Bioinformatic approaches for functional annotation and pathway inference in metagenomics data

Clostridium butyricum and carbohydrate active enzymes contribute to the reduced fat deposition in pigs

Pig gastrointestinal tracts harbor a heterogeneous and dynamic ecosystem populated with trillions of microbes, enhancing the ability of the host to harvest energy from dietary carbohydrates and contributing to host adipogenesis and fatness. However, the microbial community structure and related mechanisms responsible for the differences between the fatty phenotypes and the lean phenotypes of the pigs remained to be comprehensively elucidated. Herein, we first found significant differences in microbial composition and potential functional capacity among different gut locations in Jinhua pigs with distinct fatness phenotypes. Second, we identified that Jinhua pigs with lower fatness exhibited higher levels of short-chain fatty acids in the colon, highlighting their enhanced carbohydrate fermentation capacity. Third, we explored the differences in expressed carbohydrate-active enzyme (CAZyme) in pigs, indicating their involvement in modulating fat storage. Notably, Clostridium butyricum might be a representative bacterial species from Jinhua pigs with lower fatness, and a significantly higher percentage of its genome was dedicated to CAZyme glycoside hydrolase family 13 (GH13). Finally, a subsequent mouse intervention study substantiated the beneficial effects of C. butyricum isolated from experimental pigs, suggesting that it may possess characteristics that promote the utilization of carbohydrates and hinder fat accumulation. Remarkably, when Jinhua pigs were administered C. butyricum, similar alterations in the gut microbiome and host fatness traits were observed, further supporting the potential role of C. butyricum in modulating fatness. Taken together, our findings reveal previously overlooked links between C. butyricum and CAZyme function, providing insight into the basic mechanisms that connect gut microbiome functions to host fatness.

The role of microbial ecology in improving the performance of anaerobic digestion of sewage sludge

The use of next-generation diagnostic tools to optimise the anaerobic digestion of municipal sewage sludge has the potential to increase renewable natural gas recovery, improve the reuse of biosolid fertilisers and help operators expand circular economies globally. This review aims to provide perspectives on the role of microbial ecology in improving digester performance in wastewater treatment plants, highlighting that a systems biology approach is fundamental for monitoring mesophilic anaerobic sewage sludge in continuously stirred reactor tanks. We further highlight the potential applications arising from investigations into sludge ecology. The principal limitation for improvements in methane recoveries or in process stability of anaerobic digestion, especially after pre-treatment or during co-digestion, are ecological knowledge gaps related to the front-end metabolism (hydrolysis and fermentation). Operational problems such as stable biological foaming are a key problem, for which ecological markers are a suitable approach. However, no biomarkers exist yet to assist in monitoring and management of clade-specific foaming potentials along with other risks, such as pollutants and pathogens. Fundamental ecological principles apply to anaerobic digestion, which presents opportunities to predict and manipulate reactor functions. The path ahead for mapping ecological markers on process endpoints and risk factors of anaerobic digestion will involve numerical ecology, an expanding field that employs metrics derived from alpha, beta, phylogenetic, taxonomic, and functional diversity, as well as from phenotypes or life strategies derived from genetic potentials. In contrast to addressing operational issues (as noted above), which are effectively addressed by whole population or individual biomarkers, broad improvement and optimisation of function will require enhancement of hydrolysis and acidogenic processes. This will require a discovery-based approach, which will involve integrative research involving the proteome and metabolome. This will utilise, but overcome current limitations of DNA-centric approaches, and likely have broad application outside the specific field of anaerobic digestion.

UPIMAPI, reCOGnizer and KEGGCharter: bioinformatics tools for functional annotation and visualization of (meta)-omics datasets

Omics and meta-omics technologies are powerful approaches to explore microorganisms’ functions, but the sheer size and complexity of omics datasets often turn the analysis into a challenging task. Software developed for omics and meta-omics analyses, together with knowledgebases encompassing information on genes, proteins, taxonomic and functional annotation, among other types of information, are valuable resources for analyzing omics data. Although several bioinformatics resources are available for meta-omics analyses, many require significant computational expertise. Web interfaces are more user-friendly, but often struggle to handle large data files, such as those obtained in metagenomics, metatranscriptomics, or metaproteomics experiments.

In this work, we present three novel bioinformatics tools, which are available through user-friendly command-line interfaces, can be run sequentially or stand-alone, and combine popular resources for functional annotation. UPIMAPI performs sequence homology-based annotation and obtains data from UniProtKB (e.g., protein names, EC numbers, Gene Ontology, Taxonomy, cross-references to external databases). reCOGnizer performs multithreaded domain homology-based annotation of protein sequences with several functional databases (i.e., CDD, NCBIfam, Pfam, Protein Clusters, SMART, TIGRFAM, COG and KOG) and in addition, obtains information on domain names and descriptions and EC numbers. KEGGCharter represents omics results, including differential gene expression, in KEGG metabolic pathways. In addition, it shows the taxonomic assignment of the enzymes represented, which is particularly useful in metagenomics studies in which several microorganisms are present.

reCOGnizer, UPIMAPI and KEGGCharter together provide a comprehensive and complete functional characterization of large datasets, facilitating the interpretation of microbial activities in nature and in biotechnological processes.

Multiple profile models extract features from protein sequence data and resolve functional diversity of very different protein families

Functional classification of proteins from sequences alone has become a critical bottleneck in understanding the myriad of protein sequences that accumulate in our databases. The great diversity of homologous sequences hides, in many cases, a variety of functional activities that cannot be anticipated. Their identification appears critical for a fundamental understanding of the evolution of living organisms and for biotechnological applications. ProfileView is a sequence-based computational method, designed to functionally classify sets of homologous sequences. It relies on two main ideas: the use of multiple profile models whose construction explores evolutionary information in available databases, and a novel definition of a representation space in which to analyse sequences with multiple profile models combined together. ProfileView classifies protein families by enriching known functional groups with new sequences and discovering new groups and subgroups. We validate ProfileView on seven classes of widespread proteins involved in the interaction with nucleic acids, amino acids and small molecules, and in a large variety of functions and enzymatic reactions. Profile-View agrees with the large set of functional data collected for these proteins from the literature regarding the organisation into functional subgroups and residues that characterise the functions. In addition, ProfileView resolves undefined functional classifications and extracts the molecular determinants underlying protein functional diversity, showing its potential to select sequences towards accurate experimental design and discovery of novel biological functions. On protein families with complex domain architecture, ProfileView functional classification reconciles domain combinations, unlike phylogenetic reconstruction. ProfileView proves to outperform the functional classification approach PANTHER, the two k-mer based methods CUPP and eCAMI and a neural network approach based on Restricted Boltzmann Machines. It overcomes time complexity limitations of the latter.

