Taxonomic resolution of the ribosomal RNA operon in bacteria: implications for its use with long-read sequencing

GROND: a quality-checked and publicly available database of full-length 16S-ITS-23S rRNA operon sequences

Sequence comparison of 16S rRNA PCR amplicons is an established approach to taxonomically identify bacterial isolates and profile complex microbial communities. One potential application of recent advances in long-read sequencing technologies is to sequence entire rRNA operons and capture significantly more phylogenetic information compared to sequencing of the 16S rRNA (or regions thereof) alone, with the potential to increase the proportion of amplicons that can be reliably classified to lower taxonomic ranks. Here we describe GROND (Genome-derived Ribosomal Operon Database), a publicly available database of quality-checked 16S-ITS-23S rRNA operons, accompanied by multiple taxonomic classifications. GROND will aid researchers in analysis of their data and act as a standardised database to allow comparison of results between studies.

Harnessing the intragenomic variability of rRNA operons to improve differentiation of Vibrio species

Although the 16S rRNA gene is frequently used as a phylogenetic marker in analysis of environmental DNA, this marker often fails to distinguish closely related species, including those in the genus Vibrio. Here, we investigate whether inclusion and analysis of 23S rRNA sequence can help overcome the intrinsic weaknesses of 16S rRNA analyses for the differentiation of Vibrio species. We construct a maximum likelihood 16S rRNA gene tree to assess the use of this gene to identify clades of Vibrio species. Within the 16S rRNA tree, we identify the putative informative bases responsible for polyphyly, and demonstrate the association of these positions with tree topology. We demonstrate that concatenation of 16S and 23S rRNA genes increases the number of informative nucleotide positions, thereby overcoming ambiguities in 16S rRNA-based phylogenetic reconstructions. Finally, we experimentally demonstrate that this approach considerably improves the differentiation and identification of Vibrio species in environmental samples.

Community-scale models of microbiomes: Articulating metabolic modelling and metagenome sequencing

Building models is essential for understanding the functions and dynamics of microbial communities. Metabolic models built on genome-scale metabolic network reconstructions (GENREs) are especially relevant as a means to decipher the complex interactions occurring among species. Model reconstruction increasingly relies on metagenomics, which permits direct characterisation of naturally occurring communities that may contain organisms that cannot be isolated or cultured. In this review, we provide an overview of the field of metabolic modelling and its increasing reliance on and synergy with metagenomics and bioinformatics. We survey the means of assigning functions and reconstructing metabolic networks from (meta-)genomes, and present the variety and mathematical fundamentals of metabolic models that foster the understanding of microbial dynamics. We emphasise the characterisation of interactions and the scaling of model construction to large communities, two important bottlenecks in the applicability of these models. We give an overview of the current state of the art in metagenome sequencing and bioinformatics analysis, focusing on the reconstruction of genomes in microbial communities. Metagenomics benefits tremendously from third-generation sequencing, and we discuss the opportunities of long-read sequencing, strain-level characterisation and eukaryotic metagenomics. We aim at providing algorithmic and mathematical support, together with tool and application resources, that permit bridging the gap between metagenomics and metabolic modelling.

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

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

High-resolution phylogenetic and population genetic analysis of microbial communities with RoC-ITS

Microbial communities are inter-connected systems of incredible complexity and dynamism that play crucial roles in health, energy, and the environment. To better understand microbial communities and how they respond to change, it is important to know which microbes are present and their relative abundances at the greatest taxonomic resolution possible. Here, we describe a novel protocol (RoC-ITS) that uses the single-molecule Nanopore sequencing platform to assay the composition of microbial communities at the subspecies designation. Using rolling-circle amplification, this methodology produces long-read sequences from a circular construct containing the complete 16S ribosomal gene and the neighboring internally transcribed spacer (ITS). These long reads can be used to generate a high-fidelity circular consensus sequence. Generally, the ribosomal 16S gene provides phylogenetic information down to the species-level, while the much less conserved ITS region contains strain-level information. When linked together, this combination of markers allows for the identification of individual ribosomal units within a specific organism and the assessment of their relative stoichiometry, as well as the ability to monitor subtle shifts in microbial community composition with a single generic assay. We applied RoC-ITS to an artificial microbial community that was also sequenced using the Illumina platform, to assess its accuracy in quantifying the relative abundance and identity of each species.

