ungulates in China. Our study revealed the convergent evolution of high relative abundances of carbohydrate-active enzymes (CAZymes) in the gut microbiomes of plateau-dwelling ungulates. Notably, two significant factors contribute to this phenotype: structural variations in their gut microbiome genomes, which contain more CAZymes, and the presence of novel gut microbiota species, particularly those in the genus Cryptobacteroides, which are undergoing independent rapid evolution and speciation and have higher gene densities of CAZymes. Furthermore, these enrichment CAZymes in the gut microbiomes are highly enrichment in known metabolic pathways for short-chain fatty acid (SCFA) production. Our findings not only provide a valuable genomic resource for understanding the gut microbiomes of ungulates but also offer fresh insights into the interaction between gut microbiomes and their hosts, as well as the co-adaptation of hosts and their gut microbiomes to their environments.

MAGqual: a stand-alone pipeline to assess the quality of metagenome-assembled genomes

BACKGROUND

Metagenomics, the whole genome sequencing of microbial communities, has provided insight into complex ecosystems. It has facilitated the discovery of novel microorganisms, explained community interactions and found applications in various fields. Advances in high-throughput and third-generation sequencing technologies have further fuelled its popularity. Nevertheless, managing the vast data produced and addressing variable dataset quality remain ongoing challenges. Another challenge arises from the number of assembly and binning strategies used across studies. Comparing datasets and analysis tools is complex as it requires the quantitative assessment of metagenome quality. The inherent limitations of metagenomic sequencing, which often involves sequencing complex communities, mean community members are challenging to interrogate with traditional culturing methods leading to many lacking reference sequences. MIMAG standards aim to provide a method to assess metagenome quality for comparison but have not been widely adopted.

RESULTS

To address the need for simple and quick metagenome quality assignation, here we introduce the pipeline MAGqual (Metagenome-Assembled Genome qualifier) and demonstrate its effectiveness at determining metagenomic dataset quality in the context of the MIMAG standards.

CONCLUSIONS

The MAGqual pipeline offers an accessible way to evaluate metagenome quality and generate metadata on a large scale. MAGqual is built in Snakemake to ensure readability and scalability, and its open-source nature promotes accessibility, community development, and ease of updates. MAGqual is built in Snakemake, R, and Python and is available under the MIT license on GitHub at https://github.com/ac1513/MAGqual . Video Abstract.

Review on computer-assisted biosynthetic capacities elucidation to assess metabolic interactions and communication within microbial communities

Microbial communities thrive through interactions and communication, which are challenging to study as most microorganisms are not cultivable. To address this challenge, researchers focus on the extracellular space where communication events occur. Exometabolomics and interactome analysis provide insights into the molecules involved in communication and the dynamics of their interactions. Advances in sequencing technologies and computational methods enable the reconstruction of taxonomic and functional profiles of microbial communities using high-throughput multi-omics data. Network-based approaches, including community flux balance analysis, aim to model molecular interactions within and between communities. Despite these advances, challenges remain in computer-assisted biosynthetic capacities elucidation, requiring continued innovation and collaboration among diverse scientists. This review provides insights into the current state and future directions of computer-assisted biosynthetic capacities elucidation in studying microbial communities.

Seasonal variations of microbial communities and viral diversity in fishery-enhanced marine ranching sediments: insights into metabolic potentials and ecological interactions

BACKGROUND

The ecosystems of marine ranching have enhanced marine biodiversity and ecological balance and have promoted the natural recovery and enhancement of fishery resources. The microbial communities of these ecosystems, including bacteria, fungi, protists, and viruses, are the drivers of biogeochemical cycles. Although seasonal changes in microbial communities are critical for ecosystem functioning, the current understanding of microbial-driven metabolic properties and their viral communities in marine sediments remains limited. Here, we employed amplicon (16S and 18S) and metagenomic approaches aiming to reveal the seasonal patterns of microbial communities, bacterial-eukaryotic interactions, whole metabolic potential, and their coupling mechanisms with carbon (C), nitrogen (N), and sulfur (S) cycling in marine ranching sediments. Additionally, the characterization and diversity of viral communities in different seasons were explored in marine ranching sediments.

RESULTS

The current study demonstrated that seasonal variations dramatically affected the diversity of microbial communities in marine ranching sediments and the bacterial-eukaryotic interkingdom co-occurrence networks. Metabolic reconstruction of the 113 medium to high-quality metagenome-assembled genomes (MAGs) was conducted, and a total of 8 MAGs involved in key metabolic genes and pathways (methane oxidation - denitrification - S oxidation), suggesting a possible coupling effect between the C, N, and S cycles. In total, 338 viral operational taxonomic units (vOTUs) were identified, all possessing specific ecological characteristics in different seasons and primarily belonging to Caudoviricetes, revealing their widespread distribution and variety in marine sediment ecosystems. In addition, predicted virus-host linkages showed that high host specificity was observed, with few viruses associated with specific hosts.

