Microbial abundance, activity and population genomic profiling with mOTUs2

KMCP: accurate metagenomic profiling of both prokaryotic and viral populations by pseudo-mapping

MOTIVATION

The growing number of microbial reference genomes enables the improvement of metagenomic profiling accuracy but also imposes greater requirements on the indexing efficiency, database size and runtime of taxonomic profilers. Additionally, most profilers focus mainly on bacterial, archaeal and fungal populations, while less attention is paid to viral communities.

RESULTS

We present KMCP (K-mer-based Metagenomic Classification and Profiling), a novel k-mer-based metagenomic profiling tool that utilizes genome coverage information by splitting the reference genomes into chunks and stores k-mers in a modified and optimized Compact Bit-Sliced Signature Index for fast alignment-free sequence searching. KMCP combines k-mer similarity and genome coverage information to reduce the false positive rate of k-mer-based taxonomic classification and profiling methods. Benchmarking results based on simulated and real data demonstrate that KMCP, despite a longer running time than all other methods, not only allows the accurate taxonomic profiling of prokaryotic and viral populations but also provides more confident pathogen detection in clinical samples of low depth.

AVAILABILITY AND IMPLEMENTATION

The software is open-source under the MIT license and available at https://github.com/shenwei356/kmcp.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Quality Control in Metagenomics Data

Experiments involving metagenomics data are become increasingly commonplace. Processing such data requires a unique set of considerations. Quality control of metagenomics data is critical to extracting pertinent insights. In this chapter, we outline some considerations in terms of study design and other confounding factors that can often only be realized at the point of data analysis.In this chapter, we outline some basic principles of quality control in metagenomics, including overall reproducibility and some good practices to follow. The general quality control of sequencing data is then outlined, and we introduce ways to process this data by using bash scripts and developing pipelines in Snakemake (Python).A significant part of quality control in metagenomics is in analyzing the data to ensure you can spot relationships between variables and to identify when they might be confounded. This chapter provides a walkthrough of analyzing some microbiome data (in the R statistical language) and demonstrates a few days to identify overall differences and similarities in microbiome data. The chapter is concluded by discussing remarks about considering taxonomic results in the context of the study and interrogating sequence alignments using the command line.

Metagenome-assembled genome extraction and analysis from microbiomes using KBase

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

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.

TAMPA: interpretable analysis and visualization of metagenomics-based taxon abundance profiles

BACKGROUND

Metagenomic taxonomic profiling aims to predict the identity and relative abundance of taxa in a given whole-genome sequencing metagenomic sample. A recent surge in computational methods that aim to accurately estimate taxonomic profiles, called taxonomic profilers, has motivated community-driven efforts to create standardized benchmarking datasets and platforms, standardized taxonomic profile formats, and a benchmarking platform to assess tool performance. While this standardization is essential, there is currently a lack of tools to visualize the standardized output of the many existing taxonomic profilers. Thus, benchmarking studies rely on a single-value metrics to compare performance of tools and compare to benchmarking datasets. This is one of the major problems in analyzing metagenomic profiling data, since single metrics, such as the F1 score, fail to capture the biological differences between the datasets.

FINDINGS

Here we report the development of TAMPA (Taxonomic metagenome profiling evaluation), a robust and easy-to-use method that allows scientists to easily interpret and interact with taxonomic profiles produced by the many different taxonomic profiler methods beyond the standard metrics used by the scientific community. We demonstrate the unique ability of TAMPA to generate a novel biological hypothesis by highlighting the taxonomic differences between samples otherwise missed by commonly utilized metrics.

CONCLUSION

In this study, we show that TAMPA can help visualize the output of taxonomic profilers, enabling biologists to effectively choose the most appropriate profiling method to use on their metagenomics data. TAMPA is available on GitHub, Bioconda, and Galaxy Toolshed at https://github.com/dkoslicki/TAMPA and is released under the MIT license.

Alterations of oral microbiota and impact on the gut microbiome in type 1 diabetes mellitus revealed by integrated multi-omic analyses

BACKGROUND

Alterations to the gut microbiome have been linked to multiple chronic diseases. However, the drivers of such changes remain largely unknown. The oral cavity acts as a major route of exposure to exogenous factors including pathogens, and processes therein may affect the communities in the subsequent compartments of the gastrointestinal tract. Here, we perform strain-resolved, integrated meta-genomic, transcriptomic, and proteomic analyses of paired saliva and stool samples collected from 35 individuals from eight families with multiple cases of type 1 diabetes mellitus (T1DM).

RESULTS

We identified distinct oral microbiota mostly reflecting competition between streptococcal species. More specifically, we found a decreased abundance of the commensal Streptococcus salivarius in the oral cavity of T1DM individuals, which is linked to its apparent competition with the pathobiont Streptococcus mutans. The decrease in S. salivarius in the oral cavity was also associated with its decrease in the gut as well as higher abundances in facultative anaerobes including Enterobacteria. In addition, we found evidence of gut inflammation in T1DM as reflected in the expression profiles of the Enterobacteria as well as in the human gut proteome. Finally, we were able to follow transmitted strain-variants from the oral cavity to the gut at the individual omic levels, highlighting not only the transfer, but also the activity of the transmitted taxa along the gastrointestinal tract.

CONCLUSIONS

Alterations of the oral microbiome in the context of T1DM impact the microbial communities in the lower gut, in particular through the reduction of "mouth-to-gut" transfer of Streptococcus salivarius. Our results indicate that the observed oral-cavity-driven gut microbiome changes may contribute towards the inflammatory processes involved in T1DM. Through the integration of multi-omic analyses, we resolve strain-variant "mouth-to-gut" transfer in a disease context. Video Abstract.

Enhanced metagenomic deep learning for disease prediction and consistent signature recognition by restructured microbiome 2D representations

Metagenomic analysis has been explored for disease diagnosis and biomarker discovery. Low sample sizes, high dimensionality, and sparsity of metagenomic data challenge metagenomic investigations. Here, an unsupervised microbial embedding, grouping, and mapping algorithm (MEGMA) was developed to transform metagenomic data into individualized multichannel microbiome 2D representation by manifold learning and clustering of microbial profiles (e.g., composition, abundance, hierarchy, and taxonomy). These 2D representations enable enhanced disease prediction by established ConvNet-based AggMapNet models, outperforming the commonly used machine learning and deep learning models in metagenomic benchmark datasets. These 2D representations combined with AggMapNet explainable module robustly identified more reliable and replicable disease-prediction microbes (biomarkers). Employing the MEGMA-AggMapNet pipeline for biomarker identification from 5 disease datasets, 84% of the identified biomarkers have been described in over 74 distinct works as important for these diseases. Moreover, the method also discovered highly consistent sets of biomarkers in cross-cohort colorectal cancer (CRC) patients and microbial shifts in different CRC stages.

Evaluation of taxonomic classification and profiling methods for long-read shotgun metagenomic sequencing datasets

BACKGROUND

Long-read shotgun metagenomic sequencing is gaining in popularity and offers many advantages over short-read sequencing. The higher information content in long reads is useful for a variety of metagenomics analyses, including taxonomic classification and profiling. The development of long-read specific tools for taxonomic classification is accelerating, yet there is a lack of information regarding their relative performance. Here, we perform a critical benchmarking study using 11 methods, including five methods designed specifically for long reads. We applied these tools to several mock community datasets generated using Pacific Biosciences (PacBio) HiFi or Oxford Nanopore Technology sequencing, and evaluated their performance based on read utilization, detection metrics, and relative abundance estimates.

RESULTS

Our results show that long-read classifiers generally performed best. Several short-read classification and profiling methods produced many false positives (particularly at lower abundances), required heavy filtering to achieve acceptable precision (at the cost of reduced recall), and produced inaccurate abundance estimates. By contrast, two long-read methods (BugSeq, MEGAN-LR & DIAMOND) and one generalized method (sourmash) displayed high precision and recall without any filtering required. Furthermore, in the PacBio HiFi datasets these methods detected all species down to the 0.1% abundance level with high precision. Some long-read methods, such as MetaMaps and MMseqs2, required moderate filtering to reduce false positives to resemble the precision and recall of the top-performing methods. We found read quality affected performance for methods relying on protein prediction or exact k-mer matching, and these methods performed better with PacBio HiFi datasets. We also found that long-read datasets with a large proportion of shorter reads (< 2 kb length) resulted in lower precision and worse abundance estimates, relative to length-filtered datasets. Finally, for classification methods, we found that the long-read datasets produced significantly better results than short-read datasets, demonstrating clear advantages for long-read metagenomic sequencing.