Investigation of memory-enhancing effects of Streptococcus thermophilus EG007 in mice and elucidating molecular and metagenomic characteristics using nanopore sequencing

Over the past decades, accumulating evidences have highlighted the gut microbiota as a key player in the brain functioning via microbiota–gut–brain axis, and accordingly, the beneficial role of several probiotic strains in cognitive ability also have been actively investigated. However, the majority of the research have demonstrated the effects against age-related cognitive decline or neurological disease. To this end, we aimed to investigate lactic acid bacteria strains having beneficial effects on the cognitive function of healthy young mice and elucidate underlying characteristics by carrying out nanopore sequencing-based genomics and metagenomics analysis. 8-week consumption of Streptococcus thermophilus EG007 demonstrated marked enhancements in behavior tests assessing short-term spatial and non-spatial learning and memory. It was revealed that EG007 possessed genes encoding various metabolites beneficial for a health condition in many aspects, including gamma-aminobutyric acid producing system, a neurotransmitter associated with mood and stress response. Also, by utilizing 16S–23S rRNA operon as a taxonomic marker, we identified more accurate species-level compositional changes in gut microbiota, which was increase of certain species, previously reported to have associations with mental health or down-regulation of inflammation or infection-related species. Moreover, correlation analysis revealed that the EG007-mediated altered microbiota had a significant correlation with the memory traits.

Phylogenies of the 16S rRNA gene and its hypervariable regions lack concordance with core genome phylogenies

BACKGROUND

The 16S rRNA gene is used extensively in bacterial phylogenetics, in species delineation, and now widely in microbiome studies. However, the gene suffers from intragenomic heterogeneity, and reports of recombination and an unreliable phylogenetic signal are accumulating. Here, we compare core gene phylogenies to phylogenies constructed using core gene concatenations to estimate the strength of signal for the 16S rRNA gene, its hypervariable regions, and all core genes at the intra- and inter-genus levels. Specifically, we perform four intra-genus analyses (Clostridium, n = 65; Legionella, n = 47; Staphylococcus, n = 36; and Campylobacter, n = 17) and one inter-genus analysis [41 core genera of the human gut microbiome (31 families, 17 orders, and 12 classes), n = 82].

RESULTS

At both taxonomic levels, the 16S rRNA gene was recombinant and subject to horizontal gene transfer. At the intra-genus level, the gene showed one of the lowest levels of concordance with the core genome phylogeny (50.7% average). Concordance for hypervariable regions was lower still, with entropy masking providing little to no benefit. A major factor influencing concordance was SNP count, which showed a positive logarithmic association. Using this relationship, we determined that 690 ± 110 SNPs were required for 80% concordance (average 16S rRNA gene SNP count was 254). We also found a wide range in 16S-23S-5S rRNA operon copy number among genomes (1-27). At the inter-genus level, concordance for the whole 16S rRNA gene was markedly higher (73.8% - 10th out of 49 loci); however, the most concordant hypervariable regions (V4, V3-V4, and V1-V2) ranked in the third quartile (62.5 to 60.0%).

CONCLUSIONS

Ramifications of a poor phylogenetic performance for the 16S rRNA gene are far reaching. For example, in addition to incorrect species/strain delineation and phylogenetic inference, it has the potential to confound community diversity metrics if phylogenetic information is incorporated - for example, with popular approaches such as Faith's phylogenetic diversity and UniFrac. Our results highlight the problematic nature of these approaches and their use (along with entropy masking) is discouraged. Lastly, the wide range in 16S rRNA gene copy number among genomes also has a strong potential to confound diversity metrics. Video Abstract.

