Toward Accurate and Quantitative Comparative Metagenomics

Reliable biological and multi-omics research through biometrology

Metrology is the science of measurement and its applications, whereas biometrology is the science of biological measurement and its applications. Biometrology aims to achieve accuracy and consistency of biological measurements by focusing on the development of metrological traceability, biological reference measurement procedures, and reference materials. Irreproducibility of biological and multi-omics research results from different laboratories, platforms, and analysis methods is hampering the translation of research into clinical uses and can often be attributed to the lack of biologists' attention to the general principles of metrology. In this paper, the progresses of biometrology including metrology on nucleic acid, protein, and cell measurements and its impacts on the improvement of reliability and comparability in biological research are reviewed. Challenges in obtaining more reliable biological and multi-omics measurements due to the lack of primary reference measurement procedures and new standards for biological reference materials faced by biometrology are discussed. In the future, in addition to establishing reliable reference measurement procedures, developing reference materials from single or multiple parameters to multi-omics scale should be emphasized. Thinking in way of biometrology is warranted for facilitating the translation of high-throughput omics research into clinical practices.

CAIM: Coverage-based Analysis for Identification of Microbiome

Accurate taxonomic profiling of microbial taxa in a metagenomic sample is vital to gain insights into microbial ecology. Recent advancements in sequencing technologies have contributed tremendously toward understanding these microbes at species resolution through a whole shotgun metagenomic (WMS) approach. In this study, we developed a new bioinformatics tool, CAIM, for accurate taxonomic classification and quantification within both long- and short-read metagenomic samples using an alignment-based method. CAIM depends on two different containment techniques to identify species in metagenomic samples using their genome coverage information to filter out false positives rather than the traditional approach of relative abundance. In addition, we propose a nucleotide-count based abundance estimation, which yield lesser root mean square error than the traditional read-count approach. We evaluated the performance of CAIM on 28 metagenomic mock communities and 2 synthetic datasets by comparing it with other top-performing tools. CAIM maintained a consitently good performance across datasets in identifying microbial taxa and in estimating relative abundances than other tools. CAIM was then applied to a real dataset sequenced on both Nanopore (with and without amplification) and Illumina sequencing platforms and found high similality of taxonomic profiles between the sequencing platforms. Lastly, CAIM was applied to fecal shotgun metagenomic datasets of 232 colorectal cancer patients and 229 controls obtained from 4 different countries and primary 44 liver cancer patients and 76 controls. The predictive performance of models using the genome-coverage cutoff was better than those using the relative-abundance cutoffs in discriminating colorectal cancer and primary liver cancer patients from healthy controls with a highly confident species markers.

A Review of Web-Based Metagenomics Platforms for Analysing Next-Generation Sequence Data

Metagenomics has now evolved as a promising technology for understanding the microbial population in the environment. By metagenomics, a number of extreme and complex environment has been explored for their microbial population. Using this technology, researchers have brought out novel genes and their potential characteristics, which have robust applications in food, pharmaceutical, scientific research, and other biotechnological fields. A sequencing platform can provide a sequence of microbial populations in any given environment. The sequence needs to be analysed computationally to derive meaningful information. It is presumed that only bioinformaticians with extensive computational skills can process the sequencing data till the downstream end. However, numerous open-source software and online servers are available to analyse the metagenomic data developed for a biologist with less computational skills. This review is focused on bioinformatics tools such as Galaxy, CSI-NGS portal, ANASTASIA and SHAMAN, EBI- metagenomics, IDseq, and MG-RAST for analysing metagenomic data.

Shotgun metagenomics reveals the gut microbial diversity and functions in Vespa mandarinia (Hymenoptera: Vespidae) at multiple life stages

Wasps play important roles as predators and pollinators in the ecosystem. The Jingpo minority residing in Yunnan Province, China, has a traditional practice of using wine infused with mature wasps as a customary remedy for managing rheumatoid arthritis. The larva of the wasp is also a tasteful folk dish that has created a tremendous market. There is a paucity of survival knowledge, which has greatly restricted their potential applications in food and healthcare. Recent research has highlighted the importance of gut microbiota in insect growth. Nevertheless, there is still a lack of understanding regarding the composition, changes, and functions of the gut microbiota in Vespa mandarinia during development. In this research, the gut microbiota were investigated across three growth stages of Vespa mandarinia using a metagenomic technology. The result revealed that there are significant variations in the proportion of main gut microbes during the metamorphosis of Vespa mandarinia. Tenericutes were found to dominate during the larval stage, while Proteobacteria emerged as the dominant group post-pupation. Through a comprehensive analysis of the gut microbiota metagenome, this study revealed functional differences in the wasp gut microbiota at various growth stages. During the larval stage, the gut microbiota plays a central role in promoting metabolism. Following pupation, the gut microbiota exhibited diversified functions, likely due to the complex environments and diverse food sources encountered after metamorphosis. These functions included amino acid metabolism, compound degradation, and defense mechanisms. This research provides an extensive dataset on the gut microbiota during the metamorphosis of Vespa mandarinia, contributing to a deeper understanding of the influence of gut microbiota on wasp growth. Furthermore, this study uncovers a unique microbial treasure within insect guts, which is important for advancing the application of wasps in the fields of food and medicine.

A user's guide to the bioinformatic analysis of shotgun metagenomic sequence data for bacterial pathogen detection

Metagenomics, i.e., shotgun sequencing of the total microbial community DNA from a sample, has become a mature technique but its application to pathogen detection in clinical, environmental, and food samples is far from common or standardized. In this review, we summarize ongoing developments in metagenomic sequence analysis that facilitate its wider application to pathogen detection. We examine theoretical frameworks for estimating the limit of detection for a particular level of sequencing effort, current approaches for achieving species and strain analytical resolution, and discuss some relevant modern tools for these tasks. While these recent advances are significant and establish metagenomics as a powerful tool to provide insights not easily attained by culture-based approaches, metagenomics is unlikely to emerge as a widespread, routine monitoring tool in the near future due to its inherently high detection limits, cost, and inability to easily distinguish between viable and non-viable cells. Instead, metagenomics seems best poised for applications involving special circumstances otherwise challenging for culture-based and molecular (e.g., PCR-based) approaches such as the de novo detection of novel pathogens, cases of co-infection by more than one pathogen, and situations where it is important to assess the genomic composition of the pathogenic population(s) and/or its impact on the indigenous microbiome.

Reference-free Structural Variant Detection in Microbiomes via Long-read Coassembly Graphs

Bacterial genome dynamics are vital for understanding the mechanisms underlying microbial adaptation, growth, and their broader impact on host phenotype. Structural variants (SVs), genomic alterations of 10 base pairs or more, play a pivotal role in driving evolutionary processes and maintaining genomic heterogeneity within bacterial populations. While SV detection in isolate genomes is relatively straightforward, metagenomes present broader challenges due to absence of clear reference genomes and presence of mixed strains. In response, our proposed method rhea, forgoes reference genomes and metagenome-assembled genomes (MAGs) by encompassing a single metagenome coassembly graph constructed from all samples in a series. The log fold change in graph coverage between subsequent samples is then calculated to call SVs that are thriving or declining throughout the series. We show rhea to outperform existing methods for SV and horizontal gene transfer (HGT) detection in two simulated mock metagenomes, which is particularly noticeable as the simulated reads diverge from reference genomes and an increase in strain diversity is incorporated. We additionally demonstrate use cases for rhea on series metagenomic data of environmental and fermented food microbiomes to detect specific sequence alterations between subsequent time and temperature samples, suggesting host advantage. Our innovative approach leverages raw read patterns rather than references or MAGs to include all sequencing reads in analysis, and thus provide versatility in studying SVs across diverse and poorly characterized microbial communities for more comprehensive insights into microbial genome dynamics.

Modeling the limits of detection for antimicrobial resistance genes in agri-food samples: a comparative analysis of bioinformatics tools

BACKGROUND

Although the spread of antimicrobial resistance (AMR) through food and its production poses a significant concern, there is limited research on the prevalence of AMR bacteria in various agri-food products. Sequencing technologies are increasingly being used to track the spread of AMR genes (ARGs) in bacteria, and metagenomics has the potential to bypass some of the limitations of single isolate characterization by allowing simultaneous analysis of the agri-food product microbiome and associated resistome. However, metagenomics may still be hindered by methodological biases, presence of eukaryotic DNA, and difficulties in detecting low abundance targets within an attainable sequence coverage. The goal of this study was to assess whether limits of detection of ARGs in agri-food metagenomes were influenced by sample type and bioinformatic approaches.