Small investments with big returns: environmental genomic bioprospecting of microbial life

Microorganisms and their natural products are major drivers of ecological processes and industrial applications. Microbial bioprospecting has been critical for the advancement in various fields such as pharmaceuticals, sustainable industries, food security and bioremediation. Next generation sequencing has been paramount in the exploration of diverse environmental microbiomes. It presents a culture-independent approach to investigating hitherto uncultured taxa, resulting in the creation of massive sequence databases, which are available in the public domain. Genome mining searches available (meta)genomic data for target biosynthetic genes, and combined with the large-scale public data, this in-silico bioprospecting method presents an efficient and extensive way to uncover microbial bioproducts. Bioinformatic tools have progressed to a stage where we can recover genomes from the environment; these metagenome-assembled genomes present a way to understand the metabolic capacity of microorganisms in a physiological and ecological context. Environmental sampling been extensive across various ecological settings, including microbiomes with unique physicochemical properties that could influence the discovery of novel functions and metabolic pathways. Although in-silico methods cannot completely substitute in-vitro studies, the contextual information it provides is invaluable for understanding the ecological and taxonomic distribution of microbial genotypes and to form effective strategies for future microbial bioprospecting efforts.

Functional metagenomic profile of the lacrimal sac microbial communities in primary acquired nasolacrimal duct obstruction: The Lacriome paper 2

PURPOSE

To study the functional metagenomic profile of the microbes isolated from the lacrimal sac of patients with primary acquired nasolacrimal duct obstruction.

METHODS

A prospective study was performed on 10 consecutive lacrimal sac samples obtained for the metagenomic analysis from patients with primary acquired nasolacrimal duct obstruction ( who underwent endoscopic dacryocystorhinostomy at a tertiary care Dacryology service. The samples were collected intraoperatively soon after a full-length lacrimal sac marsupialization and immediately transported on ice to the laboratory. Following DNA extraction and library preparation, a whole shotgun metagenome sequencing was performed on the Illumina NOVASEQ 6000^TM platform. The downstream processing and bioinformatics of the samples were performed using multiple software packaged in SqueezeMeta^TM pipeline and functional analysis using the MG-RAST^TM pipeline.

RESULTS

The microbial gene mapping and protein prediction demonstrated proteins with known functions to range from 66.41% to 84.03% across the samples. The functional category distribution of Kyoto Encyclopedia of Genes and Genomes ortholog (level 1 data) showed metabolism to be the most commonly involved function followed by environmental information processes, genetic information processes and cellular processes. The functional subsystem profiling demonstrated genes associated with carbohydrate, protein and RNA metabolism, Amino acids and their derivatives, cofactors and prosthetic groups and factors involved in cell structure regulation and cell cycle control.

CONCLUSION

This is the first functional metagenomic profile of the lacrimal sac microbiota from patients with primary acquired nasolacrimal duct obstruction. Functional analysis has provided newer insights into the ecosystem dynamics and strategies of microbial communities inhabiting the lacrimal sac. Further Lacriome studies may provide clues for better understanding of the disease etiopathogenesis.

Prevotella copri increases fat accumulation in pigs fed with formula diets

BACKGROUND

Excessive fat accumulation of pigs is undesirable, as it severely affects economic returns in the modern pig industry. Studies in humans and mice have examined the role of the gut microbiome in host energy metabolism. Commercial Duroc pigs are often fed formula diets with high energy and protein contents. Whether and how the gut microbiome under this type of diet regulates swine fat accumulation is largely unknown.

RESULTS

In the present study, we systematically investigated the correlation of gut microbiome with pig lean meat percentage (LMP) in 698 commercial Duroc pigs and found that Prevotella copri was significantly associated with fat accumulation of pigs. Fat pigs had significantly higher abundance of P. copri in the gut. High abundance of P. copri was correlated with increased concentrations of serum metabolites associated with obesity, e.g., lipopolysaccharides, branched chain amino acids, aromatic amino acids, and the metabolites of arachidonic acid. Host intestinal barrier permeability and chronic inflammation response were increased. A gavage experiment using germ-free mice confirmed that the P. copri isolated from experimental pigs was a causal species increasing host fat accumulation and altering serum metabolites. Colon, adipose tissue, and muscle transcriptomes in P. copri-gavaged mice indicated that P. copri colonization activated host chronic inflammatory responses through the TLR4 and mTOR signaling pathways and significantly upregulated the expression of the genes related to lipogenesis and fat accumulation, but attenuated the genes associated with lipolysis, lipid transport, and muscle growth.

CONCLUSIONS

Taken together, the results proposed that P. copri in the gut microbial communities of pigs fed with commercial formula diets activates host chronic inflammatory responses by the metabolites through the TLR4 and mTOR signaling pathways, and increases host fat deposition significantly. The results provide fundamental knowledge for reducing fat accumulation in pigs through regulating the gut microbial composition. Video Abstract.

Composition and Metabolic Functions of the Microbiome in Fermented Grain during Light-Flavor Baijiu Fermentation

The metabolism and accumulation of flavor compounds in Chinese Baijiu are driven by microbiota succession and their inter-related metabolic processes. Changes in the microbiome composition during Baijiu production have been examined previously; however, the respective metabolic functions remain unclear. Using shotgun metagenomic sequencing and metabolomics, we examined the microbial and metabolic characteristics during light-flavor Baijiu fermentation to assess the correlations between microorganisms and their potential functions. During fermentation, the bacterial abundance increased from 58.2% to 97.65%, and fermentation resulted in the accumulation of various metabolites, among which alcohols and esters were the most abundant. Correlation analyses revealed that the levels of major metabolites were positively correlated with bacterial abundance but negatively with that of fungi. Gene annotation showed that the Lactobacillus species contained key enzyme genes for carbohydrate metabolism and contributed to the entire fermentation process. Lichtheimia ramosa, Saccharomycopsis fibuligera, Bacillus licheniformis, Saccharomyces cerevisiae, and Pichia kudriavzevii play major roles in starch degradation and ethanol production. A link was established between the composition and metabolic functions of the microbiota involved in Baijiu fermentation, which helps elucidate microbial and metabolic patterns of fermentation and provides insights into the potential optimization of Baijiu production.