CONCLUSIONS

Our critical assessment of available methods provides best-practice recommendations for current research using long reads and establishes a baseline for future benchmarking studies.

Cultivation-independent genomes greatly expand taxonomic-profiling capabilities of mOTUs across various environments

BACKGROUND

Taxonomic profiling is a fundamental task in microbiome research that aims to detect and quantify the relative abundance of microorganisms in biological samples. Available methods using shotgun metagenomic data generally depend on the deposition of sequenced and taxonomically annotated genomes, usually from cultures of isolated strains, in reference databases (reference genomes). However, the majority of microorganisms have not been cultured yet. Thus, a substantial fraction of microbial community members remains unaccounted for during taxonomic profiling, particularly in samples from underexplored environments. To address this issue, we developed the mOTU profiler, a tool that enables reference genome-independent species-level profiling of metagenomes. As such, it supports the identification and quantification of both "known" and "unknown" species based on a set of select marker genes.

RESULTS

We present mOTUs3, a command line tool that enables the profiling of metagenomes for >33,000 species-level operational taxonomic units. To achieve this, we leveraged the reconstruction of >600,000 draft genomes, most of which are metagenome-assembled genomes (MAGs), from diverse microbiomes, including soil, freshwater systems, and the gastrointestinal tract of ruminants and other animals, which we found to be underrepresented by reference genomes. Overall, two thirds of all species-level taxa lacked a reference genome. The cumulative relative abundance of these newly included taxa was low in well-studied microbiomes, such as the human body sites (6-11%). By contrast, they accounted for substantial proportions (ocean, freshwater, soil: 43-63%) or even the majority (pig, fish, cattle: 60-80%) of the relative abundance across diverse non-human-associated microbiomes. Using community-developed benchmarks and datasets, we found mOTUs3 to be more accurate than other methods and to be more congruent with 16S rRNA gene-based methods for taxonomic profiling. Furthermore, we demonstrate that mOTUs3 increases the resolution of well-known microbial groups into species-level taxa and helps identify new differentially abundant taxa in comparative metagenomic studies.

CONCLUSIONS

We developed mOTUs3 to enable accurate species-level profiling of metagenomes. Compared to other methods, it provides a more comprehensive view of prokaryotic community diversity, in particular for currently underexplored microbiomes. To facilitate comparative analyses by the research community, it is released with >11,000 precomputed profiles for publicly available metagenomes and is freely available at: https://github.com/motu-tool/mOTUs . Video Abstract.

Stronger gut microbiome modulatory effects by postbiotics than probiotics in a mouse colitis model

Probiotics are increasingly used as adjunctive therapy to manage gastrointestinal diseases, such as ulcerative colitis. However, probiotic use has posed some safety concerns. Thus, postbiotics are proposed as alternatives to probiotics in clinical applications. However, no study has directly compared the clinical benefits of probiotics and postbiotics. This study compared the beneficial effect of postbiotics and probiotics derived from the strain, Bifidobacterium adolescentis B8598, in a dextran sulfate sodium (DSS)-induced experimental colitis mouse model. Four groups of mice (n = 7 per group) were included in this work: Control (received water plus saline), DSS (received DSS without postbiotic/probiotic), Postbiotic (received DSS plus postbiotic), and Probiotic (received DSS plus probiotic). Our results showed that intragastric administration of both probiotic and postbiotic ameliorated colitis, reflected by decreased histology scores in Postbiotic and Probiotic groups compared with DSS group (P < 0.05). The fecal microbiota alpha diversity was not significantly affected by DSS-, postbiotic, or probiotic treatment. However, the postbiotic treatment showed stronger effects on modulating the fecal microbiota beta diversity, composition, and metagenomic potential than the probiotic treatment. Overall, our findings suggested that probiotics and postbiotics had similar ability to improve disease phenotype but had distinct ability to regulate the gut microbiota and metabolic pathways in the context of ulcerative colitis. In view of the smaller safety concern of postbiotics compared with probiotics and its stronger modulatory effect on the host gut microbiota, we propose that postbiotics are to be considered for use as next-generation biotherapeutics in managing ulcerative colitis or even other diseases.

Application of ecosystem-specific reference databases for increased taxonomic resolution in soil microbial profiling

Intensive agriculture systems have paved the way for a growing human population. However, the abundant use of mineral fertilizers and pesticides may negatively impact nutrient cycles and biodiversity. One potential alternative is to harness beneficial relationships between plants and plant-associated rhizobacteria to increase nutrient-use efficiency and provide pathogen resistance. Plant-associated microbiota profiling can be achieved using high-throughput 16S rRNA gene amplicon sequencing. However, interrogation of these data is limited by confident taxonomic classifications at high taxonomic resolution (genus- or species level) with the commonly applied universal reference databases. High-throughput full-length 16S rRNA gene sequencing combined with automated taxonomy assignment (AutoTax) can be used to create amplicon sequence variant resolved ecosystems-specific reference databases that are superior to the traditional universal reference databases. This approach was used here to create a custom reference database for bacteria and archaea based on 987,353 full-length 16S rRNA genes from Askov and Cologne soils. We evaluated the performance of the database using short-read amplicon data and found that it resulted in the increased genus- and species-level classification compared to commonly use universal reference databases. The custom database was utilized to evaluate the ecosystem-specific primer bias and taxonomic resolution of amplicon primers targeting the V5–V7 region of the 16S rRNA gene commonly used within the plant microbiome field. Finally, we demonstrate the benefits of custom ecosystem-specific databases through the analysis of V5–V7 amplicon data to identify new plant-associated microbes for two legumes and two cereal species.

Meta-analysis of microbiome association networks reveal patterns of dysbiosis in diseased microbiomes

The human gut microbiome is composed of a diverse and dynamic population of microbial species which play key roles in modulating host health and physiology. While individual microbial species have been found to be associated with certain disease states, increasing evidence suggests that higher-order microbial interactions may have an equal or greater contribution to host fitness. To better understand microbial community dynamics, we utilize networks to study interactions through a meta-analysis of microbial association networks between healthy and disease gut microbiomes. Taking advantage of the large number of metagenomes derived from healthy individuals and patients with various diseases, together with recent advances in network inference that can deal with sparse compositional data, we inferred microbial association networks based on co-occurrence of gut microbial species and made the networks publicly available as a resource (GitHub repository named GutNet). Through our meta-analysis of inferred networks, we were able to identify network-associated features that help stratify between healthy and disease states such as the differentiation of various bacterial phyla and enrichment of Proteobacteria interactions in diseased networks. Additionally, our findings show that the contributions of taxa in microbial associations are disproportionate to their abundances and that rarer taxa of microbial species play an integral part in shaping dynamics of microbial community interactions. Network-based meta-analysis revealed valuable insights into microbial community dynamics between healthy and disease phenotypes. We anticipate that the healthy and diseased microbiome association networks we inferred will become an important resource for human-related microbiome research.

Depression and fatigue in active IBD from a microbiome perspective-a Bayesian approach to faecal metagenomics

BACKGROUND

Extraintestinal symptoms are common in inflammatory bowel diseases (IBD) and include depression and fatigue. These are highly prevalent especially in active disease, potentially due to inflammation-mediated changes in the microbiota-gut-brain axis. The aim of this study was to investigate the associations between structural and functional microbiota characteristics and severity of fatigue and depressive symptoms in patients with active IBD.

METHODS

We included clinical data of 62 prospectively enrolled patients with IBD in an active disease state. Patients supplied stool samples and completed the questionnaires regarding depression and fatigue symptoms. Based on taxonomic and functional metagenomic profiles of faecal gut microbiota, we used Bayesian statistics to investigate the associative networks and triangle motifs between bacterial genera, functional modules and symptom severity of self-reported fatigue and depression.