RESULTS

We simulated metagenomes containing different proportions of AMR pathogens and analysed them for taxonomic composition and ARGs using several common bioinformatic tools. Kraken2/Bracken estimates of species abundance were closest to expected values. However, analysis by both Kraken2/Bracken indicated presence of organisms not included in the synthetic metagenomes. Metaphlan3/Metaphlan4 analysis of community composition was more specific but with lower sensitivity than the Kraken2/Bracken analysis. Accurate detection of ARGs dropped drastically below 5X isolate genome coverage. However, it was sometimes possible to detect ARGs and closely related alleles at lower coverage levels if using a lower ARG-target coverage cutoff (< 80%). While KMA and CARD-RGI only predicted presence of expected ARG-targets or closely related gene-alleles, SRST2 (which allows read to map to multiple targets) falsely reported presence of distantly related ARGs at all isolate genome coverage levels. The presence of background microbiota in metagenomes influenced the accuracy of ARG detection by KMA, resulting in mcr-1 detection at 0.1X isolate coverage in the lettuce but not in the beef metagenome.

CONCLUSIONS

This study demonstrates accurate detection of ARGs in synthetic metagenomes using various bioinformatic methods, provided that reads from the ARG-encoding organism exceed approximately 5X isolate coverage (i.e. 0.4% of a 40 million read metagenome). While lowering thresholds for target gene detection improved sensitivity, this led to the identification of alternative ARG-alleles, potentially confounding the identification of critical ARGs in the resistome. Further advancements in sequencing technologies providing increased coverage depth or extended read lengths may improve ARG detection in agri-food metagenomic samples, enabling use of this approach for tracking clinically important ARGs in agri-food samples.

Shotgun metagenomics and systemic targeted metabolomics highlight indole-3-propionic acid as a protective gut microbial metabolite against influenza infection

The gut-to-lung axis is critical during respiratory infections, including influenza A virus (IAV) infection. In the present study, we used high-resolution shotgun metagenomics and targeted metabolomic analysis to characterize influenza-associated changes in the composition and metabolism of the mouse gut microbiota. We observed several taxonomic-level changes on day (D)7 post-infection, including a marked reduction in the abundance of members of the Lactobacillaceae and Bifidobacteriaceae families, and an increase in the abundance of Akkermansia muciniphila. On D14, perturbation persisted in some species. Functional scale analysis of metagenomic data revealed transient changes in several metabolic pathways, particularly those leading to the production of short-chain fatty acids (SCFAs), polyamines, and tryptophan metabolites. Quantitative targeted metabolomics analysis of the serum revealed changes in specific classes of gut microbiota metabolites, including SCFAs, trimethylamine, polyamines, and indole-containing tryptophan metabolites. A marked decrease in indole-3-propionic acid (IPA) blood level was observed on D7. Changes in microbiota-associated metabolites correlated with changes in taxon abundance and disease marker levels. In particular, IPA was positively correlated with some Lactobacillaceae and Bifidobacteriaceae species (Limosilactobacillus reuteri, Lactobacillus animalis) and negatively correlated with Bacteroidales bacterium M7, viral load, and inflammation markers. IPA supplementation in diseased animals reduced viral load and lowered local (lung) and systemic inflammation. Treatment of mice with antibiotics targeting IPA-producing bacteria before infection enhanced viral load and lung inflammation, an effect inhibited by IPA supplementation. The results of this integrated metagenomic-metabolomic analysis highlighted IPA as an important contributor to influenza outcomes and a potential biomarker of disease severity.

Emergent Functional Organization of Gut Microbiomes in Health and Diseases

Continuous and significant progress in sequencing technologies and bioinformatics pipelines has revolutionized our comprehension of microbial communities, especially for human microbiomes. However, most studies have focused on studying the taxonomic composition of the microbiomes and we are still not able to characterize dysbiosis and unveil the underlying ecological consequences. This study explores the emergent organization of functional abundances and correlations of gut microbiomes in health and disease. Leveraging metagenomic sequences, taxonomic and functional tables are constructed, enabling comparative analysis. First, we show that emergent taxonomic and functional patterns are not useful to characterize dysbiosis. Then, through differential abundance analyses applied to functions, we reveal distinct functional compositions in healthy versus unhealthy microbiomes. In addition, we inquire into the functional correlation structure, revealing significant differences between the healthy and unhealthy groups, which may significantly contribute to understanding dysbiosis. Our study demonstrates that scrutinizing the functional organization in the microbiome provides novel insights into the underlying state of the microbiome. The shared data structure underlying the functional and taxonomic compositions allows for a comprehensive macroecological examination. Our findings not only shed light on dysbiosis, but also underscore the importance of studying functional interrelationships for a nuanced understanding of the dynamics of the microbial community. This research proposes a novel approach, bridging the gap between microbial ecology and functional analyses, promising a deeper understanding of the intricate world of the gut microbiota and its implications for human health.

The Identification of a Novel Spider Toxin Peptide, Lycotoxin-Pa2a, with Antibacterial and Anti-Inflammatory Activities

With the increasing challenge of controlling infectious diseases due to the emergence of antibiotic-resistant strains, the importance of discovering new antimicrobial agents is rapidly increasing. Animal venoms contain a variety of functional peptides, making them a promising platform for pharmaceutical development. In this study, a novel toxin peptide with antibacterial and anti-inflammatory activities was discovered from the spider venom gland transcriptome by implementing computational approaches. Lycotoxin-Pa2a (Lytx-Pa2a) showed homology to known-spider toxin, where functional prediction indicated the potential of both antibacterial and anti-inflammatory peptides without hemolytic activity. The colony-forming assay and minimum inhibitory concentration test showed that Lytx-Pa2a exhibited comparable or stronger antibacterial activity against pathogenic strains than melittin. Following mechanistic studies revealed that Lytx-Pa2a disrupts both cytoplasmic and outer membranes of bacteria while simultaneously inducing the accumulation of reactive oxygen species. The peptide exerted no significant toxicity when treated to human primary cells, murine macrophages, and bovine red blood cells. Moreover, Lytx-Pa2a alleviated lipopolysaccharide-induced inflammation in mouse macrophages by suppressing the expression of inflammatory mediators. These findings not only suggested that Lytx-Pa2a with dual activity can be utilized as a new antimicrobial agent for infectious diseases but also demonstrated the implementation of in silico methods for discovering a novel functional peptide, which may enhance the future utilization of biological resources.

Environmental determinants and demographic influences on global urban microbiomes, antimicrobial resistance and pathogenicity

Urban microbiome plays crucial roles in human health and are related to various diseases. The MetaSUB Consortium has conducted the most comprehensive global survey of urban microbiomes to date, profiling microbial taxa/functional genes across 60 cities worldwide. However, the influence of environmental/demographic factors on urban microbiome remains to be elucidated. We collected 35 environmental and demographic characteristics to examine their effects on global urban microbiome diversity/composition by PERMANOVA and regression models. PM10 concentration was the primary determinant factor positively associated with microbial α-diversity (observed species: p = 0.004, β = 1.66, R2 = 0.46; Fisher's alpha: p = 0.005, β = 0.68, R2 = 0.43), whereas GDP per capita was negatively associated (observed species: p = 0.046, β = -0.70, R2 = 0.10; Fisher's alpha: p = 0.004, β = -0.34, R2 = 0.22). The β-diversity of urban microbiome was shaped by seven environmental characteristics, including Köppen climate type, vegetation type, greenness fraction, soil type, PM2.5 concentration, annual average precipitation and temperature (PERMANOVA, p < 0.001, R2 = 0.01-0.06), cumulatively accounted for 20.3% of the microbial community variance. Canonical correspondence analysis (CCA) identified microbial species most strongly associated with environmental characteristic variation. Cities in East Asia with higher precipitation showed an increased abundance of Corynebacterium metruchotii, and cities in America with a higher greenness fraction exhibited a higher abundance of Corynebacterium casei. The prevalence of antimicrobial resistance (AMR) genes were negatively associated with GDP per capita and positively associated with solar radiation (p < 0.005). Total pathogens prevalence was positively associated with urban population and negatively associated with average temperature in June (p < 0.05). Our study presents the first comprehensive analysis of the influence of environmental/demographic characteristics on global urban microbiome. Our findings indicate that managing air quality and urban greenness is essential for regulating urban microbial diversity and composition. Meanwhile, socio-economic considerations, particularly reducing antibiotic usage in regions with lower GDP, are paramount in curbing the spread of antimicrobial resistance in urban environments.

Bacterial quorum sensing orchestrates longitudinal interactions to shape microbiota assembly

BACKGROUND

The mechanism of microbiota assembly is one of the main problems in microbiome research, which is also the primary theoretical basis for precise manipulation of microbial communities. Bacterial quorum sensing (QS), as the most common means for bacteria to exchange information and interactions, is characterized by universality, specificity, and regulatory power, which therefore may influence the assembly processes of human microbiota. However, the regulating role of QS in microbiota assembly is rarely reported. In this study, we developed an optimized in vitro oral biofilm microbiota assembling (OBMA) model to simulate the time-series assembly of oral biofilm microbiota (OBM), by which to excavate the QS network and its regulating power in the process.

