Historically, the analysis of airborne biological organisms relied on microscopy and culture-based techniques. However, technological advances such as PCR and next-generation sequencing now provide researchers with the ability to gather vast amounts of data on airborne environmental DNA (eDNA). Studies typically involve capturing airborne biological material, followed by nucleic acid extraction, library preparation, sequencing and taxonomic identification to characterize the eDNA at a given location. These methods have diverse applications, including pathogen detection in agriculture and human health, air quality monitoring, bioterrorism detection and biodiversity monitoring. A variety of methods are used for airborne eDNA analysis, as no single pipeline meets all needs. This review outlines current methods for sampling, extraction, sequencing and bioinformatic analysis, highlighting how different approaches can influence the resulting data and their suitability for specific use cases. It also explores current applications of airborne eDNA sampling and identifies research gaps in the field.

The microbiome of the gut is a complex ecosystem that contains a wide variety of microbial species and functional capabilities. The microbiome has a significant impact on health and disease by affecting endocrinology, physiology, and neurology. It can change the progression of certain diseases and enhance treatment responses and tolerance. The gut microbiota plays a pivotal role in human health, influencing a wide range of physiological processes. Recent advances in computational tools and artificial intelligence (AI) have revolutionized the study of gut microbiota, enabling the identification of biomarkers that are critical for diagnosing and treating various diseases. This review hunts through the cutting-edge computational methodologies that integrate multi-omics data-such as metagenomics, metaproteomics, and metabolomics-providing a comprehensive understanding of the gut microbiome's composition and function. Additionally, machine learning (ML) approaches, including deep learning and network-based methods, are explored for their ability to uncover complex patterns within microbiome data, offering unprecedented insights into microbial interactions and their link to host health. By highlighting the synergy between traditional bioinformatics tools and advanced AI techniques, this review underscores the potential of these approaches in enhancing biomarker discovery and developing personalized therapeutic strategies. The convergence of computational advancements and microbiome research marks a significant step forward in precision medicine, paving the way for novel diagnostics and treatments tailored to individual microbiome profiles. Investigators have the ability to discover connections between the composition of microorganisms, the expression of genes, and the profiles of metabolites. Individual reactions to medicines that target gut microbes can be predicted by models driven by artificial intelligence. It is possible to obtain personalized and precision medicine by first gaining an understanding of the impact that the gut microbiota has on the development of disease. The application of machine learning allows for the customization of treatments to the specific microbial environment of an individual.

Prochlorococcus and Synechococcus are key contributors to marine primary production and play essential roles in global biogeochemical cycles. Despite the ecological importance of these two picocyanobacterial genera, current genomic datasets still lack comprehensive representation of under-sampled ocean regions, associated bacteria and viruses. To address this gap, we used a combination of second- and third-generation sequencing technologies to assemble comprehensive genomic data from 105 Picocyanobacterial enrichment cultures isolated from the Indian Ocean, the South China Sea, and the western Pacific Ocean. This dataset includes 55 Prochlorococcus and 50 Synechococcus genomes with high completeness (>98%) and low contamination (<2%), along with 308 non-redundant associated bacterial genomes derived from 1,457 medium- and high-quality non-cyanobacteria metagenome-assembled genomes (MAGs, completeness ≥50% and contamination ≤10%). Additionally, 2,113 non-redundant viral operational taxonomic units (vOTUs) were derived from a total of 7632 qualified viral contigs. This dataset provides a valuable resource for improving our understanding of the complex interactions among Prochlorococcus, Synechococcus, and their associated bacteria and viruses in marine ecosystems, offering a foundation to study their ecological roles and evolutionary dynamics.

Despite effective HIV suppression, neuroinflammation and neurocognitive issues are prevalent in people with HIV (PWH) yet poorly understood. HIV infection alters the human virome, and virome perturbations have been linked to neurocognitive issues in people without HIV. Once thought to be sterile, the cerebrospinal fluid (CSF) hosts a recently discovered virome, presenting an unexplored avenue for understanding brain and mental health in PWH. This cross-sectional study analyzed 85 CSF samples (74 from PWH on suppressive antiretroviral therapy, and 11 from controls without HIV, CWH) through shotgun metagenomics for DNA/RNA viruses. Taxonomic composition (reads and contigs), α and β diversity, and relative abundance (RA) of prokaryotic (PV), human eukaryotic (hEV), and non-human eukaryotic viruses (nhEV) were evaluated in relation to HIV infection, markers of neuroinflammation and neurodegeneration, cognitive functions, and depressive symptoms. Sensitivity analyses and post-hoc cluster analysis on the RA of viral groups and blood-brain barrier permeability were also performed. Of 46 read-positive CSF samples, 93.5% contained PV sequences, 47.8% hEV, and 45.6% nhEV. Alpha diversity was lower in PWH versus CWH, although p>0.05. At β diversity analysis, HIV status explained 3.3% of the variation in viral composition (p=0.016). Contigs retained 13 samples positive for 8 hEV, 2 nhEV, and 6 PV. Higher RA of PV was correlated with higher CSF S100β (p=0.002) and β-Amyloid 1-42 fragment (βA-42, p=0.026), while higher RA of nhEV with poorer cognitive performance (p=0.022). Conversely, higher RA of hEV correlated with better cognition (p=0.003) and lower βA-42 (p=0.012). Sensitivity analyses in virome-positive samples only confirmed these findings. Three CSF clusters were identified and showed differences in astrocytosis, βA-42, tau protein, and cognitive functions. Participants with hEV-enriched CSF showed better cognitive performance compared to those with virus-devoid and nhEV-enriched CSF (models'p<0.05). This study provides the first comprehensive description of the CSF virome in PWH, revealing associations with neuroinflammation and cognition. These findings highlight the potential involvement of the CSF virome in brain health and inform about its composition, origin, and potential clinical implications in people with and without HIV.

