Shotgun metagenomics, from sampling to analysis

Establishment of a consensus protocol to explore the brain pathobiome in patients with mild cognitive impairment and Alzheimer's disease: Research outline and call for collaboration

Microbial infections of the brain can lead to dementia, and for many decades microbial infections have been implicated in Alzheimer's disease (AD) pathology. However, a causal role for infection in AD remains contentious, and the lack of standardized detection methodologies has led to inconsistent detection/identification of microbes in AD brains. There is a need for a consensus methodology; the Alzheimer's Pathobiome Initiative aims to perform comparative molecular analyses of microbes in post mortem brains versus cerebrospinal fluid, blood, olfactory neuroepithelium, oral/nasopharyngeal tissue, bronchoalveolar, urinary, and gut/stool samples. Diverse extraction methodologies, polymerase chain reaction and sequencing techniques, and bioinformatic tools will be evaluated, in addition to direct microbial culture and metabolomic techniques. The goal is to provide a roadmap for detecting infectious agents in patients with mild cognitive impairment or AD. Positive findings would then prompt tailoring of antimicrobial treatments that might attenuate or remit mounting clinical deficits in a subset of patients.

Carotenoids in Health as Studied by Omics-Related Endpoints

Carotenoids have been associated with risk reduction for several chronic diseases, including the association of their dietary intake/circulating levels with reduced incidence of obesity, type 2 diabetes, certain types of cancer, and even lower total mortality. In addition to some carotenoids constituting vitamin A precursors, they are implicated in potential antioxidant effects and pathways related to inflammation and oxidative stress, including transcription factors such as nuclear factor κB and nuclear factor erythroid 2-related factor 2. Carotenoids and metabolites may also interact with nuclear receptors, mainly retinoic acid receptor/retinoid X receptor and peroxisome proliferator-activated receptors, which play a role in the immune system and cellular differentiation. Therefore, a large number of downstream targets are likely influenced by carotenoids, including but not limited to genes and proteins implicated in oxidative stress and inflammation, antioxidation, and cellular differentiation processes. Furthermore, recent studies also propose an association between carotenoid intake and gut microbiota. While all these endpoints could be individually assessed, a more complete/integrative way to determine a multitude of health-related aspects of carotenoids includes (multi)omics-related techniques, especially transcriptomics, proteomics, lipidomics, and metabolomics, as well as metagenomics, measured in a variety of biospecimens including plasma, urine, stool, white blood cells, or other tissue cellular extracts. In this review, we highlight the use of omics technologies to assess health-related effects of carotenoids in mammalian organisms and models.

Bioinformatic approaches for studying the microbiome of fermented food

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

Life history strategies of soil bacterial communities across global terrestrial biomes

The life history strategies of soil microbes determine their metabolic potential and their response to environmental changes. Yet these strategies remain poorly understood. Here we use shotgun metagenomes from terrestrial biomes to characterize overarching covariations of the genomic traits that capture dominant life history strategies in bacterial communities. The emerging patterns show a triangle of life history strategies shaped by two trait dimensions, supporting previous theoretical and isolate-based studies. The first dimension ranges from streamlined genomes with simple metabolisms to larger genomes and expanded metabolic capacities. As metabolic capacities expand, bacterial communities increasingly differentiate along a second dimension that reflects a trade-off between increasing capacities for environmental responsiveness or for nutrient recycling. Random forest analyses show that soil pH, C:N ratio and precipitation patterns together drive the dominant life history strategy of soil bacterial communities and their biogeographic distribution. Our findings provide a trait-based framework to compare life history strategies of soil bacteria.

Challenges and opportunities in sharing microbiome data and analyses

Microbiome data, metadata and analytical workflows have become 'big' in terms of volume and complexity. Although the infrastructure and technologies to share data have been established, the interdisciplinary and multi-omic nature of the field can make resources difficult to identify and use. Following best practices for data deposition requires substantial effort, with sometimes little obvious reward. Gaps remain where microbiome-specific resources for data sharing or reproducibility do not yet exist. We outline available best practices, challenges to their adoption and opportunities in data sharing in microbiome research. We showcase examples of best practices and advocate for their enforcement and incentivization for data sharing. This includes recognition of data curation and sharing endeavours by individuals, institutions, journals and funders. Opportunities for progress include enabling microbiome-specific databases to incorporate future methods for data analysis, integration and reuse.

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

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

Meta-analysis of microbiomes reveals metagenomic features of fermented vegetables

An insightful exploration of the fermented vegetable microbiome is the key to improving food quality and sustainability. Based on 57 fermented vegetable samples from China, Ireland, the UK, and Germany retrieved from public genome databases, we conducted a high-resolution meta-analysis of the fermented vegetable microbiomes. There were significant differences in the microbiota composition and functional pathway diversity of the tested samples, as reflected by the differences in their geographical origins. Metagenomic analysis also revealed the metagenomic features of carbohydrate-active enzymes and antibiotic resistance genes in the fermented vegetable metagenomes. Five putative new species were detected by recovering 221 metagenome-assembled genomes belonging to the genera Rubrobacteraceae, Bifidobacteriaceae, and Ruminococcaceae. Our results provide new ecological insights into the implications of fermented vegetable microbiota composition and functional potential and highlight the importance of high-resolution metagenomic analysis to further investigate the fermented food microbiome.

Current Uses and Future Perspectives of Genomic Technologies in Clinical Microbiology

Recent advancements in sequencing technology and data analytics have led to a transformative era in pathogen detection and typing. These developments not only expedite the process, but also render it more cost-effective. Genomic analyses of infectious diseases are swiftly becoming the standard for pathogen analysis and control. Additionally, national surveillance systems can derive substantial benefits from genomic data, as they offer profound insights into pathogen epidemiology and the emergence of antimicrobial-resistant strains. Antimicrobial resistance (AMR) is a pressing global public health issue. While clinical laboratories have traditionally relied on culture-based antimicrobial susceptibility testing, the integration of genomic data into AMR analysis holds immense promise. Genomic-based AMR data can furnish swift, consistent, and highly accurate predictions of resistance phenotypes for specific strains or populations, all while contributing invaluable insights for surveillance. Moreover, genome sequencing assumes a pivotal role in the investigation of hospital outbreaks. It aids in the identification of infection sources, unveils genetic connections among isolates, and informs strategies for infection control. The One Health initiative, with its focus on the intricate interconnectedness of humans, animals, and the environment, seeks to develop comprehensive approaches for disease surveillance, control, and prevention. When integrated with epidemiological data from surveillance systems, genomic data can forecast the expansion of bacterial populations and species transmissions. Consequently, this provides profound insights into the evolution and genetic relationships of AMR in pathogens, hosts, and the environment.

Yeast metagenomics: analytical challenges in the analysis of the eukaryotic microbiome

Even if their impact is often underestimated, yeasts and yeast-like fungi represent the most prevalent eukaryotic members of microbial communities on Earth. They play numerous roles in natural ecosystems and in association with their hosts. They are involved in the food industry and pharmaceutical production, but they can also cause diseases in other organisms, making the understanding of their biology mandatory. The ongoing loss of biodiversity due to overexploitation of environmental resources is a growing concern in many countries. Therefore, it becomes crucial to understand the ecology and evolutionary history of these organisms to systematically classify them. To achieve this, it is essential that our knowledge of the mycobiota reaches a level similar to that of the bacterial communities. To overcome the existing challenges in the study of fungal communities, the first step should be the establishment of standardized techniques for the correct identification of species, even from complex matrices, both in wet lab practices and in bioinformatic tools.