RESULTS

Associations with moderate to strong evidence were found for 3 genera (Odoribacter, Anaerotruncus and Alistipes) and 3 functional modules (pectin, glycosaminoglycan and central carbohydrate metabolism) with regard to depression and for 4 genera (Intestinimonas, Anaerotruncus, Eubacterium and Clostridiales g.i.s) and 2 functional modules implicating amino acid and central carbohydrate metabolism with regard to fatigue.

CONCLUSIONS

This study provides the first evidence of association triplets between microbiota composition, function and extraintestinal symptoms in active IBD. Depression and fatigue were associated with lower abundances of short-chain fatty acid producers and distinct pathways implicating glycan, carbohydrate and amino acid metabolism. Our results suggest that microbiota-directed therapeutic approaches may reduce fatigue and depression in IBD and should be investigated in future research.

Genome-centric insight into metabolically active microbial population in shallow-sea hydrothermal vents

BACKGROUND

Geothermal systems have contributed greatly to both our understanding of the functions of extreme life and the evolutionary history of life itself. Shallow-sea hydrothermal systems are ecological intermediates of deep-sea systems and terrestrial springs, harboring unique and complexed ecosystems, which are well-lit and present physicochemical gradients. The microbial communities of deep-sea and terrestrial geothermal systems have been well-studied at the population genome level, yet little is known about the communities inhabiting the shallow-sea hydrothermal systems and how they compare to those inhabiting other geothermal systems.

RESULTS

Here, we used genome-resolved metagenomic and metaproteomic approaches to probe into the genetic potential and protein expression of microorganisms from the shallow-sea vent fluids off Kueishantao Island. The families Nautiliaceae and Campylobacteraceae within the Epsilonbacteraeota and the Thiomicrospiraceae within the Gammaproteobacteria were prevalent in vent fluids over a 3-year sampling period. We successfully reconstructed the in situ metabolic modules of the predominant populations within the Epsilonbacteraeota and Gammaproteobacteria by mapping the metaproteomic data back to metagenome-assembled genomes. Those active bacteria could use the reductive tricarboxylic acid cycle or Calvin-Benson-Bassham cycle for autotrophic carbon fixation, with the ability to use reduced sulfur species, hydrogen or formate as electron donors, and oxygen as a terminal electron acceptor via cytochrome bd oxidase or cytochrome bb3 oxidase. Comparative metagenomic and genomic analyses revealed dramatic differences between submarine and terrestrial geothermal systems, including microbial functional potentials for carbon fixation and energy conversion. Furthermore, shallow-sea hydrothermal systems shared many of the major microbial genera that were first isolated from deep-sea and terrestrial geothermal systems, while deep-sea and terrestrial geothermal systems shared few genera.

CONCLUSIONS

The metabolic machinery of the active populations within Epsilonbacteraeota and Gammaproteobacteria at shallow-sea vents can mirror those living at deep-sea vents. With respect to specific taxa and metabolic potentials, the microbial realm in the shallow-sea hydrothermal system presented ecological linkage to both deep-sea and terrestrial geothermal systems. Video Abstract.

Population-level Metagenomics Uncovers Distinct Effects of Multiple Medications on the Human Gut Microbiome

BACKGROUND & AIMS

Medication is a major determinant of human gut microbiome structure, and its overuse increases the risks of morbidity and mortality. However, effects of certain commonly prescribed drugs and multiple medications on the gut microbiome are still underinvestigated.

METHODS

We performed shotgun metagenomic analysis of fecal samples from 4198 individuals in the Japanese 4D (Disease, Drug, Diet, Daily life) microbiome project. A total of 759 drugs were profiled, and other metadata, such as anthropometrics, lifestyles, diets, physical activities, and diseases, were prospectively collected. Second fecal samples were collected from 243 individuals to assess the effects of drug initiation and discontinuation on the microbiome.

RESULTS

We found that numerous drugs across different treatment categories influence the microbiome; more than 70% of the drugs we profiled had not been examined before. Individuals exposed to multiple drugs, polypharmacy, showed distinct gut microbiome structures harboring significantly more abundant upper gastrointestinal species and several nosocomial pathobionts due to additive drug effects. Polypharmacy was also associated with microbial functions, including the reduction of short-chain fatty acid metabolism and increased bacterial stress responses. Even nonantibiotic drugs were significantly correlated with an increased antimicrobial resistance potential through polypharmacy. Notably, a 2-time points dataset revealed the alteration and recovery of the microbiome in response to drug initiation and cessation, corroborating the observed drug-microbe associations in the cross-sectional cohort.

CONCLUSION

Our large-scale metagenomics unravels extensive and disruptive impacts of individual and multiple drug exposures on the human gut microbiome, providing a drug-microbe catalog as a basis for a deeper understanding of the role of the microbiome in drug efficacy and toxicity.

Skin Microbiome Variation with Cancer Progression in Human Cutaneous Squamous Cell Carcinoma

The skin microbiome plays a critical role in skin homeostasis and disorders. UVR is the major cause of nonmelanoma skin cancer, but other risk factors, including immune suppression, chronic inflammation, and antibiotic usage, suggest the microbiome as an additional, unexplored risk factor and potential disease biomarker. The overarching goal was to study the skin microbiome in squamous cell carcinoma (SCC) and premalignant actinic keratosis compared with that in healthy skin to identify skin cancer‒associated changes in the skin microbiome. We performed a high-resolution analysis of shotgun metagenomes of actinic keratosis and SCC in healthy skin, revealing the microbial community shifts specific to actinic keratosis and SCC. Most prominently, the relative abundance of pathobiont Staphylococcus aureus was increased at the expense of commensal Cutibacterium acnes in SCC compared with that in healthy skin, and enrichment of functional pathways in SCC reflected this shift. Notably, C. acnes associated with lesional versus healthy skin differed at the strain level, suggesting the specific functional changes associated with its depletion in SCC. Our study revealed a transitional microbial dysbiosis from healthy skin to actinic keratosis to SCC, supporting further investigation of the skin microbiome for use as a biomarker and providing hypotheses for studies investigating how these microbes might influence skin cancer progression.

Benchmarking taxonomic classifiers with Illumina and Nanopore sequence data for clinical metagenomic diagnostic applications

Culture-independent metagenomic detection of microbial species has the potential to provide rapid and precise real-time diagnostic results. However, it is potentially limited by sequencing and taxonomic classification errors. We use simulated and real-world data to benchmark rates of species misclassification using 100 reference genomes for each of the ten common bloodstream pathogens and six frequent blood-culture contaminants (n=1568, only 68 genomes were available for Micrococcus luteus). Simulating both with and without sequencing error for both the Illumina and Oxford Nanopore platforms, we evaluated commonly used classification tools including Kraken2, Bracken and Centrifuge, utilizing mini (8 GB) and standard (30–50 GB) databases. Bracken with the standard database performed best, the median percentage of reads across both sequencing platforms identified correctly to the species level was 97.8% (IQR 92.7:99.0) [range 5:100]. For Kraken2 with a mini database, a commonly used combination, median species-level identification was 86.4% (IQR 50.5:93.7) [range 4.3:100]. Classification performance varied by species, with Escherichia coli being more challenging to classify correctly (probability of reads being assigned to the correct species: 56.1–96.0%, varying by tool used). Human read misclassification was negligible. By filtering out shorter Nanopore reads we found performance similar or superior to Illumina sequencing, despite higher sequencing error rates. Misclassification was more common when the misclassified species had a higher average nucleotide identity to the true species. Our findings highlight taxonomic misclassification of sequencing data occurs and varies by sequencing and analysis workflow. To account for ‘bioinformatic contamination’ we present a contamination catalogue that can be used in metagenomic pipelines to ensure accurate results that can support clinical decision making.