CONCLUSIONS

This finding deepens our knowledge of element cycling and viral diversity in fisheries enrichment ecosystems, providing insights into microbial-virus interactions in marine sediments and their effects on biogeochemical cycling. These findings have potential applications in marine ranching management and ecological conservation. Video Abstract.

A metagenomic approach to demystify the anaerobic digestion black box and achieve higher biogas yield: a review

The increasing reliance on fossil fuels and the growing accumulation of organic waste necessitates the exploration of sustainable energy alternatives. Anaerobic digestion (AD) presents one such solution by utilizing secondary biomass to produce biogas while reducing greenhouse gas emissions. Given the crucial role of microbial activity in anaerobic digestion, a deeper understanding of the microbial community is essential for optimizing biogas production. While metagenomics has emerged as a valuable tool for unravelling microbial composition and providing insights into the functional potential in biodigestion, it falls short of interpreting the functional and metabolic interactions, limiting a comprehensive understanding of individual roles in the community. This emphasizes the significance of expanding the scope of metagenomics through innovative tools that highlight the often-overlooked, yet crucial, role of microbiota in biomass digestion. These tools can more accurately elucidate microbial ecological fitness, shared metabolic pathways, and interspecies interactions. By addressing current limitations and integrating metagenomics with other omics approaches, more accurate predictive techniques can be developed, facilitating informed decision-making to optimize AD processes and enhance biogas yields, thereby contributing to a more sustainable future.

Graph-based self-supervised learning for repeat detection in metagenomic assembly

Repetitive DNA (repeats) poses significant challenges for accurate and efficient genome assembly and sequence alignment. This is particularly true for metagenomic data, in which genome dynamics such as horizontal gene transfer, gene duplication, and gene loss/gain complicate accurate genome assembly from metagenomic communities. Detecting repeats is a crucial first step in overcoming these challenges. To address this issue, we propose GraSSRep, a novel approach that leverages the assembly graph's structure through graph neural networks (GNNs) within a self-supervised learning framework to classify DNA sequences into repetitive and nonrepetitive categories. Specifically, we frame this problem as a node classification task within a metagenomic assembly graph. In a self-supervised fashion, we rely on a high-precision (but low-recall) heuristic to generate pseudolabels for a small proportion of the nodes. We then use those pseudolabels to train a GNN embedding and a random forest classifier to propagate the labels to the remaining nodes. In this way, GraSSRep combines sequencing features with predefined and learned graph features to achieve state-of-the-art performance in repeat detection. We evaluate our method using simulated and synthetic metagenomic data sets. The results on the simulated data highlight GraSSRep's robustness to repeat attributes, demonstrating its effectiveness in handling the complexity of repeated sequences. Additionally, experiments with synthetic metagenomic data sets reveal that incorporating the graph structure and the GNN enhances the detection performance. Finally, in comparative analyses, GraSSRep outperforms existing repeat detection tools with respect to precision and recall.

Dataset of 313 metagenome-assemble genomes from streamer hot spring water

This data report presents prokaryotic metagenome-assembled genomes (MAGs) from a hot spring stream with temperatures between 64 and 100°C. The stream water was filtered and the extracted total DNA was sequenced using the Illumina HiSeq 2500 platform. Approximately 80 Gb of raw data were generated, which were subsequently assembled using MEGAHIT v1.2.9. The MAGs were generated using MetaWRAP with binning approaches of MetaBAT2, CONCOCT and MaxBin2. We constructed 25 medium-quality and 24 high-quality archaeal MAGs, and 152 medium-quality and 112 high-quality bacterial MAGs. The fasta files of these MAGs are available in the NCBI database as well as Mendeley Data. Major phyla identified include Bacteroidota, Chloroflexota, Desulfobacterota, Firmicutes, Patescibacteria, Proteobacteria, Spirochaetota, Verrucomicrobiota, Armatimonadota, Nitrospirota, Acidobacteriota, Elusimicrobiota, Planctomycetota, Candidate division WOR-3, Aquificota, Thermoproteota, and Micrarchaeota. This dataset is valuable for studies on thermophilic genomes, reconstruction of biochemical pathways and gene discovery.

