Get the most out of your metagenome: computational analysis of environmental sequence data

Exploration of genes encoding KEGG pathway enzymes in rhizospheric microbiome of the wild plant Abutilon fruticosum

The operative mechanisms and advantageous synergies existing between the rhizobiome and the wild plant species Abutilon fruticosum were studied. Within the purview of this scientific study, the reservoir of genes in the rhizobiome, encoding the most highly enriched enzymes, was dominantly constituted by members of phylum Thaumarchaeota within the archaeal kingdom, phylum Proteobacteria within the bacterial kingdom, and the phylum Streptophyta within the eukaryotic kingdom. The ensemble of enzymes encoded through plant exudation exhibited affiliations with 15 crosstalking KEGG (Kyoto Encyclopaedia of Genes and Genomes) pathways. The ultimate goal underlying root exudation, as surmised from the present investigation, was the biosynthesis of saccharides, amino acids, and nucleic acids, which are imperative for the sustenance, propagation, or reproduction of microbial consortia. The symbiotic companionship existing between the wild plant and its associated rhizobiome amplifies the resilience of the microbial community against adverse abiotic stresses, achieved through the orchestration of ABA (abscisic acid) signaling and its cascading downstream effects. Emergent from the process of exudation are pivotal bioactive compounds including ATP, D-ribose, pyruvate, glucose, glutamine, and thiamine diphosphate. In conclusion, we hypothesize that future efforts to enhance the growth and productivity of commercially important crop plants under both favorable and unfavorable environmental conditions may focus on manipulating plant rhizobiomes.

Analysis of Antibiotic Resistance Genes in Water Reservoirs and Related Wastewater from Animal Farms in Central China

This study aimed to explore the phenotype and relationship of drug resistance genes in livestock and poultry farm wastewater and drinking water reservoirs to provide evidence for the transmission mechanisms of drug resistance genes, in order to reveal the spread of drug resistance genes in wastewater from intensive farms in Central China to urban reservoirs that serve as drinking water sources and provide preliminary data for the treatment of wastewater from animal farms to reduce the threat to human beings. DNA extraction and metagenomic sequencing were performed on eight groups of samples collected from four water reservoirs and four related wastewaters from animal farms in Central China. Metagenomic sequencing showed that the top 20 AROs with the highest abundance were vanT_gene, vanY_gene, adeF, qacG, Mtub_rpsL_STR, vanY_gene_, vanW_gene, Mtub_murA_FOF, vanY_gene, vanH_gene, FosG, rsmA, qacJ, RbpA, vanW_gene, aadA6, vanY_gene, sul4, sul1, and InuF. The resistance genes mentioned above belong to the following categories of drug resistance mechanisms: antibiotic target replacement, antibiotic target protection, antibiotic inactivation, and antibiotic efflux. The resistomes that match the top 20 genes are Streptococcus agalactiae and Streptococcus anginosus; Enterococcus faecalis; Enterococcus faecium; Actinomyces viscosus and Bacillus cereus. Enterococcus faecium; Clostridium tetani; Streptococcus agalactiae and Streptococcus anginosus; Streptococcus agalactiae and Streptococcus anginosus; Acinetobacter baumannii, Bifidobacterium bifidum, Bifidobacterium breve, Bifidobacterium longum, Corynebacterium jeikeium, Corynebacterium urealyticum, Mycobacterium kansasii, Mycobacterium tuberculosis, Schaalia odontolytica, and Trueperella pyogenes; Mycobacterium avium and Mycobacterium tuberculosis; Aeromonas caviae, Enterobacter hormaechei, Vibrio cholerae, Vibrio metoecus, Vibrio parahaemolyticus, and Vibrio vulnificus; Pseudomonas aeruginosa and Pseudomonas fluorescens; Staphylococcus aureus and Staphylococcus equorum; M. avium, Achromobacter xylosoxidans, and Acinetobacter baumannii; Sphingobium yanoikuyae, Acinetobacter indicus, Morganella morganii, Proteus mirabilis, Proteus vulgaris, Providencia rettgeri, and Providencia stuartii. Unreported drug resistance genes and drug-resistant bacteria in Central China were identified in 2023. In the transmission path of drug resistance genes, the transmission path from aquaculture wastewater to human drinking water sources cannot be ignored. For the sake of human health and ecological balance, the treatment of aquaculture wastewater needs to be further strengthened, and the effective blocking of drug resistance gene transmission needs to be considered.

Integrating shotgun metagenomics and metabolomics to elucidate the dynamics of microbial communities and metabolites in fine flavor cocoa fermentation in Hainan

The aim of this study was to investigate the dynamic profile of microorganisms and metabolites in Hainan Trinitario cocoa during a six-day spontaneous box fermentation process. Shotgun metagenomic and metabolomic approaches were employed for this investigation. The potential metabolic functions of microorganisms in cocoa fermentation were revealed through a joint analysis of microbes, functional genes, and metabolites. During the anaerobic fermentation phase, Hanseniaspora emerged as the most prevalent yeast genus, implicated in pectin decomposition and potentially involved in glycolysis and starch and sucrose metabolism. Tatumella, possessing potential for pyruvate kinase, and Fructobacillus with a preference for fructose, constituted the primary bacteria during the pre-turning fermentation stage. Upon the introduction of oxygen into the fermentation mass, acetic acid bacteria ascended to dominant within the microflora. The exponential proliferation of Acetobacter resulted in a decline in taxonomic richness and abundance. Moreover, the identification of novel species within the Komagataeibacter genus suggests that Hainan cocoa may serve as a valuable reservoir for the discovery of unique cocoa fermentation bacteria. The KEGG annotation of metabolites and enzymes also highlighted the significant involvement of phenylalanine metabolism in cocoa fermentation. This research will offer a new perspective for the selection of starter strains and the formulation of mixed starter cultures.

Statistical normalization methods in microbiome data with application to microbiome cancer research

Mounting evidence has shown that gut microbiome is associated with various cancers, including gastrointestinal (GI) tract and non-GI tract cancers. But microbiome data have unique characteristics and pose major challenges when using standard statistical methods causing results to be invalid or misleading. Thus, to analyze microbiome data, it not only needs appropriate statistical methods, but also requires microbiome data to be normalized prior to statistical analysis. Here, we first describe the unique characteristics of microbiome data and the challenges in analyzing them (Section 2). Then, we provide an overall review on the available normalization methods of 16S rRNA and shotgun metagenomic data along with examples of their applications in microbiome cancer research (Section 3). In Section 4, we comprehensively investigate how the normalization methods of 16S rRNA and shotgun metagenomic data are evaluated. Finally, we summarize and conclude with remarks on statistical normalization methods (Section 5). Altogether, this review aims to provide a broad and comprehensive view and remarks on the promises and challenges of the statistical normalization methods in microbiome data with microbiome cancer research examples.

