nf-core/mag: a best-practice pipeline for metagenome hybrid assembly and binning

Sustained bacterial N2O reduction at acidic pH

Nitrous oxide (N2O) is a climate-active gas with emissions predicted to increase due to agricultural intensification. Microbial reduction of N2O to dinitrogen (N2) is the major consumption process but microbial N2O reduction under acidic conditions is considered negligible, albeit strongly acidic soils harbor nosZ genes encoding N2O reductase. Here, we study a co-culture derived from acidic tropical forest soil that reduces N2O at pH 4.5. The co-culture exhibits bimodal growth with a Serratia sp. fermenting pyruvate followed by hydrogenotrophic N2O reduction by a Desulfosporosinus sp. Integrated omics and physiological characterization revealed interspecies nutritional interactions, with the pyruvate fermenting Serratia sp. supplying amino acids as essential growth factors to the N2O-reducing Desulfosporosinus sp. Thus, we demonstrate growth-linked N2O reduction between pH 4.5 and 6, highlighting microbial N2O reduction potential in acidic soils.

Prevalence and molecular characterization of hepatitis E virus (HEV) from wild rodents in Hubei Province, China

Hepatitis E, caused by the hepatitis E virus (HEV), is a global public health issue. Low similarity between the gene sequences of mouse and human HEV led to the belief that the risk of human infection was low. Recent reports of chronic and acute hepatitis E caused by murine HEV infection in humans in Hong Kong have raised global concerns. Therefore, it is crucial to investigate the epidemiology and prevalence of HEV in China. We comprehensively analyzed different rodent HEV strains to understand rocahepevirus occurrence in Hubei Province, China. The HEV positivity rate for was 6.43% (73/1136). We identified seven near-full-length rocahepevirus strains and detected rat HEV antigens in tissues from different mouse species. HEV has extensive tissue tropism and a high viral load in the liver. We highlight the genetic diversity of HEVs in rodents and underscore the importance of paying attention to their variation and evolution.

Ferroptosis, Inflammation, and Microbiome Alterations in the Intestine in the Göttingen Minipig Model of Hematopoietic-Acute Radiation Syndrome

Hematopoietic acute radiation syndrome (H-ARS) involves injury to multiple organ systems following total body irradiation (TBI). Our laboratory demonstrated that captopril, an angiotensin-converting enzyme inhibitor, mitigates H-ARS in Göttingen minipigs, with improved survival and hematopoietic recovery, as well as the suppression of acute inflammation. However, the effects of captopril on the gastrointestinal (GI) system after TBI are not well known. We used a Göttingen minipig H-ARS model to investigate captopril's effects on the GI following TBI (60Co 1.79 or 1.80 Gy, 0.42-0.48 Gy/min), with endpoints at 6 or 35 days. The vehicle or captopril (0.96 mg/kg) was administered orally twice daily for 12 days, starting 4 h post-irradiation. Ilea were harvested for histological, protein, and RNA analyses. TBI increased congestion and mucosa erosion and hemorrhage, which were modulated by captopril. GPX-4 and SLC7A11 were downregulated post-irradiation, consistent with ferroptosis at 6 and 35 days post-irradiation in all groups. Interestingly, p21/waf1 increased at 6 days in vehicle-treated but not captopril-treated animals. An RT-qPCR analysis showed that radiation increased the gene expression of inflammatory cytokines IL1B, TNFA, CCL2, IL18, and CXCL8, and the inflammasome component NLRP3. Captopril suppressed radiation-induced IL1B and TNFA. Rectal microbiome analysis showed that 1 day of captopril treatment with radiation decreased overall diversity, with increased Proteobacteria phyla and Escherichia genera. By 6 days, captopril increased the relative abundance of Enterococcus, previously associated with improved H-ARS survival in mice. Our data suggest that captopril mitigates senescence, some inflammation, and microbiome alterations, but not ferroptosis markers in the intestine following TBI.