Targeted domain assembly for fast functional profiling of metagenomic datasets with S3A

Motivation

The understanding of the ever-increasing number of metagenomic sequences accumulating in our databases demands for approaches that rapidly ‘explore’ the content of multiple and/or large metagenomic datasets with respect to specific domain targets, avoiding full domain annotation and full assembly.

Results

S3A is a fast and accurate domain-targeted assembler designed for a rapid functional profiling. It is based on a novel construction and a fast traversal of the Overlap-Layout-Consensus graph, designed to reconstruct coding regions from domain annotated metagenomic sequence reads. S3A relies on high-quality domain annotation to efficiently assemble metagenomic sequences and on the design of a new confidence measure for a fast evaluation of overlapping reads. Its implementation is highly generic and can be applied to any arbitrary type of annotation. On simulated data, S3A achieves a level of accuracy similar to that of classical metagenomics assembly tools while permitting to conduct a faster and sensitive profiling on domains of interest. When studying a few dozens of functional domains—a typical scenario—S3A is up to an order of magnitude faster than general purpose metagenomic assemblers, thus enabling the analysis of a larger number of datasets in the same amount of time. S3A opens new avenues to the fast exploration of the rapidly increasing number of metagenomic datasets displaying an ever-increasing size.

Availability and implementation

S3A is available at http://www.lcqb.upmc.fr/S3A_ASSEMBLER/.

Supplementary information

Supplementary data are available at Bioinformatics online.

CSN: unsupervised approach for inferring biological networks based on the genome alone

BACKGROUND

Most organisms cannot be cultivated, as they live in unique ecological conditions that cannot be mimicked in the lab. Understanding the functionality of those organisms' genes and their interactions by performing large-scale measurements of transcription levels, protein-protein interactions or metabolism, is extremely difficult and, in some cases, impossible. Thus, efficient algorithms for deciphering genome functionality based only on the genomic sequences with no other experimental measurements are needed.

RESULTS

In this study, we describe a novel algorithm that infers gene networks that we name Common Substring Network (CSN). The algorithm enables inferring novel regulatory relations among genes based only on the genomic sequence of a given organism and partial homolog/ortholog-based functional annotation. It can specifically infer the functional annotation of genes with unknown homology. This approach is based on the assumption that related genes, not necessarily homologs, tend to share sub-sequences, which may be related to common regulatory mechanisms, similar functionality of encoded proteins, common evolutionary history, and more. We demonstrate that CSNs, which are based on S. cerevisiae and E. coli genomes, have properties similar to 'traditional' biological networks inferred from experiments. Highly expressed genes tend to have higher degree nodes in the CSN, genes with similar protein functionality tend to be closer, and the CSN graph exhibits a power-law degree distribution. Also, we show how the CSN can be used for predicting gene interactions and functions.

CONCLUSIONS

The reported results suggest that 'silent' code inside the transcript can help to predict central features of biological networks and gene function. This approach can help researchers to understand the genome of novel microorganisms, analyze metagenomic data, and can help to decipher new gene functions.

AVAILABILITY

Our MATLAB implementation of CSN is available at https://www.cs.tau.ac.il/~tamirtul/CSN-Autogen.

The Microbiome-Estrogen Connection and Breast Cancer Risk

The microbiome is undoubtedly the second genome of the human body and has diverse roles in health and disease. However, translational progress is limited due to the vastness of the microbiome, which accounts for over 3.3 million genes, whose functions are still unclear. Numerous studies in the past decade have demonstrated how microbiome impacts various organ-specific cancers by altering the energy balance of the body, increasing adiposity, synthesizing genotoxins and small signaling molecules, and priming and regulating immune response and metabolism of indigestible dietary components, xenobiotics, and pharmaceuticals. In relation to breast cancer, one of the most prominent roles of the human microbiome is the regulation of steroid hormone metabolism since endogenous estrogens are the most important risk factor in breast cancer development especially in postmenopausal women. Intestinal microbes encode enzymes capable of deconjugating conjugated estrogen metabolites marked for excretion, pushing them back into the enterohepatic circulation in a biologically active form. In addition, the intestinal microbes also break down otherwise indigestible dietary polyphenols to synthesize estrogen-like compounds or estrogen mimics that exhibit varied estrogenic potency. The present account discusses the potential role of gastrointestinal microbiome in breast cancer development by mediating metabolism of steroid hormones and synthesis of biologically active estrogen mimics.

Metagenomics analysis of cocoa bean fermentation microbiome identifying species diversity and putative functional capabilities

Fermentation of Theobroma cacao L. beans is the most critical stage in the production of cocoa products such as chocolates and its derivatives. There is a limited understanding of the complex response of microbial diversity during cocoa bean fermentation. The aim of the present study was to investigate microbial communities in the cocoa bean fermentation heap using a culture-independent approach to elucidate microbial diversity, structure, functional annotation and mapping unto metabolic pathways. Genomic DNA was extracted and purified from a sample of cocoa beans fermentation heap and was followed by library preparations. Sequence data was generated on Illumina Hiseq 2000 paired-end technology (Macrogen Inc). Taxonomic analysis based on genes predicted from the metagenome identified a high percentage of Bacteria (90.0%), Yeast (9%), and bacteriophages (1%) from the cocoa microbiome. Lactobacillus (20%), Gluconacetobacter (9%), Acetobacter (7%) and Gluconobacter (6%) dominated this study. The mean species diversity, measured by Shannon alpha-diversity index, was estimated at 142.81. Assignment of metagenomic sequences to SEED database categories at 97% sequence similarity identified a genetic profile characteristic of heterotrophic lactic acid fermentation of carbohydrates and aromatic amino acids. Metabolism of aromatic compounds, amino acids and their derivatives and carbohydrates occupied 0.6%, 8% and 13% respectively. Overall, these results provide insights into the cocoa microbiome, identifying fermentation processes carried out broadly by complex microbial communities and metabolic pathways encoding aromatic compounds such as phenylacetaldehyde, butanediol, acetoin, and theobromine that are required for flavour and aroma production. The results obtained will help develop targeted inoculations to produce desired chocolate flavour or targeted metabolic pathways for the selection of microbes for good aroma and flavour compounds formation.