Abundant resistome determinants in rhizosphere soil of the wild plant Abutilon fruticosum

A metagenomic whole genome shotgun sequencing approach was used for rhizospheric soil micribiome of the wild plant Abutilon fruticosum in order to detect antibiotic resistance genes (ARGs) along with their antibiotic resistance mechanisms and to detect potential risk of these ARGs to human health upon transfer to clinical isolates. The study emphasized the potential risk to human health of such human pathogenic or commensal bacteria, being transferred via food chain or horizontally transferred to human clinical isolates. The top highly abundant rhizospheric soil non-redundant ARGs that are prevalent in bacterial human pathogens or colonizers (commensal) included mtrA, soxR, vanRO, golS, rbpA, kdpE, rpoB2, arr-1, efrA and ileS genes. Human pathogenic/colonizer bacteria existing in this soil rhizosphere included members of genera Mycobacterium, Vibrio, Klebsiella, Stenotrophomonas, Pseudomonas, Nocardia, Salmonella, Escherichia, Citrobacter, Serratia, Shigella, Cronobacter and Bifidobacterium. These bacteria belong to phyla Actinobacteria and Proteobacteria. The most highly abundant resistance mechanisms included antibiotic efflux pump, antibiotic target alteration, antibiotic target protection and antibiotic inactivation. antimicrobial resistance (AMR) families of the resistance mechanism of antibiotic efflux pump included resistance-nodulation-cell division (RND) antibiotic efflux pump (for mtrA, soxR and golS genes), major facilitator superfamily (MFS) antibiotic efflux pump (for soxR gene), the two-component regulatory kdpDE system (for kdpE gene) and ATP-binding cassette (ABC) antibiotic efflux pump (for efrA gene). AMR families of the resistance mechanism of antibiotic target alteration included glycopeptide resistance gene cluster (for vanRO gene), rifamycin-resistant beta-subunit of RNA polymerase (for rpoB2 gene) and antibiotic-resistant isoleucyl-tRNA synthetase (for ileS gene). AMR families of the resistance mechanism of antibiotic target protection included bacterial RNA polymerase-binding protein (for RbpA gene), while those of the resistance mechanism of antibiotic inactivation included rifampin ADP-ribosyltransferase (for arr-1 gene). Better agricultural and food transport practices are required especially for edible plant parts or those used in folkloric medicine.

Is type III prostatitis also associated with bacterial infection?

Objective

To explore whether type III prostatitis is related to bacterial infection by detecting the composition and function of microorganisms in expressed prostatic secretion (EPS) of patients with chronic prostatitis (CP) and healthy people.

Methods

According to the inclusion and exclusion criteria, 57 subjects were included in our study, divided into the healthy group, type II prostatitis group, and type III prostatitis group. 16s rRNA sequencing technique was used to detect and analyze the microbial composition of EPS in each group. Additionally, the metagenomics sequencing technique was used to further explore the function of different bacteria in the type III prostatitis group. Data analysis was performed by bioinformatics software, and the results were statistically significant when P<0.05.

Results

Many microorganisms exist in EPS in both CP patients and healthy populations. However, the relative abundance of Pseudomonas, Haemophilus, Sneathia, Allobaculum, and Enterococcus in CP patients (including type II and III) were significantly different. Still, the relative abundance of different bacteria in type II prostatitis patients was much higher than in type III. The metagenomics sequencing results for the type III prostatitis group showed that the different bacteria had certain biological functions.

Conclusion

Based on our sequencing results and previous studies, we suggest that type III prostatitis may also be caused by bacterial infection.

Functional annotation of rhizospheric phageome of the wild plant species Moringa oleifera

Introduction

The study aims to describe phageome of soil rhizosphere of M.oleifera in terms of the genes encoding CAZymes and other KEGG enzymes.

Methods

Genes of the rhizospheric virome of the wild plant species Moringa oleifera were investigated for their ability to encode useful CAZymes and other KEGG (Kyoto Encyclopedia of Genes and Genomes) enzymes and to resist antibiotic resistance genes (ARGs) in the soil.

Results

Abundance of these genes was higher in the rhizospheric microbiome than in the bulk soil. Detected viral families include the plant viral family Potyviridae as well as the tailed bacteriophages of class Caudoviricetes that are mainly associated with bacterial genera Pseudomonas, Streptomyces and Mycobacterium. Viral CAZymes in this soil mainly belong to glycoside hydrolase (GH) families GH43 and GH23. Some of these CAZymes participate in a KEGG pathway with actions included debranching and degradation of hemicellulose. Other actions include biosynthesizing biopolymer of the bacterial cell wall and the layered cell wall structure of peptidoglycan. Other CAZymes promote plant physiological activities such as cell-cell recognition, embryogenesis and programmed cell death (PCD). Enzymes of other pathways help reduce the level of soil H₂O₂ and participate in the biosynthesis of glycine, malate, isoprenoids, as well as isoprene that protects plant from heat stress. Other enzymes act in promoting both the permeability of bacterial peroxisome membrane and carbon fixation in plants. Some enzymes participate in a balanced supply of dNTPs, successful DNA replication and mismatch repair during bacterial cell division. They also catalyze the release of signal peptides from bacterial membrane prolipoproteins. Phages with the most highly abundant antibiotic resistance genes (ARGs) transduce species of bacterial genera Pseudomonas, Streptomyces, and Mycobacterium. Abundant mechanisms of antibiotic resistance in the rhizosphere include "antibiotic efflux pump" for ARGs soxR, OleC, and MuxB, "antibiotic target alteration" for parY mutant, and "antibiotic inactivation" for arr-1.

Discussion

These ARGs can act synergistically to inhibit several antibiotics including tetracycline, penam, cephalosporin, rifamycins, aminocoumarin, and oleandomycin. The study highlighted the issue of horizontal transfer of ARGs to clinical isolates and human gut microbiome.

Function of CAZymes encoded by highly abundant genes in rhizosphere microbiome of Moringa oleifera

Metagenomic analysis referring to CAZymes (Carbohydrate-Active enZymes) of CAZy classes encoded by the most abundant genes in rhizosphere versus bulk soil microbes of the wild plant Moringa oleifera was conducted. Results indicated that microbiome signatures and corresponding CAZy datasets differ between the two soil types. CAZy class glycoside hydrolases (GH) and its α-amylase family GH13 in rhizobiome were proven to be the most abundant among CAZy classes and families. The most abundant bacteria harboring these CAZymes include phylum Actinobacteria and its genus Streptomyces and phylum Proteobacteria and its genus Microvirga. These CAZymes participate in KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway "Starch and sucrose metabolism" and mainly use the "double displacement catalytic mechanism" in their reactions. We assume that microbiome of the wild plant Moringa oleifera is a good source of industrially important enzymes that act on starch hydrolysis and/or biosynthesis. In addition, metabolic engineering and integration of certain microbes of this microbiomes can also be used in improving growth of domestic plants and their ability to tolerate adverse environmental conditions.

Analysis of Gut Microbial Communities and Resistance Genes in Pigs and Chickens in Central China

BACKGROUND

Basic data concerning the gut microbiota of the main animal husbandry breeds (pigs and chickens) are scarce in China. The dynamics of gut microbiota (pigs and chickens) in China and antibiotic resistance genes carried by microorganisms in the natural environment are unknown.

METHODS

Free range and factory-farmed Gushi chickens and Huainan pigs were divided into eight groups. Faecal samples were collected from each group, and the metagenomic sequencing method was used to detect each group of samples.