MuDoGeR: Multi-Domain Genome recovery from metagenomes made easy

Several computational frameworks and workflows that recover genomes from prokaryotes, eukaryotes and viruses from metagenomes exist. Yet, it is difficult for scientists with little bioinformatics experience to evaluate quality, annotate genes, dereplicate, assign taxonomy and calculate relative abundance and coverage of genomes belonging to different domains. MuDoGeR is a user-friendly tool tailored for those familiar with Unix command-line environment that makes it easy to recover genomes of prokaryotes, eukaryotes and viruses from metagenomes, either alone or in combination. We tested MuDoGeR using 24 individual-isolated genomes and 574 metagenomes, demonstrating the applicability for a few samples and high throughput. While MuDoGeR can recover eukaryotic viral sequences, its characterization is predominantly skewed towards bacterial and archaeal viruses, reflecting the field's current state. However, acting as a dynamic wrapper, the MuDoGeR is designed to constantly incorporate updates and integrate new tools, ensuring its ongoing relevance in the rapidly evolving field. MuDoGeR is open-source software available at https://github.com/mdsufz/MuDoGeR. Additionally, MuDoGeR is also available as a Singularity container.

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.

Choclo virus (CHOV) recovered from deep metatranscriptomics of archived frozen tissues in natural history biorepositories

BACKGROUND

Hantaviruses are negative-stranded RNA viruses that can sometimes cause severe disease in humans; however, they are maintained in mammalian host populations without causing harm. In Panama, sigmodontine rodents serve as hosts to transmissible hantaviruses. Due to natural and anthropogenic forces, these rodent populations are having increased contact with humans.

METHODS

We extracted RNA and performed Illumina deep metatranscriptomic sequencing on Orthohantavirus seropositive museum tissues from rodents. We acquired sequence reads mapping to Choclo virus (CHOV, Orthohantavirus chocloense) from heart and kidney tissue of a two-decade old frozen museum sample from a Costa Rican pygmy rice rat (Oligoryzomys costaricensis) collected in Panama. Reads mapped to the CHOV reference were assembled and then validated by visualization of the mapped reads against the assembly.

RESULTS

We recovered a 91% complete consensus sequence from a reference-guided assembly to CHOV with an average of 16X coverage. The S and M segments used in our phylogenetic analyses were nearly complete (98% and 99%, respectively). There were 1,199 ambiguous base calls of which 93% were present in the L segment. Our assembled genome varied 1.1% from the CHOV reference sequence resulting in eight nonsynonymous mutations. Further analysis of all publicly available partial S segment sequences support a clear relationship between CHOV clinical cases and O. costaricensis acquired strains.

CONCLUSIONS

Viruses occurring at extremely low abundances can be recovered from deep metatranscriptomics of archival tissues housed in research natural history museum biorepositories. Our efforts resulted in the second CHOV genome publicly available. This genomic data is important for future surveillance and diagnostic tools as well as understanding the evolution and pathogenicity of CHOV.

Bridging the Gaps in Meta-Omic Analysis: Workflows and Reproducibility

The past few years have seen significant advances in the study of complex microbial communities associated with the evolution of sequencing technologies and increasing adoption of whole genome shotgun sequencing methods over the once more traditional Amplicon-based methods. Although these advances have broadened the horizon of meta-omic analyses in planetary health, human health, and ecology from simple sample composition studies to comprehensive taxonomic and metabolic profiles, there are still significant challenges in processing these data. First, there is a widespread lack of standardization in data processing, including software choices and the ease of installing and running attendant software. This can lead to several inconsistencies, making comparing results across studies and reproducing original results difficult. We argue that these drawbacks are especially evident in metatranscriptomic analysis, with most analyses relying on ad hoc scripts instead of pipelines implemented in workflow managers. Additional challenges rely on integrating meta-omic data, since methods have to consider the biases in the library preparation and sequencing methods and the technical noise that can arise from it. Here, we critically discuss the current limitations in metagenomics and metatranscriptomics methods with a view to catalyze future innovations in the field of Planetary Health, ecology, and allied fields of life sciences. We highlight possible solutions for these constraints to bring about more standardization, with ease of installation, high performance, and reproducibility as guiding principles.

aMeta: an accurate and memory-efficient ancient metagenomic profiling workflow

Analysis of microbial data from archaeological samples is a growing field with great potential for understanding ancient environments, lifestyles, and diseases. However, high error rates have been a challenge in ancient metagenomics, and the availability of computational frameworks that meet the demands of the field is limited. Here, we propose aMeta, an accurate metagenomic profiling workflow for ancient DNA designed to minimize the amount of false discoveries and computer memory requirements. Using simulated data, we benchmark aMeta against a current state-of-the-art workflow and demonstrate its superiority in microbial detection and authentication, as well as substantially lower usage of computer memory.