Mesophotic coral ecosystems (MCEs) host a diverse array of sponge species, which represent a promising source of bioactive compounds. Increasing evidence suggests that sponge-associated bacteria may be the primary producers of these compounds. However, cultivating these bacteria under laboratory conditions remains a significant challenge. To investigate the rich resource of bioactive compounds synthesised by mesophotic sponge-associated bacteria, we retrieved 429 metagenome-assembled genomes (MAGs) from 15 mesophotic sponges, revealing a strong correlation between bacterial diversity and sponge species. Furthermore, we identified 1637 secondary metabolite biosynthetic gene clusters (BGCs) within these MAGs. Among the identified BGCs, terpenes were the most abundant (495), followed by 369 polyketide synthases (PKSs), 293 ribosomally synthesised and post-translationally modified peptides (RiPPs) and 135 nonribosomal peptide synthetases (NRPSs). The BGCs were classified into 1086 gene cluster families (GCFs) based on sequence similarity. Notably, only five GCFs included experimentally validated reference BGCs from the Minimum Information about a Biosynthetic Gene cluster database (MIBiG). Additionally, an unusual abundance of BGCs was detected in Entotheonella sp. (s191209.Bin93) from the Tectomicrobia phylum. In contrast, members of Proteobacteria and Acidobacteriota harboured fewer BGCs (6-7 on average), yet their high abundance in MCE sponges suggests a potentially rich reservoir of BGCs. Analysis of the BGC distribution patterns revealed that a subset of BGCs, including terpene GCFs (FAM_00447 and FAM_01046), PKS GCF (FAM_00235), and RiPPs GCF (FAM_01143), were widespread across mesophotic sponges. Furthermore, 32 GCFs were consistently present in the same MAGs across different sponges, highlighting their potential key biological roles and capacity to yield novel bioactive compounds. This study not only underscores the untapped potential of mesophotic sponge-associated bacteria as a source of bioactive compounds but also provides valuable insights into the intricate interactions between sponges and their symbiotic microbial communities.

A decomposition of a network flow is a set of weighted walks whose superposition equals the flow. In this article, we give a simple and linear-time-verifiable complete characterization (flowtigs) of walks that are safe in such general flow decompositions, i.e., that are subwalks of any possible flow decomposition. We provide an O(mn)-time algorithm that identifies all maximal flowtigs and represents them inside a compact structure. On the practical side, we study flowtigs in the use-case of metagenomic assembly. By using the species abundances as flow values of the metagenomic assembly graph, we can model the possible assembly solutions as flow decompositions into weighted closed walks. On simulated data, compared to reporting unitigs or maximal safe walks based only on the graph structure, reporting flowtigs results in a notably more contiguous assembly. On real data, we frame flowtigs as a heuristic and provide an algorithm that is guided by this heuristic.

Traditional methods for studying microbial communities have been limited due to difficulties in culturing and sequencing all microbial species. Recent advances in third-generation sequencing technologies, particularly PacBio's high-fidelity (HiFi) sequencing, have significantly advanced metagenomics by providing accurate long-read sequences. This review explores the role of HiFi sequencing in overcoming the limitations of previous sequencing methods, including high error rates and fragmented assemblies. We discuss the benefits and applications of HiFi sequencing across various environments, such as the human gut and soil, which provides broader context for further exploration. Key studies are discussed to highlight HiFi sequencing's ability to recover complete and coherent microbial genomes from complex microbiomes, showcasing its superior accuracy and continuity compared to other sequencing technologies. Additionally, we explore the potential applications of HiFi sequencing in quantitative microbial analysis, as well as the detection of single nucleotide variations (SNVs) and structural variations (SVs). PacBio HiFi sequencing is establishing a new benchmark in metagenomics, with the potential to significantly enhance our understanding of microbial ecology and drive forward advancements in both environmental and clinical applications.

Gut microbiota is recognized nowadays as one of the key players in the development of several gastro-intestinal diseases. The first studies focused mainly on healthy subjects with staining of main bacterial species via culture-based techniques. Subsequently, lots of studies tried to focus on principal esophageal disease enlarged the knowledge on esophageal microbial environment and its role in pathogenesis. Gastro Esophageal Reflux Disease (GERD), the most widespread esophageal condition, seems related to a certain degree of mucosal inflammation, via interleukin (IL) 8 potentially enhanced by bacterial components, lipopolysaccharide (LPS) above all. Gram- bacteria, producing LPS), such as Campylobacter genus, have been found associated with GERD. Barrett esophagus (BE) seems characterized by a Gram- and microaerophils-shaped microbiota. Esophageal cancer (EC) development leads to an overturn in the esophageal environment with the shift from an oral-like microbiome to a prevalently low-abundant and low-diverse Gram--shaped microbiome. Although underinvestigated, also changes in the esophageal microbiome are associated with rare chronic inflammatory or neuropathic disease pathogenesis. The paucity of knowledge about the microbiota-driven mechanisms in esophageal disease pathogenesis is mainly due to the scarce sensitivity of sequencing technology and culture methods applied so far to study commensals in the esophagus. However, the recent advances in molecular techniques, especially with the advent of non-culture-based genomic sequencing tools and the implementation of multi-omics approaches, have revolutionized the microbiome field, with promises of implementing the current knowledge, discovering more mechanisms underneath, and giving insights into the development of novel therapies aimed to re-establish the microbial equilibrium for ameliorating esophageal diseases..

