New approaches for metagenome assembly with short reads

Microbiota profiling in esophageal diseases: Novel insights into molecular staining and clinical outcomes

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..

Enhancing antimicrobial resistance detection with MetaGeneMiner: Targeted gene extraction from metagenomes

BACKGROUND

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.

METHODS

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.

RESULTS

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.

CONCLUSIONS

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.

Diverse patterns of correspondence between protist metabarcodes and protist metagenome-assembled genomes

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.

Advancing metagenome-assembled genome-based pathogen identification: unraveling the power of long-read assembly algorithms in Oxford Nanopore sequencing

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.

New insights on ecological roles of waste activated sludge in nutrient-stressed co-digestion

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.

Who inhabits the built environment? A microbiological point of view on the principal bacteria colonizing our urban areas

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.

Computational and Systems Biology Advances to Enable Bioagent Agnostic Signatures

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.

A broad wastewater screening and clinical data surveillance for virus-related diseases in the metropolitan Detroit area in Michigan

BACKGROUND

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.

RESULTS

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.

CONCLUSIONS

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.

Unraveling metagenomics through long-read sequencing: a comprehensive review

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

Effect of wastewater collection and concentration methods on assessment of viral diversity

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.

NMPFamsDB: a database of novel protein families from microbial metagenomes and metatranscriptomes

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.

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

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

VIGA: a one-stop tool for eukaryotic virus identification and genome assembly from next-generation-sequencing data

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.

High-sensitivity whole-genome recovery of single viral species in environmental samples

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.

Bioinformatic approaches for studying the microbiome of fermented food

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.

Extending and improving metagenomic taxonomic profiling with uncharacterized species using MetaPhlAn 4

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.

Unraveling the functional dark matter through global metagenomics

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.

Ethanol-Producing Enterocloster bolteae Is Enriched in Chronic Hepatitis B-Associated Gut Dysbiosis: A Case-Control Culturomics Study

BACKGROUND

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.

METHODS

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.

RESULTS

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).

CONCLUSIONS

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.

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

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

ARGem: a new metagenomics pipeline for antibiotic resistance genes: metadata, analysis, and visualization

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.

Guidelines for microbiome studies in renal physiology

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.

Ariadne: synthetic long read deconvolution using assembly graphs

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.

Long-read assembled metagenomic approaches improve our understanding on metabolic potentials of microbial community in mangrove sediments

BACKGROUND

Mangrove wetlands are coastal ecosystems with important ecological features and provide habitats for diverse microorganisms with key roles in nutrient and biogeochemical cycling. However, the overall metabolic potentials and ecological roles of microbial community in mangrove sediment are remained unanswered. In current study, the microbial and metabolic profiles of prokaryotic and fungal communities in mangrove sediments were investigated using metagenomic analysis based on PacBio single-molecule real time (SMRT) and Illumina sequencing techniques.

RESULTS

Comparing to Illumina short reads, the incorporation of PacBio long reads significantly contributed to more contiguous assemblies, yielded more than doubled high-quality metagenome-assembled genomes (MAGs), and improved the novelty of the MAGs. Further metabolic reconstruction for recovered MAGs showed that prokaryotes potentially played an essential role in carbon cycling in mangrove sediment, displaying versatile metabolic potential for degrading organic carbons, fermentation, autotrophy, and carbon fixation. Mangrove fungi also functioned as a player in carbon cycling, potentially involved in the degradation of various carbohydrate and peptide substrates. Notably, a new candidate bacterial phylum named as Candidatus Cosmopoliota with a ubiquitous distribution is proposed. Genomic analysis revealed that this new phylum is capable of utilizing various types of organic substrates, anaerobic fermentation, and carbon fixation with the Wood-Ljungdahl (WL) pathway and the reverse tricarboxylic acid (rTCA) cycle.

CONCLUSIONS

The study not only highlights the advantages of HiSeq-PacBio Hybrid assembly for a more complete profiling of environmental microbiomes but also expands our understanding of the microbial diversity and potential roles of distinct microbial groups in biogeochemical cycling in mangrove sediment. Video Abstract.