The use of next generation sequencing for improving food safety: Translation into practice

Next Generation Sequencing (NGS) combined with powerful bioinformatic approaches are revolutionising food microbiology. Whole genome sequencing (WGS) of single isolates allows the most detailed comparison possible hitherto of individual strains. The two principle approaches for strain discrimination, single nucleotide polymorphism (SNP) analysis and genomic multi-locus sequence typing (MLST) are showing concordant results for phylogenetic clustering and are complementary to each other. Metabarcoding and metagenomics, applied to total DNA isolated from either food materials or the production environment, allows the identification of complete microbial populations. Metagenomics identifies the entire gene content and when coupled to transcriptomics or proteomics, allows the identification of functional capacity and biochemical activity of microbial populations. The focus of this review is on the recent use and future potential of NGS in food microbiology and on current challenges. Guidance is provided for new users, such as public health departments and the food industry, on the implementation of NGS and how to critically interpret results and place them in a broader context. The review aims to promote the broader application of NGS technologies within the food industry as well as highlight knowledge gaps and novel applications of NGS with the aim of driving future research and increasing food safety outputs from its wider use.

Shotgun Metagenomics of a Water Kefir Fermentation Ecosystem Reveals a Novel Oenococcus Species

Water kefir is a fruity, sour, slightly alcoholic and carbonated beverage, which is made by fermentation of an aqueous sucrose solution in the presence of dried figs and water kefir grains. These polysaccharide grains contain lactic acid bacteria (LAB), yeasts, and sometimes bifidobacteria and/or acetic acid bacteria, which consume sucrose to produce exopolysaccharides, lactic acid, acetic acid, ethanol, and carbon dioxide. Shotgun metagenomic sequencing was used to examine the microbial species diversity present at two time points during water kefir fermentation in detail, both in the water kefir liquor and on the water kefir grains, hence representing four samples. Lactobacillus harbinensis, Lactobacillus hilgardii, Lactobacillus nagelii, Lactobacillus paracasei, and a Lactobacillus species similar to Lactobacillus hordei/mali were present in the water kefir examined, along with Bifidobacterium aquikefiri and two yeast species, namely Saccharomyces cerevisiae and Dekkera bruxellensis. In addition, evidence for a novel Oenococcus species related to Oenococcus oeni and Oenococcus kitaharae was found. Its genome was derived from the metagenome and made available under the name of Candidatus Oenococcus aquikefiri. Through functional analysis of the four metagenomic data sets, it was possible to link the production of lactic acid, acetic acid, ethanol, and carbon dioxide to subgroups of the microbial species found. In particular, the production of mannitol from fructose was linked to L. hilgardii, Candidatus O. aquikefiri, and B. aquikefiri, whereas glycerol production was associated with S. cerevisiae. Also, there were indications of cross-feeding, for instance in the case of amino acid supply. Few bacterial species could synthesize a limited number of cofactors, making them reliant on the figs or S. cerevisiae. The LAB species in turn were found to be capable of contributing to water kefir grain growth, as dextransucrase-encoding genes were attributed to L. hilgardii, L. hordei/mali, and Candidatus O. aquikefiri.

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.

A bioinformatics pipeline integrating predictive metagenomics profiling for the analysis of 16S rDNA/rRNA sequencing data originated from foods

The recent advances in molecular biology, such as the advent of next-generation sequencing (NGS) platforms, have paved the way to new exciting tools which rapidly transform food microbiology. Nowadays, NGS methods such as 16S rDNA/rRNA metagenomics or amplicon sequencing are used for the taxonomic profiling of the food microbial communities. Although 16S rDNA/rRNA NGS-based microbial data are not suited for the investigation of the functional potential of the identified operational taxonomic units as compared to shotgun metagenomics, advances in the bioinformatics discipline allow now the performance of such studies. In this paper, a bioinformatics workflow is described integrating predictive metagenomics profiling with specific application to food microbiology data. Bioinformatics tools pertinent to each sub-module of the pipeline are suggested as well. The published 16S rDNA/rRNA amplicon data originated from an Italian Grana-type cheese, using an NGS platform, was employed to demonstrate the predictive metagenomics profiling approach. The pipeline identified the microbial community and the changes that occurred in the microbial profile during manufacture of the food product studied (taxonomic profiling). The workflow also indicated significant changes in the functional profiling of the community. The tool may help to investigate the functional potential, alterations, and interactions of a microbial community. The proposed workflow may also find an application in the investigation of the ecology of foodborne pathogens encountered in various food products.

Microbial survival strategies in ancient permafrost: insights from metagenomics

In permafrost (perennially frozen ground) microbes survive oligotrophic conditions, sub-zero temperatures, low water availability and high salinity over millennia. Viable life exists in permafrost tens of thousands of years old but we know little about the metabolic and physiological adaptations to the challenges presented by life in frozen ground over geologic time. In this study we asked whether increasing age and the associated stressors drive adaptive changes in community composition and function. We conducted deep metagenomic and 16 S rRNA gene sequencing across a Pleistocene permafrost chronosequence from 19 000 to 33 000 years before present (kyr). We found that age markedly affected community composition and reduced diversity. Reconstruction of paleovegetation from metagenomic sequence suggests vegetation differences in the paleo record are not responsible for shifts in community composition and function. Rather, we observed shifts consistent with long-term survival strategies in extreme cryogenic environments. These include increased reliance on scavenging detrital biomass, horizontal gene transfer, chemotaxis, dormancy, environmental sensing and stress response. Our results identify traits that may enable survival in ancient cryoenvironments with no influx of energy or new materials.

A Metagenomic Approach to Cyanobacterial Genomics

Cyanobacteria, or oxyphotobacteria, are primary producers that establish ecological interactions with a wide variety of organisms. Although their associations with eukaryotes have received most attention, interactions with bacterial and archaeal symbionts have also been occurring for billions of years. Due to these associations, obtaining axenic cultures of cyanobacteria is usually difficult, and most isolation efforts result in unicyanobacterial cultures containing a number of associated microbes, hence composing a microbial consortium. With rising numbers of cyanobacterial blooms due to climate change, demand for genomic evaluations of these microorganisms is increasing. However, standard genomic techniques call for the sequencing of axenic cultures, an approach that not only adds months or even years for culture purification, but also appears to be impossible for some cyanobacteria, which is reflected in the relatively low number of publicly available genomic sequences of this phylum. Under the framework of metagenomics, on the other hand, cumbersome techniques for achieving axenic growth can be circumvented and individual genomes can be successfully obtained from microbial consortia. This review focuses on approaches for the genomic and metagenomic assessment of non-axenic cyanobacterial cultures that bypass requirements for axenity. These methods enable researchers to achieve faster and less costly genomic characterizations of cyanobacterial strains and raise additional information about their associated microorganisms. While non-axenic cultures may have been previously frowned upon in cyanobacteriology, latest advancements in metagenomics have provided new possibilities for in vitro studies of oxyphotobacteria, renewing the value of microbial consortia as a reliable and functional resource for the rapid assessment of bloom-forming cyanobacteria.