Metagenome quality metrics and taxonomical annotation visualization through the integration of MAGFlow and BIgMAG

Background

Building Metagenome-Assembled Genomes (MAGs) from highly complex metagenomics datasets encompasses a series of steps covering from cleaning the sequences, assembling them to finally group them into bins. Along the process, multiple tools aimed to assess the quality and integrity of each MAG are implemented. Nonetheless, even when incorporated within end-to-end pipelines, the outputs of these pieces of software must be visualized and analyzed manually lacking integration in a complete framework.

Methods

We developed a Nextflow pipeline (MAGFlow) for estimating the quality of MAGs through a wide variety of approaches (BUSCO, CheckM2, GUNC and QUAST), as well as for annotating taxonomically the metagenomes using GTDB-Tk2. MAGFlow is coupled to a Python-Dash application (BIgMAG) that displays the concatenated outcomes from the tools included by MAGFlow, highlighting the most important metrics in a single interactive environment along with a comparison/clustering of the input data.

Results

By using MAGFlow/BIgMAG, the user will be able to benchmark the MAGs obtained through different workflows or establish the quality of the MAGs belonging to different samples following the divide and rule methodology.

Conclusions

MAGFlow/BIgMAG represents a unique tool that integrates state-of-the-art tools to study different quality metrics and extract visually as much information as possible from a wide range of genome features.

Clinical Metagenomic Next-Generation Sequencing for Diagnosis of Central Nervous System Infections: Advances and Challenges

Central nervous system (CNS) infections carry a substantial burden of morbidity and mortality worldwide, and accurate and timely diagnosis is required to optimize management. Metagenomic next-generation sequencing (mNGS) has proven to be a valuable tool in detecting pathogens in patients with suspected CNS infection. By sequencing microbial nucleic acids present in a patient's cerebrospinal fluid, brain tissue, or samples collected outside of the CNS, such as plasma, mNGS can detect a wide range of pathogens, including rare, unexpected, and/or fastidious organisms. Furthermore, its target-agnostic approach allows for the identification of both known and novel pathogens. This is particularly useful in cases where conventional diagnostic methods fail to provide an answer. In addition, mNGS can detect multiple microorganisms simultaneously, which is crucial in cases of mixed infections without a clear predominant pathogen. Overall, clinical mNGS testing can help expedite the diagnostic process for CNS infections, guide appropriate management decisions, and ultimately improve clinical outcomes. However, there are key challenges surrounding its use that need to be considered to fully leverage its clinical impact. For example, only a few specialized laboratories offer clinical mNGS due to the complexity of both the laboratory methods and analysis pipelines. Clinicians interpreting mNGS results must be aware of both false negatives-as mNGS is a direct detection modality and requires a sufficient amount of microbial nucleic acid to be present in the sample tested-and false positives-as mNGS detects environmental microbes and their nucleic acids, despite best practices to minimize contamination. Additionally, current costs and turnaround times limit broader implementation of clinical mNGS. Finally, there is uncertainty regarding the best practices for clinical utilization of mNGS, and further work is needed to define the optimal patient population(s), syndrome(s), and time of testing to implement clinical mNGS.

Exploring microbial worlds: a review of whole genome sequencing and its application in characterizing the microbial communities

The classical microbiology techniques have inherent limitations in unraveling the complexity of microbial communities, necessitating the pivotal role of sequencing in studying the diversity of microbial communities. Whole genome sequencing (WGS) enables researchers to uncover the metabolic capabilities of the microbial community, providing valuable insights into the microbiome. Herein, we present an overview of the rapid advancements achieved thus far in the use of WGS in microbiome research. There was an upsurge in publications, particularly in 2021 and 2022 with the United States, China, and India leading the metagenomics research landscape. The Illumina platform has emerged as the widely adopted sequencing technology, whereas a significant focus of metagenomics has been on understanding the relationship between the gut microbiome and human health where distinct bacterial species have been linked to various diseases. Additionally, studies have explored the impact of human activities on microbial communities, including the potential spread of pathogenic bacteria and antimicrobial resistance genes in different ecosystems. Furthermore, WGS is used in investigating the microbiome of various animal species and plant tissues such as the rhizosphere microbiome. Overall, this review reflects the importance of WGS in metagenomics studies and underscores its remarkable power in illuminating the variety and intricacy of the microbiome in different environments.

Seven hundred and ninety-seven metagenome-assembled genomes from the goat rumen during early life

The rumen microbiome plays an important role in providing energy and protein to the host. Manipulation of rumen microbiome during early life may have a long-term beneficial effect on the health, growth performance, and feed efficiency of ruminants. To better understand the profiles and functional potentials of rumen microbiome in young ruminants, metagenomic binning was performed to investigate the rumen microbiome of goat kids from one to 84 days of age. A total of 797 metagenome-assembled genomes (MAGs) were recovered from the rumen of 42 Laiwu black goat kids. Our findings provide fundamental knowledge of the rumen microbiome during early life based on metagenomic binning, which may provide insights into effective strategies to achieve long-term beneficial effects on animal health and production.