RESULTS

The resistance genes showed the following trend, from high to low relative abundance: tetW was the highest, followed by tetW/N/W, then lnuA; and others from high to low were mdtB, lnuC, ANT6-la, ErmB, mdtC, ErmQ, tetBP, vatE, evgS, acrB, cpxA, mefA, Escherichia coli-ampC, tetL, yojl, AcrF and mdtA. All groups administered enrofloxacin and oregano oil did not develop a drug-resistant phenotype during the 5-day treatment period, as grouped in this trial. In 2022, after Announcement No. 194 of the Ministry of Agriculture and Rural Affairs in China, the antimicrobial resistance (AMR) trend declined, but it did not fundamentally change, presumably due to the impact of environmental pollution caused by the long-term use of antimicrobials.

Network analysis reveals microbe-mediated impacts of aeration on deep sediment layer microbial communities

Over-aeration is a common remediation strategy for black and odorous water bodies, in which oxygen is introduced to impact aquatic microbial communities as an electron acceptor of high redox potential. In this study, black-odorous freshwater sediments were cultured for 9 weeks under aeration to investigate microbial covariations at different depths and time points. Based on community 16S rRNA gene sequencing, the microbial covariations were visualized using phylogenetic microbial ecological networks (pMENs). In the spatial scale, we identified smaller and more compact pMENs across all layers compared with the anaerobic control sediments, in terms of network size, average node connectivity, and modularity. The aerated middle layer had the most connectors, the least module hubs, a network hub, shorter average path length, and predominantly positive covariations. In addition, a significant sulfate accumulation in the aerated middle layer indicated the most intense sulfide oxidation, possibly because aeration prompted sediment surface Desulfobulbaceae, known as cable bacteria, to reach the middle layer. In the time scale, similarly, aeration led to smaller pMEN sizes and higher portions of positive covariations. Therefore, we conclude that elevated dissolved oxygen at the water-sediment interface may impact not only the surface sediment but also the subsurface and/or deep sediment microbial communities mediated by microorganisms, particularly by Desulfobulbaceae.

Abundant antibiotic resistance genes in rhizobiome of the human edible Moringa oleifera medicinal plant

Moringa oleifera (or the miracle tree) is a wild plant species widely grown for its seed pods and leaves, and is used in traditional herbal medicine. The metagenomic whole genome shotgun sequencing (mWGS) approach was used to characterize antibiotic resistance genes (ARGs) of the rhizobiomes of this wild plant and surrounding bulk soil microbiomes and to figure out the chance and consequences for highly abundant ARGs, e.g., mtrA, golS, soxR, oleC, novA, kdpE, vanRO, parY, and rbpA, to horizontally transfer to human gut pathogens via mobile genetic elements (MGEs). The results indicated that abundance of these ARGs, except for golS, was higher in rhizosphere of M. oleifera than that in bulk soil microbiome with no signs of emerging new soil ARGs in either soil type. The most highly abundant metabolic processes of the most abundant ARGs were previously detected in members of phyla Actinobacteria, Proteobacteria, Acidobacteria, Chloroflexi, and Firmicutes. These processes refer to three resistance mechanisms namely antibiotic efflux pump, antibiotic target alteration and antibiotic target protection. Antibiotic efflux mechanism included resistance-nodulation-cell division (RND), ATP-binding cassette (ABC), and major facilitator superfamily (MFS) antibiotics pumps as well as the two-component regulatory kdpDE system. Antibiotic target alteration included glycopeptide resistance gene cluster (vanRO), aminocoumarin resistance parY, and aminocoumarin self-resistance parY. While, antibiotic target protection mechanism included RbpA bacterial RNA polymerase (rpoB)-binding protein. The study supports the claim of the possible horizontal transfer of these ARGs to human gut and emergence of new multidrug resistant clinical isolates. Thus, careful agricultural practices are required especially for plants used in circles of human nutrition industry or in traditional medicine.

Use of Metagenomic Whole Genome Shotgun Sequencing Data in Taxonomic Assignment of Dipterygium glaucum Rhizosphere and Surrounding Bulk Soil Microbiomes, and Their Response to Watering

The metagenomic whole genome shotgun sequencing (mWGS) approach was used to detect signatures of the rhizosphere microbiomes of Dipterygium glaucum and surrounding bulk soil microbiomes, and to detect differential microbial responses due to watering. Preliminary results reflect the reliability of the experiment and the rationality of grouping microbiomes. Based on the abundance of non-redundant genes, bacterial genomes showed the highest level, followed by Archaeal and Eukaryotic genomes, then, the least abundant viruses. Overall results indicate that most members of bacteria have a higher abundance/relative abundance (AB/RA) pattern in the rhizosphere towards plant growth promotion, while members of eukaryota have a higher pattern in bulk soil, most likely acting as pathogens. The results also indicate the contribution of mycorrhiza (genus Rhizophagus) in mediating complex mutualistic associations between soil microbes (either beneficial or harmful) and plant roots. Some of these symbiotic relationships involve microbes of different domains responding differentially to plant root exudates. Among these are included the bacterial genus Burkholderia and eukaryotic genus Trichoderma, which have antagonistic activities against the eukaryotic genus Fusarium. Another example involves Ochrobactrum phage POA1180, its bacterial host and plant roots. One of the major challenges in plant nutrition involves other microbes that manipulate nitrogen levels in the soil. Among these are the microbes that perform contraversal actions of nitrogen fixation (the methanogen Euryarchaeota) and ammonia oxidation (Crenarchaeota). The net nitrogen level in the soil is originally based on the AB/RA of these microbes and partially on the environmental condition. Watering seems to influence the AB/RA of a large number of soil microbes, where drought-sensitive microbes (members of phyla Acidobacteria and Gemmatimonadetes) showed an increased AB/RA pattern after watering, while others (Burkholderia and Trichoderma) seem to be among microbes assisting plants to withstand abiotic stresses. This study sheds light on the efficient use of mWGS in the taxonomic assignment of soil microbes and in their response to watering. It also provides new avenues for improving biotic and abiotic resistance in domestic plant germplasm via the manipulation of soil microbes.

Reporting guidelines for human microbiome research: the STORMS checklist

The particularly interdisciplinary nature of human microbiome research makes the organization and reporting of results spanning epidemiology, biology, bioinformatics, translational medicine and statistics a challenge. Commonly used reporting guidelines for observational or genetic epidemiology studies lack key features specific to microbiome studies. Therefore, a multidisciplinary group of microbiome epidemiology researchers adapted guidelines for observational and genetic studies to culture-independent human microbiome studies, and also developed new reporting elements for laboratory, bioinformatics and statistical analyses tailored to microbiome studies. The resulting tool, called 'Strengthening The Organization and Reporting of Microbiome Studies' (STORMS), is composed of a 17-item checklist organized into six sections that correspond to the typical sections of a scientific publication, presented as an editable table for inclusion in supplementary materials. The STORMS checklist provides guidance for concise and complete reporting of microbiome studies that will facilitate manuscript preparation, peer review, and reader comprehension of publications and comparative analysis of published results.