RESULTS

By using the optimized OBMA model, we were able to restore the assembly process of OBM and generate time-series OBM metagenomes of each day. We discovered a total of 2291 QS protein homologues related to 21 QS pathways. Most of these pathways were newly reported and sequentially enriched during OBM assembling. These QS pathways formed a comprehensive longitudinal QS network that included successively enriched QS hubs, such as Streptococcus, Veillonella-Megasphaera group, and Prevotella-Fusobacteria group, for information delivery. Bidirectional cross-talk among the QS hubs was found to play critical role in the directional turnover of microbiota structure, which in turn, influenced the assembly process. Subsequent QS-interfering experiments accurately predicted and experimentally verified the directional shaping power of the longitudinal QS network in the assembly process. As a result, the QS-interfered OBM exhibited delayed and fragile maturity with prolonged membership of Streptococcus and impeded membership of Prevotella and Fusobacterium.

CONCLUSION

Our results revealed an unprecedented longitudinal QS network during OBM assembly and experimentally verified its power in predicting and manipulating the assembling process. Our work provides a new perspective to uncover underlying mechanism in natural complex microbiota assembling and a theoretical basis for ultimately precisely manipulating human microbiota through intervention in the QS network. Video Abstract.

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.

MicroEXPERT: Microbiome profiling platform with cross-study metagenome-wide association analysis functionality

The framework of the MicroEXPERT platform. Our Platform was composed of five modules. Data management module: Users upload raw data and metadata to the system using a guided workflow. Data processing module: Uploaded data is processed to generate taxonomical distribution and functional composition results. Metagenome-wide association studies module (MWAS): Various methods, including biomarker analysis, PCA, co-occurrence networks, and sample classification, are employed using metadata. Data search module: Users can query nucleotide sequences to retrieve information in the MicroEXPERT database. Data visualization module: Visualization tools are used to illustrate the metagenome analysis results.

HMDB: A curated database of genes involved in hydrocarbon monooxygenation reaction with homologous genes as background

The investigation of hydrocarbon degradation potential of environmental microorganisms is an important research topic, whether for the global carbon cycle or oil pollution remediation. Under aerobic conditions, the microorganisms employ a range of monooxygenases to use hydrocarbons substrates as a source of carbon and energy. With the explosion of sequencing data, mining genes in genomes or metagenomes has become computationally expensive and time-consuming. We proposed the HMDB, which is a professional gene database of hydrocarbon monooxygenases. HMDB contains 38 genes, which encode 11 monooxygenases responsible for the hydroxylation of 8 hydrocarbons. To reduce false positives, the strategy of using homologous genes as background noise was applied for HMDB. We added 10,095 gene sequences of homologous enzymes which took non-hydrocarbons as substrates to HMDB. The classic BLAST method and best-hit strategy were recommended for HMDB usage, but not limited. The performance of HMDB was validated using 264,402 prokaryote genomes from RefSeq and 51 metagenomes from SRA. The results showed that HMDB database had high sensitivity and low false positive rate. We release the HMDB database here, hoping to speed up the process for investigation of hydrocarbon monooxygenases in massive metagenomic data. HMDB is freely available at http://www.orgene.net/HMDB/.

Field and laboratory guidelines for reliable bioinformatic and statistical analysis of bacterial shotgun metagenomic data

Shotgun metagenomics is an increasingly cost-effective approach for profiling environmental and host-associated microbial communities. However, due to the complexity of both microbiomes and the molecular techniques required to analyze them, the reliability and representativeness of the results are contingent upon the field, laboratory, and bioinformatic procedures employed. Here, we consider 15 field and laboratory issues that critically impact downstream bioinformatic and statistical data processing, as well as result interpretation, in bacterial shotgun metagenomic studies. The issues we consider encompass intrinsic properties of samples, study design, and laboratory-processing strategies. We identify the links of field and laboratory steps with downstream analytical procedures, explain the means for detecting potential pitfalls, and propose mitigation measures to overcome or minimize their impact in metagenomic studies. We anticipate that our guidelines will assist data scientists in appropriately processing and interpreting their data, while aiding field and laboratory researchers to implement strategies for improving the quality of the generated results.

Recovery of the gut microbiome following enteric infection and persistence of antimicrobial resistance genes in specific microbial hosts

Enteric pathogens cause widespread foodborne illness and are increasingly resistant to important antibiotics yet their ecological impact on the gut microbiome and resistome is not fully understood. Herein, shotgun metagenome sequencing was applied to stool DNA from 60 patients (cases) during an enteric bacterial infection and after recovery (follow-ups). Overall, the case samples harbored more antimicrobial resistance genes (ARGs) with greater resistome diversity than the follow-up samples (p < 0.001), while follow-ups had more diverse gut microbiota (p < 0.001). Although cases were primarily defined by genera Escherichia, Salmonella, and Shigella along with ARGs for multi-compound and multidrug resistance, follow-ups had a greater abundance of Bacteroidetes and Firmicutes phyla and resistance genes for tetracyclines, macrolides, lincosamides, and streptogramins, and aminoglycosides. A host-tracking analysis revealed that Escherichia was the primary bacterial host of ARGs in both cases and follow-ups, with a greater abundance occurring during infection. Eleven distinct extended spectrum beta-lactamase (ESBL) genes were identified during infection, with some detectable upon recovery, highlighting the potential for gene transfer within the community. Because of the increasing incidence of disease caused by foodborne pathogens and their role in harboring and transferring resistance determinants, this study enhances our understanding of how enteric infections impact human gut ecology.

A standardized quantitative analysis strategy for stable isotope probing metagenomics

Stable isotope probing (SIP) facilitates culture-independent identification of active microbial populations within complex ecosystems through isotopic enrichment of nucleic acids. Many DNA-SIP studies rely on 16S rRNA gene sequences to identify active taxa, but connecting these sequences to specific bacterial genomes is often challenging. Here, we describe a standardized laboratory and analysis framework to quantify isotopic enrichment on a per-genome basis using shotgun metagenomics instead of 16S rRNA gene sequencing. To develop this framework, we explored various sample processing and analysis approaches using a designed microbiome where the identity of labeled genomes and their level of isotopic enrichment were experimentally controlled. With this ground truth dataset, we empirically assessed the accuracy of different analytical models for identifying active taxa and examined how sequencing depth impacts the detection of isotopically labeled genomes. We also demonstrate that using synthetic DNA internal standards to measure absolute genome abundances in SIP density fractions improves estimates of isotopic enrichment. In addition, our study illustrates the utility of internal standards to reveal anomalies in sample handling that could negatively impact SIP metagenomic analyses if left undetected. Finally, we present SIPmg, an R package to facilitate the estimation of absolute abundances and perform statistical analyses for identifying labeled genomes within SIP metagenomic data. This experimentally validated analysis framework strengthens the foundation of DNA-SIP metagenomics as a tool for accurately measuring the in situ activity of environmental microbial populations and assessing their genomic potential. IMPORTANCE Answering the questions, "who is eating what?" and "who is active?" within complex microbial communities is paramount for our ability to model, predict, and modulate microbiomes for improved human and planetary health. These questions can be pursued using stable isotope probing to track the incorporation of labeled compounds into cellular DNA during microbial growth. However, with traditional stable isotope methods, it is challenging to establish links between an active microorganism's taxonomic identity and genome composition while providing quantitative estimates of the microorganism's isotope incorporation rate. Here, we report an experimental and analytical workflow that lays the foundation for improved detection of metabolically active microorganisms and better quantitative estimates of genome-resolved isotope incorporation, which can be used to further refine ecosystem-scale models for carbon and nutrient fluxes within microbiomes.

Metagenomics reveals novel microbial signatures of farm exposures in house dust

Indoor home dust microbial communities, important contributors to human health, are shaped by environmental factors, including farm-related exposures. Advanced metagenomic whole genome shotgun sequencing (WGS) improves detection and characterization of microbiota in the indoor built-environment dust microbiome, compared to conventional 16S rRNA amplicon sequencing (16S). We hypothesized that the improved characterization of indoor dust microbial communities by WGS will enhance detection of exposure-outcome associations. The objective of this study was to identify novel associations of environmental exposures with the dust microbiome from the homes of 781 farmers and farm spouses enrolled in the Agricultural Lung Health Study. We examined various farm-related exposures, including living on a farm, crop versus animal production, and type of animal production, as well as non-farm exposures, including home cleanliness and indoor pets. We assessed the association of the exposures on within-sample alpha diversity and between-sample beta diversity, and the differential abundance of specific microbes by exposure. Results were compared to previous findings using 16S. We found most farm exposures were significantly positively associated with both alpha and beta diversity. Many microbes exhibited differential abundance related to farm exposures, mainly in the phyla Actinobacteria, Bacteroidetes, Firmicutes, and Proteobacteria. The identification of novel differential taxa associated with farming at the genera level, including Rhodococcus, Bifidobacterium, Corynebacterium, and Pseudomonas, was a benefit of WGS compared to 16S. Our findings indicate that characterization of dust microbiota, an important component of the indoor environment relevant to human health, is heavily influenced by sequencing techniques. WGS is a powerful tool to survey the microbial community that provides novel insights on the impact of environmental exposures on indoor dust microbiota. These findings can inform the design of future studies in environmental health.