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.

Transcriptome analysis: a powerful tool to understand individual microbial behaviors and interactions in ecosystems

Transcriptome analysis is a powerful tool for studying microbial ecology, especially individual microbial functions in an ecosystem and their interactions. With the development of high-throughput sequencing technology, great progress has been made in analytical methods for microbial communities in natural environments. 16S rRNA gene amplicon sequencing (ie microbial community structure analysis) and shotgun metagenome analysis have been widely used to determine the composition and potential metabolic capability of microorganisms in target environments without requiring culture. However, even if the types of microorganisms present and their genes are known, it is difficult to determine what they are doing in an ecosystem. Gene expression analysis (transcriptome analysis; RNA-seq) is a powerful tool to address these issues. The history and basic information of gene expression analysis, as well as examples of studies using this method to analyze microbial ecosystems, are presented.

Thanos: An R Package for the Gene-Centric Analysis of Functional Potential in Metagenomic Samples

As the amount of metagenomic sequencing continues to increase, there is a growing need for tools that help biologists make sense of the data. Specifically, researchers are often interested in the potential of a microbial community to carry out a metabolic reaction, but this analysis requires knitting together multiple software tools into a complex pipeline. Thanos offers a user-friendly R package designed for the pathway-centric analysis and visualization of the functions encoded within metagenomic samples. It allows researchers to go beyond taxonomic profiles and find out, quantitatively, which pathways are prevalent in an environment, as well as comparing different environments in terms of their functional potential. The analysis is based on the sequencing depth of the genes of interest, either in the metagenome-assembled genomes (MAGs) or in the assembled reads (contigs), using a normalization strategy that enables comparison across samples. The package can import the data from multiple formats and offers functions for the visualization of the results as bar plots of the functional profile, box plots of compare functions across samples, and annotated pathway graphs. By streamlining the analysis of the functional potential encoded in microbial communities, Thanos can enable impactful discoveries in all the fields touched by metagenomics, from human health to the environmental sciences.

Microbiome modeling: a beginner's guide

Microbiomes, comprised of diverse microbial species and viruses, play pivotal roles in human health, environmental processes, and biotechnological applications and interact with each other, their environment, and hosts via ecological interactions. Our understanding of microbiomes is still limited and hampered by their complexity. A concept improving this understanding is systems biology, which focuses on the holistic description of biological systems utilizing experimental and computational methods. An important set of such experimental methods are metaomics methods which analyze microbiomes and output lists of molecular features. These lists of data are integrated, interpreted, and compiled into computational microbiome models, to predict, optimize, and control microbiome behavior. There exists a gap in understanding between microbiologists and modelers/bioinformaticians, stemming from a lack of interdisciplinary knowledge. This knowledge gap hinders the establishment of computational models in microbiome analysis. This review aims to bridge this gap and is tailored for microbiologists, researchers new to microbiome modeling, and bioinformaticians. To achieve this goal, it provides an interdisciplinary overview of microbiome modeling, starting with fundamental knowledge of microbiomes, metaomics methods, common modeling formalisms, and how models facilitate microbiome control. It concludes with guidelines and repositories for modeling. Each section provides entry-level information, example applications, and important references, serving as a valuable resource for comprehending and navigating the complex landscape of microbiome research and modeling.

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.

Exploring high-quality microbial genomes by assembling short-reads with long-range connectivity

Although long-read sequencing enables the generation of complete genomes for unculturable microbes, its high cost limits the widespread adoption of long-read sequencing in large-scale metagenomic studies. An alternative method is to assemble short-reads with long-range connectivity, which can be a cost-effective way to generate high-quality microbial genomes. Here, we develop Pangaea, a bioinformatic approach designed to enhance metagenome assembly using short-reads with long-range connectivity. Pangaea leverages connectivity derived from physical barcodes of linked-reads or virtual barcodes by aligning short-reads to long-reads. Pangaea utilizes a deep learning-based read binning algorithm to assemble co-barcoded reads exhibiting similar sequence contexts and abundances, thereby improving the assembly of high- and medium-abundance microbial genomes. Pangaea also leverages a multi-thresholding algorithm strategy to refine assembly for low-abundance microbes. We benchmark Pangaea on linked-reads and a combination of short- and long-reads from simulation data, mock communities and human gut metagenomes. Pangaea achieves significantly higher contig continuity as well as more near-complete metagenome-assembled genomes (NCMAGs) than the existing assemblers. Pangaea also generates three complete and circular NCMAGs on the human gut microbiomes.