Metabolic Potential of the Gut Microbiome Is Significantly Impacted by Conditioning Regimen in Allogeneic Hematopoietic Stem Cell Transplantation Recipients

Allogeneic hematopoietic stem cell transplantation (aHSCT) is a putative curative treatment for malignant hematologic disorders. During transplantation, the immune system is suppressed/eradicated through a conditioning regimen (non-myeloablative or myeloablative) and replaced with a donor immune system. In our previous study, we showed changes in gut taxonomic profiles and a decrease in bacterial diversity post-transplant. In this study, we expand the cohort with 114 patients and focus on the impact of the conditioning regimens on taxonomic features and the metabolic functions of the gut bacteria. This is, to our knowledge, the first study to examine the metabolic potential of the gut microbiome in this patient group. Adult aHSCT recipients with shotgun sequenced stool samples collected day −30 to +28 relative to aHSCT were included. One sample was selected per patient per period: pre-aHSCT (day −30–0) and post-aHSCT (day 1–28). In total, 254 patients and 365 samples were included. Species richness, alpha diversity, gene richness and metabolic richness were all lower post-aHSCT than pre-aHSCT and the decline was more pronounced for the myeloablative group. The myeloablative group showed a decline in 36 genera and an increase in 15 genera. For the non-myeloablative group, 30 genera decreased and 16 increased with lower fold changes than observed in the myeloablative group. For the myeloablative group, 32 bacterial metabolic functions decreased, and one function increased. For the non-myeloablative group, three functions decreased, and two functions increased. Hence, the changes in taxonomy post-aHSCT caused a profound decline in bacterial metabolic functions especially in the myeloablative group, thus providing new evidence for associations of myeloablative conditioning and gut dysbiosis from a functional perspective.

Drivers and determinants of strain dynamics following fecal microbiota transplantation

Fecal microbiota transplantation (FMT) is a therapeutic intervention for inflammatory diseases of the gastrointestinal tract, but its clinical mode of action and subsequent microbiome dynamics remain poorly understood. Here we analyzed metagenomes from 316 FMTs, sampled pre and post intervention, for the treatment of ten different disease indications. We quantified strain-level dynamics of 1,089 microbial species, complemented by 47,548 newly constructed metagenome-assembled genomes. Donor strain colonization and recipient strain resilience were mostly independent of clinical outcomes, but accurately predictable using LASSO-regularized regression models that accounted for host, microbiome and procedural variables. Recipient factors and donor–recipient complementarity, encompassing entire microbial communities to individual strains, were the main determinants of strain population dynamics, providing insights into the underlying processes that shape the post-FMT gut microbiome. Applying an ecology-based framework to our findings indicated parameters that may inform the development of more effective, targeted microbiome therapies in the future, and suggested how patient stratification can be used to enhance donor microbiota colonization or the displacement of recipient microbes in clinical practice.

Trade-offs of lipid remodeling in a marine predator-prey interaction in response to phosphorus limitation

Microbial growth is often limited by key nutrients like phosphorus (P) across the global ocean. A major response to P limitation is the replacement of membrane phospholipids with non-P lipids to reduce their cellular P quota. However, the biological “costs” of lipid remodeling are largely unknown. Here, we uncover a predator–prey interaction trade-off whereby a lipid-remodeled bacterial prey cell becomes more susceptible to digestion by a protozoan predator facilitating its rapid growth. Thus, we highlight a complex interplay between adaptation to the abiotic environment and consequences for biotic interactions (grazing), which may have important implications for the stability and structuring of microbial communities and the performance of the marine food web.

Phosphorus (P) is a key nutrient limiting bacterial growth and primary production in the oceans. Unsurprisingly, marine microbes have evolved sophisticated strategies to adapt to P limitation, one of which involves the remodeling of membrane lipids by replacing phospholipids with non-P-containing surrogate lipids. This strategy is adopted by both cosmopolitan marine phytoplankton and heterotrophic bacteria and serves to reduce the cellular P quota. However, little, if anything, is known of the biological consequences of lipid remodeling. Here, using the marine bacterium Phaeobacter sp. MED193 and the ciliate Uronema marinum as a model, we sought to assess the effect of remodeling on bacteria–protist interactions. We discovered an important trade-off between either escape from ingestion or resistance to digestion. Thus, Phaeobacter grown under P-replete conditions was readily ingested by Uronema, but not easily digested, supporting only limited predator growth. In contrast, following membrane lipid remodeling in response to P depletion, Phaeobacter was less likely to be captured by Uronema, thanks to the reduced expression of mannosylated glycoconjugates. However, once ingested, membrane-remodeled cells were unable to prevent phagosome acidification, became more susceptible to digestion, and, as such, allowed rapid growth of the ciliate predator. This trade-off between adapting to a P-limited environment and susceptibility to protist grazing suggests the more efficient removal of low-P prey that potentially has important implications for the functioning of the marine microbial food web in terms of trophic energy transfer and nutrient export efficiency.

Extensive gut virome variation and its associations with host and environmental factors in a population-level cohort

Indigenous bacteriophage communities (virome) in the human gut have a huge impact on the structure and function of gut bacterial communities (bacteriome), but virome variation at a population scale is not fully investigated yet. Here, we analyse the gut dsDNA virome in the Japanese 4D cohort of 4198 deeply phenotyped individuals. By assembling metagenomic reads, we discover thousands of high-quality phage genomes including previously uncharacterised phage clades with different bacterial hosts than known major ones. The distribution of host bacteria is a strong determinant for the distribution of phages in the gut, and virome diversity is highly correlated with anti-viral defence mechanisms of the bacteriome, such as CRISPR-Cas and restriction-modification systems. We identify 97 various intrinsic/extrinsic factors that significantly affect the virome structure, including age, sex, lifestyle, and diet, most of which showed consistent associations with both phages and their predicted bacterial hosts. Among the metadata categories, disease and medication have the strongest effects on the virome structure. Overall, these results present a basis to understand the symbiotic communities of bacteria and their viruses in the human gut, which will facilitate the medical and industrial applications of indigenous viruses.

StrainPanDA: Linked reconstruction of strain composition and gene content profiles via pangenome-based decomposition of metagenomic data

Microbial strains of variable functional capacities coexist in microbiomes. Current bioinformatics methods of strain analysis cannot provide the direct linkage between strain composition and their gene contents from metagenomic data. Here we present Strain-level Pangenome Decomposition Analysis (StrainPanDA), a novel method that uses the pangenome coverage profile of multiple metagenomic samples to simultaneously reconstruct the composition and gene content variation of coexisting strains in microbial communities. We systematically validate the accuracy and robustness of StrainPanDA using synthetic data sets. To demonstrate the power of gene-centric strain profiling, we then apply StrainPanDA to analyze the gut microbiome samples of infants, as well as patients treated with fecal microbiota transplantation. We show that the linked reconstruction of strain composition and gene content profiles is critical for understanding the relationship between microbial adaptation and strain-specific functions (e.g., nutrient utilization and pathogenicity). Finally, StrainPanDA has minimal requirements for computing resources and can be scaled to process multiple species in a community in parallel. In short, StrainPanDA can be applied to metagenomic data sets to detect the association between molecular functions and microbial/host phenotypes to formulate testable hypotheses and gain novel biological insights at the strain or subspecies level.

expam-high-resolution analysis of metagenomes using distance trees

Summary

Shotgun metagenomic sequencing provides the capacity to understand microbial community structure and function at unprecedented resolution; however, the current analytical methods are constrained by a focus on taxonomic classifications that may obfuscate functional relationships. Here, we present expam, a tree-based, taxonomy agnostic tool for the identification of biologically relevant clades from shotgun metagenomic sequencing.

Availability and implementation

expam is an open-source Python application released under the GNU General Public Licence v3.0. expam installation instructions, source code and tutorials can be found at https://github.com/seansolari/expam.

Supplementary information

Supplementary data are available at Bioinformatics online.