Gut microbial signatures and their functions in Behcet's uveitis and Vogt-Koyanagi-Harada disease

BACKGROUND

A number of public metagenomic studies reveal an association between the gut microbiome and various immune-mediated diseases including Behcet's uveitis (BU) and Vogt-Koyanagi-Harada disease (VKH). Integrated-analysis and subsequent validation of these results could be a potentially powerful way to understand the microbial signatures and their functions in these two uveitis entities.

METHODS

We integrated the sequencing data of our previous metagenomic studies on two major uveitis entities, BU and VKH as well as four other publicly available immune-mediated diseases datasets, including Ankylosing Spondylitis (AS), Rheumatoid Arthritis (RA), Crohn's disease (CD) and Ulcerative Colitis (UC). Alpha-diversity and beta-diversity analysis were used to compare the gut microbiome signatures between both uveitis entities and other immune-mediated diseases and healthy controls. Amino acid homology between microbial proteins and a uveitogenic peptide of the interphotoreceptor retinoid-binding protein (IRBP)161-180 was investigated using a similarity search in the NCBI protein BLAST program (BLASTP). Enzyme-linked Immunosorbent Assay (ELISA) was performed to evaluate the cross-reactive responses of experimental autoimmune uveitis (EAU)-derived lymphocytes and BU patients-derived peripheral blood mononuclear cells (PBMCs) against homologous peptides. The area under the curve (AUC) analysis was used to test the sensitivity and specificity of gut microbial biomarkers.

RESULTS

Depleted Dorea, Blautia, Coprococcus, Erysipelotrichaceae and Lachnospiraceae as well as enriched Bilophila and Stenotrophomonas were identified in BU patients. An enriched Alistipes along with a lower level of Dorea were observed in VKH patients. A peptide antigen (SteTDR) encoded by BU specifically enriched Stenotrophomonas was identified to share homology with IRBP161-180. In vitro experiments showed that lymphocytes from EAU or PBMCs from BU patients reacted to this peptide antigen as shown by the production of IFN-γ and IL-17. Addition of the SteTDR peptide to the classical IRBP immunization protocol exacerbated EAU severity. Gut microbial marker profiles consisted of 24 species and 32 species respectively differentiated BU and VKH from each other as well as from the other four immune-mediated diseases and healthy controls. Protein annotation identified 148 and 119 specific microbial proteins associated with BU and VKH, respectively. For metabolic function analysis, 108 and 178 metabolic pathways were shown to be associated with BU and VKH, respectively.

CONCLUSIONS

Our study revealed specific gut microbial signatures and their potentially functional roles in BU and VKH pathogenesis that differ significantly from other immune-mediated diseases as well as healthy controls.

Exploration of Bioinformatics on Microbial Fuel Cell Technology: Trends, Challenges, and Future Prospects

Microbial fuel cells (MFCs) are a cost-effective and environmentally friendly alternative energy method. MFC technology has gained much interest in recent decades owing to its effectiveness in remediating wastewater and generating bioelectricity. The microbial fuel cell generates energy mainlybecause of oxidation-reduction reactions. In this reaction, electrons were transferred between two reactants. Bioinformatics is expanding across a wide range of microbial fuel cell technology. Electroactive species in the microbial community were evaluated using bioinformatics methodologies in whole genome sequencing, RNA sequencing, transcriptomics, metagenomics, and phylogenetics. Technology advancements in microbial fuel cells primarily produce power from organic and inorganic waste from various sources. Reduced chemical oxygen demand and waste degradation are two added advantages for microbial fuel cells. From plants, bacteria, and algae, microbial fuel cells were developed. Due to the rapid advancement of sequencing techniques, bioinformatics approaches are currently widely used in the technology of microbial fuel cells. In addition, they play an important role in determining the composition of electroactive species in microorganisms. The metabolic pathway is also possible to determine with bioinformatics resources. A computational technique that reveals the nature of the mediators and the substrate was also used to predict the electrochemical properties. Computational strategies were used to tackle significant challenges in experimental procedures, such as optimization and understanding microbiological systems. The main focus of this review is on utilizing bioinformatics techniques to improve microbial fuel cell technology.

Sediment metagenomics reveals the impacts of poultry industry wastewater on antibiotic resistance and nitrogen cycling genes in tidal creek ecosystems

The intensification of the poultry industry may lead to the increased spread of antibiotic resistance genes (ARGs) in the environment. However, the impacts of wastewater discharge from poultry processing plants on the sediment resistome are relatively unexplored. Furthermore, its relationships with important biogeochemical pathways, such as the N cycle, are virtually unknown. The overall objective of this study was to examine the abundance and diversity of antibiotic resistance and N cycling genes in sediment microbial communities impacted by poultry industry wastewater. We performed a metagenomic investigation of sediments in an impacted and a reference tidal creek. We also quantified the abundance of the clinical class 1 integron-integrase gene (intI1) through qPCR as a secondary marker of anthropogenic contamination. Abundance and diversity of ARGs were substantially higher in the impacted tidal creek, especially near the wastewater discharge. Abundances of ARGs conferring resistance to macrolides, tetracyclines, and streptogramins were also higher in the impacted creek than the reference creek. From the N cycling genes detected in the metagenomes, nrfA, the genetic marker for dissimilatory nitrate reduction to ammonia (DNRA), had the strongest positive relationship with the total abundance of ARGs, which may indicate an increased potential of eutrophication in ARG-impacted ecosystems due to nitrogen retention. This study demonstrates that wastewater discharge from a poultry processing plant can increase the spread of ARGs, which may result in negative impacts on ecosystem health.

Phenotype-specific signatures of systems-level gut microbiome associated with childhood airway allergies

BACKGROUND

Perturbation of gut symbiosis has been linked to childhood allergic diseases. However, the underlying host-microbe interaction connected with specific phenotypes is poorly understood.

METHODS

To address this, integrative analyses of stool metagenomic and metabolomic profiles associated with IgE reactions in 56 children with mite-sensitized airway allergies (25 with rhinitis and 31 with asthma) and 28 nonallergic healthy controls were conducted.

RESULTS

We noted a decrease in the number and abundance of gut microbiome-encoded carbohydrate-active enzyme (CAZyme) genes, accompanied with a reduction in species richness, in the asthmatic gut microflora but not in that from allergic rhinitis. Such loss of CAZymes was consistent with the observation that a CAZyme-linked decrease in fecal butyrate was found in asthmatics and negatively correlated with mite-specific IgE responses. Different from the CAZymes, we demonstrated an increase in α diversity at the virulome levels in asthmatic gut microbiota and identified phenotype-specific variations of gut virulome. Moreover, use of fecal metagenomic and metabolomic signatures resulted in distinct effects on differentiating rhinitis and asthma from nonallergic healthy controls.

CONCLUSION

Overall, our integrative analyses reveal several signatures of systems-level gut microbiome in robust associations with fecal metabolites and disease phenotypes, which may be of etiological and diagnostic implications in childhood airway allergies.

Ecological evolution during the three-year restoration using rhizosphere soil cover method at a Lead-Zinc tailing pond in Karst areas

A major challenge for the restoration of the Lead-Zinc tailing pond in Karst areas lies in how to establish vegetation with less soil and restore the ecological functions of the substrate. In this study, a novel method, rhizosphere soil cover method (RSC), was applied to recover the vegetation at a Pb-Zn tailing pond in Karst areas. Two local tolerate plants, Miscanthus sinensis and Pueraria phaseoloides, were planted as pioneer species. Although 68 % of the tailing pond was not covered with soil, the vegetation coverage has reached over 90 % after restoration for three years. Compared with the natural revegetation process (vegetation coverage was <5 % after 20 years of natural succession), the revegetation in the tailing pond was accelerated by RSC and planting pioneer species. Both the plant's diversity and richness have significantly increased in the tailings pond during the restoration (p < 0.05). The important value indicators of M. sinensis and P. phaseoloides were the highest in the plant community, indicating the dominant role of these two plants in revegetation. Moreover, the total organic carbon, total nitrogen, total phosphorus, and total potassium in the tailings increased annually (p < 0.05), which demonstrated that the revegetation has improved the chemical properties in the substrate. In addition, the Shannon diversity index of bacteria in the tailings increased significantly from 4.11 to 5.51. The relative abundance of microbial genes related to carbon fixation and nitrogen fixation in the tailings increased by 17 % and 43 %, respectively. Meanwhile, the physicochemical properties, microbial community structure, and nutrient cycling function in the tailings without topsoil were improved more obviously than those in soils. It is thereby concluded that RSC is an efficient means for ecological restoration of the tailing ponds in Karst areas to improve the ecosystem structure and function of Pb-Zn tailings.