The buzz about honey-based biosurveys

Approximately 1.8 million metric tonnes of honey are produced globally every year. The key source behind this output, the honey bee (Apis mellifera), works tirelessly to create the delicious condiment that is consumed worldwide. The honey that finds its way into jars on store shelves contains a myriad of information about its biogeographical origins, such as the bees that produced it, the botanical constituents, and traces of other organisms or pathogens that have come in contact with the product or its producer. With the ongoing threat of honey bee decline and overall global biodiversity loss, access to ecological information has become an key factor in preventing the loss of species. This review delves into the various molecular techniques developed to characterize the collective DNA harnessed within honey samples, and how it can be used to elucidate the ecological interactions between honey bees and the environment. We also explore how these DNA-based methods can be used for large-scale biogeographical studies through the environmental DNA collected by foraging honey bees. Further development of these techniques can assist in the conservation of biodiversity by detecting ecosystem perturbations, with the potential to be expanded towards other critical flying pollinators.

A CRISPR-based strategy for targeted sequencing in biodiversity science

Many applications in molecular ecology require the ability to match specific DNA sequences from single- or mixed-species samples with a diagnostic reference library. Widely used methods for DNA barcoding and metabarcoding employ PCR and amplicon sequencing to identify taxa based on target sequences, but the target-specific enrichment capabilities of CRISPR-Cas systems may offer advantages in some applications. We identified 54,837 CRISPR-Cas guide RNAs that may be useful for enriching chloroplast DNA across phylogenetically diverse plant species. We tested a subset of 17 guide RNAs in vitro to enrich plant DNA strands ranging in size from diagnostic DNA barcodes of 1,428 bp to entire chloroplast genomes of 121,284 bp. We used an Oxford Nanopore sequencer to evaluate sequencing success based on both single- and mixed-species samples, which yielded mean chloroplast sequence lengths of 2,530-11,367 bp, depending on the experiment. In comparison to mixed-species experiments, single-species experiments yielded more on-target sequence reads and greater mean pairwise identity between contigs and the plant species' reference genomes. But nevertheless, these mixed-species experiments yielded sufficient data to provide ≥48-fold increase in sequence length and better estimates of relative abundance for a commercially prepared mixture of plant species compared to DNA metabarcoding based on the chloroplast trnL-P6 marker. Prior work developed CRISPR-based enrichment protocols for long-read sequencing and our experiments pioneered its use for plant DNA barcoding and chloroplast assemblies that may have advantages over workflows that require PCR and short-read sequencing. Future work would benefit from continuing to develop in vitro and in silico methods for CRISPR-based analyses of mixed-species samples, especially when the appropriate reference genomes for contig assembly cannot be known a priori.

Metagenomics for Pathogen Detection During a Mass Mortality Event in Songbirds

Mass mortality events in wildlife can be indications of an emerging infectious disease. During the spring and summer of 2021, hundreds of dead passerines were reported across the eastern US. Birds exhibited a range of clinical signs including swollen conjunctiva, ocular discharge, ataxia, and nystagmus. As part of the diagnostic investigation, high-throughput metagenomic next-generation sequencing was performed across three molecular laboratories on samples from affected birds. Many potentially pathogenic microbes were detected, with bacteria forming the largest proportion; however, no singular agent was consistently identified, with many of the detected microbes also found in unaffected (control) birds and thus considered to be subclinical infections. Congruent results across laboratories have helped drive further investigation into alternative causes, including environmental contaminants and nutritional deficiencies. This work highlights the utility of metagenomic approaches in investigations of emerging diseases and provides a framework for future wildlife mortality events.

Current status and future developments of assessing microbiome composition and dynamics in anaerobic digestion systems using metagenomic approaches

The metagenomic approach stands as a powerful technique for examining the composition of microbial communities and their involvement in various anaerobic digestion (AD) systems. Understanding the structure, function, and dynamics of microbial communities becomes pivotal for optimizing the biogas process, enhancing its stability and improving overall performance. Currently, taxonomic profiling of biogas-producing communities relies mainly on high-throughput 16S rRNA sequencing, offering insights into the bacterial and archaeal structures of AD assemblages and their correlations with fed substrates and process parameters. To delve even deeper, shotgun and genome-centric metagenomic approaches are employed to recover individual genomes from the metagenome. This provides a nuanced understanding of collective functionalities, interspecies interactions, and microbial associations with abiotic factors. The application of OMICs in AD systems holds the potential to revolutionize the field, leading to more efficient and sustainable waste management practices particularly through the implementation of precision anaerobic digestion systems. As ongoing research in this area progresses, anticipations are high for further exciting developments in the future. This review serves to explore the current landscape of metagenomic analyses, with focus on advancing our comprehension and critically evaluating biases and recommendations in the analysis of microbial communities in anaerobic digesters. Its objective is to explore how contemporary metagenomic approaches can be effectively applied to enhance our understanding and contribute to the refinement of the AD process. This marks a substantial stride towards achieving a more comprehensive understanding of anaerobic digestion systems.