Antimicrobial resistance (AMR) is an escalating global health threat, often driven by the horizontal gene transfer (HGT) of resistance genes. Detecting AMR genes and understanding their genomic context within bacterial populations is crucial for mitigating the spread of resistance. In this study, we evaluate the performance of three sequence alignment tools-Bandage, SPAligner, and GraphAligner-in identifying AMR gene sequences from assembly and de Bruijn graphs, which are commonly used in microbial genome assembly. Efficiently identifying these genes allows for the detection of neighboring genetic elements and possible HGT events, contributing to a deeper understanding of AMR dissemination. We compare the performance of the tools both qualitatively and quantitatively, analyzing the precision, computational efficiency, and accuracy in detecting AMR-related sequences. Our analysis reveals that Bandage offers the most precise and efficient identification of AMR gene sequences, followed by GraphAligner and SPAligner. The comparison includes evaluating the similarity of paths returned by each tool and measuring output accuracy using a modified edit distance metric. These results highlight Bandage's potential for contributing to the accurate identification and study of AMR genes in bacterial populations, offering important insights into resistance mechanisms and potential targets for mitigating AMR spread.

Assembly of metagenomic samples can provide essential information about the mobility potential and taxonomic origin of antibiotic resistance genes (ARGs) and inform interventions to prevent further spread of resistant bacteria. However, similar to other conserved regions, such as ribosomal RNA genes and mobile genetic elements, almost identical ARGs typically occur in multiple genomic contexts across different species, representing a considerable challenge for the assembly process. Usually, this results in many fragmented contigs of unclear origin, complicating the risk assessment of ARG detections. To systematically investigate the impact of this issue on detection, quantification and contextualization of ARGs, we evaluated the performance of different assembly approaches, including genomic-, metagenomic- and transcriptomic-specialized assemblers. We quantified recovery and accuracy rates of each tool for ARGs both from in silico spiked metagenomic samples as well as real samples sequenced using both long- and short-read sequencing technologies.

The results revealed that none of the investigated tools can accurately capture genomic contexts present in samples of high complexity. The transcriptomic assembler Trinity showed a better performance in terms of reconstructing longer and fewer contigs matching unique genomic contexts, which can be beneficial for deciphering the taxonomic origin of ARGs. The currently commonly used metagenomic assembly tools metaSPAdes and MEGAHIT were able to identify the ARG repertoire but failed to fully recover the diversity of genomic contexts present in a sample. On top of that, in a complex scenario MEGAHIT produced very short contigs, which can lead to considerable underestimation of the resistome in a given sample.

Our study shows that metaSPAdes and Trinity would be the preferable tools in terms of accuracy to recover correct genomic contexts around ARGs in metagenomic samples characterized by uneven coverages. Overall, the inability of assemblers to reconstruct long ARG-containing contigs has impacts on ARG quantification, suggesting that directly mapping reads to an ARG database should be performed as a complementary strategy to get accurate ARG abundance and diversity measures.

Different strains of identical species can vary substantially in terms of their spectrum of biomedically relevant phenotypes. Reconstructing the genomes of microbial communities at the level of their strains poses significant challenges, because sequencing errors can obscure strain-specific variants. Next-generation sequencing (NGS) reads are too short to resolve complex genomic regions. Third-generation sequencing (TGS) reads, although longer, are prone to higher error rates or substantially more expensive. Limiting TGS coverage to reduce costs compromises the accuracy of the assemblies. This explains why prior approaches agree on losses in strain awareness, accuracy, tendentially excessive costs, or combinations thereof. We introduce HyLight, a metagenome assembly approach that addresses these challenges by implementing the complementary strengths of TGS and NGS data. HyLight employs strain-resolved overlap graphs (OG) to accurately reconstruct individual strains within microbial communities. Our experiments demonstrate that HyLight produces strain-aware and contiguous assemblies at minimal error content, while significantly reducing costs because utilizing low-coverage TGS data. HyLight achieves an average improvement of 19.05% in preserving strain identity and demonstrates near-complete strain awareness across diverse datasets. In summary, HyLight offers considerable advances in metagenome assembly, insofar as it delivers significantly enhanced strain awareness, contiguity, and accuracy without the typical compromises observed in existing approaches.

Advances in omics technologies have enabled the in-depth study of microbial communities and their metabolic profiles from all environments. Here metagenomes were sampled from piranha (Serrasalmus rhombeus) and from river water from the Rio São Benedito (Amazon Basin). Shotgun metagenome sequencing was used to explore diversity and to test whether fish microbiomes are a good proxy for river microbiome studies. The results showed that the fish microbiomes were not significantly different from the river water microbiomes at higher taxonomic ranks. However, at the genus level, fish microbiome alpha diversity decreased, and beta diversity increased. This result repeated for functional gene abundances associated with specific metabolic categories (SEED level 3). A clear delineation between water and fish was seen for beta diversity. The piranha microbiome provides a good and representative subset of its river water microbiome. Variations seen in beta biodiversity were expected and can be explained by temporal variations in the fish microbiome in response to stronger selective forces on its biodiversity. Metagenome assembled genomes construction was better from the fish samples. This study has revealed that the microbiome of a piranha tells us a lot about its river water microbiome and function.