An improved workflow for accurate and robust healthcare environmental surveillance using metagenomics

BACKGROUND

Effective surveillance of microbial communities in the healthcare environment is increasingly important in infection prevention. Metagenomics-based techniques are promising due to their untargeted nature but are currently challenged by several limitations: (1) they are not powerful enough to extract valid signals out of the background noise for low-biomass samples, (2) they do not distinguish between viable and nonviable organisms, and (3) they do not reveal the microbial load quantitatively. An additional practical challenge towards a robust pipeline is the inability to efficiently allocate sequencing resources a priori. Assessment of sequencing depth is generally practiced post hoc, if at all, for most microbiome studies, regardless of the sample type. This practice is inefficient at best, and at worst, poor sequencing depth jeopardizes the interpretation of study results. To address these challenges, we present a workflow for metagenomics-based environmental surveillance that is appropriate for low-biomass samples, distinguishes viability, is quantitative, and estimates sequencing resources.

RESULTS

The workflow was developed using a representative microbiome sample, which was created by aggregating 120 surface swabs collected from a medical intensive care unit. Upon evaluating and optimizing techniques as well as developing new modules, we recommend best practices and introduce a well-structured workflow. We recommend adopting liquid-liquid extraction to improve DNA yield and only incorporating whole-cell filtration when the nonbacterial proportion is large. We suggest including propidium monoazide treatment coupled with internal standards and absolute abundance profiling for viability assessment and involving cultivation when demanding comprehensive profiling. We further recommend integrating internal standards for quantification and additionally qPCR when we expect poor taxonomic classification. We also introduce a machine learning-based model to predict required sequencing effort from accessible sample features. The model helps make full use of sequencing resources and achieve desired outcomes. Video Abstract CONCLUSIONS: This workflow will contribute to more accurate and robust environmental surveillance and infection prevention. Lessons gained from this study will also benefit the continuing development of methods in relevant fields.

Genotyping Microbial Communities with MIDAS2: From Metagenomic Reads to Allele Tables

The Metagenomic Intra-Species Diversity Analysis System 2 (MIDAS2) is a scalable pipeline that identifies single nucleotide variants and gene copy number variants in metagenomes using comprehensive reference databases built from public microbial genome collections (metagenotyping). MIDAS2 is the first metagenotyping tool with functionality to control metagenomic read mapping filters and to customize the reference database to the microbial community, features that improve the precision and recall of detected variants. In this article we present four basic protocols for the most common use cases of MIDAS2, along with supporting protocols for installation and use. In addition, we provide in-depth guidance on adjusting command line parameters, editing the reference database, optimizing hardware utilization, and understanding the metagenotyping results. All the steps of metagenotyping, from raw sequencing reads to population genetic analysis, are demonstrated with example data in two downloadable sequencing libraries of single-end metagenomic reads representing a mixture of multiple bacterial species. This set of protocols empowers users to accurately genotype hundreds of species in thousands of samples, providing rich genetic data for studying the evolution and strain-level ecology of microbial communities. © 2022 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Species prescreening Basic Protocol 2: Download MIDAS reference database Basic Protocol 3: Population single nucleotide variant calling Basic Protocol 4: Pan-genome copy number variant calling Support Protocol 1: Installing MIDAS2 Support Protocol 2: Command line inputs Support Protocol 3: Metagenotyping with a custom collection of genomes Support Protocol 4: Metagenotyping with advanced parameters.

Wochenende — modular and flexible alignment-based shotgun metagenome analysis

Background

Shotgun metagenome analysis provides a robust and verifiable method for comprehensive microbiome analysis of fungal, viral, archaeal and bacterial taxonomy, particularly with regard to visualization of read mapping location, normalization options, growth dynamics and functional gene repertoires. Current read classification tools use non-standard output formats, or do not fully show information on mapping location. As reference datasets are not perfect, portrayal of mapping information is critical for judging results effectively.

Results

Our alignment-based pipeline, Wochenende, incorporates flexible quality control, trimming, mapping, various filters and normalization. Results are completely transparent and filters can be adjusted by the user. We observe stringent filtering of mismatches and use of mapping quality sharply reduces the number of false positives. Further modules allow genomic visualization and the calculation of growth rates, as well as integration and subsequent plotting of pipeline results as heatmaps or heat trees. Our novel normalization approach additionally allows calculation of absolute abundance profiles by comparison with reads assigned to the human host genome.

Conclusion

Wochenende has the ability to find and filter alignments to all kingdoms of life using both short and long reads, and requires only good quality reference genomes. Wochenende automatically combines multiple available modules ranging from quality control and normalization to taxonomic visualization. Wochenende is available at https://github.com/MHH-RCUG/nf_wochenende.

AsgeneDB: a curated orthology arsenic metabolism gene database and computational tool for metagenome annotation

Arsenic (As) is the most ubiquitous toxic metalloid in nature. Microbe-mediated As metabolism plays an important role in global As biogeochemical processes, greatly changing its toxicity and bioavailability. While metagenomic sequencing may advance our understanding of the As metabolism capacity of microbial communities in different environments, accurate metagenomic profiling of As metabolism remains challenging due to low coverage and inaccurate definitions of As metabolism gene families in public orthology databases. Here we developed a manually curated As metabolism gene database (AsgeneDB) comprising 400 242 representative sequences from 59 As metabolism gene families, which are affiliated with 1653 microbial genera from 46 phyla. AsgeneDB achieved 100% annotation sensitivity and 99.96% annotation accuracy for an artificial gene dataset. We then applied AsgeneDB for functional and taxonomic profiling of As metabolism in metagenomes from various habitats (freshwater, hot spring, marine sediment and soil). The results showed that AsgeneDB substantially improved the mapping ratio of short reads in metagenomes from various environments. Compared with other databases, AsgeneDB provides more accurate, more comprehensive and faster analysis of As metabolic genes. In addition, we developed an R package, Asgene, to facilitate the analysis of metagenome sequencing data. Therefore, AsgeneDB and the associated Asgene package will greatly promote the study of As metabolism in microbial communities in various environments.

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

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

Applications of massively parallel sequencing in forensic genetics

Massively parallel sequencing, also referred to as next-generation sequencing, has positively changed DNA analysis, allowing further advances in genetics. Its capability of dealing with low quantity/damaged samples makes it an interesting instrument for forensics. The main advantage of MPS is the possibility of analyzing simultaneously thousands of genetic markers, generating high-resolution data. Its detailed sequence information allowed the discovery of variations in core forensic short tandem repeat loci, as well as the identification of previous unknown polymorphisms. Furthermore, different types of markers can be sequenced in a single run, enabling the emergence of DIP-STRs, SNP-STR haplotypes, and microhaplotypes, which can be very useful in mixture deconvolution cases. In addition, the multiplex analysis of different single nucleotide polymorphisms can provide valuable information about identity, biogeographic ancestry, paternity, or phenotype. DNA methylation patterns, mitochondrial DNA, mRNA, and microRNA profiling can also be analyzed for different purposes, such as age inference, maternal lineage analysis, body-fluid identification, and monozygotic twin discrimination. MPS technology also empowers the study of metagenomics, which analyzes genetic material from a microbial community to obtain information about individual identification, post-mortem interval estimation, geolocation inference, and substrate analysis. This review aims to discuss the main applications of MPS in forensic genetics.

A machine learning framework for discovery and enrichment of metagenomics metadata from open access publications

Metagenomics is a culture-independent method for studying the microbes inhabiting a particular environment. Comparing the composition of samples (functionally/taxonomically), either from a longitudinal study or cross-sectional studies, can provide clues into how the microbiota has adapted to the environment. However, a recurring challenge, especially when comparing results between independent studies, is that key metadata about the sample and molecular methods used to extract and sequence the genetic material are often missing from sequence records, making it difficult to account for confounding factors. Nevertheless, these missing metadata may be found in the narrative of publications describing the research. Here, we describe a machine learning framework that automatically extracts essential metadata for a wide range of metagenomics studies from the literature contained in Europe PMC. This framework has enabled the extraction of metadata from 114,099 publications in Europe PMC, including 19,900 publications describing metagenomics studies in European Nucleotide Archive (ENA) and MGnify. Using this framework, a new metagenomics annotations pipeline was developed and integrated into Europe PMC to regularly enrich up-to-date ENA and MGnify metagenomics studies with metadata extracted from research articles. These metadata are now available for researchers to explore and retrieve in the MGnify and Europe PMC websites, as well as Europe PMC annotations API.