Enhancing Clinical Utility: Utilization of International Standards and Guidelines for Metagenomic Sequencing in Infectious Disease Diagnosis

Metagenomic sequencing has emerged as a transformative tool in infectious disease diagnosis, offering a comprehensive and unbiased approach to pathogen detection. Leveraging international standards and guidelines is essential for ensuring the quality and reliability of metagenomic sequencing in clinical practice. This review explores the implications of international standards and guidelines for the application of metagenomic sequencing in infectious disease diagnosis. By adhering to established standards, such as those outlined by regulatory bodies and expert consensus, healthcare providers can enhance the accuracy and clinical utility of metagenomic sequencing. The integration of international standards and guidelines into metagenomic sequencing workflows can streamline diagnostic processes, improve pathogen identification, and optimize patient care. Strategies in implementing these standards for infectious disease diagnosis using metagenomic sequencing are discussed, highlighting the importance of standardized approaches in advancing precision infectious disease diagnosis initiatives.

Mining the microbiome of Lake Afdera to gain insights into microbial diversity and biosynthetic potential

Microorganisms inhabiting hypersaline environments have received significant attention due to their ability to thrive under poly-extreme conditions, including high salinity, elevated temperatures and heavy metal stress. They are believed to possess biosynthetic gene clusters (BGCs) that encode secondary metabolites as survival strategy and offer potential biotechnological applications. In this study, we mined BGCs in shotgun metagenomic sequences generated from Lake Afdera, a hypersaline lake in the Afar Depression, Ethiopia. The microbiome of Lake Afdera is predominantly bacterial, with Acinetobacter (18.6%) and Pseudomonas (11.8%) being ubiquitously detected. A total of 94 distinct BGCs were identified in the metagenomic data. These BGCs are found to encode secondary metabolites with two main categories of functions: (i) potential pharmaceutical applications (nonribosomal peptide synthase NRPs, polyketide synthase, others) and (ii) miscellaneous roles conferring adaptation to extreme environment (bacteriocins, ectoine, others). Notably, NRPs (20.6%) and bacteriocins (10.6%) were the most abundant. Furthermore, our metagenomic analysis predicted gene clusters that enable microbes to defend against a wide range of toxic metals, oxidative stress and osmotic stress. These findings suggest that Lake Afdera is a rich biological reservoir, with the predicted BGCs playing critical role in the survival and adaptation of extremophiles.

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.

When Livestock Genomes Meet Third-Generation Sequencing Technology: From Opportunities to Applications

Third-generation sequencing technology has found widespread application in the genomic, transcriptomic, and epigenetic research of both human and livestock genetics. This technology offers significant advantages in the sequencing of complex genomic regions, the identification of intricate structural variations, and the production of high-quality genomes. Its attributes, including long sequencing reads, obviation of PCR amplification, and direct determination of DNA/RNA, contribute to its efficacy. This review presents a comprehensive overview of third-generation sequencing technologies, exemplified by single-molecule real-time sequencing (SMRT) and Oxford Nanopore Technology (ONT). Emphasizing the research advancements in livestock genomics, the review delves into genome assembly, structural variation detection, transcriptome sequencing, and epigenetic investigations enabled by third-generation sequencing. A comprehensive analysis is conducted on the application and potential challenges of third-generation sequencing technology for genome detection in livestock. Beyond providing valuable insights into genome structure analysis and the identification of rare genes in livestock, the review ventures into an exploration of the genetic mechanisms underpinning exemplary traits. This review not only contributes to our understanding of the genomic landscape in livestock but also provides fresh perspectives for the advancement of research in this domain.

Genome-resolved metagenomics of Venice Lagoon surface sediment bacteria reveals high biosynthetic potential and metabolic plasticity as successful strategies in an impacted environment