Trends in Free-access Genomic Data Accelerate Advances in Cyanobacteria Taxonomy

Free access databases of DNA sequences containing microbial genetic information have changed the way scientists look at the microbial world. Currently, the NCBI database includes about 516 distinct search results for Cyanobacterial genomes distributed in a taxonomy based on a polyphasic approach. While their classification and taxonomic relationships are widely used as is, recent proposals to alter their grouping include further exploring the relationship between Cyanobacteria and Melainabacteria. Nowadays, most cyanobacteria still are named under the Botanical Code; however, there is a proposal made by the Genome Taxonomy Database (GTDB) to harmonize cyanobacteria nomenclature with the other bacteria, an initiative to standardize microbial taxonomy based on genome phylogeny, in order to contribute to an overall better phylogenetic resolution of microbiota. Furthermore, the assembly level of the genomes and their geographical origin demonstrates some trends of cyanobacteria genomics on the scientific community, such as low availability of complete genomes and underexplored sampling locations. By describing how available cyanobacterial genomes from free-access databases fit within different taxonomic classifications, this mini-review provides a holistic view of the current knowledge of cyanobacteria and indicates some steps towards improving our efforts to create a more cohesive and inclusive classifying system, which can be greatly improved by using large-scale sequencing and metagenomic techniques.

MetagenoNets: comprehensive inference and meta-insights for microbial correlation networks

Microbial association networks are frequently used for understanding and comparing community dynamics from microbiome datasets. Inferring microbial correlations for such networks and obtaining meaningful biological insights, however, requires a lengthy data management workflow, choice of appropriate methods, statistical computations, followed by a different pipeline for suitably visualizing, reporting and comparing the associations. The complexity is further increased with the added dimension of multi-group 'meta-data' and 'inter-omic' functional profiles that are often associated with microbiome studies. This not only necessitates the need for categorical networks, but also integrated and bi-partite networks. Multiple options of network inference algorithms further add to the efforts required for performing correlation-based microbiome interaction studies. We present MetagenoNets, a web-based application, which accepts multi-environment microbial abundance as well as functional profiles, intelligently segregates 'continuous and categorical' meta-data and allows inference as well as visualization of categorical, integrated (inter-omic) and bi-partite networks. Modular structure of MetagenoNets ensures logical flow of analysis (inference, integration, exploration and comparison) in an intuitive and interactive personalized dashboard driven framework. Dynamic choice of filtration, normalization, data transformation and correlation algorithms ensures, that end-users get a one-stop solution for microbial network analysis. MetagenoNets is freely available at https://web.rniapps.net/metagenonets.

Nitrilase: a promising biocatalyst in industrial applications for green chemistry

Nitrilases are widely distributed in nature and are able to hydrolyze nitriles into their corresponding carboxylic acids and ammonia. In industry, nitrilases have been used as green biocatalysts for the production of high value-added products. To date, biocatalysts are considered to be important alternatives to chemical catalysts due to increasing environmental problems and resource scarcity. This review provides an overview of recent advances of nitrilases in aspects of distribution, enzyme screening, molecular structure and catalytic mechanism, protein engineering, and their potential applications in industry.

From bag-of-genes to bag-of-genomes: metabolic modelling of communities in the era of metagenome-assembled genomes

Metagenomic sequencing of complete microbial communities has greatly enhanced our understanding of the taxonomic composition of microbiotas. This has led to breakthrough developments in bioinformatic disciplines such as assembly, gene clustering, metagenomic binning of species genomes and the discovery of an incredible, so far undiscovered, taxonomic diversity. However, functional annotations and estimating metabolic processes from single species - or communities - is still challenging. Earlier approaches relied mostly on inferring the presence of key enzymes for metabolic pathways in the whole metagenome, ignoring the genomic context of such enzymes, resulting in the 'bag-of-genes' approach to estimate functional capacities of microbiotas. Here, we review recent developments in metagenomic bioinformatics, with a special focus on emerging technologies to simulate and estimate metabolic information, that can be derived from metagenomic assembled genomes. Genome-scale metabolic models can be used to model the emergent properties of microbial consortia and whole communities, and the progress in this area is reviewed. While this subfield of metagenomics is still in its infancy, it is becoming evident that there is a dire need for further bioinformatic tools to address the complex combinatorial problems in modelling the metabolism of large communities as a 'bag-of-genomes'.

Pre- and post-sequencing recommendations for functional annotation of human fecal metagenomes

Background

Shotgun metagenomes are often assembled prior to annotation of genes which biases the functional capacity of a community towards its most abundant members. For an unbiased assessment of community function, short reads need to be mapped directly to a gene or protein database. The ability to detect genes in short read sequences is dependent on pre- and post-sequencing decisions. The objective of the current study was to determine how library size selection, read length and format, protein database, e-value threshold, and sequencing depth impact gene-centric analysis of human fecal microbiomes when using DIAMOND, an alignment tool that is up to 20,000 times faster than BLASTX.

Results

Using metagenomes simulated from a database of experimentally verified protein sequences, we find that read length, e-value threshold, and the choice of protein database dramatically impact detection of a known target, with best performance achieved with longer reads, stricter e-value thresholds, and a custom database. Using publicly available metagenomes, we evaluated library size selection, paired end read strategy, and sequencing depth. Longer read lengths were acheivable by merging paired ends when the sequencing library was size-selected to enable overlaps. When paired ends could not be merged, a congruent strategy in which both ends are independently mapped was acceptable. Sequencing depths of 5 million merged reads minimized the error of abundance estimates of specific target genes, including an antimicrobial resistance gene.

Conclusions

Shotgun metagenomes of DNA extracted from human fecal samples sequenced using the Illumina platform should be size-selected to enable merging of paired end reads and should be sequenced in the PE150 format with a minimum sequencing depth of 5 million merge-able reads to enable detection of specific target genes. Expecting the merged reads to be 180-250 bp in length, the appropriate e-value threshold for DIAMOND would then need to be more strict than the default. Accurate and interpretable results for specific hypotheses will be best obtained using small databases customized for the research question.

The Planktonic Core Microbiome and Core Functions in the Cattle Rumen by Next Generation Sequencing

The cow rumen harbors a great variety of diverse microbes, which form a complex, organized community. Understanding the behavior of this multifarious network is crucial in improving ruminant nutrient use efficiency. The aim of this study was to expand our knowledge by examining 10 Holstein dairy cow rumen fluid fraction whole metagenome and transcriptome datasets. DNA and mRNA sequence data, generated by Ion Torrent, was subjected to quality control and filtering before analysis for core elements. The taxonomic core microbiome consisted of 48 genera belonging to Bacteria (47) and Archaea (1). The genus Prevotella predominated the planktonic core community. Core functional groups were identified using co-occurrence analysis and resulted in 587 genes, from which 62 could be assigned to metabolic functions. Although this was a minimal functional core, it revealed key enzymes participating in various metabolic processes. A diverse and rich collection of enzymes involved in carbohydrate metabolism and other functions were identified. Transcripts coding for enzymes active in methanogenesis made up 1% of the core functions. The genera associated with the core enzyme functions were also identified. Linking genera to functions showed that the main metabolic pathways are primarily provided by Bacteria and several genera may serve as a “back-up” team for the central functions. The key actors in most essential metabolic routes belong to the genus Prevotella. Confirming earlier studies, the genus Methanobrevibacter carries out the overwhelming majority of rumen methanogenesis and therefore methane emission mitigation seems conceivable via targeting the hydrogenotrophic methanogenesis.