Microbial Identification Using rRNA Operon Region: Database and Tool for Metataxonomics with Long-Read Sequence

Recent development of long-read sequencing platforms has enabled researchers to explore bacterial community structure through analysis of full-length 16S rRNA gene (∼1,500 bp) or 16S-ITS-23S rRNA operon region (∼4,300 bp), resulting in higher taxonomic resolution than short-read sequencing platforms. Despite the potential of long-read sequencing in metagenomics, resources and protocols for this technology are scarce. Here, we describe MIrROR, the database and analysis tool for metataxonomics using the bacterial 16S-ITS-23S rRNA operon region. We collected 16S-ITS-23S rRNA operon sequences extracted from bacterial genomes from NCBI GenBank and performed curation. A total of 97,781 16S-ITS-23S rRNA operon sequences covering 9,485 species from 43,653 genomes were obtained. For user convenience, we provide an analysis tool based on a mapping strategy that can be used for taxonomic profiling with MIrROR database. To benchmark MIrROR, we compared performance against publicly available databases and tool with mock communities and simulated data sets. Our platform showed promising results in terms of the number of species covered and the accuracy of classification. To encourage active 16S-ITS-23S rRNA operon analysis in the field, BLAST function and taxonomic profiling results with 16S-ITS-23S rRNA operon studies, which have been reported as BioProject on NCBI are provided. MIrROR (http://mirror.egnome.co.kr/) will be a useful platform for researchers who want to perform high-resolution metagenome analysis with a cost-effective sequencer such as MinION from Oxford Nanopore Technologies. IMPORTANCE Metabarcoding is a powerful tool to investigate community diversity in an economic and efficient way by amplifying a specific gene marker region. With the advancement of long-read sequencing technologies, the field of metabarcoding has entered a new phase. The technologies have brought a need for development in several areas, including new markers that long-read can cover, database for the markers, tools that reflect long-read characteristics, and compatibility with downstream analysis tools. By constructing MIrROR, we met the need for a database and tools for the 16S-ITS-23S rRNA operon region, which has recently been shown to have sufficient resolution at the species level. Bacterial community analysis using the 16S-ITS-23S rRNA operon region with MIrROR will provide new insights from various research fields.

Tatajuba: exploring the distribution of homopolymer tracts

Length variation of homopolymeric tracts, which induces phase variation, is known to regulate gene expression leading to phenotypic variation in a wide range of bacterial species. There is no specialized bioinformatics software which can, at scale, exhaustively explore and describe these features from sequencing data. Identifying these is non-trivial as sequencing and bioinformatics methods are prone to introducing artefacts when presented with homopolymeric tracts due to the decreased base diversity. We present tatajuba, which can automatically identify potential homopolymeric tracts and help predict their putative phenotypic impact, allowing for rapid investigation. We use it to detect all tracts in two separate datasets, one of Campylobacter jejuni and one of three Bordetella species, and to highlight those tracts that are polymorphic across samples. With this we confirm homopolymer tract variation with phenotypic impact found in previous studies and additionally find many more with potential variability. The software is written in C and is available under the open source licence GNU GPLv3.

Synergies of Systems Biology and Synthetic Biology in Human Microbiome Studies

A number of studies have shown that the microbial communities of the human body are integral for the maintenance of human health. Advances in next-generation sequencing have enabled rapid and large-scale quantification of the composition of microbial communities in health and disease. Microorganisms mediate diverse host responses including metabolic pathways and immune responses. Using a system biology approach to further understand the underlying alterations of the microbiota in physiological and pathological states can help reveal potential novel therapeutic and diagnostic interventions within the field of synthetic biology. Tools such as biosensors, memory arrays, and engineered bacteria can rewire the microbiome environment. In this article, we review the computational tools used to study microbiome communities and the current limitations of these methods. We evaluate how genome-scale metabolic models (GEMs) can advance our understanding of the microbe-microbe and microbe-host interactions. Moreover, we present how synergies between these system biology approaches and synthetic biology can be harnessed in human microbiome studies to improve future therapeutics and diagnostics and highlight important knowledge gaps for future research in these rapidly evolving fields.