Metagenomics: Probing pollutant fate in natural and engineered ecosystems

Polluted environments are a reservoir of microbial species able to degrade or to convert pollutants to harmless compounds. The proper management of microbial resources requires a comprehensive characterization of their genetic pool to assess the fate of contaminants and increase the efficiency of bioremediation processes. Metagenomics offers appropriate tools to describe microbial communities in their whole complexity without lab-based cultivation of individual strains. After a decade of use of metagenomics to study microbiomes, the scientific community has made significant progress in this field. In this review, we survey the main steps of metagenomics applied to environments contaminated with organic compounds or heavy metals. We emphasize technical solutions proposed to overcome encountered obstacles. We then compare two metagenomic approaches, i.e. library-based targeted metagenomics and direct sequencing of metagenomes. In the former, environmental DNA is cloned inside a host, and then clones of interest are selected based on (i) their expression of biodegradative functions or (ii) sequence homology with probes and primers designed from relevant, already known sequences. The highest score for the discovery of novel genes and degradation pathways has been achieved so far by functional screening of large clone libraries. On the other hand, direct sequencing of metagenomes without a cloning step has been more often applied to polluted environments for characterization of the taxonomic and functional composition of microbial communities and their dynamics. In this case, the analysis has focused on 16S rRNA genes and marker genes of biodegradation. Advances in next generation sequencing and in bioinformatic analysis of sequencing data have opened up new opportunities for assessing the potential of biodegradation by microbes, but annotation of collected genes is still hampered by a limited number of available reference sequences in databases. Although metagenomics is still facing technical and computational challenges, our review of the recent literature highlights its value as an aid to efficiently monitor the clean-up of contaminated environments and develop successful strategies to mitigate the impact of pollutants on ecosystems.

A Bayesian inference method for the analysis of transcriptional regulatory networks in metagenomic data

Background

Metagenomics enables the analysis of bacterial population composition and the study of emergent population features, such as shared metabolic pathways. Recently, we have shown that metagenomics datasets can be leveraged to characterize population-wide transcriptional regulatory networks, or meta-regulons, providing insights into how bacterial populations respond collectively to specific triggers. Here we formalize a Bayesian inference framework to analyze the composition of transcriptional regulatory networks in metagenomes by determining the probability of regulation of orthologous gene sequences. We assess the performance of this approach on synthetic datasets and we validate it by analyzing the copper-homeostasis network of Firmicutes species in the human gut microbiome.

Results

Assessment on synthetic datasets shows that our method provides a robust and interpretable metric for assessing putative regulation by a transcription factor on sets of promoter sequences mapping to an orthologous gene cluster. The inference framework integrates the regulatory contribution of secondary sites and can discern false positives arising from multiple instances of a clonal sequence. Posterior probabilities for orthologous gene clusters decline sharply when less than 20 % of mapped promoters have binding sites, but we introduce a sensitivity adjustment procedure to speed up computation that enhances regulation assessment in heterogeneous ortholog clusters. Analysis of the copper-homeostasis regulon governed by CsoR in the human gut microbiome Firmicutes reveals that CsoR controls itself and copper-translocating P-type ATPases, but not CopZ-type copper chaperones. Our analysis also indicates that CsoR frequently targets promoters with dual CsoR-binding sites, suggesting that it exploits higher-order binding conformations to fine-tune its activity.

Conclusions

We introduce and validate a method for the analysis of transcriptional regulatory networks from metagenomic data that enables inference of meta-regulons in a systematic and interpretable way. Validation of this method on the CsoR meta-regulon of gut microbiome Firmicutes illustrates the usefulness of the approach, revealing novel properties of the copper-homeostasis network in poorly characterized bacterial species and putting forward evidence of new mechanisms of DNA binding for this transcriptional regulator. Our approach will enable the comparative analysis of regulatory networks across metagenomes, yielding novel insights into the evolution of transcriptional regulatory networks.

Electronic supplementary material

The online version of this article (doi:10.1186/s13015-016-0082-8) contains supplementary material, which is available to authorized users.

An automated system for evaluation of the potential functionome: MAPLE version 2.1.0

Metabolic and physiological potential evaluator (MAPLE) is an automatic system that can perform a series of steps used in the evaluation of potential comprehensive functions (functionome) harboured in the genome and metagenome. MAPLE first assigns KEGG Orthology (KO) to the query gene, maps the KO-assigned genes to the Kyoto Encyclopedia of Genes and Genomes (KEGG) functional modules, and then calculates the module completion ratio (MCR) of each functional module to characterize the potential functionome in the user’s own genomic and metagenomic data. In this study, we added two more useful functions to calculate module abundance and Q-value, which indicate the functional abundance and statistical significance of the MCR results, respectively, to the new version of MAPLE for more detailed comparative genomic and metagenomic analyses. Consequently, MAPLE version 2.1.0 reported significant differences in the potential functionome, functional abundance, and diversity of contributors to each function among four metagenomic datasets generated by the global ocean sampling expedition, one of the most popular environmental samples to use with this system. MAPLE version 2.1.0 is now available through the web interface (http://www.genome.jp/tools/maple/) 17 June 2016, date last accessed.

An analysis of the validity and utility of the proximon proposition

The utilization of metagenomic functional interactions represents a key technique for metagenomic functional annotation efforts. By definition, metagenomic operons represent such interactions, but many operon predictions protocols rely on information about orthology and/or gene function that is frequently unavailable for metagenomic genes. Recently, the concept of the metagenomic proximon was proposed for use in metagenomic scenarios where supplemental information is sparse. In this paper, we examine the validity and utility of the proximon proposition by measuring the extent to which proximons emulate actual operons. Using the Escherichia coli K-12 genome, we compare proximons and operons from the same genome and observe the configurations and cardinalities among their corresponding mappings. The results demonstrate that the vast majority of proximons map discretely to a single operon in a conservative fashion where a typical proximon is synonymous to an equivalent or truncated operon. However, a large proportion of operons had no corresponding mappings to any proximon. Various perspectives of operon and proximon intersection are discussed, along with the potential limitations for proximon detection and usage.