Current and emerging trends in techniques for plant pathogen detection

Plant pathogenic microorganisms cause substantial yield losses in several economically important crops, resulting in economic and social adversity. The spread of such plant pathogens and the emergence of new diseases is facilitated by human practices such as monoculture farming and global trade. Therefore, the early detection and identification of pathogens is of utmost importance to reduce the associated agricultural losses. In this review, techniques that are currently available to detect plant pathogens are discussed, including culture-based, PCR-based, sequencing-based, and immunology-based techniques. Their working principles are explained, followed by an overview of the main advantages and disadvantages, and examples of their use in plant pathogen detection. In addition to the more conventional and commonly used techniques, we also point to some recent evolutions in the field of plant pathogen detection. The potential use of point-of-care devices, including biosensors, have gained in popularity. These devices can provide fast analysis, are easy to use, and most importantly can be used for on-site diagnosis, allowing the farmers to take rapid disease management decisions.

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

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

Identification of the EH CRISPR-Cas9 system on a metagenome and its application to genome engineering

Non-coding RNAs (crRNAs) produced from clustered regularly interspaced short palindromic repeats (CRISPR) loci and CRISPR-associated (Cas) proteins of the prokaryotic CRISPR-Cas systems form complexes that interfere with the spread of transmissible genetic elements through Cas-catalysed cleavage of foreign genetic material matching the guide crRNA sequences. The easily programmable targeting of nucleic acids enabled by these ribonucleoproteins has facilitated the implementation of CRISPR-based molecular biology tools for in vivo and in vitro modification of DNA and RNA targets. Despite the diversity of DNA-targeting Cas nucleases so far identified, native and engineered derivatives of the Streptococcus pyogenes SpCas9 are the most widely used for genome engineering, at least in part due to their catalytic robustness and the requirement of an exceptionally short motif (5'-NGG-3' PAM) flanking the target sequence. However, the large size of the SpCas9 variants impairs the delivery of the tool to eukaryotic cells and smaller alternatives are desirable. Here, we identify in a metagenome a new CRISPR-Cas9 system associated with a smaller Cas9 protein (EHCas9) that targets DNA sequences flanked by 5'-NGG-3' PAMs. We develop a simplified EHCas9 tool that specifically cleaves DNA targets and is functional for genome editing applications in prokaryotes and eukaryotic cells.

Detection of Viral Genes in Metarhizium anisopliae JEF-290-infected longhorned tick, Haemaphysalis longicornis using transcriptome analysis

Ticks are carriers of viruses that can cause disease in humans and animals. The longhorned ticks (Haemaphysalis longicornis; LHT), for example, mediates the severe fever with thrombocytopenia syndrome virus (SFTSV) in humans, and the population of ticks is growing due to increases in temperature caused by climate change. As ticks carry primarily RNA viruses, there is a need to study the possibility of detecting new viruses through tick virome analysis. In this study, viruses in LHTs collected in Korea were investigated and virus titers in ticks exposed to the entomopathogenic fungus Metarhizium anisopliae JEF-290 were analyzed. Total RNA was extracted from the collected ticks, and short reads were obtained from Illumina sequencing. A total of 50,024 contigs with coding capacity were obtained after de novo assembly of the reads in the metaSPAdes genome assembler. A series of BLAST-based analyses using the GenBank database was performed to screen viral contigs, and three putative virus species were identified from the tick meta-transcriptome, such as Alongshan virus (ALSV), Denso virus and Taggert virus. Measurements of virus-expression levels of infected and non-infected LHTs failed to detect substantial differences in expression levels. However, we suggest that LHT can spread not only SFTSV, but also various other disease-causing viruses over large areas of the world. From the phylogenetic analysis of ALSV glycoproteins, genetic differences in the ALSV could be due to host differences as well as regional differences. Viral metagenome analysis can be used as a tool to manage future outbreaks of disease caused by ticks by detecting unknown viruses.

Comparison of long- and short-read metagenomic assembly for low-abundance species and resistance genes

