Microbial Malaise: How Can We Classify the Microbiome?

Genome-resolved metagenomics: a game changer for microbiome medicine

Recent substantial evidence implicating commensal bacteria in human diseases has given rise to a new domain in biomedical research: microbiome medicine. This emerging field aims to understand and leverage the human microbiota and derivative molecules for disease prevention and treatment. Despite the complex and hierarchical organization of this ecosystem, most research over the years has relied on 16S amplicon sequencing, a legacy of bacterial phylogeny and taxonomy. Although advanced sequencing technologies have enabled cost-effective analysis of entire microbiota, translating the relatively short nucleotide information into the functional and taxonomic organization of the microbiome has posed challenges until recently. In the last decade, genome-resolved metagenomics, which aims to reconstruct microbial genomes directly from whole-metagenome sequencing data, has made significant strides and continues to unveil the mysteries of various human-associated microbial communities. There has been a rapid increase in the volume of whole metagenome sequencing data and in the compilation of novel metagenome-assembled genomes and protein sequences in public depositories. This review provides an overview of the capabilities and methods of genome-resolved metagenomics for studying the human microbiome, with a focus on investigating the prokaryotic microbiota of the human gut. Just as decoding the human genome and its variations marked the beginning of the genomic medicine era, unraveling the genomes of commensal microbes and their sequence variations is ushering us into the era of microbiome medicine. Genome-resolved metagenomics stands as a pivotal tool in this transition and can accelerate our journey toward achieving these scientific and medical milestones.

medna-metadata: an open-source data management system for tracking environmental DNA samples and metadata

MOTIVATION

Environmental DNA (eDNA), as a rapidly expanding research field, stands to benefit from shared resources including sampling protocols, study designs, discovered sequences, and taxonomic assignments to sequences. High-quality community shareable eDNA resources rely heavily on comprehensive metadata documentation that captures the complex workflows covering field sampling, molecular biology lab work, and bioinformatic analyses. There are limited sources that provide documentation of database development on comprehensive metadata for eDNA and these workflows and no open-source software.

RESULTS

We present medna-metadata, an open-source, modular system that aligns with Findable, Accessible, Interoperable, and Reusable guiding principles that support scholarly data reuse and the database and application development of a standardized metadata collection structure that encapsulates critical aspects of field data collection, wet lab processing, and bioinformatic analysis. Medna-metadata is showcased with metabarcoding data from the Gulf of Maine (Polinski et al., 2019).

AVAILABILITY AND IMPLEMENTATION

The source code of the medna-metadata web application is hosted on GitHub (https://github.com/Maine-eDNA/medna-metadata). Medna-metadata is a docker-compose installable package. Documentation can be found at https://medna-metadata.readthedocs.io/en/latest/?badge=latest. The application is implemented in Python, PostgreSQL and PostGIS, RabbitMQ, and NGINX, with all major browsers supported. A demo can be found at https://demo.metadata.maine-edna.org/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Evaluating hierarchical machine learning approaches to classify biological databases

The rate of biological data generation has increased dramatically in recent years, which has driven the importance of databases as a resource to guide innovation and the generation of biological insights. Given the complexity and scale of these databases, automatic data classification is often required. Biological data sets are often hierarchical in nature, with varying degrees of complexity, imposing different challenges to train, test and validate accurate and generalizable classification models. While some approaches to classify hierarchical data have been proposed, no guidelines regarding their utility, applicability and limitations have been explored or implemented. These include ‘Local’ approaches considering the hierarchy, building models per level or node, and ‘Global’ hierarchical classification, using a flat classification approach. To fill this gap, here we have systematically contrasted the performance of ‘Local per Level’ and ‘Local per Node’ approaches with a ‘Global’ approach applied to two different hierarchical datasets: BioLip and CATH. The results show how different components of hierarchical data sets, such as variation coefficient and prediction by depth, can guide the choice of appropriate classification schemes. Finally, we provide guidelines to support this process when embarking on a hierarchical classification task, which will help optimize computational resources and predictive performance.

Taxonomic classification of the bacterial endosymbiont Wolbachia based on next-generation sequencing: is there molecular evidence for its presence in tardigrades?