The Road to Metagenomics: From Microbiology to DNA Sequencing Technologies and Bioinformatics

The study of microorganisms that pervade each and every part of this planet has encountered many challenges through time such as the discovery of unknown organisms and the understanding of how they interact with their environment. The aim of this review is to take the reader along the timeline and major milestones that led us to modern metagenomics. This new and thriving area is likely to be an important contributor to solve different problems. The transition from classical microbiology to modern metagenomics studies has required the development of new branches of knowledge and specialization. Here, we will review how the availability of high-throughput sequencing technologies has transformed microbiology and bioinformatics and how to tackle the inherent computational challenges that arise from the DNA sequencing revolution. New computational methods are constantly developed to collect, process, and extract useful biological information from a variety of samples and complex datasets, but metagenomics needs the integration of several of these computational methods. Despite the level of specialization needed in bioinformatics, it is important that life-scientists have a good understanding of it for a correct experimental design, which allows them to reveal the information in a metagenome.

VDJtools: Unifying Post-analysis of T Cell Receptor Repertoires

Despite the growing number of immune repertoire sequencing studies, the field still lacks software for analysis and comprehension of this high-dimensional data. Here we report VDJtools, a complementary software suite that solves a wide range of T cell receptor (TCR) repertoires post-analysis tasks, provides a detailed tabular output and publication-ready graphics, and is built on top of a flexible API. Using TCR datasets for a large cohort of unrelated healthy donors, twins, and multiple sclerosis patients we demonstrate that VDJtools greatly facilitates the analysis and leads to sound biological conclusions. VDJtools software and documentation are available at https://github.com/mikessh/vdjtools.

riboFrame: An Improved Method for Microbial Taxonomy Profiling from Non-Targeted Metagenomics

Non-targeted metagenomics offers the unprecedented possibility of simultaneously investigate the microbial profile and the genetic capabilities of a sample by a direct analysis of its entire DNA content. The assessment of the microbial taxonomic composition is frequently obtained by mapping reads to genomic databases that, although growing, are still limited and biased. Here we present riboFrame, a novel procedure for microbial profiling based on the identification and classification of 16S rDNA sequences in non-targeted metagenomics datasets. Reads overlapping the 16S rDNA genes are identified using Hidden Markov Models and a taxonomic assignment is obtained by naïve Bayesian classification. All reads identified as ribosomal are coherently positioned in the 16S rDNA gene, allowing the use of the topology of the gene (i.e., the secondary structure and the location of variable regions) to guide the abundance analysis. We tested and verified the effectiveness of our method on simulated ribosomal data, on simulated metagenomes and on a real dataset. riboFrame exploits the taxonomic potentialities of the 16S rDNA gene in the context of non-targeted metagenomics, giving an accurate perspective on the microbial profile in metagenomic samples.

Environmental genes and genomes: understanding the differences and challenges in the approaches and software for their analyses

DNA-based taxonomic and functional profiling is widely used for the characterization of organismal communities across a rapidly increasing array of research areas that include the role of microbiomes in health and disease, biomonitoring, and estimation of both microbial and metazoan species richness. Two principal approaches are currently used to assign taxonomy to DNA sequences: DNA metabarcoding and metagenomics. When initially developed, each of these approaches mandated their own particular methods for data analysis; however, with the development of high-throughput sequencing (HTS) techniques they have begun to share many aspects in data set generation and processing. In this review we aim to define the current characteristics, goals and boundaries of each field, and describe the different software used for their analysis. We argue that an appreciation of the potential and limitations of each method can help underscore the improvements required by each field so as to better exploit the richness of current HTS-based data sets.

Challenges and opportunities in understanding microbial communities with metagenome assembly (accompanied by IPython Notebook tutorial)

Metagenomic investigations hold great promise for informing the genetics, physiology, and ecology of environmental microorganisms. Current challenges for metagenomic analysis are related to our ability to connect the dots between sequencing reads, their population of origin, and their encoding functions. Assembly-based methods reduce dataset size by extending overlapping reads into larger contiguous sequences (contigs), providing contextual information for genetic sequences that does not rely on existing references. These methods, however, tend to be computationally intensive and are again challenged by sequencing errors as well as by genomic repeats While numerous tools have been developed based on these methodological concepts, they present confounding choices and training requirements to metagenomic investigators. To help with accessibility to assembly tools, this review also includes an IPython Notebook metagenomic assembly tutorial. This tutorial has instructions for execution any operating system using Amazon Elastic Cloud Compute and guides users through downloading, assembly, and mapping reads to contigs of a mock microbiome metagenome. Despite its challenges, metagenomic analysis has already revealed novel insights into many environments on Earth. As software, training, and data continue to emerge, metagenomic data access and its discoveries will to grow.

FCMM: A comparative metagenomic approach for functional characterization of multiple metagenome samples

Next-generation sequencing (NGS) technologies make it possible to obtain the entire genomic content of microorganisms in metagenome samples. Thus, many studies have developed methods for the processing and analysis of metagenomic NGS reads, including analyses for predicting functions and their enrichments in environmental metagenome samples. Especially, comparative functional studies by using multi-metagenome samples are essential for identifying and comparing different characteristics of multiple environmental samples. In this paper, we introduce a pipeline for functional characterization of multiple metagenome samples to infer major functions as well as their quantitative scores in a comparative metagenomics manner. The pipeline performs the annotation of functions related to expected proteins in the metagenome samples, calculates their enrichment scores based on the reads per kilobase per million reads (RPKM) measure, and predicts the relative abundance of associated functions by a statistical test. The results from single sample analysis are then used to find common and sample-specific major functions. By applying the pipeline to six different environmental metagenome samples, including two ocean (Antarctica aquatic and Baltic Sea) and four terrestrial (Acid mine drainage, human gut microbiome, Amazon River, and Wasca soil) samples, we were able to predict common functions as well as environment-specific functions. Our pipeline is available at http://bioinfo.konkuk.ac.kr/FCMM/.

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.