Ultra-resolution Metagenomics: When Enough Is Not Enough

Technological advances in community sequencing have steadily increased the taxonomic resolution at which microbes can be delineated. In high-resolution metagenomics, bacterial strains can now be resolved, enhancing medical microbiology and the description of microbial evolution in vivo. In the Hildebrand lab, we are researching novel approaches to further increase the phylogenetic resolution of metagenomics. I propose that ultra-resolution metagenomics will be the next qualitative level of community sequencing, classified by the accurate resolution of ultra-rare genetic events, such as subclonal mutations present in all populations of evolving cells. This will be used to quantify evolutionary processes at ecologically relevant scales, monitor the progress of infections within a patient, and accurately track pathogens in food and infection chains. However, to develop this next metagenomic generation, we first need to understand the currently imposed limits of sequencing technologies, metagenomic strain delineation, and genome reconstructions.

Evolution of Pig Fecal Microbiota Composition and Diversity in Response to Enterotoxigenic Escherichia coli Infection and Colistin Treatment in Weaned Piglets

The intestinal microbiota plays several important roles in pig health and growth. The aim of the current study was to characterize the changes in the fecal microbiota diversity and composition of weaned piglets following an oral challenge with an ETEC: F4 strain and/or a treatment with colistin sulfate (CS). Twenty-eight piglets were used in this experiment and were divided into four groups: challenged untreated, challenged treated, unchallenged treated, and unchallenged untreated. Rectal swab samples were collected at five sampling times throughout the study. Total genomic DNA was used to assess the fecal microbiota diversity and composition using the V4 region of the 16S rRNA gene. The relative abundance, the composition, and the community structure of piglet fecal microbiota was highly affected by the ETEC: F4 challenge throughout the experiment, while the oral treatment with CS, a narrow spectrum antibiotic, resulted in a significant decrease of E. coli/Shigella populations during the treatment period only. This study was the first to identify some gut microbiota subgroups (e.g., Streptococcus, Lachnospiraceae) that are associated with healthy piglets as compared to ETEC: F4 challenged animals. These key findings might contribute to the development of alternative strategies to reduce the use of antimicrobials in the control of post-weaning diarrhea in pigs.

Multi-omics data integration considerations and study design for biological systems and disease

With the advancement of next-generation sequencing and mass spectrometry, there is a growing need for the ability to merge biological features in order to study a system as a whole. Features such as the transcriptome, methylome, proteome, histone post-translational modifications and the microbiome all influence the host response to various diseases and cancers. Each of these platforms have technological limitations due to sample preparation steps, amount of material needed for sequencing, and sequencing depth requirements. These features provide a snapshot of one level of regulation in a system. The obvious next step is to integrate this information and learn how genes, proteins, and/or epigenetic factors influence the phenotype of a disease in context of the system. In recent years, there has been a push for the development of data integration methods. Each method specifically integrates a subset of omics data using approaches such as conceptual integration, statistical integration, model-based integration, networks, and pathway data integration. In this review, we discuss considerations of the study design for each data feature, the limitations in gene and protein abundance and their rate of expression, the current data integration methods, and microbiome influences on gene and protein expression. The considerations discussed in this review should be regarded when developing new algorithms for integrating multi-omics data.

High-accuracy long-read amplicon sequences using unique molecular identifiers with Nanopore or PacBio sequencing

High-throughput amplicon sequencing of large genomic regions remains challenging for short-read technologies. Here, we report a high-throughput amplicon sequencing approach combining unique molecular identifiers (UMIs) with Oxford Nanopore Technologies (ONT) or Pacific Biosciences circular consensus sequencing, yielding high-accuracy single-molecule consensus sequences of large genomic regions. We applied our approach to sequence ribosomal RNA operon amplicons (~4,500 bp) and genomic sequences (>10,000 bp) of reference microbial communities in which we observed a chimera rate <0.02%. To reach a mean UMI consensus error rate <0.01%, a UMI read coverage of 15× (ONT R10.3), 25× (ONT R9.4.1) and 3× (Pacific Biosciences circular consensus sequencing) is needed, which provides a mean error rate of 0.0042%, 0.0041% and 0.0007%, respectively.