Accurately and efficiently extracting microbial genomic sequences from complex metagenomic data is crucial for advancing our understanding in fields such as clinical diagnostics, environmental microbiology, and biodiversity. As sequencing technologies evolve, this task becomes increasingly challenging due to the intricate nature of microbial communities and the vast amount of data generated. Especially in intensive care units (ICUs), infections caused by antibiotic-resistant bacteria are increasingly prevalent among critically ill patients, significantly impacting the effectiveness of treatments and patient prognoses. Therefore, obtaining timely and accurate information about infectious pathogens is of paramount importance for the treatment of patients with severe infections, which enables precisely targeted anti-infection therapies, and a tool that can extract microbial genomic sequences from metagenomic dataset would be of help.

We developed MetaGeneMiner to help with retrieving specific microbial genomic sequences from metagenomes using a k-mer-based approach. It facilitates the rapid and accurate identification and analysis of pathogens. The tool is designed to be user-friendly and efficient on standard personal computers, allowing its use across a wide variety of settings. We validated MetaGeneMiner using eight metagenomic samples from ICU patients, which demonstrated its efficiency and accuracy.

The software extensively retrieved coding sequences of pathogens Acinetobacter baumannii and herpes simplex virus type 1 and detected a variety of resistance genes. All documentation and source codes for MetaGeneMiner are freely available at https://gitee.com/sculab/MetaGeneMiner .

It is foreseeable that MetaGeneMiner possesses the potential for applications across multiple domains, including clinical diagnostics, environmental microbiology, gut microbiome research, as well as biodiversity and conservation biology. Particularly in ICU settings, MetaGeneMiner introduces a novel, rapid, and precise method for diagnosing and treating infections in critically ill patients. This tool is capable of efficiently identifying infectious pathogens, guiding personalized and precise treatment strategies, and monitoring the development of antibiotic resistance, significantly impacting the diagnosis and treatment of severe infections.

All species shed DNA during life or in death, providing an opportunity to monitor biodiversity via environmental DNA (eDNA). In recent years, combining eDNA, high-throughput sequencing technologies, bioinformatics, and increasingly complete sequence databases has promised a non-invasive and non-destructive environmental monitoring tool. Modern agricultural systems are often large monocultures and so are highly vulnerable to disease outbreaks. Pest and pathogen monitoring in agricultural ecosystems is key for efficient and early disease prevention, lower pesticide use, and better food security. Although the air is rich in biodiversity, it has the lowest DNA concentration of all environmental media and yet is the route for windborne spread of many damaging crop pathogens. Our work suggests that ecosystems can be monitored efficiently using airborne nucleic acid information. Here, we show that the airborne DNA of microbes can be recovered, shotgun sequenced, and taxonomically classified, including down to the species level. We show that by monitoring a field growing key crops we can identify the presence of agriculturally significant pathogens and quantify their changing abundance over a period of 1.5 months, often correlating with weather variables. We add to the evidence that aerial eDNA can be used as a source for biomonitoring in terrestrial ecosystems, specifically highlighting agriculturally relevant species and how pathogen levels correlate with weather conditions. Our ability to detect dynamically changing levels of species and strains highlights the value of airborne eDNA in agriculture, monitoring biodiversity changes, and tracking taxa of interest.

Characterizing unknown viruses is essential for understanding viral ecology and preparing against viral outbreaks. Recovering complete genome sequences from environmental samples remains computationally challenging using metagenomics, especially for low-abundance species with uneven coverage. We present an experimental method for reliably recovering complete viral genomes from complex environmental samples. Individual genomes are encapsulated into droplets and amplified using multiple displacement amplification. A unique gene detection assay, which employs an RNA-based probe and an exonuclease, selectively identifies droplets containing the target viral genome. Labeled droplets are sorted using a microfluidic sorter, and genomes are extracted for sequencing. We demonstrate this method's efficacy by spiking two known viral genomes, Simian virus 40 (SV40, 5,243 bp) and Human Adenovirus 5 (HAd5, 35,938 bp), into a sewage sample with a final abundance in the droplets of around 0.1% and 0.015%, respectively. We achieve 100% recovery of the complete sequence of the spiked-in SV40 genome with uniform coverage distribution. For the larger HAd5 genome, we cover approximately 99.4% of its sequence. Notably, genome recovery is achieved with as few as one sorted droplet, which enables the recovery of any desired genomes in complex environmental samples, regardless of their abundance. This method enables single-genome whole-genome amplification and targeting characterizations of rare viral species and will facilitate our ability to access the mutational profile in single-virus genomes and contribute to an improved understanding of viral ecology.

The causative agents of Acute Encephalitis Syndrome remain unknown in 68-75% of the cases. In Nepal, the cases are tested only for Japanese encephalitis, which constitutes only about 15% of the cases. However, there could be several organisms, including vaccine-preventable etiologies that cause acute encephalitis, when identified could direct public health efforts for prevention, including addressing gaps in vaccine coverage.

This study employs metagenomic next-generation-sequencing in the investigation of underlying causative etiologies contributing to acute encephalitis syndrome in Nepal.