Recent technological and computational advances have made metagenomic assembly a viable approach to achieving high-resolution views of complex microbial communities. In previous benchmarking, short-read (SR) metagenomic assemblers had the highest accuracy, long-read (LR) assemblers generated the most contiguous sequences and hybrid (HY) assemblers balanced length and accuracy. However, no assessments have specifically compared the performance of these assemblers on low-abundance species, which include clinically relevant organisms in the gut. We generated semi-synthetic LR and SR datasets by spiking small and increasing amounts of Escherichia coli isolate reads into fecal metagenomes and, using different assemblers, examined E. coli contigs and the presence of antibiotic resistance genes (ARGs). For ARG assembly, although SR assemblers recovered more ARGs with high accuracy, even at low coverages, LR assemblies allowed for the placement of ARGs within longer, E. coli-specific contigs, thus pinpointing their taxonomic origin. HY assemblies identified resistance genes with high accuracy and had lower contiguity than LR assemblies. Each assembler type's strengths were maintained even when our isolate was spiked in with a competing strain, which fragmented and reduced the accuracy of all assemblies. For strain characterization and determining gene context, LR assembly is optimal, while for base-accurate gene identification, SR assemblers outperform other options. HY assembly offers contiguity and base accuracy, but requires generating data on multiple platforms, and may suffer high misassembly rates when strain diversity exists. Our results highlight the trade-offs associated with each approach for recovering low-abundance taxa, and that the optimal approach is goal-dependent.

Cobalamin producers and prokaryotic consumers in the Northwest Atlantic

Cobalamin availability can influence primary productivity and ecological interactions in marine microbial communities. The characterization of cobalamin sources and sinks is a first step in investigating cobalamin dynamics and its impact on productivity. Here, we identify potential cobalamin sources and sinks on the Scotian Shelf and Slope in the Northwest Atlantic Ocean. Functional and taxonomic annotation of bulk metagenomic reads, combined with analysis of genome bins, were used to identify potential cobalamin sources and sinks. Cobalamin synthesis potential was mainly attributed to Rhodobacteraceae, Thaumarchaeota, and cyanobacteria (Synechococcus and Prochlorococcus). Cobalamin remodelling potential was mainly attributed to Alteromonadales, Pseudomonadales, Rhizobiales, Oceanospirilalles, Rhodobacteraceae, and Verrucomicrobia, while potential cobalamin consumers include Flavobacteriaceae, Actinobacteria, Porticoccaceae, Methylophiliaceae, and Thermoplasmatota. These complementary approaches identified taxa with the potential to be involved in cobalamin cycling on the Scotian Shelf and revealed genomic information required for further characterization. The Cob operon of Rhodobacterales bacterium HTCC2255, a strain with known importance in cobalamin cycling, was similar to a major cobalamin producer bin, suggesting that a related strain may represent a critical cobalamin source in this region. These results enable future inquiries that will enhance our understanding of how cobalamin shapes microbial interdependencies and productivity in this region.

Predicting metabolomic profiles from microbial composition through neural ordinary differential equations

Characterizing the metabolic profile of a microbial community is crucial for understanding its biological function and its impact on the host or environment. Metabolomics experiments directly measuring these profiles are difficult and expensive, while sequencing methods quantifying the species composition of microbial communities are well-developed and relatively cost-effective. Computational methods that are capable of predicting metabolomic profiles from microbial compositions can save considerable efforts needed for metabolomic profiling experimentally. Yet, despite existing efforts, we still lack a computational method with high prediction power, general applicability, and great interpretability. Here we develop a method - mNODE (Metabolomic profile predictor using Neural Ordinary Differential Equations), based on a state-of-the-art family of deep neural network models. We show compelling evidence that mNODE outperforms existing methods in predicting the metabolomic profiles of human microbiomes and several environmental microbiomes. Moreover, in the case of human gut microbiomes, mNODE can naturally incorporate dietary information to further enhance the prediction of metabolomic profiles. Besides, susceptibility analysis of mNODE enables us to reveal microbe-metabolite interactions, which can be validated using both synthetic and real data. The presented results demonstrate that mNODE is a powerful tool to investigate the microbiome-diet-metabolome relationship, facilitating future research on precision nutrition.

Antibody sequences assembly method based on weighted de Bruijn graph

With the development of next-generation protein sequencing technologies, sequence assembly algorithm has become a key technology for de novo sequencing process. At present, the existing methods can address the assembly of an unknown single protein chain. However, for monoclonal antibodies with light and heavy chains, the assembly is still an unsolved question. To address this problem, we propose a new assembly method, DBAS, which integrates the quality scores and sequence alignment scores from de novo sequencing peptides into a weighted de Bruijn graph to assemble the final protein sequences. The established method is used to assembling sequences from two datasets with mixed light and heavy chains from antibodies. The results show that the DBAS can assemble long antibody sequences for both mixed light and heavy chains and single chains. In addition, DBAS is able to distinguish the light and heavy chains by using BLAST sequence alignment. The results show that the algorithm has good performance for both target sequence coverage and contig assembly accuracy.