A multi-source domain annotation pipeline for quantitative metagenomic and metatranscriptomic functional profiling

BACKGROUND

Biochemical and regulatory pathways have until recently been thought and modelled within one cell type, one organism and one species. This vision is being dramatically changed by the advent of whole microbiome sequencing studies, revealing the role of symbiotic microbial populations in fundamental biochemical functions. The new landscape we face requires the reconstruction of biochemical and regulatory pathways at the community level in a given environment. In order to understand how environmental factors affect the genetic material and the dynamics of the expression from one environment to another, we want to evaluate the quantity of gene protein sequences or transcripts associated to a given pathway by precisely estimating the abundance of protein domains, their weak presence or absence in environmental samples.

RESULTS

MetaCLADE is a novel profile-based domain annotation pipeline based on a multi-source domain annotation strategy. It applies directly to reads and improves identification of the catalog of functions in microbiomes. MetaCLADE is applied to simulated data and to more than ten metagenomic and metatranscriptomic datasets from different environments where it outperforms InterProScan in the number of annotated domains. It is compared to the state-of-the-art non-profile-based and profile-based methods, UProC and HMM-GRASPx, showing complementary predictions to UProC. A combination of MetaCLADE and UProC improves even further the functional annotation of environmental samples.

CONCLUSIONS

Learning about the functional activity of environmental microbial communities is a crucial step to understand microbial interactions and large-scale environmental impact. MetaCLADE has been explicitly designed for metagenomic and metatranscriptomic data and allows for the discovery of patterns in divergent sequences, thanks to its multi-source strategy. MetaCLADE highly improves current domain annotation methods and reaches a fine degree of accuracy in annotation of very different environments such as soil and marine ecosystems, ancient metagenomes and human tissues.

Discovering viral genomes in human metagenomic data by predicting unknown protein families

Massive amounts of metagenomics data are currently being produced, and in all such projects a sizeable fraction of the resulting data shows no or little homology to known sequences. It is likely that this fraction contains novel viruses, but identification is challenging since they frequently lack homology to known viruses. To overcome this problem, we developed a strategy to detect ORFan protein families in shotgun metagenomics data, using similarity-based clustering and a set of filters to extract bona fide protein families. We applied this method to 17 virus-enriched libraries originating from human nasopharyngeal aspirates, serum, feces, and cerebrospinal fluid samples. This resulted in 32 predicted putative novel gene families. Some families showed detectable homology to sequences in metagenomics datasets and protein databases after reannotation. Notably, one predicted family matches an ORF from the highly variable Torque Teno virus (TTV). Furthermore, follow-up from a predicted ORFan resulted in the complete reconstruction of a novel circular genome. Its organisation suggests that it most likely corresponds to a novel bacteriophage in the microviridae family, hence it was named bacteriophage HFM.

Assessing Species Diversity Using Metavirome Data: Methods and Challenges

Assessing biodiversity is an important step in the study of microbial ecology associated with a given environment. Multiple indices have been used to quantify species diversity, which is a key biodiversity measure. Measuring species diversity of viruses in different environments remains a challenge relative to measuring the diversity of other microbial communities. Metagenomics has played an important role in elucidating viral diversity by conducting metavirome studies; however, metavirome data are of high complexity requiring robust data preprocessing and analysis methods. In this review, existing bioinformatics methods for measuring species diversity using metavirome data are categorised broadly as either sequence similarity-dependent methods or sequence similarity-independent methods. The former includes a comparison of DNA fragments or assemblies generated in the experiment against reference databases for quantifying species diversity, whereas estimates from the latter are independent of the knowledge of existing sequence data. Current methods and tools are discussed in detail, including their applications and limitations. Drawbacks of the state-of-the-art method are demonstrated through results from a simulation. In addition, alternative approaches are proposed to overcome the challenges in estimating species diversity measures using metavirome data.

Practical considerations for large-scale gut microbiome studies

First insights on the human gut microbiome have been gained from medium-sized, cross-sectional studies. However, given the modest portion of explained variance of currently identified covariates and the small effect size of gut microbiota modulation strategies, upscaling seems essential for further discovery and characterisation of the multiple influencing factors and their relative contribution. In order to guide future research projects and standardisation efforts, we here review currently applied collection and preservation methods for gut microbiome research. We discuss aspects such as sample quality, applicable omics techniques, user experience and time and cost efficiency. In addition, we evaluate the protocols of a large-scale microbiome cohort initiative, the Flemish Gut Flora Project, to give an idea of perspectives, and pitfalls of large-scale faecal sampling studies. Although cryopreservation can be regarded as the gold standard, freezing protocols generally require more resources due to cold chain management. However, here we show that much can be gained from an optimised transport chain and sample aliquoting before freezing. Other protocols can be useful as long as they preserve the microbial signature of a sample such that relevant conclusions can be drawn regarding the research question, and the obtained data are stable and reproducible over time.

Relating genomic characteristics to environmental preferences and ubiquity in different microbial taxa

Background

Despite the important role that microorganisms play in environmental processes, the low percentage of cultured microbes (5%) has limited, until now, our knowledge of their ecological strategies. However, the development of high-throughput sequencing has generated a huge amount of genomic and metagenomic data without the need of culturing that can be used to study ecological questions. This study aims to estimate the functional capabilities, genomic sizes and 16S copy number of different taxa in relation to their ubiquity and their environmental preferences.

Results

To achieve this goal, we compiled data regarding the presence of each prokaryotic genera in diverse environments. Then, genomic characteristics such as genome size, 16S rRNA gene copy number, and functional content of the genomes were related to their ubiquity and different environmental preferences of the corresponding taxa. The results showed clear correlations between genomic characteristics and environmental conditions.

Conclusions

Ubiquity and adaptation were linked to genome size, while 16S copy number was not directly related to ubiquity. We observed that different combinations of these two characteristics delineate the different environments. Besides, the analysis of functional classes showed some clear signatures linked to particular environments.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-017-3888-y) contains supplementary material, which is available to authorized users.

Comparative Metagenomic Analysis Reveals Mechanisms for Stress Response in Hypoliths from Extreme Hyperarid Deserts

Understanding microbial adaptation to environmental stressors is crucial for interpreting broader ecological patterns. In the most extreme hot and cold deserts, cryptic niche communities are thought to play key roles in ecosystem processes and represent excellent model systems for investigating microbial responses to environmental stressors. However, relatively little is known about the genetic diversity underlying such functional processes in climatically extreme desert systems. This study presents the first comparative metagenome analysis of cyanobacteria-dominated hypolithic communities in hot (Namib Desert, Namibia) and cold (Miers Valley, Antarctica) hyperarid deserts. The most abundant phyla in both hypolith metagenomes were Actinobacteria, Proteobacteria, Cyanobacteria and Bacteroidetes with Cyanobacteria dominating in Antarctic hypoliths. However, no significant differences between the two metagenomes were identified. The Antarctic hypolithic metagenome displayed a high number of sequences assigned to sigma factors, replication, recombination and repair, translation, ribosomal structure, and biogenesis. In contrast, the Namib Desert metagenome showed a high abundance of sequences assigned to carbohydrate transport and metabolism. Metagenome data analysis also revealed significant divergence in the genetic determinants of amino acid and nucleotide metabolism between these two metagenomes and those of soil from other polar deserts, hot deserts, and non-desert soils. Our results suggest extensive niche differentiation in hypolithic microbial communities from these two extreme environments and a high genetic capacity for survival under environmental extremes.