In this study, we investigated 90, Japanese-encephalitis-negative, banked cerebrospinal fluid samples that were collected as part of a national surveillance network in 2016 and 2017. Randomization was done to include three age groups (< 5-years; 5-14-years; >15-years). Only some metadata (age and gender) were available. The investigation was performed in two batches which included total nucleic-acid extraction, followed by individual library preparation (DNA and RNA) and sequencing on Illumina iSeq100. The genomic data were interpreted using Chan Zuckerberg-ID and confirmed with polymerase-chain-reaction.

Human-alphaherpes-virus 2 and Enterovirus-B were seen in two samples. These hits were confirmed by qPCR and semi-nested PCR respectively. Most of the other samples were marred by low abundance of pathogen, possible freeze-thaw cycles, lack of process controls and associated clinical metadata.

From this study, two documented causative agents were revealed through metagenomic next-generation-sequencing. Insufficiency of clinical metadata, process controls, low pathogen abundance and absence of standard procedures to collect and store samples in nucleic-acid protectants could have impeded the study and incorporated ambiguity while correlating the identified hits to infection. Therefore, there is need of standardized procedures for sample collection, inclusion of process controls and clinical metadata. Despite challenging conditions, this study highlights the usefulness of mNGS to investigate diseases with unknown etiologies and guide development of adequate clinical-management-algorithms and outbreak investigations in Nepal.

Two common approaches to study the composition of environmental protist communities are metabarcoding and metagenomics. Raw metabarcoding data are usually processed into Operational Taxonomic Units (OTUs) or amplicon sequence variants (ASVs) through clustering or denoising approaches, respectively. Analogous approaches are used to assemble metagenomic reads into metagenome-assembled genomes (MAGs). Understanding the correspondence between the data produced by these two approaches can help to integrate information between the datasets and to explain how metabarcoding OTUs and MAGs are related with the underlying biological entities they are hypothesised to represent. MAGs do not contain the commonly used barcoding loci, therefore sequence homology approaches cannot be used to match OTUs and MAGs. We made an attempt to match V9 metabarcoding OTUs from the 18S rRNA gene (V9 OTUs) and MAGs from the Tara Oceans expedition based on the correspondence of their relative abundances across the same set of samples. We evaluated several metrics for detecting correspondence between features in these two datasets and developed controls to filter artefacts of data structure and processing. After selecting the best-performing metrics, ranking the V9 OTU/MAG matches by their proportionality/correlation coefficients and applying a set of selection criteria, we identified candidate matches between V9 OTUs and MAGs. In some cases, V9 OTUs and MAGs could be matched with a one-to-one correspondence, implying that they likely represent the same underlying biological entity. More generally, matches we observed could be classified into 4 scenarios: one V9 OTU matches many MAGs; many V9 OTUs match many MAGs; many V9 OTUs match one MAG; one V9 OTU matches one MAG. Notably, we found some instances in which different OTU-MAG matches from the same taxonomic group were not classified in the same scenario, with all four scenarios possible even within the same taxonomic group, illustrating that factors beyond taxonomic lineage influence the relationship between OTUs and MAGs. Overall, each scenario produces a different interpretation of V9 OTUs, MAGs and how they compare in terms of the genomic and ecological diversity they represent.

Oxford Nanopore sequencing is one of the high-throughput sequencing technologies that facilitates the reconstruction of metagenome-assembled genomes (MAGs). This study aimed to assess the potential of long-read assembly algorithms in Oxford Nanopore sequencing to enhance the MAG-based identification of bacterial pathogens using both simulated and mock communities. Simulated communities were generated to mimic those on fresh spinach and in surface water. Long reads were produced using R9.4.1+SQK-LSK109 and R10.4 + SQK-LSK112, with 0.5, 1, and 2 million reads. The simulated bacterial communities included multidrug-resistant Salmonella enterica serotypes Heidelberg, Montevideo, and Typhimurium in the fresh spinach community individually or in combination, as well as multidrug-resistant Pseudomonas aeruginosa in the surface water community. Real data sets of the ZymoBIOMICS HMW DNA Standard were also studied. A bioinformatic pipeline (MAGenie, freely available at https://github.com/jackchen129/MAGenie) that combines metagenome assembly, taxonomic classification, and sequence extraction was developed to reconstruct draft MAGs from metagenome assemblies. Five assemblers were evaluated based on a series of genomic analyses. Overall, Flye outperformed the other assemblers, followed by Shasta, Raven, and Unicycler, while Canu performed least effectively. In some instances, the extracted sequences resulted in draft MAGs and provided the locations and structures of antimicrobial resistance genes and mobile genetic elements. Our study showcases the viability of utilizing the extracted sequences for precise phylogenetic inference, as demonstrated by the consistent alignment of phylogenetic topology between the reference genome and the extracted sequences. R9.4.1+SQK-LSK109 was more effective in most cases than R10.4+SQK-LSK112, and greater sequencing depths generally led to more accurate results.IMPORTANCEBy examining diverse bacterial communities, particularly those housing multiple Salmonella enterica serotypes, this study holds significance in uncovering the potential of long-read assembly algorithms to improve metagenome-assembled genome (MAG)-based pathogen identification through Oxford Nanopore sequencing. Our research demonstrates that long-read assembly stands out as a promising avenue for boosting precision in MAG-based pathogen identification, thus advancing the development of more robust surveillance measures. The findings also support ongoing endeavors to fine-tune a bioinformatic pipeline for accurate pathogen identification within complex metagenomic samples.