Evaluation of metagenomic assembly methods for the detection and characterization of antimicrobial resistance determinants and associated mobilizable elements

Antimicrobial resistance genes (ARGs) can be transferred between members of a bacterial population by mobile genetic elements (MGE). Understanding the risk of these transfer events is important in monitoring and predicting antimicrobial resistance (AMR), especially in the context of a One Health Continuum. However, there is no universally accepted method for detection of ARGs and MGEs, and especially for determining their linkages. This study used publicly available shotgun metagenomic DNA short-read (Illumina, 100 bp paired-end) sequence data from samples across the One Health Continuum (including beef cattle composite feces from feedlots, catch basin water at feedlots, agricultural soil from feedlot manured surrounding fields, and urban/municipal sewage influent from two municipal wastewater treatment plants) to develop a workflow to identify and associate ARGs and MGEs. ARG- and MGE-based targeted-assemblies with available short-read data were unable to meet this analysis goal. In contrast, de novo assembly of contigs provided enough sequence context to associate ARGs and MGEs, without compromising discovery rate. However, to estimate the relative abundance of these elements, unassembled sequence data must still be used.

Natural products from reconstructed bacterial genomes of the Middle and Upper Paleolithic

Major advances over the past decade in the field of ancient DNA are providing access to past paleogenomic diversity, but the diverse functions and biosynthetic capabilities of this growing paleome remain largely elusive. Here, we investigated the dental calculus of 12 Neanderthals and 52 anatomically modern humans spanning 100 kya to the present and reconstructed 459 bacterial metagenome-assembled genomes (MAGs). We identified a biosynthetic gene cluster (BGC) shared by seven Middle and Upper Paleolithic individuals that allows for the heterologous production of a class of previously unknown metabolites we name paleofurans. This paleobiotechnological approach demonstrates that viable biosynthetic machinery can be produced from the preserved genetic material of ancient organisms, allowing access to natural products from the Pleistocene and providing a promising area for natural product exploration.

Metagenomic Antimicrobial Susceptibility Testing from Simulated Native Patient Samples

Genomic antimicrobial susceptibility testing (AST) has been shown to be accurate for many pathogens and antimicrobials. However, these methods have not been systematically evaluated for clinical metagenomic data. We investigate the performance of in-silico AST from clinical metagenomes (MG-AST). Using isolate sequencing data from a multi-center study on antimicrobial resistance (AMR) as well as shotgun-sequenced septic urine samples, we simulate over 2000 complicated urinary tract infection (cUTI) metagenomes with known resistance phenotype to 5 antimicrobials. Applying rule-based and machine learning-based genomic AST classifiers, we explore the impact of sequencing depth and technology, metagenome complexity, and bioinformatics processing approaches on AST accuracy. By using an optimized metagenomics assembly and binning workflow, MG-AST achieved balanced accuracy within 5.1% of isolate-derived genomic AST. For poly-microbial infections, taxonomic sample complexity and relatedness of taxa in the sample is a key factor influencing metagenomic binning and downstream MG-AST accuracy. We show that the reassignment of putative plasmid contigs by their predicted host range and investigation of whole resistome capabilities improved MG-AST performance on poly-microbial samples. We further demonstrate that machine learning-based methods enable MG-AST with superior accuracy compared to rule-based approaches on simulated native patient samples.

metaGOflow: a workflow for the analysis of marine Genomic Observatories shotgun metagenomics data

BACKGROUND

Genomic Observatories (GOs) are sites of long-term scientific study that undertake regular assessments of the genomic biodiversity. The European Marine Omics Biodiversity Observation Network (EMO BON) is a network of GOs that conduct regular biological community samplings to generate environmental and metagenomic data of microbial communities from designated marine stations around Europe. The development of an effective workflow is essential for the analysis of the EMO BON metagenomic data in a timely and reproducible manner.