Deciphering ocean carbon in a changing world

Dissolved organic matter (DOM) in the oceans is one of the largest pools of reduced carbon on Earth, comparable in size to the atmospheric CO2 reservoir. A vast number of compounds are present in DOM, and they play important roles in all major element cycles, contribute to the storage of atmospheric CO2 in the ocean, support marine ecosystems, and facilitate interactions between organisms. At the heart of the DOM cycle lie molecular-level relationships between the individual compounds in DOM and the members of the ocean microbiome that produce and consume them. In the past, these connections have eluded clear definition because of the sheer numerical complexity of both DOM molecules and microorganisms. Emerging tools in analytical chemistry, microbiology, and informatics are breaking down the barriers to a fuller appreciation of these connections. Here we highlight questions being addressed using recent methodological and technological developments in those fields and consider how these advances are transforming our understanding of some of the most important reactions of the marine carbon cycle.

Long-Term Oil Contamination Alters the Molecular Ecological Networks of Soil Microbial Functional Genes

With knowledge on microbial composition and diversity, investigation of within-community interactions is a further step to elucidate microbial ecological functions, such as the biodegradation of hazardous contaminants. In this work, microbial functional molecular ecological networks were studied in both contaminated and uncontaminated soils to determine the possible influences of oil contamination on microbial interactions and potential functions. Soil samples were obtained from an oil-exploring site located in South China, and the microbial functional genes were analyzed with GeoChip, a high-throughput functional microarray. By building random networks based on null model, we demonstrated that overall network structures and properties were significantly different between contaminated and uncontaminated soils (P < 0.001). Network connectivity, module numbers, and modularity were all reduced with contamination. Moreover, the topological roles of the genes (module hub and connectors) were altered with oil contamination. Subnetworks of genes involved in alkane and polycyclic aromatic hydrocarbon degradation were also constructed. Negative co-occurrence patterns prevailed among functional genes, thereby indicating probable competition relationships. The potential "keystone" genes, defined as either "hubs" or genes with highest connectivities in the network, were further identified. The network constructed in this study predicted the potential effects of anthropogenic contamination on microbial community co-occurrence interactions.

Advances in genomics, transcriptomics and proteomics of toxin-producing cyanobacteria

A common misconception persists that the genomes of toxic and non-toxic cyanobacterial strains are largely conserved with the exception of the presence or absence of the genes responsible for toxin production. Implementation of -omics era technologies has challenged this paradigm, with comparative analyses providing increased insight into the differences between strains of the same species. The implementation of genomic, transcriptomic and proteomic approaches has revealed distinct profiles between toxin-producing and non-toxic strains. Further, metagenomics and metaproteomics highlight the genomic potential and functional state of toxic bloom events over time. In this review, we highlight how these technologies have shaped our understanding of the complex relationship between these molecules, their producers and the environment at large within which they persist.

Elevated nutrients change bacterial community composition and connectivity: high throughput sequencing of young marine biofilms

Biofilms are integral to many marine processes but their formation and function may be affected by anthropogenic inputs that alter environmental conditions, including fertilisers that increase nutrients. Density composition and connectivity of biofilms developed in situ (under ambient and elevated nutrients) were compared using 454-pyrosequencing of the 16S gene. Elevated nutrients shifted community composition from bacteria involved in higher processes (eg Pseudoalteromonas spp. invertebrate recruitment) towards more nutrient-tolerant bacterial species (eg Terendinibacter sp.). This may enable the persistence of biofilm communities by increasing resistance to nutrient inputs. A core biofilm microbiome was identified (predominantly Alteromonadales and Oceanospirillales) and revealed shifts in abundances of core microbes that could indicate enrichment by fertilisers. Fertiliser decreased density and connectivity within biofilms indicating that associations were disrupted perhaps via changes to energetic allocations within the core microbiome. Density composition and connectivity changes suggest nutrients can affect the stability and function of these important marine communities.

Effect of the Organic Loading Rate Increase and the Presence of Zeolite on Microbial Community Composition and Process Stability During Anaerobic Digestion of Chicken Wastes

This study investigates the effect of the organic loading rate (OLR) increase from 1.0 to 3.5 g VS L(-1) day(-1) at constant hydraulic retention time (HRT) of 35 days on anaerobic reactors' performance and microbial diversity during mesophilic anaerobic digestion of ammonium-rich chicken wastes in the absence/presence of zeolite. The effects of anaerobic process parameters on microbial community structure and dynamics were evaluated using a 16S ribosomal RNA gene-based pyrosequencing approach. Maximum 12 % of the total ammonia nitrogen (TAN) was efficiently removed by zeolite in the fixed zeolite reactor (day 87). In addition, volatile fatty acids (VFA) in the fixed zeolite reactor accumulated in lower concentrations at high OLR of 3.2-3.5 g VS L(-1) day(-1). Microbial communities in the fixed zeolite reactor and reactor without zeolite were dominated by various members of Bacteroidales and Methanobacterium sp. at moderate TAN and VFA levels. The increase of the OLR accompanied by TAN and VFA accumulation and increase in pH led to the predominance of representatives of the family Erysipelotrichaceae and genera Clostridium and Methanosarcina. Methanosarcina sp. reached relative abundances of 94 and 57 % in the fixed zeolite reactor and reactor without zeolite at the end of the experimental period, respectively. In addition, the diminution of Synergistaceae and Crenarchaeota and increase in the abundance of Acholeplasmataceae in parallel with the increase of TAN, VFA, and pH values were observed.

Multi-Layer and Recursive Neural Networks for Metagenomic Classification

Recent advances in machine learning, specifically in deep learning with neural networks, has made a profound impact on fields such as natural language processing, image classification, and language modeling; however, feasibility and potential benefits of the approaches to metagenomic data analysis has been largely under-explored. Deep learning exploits many layers of learning nonlinear feature representations, typically in an unsupervised fashion, and recent results have shown outstanding generalization performance on previously unseen data. Furthermore, some deep learning methods can also represent the structure in a data set. Consequently, deep learning and neural networks may prove to be an appropriate approach for metagenomic data. To determine whether such approaches are indeed appropriate for metagenomics, we experiment with two deep learning methods: i) a deep belief network, and ii) a recursive neural network, the latter of which provides a tree representing the structure of the data. We compare these approaches to the standard multi-layer perceptron, which has been well-established in the machine learning community as a powerful prediction algorithm, though its presence is largely missing in metagenomics literature. We find that traditional neural networks can be quite powerful classifiers on metagenomic data compared to baseline methods, such as random forests. On the other hand, while the deep learning approaches did not result in improvements to the classification accuracy, they do provide the ability to learn hierarchical representations of a data set that standard classification methods do not allow. Our goal in this effort is not to determine the best algorithm in terms accuracy-as that depends on the specific application-but rather to highlight the benefits and drawbacks of each of the approach we discuss and provide insight on how they can be improved for predictive metagenomic analysis.