There have been extensive applications of waste activated sludge (WAS) in anaerobic co-digestion (AcoD). Nonetheless, mechanisms through which AcoD systems maintain stability, particularly under nutrient-stressed conditions, are under-appreciated. In this study, the role of WAS in a nutrient-stressed WAS-food waste AcoD system was re-evaluated. Our findings demonstrated that WAS-based co-digestion increased methane production (by 20-60%) as WAS bolsters such systems' resilience via establishing a core niche-based microbial balance. The carbon utilization investigation suggested a microbial niche balance is attainable if two conditions are satisfied: 1) hydrolysis efficiency is greater than 50%; and 2) both the acidogenesis-to-hydrolysis and acetogenesis-to-hydrolysis efficiencies surpass 0.5. Metagenomic assembly genome (MAG) analysis indicated that the versatile metabolic characteristics strengthened the microbial niche balance, rendering the system resilient and efficient through a syntrophic mode, contributing to both acidogenesis and acetogenesis. The findings of this study provide new insights into the ecological effects of WAS on AcoD.

Modern lifestyle greatly influences human well-being. Indeed, nowadays people are centered in the cities and this trend is growing with the ever-increasing population. The main habitat for modern humans is defined as the built environment (BE). The modulation of life quality in the BE is primarily mediated by a biodiversity of microbes. They derive from different sources, such as soil, water, air, pets, and humans. Humans are the main source and vector of bacterial diversity in the BE leaving a characteristic microbial fingerprint on the surfaces and spaces. This review, focusing on articles published from the early 2000s, delves into bacterial populations present in indoor and outdoor urban environments, exploring the characteristics of primary bacterial niches in the BE and their native habitats. It elucidates bacterial interconnections within this context and among themselves, shedding light on pathways for adaptation and survival across diverse environmental conditions. Given the limitations of culture-based methods, emphasis is placed on culture-independent approaches, particularly high-throughput techniques to elucidate the genetic and -omic features of BE bacteria. By elucidating these microbiota profiles, the review aims to contribute to understanding the implications for human health and the assessment of urban environmental quality in modern cities.

Enumerated threat agent lists have long driven biodefense priorities. The global SARS-CoV-2 pandemic demonstrated the limitations of searching for known threat agents as compared to a more agnostic approach. Recent technological advances are enabling agent-agnostic biodefense, especially through the integration of multi-modal observations of host-pathogen interactions directed by a human immunological model. Although well-developed technical assays exist for many aspects of human-pathogen interaction, the analytic methods and pipelines to combine and holistically interpret the results of such assays are immature and require further investments to exploit new technologies. In this manuscript, we discuss potential immunologically based bioagent-agnostic approaches and the computational tool gaps the community should prioritize filling.

Periodic bioinformatics-based screening of wastewater for assessing the diversity of potential human viral pathogens circulating in a given community may help to identify novel or potentially emerging infectious diseases. Any identified contigs related to novel or emerging viruses should be confirmed with targeted wastewater and clinical testing.

During the COVID-19 pandemic, untreated wastewater samples were collected for a 1-year period from the Great Lakes Water Authority Wastewater Treatment Facility in Detroit, MI, USA, and viral population diversity from both centralized interceptor sites and localized neighborhood sewersheds was investigated. Clinical cases of the diseases caused by human viruses were tabulated and compared with data from viral wastewater monitoring. In addition to Betacoronavirus, comparison using assembled contigs against a custom Swiss-Prot human virus database indicated the potential prevalence of other pathogenic virus genera, including: Orthopoxvirus, Rhadinovirus, Parapoxvirus, Varicellovirus, Hepatovirus, Simplexvirus, Bocaparvovirus, Molluscipoxvirus, Parechovirus, Roseolovirus, Lymphocryptovirus, Alphavirus, Spumavirus, Lentivirus, Deltaretrovirus, Enterovirus, Kobuvirus, Gammaretrovirus, Cardiovirus, Erythroparvovirus, Salivirus, Rubivirus, Orthohepevirus, Cytomegalovirus, Norovirus, and Mamastrovirus. Four nearly complete genomes were recovered from the Astrovirus, Enterovirus, Norovirus and Betapolyomavirus genera and viral species were identified.

The presented findings in wastewater samples are primarily at the genus level and can serve as a preliminary "screening" tool that may serve as indication to initiate further testing for the confirmation of the presence of species that may be associated with human disease. Integrating innovative environmental microbiology technologies like metagenomic sequencing with viral epidemiology offers a significant opportunity to improve the monitoring of, and predictive intelligence for, pathogenic viruses, using wastewater.

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.