The Contribution of the Human Oral Microbiome to Oral Disease: A Review

The oral microbiome is an emerging field that has been a topic of discussion since the development of next generation sequencing and the implementation of the human microbiome project. This article reviews the current literature surrounding the oral microbiome, briefly highlighting most recent methods of microbiome characterization including cutting edge omics, databases for the microbiome, and areas with current gaps in knowledge. This article also describes reports on microorganisms contained in the oral microbiome which include viruses, archaea, fungi, and bacteria, and provides an in-depth analysis of their significant roles in tissue homeostasis. Finally, we detail key bacteria involved in oral disease, including oral cancer, and the current research surrounding their role in stimulation of inflammatory cytokines, the role of gingival crevicular fluid in periodontal disease, the creation of a network of interactions between microorganisms, the influence of the planktonic microbiome and cospecies biofilms, and the implications of antibiotic resistance. This paper provides a comprehensive literature analysis while also identifying gaps in knowledge to enable future studies to be conducted.

Capturing variation in metagenomic assembly graphs with MetaCortex

MOTIVATION

The assembly of contiguous sequence from metagenomic samples presents a particular challenge, due to the presence of multiple species, often closely related, at varying levels of abundance. Capturing diversity within species, for example, viral haplotypes, or bacterial strain-level diversity, is even more challenging.

RESULTS

We present MetaCortex, a metagenome assembler that captures intra-species diversity by searching for signatures of local variation along assembled sequences in the underlying assembly graph and outputting these sequences in sequence graph format. We show that MetaCortex produces accurate assemblies with higher genome coverage and contiguity than other popular metagenomic assemblers on mock viral communities with high levels of strain-level diversity and on simulated communities containing simulated strains.

AVAILABILITY AND IMPLEMENTATION

Source code is freely available to download from https://github.com/SR-Martin/metacortex, is implemented in C and supported on MacOS and Linux. The version used for the results presented in this article is available at doi.org/10.5281/zenodo.7273627.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Assembly and Annotation of Viral Metagenomes from Short-Read Sequencing Data

Viral metagenomics enables the detection, characterization, and quantification of viral sequences present in shotgun-sequenced datasets of purified virus-like particles and whole metagenomes. Next generation sequencing (Illumina) derived short single or paired-end read runs are a principal platform for metagenomics, and assembly of short reads allows for the identification of distinguishing viral signatures and complex genomic features for taxonomy and functional annotation. Here we describe the identification and characterization of viral genome sequences, bacteriophages, and eukaryotic viruses, from a cohort of human stool samples, using multiple methods. Following the purification of virus-like particles, sequencing, quality refinement, and genome assembly, we begin the protocol with raw short reads deposited in an open-source nucleotide archive. We highlight the use of VIBRANT, an automated computational tool for the characterization of microbial viruses and their viral community function. Finally, we also describe an alternative assembly-free option of mapping reads to established databases of reference genomes and previously characterized metagenome-assembled viral genomes.

Identification of PKS Gene Clusters from Metagenomic Libraries Using a Next-Generation Sequencing Approach

Microbial secondary metabolites have been an important source of bioactive compounds with diverse applications from medicine to agriculture, noticeably those encoded by polyketide synthase (PKS) clusters due to their astounding chemical diversity. While most discovered compounds originate from culturable microorganisms, yet-to-be cultured microbes represent a reservoir of previously inaccessible compounds. The advent and development of metagenomics have allowed not only the characterization of these microorganisms but also their metabolic potential, making viable the prospection of environmental PKS for natural product discovery.Study of environmental PKSs often relies on the construction of metagenomic libraries and their mining, with clones containing PKS clusters identified via amplification of conserved domains and then screened for an activity of interest. Compounds produced by clones exhibiting the desired bioactivity can be isolated and characterized. However, these approaches can be less sensitive and biased against more divergent clusters, in addition to precluding the use of bioinformatics for cluster characterization prior to expression. While direct shotgun sequencing of metagenomes has identified and profiled a great number of PKSs from different environments and yet-to-be cultured microorganisms, it does not lend itself well to heterologous expression, the cruxes of natural product discovery.Here, we describe a strategy for sequencing entire metagenomic libraries while maintaining correspondence between sequence and clone, allowing the full characterization and annotation of all clusters present in a library using bioinformatic tools and then seamlessly passing clones of interest for activity screening through heterologous expression. Once a library is sequenced, the methods herein can be adapted for the mining of any biosynthetic gene cluster of interest within a metagenomic library.