Bacteria living in sediments play essential roles in marine ecosystems and deeper insights into the ecology and biogeochemistry of these largely unexplored organisms can be obtained from 'omics' approaches. Here, we characterized metagenome-assembled-genomes (MAGs) from the surface sediment microbes of the Venice Lagoon (northern Adriatic Sea) in distinct sub-basins exposed to various natural and anthropogenic pressures. MAGs were explored for biodiversity, major marine metabolic processes, anthropogenic activity-related functions, adaptations at the microscale, and biosynthetic gene clusters. Starting from 126 MAGs, a non-redundant dataset of 58 was compiled, the majority of which (35) belonged to (Alpha- and Gamma-) Proteobacteria. Within the broad microbial metabolic repertoire (including C, N, and S metabolisms) the potential to live without oxygen emerged as one of the most important features. Mixotrophy was also found as a successful lifestyle. Cluster analysis showed that different MAGs encoded the same metabolic patterns (e.g., C fixation, sulfate oxidation) thus suggesting metabolic redundancy. Antibiotic and toxic compounds resistance genes were coupled, a condition that could promote the spreading of these genetic traits. MAGs showed a high biosynthetic potential related to antimicrobial and biotechnological classes and to organism defense and interactions as well as adaptive strategies for micronutrient uptake and cellular detoxification. Our results highlighted that bacteria living in an impacted environment, such as the surface sediments of the Venice Lagoon, may benefit from metabolic plasticity as well as from the synthesis of a wide array of secondary metabolites, promoting ecosystem resilience and stability toward environmental pressures.

Supplementary Information

The online version contains supplementary material available at 10.1007/s42995-023-00192-z.

Decoupling the dynamic mechanism revealed by FGFR2 mutation-induced population shift

The fibroblast growth factor receptor 2 (FGFR2) is a key component in cellular signaling networks, and its dysfunctional activation has been implicated in various diseases including cancer and developmental disorders. Mutations at the activation loop (A-loop) have been suggested to trigger an increased basal kinase activity. However, the molecular mechanism underlying this highly dynamic process has not been fully understood due to the limitation of static structural information. Here, we conducted multiple, large-scale Gaussian accelerated molecular dynamics simulations of five (K659E, K659N, K659M, K659Q, and K659T) FGFR2 mutants at the A-loop, and comprehensively analyzed the dynamic molecular basis of FGFR2 activation. The results quantified the population shift of each system, revealing that all mutants had a higher proportion of active-like states. Using Markov state models, we extracted the representative structure of different conformational states and identified key residues related to the increased kinase activity. Furthermore, community network analysis showed enhanced information connections in the mutants, highlighting the long-range allosteric communication between the A-loop and the hinge region. Our findings may provide insights into the dynamic mechanism for FGFR2 dysfunctional activation and allosteric drug discovery.Communicated by Ramaswamy H. Sarma.

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.

- Invited Review - Understanding the functionality of the rumen microbiota: searching for better opportunities for rumen microbial manipulation

Rumen microbiota play a central role in the digestive process of ruminants. Their remarkable ability to break down complex plant fibers and proteins, converting them into essential organic compounds that provide animals with energy and nutrition. Research on rumen microbiota not only contributes to improving animal production performance and enhancing feed utilization efficiency but also holds the potential to reduce methane emissions and environmental impact. Nevertheless, studies on rumen microbiota face numerous challenges, including complexity, difficulties in cultivation, and obstacles in functional analysis. This review provides an overview of microbial species involved in the degradation of macromolecules, the fermentation processes, and methane production in the rumen, all based on cultivation methods. Additionally, the review introduces the applications, advantages, and limitations of emerging omics technologies such as metagenomics, metatranscriptomics, metaproteomics, and metabolomics, in investigating the functionality of rumen microbiota. Finally, the article offers a forward-looking perspective on the new horizons and technologies in the field of rumen microbiota functional research. These emerging technologies, with continuous refinement and mutual complementation, have deepened our understanding of rumen microbiota functionality, thereby enabling effective manipulation of the rumen microbial community.

metaProbiotics: a tool for mining probiotic from metagenomic binning data based on a language model

Beneficial bacteria remain largely unexplored. Lacking systematic methods, understanding probiotic community traits becomes challenging, leading to various conclusions about their probiotic effects among different publications. We developed language model-based metaProbiotics to rapidly detect probiotic bins from metagenomes, demonstrating superior performance in simulated benchmark datasets. Testing on gut metagenomes from probiotic-treated individuals, it revealed the probioticity of intervention strains-derived bins and other probiotic-associated bins beyond the training data, such as a plasmid-like bin. Analyses of these bins revealed various probiotic mechanisms and bai operon as probiotic Ruminococcaceae's potential marker. In different health-disease cohorts, these bins were more common in healthy individuals, signifying their probiotic role, but relevant health predictions based on the abundance profiles of these bins faced cross-disease challenges. To better understand the heterogeneous nature of probiotics, we used metaProbiotics to construct a comprehensive probiotic genome set from global gut metagenomic data. Module analysis of this set shows that diseased individuals often lack certain probiotic gene modules, with significant variation of the missing modules across different diseases. Additionally, different gene modules on the same probiotic have heterogeneous effects on various diseases. We thus believe that gene function integrity of the probiotic community is more crucial in maintaining gut homeostasis than merely increasing specific gene abundance, and adding probiotics indiscriminately might not boost health. We expect that the innovative language model-based metaProbiotics tool will promote novel probiotic discovery using large-scale metagenomic data and facilitate systematic research on bacterial probiotic effects. The metaProbiotics program can be freely downloaded at https://github.com/zhenchengfang/metaProbiotics.