A revisit to universal single-copy genes in bacterial genomes

Universal single-copy genes (USCGs) are widely used for species classification and taxonomic profiling. Despite many studies on USCGs, our understanding of USCGs in bacterial genomes might be out of date, especially how different the USCGs are in different studies, how well a set of USCGs can distinguish two bacterial species, whether USCGs can separate different strains of a bacterial species, to name a few. To fill the void, we studied USCGs in the most updated complete bacterial genomes. We showed that different USCG sets are quite different while coming from highly similar functional categories. We also found that although USCGs occur once in almost all bacterial genomes, each USCG does occur multiple times in certain genomes. We demonstrated that USCGs are reliable markers to distinguish different species while they cannot distinguish different strains of most bacterial species. Our study sheds new light on the usage and limitations of USCGs, which will facilitate their applications in evolutionary, phylogenomic, and metagenomic studies.

FunOMIC: Pipeline with built-in fungal taxonomic and functional databases for human mycobiome profiling

While analysis of the bacterial microbiome has become routine, that of the fungal microbiome is still hampered by the lack of robust databases and bioinformatic pipelines. Here, we present FunOMIC, a pipeline with built-in taxonomic (1.6 million marker genes) and functional (3.4 million non-redundant fungal proteins) databases for the identification of fungi. Applied to more than 2,600 human metagenomic samples, the tool revealed fungal species associated with geography, body sites, and diseases. Correlation network analysis provided new insights into inter-kingdom interactions. With this pipeline and two of the most comprehensive fungal databases, we foresee a fast-growing resource for mycobiome studies.

The Core Human Microbiome: Does It Exist and How Can We Find It? A Critical Review of the Concept

The core microbiome, which refers to a set of consistent microbial features across populations, is of major interest in microbiome research and has been addressed by numerous studies. Understanding the core microbiome can help identify elements that lead to dysbiosis, and lead to treatments for microbiome-related health states. However, defining the core microbiome is a complex task at several levels. In this review, we consider the current state of core human microbiome research. We consider the knowledge that has been gained, the factors limiting our ability to achieve a reliable description of the core human microbiome, and the fields most likely to improve that ability. DNA sequencing technologies and the methods for analyzing metagenomics and amplicon data will most likely facilitate higher accuracy and resolution in describing the microbiome. However, more effort should be invested in characterizing the microbiome’s interactions with its human host, including the immune system and nutrition. Other components of this holobiontic system should also be emphasized, such as fungi, protists, lower eukaryotes, viruses, and phages. Most importantly, a collaborative effort of experts in microbiology, nutrition, immunology, medicine, systems biology, bioinformatics, and machine learning is probably required to identify the traits of the core human microbiome.

Gut Microbiota Composition and Metabolic Potential of Long-Living People in China

Individuals with naturally long-life spans have been extensively studied to gain a greater understanding of what factors contribute to their overall health and ability to delay or avoid certain diseases. Our previous work showed that gut microbiota can be a new avenue in healthy aging studies. In the present study, a total of 86 Chinese individuals were assigned into three groups: the long-living group (90 + years old; n = 28), the elderly group (65–75 years old; n = 31), and the young group (24–48 years old; n = 27). These groups were used to explore the composition and functional genes in the microbiota community by using the metagenomic sequencing method. We found that long-living individuals maintained high diversity in gene composition and functional profiles. Furthermore, their microbiota displays less inter-individual variation than that of elderly adults. In the taxonomic composition, it was shown that long-living people contained more short-chain fatty acid (SCFA)-producing bacteria and a decrease in certain pathogenic bacteria. Functional analysis also showed that the long-living people were enriched in metabolism metabolites methanol, trimethylamine (TMA), and CO₂ to methane, and lysine biosynthesis, but the genes related to riboflavin (vitamin B2) metabolism and tryptophan biosynthesis were significantly reduced in long-living individuals. Further, we found that long-living people with enriched SCFA- and lactic-producing bacteria and related genes, highly centered on producing key lactic acid genes (ldhA) and the genes of lysine that are metabolized to the butyrate pathway. In addition, we compared the gut microbiota signatures of longevity in different regions and found that the composition of the gut microbiota of the long-lived Chinese and Italian people was quite different, but both groups were enriched in genes related to methane production and glucose metabolism. In terms of SCFA metabolism, the Chinese long-living people were enriched with bacteria and genes related to butyric acid production, while the Italian long-living people were enriched with more acetic acid-related genes. These findings suggest that the gut microbiota of Chinese long-living individuals include more SCFA-producing bacteria and genes, metabolizes methanol, TMA, and CO₂, and contains fewer pathogenic bacteria, thereby potentially contributing to the healthy aging of humans.

Exaggerated trans-membrane charge of ammonium transporters in nutrient-poor marine environments

Transporter proteins are a vital interface between cells and their environment. In nutrient-limited environments, microbes with transporters that are effective at bringing substrates into their cells will gain a competitive advantage over variants with reduced transport function. Microbial ammonium transporters (Amt) bring ammonium into the cytoplasm from the surrounding periplasm space, but diagnosing Amt adaptations to low nutrient environments solely from sequence data has been elusive. Here, we report altered Amt sequence amino acid distribution from deep marine samples compared to variants sampled from shallow water in two important microbial lineages of the marine water column community-Marine Group I Archaea (Thermoproteota) and the uncultivated gammaproteobacterial lineage SAR86. This pattern indicates an evolutionary pressure towards an increasing dipole in Amt for these clades in deep ocean environments and is predicted to generate stronger electric fields facilitating ammonium acquisition. This pattern of increasing dipole charge with depth was not observed in lineages capable of accessing alternative nitrogen sources, including the abundant alphaproteobacterial clade SAR11. We speculate that competition for ammonium in the deep ocean drives transporter sequence evolution. The low concentration of ammonium in the deep ocean is therefore likely due to rapid uptake by Amts concurrent with decreasing nutrient flux.

A faecal microbiota signature with high specificity for pancreatic cancer

BACKGROUND

Recent evidence suggests a role for the microbiome in pancreatic ductal adenocarcinoma (PDAC) aetiology and progression.

OBJECTIVE

To explore the faecal and salivary microbiota as potential diagnostic biomarkers.

METHODS

We applied shotgun metagenomic and 16S rRNA amplicon sequencing to samples from a Spanish case-control study (n=136), including 57 cases, 50 controls, and 29 patients with chronic pancreatitis in the discovery phase, and from a German case-control study (n=76), in the validation phase.

RESULTS

Faecal metagenomic classifiers performed much better than saliva-based classifiers and identified patients with PDAC with an accuracy of up to 0.84 area under the receiver operating characteristic curve (AUROC) based on a set of 27 microbial species, with consistent accuracy across early and late disease stages. Performance further improved to up to 0.94 AUROC when we combined our microbiome-based predictions with serum levels of carbohydrate antigen (CA) 19-9, the only current non-invasive, Food and Drug Administration approved, low specificity PDAC diagnostic biomarker. Furthermore, a microbiota-based classification model confined to PDAC-enriched species was highly disease-specific when validated against 25 publicly available metagenomic study populations for various health conditions (n=5792). Both microbiome-based models had a high prediction accuracy on a German validation population (n=76). Several faecal PDAC marker species were detectable in pancreatic tumour and non-tumour tissue using 16S rRNA sequencing and fluorescence in situ hybridisation.

CONCLUSION

Taken together, our results indicate that non-invasive, robust and specific faecal microbiota-based screening for the early detection of PDAC is feasible.

Metagenomic Identification of Microbial Signatures Predicting Pancreatic Cancer From a Multinational Study

BACKGROUND & AIMS

To identify gut and oral metagenomic signatures that accurately predict pancreatic ductal carcinoma (PDAC) and to validate these signatures in independent cohorts.

METHODS

We conducted a multinational study and performed shotgun metagenomic analysis of fecal and salivary samples collected from patients with treatment-naïve PDAC and non-PDAC controls in Japan, Spain, and Germany. Taxonomic and functional profiles of the microbiomes were characterized, and metagenomic classifiers to predict PDAC were constructed and validated in external datasets.