Screening currency notes for microbial pathogens and antibiotic resistance genes using a shotgun metagenomic approach

Fomites are a well-known source of microbial infections and previous studies have provided insights into the sojourning microbiome of fomites from various sources. Paper currency notes are one of the most commonly exchanged objects and its potential to transmit pathogenic organisms has been well recognized. Approaches to identify the microbiome associated with paper currency notes have been largely limited to culture dependent approaches. Subsequent studies portrayed the use of 16S ribosomal RNA based approaches which provided insights into the taxonomical distribution of the microbiome. However, recent techniques including shotgun sequencing provides resolution at gene level and enable estimation of their copy numbers in the metagenome. We investigated the microbiome of Indian paper currency notes using a shotgun metagenome sequencing approach. Metagenomic DNA isolated from samples of frequently circulated denominations of Indian currency notes were sequenced using Illumina Hiseq sequencer. Analysis of the data revealed presence of species belonging to both eukaryotic and prokaryotic genera. The taxonomic distribution at kingdom level revealed contigs mapping to eukaryota (70%), bacteria (9%), viruses and archae (~1%). We identified 78 pathogens including Staphylococcus aureus, Corynebacterium glutamicum, Enterococcus faecalis, and 75 cellulose degrading organisms including Acidothermus cellulolyticus, Cellulomonas flavigena and Ruminococcus albus. Additionally, 78 antibiotic resistance genes were identified and 18 of these were found in all the samples. Furthermore, six out of 78 pathogens harbored at least one of the 18 common antibiotic resistance genes. To the best of our knowledge, this is the first report of shotgun metagenome sequence dataset of paper currency notes, which can be useful for future applications including as bio-surveillance of exchangeable fomites for infectious agents.

Insights from 20 years of bacterial genome sequencing

Since the first two complete bacterial genome sequences were published in 1995, the science of bacteria has dramatically changed. Using third-generation DNA sequencing, it is possible to completely sequence a bacterial genome in a few hours and identify some types of methylation sites along the genome as well. Sequencing of bacterial genome sequences is now a standard procedure, and the information from tens of thousands of bacterial genomes has had a major impact on our views of the bacterial world. In this review, we explore a series of questions to highlight some insights that comparative genomics has produced. To date, there are genome sequences available from 50 different bacterial phyla and 11 different archaeal phyla. However, the distribution is quite skewed towards a few phyla that contain model organisms. But the breadth is continuing to improve, with projects dedicated to filling in less characterized taxonomic groups. The clustered regularly interspaced short palindromic repeats (CRISPR)-Cas system provides bacteria with immunity against viruses, which outnumber bacteria by tenfold. How fast can we go? Second-generation sequencing has produced a large number of draft genomes (close to 90 % of bacterial genomes in GenBank are currently not complete); third-generation sequencing can potentially produce a finished genome in a few hours, and at the same time provide methlylation sites along the entire chromosome. The diversity of bacterial communities is extensive as is evident from the genome sequences available from 50 different bacterial phyla and 11 different archaeal phyla. Genome sequencing can help in classifying an organism, and in the case where multiple genomes of the same species are available, it is possible to calculate the pan- and core genomes; comparison of more than 2000 Escherichia coli genomes finds an E. coli core genome of about 3100 gene families and a total of about 89,000 different gene families. Why do we care about bacterial genome sequencing? There are many practical applications, such as genome-scale metabolic modeling, biosurveillance, bioforensics, and infectious disease epidemiology. In the near future, high-throughput sequencing of patient metagenomic samples could revolutionize medicine in terms of speed and accuracy of finding pathogens and knowing how to treat them.

Insights into novel antimicrobial compounds and antibiotic resistance genes from soil metagenomes

In recent years a major worldwide problem has arisen with regard to infectious diseases caused by resistant bacteria. Resistant pathogens are related to high mortality and also to enormous healthcare costs. In this field, cultured microorganisms have been commonly focused in attempts to isolate antibiotic resistance genes or to identify antimicrobial compounds. Although this strategy has been successful in many cases, most of the microbial diversity and related antimicrobial molecules have been completely lost. As an alternative, metagenomics has been used as a reliable approach to reveal the prospective reservoir of antimicrobial compounds and antibiotic resistance genes in the uncultured microbial community that inhabits a number of environments. In this context, this review will focus on resistance genes as well as on novel antibiotics revealed by a metagenomics approach from the soil environment. Biotechnology prospects are also discussed, opening new frontiers for antibiotic development.

Microbial communities evolve faster in extreme environments

Evolutionary analysis of microbes at the community level represents a new research avenue linking ecological patterns to evolutionary processes, but remains insufficiently studied. Here we report a relative evolutionary rates (rERs) analysis of microbial communities from six diverse natural environments based on 40 metagenomic samples. We show that the rERs of microbial communities are mainly shaped by environmental conditions, and the microbes inhabiting extreme habitats (acid mine drainage, saline lake and hot spring) evolve faster than those populating benign environments (surface ocean, fresh water and soil). These findings were supported by the observation of more relaxed purifying selection and potentially frequent horizontal gene transfers in communities from extreme habitats. The mechanism of high rERs was proposed as high mutation rates imposed by stressful conditions during the evolutionary processes. This study brings us one stage closer to an understanding of the evolutionary mechanisms underlying the adaptation of microbes to extreme environments.

Metagenomic technology and genome mining: emerging areas for exploring novel nitrilases

Nitrilase is one of the most important members in the nitrilase superfamily and it is widely used for bioproduction of commodity chemicals and pharmaceutical intermediates as well as bioremediation of nitrile-contaminated wastes. However, its application was hindered by several limitations. Searching for new nitrilases and improving their application performances are the driving force for researchers. Genetic data resources in various databases are quite rich in post-genomic era. Besides, more than 99 % of microbes in the environment are unculturable. Metagenomic technology and genome mining are thus becoming burgeoning areas and provide unprecedented opportunities for searching more useful novel nitrilases due to the abundance of already existing but unexplored gene resources, namely uncharacterized genome information in the database and unculturable microbes in the natural environment. These techniques seem to be innovative and highly efficient. This study reviews the current status and future directions of metagenomics and genome mining in nitrilase exploration. Moreover, it discussed their utilization in coping with the challenges for nitrilase application. In the next several years, with the rapid development of nitrile biocatalysis, these two techniques would be bound to attract increasing attentions and even become a dominant trend for finding more novel nitrilases. Also, this review would provide guidance for exploitation of other commercially important enzymes.