LMAS: evaluating metagenomic short de novo assembly methods through defined communities

BACKGROUND

The de novo assembly of raw sequence data is key in metagenomic analysis. It allows recovering draft genomes from a pool of mixed raw reads, yielding longer sequences that offer contextual information and provide a more complete picture of the microbial community.

FINDINGS

To better compare de novo assemblers for metagenomic analysis, LMAS (Last Metagenomic Assembler Standing) was developed as a flexible platform allowing users to evaluate assembler performance given known standard communities. Overall, in our test datasets, k-mer De Bruijn graph assemblers outperformed the alternative approaches but came with a greater computational cost. Furthermore, assemblers branded as metagenomic specific did not consistently outperform other genomic assemblers in metagenomic samples. Some assemblers still in use, such as ABySS, MetaHipmer2, minia, and VelvetOptimiser, perform relatively poorly and should be used with caution when assembling complex samples. Meaningful strain resolution at the single-nucleotide polymorphism level was not achieved, even by the best assemblers tested.

CONCLUSIONS

The choice of a de novo assembler depends on the computational resources available, the replicon of interest, and the major goals of the analysis. No single assembler appeared an ideal choice for short-read metagenomic prokaryote replicon assembly, each showing specific strengths. The choice of metagenomic assembler should be guided by user requirements and characteristics of the sample of interest, and LMAS provides an interactive evaluation platform for this purpose. LMAS is open source, and the workflow and its documentation are available at https://github.com/B-UMMI/LMAS and https://lmas.readthedocs.io/, respectively.

Novel Virus Identification through Metagenomics: A Systematic Review

Metagenomic Next Generation Sequencing (mNGS) allows the evaluation of complex microbial communities, avoiding isolation and cultivation of each microbial species, and does not require prior knowledge of the microbial sequences present in the sample. Applications of mNGS include virome characterization, new virus discovery and full-length viral genome reconstruction, either from virus preparations enriched in culture or directly from clinical and environmental specimens. Here, we systematically reviewed studies that describe novel virus identification through mNGS from samples of different origin (plant, animal and environment). Without imposing time limits to the search, 379 publications were identified that met the search parameters. Sample types, geographical origin, enrichment and nucleic acid extraction methods, sequencing platforms, bioinformatic analytical steps and identified viral families were described. The review highlights mNGS as a feasible method for novel virus discovery from samples of different origins, describes which kind of heterogeneous experimental and analytical protocols are currently used and provides useful information such as the different commercial kits used for the purification of nucleic acids and bioinformatics analytical pipelines.

Critical Assessment of Short-Read Assemblers for the Metagenomic Identification of Foodborne and Waterborne Pathogens Using Simulated Bacterial Communities

Metagenomics offers the highest level of strain discrimination of bacterial pathogens from complex food and water microbiota. With the rapid evolvement of assembly algorithms, defining an optimal assembler based on the performance in the metagenomic identification of foodborne and waterborne pathogens is warranted. We aimed to benchmark short-read assemblers for the metagenomic identification of foodborne and waterborne pathogens using simulated bacterial communities. Bacterial communities on fresh spinach and in surface water were simulated by generating paired-end short reads of Illumina HiSeq, MiSeq, and NovaSeq at different sequencing depths. Multidrug-resistant Salmonella Indiana SI43 and Pseudomonas aeruginosa PAO1 were included in the simulated communities on fresh spinach and in surface water, respectively. ABySS, IDBA-UD, MaSuRCA, MEGAHIT, metaSPAdes, and Ray Meta were benchmarked in terms of assembly quality, identifications of plasmids, virulence genes, Salmonella pathogenicity island, antimicrobial resistance genes, chromosomal point mutations, serotyping, multilocus sequence typing, and whole-genome phylogeny. Overall, MEGHIT, metaSPAdes, and Ray Meta were more effective for metagenomic identification. We did not obtain an optimal assembler when using the extracted reads classified as Salmonella or P. aeruginosa for downstream genomic analyses, but the extracted reads showed consistent phylogenetic topology with the reference genome when they were aligned with Salmonella or P. aeruginosa strains. In most cases, HiSeq, MiSeq, and NovaSeq were comparable at the same sequencing depth, while higher sequencing depths generally led to more accurate results. As assembly algorithms advance and mature, the evaluation of assemblers should be a continuous process.