Four functional profiles for fibre and mucin metabolism in the human gut microbiome

BACKGROUND

With the emergence of metagenomic data, multiple links between the gut microbiome and the host health have been shown. Deciphering these complex interactions require evolved analysis methods focusing on the microbial ecosystem functions. Despite the fact that host or diet-derived fibres are the most abundant nutrients available in the gut, the presence of distinct functional traits regarding fibre and mucin hydrolysis, fermentation and hydrogenotrophic processes has never been investigated.

RESULTS

After manually selecting 91 KEGG orthologies and 33 glycoside hydrolases further aggregated in 101 functional descriptors representative of fibre and mucin degradation pathways in the gut microbiome, we used nonnegative matrix factorization to mine metagenomic datasets. Four distinct metabolic profiles were further identified on a training set of 1153 samples, thoroughly validated on a large database of 2571 unseen samples from 5 external metagenomic cohorts and confirmed with metatranscriptomic data. Profiles 1 and 2 are the main contributors to the fibre-degradation-related metagenome: they present contrasted involvement in fibre degradation and sugar metabolism and are differentially linked to dysbiosis, metabolic disease and inflammation. Profile 1 takes over Profile 2 in healthy samples, and unbalance of these profiles characterize dysbiotic samples. Furthermore, high fibre diet favours a healthy balance between profiles 1 and profile 2. Profile 3 takes over profile 2 during Crohn's disease, inducing functional reorientations towards unusual metabolism such as fucose and H2S degradation or propionate, acetone and butanediol production. Profile 4 gathers under-represented functions, like methanogenesis. Two taxonomic makes up of the profiles were investigated, using either the covariation of 203 prevalent genomes or metagenomic species, both providing consistent results in line with their functional characteristics. This taxonomic characterization showed that profiles 1 and 2 were respectively mainly composed of bacteria from the phyla Bacteroidetes and Firmicutes while profile 3 is representative of Proteobacteria and profile 4 of methanogens.

CONCLUSIONS

Integrating anaerobic microbiology knowledge with statistical learning can narrow down the metagenomic analysis to investigate functional profiles. Applying this approach to fibre degradation in the gut ended with 4 distinct functional profiles that can be easily monitored as markers of diet, dysbiosis, inflammation and disease. Video Abstract.

The application of multi-omics in the respiratory microbiome: Progresses, challenges and promises

The study of the respiratory microbiome has entered a multi-omic era. Through integrating different omic data types such as metagenome, metatranscriptome, metaproteome, metabolome, culturome and radiome surveyed from respiratory specimens, holistic insights can be gained on the lung microbiome and its interaction with host immunity and inflammation in respiratory diseases. The power of multi-omics have moved the field forward from associative assessment of microbiome alterations to causative understanding of the lung microbiome in the pathogenesis of chronic, acute and other types of respiratory diseases. However, the application of multi-omics in respiratory microbiome remains with unique challenges from sample processing, data integration, and downstream validation. In this review, we first introduce the respiratory sample types and omic data types applicable to studying the respiratory microbiome. We next describe approaches for multi-omic integration, focusing on dimensionality reduction, multi-omic association and prediction. We then summarize progresses in the application of multi-omics to studying the microbiome in respiratory diseases. We finally discuss current challenges and share our thoughts on future promises in the field.

The role of gut archaea in the pig gut microbiome: a mini-review

The gastrointestinal microbiota of swine harbors an essential but often overlooked component: the gut archaea. These enigmatic microorganisms play pivotal roles in swine growth, health, and yield quality. Recent insights indicate that the diversity of gut archaea is influenced by various factors including breed, age, and diet. Such factors orchestrate the metabolic interactions within the porcine gastrointestinal environment. Through symbiotic relationships with bacteria, these archaea modulate the host's energy metabolism and digestive processes. Contemporary research elucidates a strong association between the abundance of these archaea and economically significant traits in swine. This review elucidates the multifaceted roles of gut archaea in swine and underscores the imperative for strategic interventions to modulate their population and functionality. By exploring the probiotic potential of gut archaea, we envisage novel avenues to enhance swine growth, health, and product excellence. By spotlighting this crucial, yet under-investigated, facet of the swine gut microbiome, we aim to galvanize further scientific exploration into harnessing their myriad benefits.

An integrated neuroimaging-omics approach for the gut-brain communication pathways in Alzheimer's disease

A key role of the gut microbiota in the pathogenesis of neurodegenerative diseases, such as Alzheimer's disease (AD), has been identified over the past decades. Increasing clinical and preclinical evidence implicates that there is bidirectional communication between the gut microbiota and the central nervous system (CNS), which is also known as the microbiota-gut-brain axis. Nevertheless, current knowledge on the interplay between gut microbiota and the brain remains largely unclear. One of the primary mediating factors by which the gut microbiota interacts with the host is peripheral metabolites, including blood or gut-derived metabolites. However, mechanistic knowledge about the effect of the microbiome and metabolome signaling on the brain is limited. Neuroimaging techniques, such as multi-modal magnetic resonance imaging (MRI), and fluorodeoxyglucose-positron emission tomography (FDG-PET), have the potential to directly elucidate brain structural and functional changes corresponding with alterations of the gut microbiota and peripheral metabolites in vivo. Employing a combination of gut microbiota, metabolome, and advanced neuroimaging techniques provides a future perspective in illustrating the microbiota-gut-brain pathway and further unveiling potential therapeutic targets for AD treatments.

Present and future outlooks on environmental DNA-based methods for antibiotic discovery

Novel antibiotics are in constant demand to combat a global increase in antibiotic-resistant infections. Bacterial natural products have been a long-standing source of antibiotic compounds, and metagenomic mining of environmental DNA (eDNA) has increasingly provided new antibiotic leads. The metagenomic small-molecule discovery pipeline can be divided into three main steps: surveying eDNA, retrieving a sequence of interest, and accessing the encoded natural product. Improvements in sequencing technology, bioinformatic algorithms, and methods for converting biosynthetic gene clusters into small molecules are steadily increasing our ability to discover metagenomically encoded antibiotics. We predict that, over the next decade, ongoing technological improvements will dramatically increase the rate at which antibiotics are discovered from metagenomes.

Global scale phylogeography of functional traits and microdiversity in Prochlorococcus

Prochlorococcus is the most numerically abundant photosynthetic organism in the surface ocean. The Prochlorococcus high-light and warm-water adapted ecotype (HLII) is comprised of extensive microdiversity, but specific functional differences between microdiverse sub-clades remain elusive. Here we characterized both functional and phylogenetic diversity within the HLII ecotype using Bio-GO-SHIP metagenomes. We found widespread variation in gene frequency connected to local environmental conditions. Metagenome-assembled marker genes and genomes revealed a globally distributed novel HLII haplotype defined by adaptation to chronically low P conditions (HLII-P). Environmental correlation analysis revealed different factors were driving gene abundances verses phylogenetic differences. An analysis of cultured HLII genomes and metagenome-assembled genomes revealed a subclade within HLII, which corresponded to the novel HLII-P haplotype. This work represents the first global assessment of the HLII ecotype's phylogeography and corresponding functional differences. These findings together expand our understanding of how microdiversity structures functional differences and reveals the importance of nutrients as drivers of microdiversity in Prochlorococcus.