We used high-throughput sequencing of 16S rRNA to test whether tardigrade species are infected with Wolbachia parasites. We applied SILVA and Greengenes databases that allowed taxonomic classification of bacterial sequences to OTUs. The results obtained from both databases differed considerably in the number of OTUs, and only the Greengenes database allowed identification of Wolbachia (infection was also supported by comparison of sequences to NCBI database). The putative bacterial endosymbiont Wolbachia was discovered only in adult eutardigrades, while bacteria identified down to the order Rickettsiales were detected in both eutardigrade eggs and adult specimens. Nevertheless, the frequency of Wolbachia in the bacterial communities of the studied eutardigrades was low. Similarly, in our positive control, i.e., a fairy shrimp Streptocephalus cafer, which was found to be infected with Wolbachia in our previous study using Sanger sequencing, only the Rickettsiales were detected. We also carried out phylogenetic reconstruction using Wolbachia sequences from the SILVA and Greengenes databases, Alphaproteobacteria putative endosymbionts and Rickettsiales OTUs obtained in previous studies on the microbial community of tardigrades, and Rickettsiales and Wolbachia OTUs obtained in the current study. Our discovery of Wolbachia in tardigrades can fuel new research to uncover the specifics of this interaction.

Assessment of phylo-functional coherence along the bacterial phylogeny and taxonomy

In this report we use available curated phylogenies, taxonomy, and genome annotations to assess the phylogenetic and gene content similarity associated with each different taxon and taxonomic rank. Subsequently, we employ the same data to assess the frontiers of functional coherence along the bacterial phylogeny. Our results show that within-group phylogenetic and gene content similarity of taxa in the same rank are not homogenous, and that these values show extensive overlap between ranks. Functional coherence along the 16S rRNA gene-based phylogeny was limited to 44 particular nodes presenting large variations in phylogenetic depth. For instance, the deep subtree affiliated to class Actinobacteria presented functional coherence, while the shallower family Enterobacteriaceae-affiliated subtree did not. On the other hand, functional coherence along the genome-based phylogeny delimited deep subtrees affiliated to phyla Actinobacteriota, Deinococcota, Chloroflexota, Firmicutes, and a subtree containing the rest of the bacterial phyla. The results presented here can be used to guide the exploration of results in many microbial ecology and evolution research scenarios. Moreover, we provide dedicated scripts and files that can be used to continue the exploration of functional coherence along the bacterial phylogeny employing different parameters or input data (https://git.io/Jec5U).

16S Metagenomics Reveals Dysbiosis of Nasal Core Microbiota in Children With Chronic Nasal Inflammation: Role of Adenoid Hypertrophy and Allergic Rhinitis

Allergic rhinitis (AR) and adenoid hypertrophy (AH) are, in children, the main cause of partial or complete upper airway obstruction and reduction in airflow. However, limited data exist about the impact of the increased resistance to airflow, on the nasal microbial composition of children with AR end AH. Allergic rhinitis (AR) as well as adenoid hypertrophy (AH), represent extremely common pathologies in this population. Their known inflammatory obstruction is amplified when both pathologies coexist. In our study, the microbiota of anterior nares of 75 pediatric subjects with AR, AH or both conditions, was explored by 16S rRNA-based metagenomic approach. Our data show for the first time, that in children, the inflammatory state is associated to similar changes in the microbiota composition of AR and AH subjects respect to the healthy condition. Together with such alterations, we observed a reduced variability in the between-subject biodiversity on the other hand, these same alterations resulted amplified by the nasal obstruction that could constitute a secondary risk factor for dysbiosis. Significant differences in the relative abundance of specific microbial groups were found between diseased phenotypes and the controls. Most of these taxa belonged to a stable and quantitatively dominating component of the nasal microbiota and showed marked potentials in discriminating the controls from diseased subjects. A pauperization of the nasal microbial network was observed in diseased status in respect to the number of involved taxa and connectivity. Finally, while stable co-occurrence relationships were observed within both control- and diseases-associated microbial groups, only negative correlations were present between them, suggesting that microbial subgroups potentially act as maintainer of the eubiosis state in the nasal ecosystem. In the nasal ecosystem, inflammation-associated shifts seem to impact the more intimate component of the microbiota rather than representing the mere loss of microbial diversity. The discriminatory potential showed by differentially abundant taxa provide a starting point for future research with the potential to improve patient outcomes. Overall, our results underline the association of AH and AR with the impairment of the microbial interplay leading to unbalanced ecosystems.