Applying meta-pathway analyses through metagenomics to identify the functional properties of the major bacterial communities of a single spontaneous cocoa bean fermentation process sample

A high-resolution functional metagenomic analysis of a representative single sample of a Brazilian spontaneous cocoa bean fermentation process was carried out to gain insight into its bacterial community functioning. By reconstruction of microbial meta-pathways based on metagenomic data, the current knowledge about the metabolic capabilities of bacterial members involved in the cocoa bean fermentation ecosystem was extended. Functional meta-pathway analysis revealed the distribution of the metabolic pathways between the bacterial members involved. The metabolic capabilities of the lactic acid bacteria present were most associated with the heterolactic fermentation and citrate assimilation pathways. The role of Enterobacteriaceae in the conversion of substrates was shown through the use of the mixed-acid fermentation and methylglyoxal detoxification pathways. Furthermore, several other potential functional roles for Enterobacteriaceae were indicated, such as pectinolysis and citrate assimilation. Concerning acetic acid bacteria, metabolic pathways were partially reconstructed, in particular those related to responses toward stress, explaining their metabolic activities during cocoa bean fermentation processes. Further, the in-depth metagenomic analysis unveiled functionalities involved in bacterial competitiveness, such as the occurrence of CRISPRs and potential bacteriocin production. Finally, comparative analysis of the metagenomic data with bacterial genomes of cocoa bean fermentation isolates revealed the applicability of the selected strains as functional starter cultures.

RIEMS: a software pipeline for sensitive and comprehensive taxonomic classification of reads from metagenomics datasets

BACKGROUND

Fuelled by the advent and subsequent development of next generation sequencing technologies, metagenomics became a powerful tool for the analysis of microbial communities both scientifically and diagnostically. The biggest challenge is the extraction of relevant information from the huge sequence datasets generated for metagenomics studies. Although a plethora of tools are available, data analysis is still a bottleneck.

RESULTS

To overcome the bottleneck of data analysis, we developed an automated computational workflow called RIEMS - Reliable Information Extraction from Metagenomic Sequence datasets. RIEMS assigns every individual read sequence within a dataset taxonomically by cascading different sequence analyses with decreasing stringency of the assignments using various software applications. After completion of the analyses, the results are summarised in a clearly structured result protocol organised taxonomically. The high accuracy and performance of RIEMS analyses were proven in comparison with other tools for metagenomics data analysis using simulated sequencing read datasets.

CONCLUSIONS

RIEMS has the potential to fill the gap that still exists with regard to data analysis for metagenomics studies. The usefulness and power of RIEMS for the analysis of genuine sequencing datasets was demonstrated with an early version of RIEMS in 2011 when it was used to detect the orthobunyavirus sequences leading to the discovery of Schmallenberg virus.

Deciphering the human microbiome using next-generation sequencing data and bioinformatics approaches

The human microbiome is one of the key factors affecting the host immune system and metabolic functions that are not encoded in the human genome. Culture-independent analysis of the human microbiome using metagenomics approach allows us to investigate the compositions and functions of the human microbiome. Computational methods analyze the microbial community by using specific marker genes or by using shotgun sequencing of the entire microbial community. Taxonomy profiling is conducted by using the reference sequences or by de novo clustering of the specific region of sequences. Functional profiling, which is mainly based on the sequence similarity, is more challenging since about half of ORFs predicted in the metagenomic data could not find homology with known protein families. This review examines computational methods that are valuable for the analysis of human microbiome, and highlights the results of several large-scale human microbiome studies. It is becoming increasingly evident that dysbiosis of the gut microbiome is strongly associated with the development of immune disorder and metabolic dysfunction.

Analysis of plant microbe interactions in the era of next generation sequencing technologies

Next generation sequencing (NGS) technologies have impressively accelerated research in biological science during the last years by enabling the production of large volumes of sequence data to a drastically lower price per base, compared to traditional sequencing methods. The recent and ongoing developments in the field allow addressing research questions in plant-microbe biology that were not conceivable just a few years ago. The present review provides an overview of NGS technologies and their usefulness for the analysis of microorganisms that live in association with plants. Possible limitations of the different sequencing systems, in particular sources of errors and bias, are critically discussed and methods are disclosed that help to overcome these shortcomings. A focus will be on the application of NGS methods in metagenomic studies, including the analysis of microbial communities by amplicon sequencing, which can be considered as a targeted metagenomic approach. Different applications of NGS technologies are exemplified by selected research articles that address the biology of the plant associated microbiota to demonstrate the worth of the new methods.

Comparative metagenomic analysis of human gut microbiome composition using two different bioinformatic pipelines

Technological advances in next-generation sequencing-based approaches have greatly impacted the analysis of microbial community composition. In particular, 16S rRNA-based methods have been widely used to analyze the whole set of bacteria present in a target environment. As a consequence, several specific bioinformatic pipelines have been developed to manage these data. MetaGenome Rapid Annotation using Subsystem Technology (MG-RAST) and Quantitative Insights Into Microbial Ecology (QIIME) are two freely available tools for metagenomic analyses that have been used in a wide range of studies. Here, we report the comparative analysis of the same dataset with both QIIME and MG-RAST in order to evaluate their accuracy in taxonomic assignment and in diversity analysis. We found that taxonomic assignment was more accurate with QIIME which, at family level, assigned a significantly higher number of reads. Thus, QIIME generated a more accurate BIOM file, which in turn improved the diversity analysis output. Finally, although informatics skills are needed to install QIIME, it offers a wide range of metrics that are useful for downstream applications and, not less important, it is not dependent on server times.

Characterization of the SOS meta-regulon in the human gut microbiome

MOTIVATION

Data from metagenomics projects remain largely untapped for the analysis of transcriptional regulatory networks. Here, we provide proof-of-concept that metagenomic data can be effectively leveraged to analyze regulatory networks by characterizing the SOS meta-regulon in the human gut microbiome.

RESULTS

We combine well-established in silico and in vitro techniques to mine the human gut microbiome data and determine the relative composition of the SOS network in a natural setting. Our analysis highlights the importance of translesion synthesis as a primary function of the SOS response. We predict the association of this network with three novel protein clusters involved in cell wall biogenesis, chromosome partitioning and restriction modification, and we confirm binding of the SOS response transcriptional repressor to sites in the promoter of a cell wall biogenesis enzyme, a phage integrase and a death-on-curing protein. We discuss the implications of these findings and the potential for this approach for metagenome analysis.