Rapid and sensitive single-sample viral metagenomics using Nanopore Flongle sequencing

The ability of viral metagenomic Next-Generation Sequencing (mNGS) to unbiasedly detect nucleic acids in a clinical sample is a powerful tool for advanced diagnosis of viral infections. When clinical symptoms do not provide a clear differential diagnosis, extensive laboratory testing with virus-specific PCR and serology can be replaced by a single viral mNGS analysis. However, widespread diagnostic use of viral mNGS is thus far limited by long sample-to-result times, as most protocols rely on Illumina sequencing, which provides high and accurate sequencing output but is time-consuming and expensive. Here, we describe the development of an mNGS protocol based on the more cost-effective Nanopore Flongle sequencing with decreased turnaround time and lower, yet sufficient sequencing output to provide sensitive virus detection. Sample preparation (6 h) and sequencing (2 h) times are substantially reduced compared to Illumina mNGS and allow detection of DNA/RNA viruses at low input (up to 33-38 cycle threshold of specific qPCR). Although Flongles yield lower sequencing output, direct comparison with Illumina mNGS on diverse clinical samples showed similar results. Collectively, the novel Nanopore mNGS approach is specifically tailored for use in clinical diagnostics and provides a rapid and cost-effective mNGS strategy for individual testing of severe cases.

happi: a hierarchical approach to pangenomics inference

Recovering metagenome-assembled genomes (MAGs) from shotgun sequencing data is an increasingly common task in microbiome studies, as MAGs provide deeper insight into the functional potential of both culturable and non-culturable microorganisms. However, metagenome-assembled genomes vary in quality and may contain omissions and contamination. These errors present challenges for detecting genes and comparing gene enrichment across sample types. To address this, we propose happi, an approach to testing hypotheses about gene enrichment that accounts for genome quality. We illustrate the advantages of happi over existing approaches using published Saccharibacteria MAGs, Streptococcus thermophilus MAGs, and via simulation.

High potential for biomass-degrading CAZymes revealed by pine forest soil metagenomics

The undisturbed environment in Netarhat, with its high levels of accumulated lignocellulosic biomass, presents an opportunity to identify microbes for biomass digestion. This study focuses on the bioprospecting of native soil microbes from the Netarhat forest in Jharkhand, India, with the potential for lignocellulosic substrate digestion. These biocatalysts could help overcome the bottleneck of biomass saccharification and reduce the overall cost of biofuel production, replacing harmful fossil fuels. The study used metagenomic analysis of pine forest soil via whole genome shotgun sequencing, revealing that most of the reads matched with the bacterial species, very low percentage of reads (0.1%) belongs to fungal species, with 13% of unclassified reads. Actinobacteria were found to be predominant among the bacterial species. MetaErg annotation identified 11,830 protein family genes and 2 metabolic marker genes in the soil samples. Based on the Carbohydrate Active EnZyme (CAZy) database, 3,996 carbohydrate enzyme families were identified, with family Glycosyl hydrolase (GH) dominating with 1,704 genes. Most observed GH families in the study were GH0, 3, 5, 6. 9, 12. 13, 15, 16, 39, 43, 57, and 97. Modelling analysis of a representative GH 43 gene suggested a strong affinity for cellulose than xylan. This study highlights the lignocellulosic digestion potential of the native microfauna of the lesser-known pine forest of Netarhat.Communicated by Ramaswamy H. Sarma.

Integrated multi-omics analyses of microbial communities: a review of the current state and future directions

Integrated multi-omics analyses of microbiomes have become increasingly common in recent years as the emerging omics technologies provide an unprecedented opportunity to better understand the structural and functional properties of microbial communities. Consequently, there is a growing need for and interest in the concepts, approaches, considerations, and available tools for investigating diverse environmental and host-associated microbial communities in an integrative manner. In this review, we first provide a general overview of each omics analysis type, including a brief history, typical workflow, primary applications, strengths, and limitations. Then, we inform on both experimental design and bioinformatics analysis considerations in integrated multi-omics analyses, elaborate on the current approaches and commonly used tools, and highlight the current challenges. Finally, we discuss the expected key advances, emerging trends, potential implications on various fields from human health to biotechnology, and future directions.

Capturing the microbial dark matter in desert soils using culturomics-based metagenomics and high-resolution analysis

Deserts occupy one-third of the Earth's terrestrial surface and represent a potentially significant reservoir of microbial biodiversity, yet the majority of desert microorganisms remain uncharacterized and are seen as "microbial dark matter". Here, we introduce a multi-omics strategy, culturomics-based metagenomics (CBM) that integrates large-scale cultivation, full-length 16S rRNA gene amplicon, and shotgun metagenomic sequencing. The results showed that CBM captured a significant amount of taxonomic and functional diversity missed in direct sequencing by increasing the recovery of amplicon sequence variants (ASVs) and high/medium-quality metagenome-assembled genomes (MAGs). Importantly, CBM allowed the post hoc recovery of microbes of interest (e.g., novel or specific taxa), even those with extremely low abundance in the culture. Furthermore, strain-level analyses based on CBM and direct sequencing revealed that the desert soils harbored a considerable number of novel bacterial candidates (1941, 51.4%), of which 1095 (from CBM) were culturable. However, CBM would not exactly reflect the relative abundance of true microbial composition and functional pathways in the in situ environment, and its use coupled with direct metagenomic sequencing could provide greater insight into desert microbiomes. Overall, this study exemplifies the CBM strategy with high-resolution is an ideal way to deeply explore the untapped novel bacterial resources in desert soils, and substantially expands our knowledge on the microbial dark matter hidden in the vast expanse of deserts.

mStrain: strain-level identification of Yersinia pestis using metagenomic data

Motivation

High-resolution target pathogen detection using metagenomic sequencing data represents a major challenge due to the low concentration of target pathogens in samples. We introduced mStrain, a novel Yesinia pestis strain/lineage-level identification tool that utilizes metagenomic data. mStrain successfully identified Y. pestis at the strain/lineage level by extracting sufficient information regarding single-nucleotide polymorphisms (SNPs), which can therefore be an effective tool for identification and source tracking of Y. pestis based on metagenomic data during a plague outbreak.

Definition

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Strain-level identification

Assigning the reads in the metagenomic sequencing data to an exactly known or most closely representative Y. pestis strain.

Lineage-level identification

Assigning the reads in the metagenomic sequencing data to a specific lineage on the phylogenetic tree.

canoSNPs

The unique and typical SNPs present in all representative strains.

Ancestor/derived state

An SNP is defined as the ancestor state when consistent with the allele of Yersinia pseudotuberculosis strain IP32953; otherwise, the SNP is defined as the derived state.

Availability and implementation

The code for running mStrain, the test dataset, and instructions for running the code can be found at the following GitHub repository: https://github.com/xwqian1123/mStrain.

Consistency across multi-omics layers in a drug-perturbed gut microbial community

Multi-omics analyses are used in microbiome studies to understand molecular changes in microbial communities exposed to different conditions. However, it is not always clear how much each omics data type contributes to our understanding and whether they are concordant with each other. Here, we map the molecular response of a synthetic community of 32 human gut bacteria to three non-antibiotic drugs by using five omics layers (16S rRNA gene profiling, metagenomics, metatranscriptomics, metaproteomics and metabolomics). We find that all the omics methods with species resolution are highly consistent in estimating relative species abundances. Furthermore, different omics methods complement each other for capturing functional changes. For example, while nearly all the omics data types captured that the antipsychotic drug chlorpromazine selectively inhibits Bacteroidota representatives in the community, the metatranscriptome and metaproteome suggested that the drug induces stress responses related to protein quality control. Metabolomics revealed a decrease in oligosaccharide uptake, likely caused by Bacteroidota depletion. Our study highlights how multi-omics datasets can be utilized to reveal complex molecular responses to external perturbations in microbial communities.