RESULTS

Comparative metagenomics revealed dysbiosis of both the gut and oral microbiomes and identified 30 gut and 18 oral species significantly associated with PDAC in the Japanese cohort. These microbial signatures achieved high area under the curve values of 0.78 to 0.82. The prediction model trained on the Japanese gut microbiome also had high predictive ability in Spanish and German cohorts, with respective area under the curve values of 0.74 and 0.83, validating its high confidence and versatility for PDAC prediction. Significant enrichments of Streptococcus and Veillonella spp and a depletion of Faecalibacterium prausnitzii were common gut signatures for PDAC in all the 3 cohorts. Prospective follow-up data revealed that patients with certain gut and oral microbial species were at higher risk of PDAC-related mortality. Finally, 58 bacteriophages that could infect microbial species consistently enriched in patients with PDAC across the 3 countries were identified.

CONCLUSIONS

Metagenomics targeting the gut and oral microbiomes can provide a powerful source of biomarkers for identifying individuals with PDAC and their prognoses. The identification of shared gut microbial signatures for PDAC in Asian and European cohorts indicates the presence of robust and global gut microbial biomarkers.

Biosynthetic potential of the global ocean microbiome

Natural microbial communities are phylogenetically and metabolically diverse. In addition to underexplored organismal groups¹, this diversity encompasses a rich discovery potential for ecologically and biotechnologically relevant enzymes and biochemical compounds^2,3. However, studying this diversity to identify genomic pathways for the synthesis of such compounds⁴ and assigning them to their respective hosts remains challenging. The biosynthetic potential of microorganisms in the open ocean remains largely uncharted owing to limitations in the analysis of genome-resolved data at the global scale. Here we investigated the diversity and novelty of biosynthetic gene clusters in the ocean by integrating around 10,000 microbial genomes from cultivated and single cells with more than 25,000 newly reconstructed draft genomes from more than 1,000 seawater samples. These efforts revealed approximately 40,000 putative mostly new biosynthetic gene clusters, several of which were found in previously unsuspected phylogenetic groups. Among these groups, we identified a lineage rich in biosynthetic gene clusters (‘Candidatus Eudoremicrobiaceae’) that belongs to an uncultivated bacterial phylum and includes some of the most biosynthetically diverse microorganisms in this environment. From these, we characterized the phospeptin and pythonamide pathways, revealing cases of unusual bioactive compound structure and enzymology, respectively. Together, this research demonstrates how microbiomics-driven strategies can enable the investigation of previously undescribed enzymes and natural products in underexplored microbial groups and environments.

Global ocean microbiome survey reveals the bacterial family ‘Candidatus Eudoremicrobiaceae’, which includes some of the most biosynthetically diverse microorganisms in the ocean environment.

Identification of nosZ-expressing microorganisms consuming trace N2O in microaerobic chemostat consortia dominated by an uncultured Burkholderiales

Microorganisms possessing N₂O reductases (NosZ) are the only known environmental sink of N₂O. While oxygen inhibition of NosZ activity is widely known, environments where N₂O reduction occurs are often not devoid of O₂. However, little is known regarding N₂O reduction in microoxic systems. Here, 1.6-L chemostat cultures inoculated with activated sludge samples were sustained for ca. 100 days with low concentration (<2 ppmv) and feed rate (<1.44 µmoles h⁻¹) of N₂O, and the resulting microbial consortia were analyzed via quantitative PCR (qPCR) and metagenomic/metatranscriptomic analyses. Unintended but quantified intrusion of O₂ sustained dissolved oxygen concentration above 4 µM; however, complete N₂O reduction of influent N₂O persisted throughout incubation. Metagenomic investigations indicated that the microbiomes were dominated by an uncultured taxon affiliated to Burkholderiales, and, along with the qPCR results, suggested coexistence of clade I and II N₂O reducers. Contrastingly, metatranscriptomic nosZ pools were dominated by the Dechloromonas-like nosZ subclade, suggesting the importance of the microorganisms possessing this nosZ subclade in reduction of trace N₂O. Further, co-expression of nosZ and ccoNO/cydAB genes found in the metagenome-assembled genomes representing these putative N₂O-reducers implies a survival strategy to maximize utilization of scarcely available electron acceptors in microoxic environmental niches.

The Relationship Between Gut Microbiome and Bile Acids in Primates With Diverse Diets

Primates have evolved a variety of feeding habits and intestinal physiological structure. Gut microbiome act as metabolic organs in many biological processes and play a vital role in adaptation to dietary niches. Gut microbiome also convert primary bile acids (BAs) to secondary. BAs profile and gut microbiome are together influenced by diets and play a significant role in nutrient absorption. The regulation between gut microbiome and BAs metabolism is bidirectional although the relationship in primates consuming diverse diets is still unclear. Here, we investigated gut microbiome structures, fecal BAs profile, and their relationship in primates preferring three distinct diets. We found that gut microbiome communities are well differentiated among dietary groups. Folivorous primates had higher Firmicutes abundance and lower Prevotella to Bacaeroides ratios, possibly related to fiber consumption. Frugivorous primates are colonized predominantly by Prevotella and Bacteroides, pointing to an increased adaptation to high-sugar and simple carbohydrate diets. Likewise, BA profiles differ according to diet in a manner predictable from the known effects of BAs on metabolism. Folivorous primates have high conjugated bile acid levels and low unconjugated to conjugated BA ratios, consistent with their fiber-rich leaf-eating diet. Much of the differentiation in secondary and unconjugated BAs is associated with microbiome composition shifts and individual bile acid concentrations are correlated with the abundance of distinct bacterial taxonomic groups. Omnivores have higher concentrations of secondary BAs, mainly lithocholic acid (LCA). These levels are significantly positively correlated with the presence of Clostrida species, showing that the digestion requirements of omnivores are different from plant-eating primates. In conclusion, gut microbiome and BAs can respond to changes in diet and are associated with nutrient component consumption in each diet primate group. Our study is the first to demonstrate BA profile differentiation among primates preferring diverse diets. BAs thus appear to work with gut microbiome to help primates adapt to their diet.

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

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

Mechanistic insights into the key marine dimethylsulfoniopropionate synthesis enzyme DsyB/DSYB

Marine algae and bacteria produce approximately eight billion tonnes of the organosulfur molecule dimethylsulfoniopropionate (DMSP) in Earth's surface oceans annually. DMSP is an antistress compound and, once released into the environment, a major nutrient, signaling molecule, and source of climate-active gases. The methionine transamination pathway for DMSP synthesis is used by most known DMSP-producing algae and bacteria. The S-directed S-adenosylmethionine (SAM)-dependent 4-methylthio-2-hydroxybutyrate (MTHB) S-methyltransferase, encoded by the dsyB/DSYB gene, is the key enzyme of this pathway, generating S-adenosylhomocysteine (SAH) and 4-dimethylsulfonio-2-hydroxybutyrate (DMSHB). DsyB/DSYB, present in most haptophyte and dinoflagellate algae with the highest known intracellular DMSP concentrations, is shown to be far more abundant and transcribed in marine environments than any other known S-methyltransferase gene in DMSP synthesis pathways. Furthermore, we demonstrate in vitro activity of the bacterial DsyB enzyme from Nisaea denitrificans and provide its crystal structure in complex with SAM and SAH-MTHB, which together provide the first important mechanistic insights into a DMSP synthesis enzyme. Structural and mutational analyses imply that DsyB adopts a proximity and desolvation mechanism for the methyl transfer reaction. Sequence analysis suggests that this mechanism may be common to all bacterial DsyB enzymes and also, importantly, eukaryotic DSYB enzymes from e.g., algae that are the major DMSP producers in Earth's surface oceans.

Multi-Omics Strategies for Investigating the Microbiome in Toxicology Research

Microbial communities on and within the host contact environmental pollutants, toxic compounds, and other xenobiotic compounds. These communities of bacteria, fungi, viruses, and archaea possess diverse metabolic potential to catabolize compounds and produce new metabolites. Microbes alter chemical disposition thus making the microbiome a natural subject of interest for toxicology. Sequencing and metabolomics technologies permit the study of microbiomes altered by acute or long-term exposure to xenobiotics. These investigations have already contributed to and are helping to re-interpret traditional understandings of toxicology. The purpose of this review is to provide a survey of the current methods used to characterize microbes within the context of toxicology. This will include discussion of commonly used techniques for conducting omic-based experiments, their respective strengths and deficiencies, and how forward-looking techniques may address present shortcomings. Finally, a perspective will be provided regarding common assumptions that currently impede microbiome studies from producing causal explanations of toxicologic mechanisms.