Meta-analysis of the microbial biomarkers in the gut - lung crosstalk in COVID-19, community acquired pneumonia and Clostridium difficile infections

Respiratory infections are the leading causes of mortality and the current pandemic COVID-19 is one such trauma that imposed catastrophic devastation to the health and economy of the world. Unraveling the correlations and interplay of the human microbiota in the gut- lung axis would offer incredible solutions to the underlying mystery of the disease progression. The study compared the microbiota profiles of six samples namely healthy gut, healthy lung, COVID-19 infected gut, COVID-19 infected lungs, Clostridium difficile infected gut and community acquired pneumonia infected lungs. The metagenome datasets were processed, normalized, classified and the rarefaction curves were plotted. The microbial biomarkers for COVID-19 infections were identified as the abundance of Candida and Escherichia in lungs with Ruminococcus in the gut. Candida and Staphylococcus could play a vital role as putative prognostic biomarkers of community acquired pneumonia whereas abundance of Faecalibacterium and Clostridium are associated with the Clostridium difficile infections in gut. A machine learning random forest classifier applied to the datasets efficiently classified the biomarkers. The study offers an extensive and incredible understanding of the existence of gut lung axis during dysbiosis of two anatomically different organs.

A High-Throughput Absolute Abundance Quantification Method for the Characterisation of Daqu Core Fungal Communities

An inherent issue in high-throughput sequencing applications is that they provide compositional data for relative abundance. This often obscures the true biomass and potential functions of fungi in the community. Therefore, we presented a high-throughput absolute quantification (HAQ) method to quantitatively estimate the fungal abundance in Daqu. In this study, five internal standard plasmids (ISPs) were designed for the fungal ITS2 subregion with high length variations. Five ISPs were then utilised to establish standard curves with a quantitative concentration range of 103–107 cells/g, and this was used to quantify the core fungi, including Basidiomycota, Ascomycota, and Mucoromycota. Using three types of mature Daqu from different regions, we demonstrated that the HAQ method yielded community profiles substantially different from those derived using relative abundances. Then, the HAQ method was applied to the Daqu during fermentation. The initial formation of the Daqu surface occurred in the fourth stage, which was mainly driven by moisture. The key fungi that caused the initial formation of the Daqu surface included Hyphopichia burtonii, Saccharomycopsis fibuligera, and Pichia kudriavzevii. The initial formation of the Daqu core occurred in the fifth stage, which was mainly affected by moisture and reducing the sugar content. The key fungi that cause the initial formation of the Daqu core included S. fibuligera and Paecilomyces verrucosus. We conclude that the HAQ method, when applied to ITS2 gene fungal community profiling, is quantitative and that its use will greatly improve our understanding of the fungal ecosystem in Daqu.

Microbiome Analysis for Wastewater Surveillance during COVID-19

Wastewater surveillance (WS), when coupled with advanced molecular techniques, offers near real-time monitoring of community-wide transmission of SARS-CoV-2 and allows assessing and mitigating COVID-19 outbreaks, by evaluating the total microbial assemblage in a community. Composite wastewater samples (24 h) were collected weekly from a manhole between December 2020 and November 2021 in Maryland, USA. RT-qPCR results showed concentrations of SARS-CoV-2 RNA recovered from wastewater samples reflected incidence of COVID-19 cases. When a drastic increase in COVID-19 was detected in February 2021, samples were selected for microbiome analysis (DNA metagenomics, RNA metatranscriptomics, and targeted SARS-CoV-2 sequencing). Targeted SARS-CoV-2 sequencing allowed for detection of important genetic mutations, such as spike: K417N, D614G, P681H, T716I, S982A, and D1118H, commonly associated with increased cell entry and reinfection. Microbiome analysis (DNA and RNA) provided important insight with respect to human health-related factors, including detection of pathogens and their virulence/antibiotic resistance genes. Specific microbial species comprising the wastewater microbiome correlated with incidence of SARS-CoV-2 RNA, suggesting potential association with SARS-CoV-2 infection. Climatic conditions, namely, temperature, were related to incidence of COVID-19 and detection of SARS-CoV-2 in wastewater, having been monitored as part of an environmental risk score assessment carried out in this study. In summary, the wastewater microbiome provides useful public health information, and hence, a valuable tool to proactively detect and characterize pathogenic agents circulating in a community. In effect, metagenomics of wastewater can serve as an early warning system for communicable diseases, by providing a larger source of information for health departments and public officials. IMPORTANCE Traditionally, testing for COVID-19 is done by detecting SARS-CoV-2 in samples collected from nasal swabs and/or saliva. However, SARS-CoV-2 can also be detected in feces of infected individuals. Therefore, wastewater samples can be used to test all individuals of a community contributing to the sewage collection system, i.e., the infrastructure, such as gravity pipes, manholes, tanks, lift stations, control structures, and force mains, that collects used water from residential and commercial sources and conveys the flow to a wastewater treatment plant. Here, we profile community wastewater collected from a manhole, detect presence of SARS-CoV-2, identify genetic mutations of SARS-CoV-2, and perform COVID-19 risk score assessment of the study area. Using metagenomics analysis, we also detect other microorganisms (bacteria, fungi, protists, and viruses) present in the samples. Results show that by analyzing all microorganisms present in wastewater, pathogens circulating in a community can provide an early warning for contagious diseases.

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.

Exosome Carrier Effects; Resistance to Digestion in Phagolysosomes May Assist Transfers to Targeted Cells; II Transfers of miRNAs Are Better Analyzed via Systems Approach as They Do Not Fit Conventional Reductionist Stoichiometric Concepts

Carrier effects of extracellular vesicles (EV) like exosomes refer to properties of the vesicles that contribute to the transferred biologic effects of their contents to targeted cells. This can pertain to ingested small amounts of xenogeneic plant miRNAs and oral administration of immunosuppressive exosomes. The exosomes contribute carrier effects on transfers of miRNAs by contributing both to the delivery and the subsequent functional intracellular outcomes. This is in contrast to current quantitative canonical rules that dictate just the minimum copies of a miRNA for functional effects, and thus successful transfers, independent of the EV carrier effects. Thus, we argue here that transfers by non-canonical minute quantities of miRNAs must consider the EV carrier effects of functional low levels of exosome transferred miRNA that may not fit conventional reductionist stoichiometric concepts. Accordingly, we have examined traditional stoichiometry vs. systems biology that may be more appropriate for delivered exosome functional responses. Exosome carrier properties discussed include; their required surface activating interactions with targeted cells, potential alternate targets beyond mRNAs, like reaching a threshold, three dimensional aspects of the RNAs, added EV kinetic dynamic aspects making transfers four dimensional, and unique intracellular release from EV that resist intracellular digestion in phagolysosomes. Together these EV carrier considerations might allow systems analysis. This can then result in a more appropriate understanding of transferred exosome carrier-assisted functional transfers. A plea is made that the miRNA expert community, in collaboration with exosome experts, perform new experiments on molecular and quantitative miRNA functional effects in systems that include EVs, like variation in EV type and surface constituents, delivery, dose and time to hopefully create more appropriate and truly current canonical concepts of the consequent miRNA functional transfers by EVs like exosomes.

Polymer-based chemical-nose systems for optical-pattern recognition of gut microbiota

Gut-microbiota analysis has been recognized as crucial in health management and disease treatment. Metagenomics, a current standard examination method for the gut microbiome, is effective but requires both expertise and significant amounts of general resources. Here, we show highly accessible sensing systems based on the so-called chemical-nose strategy to transduce the characteristics of microbiota into fluorescence patterns. The fluorescence patterns, generated by twelve block copolymers with aggregation-induced emission (AIE) units, were analyzed using pattern-recognition algorithms, which identified 16 intestinal bacterial strains in a way that correlates with their genome-based taxonomic classification. Importantly, the chemical noses classified artificial models of obesity-associated gut microbiota, and further succeeded in detecting sleep disorder in mice through comparative analysis of normal and abnormal mouse gut microbiota. Our techniques thus allow analyzing complex bacterial samples far more quickly, simply, and inexpensively than common metagenome-based methods, which offers a powerful and complementary tool for the practical analysis of the gut microbiome.