Microbiome and metabolome in home-made fermented soybean foods of India revealed by metagenome-assembled genomes and metabolomics

Grep-chhurpi, peha, peron namsing and peruñyaan are lesser-known home-made fermented soybean foods prepared by the native people of Arunachal Pradesh in India. Present work aims to study the microbiome, their functional annotations, metabolites and recovery of metagenome-assembled genomes (MAGs) in these four fermented soybean foods. Metagenomes revealed the dominance of bacteria (97.80 %) with minor traces of viruses, eukaryotes and archaea. Bacillota is the most abundant phylum with Bacillus subtilis as the abundant species. Metagenome also revealed the abundance of lactic acid bacteria such as Enterococcus casseliflavus, Enterococcus faecium, Mammaliicoccus sciuri and Staphylococcus saprophyticus in all samples. B. subtilis was the major species found in all products. Predictive metabolic pathways showed the abundance of genes associated with metabolisms. Metabolomics analysis revealed both targeted and untargeted metabolites, which suggested their role in flavour development and therapeutic properties. High-quality MAGs, identified as B. subtilis, Enterococcus faecalis, Pediococcus acidilactici and B. velezensis, showed the presence of several biomarkers corresponding to various bio-functional properties. Gene clusters of secondary metabolites (antimicrobial peptides) and CRISPR-Cas systems were detected in all MAGs. This present work also provides key elements related to the cultivability of identified species of MAGs for future use as starter cultures in fermented soybean food product development. Additionally, comparison of microbiome and metabolites of grep-chhurpi, peron namsing and peruñyaan with that of other fermented soybean foods of Asia revealed a distinct difference.

Shotgun Metagenomics-Guided Prediction Reveals the Metal Tolerance and Antibiotic Resistance of Microbes in Poly-Extreme Environments in the Danakil Depression, Afar Region

The occurrence and spread of antibiotic resistance genes (ARGs) in environmental microorganisms, particularly in poly-extremophilic bacteria, remain underexplored and have received limited attention. This study aims to investigate the prevalence of ARGs and metal resistance genes (MRGs) in shotgun metagenome sequences obtained from water and salt crust samples collected from Lake Afdera and the Assale salt plain in the Danakil Depression, northern Ethiopia. Potential ARGs were characterized by the comprehensive antibiotic research database (CARD), while MRGs were identified by using BacMetScan V.1.0. A total of 81 ARGs and 39 MRGs were identified at the sampling sites. We found a copA resistance gene for copper and the β-lactam encoding resistance genes were the most abundant the MRG and ARG in the study area. The abundance of MRGs is positively correlated with mercury (Hg) concentration, highlighting the importance of Hg in the selection of MRGs. Significant correlations also exist between heavy metals, Zn and Cd, and ARGs, which suggests that MRGs and ARGs can be co-selected in the environment contaminated by heavy metals. A network analysis revealed that MRGs formed a complex network with ARGs, primarily associated with β-lactams, aminoglycosides, and tetracyclines. This suggests potential co-selection mechanisms, posing concerns for both public health and ecological balance.

The potential role of the microbiota in prostate cancer pathogenesis and treatment

The human body hosts a complex and dynamic population of trillions of microorganisms - the microbiota - which influences the body in homeostasis and disease, including cancer. Several epidemiological studies have associated specific urinary and gut microbial species with increased risk of prostate cancer; however, causal mechanistic data remain elusive. Studies have associated bacterial generation of genotoxins with the occurrence of TMPRSS2-ERG gene fusions, a common, early oncogenic event during prostate carcinogenesis. A subsequent study demonstrated the role of the gut microbiota in prostate cancer endocrine resistance, which occurs, at least partially, through the generation of androgenic steroids fuelling oncogenic signalling via the androgen receptor. These studies present mechanistic evidence of how the host microbiota might be implicated in prostate carcinogenesis and tumour progression. Importantly, these findings also reveal potential avenues for the detection and treatment of prostate cancer through the profiling and modulation of the host microbiota. The latter could involve approaches such as the use of faecal microbiota transplantation, prebiotics, probiotics, postbiotics or antibiotics, which can be used independently or combined with existing treatments to reverse therapeutic resistance and improve clinical outcomes in patients with prostate cancer.