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.

MIntO: A Modular and Scalable Pipeline For Microbiome Metagenomic and Metatranscriptomic Data Integration

Omics technologies have revolutionized microbiome research allowing the characterization of complex microbial communities in different biomes without requiring their cultivation. As a consequence, there has been a great increase in the generation of omics data from metagenomes and metatranscriptomes. However, pre-processing and analysis of these data have been limited by the availability of computational resources, bioinformatics expertise and standardized computational workflows to obtain consistent results that are comparable across different studies. Here, we introduce MIntO (Microbiome Integrated meta-Omics), a highly versatile pipeline that integrates metagenomic and metatranscriptomic data in a scalable way. The distinctive feature of this pipeline is the computation of gene expression profile through integrating metagenomic and metatranscriptomic data taking into account the community turnover and gene expression variations to disentangle the mechanisms that shape the metatranscriptome across time and between conditions. The modular design of MIntO enables users to run the pipeline using three available modes based on the input data and the experimental design, including de novo assembly leading to metagenome-assembled genomes. The integrated pipeline will be relevant to provide unique biochemical insights into microbial ecology by linking functions to retrieved genomes and to examine gene expression variation. Functional characterization of community members will be crucial to increase our knowledge of the microbiome’s contribution to human health and environment. MIntO v1.0.1 is available at https://github.com/arumugamlab/MIntO.

Evaluation of Experimental Protocols for Shotgun Whole-Genome Metagenomic Discovery of Antibiotic Resistance Genes

Shotgun metagenomics has enabled the discovery of antibiotic resistance genes (ARGs). Although there have been numerous studies benchmarking the bioinformatics methods for shotgun metagenomic data analysis, there has not yet been a study that systematically evaluates the performance of different experimental protocols on metagenomic species profiling and ARG detection. In this study, we generated 35 whole genome shotgun metagenomic sequencing data sets for five samples (three human stool and two microbial standard) using seven experimental protocols (KAPA or Flex kits at 50ng, 10ng, or 5ng input amounts; XT kit at 1ng input amount). Using this comprehensive resource, we evaluated the seven protocols in terms of robust detection of ARGs and microbial abundance estimation at various sequencing depths. We found that the data generated by the seven protocols are largely similar. The inter-protocol variability is significantly smaller than the variability between samples or sequencing depths. We found that a sequencing depth of more than 30M is suitable for human stool samples. A higher input amount (50ng) is generally favorable for the KAPA and Flex kits. This systematic benchmarking study sheds light on the impact of sequencing depth, experimental protocol, and DNA input amount on ARG detection in human stool samples.

Dissecting the dominant hot spring microbial populations based on community-wide sampling at single-cell genomic resolution

With advances in DNA sequencing and miniaturized molecular biology workflows, rapid and affordable sequencing of single-cell genomes has become a reality. Compared to 16S rRNA gene surveys and shotgun metagenomics, large-scale application of single-cell genomics to whole microbial communities provides an integrated snapshot of community composition and function, directly links mobile elements to their hosts, and enables analysis of population heterogeneity of the dominant community members. To that end, we sequenced nearly 500 single-cell genomes from a low diversity hot spring sediment sample from Dewar Creek, British Columbia, and compared this approach to 16S rRNA gene amplicon and shotgun metagenomics applied to the same sample. We found that the broad taxonomic profiles were similar across the three sequencing approaches, though several lineages were missing from the 16S rRNA gene amplicon dataset, likely the result of primer mismatches. At the functional level, we detected a large array of mobile genetic elements present in the single-cell genomes but absent from the corresponding same species metagenome-assembled genomes. Moreover, we performed a single-cell population genomic analysis of the three most abundant community members, revealing differences in population structure based on mutation and recombination profiles. While the average pairwise nucleotide identities were similar across the dominant species-level lineages, we observed differences in the extent of recombination between these dominant populations. Most intriguingly, the creek's Hydrogenobacter sp. population appeared to be so recombinogenic that it more closely resembled a sexual species than a clonally evolving microbe. Together, this work demonstrates that a randomized single-cell approach can be useful for the exploration of previously uncultivated microbes from community composition to population structure.

Microbial Consortiums of Putative Degraders of Low-Density Polyethylene-Associated Compounds in the Ocean

Polyethylene (PE) is one of the most abundant plastics in the ocean. The development of a biofilm on PE in the ocean has been reported, yet whether some of the biofilm-forming organisms can biodegrade this plastic in the environment remains unknown. Via metagenomics analysis, we taxonomically and functionally analyzed three biofilm communities using low-density polyethylene (LDPE) as their sole carbon source for 2 years. Several of the taxa that increased in relative abundance over time were closely related to known degraders of alkane and other hydrocarbons. Alkane degradation has been proposed to be involved in PE degradation, and most of the organisms increasing in relative abundance over time harbored genes encoding proteins essential in alkane degradation, such as the genes alkB and CYP153, encoding an alkane monooxygenase and a cytochrome P450 alkane hydroxylase, respectively. Weight loss of PE sheets when incubated with these communities and chemical and electron microscopic analyses provided evidence for alteration of the PE surface over time. Taken together, these results provide evidence for the utilization of LDPE-associated compounds by the prokaryotic communities. This report identifies a group of genes potentially involved in the degradation of the LDPE polymeric structure and/or associated plastic additives in the ocean and describes a phylogenetically diverse community of plastic biofilm-dwelling microbes with the potential for utilizing LDPE-associated compounds as carbon and energy source. IMPORTANCE Low-density polyethylene (LDPE) is one of the most used plastics worldwide, and a large portion of it ends up in the ocean. Very little is known about its fate in the ocean and whether it can be biodegraded by microorganisms. By combining 2-year incubations with metagenomics, respiration measurements, and LDPE surface analysis, we identified bacteria and associated genes and metabolic pathways potentially involved in LDPE biodegradation. After 2 years of incubation, two of the microbial communities exhibited very similar taxonomic compositions mediating changes to the LDPE pieces they were incubated with. We provide evidence that there are plastic-biofilm dwelling bacteria in the ocean that might have the potential to degrade LDPE-associated compounds and that alkane degradation pathways might be involved.

Human gut metatranscriptome changes induced by a fermented milk product are associated with improved tolerance to a flatulogenic diet

Healthy plant-based diets rich in fermentable residues may induce gas-related symptoms, possibly mediated by the gut microbiota. We previously showed that consumption of a fermented milk product (FMP) containing Bifidobacterium animalis subsp. lactis CNCM I-2494 and lactic acid bacteria improved gastrointestinal (GI) comfort in response to a flatulogenic dietary challenge in healthy individuals. To study the effects of the FMP on gut microbiota activity from those participants, we conducted a metatranscriptomic analysis of fecal samples (n = 262), which were collected during the ingestion of a habitual diet and two series of a 3-day high-residue challenge diet, before and following 28-days of FMP consumption.

Most of the FMP species were detected or found enriched upon consumption of the product. FMP mitigated the effect of a flatulogenic diet on gas-related symptoms in several ways. First, FMP consumption was associated with the depletion of gas-producing bacteria and increased hydrogen to methane conversion. It also led to the upregulation of activities such as replication and downregulation of functions related to motility and chemotaxis. Furthermore, upon FMP intake, metabolic activities such as carbohydrate metabolism, attributed to B. animalis and S. thermophilus, were enriched; these activities were coincidentally found to be negatively associated with several GI symptoms. Finally, a more connected microbial ecosystem or mutualistic relationship among microbes was found in responders to the FMP intervention.

Taken together, these findings suggest that consumption of the FMP improved the tolerance of a flatulogenic diet through active interactions with the resident gut microbiota.