Comparison of Metagenomics and Metatranscriptomics Tools: A Guide to Making the Right Choice

The study of microorganisms is a field of great interest due to their environmental (e.g., soil contamination) and biomedical (e.g., parasitic diseases, autism) importance. The advent of revolutionary next-generation sequencing techniques, and their application to the hypervariable regions of the 16S, 18S or 23S ribosomal subunits, have allowed the research of a large variety of organisms more in-depth, including bacteria, archaea, eukaryotes and fungi. Additionally, together with the development of analysis software, the creation of specific databases (e.g., SILVA or RDP) has boosted the enormous growth of these studies. As the cost of sequencing per sample has continuously decreased, new protocols have also emerged, such as shotgun sequencing, which allows the profiling of all taxonomic domains in a sample. The sequencing of hypervariable regions and shotgun sequencing are technologies that enable the taxonomic classification of microorganisms from the DNA present in microbial communities. However, they are not capable of measuring what is actively expressed. Conversely, we advocate that metatranscriptomics is a "new" technology that makes the identification of the mRNAs of a microbial community possible, quantifying gene expression levels and active biological pathways. Furthermore, it can be also used to characterise symbiotic interactions between the host and its microbiome. In this manuscript, we examine the three technologies above, and discuss the implementation of different software and databases, which greatly impact the obtaining of reliable results. Finally, we have developed two easy-to-use pipelines leveraging Nextflow technology. These aim to provide everything required for an average user to perform a metagenomic analysis of marker genes with QIMME2 and a metatranscriptomic study using Kraken2/Bracken.

Persistent memory as an effective alternative to random access memory in metagenome assembly

BACKGROUND

The assembly of metagenomes decomposes members of complex microbe communities and allows the characterization of these genomes without laborious cultivation or single-cell metagenomics. Metagenome assembly is a process that is memory intensive and time consuming. Multi-terabyte sequences can become too large to be assembled on a single computer node, and there is no reliable method to predict the memory requirement due to data-specific memory consumption pattern. Currently, out-of-memory (OOM) is one of the most prevalent factors that causes metagenome assembly failures.

RESULTS

In this study, we explored the possibility of using Persistent Memory (PMem) as a less expensive substitute for dynamic random access memory (DRAM) to reduce OOM and increase the scalability of metagenome assemblers. We evaluated the execution time and memory usage of three popular metagenome assemblers (MetaSPAdes, MEGAHIT, and MetaHipMer2) in datasets up to one terabase. We found that PMem can enable metagenome assemblers on terabyte-sized datasets by partially or fully substituting DRAM. Depending on the configured DRAM/PMEM ratio, running metagenome assemblies with PMem can achieve a similar speed as DRAM, while in the worst case it showed a roughly two-fold slowdown. In addition, different assemblers displayed distinct memory/speed trade-offs in the same hardware/software environment.

CONCLUSIONS

We demonstrated that PMem is capable of expanding the capacity of DRAM to allow larger metagenome assembly with a potential tradeoff in speed. Because PMem can be used directly without any application-specific code modification, these findings are likely to be generalized to other memory-intensive bioinformatics applications.

Bifidobacterium animalis subsp. lactis Probio-M8 undergoes host adaptive evolution by glcU mutation and translocates to the infant's gut via oral-/entero-mammary routes through lactation

BACKGROUND

Most previous studies attempting to prove the phenomenon of mother-to-infant microbiota transmission were observational, performed only at genus/species-level resolution, and relied entirely on non-culture-based methodologies, impeding interpretation.

RESULTS

This work aimed to use a biomarker strain, Bifidobacterium animalis subsp. lactis Probio-M8 (M8), to directly evaluate the vertical transmission of maternally ingested bacteria by integrated culture-dependent/-independent methods. Our culture and metagenomics results showed that small amounts of maternally ingested bacteria could translocate to the infant gut via oral-/entero-mammary routes through lactation. Interestingly, many mother-infant-pair-recovered M8 homologous isolates exhibited high-frequency nonsynonymous mutations in a sugar transporter gene (glcU) and altered carbohydrate utilization preference/capacity compared with non-mutant isolates, suggesting that M8 underwent adaptive evolution for better survival in simple sugar-deprived lower gut environments.