Clustering microbiome data using mixtures of logistic normal multinomial models

Discrete data such as counts of microbiome taxa resulting from next-generation sequencing are routinely encountered in bioinformatics. Taxa count data in microbiome studies are typically high-dimensional, over-dispersed, and can only reveal relative abundance therefore being treated as compositional. Analyzing compositional data presents many challenges because they are restricted to a simplex. In a logistic normal multinomial model, the relative abundance is mapped from a simplex to a latent variable that exists on the real Euclidean space using the additive log-ratio transformation. While a logistic normal multinomial approach brings flexibility for modeling the data, it comes with a heavy computational cost as the parameter estimation typically relies on Bayesian techniques. In this paper, we develop a novel mixture of logistic normal multinomial models for clustering microbiome data. Additionally, we utilize an efficient framework for parameter estimation using variational Gaussian approximations (VGA). Adopting a variational Gaussian approximation for the posterior of the latent variable reduces the computational overhead substantially. The proposed method is illustrated on simulated and real datasets.

Microbiomes, Their Function, and Cancer: How Metatranscriptomics Can Close the Knowledge Gap

The interaction between the microbial communities in the human body and the onset and progression of cancer has not been investigated until recently. The vast majority of the metagenomics research in this area has concentrated on the composition of microbiomes, attempting to link the overabundance or depletion of certain microorganisms to cancer proliferation, metastatic behaviour, and its resistance to therapies. However, studies elucidating the functional implications of the microbiome activity in cancer patients are still scarce; in particular, there is an overwhelming lack of studies assessing such implications directly, through analysis of the transcriptome of the bacterial community. This review summarises the contributions of metagenomics and metatranscriptomics to the knowledge of the microbial environment associated with several cancers; most importantly, it highlights all the advantages that metatranscriptomics has over metagenomics and suggests how such an approach can be leveraged to advance the knowledge of the cancer bacterial environment.

Extended Spectrum β-Lactamase-Producing Enterobacterales of Shrimp and Salmon Available for Purchase by Consumers in Canada-A Risk Profile Using the Codex Framework

The extended-spectrum β-lactamase (ESBL)-producing Enterobacterales (ESBL-EB) encompass several important human pathogens and are found on the World Health Organization (WHO) priority pathogens list of antibiotic-resistant bacteria. They are a group of organisms which demonstrate resistance to third-generation cephalosporins (3GC) and their presence has been documented worldwide, including in aquaculture and the aquatic environment. This risk profile was developed following the Codex Guidelines for Risk Analysis of Foodborne Antimicrobial Resistance with the objectives of describing the current state of knowledge of ESBL-EB in relation to retail shrimp and salmon available to consumers in Canada, the primary aquacultured species consumed in Canada. The risk profile found that Enterobacterales and ESBL-EB have been found in multiple aquatic environments, as well as multiple host species and production levels. Although the information available did not permit the conclusion as to whether there is a human health risk related to ESBLs in Enterobacterales in salmon and shrimp available for consumption by Canadians, ESBL-EB in imported seafood available at the retail level in Canada have been found. Surveillance activities to detect ESBL-EB in seafood are needed; salmon and shrimp could be used in initial surveillance activities, representing domestic and imported products.

Phytoremediation: A green and low-cost technology to remediate herbicides in the environment

Pesticide dependence is one of the main disadvantages of agriculture. Despite the advances in biological control and integrated management of plant pests and diseases in recent years, herbicides are still essential for weed control and constitute the main class of pesticides worldwide. Herbicide residues in water, soil, air, and non-target organisms are among the biggest agricultural and environmental sustainability obstacles. Therefore, we suggest an environmentally viable alternative to reduce the harmful effects of herbicide residues, a technology called phytoremediation. Remediating plants were grouped into herbaceous, arboreal, and aquatic macrophytes. Phytoremediation can reduce the loss of at least 50% of all herbicide residues to the environment. Among the herbaceous species reported as phytoremediators of herbicides, the Fabaceae family was mentioned in more than 50% of reports. This family is also among the main species of trees reported. Regarding the most reported groups of herbicides, it is observed that most of them, regardless of the group of plants, are triazines. Processes such as extraction or accumulation are the best known and reported for most herbicides. The phytoremediation may be effective against chronic or unknown herbicide toxicity. This tool can be included in proposals for management plans and specific legislation in countries, guaranteeing public policies to maintain environmental quality.

Exploring the Microbiome in Human Reproductive Tract: High-Throughput Methods for the Taxonomic Characterization of Microorganisms

Microorganisms are important due to their widespread presence and multifaceted roles across various domains of life, ecology, and industries. In humans, they underlie the proper functioning of multiple systems crucial to well-being, including immunological and metabolic functions. Emerging research addressing the presence and roles of microorganisms within human reproduction is increasingly relevant. Studies implementing new methodologies (e.g., to investigate vaginal, uterine, and semen microenvironments) can now provide relevant insights into fertility, reproductive health, or pregnancy outcomes. In that sense, cutting-edge sequencing techniques, as well as others such as meta-metabolomics, culturomics, and meta-proteomics, are becoming more popular and accessible worldwide, allowing the characterization of microbiomes at unprecedented resolution. However, they frequently involve rather complex laboratory protocols and bioinformatics analyses, for which researchers may lack the required expertise. A suitable pipeline would successfully enable both taxonomic classification and functional profiling of the microbiome, providing easy-to-understand biological interpretations. However, the selection of an appropriate methodology would be crucial, as it directly impacts the reproducibility, accuracy, and quality of the results and observations. This review focuses on the different current microbiome-related techniques in the context of human reproduction, encompassing niches like vagina, endometrium, and seminal fluid. The most standard and reliable methods are 16S rRNA gene sequencing, metagenomics, and meta-transcriptomics, together with complementary approaches including meta-proteomics, meta-metabolomics, and culturomics. Finally, we also offer case examples and general recommendations about the most appropriate methods and workflows and discuss strengths and shortcomings for each technique.

Microorganisms and dissolved metabolites distinguish Florida's Coral Reef habitats

As coral reef ecosystems experience unprecedented change, effective monitoring of reef features supports management, conservation, and intervention efforts. Omic techniques show promise in quantifying key components of reef ecosystems including dissolved metabolites and microorganisms that may serve as invisible sensors for reef ecosystem dynamics. Dissolved metabolites are released by reef organisms and transferred among microorganisms, acting as chemical currencies and contributing to nutrient cycling and signaling on reefs. Here, we applied four omic techniques (taxonomic microbiome via amplicon sequencing, functional microbiome via shotgun metagenomics, targeted metabolomics, and untargeted metabolomics) to waters overlying Florida's Coral Reef, as well as microbiome profiling on individual coral colonies from these reefs to understand how microbes and dissolved metabolites reflect biogeographical, benthic, and nutrient properties of this 500-km barrier reef. We show that the microbial and metabolite omic approaches each differentiated reef habitats based on geographic zone. Further, seawater microbiome profiling and targeted metabolomics were significantly related to more reef habitat characteristics, such as amount of hard and soft coral, compared to metagenomic sequencing and untargeted metabolomics. Across five coral species, microbiomes were also significantly related to reef zone, followed by species and disease status, suggesting that the geographic water circulation patterns in Florida also impact the microbiomes of reef builders. A combination of differential abundance and indicator species analyses revealed metabolite and microbial signatures of specific reef zones, which demonstrates the utility of these techniques to provide new insights into reef microbial and metabolite features that reflect broader ecosystem processes.