Critical Assessment of Metagenome Interpretation: the second round of challenges

Evaluating metagenomic software is key for optimizing metagenome interpretation and focus of the Initiative for the Critical Assessment of Metagenome Interpretation (CAMI). The CAMI II challenge engaged the community to assess methods on realistic and complex datasets with long- and short-read sequences, created computationally from around 1,700 new and known genomes, as well as 600 new plasmids and viruses. Here we analyze 5,002 results by 76 program versions. Substantial improvements were seen in assembly, some due to long-read data. Related strains still were challenging for assembly and genome recovery through binning, as was assembly quality for the latter. Profilers markedly matured, with taxon profilers and binners excelling at higher bacterial ranks, but underperforming for viruses and Archaea. Clinical pathogen detection results revealed a need to improve reproducibility. Runtime and memory usage analyses identified efficient programs, including top performers with other metrics. The results identify challenges and guide researchers in selecting methods for analyses.

This study presents the results of the second round of the Critical Assessment of Metagenome Interpretation challenges (CAMI II), which is a community-driven effort for comprehensively benchmarking tools for metagenomics data analysis.

Effects of Resistant Starch on Symptoms, Fecal Markers, and Gut Microbiota in Parkinson's Disease - The RESISTA-PD Trial

The composition of the gut microbiota is linked to multiple diseases, including Parkinson's disease (PD). Abundance of bacteria producing short-chain fatty acids (SCFAs) and fecal SCFA concentrations are reduced in PD. SCFAs exert various beneficial functions in humans. In the interventional, monocentric, open-label clinical trial "Effects of Resistant Starch on Bowel Habits, Short Chain Fatty Acids and Gut Microbiota in Parkinson'sDisease" (RESISTA-PD; ID: NCT02784145), we aimed at altering fecal SCFAs by an 8-week prebiotic intervention with resistant starch (RS). We enrolled 87 subjects in three study-arms: 32 PD patients received RS (PD + RS), 30 control subjects received RS, and 25 PD patients received solely dietary instructions. We performed paired-end 100 bp length metagenomic sequencing of fecal samples using the BGISEQ platform at an average of 9.9 GB. RS was well-tolerated. In the PD + RS group, fecal butyrate concentrations increased significantly, and fecal calprotectin concentrations dropped significantly after 8 weeks of RS intervention. Clinically, we observed a reduction in non-motor symptom load in the PD + RS group. The reference-based analysis of metagenomes highlighted stable alpha-diversity and beta-diversity across the three groups, including bacteria producing SCFAs. Reference-free analysis suggested punctual, yet pronounced differences in the metagenomic signature in the PD + RS group. RESISTA-PD highlights that a prebiotic treatment with RS is safe and well-tolerated in PD. The stable alpha-diversity and beta-diversity alongside altered fecal butyrate and calprotectin concentrations call for long-term studies, also investigating whether RS is able to modify the clinical course of PD.

Metagenomic strain detection with SameStr: identification of a persisting core gut microbiota transferable by fecal transplantation

BACKGROUND

The understanding of how microbiomes assemble, function, and evolve requires metagenomic tools that can resolve microbiota compositions at the strain level. However, the identification and tracking of microbial strains in fecal metagenomes is challenging and available tools variably classify subspecies lineages, which affects their applicability to infer microbial persistence and transfer.

RESULTS

We introduce SameStr, a bioinformatic tool that identifies shared strains in metagenomes by determining single-nucleotide variants (SNV) in species-specific marker genes, which are compared based on a maximum variant profile similarity. We validated SameStr on mock strain populations, available human fecal metagenomes from healthy individuals and newly generated data from recurrent Clostridioides difficile infection (rCDI) patients treated with fecal microbiota transplantation (FMT). SameStr demonstrated enhanced sensitivity to detect shared dominant and subdominant strains in related samples (where strain persistence or transfer would be expected) when compared to other tools, while being robust against false-positive shared strain calls between unrelated samples (where neither strain persistence nor transfer would be expected). We applied SameStr to identify strains that are stably maintained in fecal microbiomes of healthy adults over time (strain persistence) and that successfully engraft in rCDI patients after FMT (strain engraftment). Taxonomy-dependent strain persistence and engraftment frequencies were positively correlated, indicating that a specific core microbiota of intestinal species is adapted to be competitive both in healthy microbiomes and during post-FMT microbiome assembly. We explored other use cases for strain-level microbiota profiling, as a metagenomics quality control measure and to identify individuals based on the persisting core gut microbiota.

CONCLUSION

SameStr provides for a robust identification of shared strains in metagenomic sequence data with sufficient specificity and sensitivity to examine strain persistence, transfer, and engraftment in human fecal microbiomes. Our findings identify a persisting healthy adult core gut microbiota, which should be further studied to shed light on microbiota contributions to chronic diseases. Video abstract.

Efficient and Quality-Optimized Metagenomic Pipeline Designed for Taxonomic Classification in Routine Microbiological Clinical Tests

Metagenomics analysis is now routinely used for clinical diagnosis in several diseases, and we need confidence in interpreting metagenomics analysis of microbiota. Particularly from the side of clinical microbiology, we consider that it would be a major milestone to further advance microbiota studies with an innovative and significant approach consisting of processing steps and quality assessment for interpreting metagenomics data used for diagnosis. Here, we propose a methodology for taxon identification and abundance assessment of shotgun sequencing data of microbes that are well fitted for clinical setup. Processing steps of quality controls have been developed in order (i) to avoid low-quality reads and sequences, (ii) to optimize abundance thresholds and profiles, (iii) to combine classifiers and reference databases for best classification of species and abundance profiles for both prokaryotic and eukaryotic sequences, and (iv) to introduce external positive control. We find that the best strategy is to use a pipeline composed of a combination of different but complementary classifiers such as Kraken2/Bracken and Kaiju. Such improved quality assessment will have a major impact on the robustness of biological and clinical conclusions drawn from metagenomic studies.

Surgical Treatment for Colorectal Cancer Partially Restores Gut Microbiome and Metabolome Traits

The gut microbiome and metabolites are associated with CRC progression and carcinogenesis. Postoperative CRC patients are reported to be at an increased CRC risk; however, how gut microbiome and metabolites are related to CRC risk in postoperative patients remains only partially understood.

Characterization and Demonstration of Mock Communities as Control Reagents for Accurate Human Microbiome Community Measurements

Standardization and quality assurance of microbiome community analysis by high-throughput DNA sequencing require widely accessible and well-characterized reference materials. Here, we report on newly developed DNA and whole-cell mock communities to serve as control reagents for human gut microbiota measurements by shotgun metagenomics and 16S rRNA gene amplicon sequencing. The mock communities were formulated as near-even blends of up to 20 bacterial species prevalent in the human gut, span a wide range of genomic guanine-cytosine (GC) contents, and include multiple strains with Gram-positive type cell walls. Through a collaborative study, we carefully characterized the mock communities by shotgun metagenomics, using previously developed standardized protocols for DNA extraction and sequencing library construction. Further, we validated fitness of the mock communities for revealing technically meaningful differences among protocols for DNA extraction and metagenome/16S rRNA gene amplicon library construction. Finally, we used the mock communities to reveal varying performance of metagenome-based taxonomic profilers and the impact of trimming and filtering of sequencing reads on observed species profiles. The latter showed that aggressive preprocessing of reads may result in substantial GC-dependent bias and should thus be carefully evaluated to minimize unintended effects on species abundances. Taken together, the mock communities are expected to support a myriad of applications that rely on well-characterized control reagents, ranging from evaluation and optimization of methods to assessment of reproducibility in interlaboratory studies and routine quality control.

IMPORTANCE Application of high-throughput DNA sequencing has greatly accelerated human microbiome research and its translation into new therapeutic and diagnostic capabilities. Microbiome community analyses results can, however, vary considerably across studies or laboratories, and establishment of measurement standards to improve accuracy and reproducibility has become a priority. The here-developed mock communities, which are available from the NITE Biological Resource Center (NBRC) at the National Institute of Technology and Evaluation (NITE, Japan), provide well-characterized control reagents that allow users to judge the accuracy of their measurement results. Widespread and consistent adoption of the mock communities will improve reproducibility and comparability of microbiome community analyses, thereby supporting and accelerating human microbiome research and development.