CONCLUSIONS

This study presented direct and strain-level evidence of mother-to-infant bacterial transmission through lactation and provided insights into the impact of milk microbiota on infant gut colonization. Video Abstract.

Altered gut microbiome diversity and function in patients with propionic acidemia

Propionic acidemia (PA) is an inherited metabolic disorder of propionate metabolism, where the gut microbiota may play a role in pathophysiology and therefore, represent a relevant therapeutic target. Little is known about the gut microbiota composition and activity in patients with PA. Although clinical practice varies between metabolic treatment centers, management of PA requires combined dietary and pharmaceutical treatments, both known to affect the gut microbiota. This study aimed to characterize the gut microbiota and its metabolites in fecal samples of patients with PA compared with healthy controls from the same household. Eight patients (aged 3-14y) and 8 controls (4-31y) were recruited from Center 1 (UK) and 7 patients (11-33y) and 6 controls (15-54y) from Center 2 (Austria). Stool samples were collected 4 times over 3 months, alongside data on dietary intakes and medication usage. Several microbial taxa differed between patients with PA and controls, particularly for Center 1, e.g., Proteobacteria levels were increased, whereas butyrate-producing genera, such as Roseburia and Faecalibacterium, were decreased. Most measured microbial metabolites were lower in patients with PA, and butyrate was particularly depleted in patients from Center 1. Furthermore, microbiota profile of these patients showed the lowest compositional and functional diversity, and lowest stability over 3 months. As the first study to map the gut microbiota of patients with PA, this work represents an important step forward for developing new therapeutic strategies to further improve PA clinical status. New dietary strategies should consider microbial propionate production as well as butyrate production and microbiota stability.

The Application of Metagenomics to Study Microbial Communities and Develop Desirable Traits in Fermented Foods

The microbial communities present within fermented foods are diverse and dynamic, producing a variety of metabolites responsible for the fermentation processes, imparting characteristic organoleptic qualities and health-promoting traits, and maintaining microbiological safety of fermented foods. In this context, it is crucial to study these microbial communities to characterise fermented foods and the production processes involved. High Throughput Sequencing (HTS)-based methods such as metagenomics enable microbial community studies through amplicon and shotgun sequencing approaches. As the field constantly develops, sequencing technologies are becoming more accessible, affordable and accurate with a further shift from short read to long read sequencing being observed. Metagenomics is enjoying wide-spread application in fermented food studies and in recent years is also being employed in concert with synthetic biology techniques to help tackle problems with the large amounts of waste generated in the food sector. This review presents an introduction to current sequencing technologies and the benefits of their application in fermented foods.

MLR-OOD: A Markov Chain Based Likelihood Ratio Method for Out-Of-Distribution Detection of Genomic Sequences

Machine learning or deep learning models have been widely used for taxonomic classification of metagenomic sequences and many studies reported high classification accuracy. Such models are usually trained based on sequences in several training classes in hope of accurately classifying unknown sequences into these classes. However, when deploying the classification models on real testing data sets, sequences that do not belong to any of the training classes may be present and are falsely assigned to one of the training classes with high confidence. Such sequences are referred to as out-of-distribution (OOD) sequences and are ubiquitous in metagenomic studies. To address this problem, we develop a deep generative model-based method, MLR-OOD, that measures the probability of a testing sequencing belonging to OOD by the likelihood ratio of the maximum of the in-distribution (ID) class conditional likelihoods and the Markov chain likelihood of the testing sequence measuring the sequence complexity. We compose three different microbial data sets consisting of bacterial, viral, and plasmid sequences for comprehensively benchmarking OOD detection methods. We show that MLR-OOD achieves the state-of-the-art performance demonstrating the generality of MLR-OOD to various types of microbial data sets. It is also shown that MLR-OOD is robust to the GC content, which is a major confounding effect for OOD detection of genomic sequences. In conclusion, MLR-OOD will greatly reduce false positives caused by OOD sequences in metagenomic sequence classification.