Isolation an Aldehyde Dehydrogenase Gene from Metagenomics Based on Semi-nest Touch-Down PCR

Culture-independent approaches to analyze metagenome are practical choices for rapid exploring useful genes. The mg-MSDH gene, acquired from the hot spring metagenomic, was retrieved full lengths of functional gene using semi-nest touch-down PCR. Two pairs of degenerate primers were used to separate seven conserve partial sequences by semi-nest touch-down PCR. One of them showed similarity with aldehyde dehydrogenase was used as a target fragment for isolating full-length sequence. The full-length mg-MSDH sequence contained a 1,473 bp coding sequence encoding a 490-amino-acid polypeptide and assigned an accession number JQ715422 in Genbank. The upstream sequences TAGGAG of the start codon (GTG), suggested that was a ribosome binding site. The coding sequence of mg-MSDH was ligated to pET-303 vector and the reconstructive plasmid was successfully overexpressed in E. coli. The purified recombinant mg-MSDH enzyme showed propionaldehyde oxidative activity of 3.0 U mg(-1) at 37 °C.

Bioinformatic approaches for functional annotation and pathway inference in metagenomics data

Metagenomic approaches are increasingly recognized as a baseline for understanding the ecology and evolution of microbial ecosystems. The development of methods for pathway inference from metagenomics data is of paramount importance to link a phenotype to a cascade of events stemming from a series of connected sets of genes or proteins. Biochemical and regulatory pathways have until recently been thought and modelled within one cell type, one organism, one species. This vision is being dramatically changed by the advent of whole microbiome sequencing studies, revealing the role of symbiotic microbial populations in fundamental biochemical functions. The new landscape we face requires a clear picture of the potentialities of existing tools and development of new tools to characterize, reconstruct and model biochemical and regulatory pathways as the result of integration of function in complex symbiotic interactions of ontologically and evolutionary distinct cell types.

Virtual metagenome reconstruction from 16S rRNA gene sequences

Microbial ecologists have investigated roles of species richness and diversity in a wide variety of ecosystems. Recently, metagenomics have been developed to measure functions in ecosystems, but this approach is cost-intensive. Here we describe a novel method for the rapid and efficient reconstruction of a virtual metagenome in environmental microbial communities without using large-scale genomic sequencing. We demonstrate this approach using 16S rRNA gene sequences obtained from denaturing gradient gel electrophoresis analysis, mapped to fully sequenced genomes, to reconstruct virtual metagenome-like organizations. Furthermore, we validate a virtual metagenome using a published metagenome for cocoa bean fermentation samples, and show that metagenomes reconstructed from biofilm formation samples allow for the study of the gene pool dynamics that are necessary for biofilm growth.

Genomic approaches to studying the human microbiota

The human body is colonized by a vast array of microbes, which form communities of bacteria, viruses and microbial eukaryotes that are specific to each anatomical environment. Every community must be studied as a whole because many organisms have never been cultured independently, and this poses formidable challenges. The advent of next-generation DNA sequencing has allowed more sophisticated analysis and sampling of these complex systems by culture-independent methods. These methods are revealing differences in community structure between anatomical sites, between individuals, and between healthy and diseased states, and are transforming our view of human biology.

Estimation of metagenome size and structure in an experimental soil microbiota from low coverage next-generation sequence data

AIMS

A major challenge in metagenome studies is to estimate the true size of all combined genomes. Here, we present a novel approach to estimate the size of all combined genomes for low coverage next-generation sequencing (NGS) data through empirically determined copy numbers of random DNA fragments.

METHODS AND RESULTS

Size estimates were made based on analyses of two experimental soil micro-ecosystems - simulating soil with and without earthworms. Our analyses showed combined genome sizes of about log 11 nucleotides for each of the soil micro-ecosystems, as estimated from qPCR determined copy numbers of random DNA fragments. This corresponds to more than 20000 unique bacterial genomes in each sample. There seemed, however, to be a bacterial subpopulation in the earthworm soil, not being present in the nonearthworm soil. To describe the structure of the metagenomes, both total DNA and amplified 16S rRNA gene sequence libraries were generated with 454-sequencing. Bioinformatic analysis of 454 sequence libraries showed a large functional but low taxonomic overlap between the samples with and without earthworms. A neutrality test indicated that rare species have a competitive advantage over abundant species in both micro-ecosystems providing a potential explanation for the large metagenome sizes.

CONCLUSIONS

We have shown that the soil metagenome is very large and that the large size is probably a consequence of top-down selection of the dominant bacterial species.

SIGNIFICANCE AND IMPACT OF THE STUDY

Estimates of metagenome size from low coverage NGS data will be important for guiding future NGS set-ups.

Functional assignment of metagenomic data: challenges and applications

Metagenomic sequencing provides a unique opportunity to explore earth's limitless environments harboring scores of yet unknown and mostly unculturable microbes and other organisms. Functional analysis of the metagenomic data plays a central role in projects aiming to explore the most essential questions in microbiology, namely 'In a given environment, among the microbes present, what are they doing, and how are they doing it?' Toward this goal, several large-scale metagenomic projects have recently been conducted or are currently underway. Functional analysis of metagenomic data mainly suffers from the vast amount of data generated in these projects. The shear amount of data requires much computational time and storage space. These problems are compounded by other factors potentially affecting the functional analysis, including, sample preparation, sequencing method and average genome size of the metagenomic samples. In addition, the read-lengths generated during sequencing influence sequence assembly, gene prediction and subsequently the functional analysis. The level of confidence for functional predictions increases with increasing read-length. Usually, the most reliable functional annotations for metagenomic sequences are achieved using homology-based approaches against publicly available reference sequence databases. Here, we present an overview of the current state of functional analysis of metagenomic sequence data, bottlenecks frequently encountered and possible solutions in light of currently available resources and tools. Finally, we provide some examples of applications from recent metagenomic studies which have been successfully conducted in spite of the known difficulties.

Computational tools for viral metagenomics and their application in clinical research

There are 100 times more virions than eukaryotic cells in a healthy human body. The characterization of human-associated viral communities in a non-pathological state and the detection of viral pathogens in cases of infection are essential for medical care and epidemic surveillance. Viral metagenomics, the sequenced-based analysis of the complete collection of viral genomes directly isolated from an organism or an ecosystem, bypasses the “single-organism-level” point of view of clinical diagnostics and thus the need to isolate and culture the targeted organism. The first part of this review is dedicated to a presentation of past research in viral metagenomics with an emphasis on human-associated viral communities (eukaryotic viruses and bacteriophages). In the second part, we review more precisely the computational challenges posed by the analysis of viral metagenomes, and we illustrate the problem of sequences that do not have homologs in public databases and the possible approaches to characterize them.

Microbial metabolomics

Microbial metabolomics constitutes an integrated component of systems biology. By studying the complete set of metabolites within a microorganism and monitoring the global outcome of interactions between its development processes and the environment, metabolomics can potentially provide a more accurate snap shot of the actual physiological state of the cell. Recent advancement of technologies and post-genomic developments enable the study and analysis of metabolome. This unique contribution resulted in many scientific disciplines incorporating metabolomics as one of their “omics” platforms. This review focuses on metabolomics in microorganisms and utilizes selected topics to illustrate its impact on the understanding of systems microbiology.