Detection of blaCTX-M and blaDHA genes in stool samples of healthy people: comparison of culture- and shotgun metagenomic-based approaches

We implemented culture- and shotgun metagenomic sequencing (SMS)-based methods to assess the gut colonization with extended-spectrum cephalosporin-resistant Enterobacterales (ESC-R-Ent) in 42 volunteers. Both methods were performed using native and pre-enriched (broth supplemented with cefuroxime) stools. Native culture screening on CHROMID® ESBL plates resulted in 17 positive samples, whereas the pre-enriched culture (gold-standard) identified 23 carriers. Overall, 26 ESC-R-Ent strains (24 Escherichia coli) were identified: 25 CTX-M and 3 DHA-1 producers (2 co-producing CTX-Ms). Using the SMS on native stool ("native SMS") with thresholds ≥60% for both identity and coverage, only 7 of the 23 pre-enriched culture-positive samples resulted positive for blaCTX-M/blaDHA genes (native SMS reads mapping to blaCTX-M/blaDHAs identified in gold-standard: sensitivity, 59.0%; specificity 100%). Moreover, an average of 31.5 and 24.6 antimicrobial resistance genes (ARGs) were detected in the 23 pre-enriched culture-positive and the 19 negative samples, respectively. When the pre-enriched SMS was implemented, more blaCTX-M/blaDHA genes were detected than in the native assay, including in stools that were pre-enriched culture-negative (pre-enriched SMS reads mapping to blaCTX-M/blaDHAs identified in gold-standard: sensitivity, 78.3%; specificity 75.0%). In addition, the pre-enriched SMS identified on average 38.6 ARGs/sample, whereas for the corresponding native SMS it was 29.4 ARGs/sample. Notably, stools resulting false-negative by using the native SMS had lower concentrations of ESC-R-Ent (average: ~105 vs. ~107 CFU/g) and E. coli classified reads (average: 193,959 vs. 1.45 million) than those of native SMS positive samples. Finally, the detection of blaCTX-M/blaDHA genes was compared with two well-established bioinformatic tools. In conclusion, only the pre-enriched SMS assured detection of most carriers of ESC-R-Ent. However, its performance was not comparable to the pre-enriched culture-based approach.

Climate Change Causes Salinity To Become Determinant in Shaping the Microeukaryotic Spatial Distribution among the Lakes of the Inner Mongolia-Xinjiang Plateau

Climate change greatly affects lake microorganisms in arid and semiarid zones, which alters ecosystem functions and the ecological security of lakes. However, the responses of lake microorganisms, especially microeukaryotes, to climate change are poorly understood. Here, using 18S ribosomal RNA (rRNA) high-throughput sequencing, we investigated the distribution patterns of microeukaryotic communities and whether and how climate change directly or indirectly affected the microeukaryotic communities on the Inner Mongolia-Xinjiang Plateau. Our results showed that climate change, as the main driving force of lake change, drives salinity to become a determinant of the microeukaryotic community among the lakes of the Inner Mongolia-Xinjiang Plateau. Salinity shapes the diversity and trophic level of the microeukaryotic community and further affects lake carbon cycling. Co-occurrence network analysis further revealed that increasing salinity reduced the complexity but improved the stability of microeukaryotic communities and changed ecological relationships. Meanwhile, increasing salinity enhanced the importance of deterministic processes in microeukaryotic community assembly, and the dominance of stochastic processes in freshwater lakes transformed into deterministic processes in salt lakes. Furthermore, we established lake biomonitoring and climate sentinel models by integrating microeukaryotic information, which would provide substantial improvements to our predictive ability of lake responses to climate change. IMPORTANCE Our findings have important implications for understanding the distribution patterns and the driving mechanisms of microeukaryotic communities among the lakes of the Inner Mongolia-Xinjiang Plateau and whether and how climate change directly or indirectly affects microeukaryotic communities. Our study also establishes the groundwork to use the lake microbiome for the assessment of aquatic ecological health and climate change, which is critical for ecosystem management and for projecting the ecological consequences of future climate warming.

Correlation between Targeted qPCR Assays and Untargeted DNA Shotgun Metagenomic Sequencing for Assessing the Fecal Microbiota in Dogs

DNA shotgun sequencing is an untargeted approach for identifying changes in relative abundances, while qPCR allows reproducible quantification of specific bacteria. The canine dysbiosis index (DI) assesses the canine fecal microbiota by using a mathematical algorithm based on qPCR results. We evaluated the correlation between qPCR and shotgun sequencing using fecal samples from 296 dogs with different clinical phenotypes. While significant correlations were found between qPCR and sequencing, certain taxa were only detectable by qPCR and not by sequencing. Based on sequencing, less than 2% of bacterial species (17/1190) were consistently present in all healthy dogs (n = 76). Dogs with an abnormal DI had lower alpha-diversity compared to dogs with normal DI. Increases in the DI correctly predicted the gradual shifts in microbiota observed by sequencing: minor changes (R = 0.19, DI < 0 with any targeted taxa outside the reference interval, RI), mild-moderate changes (R = 0.24, 0 < DI < 2), and significant dysbiosis (R = 0.54, 0.73, and 0.91 for DI > 2, DI > 5, and DI > 8, respectively), compared to dogs with a normal DI (DI < 0, all targets within the RI), as higher R-values indicated larger dissimilarities. In conclusion, the qPCR-based DI is an effective indicator of overall microbiota shifts observed by shotgun sequencing in dogs.

Taxonomy, Sequence Variance and Functional Profiling of the Microbial Community of Long-Ripened Cheddar Cheese Using Shotgun Metagenomics

Shotgun metagenomic sequencing was used to investigate the diversity of the microbial community of Cheddar cheese ripened over 32 months. The changes in taxa abundance were compared from assembly-based, non-assembly-based, and mOTUs2 sequencing pipelines to delineate the community profile for each age group. Metagenomic assembled genomes (MAGs) passing the quality threshold were obtained for 11 species from 58 samples. Although Lactococcus cremoris and Lacticaseibacillus paracasei were dominant across the shotgun samples, other species were identified using MG-RAST. NMDS analysis of the beta diversity of the microbial community revealed the similarity of the cheeses in older age groups (7 months to 32 months). As expected, the abundance of Lactococcus cremoris consistently decreased over ripening, while the proportion of permeable cells increased. Over the ripening period, the relative abundance of viable Lacticaseibacillus paracasei progressively increased, but at a variable rate among trials. Reads attributed to Siphoviridae and Ascomycota remained below 1% relative abundance. The functional profiles of PMA-treated cheeses differed from those of non-PMA-treated cheeses. Starter rotation was reflected in the single nucleotide variant profiles of Lactococcus cremoris (SNVs of this species using mOTUs2), while the incoming milk was the leading factor in discriminating Lacticaseibacillus paracasei/casei SNV profiles. The relative abundance estimates from Kraken2, non-assembly-based (MG-RAST) and marker gene clusters (mOTUs2) were consistent across age groups for the two dominant taxa. Metagenomics enabled sequence variant analysis below the bacterial species level and functional profiling that may affect the metabolic interactions between subpopulations in cheese during ripening, which could help explain the overall flavour development of cheese. Future work will integrate microbial variants with volatile profiles to associate the development of compounds related to cheese flavour at each ripening stage.

Metagenome Analysis of Speleothem Microbiome from Subterranean Cave Reveals Insight into Community Structure, Metabolic Potential, and BGCs Diversity

The Borra caves, the second largest subterranean karst cave ecosystem in the Indian sub-continent, are located at the Ananthagiri hills of Araku Valley in the Alluri district of Andhra Pradesh, India. The present investigation applied a shotgun metagenomic approach to gain insights into the microbial community structure, metabolic potential, and biosynthetic gene cluster (BGC) diversity of the microbes colonizing the surface of the speleothems from the aphotic zone of Borra caves. The taxonomic analysis of the metagenome data illustrated that the speleothem-colonizing core microbial community was dominated mainly by Alpha-, Beta-, and Gamma-Proteobacteria, Actinobacteria, Firmicutes, and Bacteroidetes. The key energy metabolic pathways analysis provides strong evidence of chemolithoautotrophic and chemoheterotrophic modes of nutrition in the speleothem-colonizing microbial community. Metagenome data suggests that sulfur reducers and sulfur-disproportionating microbes might play a vital role in energy generation in this ecosystem. Our metagenome data also suggest that the dissimilatory nitrifiers and nitrifying denitrifiers might play an essential role in conserving nitrogen pools in the ecosystem. Furthermore, metagenome-wide BGCs mining retrieved 451 putative BGCs; NRPS was the most abundant (24%). Phylogenetic analysis of the C domain of NRPS showed that sequences were distributed across all six function categories of the known C domain, including several novel subclades. For example, a novel subclade had been recovered within the LCL domain clade as a sister subclade of immunosuppressant cyclosporin encoding C domain sequences. Our result suggested that subterranean cave microbiomes might be a potential reservoir of novel microbial metabolites.