Monitoring of potentially pathogenic human viruses in wastewater is of crucial importance to understand disease trends in communities, predict potential outbreaks, and boost preparedness and response by public health departments. High throughput metagenomic sequencing opens an opportunity to expand the capabilities of wastewater surveillance. However, there are major bottlenecks in the metagenomic enabled wastewater surveillance, including the complexities in selecting appropriate sampling and concentration/virus enrichment methods as well as in bioinformatic analysis of complex samples with low human virus concentrations. To evaluate the abilities of two commonly used sampling and concentration methods in virus identification, virus communities concentrated with Virus Adsorption-Elution (VIRADEL) and PolyEthylene Glycol (PEG) precipitation were compared for three interceptor sites. Results indicated that more viral reads were obtained by the VIRADEL concentration method, with 2.84 ± 0.57 % viral reads in the sample. For samples concentrated with PEG, the average proportion of viral reads in the sample was 0.63 ± 0.19 %. In all wastewater samples, bacteriophage affiliated with the families Siphoviridae, Myoviridae and Podoviridae were found to be the abundant populations. Comparison against a custom Swiss-Prot human virus database indicated that the relatively abundant human viruses (average proportions in human virus community greater than 1.00 %) in samples concentrated with the VIRADEL method were Orthopoxvirus, Rhadinovirus, Parapoxvirus, Varicellovirus, Hepatovirus, Simplexvirus, Molluscipoxvirus, Parechovirus, Lymphocryptovirus, and Spumavirus. In samples concentrated with the PEG method, fewer human viruses were found to be relatively abundant. These were Orthopoxvirus, Rhadinovirus, Varicellovirus, Simplexvirus, Molluscipoxvirus, Lymphocryptovirus, and Betacoronavirus. Contigs of Betacoronavirus, which contains severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), were identified in VIRADEL and PEG samples. Our study demonstrates the feasibility of using metagenomics in wastewater surveillance as a first screening tool and the need for selecting the appropriate virus concentration methods and optimizing bioinformatic approaches in analyzing metagenomic data of wastewater samples.

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

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

Identification of viruses and further assembly of viral genomes from the next-generation-sequencing data are essential steps in virome studies. This study presented a one-stop tool named VIGA (available at https://github.com/viralInformatics/VIGA) for eukaryotic virus identification and genome assembly from NGS data. It was composed of four modules, namely, identification, taxonomic annotation, assembly and novel virus discovery, which integrated several third-party tools such as BLAST, Trinity, MetaCompass and RagTag. Evaluation on multiple simulated and real virome datasets showed that VIGA assembled more complete virus genomes than its competitors on both the metatranscriptomic and metagenomic data and performed well in assembling virus genomes at the strain level. Finally, VIGA was used to investigate the virome in metatranscriptomic data from the Human Microbiome Project and revealed different composition and positive rate of viromes in diseases of prediabetes, Crohn's disease and ulcerative colitis. Overall, VIGA would help much in identification and characterization of viromes, especially the known viruses, in future studies.

Characterizing unknown viruses is essential for understanding viral ecology and preparing against viral outbreaks. Recovering complete genome sequences from environmental samples remains computationally challenging using metagenomics, especially for low-abundance species with uneven coverage. This work presents a method for reliably recovering complete viral genomes from complex environmental samples. Individual genomes are encapsulated into droplets and amplified using multiple displacement amplification. A novel gene detection assay, which employs an RNA-based probe and an exonuclease, selectively identifies droplets containing the target viral genome. Labeled droplets are sorted using a microfluidic sorter, and genomes are extracted for sequencing. Validation experiments using a sewage sample spiked with two known viruses demonstrate the method's efficacy. We achieve 100% recovery of the spiked-in SV40 (Simian virus 40, 5243bp) genome sequence with uniform coverage distribution, and approximately 99.4% for the larger HAd5 genome (Human Adenovirus 5, 35938bp). Notably, genome recovery is achieved with as few as one sorted droplet, which enables the recovery of any desired genomes in complex environmental samples, regardless of their abundance. This method enables targeted characterizations of rare viral species and whole-genome amplification of single genomes for accessing the mutational profile in single virus genomes, contributing to an improved understanding of viral ecology.

High-throughput DNA sequencing-based approaches continue to revolutionise our understanding of microbial ecosystems, including those associated with fermented foods. Metagenomic and metatranscriptomic approaches are state-of-the-art biological profiling methods and are employed to investigate a wide variety of characteristics of microbial communities, such as taxonomic membership, gene content and the range and level at which these genes are expressed. Individual groups and consortia of researchers are utilising these approaches to produce increasingly large and complex datasets, representing vast populations of microorganisms. There is a corresponding requirement for the development and application of appropriate bioinformatic tools and pipelines to interpret this data. This review critically analyses the tools and pipelines that have been used or that could be applied to the analysis of metagenomic and metatranscriptomic data from fermented foods. In addition, we critically analyse a number of studies of fermented foods in which these tools have previously been applied, to highlight the insights that these approaches can provide.

Metagenomic assembly enables new organism discovery from microbial communities, but it can only capture few abundant organisms from most metagenomes. Here we present MetaPhlAn 4, which integrates information from metagenome assemblies and microbial isolate genomes for more comprehensive metagenomic taxonomic profiling. From a curated collection of 1.01 M prokaryotic reference and metagenome-assembled genomes, we define unique marker genes for 26,970 species-level genome bins, 4,992 of them taxonomically unidentified at the species level. MetaPhlAn 4 explains ~20% more reads in most international human gut microbiomes and >40% in less-characterized environments such as the rumen microbiome and proves more accurate than available alternatives on synthetic evaluations while also reliably quantifying organisms with no cultured isolates. Application of the method to >24,500 metagenomes highlights previously undetected species to be strong biomarkers for host conditions and lifestyles in human and mouse microbiomes and shows that even previously uncharacterized species can be genetically profiled at the resolution of single microbial strains.