Complex genome assembly based on long-read sequencing

High-quality genome chromosome-scale sequences provide an important basis for genomics downstream analysis, especially the construction of haplotype-resolved and complete genomes, which plays a key role in genome annotation, mutation detection, evolutionary analysis, gene function research, comparative genomics and other aspects. However, genome-wide short-read sequencing is difficult to produce a complete genome in the face of a complex genome with high duplication and multiple heterozygosity. The emergence of long-read sequencing technology has greatly improved the integrity of complex genome assembly. We review a variety of computational methods for complex genome assembly and describe in detail the theories, innovations and shortcomings of collapsed, semi-collapsed and uncollapsed assemblers based on long reads. Among the three methods, uncollapsed assembly is the most correct and complete way to represent genomes. In addition, genome assembly is closely related to haplotype reconstruction, that is uncollapsed assembly realizes haplotype reconstruction, and haplotype reconstruction promotes uncollapsed assembly. We hope that gapless, telomere-to-telomere and accurate assembly of complex genomes can be truly routinely achieved using only a simple process or a single tool in the future.

Generation and application of pseudo-long reads for metagenome assembly

BACKGROUND

Metagenomic assembly using high-throughput sequencing data is a powerful method to construct microbial genomes in environmental samples without cultivation. However, metagenomic assembly, especially when only short reads are available, is a complex and challenging task because mixed genomes of multiple microorganisms constitute the metagenome. Although long read sequencing technologies have been developed and have begun to be used for metagenomic assembly, many metagenomic studies have been performed based on short reads because the generation of long reads requires higher sequencing cost than short reads.

RESULTS

In this study, we present a new method called PLR-GEN. It creates pseudo-long reads from metagenomic short reads based on given reference genome sequences by considering small sequence variations existing in individual genomes of the same or different species. When applied to a mock community data set in the Human Microbiome Project, PLR-GEN dramatically extended short reads in length of 101 bp to pseudo-long reads with N50 of 33 Kbp and 0.4% error rate. The use of these pseudo-long reads generated by PLR-GEN resulted in an obvious improvement of metagenomic assembly in terms of the number of sequences, assembly contiguity, and prediction of species and genes.

CONCLUSIONS

PLR-GEN can be used to generate artificial long read sequences without spending extra sequencing cost, thus aiding various studies using metagenomes.

Microbiome and related structural features of Earth's most archaic plant indicate early plant symbiosis attributes

Origin of earliest land plants from ancestral algae dramatically accelerated the evolution of Earth’s terrestrial ecosystems, in which microbial symbioses have played key roles. Recent molecular diversification analyses identify the rare, geographically-limited moss Takakia as Earth’s most archaic modern land plant. Despite occupying a phylogenetic position pivotal for understanding earliest plants, Takakia microbial associations are poorly known. Here, we describe symbiosis-related structural features and contig-based metagenomic data that illuminate the evolutionary transition from streptophyte algae to early embryophytes. We observed that T. lepidozioides shares with streptophyte algae secretion of microbe-harboring mucilage and bacterial taxa such as Rhizobium and genes indicating nitrogen fixation. We find that Takakia root-analogs produce lateral mucilage organs that are more complex than generally understood, having structural analogies to angiosperm lateral roots adapted for N-fixation symbioses, including presence of intracellular microbes. We also find structural and metagenomic evidence for mycorrhiza-like species of glomalean fungi (including Rhizophagus irregularis) not previously known for mosses, as well as ascomycete fungi (e.g. Rhizoscyphus ericae) that associate with other early-diverging plants. Because Takakia is the oldest known modern plant genus, this study of plants of a remote locale not strongly influenced by human activities may indicate microbiome features of early land plants.

The evolution of bacterial genome assemblies - where do we need to go next?

Genome sequencing has fundamentally changed our ability to decipher and understand the genetic blueprint of life and how it changes over time in response to environmental and evolutionary pressures. The pace of sequencing is still increasing in response to advances in technologies, paving the way from sequenced genes to genomes to metagenomes to metagenome-assembled genomes (MAGs). Our ability to interrogate increasingly complex microbial communities through metagenomes and MAGs is opening up a tantalizing future where we may be able to delve deeper into the mechanisms and genetic responses emerging over time. In the near future, we will be able to detect MAG assembly variations within strains originating from diverging sub-populations, and one of the emerging challenges will be to capture these variations in a biologically relevant way. Here, we present a brief overview of sequencing technologies and the current state of metagenome assemblies to suggest the need to develop new data formats that can capture the genetic variations within strains and communities, which previously remained invisible due to sequencing technology limitations.