Touching the (almost) untouchable: a minimally invasive workflow for microbiological and biomolecular analyses of cultural heritage objects

Microbiological and biomolecular approaches to cultural heritage research have expanded the established research horizon from the prevalent focus on the cultural objects' conservation and human health protection to the relatively recent applications to provenance inquiry and assessment of environmental impacts in a global context of a changing climate. Standard microbiology and molecular biology methods developed for other materials, specimens, and contexts could, in principle, be applied to cultural heritage research. However, given certain characteristics common to several heritage objects-such as uniqueness, fragility, high value, and restricted access, tailored approaches are required. In addition, samples of heritage objects may yield low microbial biomass, rendering them highly susceptible to cross-contamination. Therefore, dedicated methodology addressing these limitations and operational hurdles is needed. Here, we review the main experimental challenges and propose a standardized workflow to study the microbiome of cultural heritage objects, illustrated by the exploration of bacterial taxa. The methodology was developed targeting the challenging side of the spectrum of cultural heritage objects, such as the delicate written record, while retaining flexibility to adapt and/or upscale it to heritage artifacts of a more robust constitution or larger dimensions. We hope this tailored review and workflow will facilitate the interdisciplinary inquiry and interactions among the cultural heritage research community.

Human microbiome myths and misconceptions

Over the past two decades, interest in human microbiome research has increased exponentially. Regrettably, this increased activity has brought with it a degree of hype and misinformation, which can undermine progress and public confidence in the research. Here we highlight selected human microbiome myths and misconceptions that lack a solid evidence base. By presenting these examples, we hope to draw increased attention to the implications of inaccurate dogma becoming embedded in the literature, and the importance of acknowledging nuance when describing the complex human microbiome.

Effect of a multichannel oral irrigator on periodontal health and the oral microbiome

Oral biofilms or dental plaques are one of the major etiological factors for diverse oral diseases. We aimed to evaluate the effect of a multichannel oral irrigator (MCOI) on periodontal health in 29 participants randomly divided into two groups: the MCOI group and the control group. To evaluate the effect of the MCOI on periodontal health, the modified Quigley Hein Plaque Index (PI), Mühlemann-Son Sulcus Bleeding Index (SBI), bleeding on probing (BOP), and swelling were evaluated and compared before and after MCOI use for 3 days. Although PI and SBI showed statistically significant increases in the control group, the MCOI group showed no significant changes in either parameter. Moreover, the percentage of BOP was significantly lower in the MCOI group. Saliva samples were analyzed by 16s rRNA amplicon sequencing to investigate changes in the oral microbiome. Sequencing results showed that Porphyromonas spp. were significantly increased in the control group, whereas no significant change was detected in the MCOI group. Using the MCOI, enriched populations and functional pathways were detected in pioneer species comprising non-mutans streptococci. These findings provide evidence of the effectiveness of the MCOI in maintaining periodontal health and a healthy microbial ecology in the oral cavity.

Longitudinal development of the airway metagenome of preterm very low birth weight infants during the first two years of life

Preterm birth is accompanied with many complications and requires severe therapeutic regimens at the neonatal intensive care unit. The influence of the above-mentioned factors on the premature-born infants' respiratory metagenome or more generally its maturation is unknown. We therefore applied shotgun metagenome sequencing of oropharyngeal swabs to analyze the airway metagenome development of 24 preterm infants from one week postpartum to 15 months of age. Beta diversity analysis revealed a distinct clustering of airway microbial communities from hospitalized preterms and samples after hospital discharge. At nine and 15 months of age, the preterm infants lost their hospital-acquired individual metagenome signatures towards a common taxonomic structure. However, ecological network analysis and Random Forest classification of cross-sectional data revealed that by this age the preterm infants did not succeed in establishing the uniform and stable bacterial community structures that are characteristic for healthy full-term infants.

The predictive power of phylogeny on growth rates in soil bacterial communities

Predicting ecosystem function is critical to assess and mitigate the impacts of climate change. Quantitative predictions of microbially mediated ecosystem processes are typically uninformed by microbial biodiversity. Yet new tools allow the measurement of taxon-specific traits within natural microbial communities. There is mounting evidence of a phylogenetic signal in these traits, which may support prediction and microbiome management frameworks. We investigated phylogeny-based trait prediction using bacterial growth rates from soil communities in Arctic, boreal, temperate, and tropical ecosystems. Here we show that phylogeny predicts growth rates of soil bacteria, explaining an average of 31%, and up to 58%, of the variation within ecosystems. Despite limited overlap in community composition across these ecosystems, shared nodes in the phylogeny enabled ancestral trait reconstruction and cross-ecosystem predictions. Phylogenetic relationships could explain up to 38% (averaging 14%) of the variation in growth rates across the highly disparate ecosystems studied. Our results suggest that shared evolutionary history contributes to similarity in the relative growth rates of related bacteria in the wild, allowing phylogeny-based predictions to explain a substantial amount of the variation in taxon-specific functional traits, within and across ecosystems.

Topic modeling for multi-omic integration in the human gut microbiome and implications for Autism

While healthy gut microbiomes are critical to human health, pertinent microbial processes remain largely undefined, partially due to differential bias among profiling techniques. By simultaneously integrating multiple profiling methods, multi-omic analysis can define generalizable microbial processes, and is especially useful in understanding complex conditions such as Autism. Challenges with integrating heterogeneous data produced by multiple profiling methods can be overcome using Latent Dirichlet Allocation (LDA), a promising natural language processing technique that identifies topics in heterogeneous documents. In this study, we apply LDA to multi-omic microbial data (16S rRNA amplicon, shotgun metagenomic, shotgun metatranscriptomic, and untargeted metabolomic profiling) from the stool of 81 children with and without Autism. We identify topics, or microbial processes, that summarize complex phenomena occurring within gut microbial communities. We then subset stool samples by topic distribution, and identify metabolites, specifically neurotransmitter precursors and fatty acid derivatives, that differ significantly between children with and without Autism. We identify clusters of topics, deemed "cross-omic topics", which we hypothesize are representative of generalizable microbial processes observable regardless of profiling method. Interpreting topics, we find each represents a particular diet, and we heuristically label each cross-omic topic as: healthy/general function, age-associated function, transcriptional regulation, and opportunistic pathogenesis.

Efficient High-Quality Metagenome Assembly from Long Accurate Reads using Minimizer-space de Bruijn Graphs

We introduce a novel metagenomics assembler for high-accuracy long reads. Our approach, implemented as metaMDBG, combines highly efficient de Bruijn graph assembly in minimizer space, with both a multi-k' approach for dealing with variations in genome coverage depth and an abundance-based filtering strategy for simplifying strain complexity. The resulting algorithm is more efficient than the state-of-the-art but with better assembly results. metaMDBG was 1.5 to 12 times faster than competing assemblers and requires between one-tenth and one-thirtieth of the memory across a range of data sets. We obtained up to twice as many high-quality circularised prokaryotic metagenome assembled genomes (MAGs) on the most complex communities, and a better recovery of viruses and plasmids. metaMDBG performs particularly well for abundant organisms whilst being robust to the presence of strain diversity. The result is that for the first time it is possible to efficiently reconstruct the majority of complex communities by abundance as near-complete MAGs.