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

Hepatitis B virus (HBV) infection is a global health epidemic that causes fatal complications, leading to liver cirrhosis and hepatocellular carcinoma. The link between HBV-related dysbiosis and specific bacterial taxa is still under investigation. Enterocloster is emerging as a new genus (formerly Clostridium), including Enterocloster bolteae, a gut pathogen previously associated with dysbiosis and human diseases such as autism, multiple sclerosis, and inflammatory bowel diseases. Its role in liver diseases, especially HBV infection, is not reported.

The fecal samples of eight patients with chronic HBV infection and ten healthy individuals were analyzed using the high-throughput culturomics approach and compared to 16S rRNA sequencing. Quantification of ethanol, known for its damaging effect on the liver, produced from bacterial strains enriched in chronic HBV was carried out by gas chromatography-mass spectrometry.

Using culturomics, 29,120 isolated colonies were analyzed by Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry (MALDI-TOF); 340 species were identified (240 species in chronic HBV samples, 254 species in control samples) belonging to 169 genera and 6 phyla. In the chronic HBV group, 65 species were already known in the literature; 48 were associated with humans but had not been previously found in the gut, and 17 had never been associated with humans previously. Six species were newly isolated in our study. By comparing bacterial species frequency, three bacterial genera were serendipitously found with significantly enriched bacterial diversity in patients with chronic HBV: Enterocloster, Clostridium, and Streptococcus (p = 0.0016, p = 0.041, p = 0.053, respectively). However, metagenomics could not identify this enrichment, possibly concerning its insufficient taxonomical resolution (equivocal assignment of operational taxonomic units). At the species level, the significantly enriched species in the chronic HBV group almost all belonged to class Clostridia, such as Clostridium perfringens, Clostridium sporogenes, Enterocloster aldenensis, Enterocloster bolteae, Enterocloster clostridioformis, and Clostridium innocuum. Two E. bolteae strains, isolated from two patients with chronic HBV infection, showed high ethanol production (27 and 200 mM).

Culturomics allowed us to identify Enterocloster species, specifically, E. bolteae, enriched in the gut microbiota of patients with chronic HBV. These species had never been isolated in chronic HBV infection before. Moreover, ethanol production by E. bolteae strains isolated from the chronic HBV group could contribute to liver disease progression. Additionally, culturomics might be critical for better elucidating the relationship between dysbiosis and chronic HBV infection in the future.

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

Antibiotic resistance is of crucial interest to both human and animal medicine. It has been recognized that increased environmental monitoring of antibiotic resistance is needed. Metagenomic DNA sequencing is becoming an attractive method to profile antibiotic resistance genes (ARGs), including a special focus on pathogens. A number of computational pipelines are available and under development to support environmental ARG monitoring; the pipeline we present here is promising for general adoption for the purpose of harmonized global monitoring. Specifically, ARGem is a user-friendly pipeline that provides full-service analysis, from the initial DNA short reads to the final visualization of results. The capture of extensive metadata is also facilitated to support comparability across projects and broader monitoring goals. The ARGem pipeline offers efficient analysis of a modest number of samples along with affordable computational components, though the throughput could be increased through cloud resources, based on the user's configuration. The pipeline components were carefully assessed and selected to satisfy tradeoffs, balancing efficiency and flexibility. It was essential to provide a step to perform short read assembly in a reasonable time frame to ensure accurate annotation of identified ARGs. Comprehensive ARG and mobile genetic element databases are included in ARGem for annotation support. ARGem further includes an expandable set of analysis tools that include statistical and network analysis and supports various useful visualization techniques, including Cytoscape visualization of co-occurrence and correlation networks. The performance and flexibility of the ARGem pipeline is demonstrated with analysis of aquatic metagenomes. The pipeline is freely available at https://github.com/xlxlxlx/ARGem.

Gut microbiome research has increased dramatically in the last decade, including in renal health and disease. The field is moving from experiments showing mere association to causation using both forward and reverse microbiome approaches, leveraging tools such as germ-free animals, treatment with antibiotics, and fecal microbiota transplantations. However, we are still seeing a gap between discovery and translation that needs to be addressed, so that patients can benefit from microbiome-based therapies. In this guideline paper, we discuss the key considerations that affect the gut microbiome of animals and clinical studies assessing renal function, many of which are often overlooked, resulting in false-positive results. For animal studies, these include suppliers, acclimatization, baseline microbiota and its normalization, littermates and cohort/cage effects, diet, sex differences, age, circadian differences, antibiotics and sweeteners, and models used. Clinical studies have some unique considerations, which include sampling, gut transit time, dietary records, medication, and renal phenotypes. We provide best-practice guidance on sampling, storage, DNA extraction, and methods for microbial DNA sequencing (both 16S rRNA and shotgun metagenome). Finally, we discuss follow-up analyses, including tools available, metrics, and their interpretation, and the key challenges ahead in the microbiome field. By standardizing study designs, methods, and reporting, we will accelerate the findings from discovery to translation and result in new microbiome-based therapies that may improve renal health.

Synthetic long read sequencing techniques such as UST's TELL-Seq and Loop Genomics' LoopSeq combine 3[Formula: see text] barcoding with standard short-read sequencing to expand the range of linkage resolution from hundreds to tens of thousands of base-pairs. However, the lack of a 1:1 correspondence between a long fragment and a 3[Formula: see text] unique molecular identifier confounds the assignment of linkage between short reads. We introduce Ariadne, a novel assembly graph-based synthetic long read deconvolution algorithm, that can be used to extract single-species read-clouds from synthetic long read datasets to improve the taxonomic classification and de novo assembly of complex populations, such as metagenomes.