Meta-omic platforms to assist in the understanding of NAFLD gut microbiota alterations: tools and applications

Non-alcoholic fatty liver disease (NAFLD) is the most common cause of chronic liver disease worldwide as a result of the increasing prevalence of obesity, starting from early life stages. It is characterized by a spectrum of liver diseases ranging from simple fatty liver (NAFL) to steatohepatitis (NASH), with a possible progression to fibrosis, thus increasing liver-related morbidity and mortality. NAFLD development is driven by the co-action of several risk factors, including obesity and metabolic syndrome, which may be both genetically induced and diet-related. Recently, particular attention has been paid to the gut-liver axis, which may play a physio-pathological role in the onset and progression of the disease. The gut microbiota is intended to act as a bioreactor that can guarantee autonomous metabolic and immunological functions and that can drive functional strategies within the environment of the body in response to external stimuli. The complexity of the gut microbiota suggests that it behaves as an organ. Therefore, the concept of the gut-liver axis must be complemented with the gut-microbiota-liver network due to the high intricacy of the microbiota components and metabolic activities; these activities form the active diet-driven power plant of the host. Such complexity can only be revealed using systems biology, which can integrate clinical phenomics and gut microbiota data.

Inference of phenotype-defining functional modules of protein families for microbial plant biomass degraders

BACKGROUND

Efficient industrial processes for converting plant lignocellulosic materials into biofuels are a key to global efforts to come up with alternative energy sources to fossil fuels. Novel cellulolytic enzymes have been discovered in microbial genomes and metagenomes of microbial communities. However, the identification of relevant genes without known homologs, and the elucidation of the lignocellulolytic pathways and protein complexes for different microorganisms remain challenging.

RESULTS

We describe a new computational method for the targeted discovery of functional modules of plant biomass-degrading protein families, based on their co-occurrence patterns across genomes and metagenome datasets, and the strength of association of these modules with the genomes of known degraders. From approximately 6.4 million family annotations for 2,884 microbial genomes, and 332 taxonomic bins from 18 metagenomes, we identified 5 functional modules that are distinctive for plant biomass degraders, which we term "plant biomass degradation modules" (PDMs). These modules incorporate protein families involved in the degradation of cellulose, hemicelluloses, and pectins, structural components of the cellulosome, and additional families with potential functions in plant biomass degradation. The PDMs were linked to 81 gene clusters in genomes of known lignocellulose degraders, including previously described clusters of lignocellulolytic genes. On average, 70% of the families of each PDM were found to map to gene clusters in known degraders, which served as an additional confirmation of their functional relationships. The presence of a PDM in a genome or taxonomic metagenome bin furthermore allowed us to accurately predict the ability of any particular organism to degrade plant biomass. For 15 draft genomes of a cow rumen metagenome, we used cross-referencing to confirmed cellulolytic enzymes to validate that the PDMs identified plant biomass degraders within a complex microbial community.

CONCLUSIONS

Functional modules of protein families that are involved in different aspects of plant cell wall degradation can be inferred from co-occurrence patterns across (meta-)genomes with a probabilistic topic model. PDMs represent a new resource of protein families and candidate genes implicated in microbial plant biomass degradation. They can also be used to predict the plant biomass degradation ability for a genome or taxonomic bin. The method is also suitable for characterizing other microbial phenotypes.

Biofilm-growing bacteria involved in the corrosion of concrete wastewater pipes: protocols for comparative metagenomic analyses

Advances in high-throughput next-generation sequencing (NGS) technology for direct sequencing of environmental DNA (i.e., shotgun metagenomics) are transforming the field of microbiology. NGS technologies are now regularly being applied in comparative metagenomic studies, which provide the data for functional annotations, taxonomic comparisons, community profile, and metabolic reconstructions. For example, comparative metagenomic analysis of corroded pipes unveiled novel insights on the bacterial populations associated with the sulfur and nitrogen cycle, which may be directly or indirectly implicated in concrete wastewater pipe corrosion. The objective of this chapter is to describe the steps involved in the taxonomic and functional analysis of metagenome datasets from biofilm involved in microbial-induced concrete corrosion (MICC).

Accelerating in silico research with workflows: a lesson in Simplicity

Bioinformatics is the application of computer science and related disciplines to the field of molecular biology. While there are currently several web based and desktop tools available for biologists to perform routine bioinformatics tasks, these tools often require users to manually and repeatedly co-ordinate multiple applications before reaching a result. In an effort to reduce time and error, workflow tools have been developed to automate these tasks. However, many of these tools require expert knowledge of the techniques and supporting databases which more often than not lies outside the scope of most biologists. Herein, we describe the development of sequence information management platform (Simplicity), a workflow-based bioinformatics management tool, which allows non-bioinformaticians to rapidly annotate large amounts of DNA and protein sequence data.

Pro-inflammatory flagellin proteins of prevalent motile commensal bacteria are variably abundant in the intestinal microbiome of elderly humans

Some Eubacterium and Roseburia species are among the most prevalent motile bacteria present in the intestinal microbiota of healthy adults. These flagellate species contribute “cell motility” category genes to the intestinal microbiome and flagellin proteins to the intestinal proteome. We reviewed and revised the annotation of motility genes in the genomes of six Eubacterium and Roseburia species that occur in the human intestinal microbiota and examined their respective locus organization by comparative genomics. Motility gene order was generally conserved across these loci. Five of these species harbored multiple genes for predicted flagellins. Flagellin proteins were isolated from R. inulinivorans strain A2-194 and from E. rectale strains A1-86 and M104/1. The amino-termini sequences of the R. inulinivorans and E. rectale A1-86 proteins were almost identical. These protein preparations stimulated secretion of interleukin-8 (IL-8) from human intestinal epithelial cell lines, suggesting that these flagellins were pro-inflammatory. Flagellins from the other four species were predicted to be pro-inflammatory on the basis of alignment to the consensus sequence of pro-inflammatory flagellins from the β- and γ- proteobacteria. Many fliC genes were deduced to be under the control of σ²⁸. The relative abundance of the target Eubacterium and Roseburia species varied across shotgun metagenomes from 27 elderly individuals. Genes involved in the flagellum biogenesis pathways of these species were variably abundant in these metagenomes, suggesting that the current depth of coverage used for metagenomic sequencing (3.13–4.79 Gb total sequence in our study) insufficiently captures the functional diversity of genomes present at low (≤1%) relative abundance. E. rectale and R. inulinivorans thus appear to synthesize complex flagella composed of flagellin proteins that stimulate IL-8 production. A greater depth of sequencing, improved evenness of sequencing and improved metagenome assembly from short reads will be required to facilitate in silico analyses of complete complex biochemical pathways for low-abundance target species from shotgun metagenomes.