Influence of conservation agriculture-based production systems on bacterial diversity and soil quality in rice-wheat-greengram cropping system in eastern Indo-Gangetic Plains of India

Introduction

Conservation agriculture (CA) is gaining attention in the South Asia as an environmentally benign and sustainable food production system. The knowledge of the soil bacterial community composition along with other soil properties is essential for evaluating the CA-based management practices for achieving the soil environment sustainability and climate resilience in the rice-wheat-greengram system. The long-term effects of CA-based tillage-cum-crop establishment (TCE) methods on earthworm population, soil parameters as well as microbial diversity have not been well studied.

Methods

Seven treatments (or scenarios) were laid down with the various tillage (wet, dry, or zero-tillage), establishment method (direct-or drill-seeding or transplantation) and residue management practices (mixed with the soil or kept on the soil surface). The soil samples were collected after 7 years of experimentation and analyzed for the soil quality and bacterial diversity to examine the effect of tillage-cum-crop establishment methods.

Results and Discussion

Earthworm population (3.6 times), soil organic carbon (11.94%), macro (NPK) (14.50-23.57%) and micronutrients (Mn, and Cu) (13.25 and 29.57%) contents were appreciably higher under CA-based TCE methods than tillage-intensive farming practices. Significantly higher number of OTUs (1,192 ± 50) and Chao1 (1415.65 ± 14.34) values were observed in partial CA-based production system (p ≤ 0.05). Forty-two (42) bacterial phyla were identified across the scenarios, and Proteobacteria, Actinobacteria, and Firmicutes were the most dominant in all the scenarios. The CA-based scenarios harbor a high abundance of Proteobacteria (2-13%), whereas the conventional tillage-based scenarios were dominated by the bacterial phyla Acidobacteria and Chloroflexi and found statistically differed among the scenarios (p ≤ 0.05). Composition of the major phyla, i.e., Proteobacteria, Actinobacteria, and Firmicutes were associated differently with either CA or farmers-based tillage management practices. Overall, the present study indicates the importance of CA-based tillage-cum-crop establishment methods in shaping the bacterial diversity, earthworms population, soil organic carbon, and plant nutrient availability, which are crucial for sustainable agricultural production and resilience in agro-ecosystem.

Vibrio-Sequins - dPCR-traceable DNA standards for quantitative genomics of Vibrio spp

BACKGROUND

Vibrio spp. are a diverse group of ecologically important marine bacteria responsible for several foodborne outbreaks of gastroenteritis around the world. Their detection and characterization are moving away from conventional culture-based methods towards next generation sequencing (NGS)-based approaches. However, genomic methods are relative in nature and suffer from technical biases arising from library preparation and sequencing. Here, we introduce a quantitative NGS-based method that enables the quantitation of Vibrio spp. at the limit of quantification (LOQ) through artificial DNA standards and their absolute quantification via digital PCR (dPCR).

RESULTS

We developed six DNA standards, called Vibrio-Sequins, together with optimized TaqMan assays for their quantification in individually sequenced DNA libraries via dPCR. To enable Vibrio-Sequin quantification, we validated three duplex dPCR methods to quantify the six targets. LOQs were ranging from 20 to 120 cp/µl for the six standards, whereas the limit of detection (LOD) was ~ 10 cp/µl for all six assays. Subsequently, a quantitative genomics approach was applied to quantify Vibrio-DNA in a pooled DNA mixture derived from several Vibrio species in a proof-of-concept study, demonstrating the increased power of our quantitative genomic pipeline through the coupling of NGS and dPCR.

CONCLUSIONS

We significantly advance existing quantitative (meta)genomic methods by ensuring metrological traceability of NGS-based DNA quantification. Our method represents a useful tool for future metagenomic studies aiming at quantifying microbial DNA in an absolute manner. The inclusion of dPCR into sequencing-based methods supports the development of statistical approaches for the estimation of measurement uncertainties (MU) for NGS, which is still in its infancy.

Computational methods and challenges in analyzing intratumoral microbiome data

The human microbiome is intimately related to cancer biology and plays a vital role in the efficacy of cancer treatments, including immunotherapy. Extraordinary evidence has revealed that several microbes influence tumor development through interaction with the host immune system, that is, immuno-oncology-microbiome (IOM). This review focuses on the intratumoral microbiome in IOM and describes the available data and computational methods for discovering biological insights of microbial profiling from host bulk, single-cell, and spatial sequencing data. Critical challenges in data analysis and integration are discussed. Specifically, the microorganisms associated with cancer and cancer treatment in the context of IOM are collected and integrated from the literature. Lastly, we provide our perspectives for future directions in IOM research.

Skin Microbiota and the Cosmetic Industry

Skin harbors an important microbial ecosystem - the skin microbiota that is in homeostasis with its host and is beneficial for human health. Cosmetic products have the potential to interfere with this microbial community; therefore their impact should be assessed. The aim of this review is to highlight the importance of skin microbiota in the cosmetic industry. Several studies determined that cosmetic ingredients have the potential to disrupt the skin microbiota equilibrium leading to the development of skin diseases and dysregulation of immune response. These studies led their investigation by using different methodologies and models, concluding that methods must be chosen according to the aim of the study, the skin site to be evaluated, and the target population of the cosmetics. Overall, it is crucial to test the impact of cosmetics in the skin microbiota and to stablish standard procedures, as well as specific criteria that allow to classify a cosmetic product as skin microbiota friendly.

Coriolis: enabling metagenomic classification on lightweight mobile devices

MOTIVATION

The introduction of portable DNA sequencers such as the Oxford Nanopore Technologies MinION has enabled real-time and in the field DNA sequencing. However, in the field sequencing is actionable only when coupled with in the field DNA classification. This poses new challenges for metagenomic software since mobile deployments are typically in remote locations with limited network connectivity and without access to capable computing devices.

RESULTS

We propose new strategies to enable in the field metagenomic classification on mobile devices. We first introduce a programming model for expressing metagenomic classifiers that decomposes the classification process into well-defined and manageable abstractions. The model simplifies resource management in mobile setups and enables rapid prototyping of classification algorithms. Next, we introduce the compact string B-tree, a practical data structure for indexing text in external storage, and we demonstrate its viability as a strategy to deploy massive DNA databases on memory-constrained devices. Finally, we combine both solutions into Coriolis, a metagenomic classifier designed specifically to operate on lightweight mobile devices. Through experiments with actual MinION metagenomic reads and a portable supercomputer-on-a-chip, we show that compared with the state-of-the-art solutions Coriolis offers higher throughput and lower resource consumption without sacrificing quality of classification.

AVAILABILITY AND IMPLEMENTATION

Source code and test data are available from http://score-group.org/?id=smarten.

SemiBin2: self-supervised contrastive learning leads to better MAGs for short- and long-read sequencing

MOTIVATION

Metagenomic binning methods to reconstruct metagenome-assembled genomes (MAGs) from environmental samples have been widely used in large-scale metagenomic studies. The recently proposed semi-supervised binning method, SemiBin, achieved state-of-the-art binning results in several environments. However, this required annotating contigs, a computationally costly and potentially biased process.