The Isolation and Characterization of Rare Mycobiome Associated With Spacecraft Assembly Cleanrooms

Ensuring biological cleanliness while assembling and launching spacecraft is critical for robotic exploration of the solar system. To date, when preventing forward contamination of other celestial bodies, NASA Planetary Protection policies have focused on endospore-forming bacteria while fungi were neglected. In this study, for the first time the mycobiome of two spacecraft assembly facilities at Jet Propulsion Laboratory (JPL) and Kennedy Space Center (KSC) was assessed using both cultivation and sequencing techniques. To facilitate enumeration of viable fungal populations and downstream molecular analyses, collected samples were first treated with chloramphenicol for 24 h and then with propidium monoazide (PMA). Among cultivable fungi, 28 distinct species were observed, 16 at JPL and 16 at KSC facilities, while 13 isolates were potentially novel species. Only four isolated species Aureobasidium melanogenum, Penicillium fuscoglaucum, Penicillium decumbens, and Zalaria obscura were present in both cleanroom facilities, which suggests that mycobiomes differ significantly between distant locations. To better visualize the biogeography of all isolated strains the network analysis was undertaken and confirmed higher abundance of Malassezia globosa and Cyberlindnera jadinii. When amplicon sequencing was performed, JPL-SAF and KSC-PHSF showed differing mycobiomes. Metagenomic fungal reads were dominated by Ascomycota (91%) and Basidiomycota (7.15%). Similar to amplicon sequencing, the number of fungal reads changed following antibiotic treatment in both cleanrooms; however, the opposite trends were observed. Alas, treatment with the antibiotic did not allow for definitive ascribing changes observed in fungal populations between treated and untreated samples in both cleanrooms. Rather, these substantial differences in fungal abundance might be attributed to several factors, including the geographical location, climate and the in-house cleaning procedures used to maintain the cleanrooms. This study is a first step in characterizing cultivable and viable fungal populations in cleanrooms to assess fungal potential as biocontaminants during interplanetary explorations. The outcomes of this and future studies could be implemented in other cleanrooms that require to reduce microbial burden, like intensive care units, operating rooms, or cleanrooms in the semiconducting and pharmaceutical industries.

Organic fertilizer potentiates the transfer of typical antibiotic resistance gene among special bacterial species

The propagation of antibiotic resistance genes (ARGs) in environments has evoked many attentions, however, how to identify their host pathogenic bacteria in situ remains a great challenge. Here we explored the bacterial host distribution and dissemination of a typical ARG, sul1 gene, in agricultural soils through the simultaneous detection of sul1 and its host 16S rRNA gene by emulsion paired isolation and concatenation PCR (epicPCR). Compared to chemical fertilizer, organic fertilizer (chicken manure) led to a higher prevalence of sul1 gene in the soil, and dominant bacterial hosts of sul1 gene were classified into Proteobacteria and Bacteroidetes phyla. Additionally, significant higher diversity of antibiotic resistance bacteria (ARB), higher rate of horizontal gene transfer (HGT), higher rate of mobile genetic elements (MGE) and higher proportion of pathogens were all observed in the treatment of organic fertilizer. This study alerts potential health risks of manure applications in agricultural soils.

Colonization of the live biotherapeutic product VE303 and modulation of the microbiota and metabolites in healthy volunteers

Manipulation of the gut microbiota via fecal microbiota transplantation (FMT) has shown clinical promise in diseases such as recurrent Clostridioides difficile infection (rCDI). However, the variable nature of this approach makes it challenging to describe the relationship between fecal strain colonization, corresponding microbiota changes, and clinical efficacy. Live biotherapeutic products (LBPs) consisting of defined consortia of clonal bacterial isolates have been proposed as an alternative therapeutic class because of their promising preclinical results and safety profile. We describe VE303, an LBP comprising 8 commensal Clostridia strains under development for rCDI, and its early clinical development in healthy volunteers (HVs). In a phase 1a/b study in HVs, VE303 is determined to be safe and well-tolerated at all doses tested. VE303 strains optimally colonize HVs if dosed over multiple days after vancomycin pretreatment. VE303 promotes the establishment of a microbiota community known to provide colonization resistance.

MetaPop: a pipeline for macro- and microdiversity analyses and visualization of microbial and viral metagenome-derived populations

BACKGROUND

Microbes and their viruses are hidden engines driving Earth's ecosystems from the oceans and soils to humans and bioreactors. Though gene marker approaches can now be complemented by genome-resolved studies of inter-(macrodiversity) and intra-(microdiversity) population variation, analytical tools to do so remain scattered or under-developed.

RESULTS

Here, we introduce MetaPop, an open-source bioinformatic pipeline that provides a single interface to analyze and visualize microbial and viral community metagenomes at both the macro- and microdiversity levels. Macrodiversity estimates include population abundances and α- and β-diversity. Microdiversity calculations include identification of single nucleotide polymorphisms, novel codon-constrained linkage of SNPs, nucleotide diversity (π and θ), and selective pressures (pN/pS and Tajima's D) within and fixation indices (FST) between populations. MetaPop will also identify genes with distinct codon usage. Following rigorous validation, we applied MetaPop to the gut viromes of autistic children that underwent fecal microbiota transfers and their neurotypical peers. The macrodiversity results confirmed our prior findings for viral populations (microbial shotgun metagenomes were not available) that diversity did not significantly differ between autistic and neurotypical children. However, by also quantifying microdiversity, MetaPop revealed lower average viral nucleotide diversity (π) in autistic children. Analysis of the percentage of genomes detected under positive selection was also lower among autistic children, suggesting that higher viral π in neurotypical children may be beneficial because it allows populations to better "bet hedge" in changing environments. Further, comparisons of microdiversity pre- and post-FMT in autistic children revealed that the delivery FMT method (oral versus rectal) may influence viral activity and engraftment of microdiverse viral populations, with children who received their FMT rectally having higher microdiversity post-FMT. Overall, these results show that analyses at the macro level alone can miss important biological differences.

CONCLUSIONS

These findings suggest that standardized population and genetic variation analyses will be invaluable for maximizing biological inference, and MetaPop provides a convenient tool package to explore the dual impact of macro- and microdiversity across microbial communities. Video abstract.

Intestinal butyrate-metabolizing species contribute to autoantibody production and bone erosion in rheumatoid arthritis

The imbalance between pathogenic and beneficial species of the intestinal microbiome and metabolism in rheumatoid arthritis (RA) remains unclarified. Here, using shotgun-based metagenome sequencing for a treatment-naïve patient cohort and a "quasi-paired cohort" method, we observed a deficiency of butyrate-producing species and an overwhelming number of butyrate consumers in RA patients. These outcomes mainly occurred in patients with positive ACPA, with a mean AUC of 0.94. This panel was also validated in established RA with an AUC of 0.986 in those with joint deformity. In addition, we showed that butyrate promoted Tregs, while suppressing Tconvs and osteoclasts, due to potentiation of the reduction in HDAC expression and down-regulation of proinflammatory cytokine genes. Dietary butyrate supplementation conferred anti-inflammatory benefits in a mouse model by rebalancing TFH cells and Tregs, as well as reducing antibody production. These findings reveal the critical role of butyrate-metabolizing species and suggest the potential of butyrate-based therapies for RA patients.

Diet, microbe, and autism: Cause or consequence?

Numerous studies have shown the possible contributions of the gut microbiome to the pathogenesis of autism spectrum disorder (ASD). However, recently in Cell, Yap et al. found that autism-related dietary preferences may mediate the ASD-microbiome associations, while the direct associations between ASD and gut microbiota are negligible.

Metagenomics combined with activity-based proteomics point to gut bacterial enzymes that reactivate mycophenolate

Mycophenolate mofetil (MMF) is an important immunosuppressant prodrug prescribed to prevent organ transplant rejection and to treat autoimmune diseases. MMF usage, however, is limited by severe gastrointestinal toxicity that is observed in approximately 45% of MMF recipients. The active form of the drug, mycophenolic acid (MPA), undergoes extensive enterohepatic recirculation by bacterial β-glucuronidase (GUS) enzymes, which reactivate MPA from mycophenolate glucuronide (MPAG) within the gastrointestinal tract. GUS enzymes demonstrate distinct substrate preferences based on their structural features, and gut microbial GUS enzymes that reactivate MPA have not been identified. Here, we compare the fecal microbiomes of transplant recipients receiving MMF to healthy individuals using shotgun metagenomic sequencing. We find that neither microbial composition nor the presence of specific structural classes of GUS genes are sufficient to explain the differences in MPA reactivation measured between fecal samples from the two cohorts. We next employed a GUS-specific activity-based chemical probe and targeted metaproteomics to identify and quantify the GUS proteins present in the human fecal samples. The identification of specific GUS enzymes was improved by using the metagenomics data collected from the fecal samples. We found that the presence of GUS enzymes that bind the flavin mononucleotide (FMN) is significantly correlated with efficient MPA reactivation. Furthermore, structural analysis identified motifs unique to these FMN-binding GUS enzymes that provide molecular support for their ability to process this drug glucuronide. These results indicate that FMN-binding GUS enzymes may be responsible for reactivation of MPA and could be a driving force behind MPA-induced GI toxicity.