FINDINGS

Based on the established MGnify resource, we developed metaGOflow. metaGOflow supports the fast inference of taxonomic profiles from GO-derived data based on ribosomal RNA genes and their functional annotation using the raw reads. Thanks to the Research Object Crate packaging, relevant metadata about the sample under study, and the details of the bioinformatics analysis it has been subjected to, are inherited to the data product while its modular implementation allows running the workflow partially. The analysis of 2 EMO BON samples and 1 Tara Oceans sample was performed as a use case.

CONCLUSIONS

metaGOflow is an efficient and robust workflow that scales to the needs of projects producing big metagenomic data such as EMO BON. It highlights how containerization technologies along with modern workflow languages and metadata package approaches can support the needs of researchers when dealing with ever-increasing volumes of biological data. Despite being initially oriented to address the needs of EMO BON, metaGOflow is a flexible and easy-to-use workflow that can be broadly used for one-sample-at-a-time analysis of shotgun metagenomics data.

Metaphor-A workflow for streamlined assembly and binning of metagenomes

Recent advances in bioinformatics and high-throughput sequencing have enabled the large-scale recovery of genomes from metagenomes. This has the potential to bring important insights as researchers can bypass cultivation and analyze genomes sourced directly from environmental samples. There are, however, technical challenges associated with this process, most notably the complexity of computational workflows required to process metagenomic data, which include dozens of bioinformatics software tools, each with their own set of customizable parameters that affect the final output of the workflow. At the core of these workflows are the processes of assembly-combining the short-input reads into longer, contiguous fragments (contigs)-and binning, clustering these contigs into individual genome bins. The limitations of assembly and binning algorithms also pose different challenges depending on the selected strategy to execute them. Both of these processes can be done for each sample separately or by pooling together multiple samples to leverage information from a combination of samples. Here we present Metaphor, a fully automated workflow for genome-resolved metagenomics (GRM). Metaphor differs from existing GRM workflows by offering flexible approaches for the assembly and binning of the input data and by combining multiple binning algorithms with a bin refinement step to achieve high-quality genome bins. Moreover, Metaphor generates reports to evaluate the performance of the workflow. We showcase the functionality of Metaphor on different synthetic datasets and the impact of available assembly and binning strategies on the final results.

Optimized Method for Bacterial Nucleic Acid Extraction from Positive Blood Culture Broth for Whole-Genome Sequencing, Resistance Phenotype Prediction, and Downstream Molecular Applications

The application of direct metagenomic sequencing from positive blood culture broth may solve the challenges of sequencing from low-bacterial-load blood samples in patients with sepsis. Forty prospectively collected blood culture broth samples growing Gram-negative bacteria were extracted using commercially available kits to achieve high-quality DNA.

Gut Microbiome Changes Occurring with Norovirus Infection and Recovery in Infants Enrolled in a Longitudinal Birth Cohort in Leon, Nicaragua

Noroviruses are associated with one fifth of diarrheal illnesses globally and are not yet preventable with vaccines. Little is known about the effects of norovirus infection on infant gut microbiome health, which has a demonstrated role in protecting hosts from pathogens and a possible role in oral vaccine performance. In this study, we characterized infant gut microbiome changes occurring with norovirus-associated acute gastroenteritis (AGE) and the extent of recovery. Metagenomic sequencing was performed on the stools of five infants participating in a longitudinal birth cohort study conducted in León, Nicaragua. Taxonomic and functional diversities of gut microbiomes were profiled at time points before, during, and after norovirus infection. Initially, the gut microbiomes resembled those of breastfeeding infants, rich in probiotic species. When disturbed by AGE, Gammaproteobacteria dominated, particularly Pseudomonas species. Alpha diversity increased but the genes involved in carbohydrate metabolism and glycan biosynthesis decreased. After the symptoms subsided, the gut microbiomes rebounded with their taxonomic and functional communities resembling those of the pre-infection microbiomes. In this study, during disruptive norovirus-associated AGE, the gut microbiome was temporarily altered, returning to a pre-infection composition a median of 58 days later. Our study provides new insights for developing probiotic treatments and furthering our understanding of the role that episodes of AGE have in shaping the infant gut microbiome, their long-term outcomes, and implications for oral vaccine effectiveness.