RESULTS

We propose SemiBin2, which uses self-supervised learning to learn feature embeddings from the contigs. In simulated and real datasets, we show that self-supervised learning achieves better results than the semi-supervised learning used in SemiBin1 and that SemiBin2 outperforms other state-of-the-art binners. Compared to SemiBin1, SemiBin2 can reconstruct 8.3-21.5% more high-quality bins and requires only 25% of the running time and 11% of peak memory usage in real short-read sequencing samples. To extend SemiBin2 to long-read data, we also propose ensemble-based DBSCAN clustering algorithm, resulting in 13.1-26.3% more high-quality genomes than the second best binner for long-read data.

AVAILABILITY AND IMPLEMENTATION

SemiBin2 is available as open source software at https://github.com/BigDataBiology/SemiBin/ and the analysis scripts used in the study can be found at https://github.com/BigDataBiology/SemiBin2_benchmark.

Metagenome of Gut Microbiota Provides a Novel Insight into the Pathogenicity of Balantioides coli in Weaned Piglets

Balantioides coli plays an important role in the diarrhea of weaned piglets, but its pathogenic potential and interaction with gut microbes remain unclear. To investigate the impact of B. coli colonization on the gut bacterial structure and function of weaned piglets, a metagenomic analysis based on shotgun sequencing was performed on fresh fecal samples collected from ten B. coli-colonized piglets and eight B. coli-free ones in this study. The results showed that decreasing diversity and shifted composition and function of the bacterial community were detected in the weaned piglets infected by B. coli. In contrast to the B. coli-negative group, the relative abundances of some members of the Firmicutes phylum including Clostridium, Ruminococcus species, and Intestinimonas butyriciproducens, which produce short-chain fatty acids, were significantly reduced in the B. coli-positive group. Notably, some species of the Prevotella genus (such as Prevotella sp. CAG:604 and Prevotella stercorea) were significantly increased in abundance in the B. coli-positive piglets. A functional analysis of the gut microbiota demonstrated that the differential gene sets for the metabolism of carbohydrates and amino acids were abundant in both groups, and the more enriched pathways in B. coli-infected piglets were associated with the sugar-specific phosphotransferase system (PTS) and the two-component regulatory system, as well as lipopolysaccharide (LPS) biosynthesis. Furthermore, several species of Prevotella were significantly positively correlated to the synthesis of lipid A, leading to the exporting of endotoxins and, thereby, inducing inflammation in the intestines of weaned piglets. Taken together, these findings revealed that colonization by B. coli was distinctly associated with the dysbiosis of gut bacterial structure and function in weaned piglets. Lower relative abundances of Clostridiaceae and Ruminococcaceae and higher abundances of Prevotella species were biomarkers of B. coli infection in weaned piglets.

Comparison of DNA extraction methods for 16S rRNA gene sequencing in the analysis of the human gut microbiome

The gut microbiome is widely analyzed using high-throughput sequencing, such as 16S rRNA gene amplicon sequencing and shotgun metagenomic sequencing (SMS). DNA extraction is known to have a large impact on the metagenomic analyses. The aim of this study was to compare DNA extraction protocols for 16S sequencing. In that context, four commonly used DNA extraction methods were compared for the analysis of the gut microbiota. Commercial versions were evaluated against modified protocols using a stool preprocessing device (SPD, bioMérieux) upstream DNA extraction. Stool samples from nine healthy volunteers and nine patients with a Clostridium difficile infection were extracted with all protocols and 16S sequenced. Protocols were ranked using wet- and dry-lab criteria, including quality controls of the extracted genomic DNA, alpha-diversity, accuracy using a mock community of known composition and repeatability across technical replicates. SPD improved overall efficiency of three of the four tested protocols compared with their commercial version, in terms of DNA extraction yield, sample alpha-diversity, and recovery of Gram-positive bacteria. The best overall performance was obtained for the S-DQ protocol, SPD combined with the DNeasy PowerLyser PowerSoil protocol from QIAGEN. Based on this evaluation, we strongly believe that the use of such stool preprocessing device improves both the standardization and the quality of the DNA extraction in the human gut microbiome studies.

Site-specialization of human oral Gemella species

Gemella species are core members of the human oral microbiome in healthy subjects and are regarded as commensals, although they can cause opportunistic infections. Our objective was to evaluate the site-specialization of Gemella species among various habitats within the mouth by combining pangenomics and metagenomics. With pangenomics, we identified genome relationships and categorized genes as core and accessory to each species. With metagenomics, we identified the primary oral habitat of individual genomes. Our results establish that the genomes of three species, G. haemolysans, G. sanguinis and G. morbillorum, are abundant and prevalent in human mouths at different oral sites: G. haemolysans on buccal mucosa and keratinized gingiva; G. sanguinis on tongue dorsum, throat, and tonsils; and G. morbillorum in dental plaque. The gene-level basis of site-specificity was investigated by identifying genes that were core to Gemella genomes at a specific oral site but absent from other Gemella genomes. The riboflavin biosynthesis pathway was present in G. haemolysans genomes associated with buccal mucosa but absent from the rest of the genomes. Overall, metapangenomics show that Gemella species have clear ecological preferences in the oral cavity of healthy humans and provides an approach to identifying gene-level drivers of site specificity.

Species-Level Taxonomic Characterization of Uncultured Core Gut Microbiota of Plateau Pika

Rarely has the vast diversity of bacteria on Earth been profiled, particularly on inaccessible plateaus. These uncultured microbes, which are also known as "microbial dark matter," may play crucial roles in maintaining the ecosystem and are linked to human health, regarding pathogenicity and prebioticity. The plateau pika (Ochotona curzoniae) is a small burrowing steppe lagomorph that is endemic to the Qinghai-Tibetan Plateau and is a keystone species in the maintenance of ecological balance. We used a combination of full-length 16S rRNA amplicon sequencing, shotgun metagenomics, and metabolomics to elucidate the species-level community structure and the metabolic potential of the gut microbiota of the plateau pika. Using a full-length 16S rRNA metataxonomic approach, we clustered 618 (166 ± 35 per sample) operational phylogenetic units (OPUs) from 105 plateau pika samples and assigned them to 215 known species, 226 potentially new species, and 177 higher hierarchical taxa. Notably, 39 abundant OPUs (over 60% total relative abundance) are found in over 90% of the samples, thereby representing a "core microbiota." They are all classified as novel microbial lineages, from the class to the species level. Using metagenomic reads, we independently assembled and binned 109 high-quality, species-level genome bins (SGBs). Then, a precise taxonomic assignment was performed to clarify the phylogenetic consistency of the SGBs and the 16S rRNA amplicons. Thus, the majority of the core microbes possess their genomes. SGBs belonging to the genus Treponema, the families Muribaculaceae, Lachnospiraceae, and Oscillospiraceae, and the order Eubacteriales are abundant in the metagenomic samples. In addition, multiple CAZymes are detected in these SGBs, indicating their efficient utilization of plant biomass. As the most widely connected metabolite with the core microbiota, tryptophan may relate to host environmental adaptation. Our investigation allows for a greater comprehension of the composition and functional capacity of the gut microbiota of the plateau pika. IMPORTANCE The great majority of microbial species remain uncultured, severely limiting their taxonomic characterization and biological understanding. The plateau pika (Ochotona curzoniae) is a small burrowing steppe lagomorph that is endemic to the Qinghai-Tibetan Plateau and is considered to be the keystone species in the maintenance of ecological stability. We comprehensively investigated the gut microbiota of the plateau pika via a multiomics endeavor. Combining full-length 16S rRNA metataxonomics, shotgun metagenomics, and metabolomics, we elucidated the species-level taxonomic assignment of the core uncultured intestinal microbiota of the plateau pika and revealed their correlation to host nutritional metabolism and adaptation. Our findings provide insights into the microbial diversity and biological significance of alpine animals.