Building up a clinical microbiota profiling: a quality framework proposal

Extensive characterization of the human microbiota has revealed promising relationships between microbial composition and health or disease, generating interest in biomarkers derived from microbiota profiling. However, microbiota complexity and technical challenges strongly influencing the results limit the generalization of microbiota profiling and question its clinical utility. In addition, no quality management scheme has been adapted to the specificities of microbiota profiling, notably due to the heterogeneity in methods and results. In this review, we discuss possible adaptation of classical quality management tools routinely used in diagnostic laboratories to microbiota profiling and propose a specific framework. Multiple quality controls are needed to cover all steps, from sampling to data processing. Standard operating procedures, primarily developed for wet lab analyses, must be adapted to the use of bioinformatic tools. Finally, requirements for test validation and proficiency testing must take into account expected discrepancies in results due to the heterogeneity of the processes. The proposed quality management framework should support the implementation of routine microbiota profiling by clinical laboratories to support patient care. Furthermore, its use in research laboratories would improve publication reproducibility as well as transferability of methods and results to routine practice.

A Genome-Phenome Association study in native microbiomes identifies a mechanism for cytosine modification in DNA and RNA

Shotgun metagenomic sequencing is a powerful approach to study microbiomes in an unbiased manner and of increasing relevance for identifying novel enzymatic functions. However, the potential of metagenomics to relate from microbiome composition to function has thus far been underutilized. Here, we introduce the Metagenomics Genome-Phenome Association (MetaGPA) study framework, which allows linking genetic information in metagenomes with a dedicated functional phenotype. We applied MetaGPA to identify enzymes associated with cytosine modifications in environmental samples. From the 2365 genes that met our significance criteria, we confirm known pathways for cytosine modifications and proposed novel cytosine-modifying mechanisms. Specifically, we characterized and identified a novel nucleic acid-modifying enzyme, 5-hydroxymethylcytosine carbamoyltransferase, that catalyzes the formation of a previously unknown cytosine modification, 5-carbamoyloxymethylcytosine, in DNA and RNA. Our work introduces MetaGPA as a novel and versatile tool for advancing functional metagenomics.

Many industrial processes, such as starch processing and oil refinement, use chemicals that cause harm to the environment. These can often be switched to more sustainable biological processes that are powered by proteins called enzymes.

Enzymes are micro-factories that speed up biochemical reactions in most living things. Communities of microorganisms (also known as microbiomes) are an amazing but often untapped resource for discovering enzymes that can be harnessed for industrial purposes. To gain a better picture of the microbes present within a population, researchers often extract and sequence the genetic material of all microorganisms in an environmental sample, also known as the metagenome. While current methods for analyzing the metagenome are good at identifying new species, they often provide limited information about the microorganism’s functional role within the community. This makes it difficult to find new enzymes that may be useful for industry.

Here, Yang, Lin et al. have developed a new technique called Metagenomics Genome-Phenome Association, or MetaGPA for short. The method works in a similar way to genome-wide association studies (GWAS) which are used to identify genes involved in human disease. However, instead of disease associated genes in humans, MetaGPA finds microbial genes that are associated with a biological process useful for biotechnology.

Like GWAS, the new approach created by Yang, Lin et al. compares two groups: the first contains microorganisms that carry out a specific process, and the second contains all organisms in the microbiome. The metagenome of each group is extracted and a computational pipeline is then applied to identify genes, including those coding for enzymes, that are found more often in the group performing the desired task.

To test the technique, Yang, Lin et al. used MetGPA to find new enzymes involved in DNA modification. Microbiome samples were collected from coastal water and sewage, and the computational pipeline was applied to discover genes that are associated with this process. Further analysis revealed that one of the identified genes codes for an enzyme that introduces a previously unknown change to DNA.

MetaGPA could be applied to other processes and microbiomes, and, if successful, may help researchers to identify more diverse enzymes than is currently available. This could scale up the discovery of new enzymes that can be used to power industrial reactions.

Single-cell RNA-seq describes the transcriptome landscape and identifies critical transcription factors in the leaf blade of the allotetraploid peanut (Arachis hypogaea L.)

Single-cell RNA-seq (scRNA-seq) has been highlighted as a powerful tool for the description of human cell transcriptome, but the technology has not been broadly applied in plant cells. Herein, we describe the successful development of a robust protoplast cell isolation system in the peanut leaf. A total of 6,815 single cells were divided into eight cell clusters based on reported marker genes by applying scRNA-seq. Further, a pseudo-time analysis was used to describe the developmental trajectory and interaction network of transcription factors (TFs) of distinct cell types during leaf growth. The trajectory enabled re-investigation of the primordium-driven development processes of the mesophyll and epidermis. These results suggest that palisade cells likely differentiate into spongy cells, while the epidermal cells originated earlier than the primordium. Subsequently, the developed method integrated multiple technologies to efficiently validate the scRNA-seq result in a homogenous cell population. The expression levels of several TFs were strongly correlated with epidermal ontogeny in accordance with obtained scRNA-seq values. Additionally, peanut AHL23 (AT-HOOK MOTIF NUCLEAR LOCALIZED PROTEIN 23), which is localized in nucleus, promoted leaf growth when ectopically expressed in Arabidopsis by modulating the phytohormone pathway. Together, our study displays that application of scRNA-seq can provide new hypotheses regarding cell differentiation in the leaf blade of Arachis hypogaea. We believe that this approach will enable significant advances in the functional study of leaf blade cells in the allotetraploid peanut and other plant species.

Microbial Ecology of Oxygen Minimum Zones Amidst Ocean Deoxygenation

Oxygen minimum zones (OMZs) have substantial effects on the global ecology and biogeochemical processes of marine microbes. However, the diversity and activity of OMZ microbes and their trophic interactions are only starting to be documented, especially in regard to the potential roles of viruses and protists. OMZs have expanded over the past 60 years and are predicted to expand due to anthropogenic climate change, furthering the need to understand these regions. This review summarizes the current knowledge of OMZ formation, the biotic and abiotic factors involved in OMZ expansion, and the microbial ecology of OMZs, emphasizing the importance of bacteria, archaea, viruses, and protists. We describe the recognized roles of OMZ microbes in carbon, nitrogen, and sulfur cycling, the potential of viruses in altering host metabolisms involved in these cycles, and the control of microbial populations by grazers and viruses. Further, we highlight the microbial community composition and roles of these organisms in oxic and anoxic depths within the water column and how these differences potentially inform how microbial communities will respond to deoxygenation. Additionally, the current literature on the alteration of microbial communities by other key climate change parameters such as temperature and pH are considered regarding how OMZ microbes might respond to these pressures. Finally, we discuss what knowledge gaps are present in understanding OMZ microbial communities and propose directions that will begin to close these gaps.

Cultivation of common bacterial species and strains from human skin, oral, and gut microbiota

BACKGROUND

Genomics-driven discoveries of microbial species have provided extraordinary insights into the biodiversity of human microbiota. In addition, a significant portion of genetic variation between microbiota exists at the subspecies, or strain, level. High-resolution genomics to investigate species- and strain-level diversity and mechanistic studies, however, rely on the availability of individual microbes from a complex microbial consortia. High-throughput approaches are needed to acquire and identify the significant species- and strain-level diversity present in the oral, skin, and gut microbiome. Here, we describe and validate a streamlined workflow for cultivating dominant bacterial species and strains from the skin, oral, and gut microbiota, informed by metagenomic sequencing, mass spectrometry, and strain profiling.

RESULTS

Of total genera discovered by either metagenomic sequencing or culturomics, our cultivation pipeline recovered between 18.1-44.4% of total genera identified. These represented a high proportion of the community composition reconstructed with metagenomic sequencing, ranging from 66.2-95.8% of the relative abundance of the overall community. Fourier-Transform Infrared spectroscopy (FT-IR) was effective in differentiating genetically distinct strains compared with whole-genome sequencing, but was less effective as a proxy for genetic distance.

CONCLUSIONS

Use of a streamlined set of conditions selected for cultivation of skin, oral, and gut microbiota facilitates recovery of dominant microbes and their strain variants from a relatively large sample set. FT-IR spectroscopy allows rapid differentiation of strain variants, but these differences are limited in recapitulating genetic distance. Our data highlights the strength of our cultivation and characterization pipeline, which is in throughput, comparisons with high-resolution genomic data, and rapid identification of strain variation.