A conceptual framework for the phylogenetically constrained assembly of microbial communities

Microbial communities play essential and preponderant roles in all ecosystems. Understanding the rules that govern microbial community assembly will have a major impact on our ability to manage microbial ecosystems, positively impacting, for instance, human health and agriculture. Here, I present a phylogenetically constrained community assembly principle grounded on the well-supported facts that deterministic processes have a significant impact on microbial community assembly, that microbial communities show significant phylogenetic signal, and that microbial traits and ecological coherence are, to some extent, phylogenetically conserved. From these facts, I derive a few predictions which form the basis of the framework. Chief among them is the existence, within most microbial ecosystems, of phylogenetic core groups (PCGs), defined as discrete portions of the phylogeny of varying depth present in all instances of the given ecosystem, and related to specific niches whose occupancy requires a specific phylogenetically conserved set of traits. The predictions are supported by the recent literature, as well as by dedicated analyses. Integrating the effect of ecosystem patchiness, microbial social interactions, and scale sampling pitfalls takes us to a comprehensive community assembly model that recapitulates the characteristics most commonly observed in microbial communities. PCGs' identification is relatively straightforward using high-throughput 16S amplicon sequencing, and subsequent bioinformatic analysis of their phylogeny, estimated core pan-genome, and intra-group co-occurrence should provide valuable information on their ecophysiology and niche characteristics. Such a priori information for a significant portion of the community could be used to prime complementing analyses, boosting their usefulness. Thus, the use of the proposed framework could represent a leap forward in our understanding of microbial community assembly and function.

Challenges in the construction of knowledge bases for human microbiome-disease associations

The last few years have seen tremendous growth in human microbiome research, with a particular focus on the links to both mental and physical health and disease. Medical and experimental settings provide initial sources of information about these links, but individual studies produce disconnected pieces of knowledge bounded in context by the perspective of expert researchers reading full-text publications. Building a knowledge base (KB) consolidating these disconnected pieces is an essential first step to democratize and accelerate the process of accessing the collective discoveries of human disease connections to the human microbiome. In this article, we survey the existing tools and development efforts that have been produced to capture portions of the information needed to construct a KB of all known human microbiome-disease associations and highlight the need for additional innovations in natural language processing (NLP), text mining, taxonomic representations, and field-wide vocabulary standardization in human microbiome research. Addressing these challenges will enable the construction of KBs that help identify new insights amenable to experimental validation and potentially clinical decision support.

Taxonomy based performance metrics for evaluating taxonomic assignment methods

Background

Metagenomics experiments often make inferences about microbial communities by sequencing 16S and 18S rRNA, and taxonomic assignment is a fundamental step in such studies. This paper addresses the weaknesses in two types of metrics commonly used by previous studies for measuring the performance of existing taxonomic assignment methods: Sequence count based metrics and Binary error measurement. These metrics made performance evaluation results biased, less informative and mutually incomparable.

Results

We investigated weaknesses in two types of metrics and proposed new performance metrics including Average Taxonomy Distance (ATD) and ATD_by_Taxa, together with the visualized ATD plot.

Conclusions

By comparing the evaluation results from four popular taxonomic assignment methods across three test data sets, we found the new metrics more robust, informative and comparable.

The Majority of Active Rhodobacteraceae in Marine Sediments Belong to Uncultured Genera: A Molecular Approach to Link Their Distribution to Environmental Conditions

General studies on benthic microbial communities focus on fundamental biogeochemical processes or the most abundant constituents. Thereby, minor fractions such as the Rhodobacteraceae are frequently neglected. Even though this family belongs to the most widely distributed bacteria in the marine environment, their proportion on benthic microbial communities is usually within or below the single digit range. Thus, knowledge on these community members is limited, even though their absolute numbers might exceed those from the pelagic zone by orders of magnitudes. To unravel the distribution and diversity of benthic, metabolically active Rhodobacteraceae, we have now analyzed an already existing library of bacterial 16S rRNA transcripts. The dataset originated from 154 individual sediment samples comprising seven oceanic regions and a broad variety of environmental conditions. Across all samples, a total of 0.7% of all 16S rRNA transcripts was annotated as Rhodobacteraceae. Among those, Sulfitobacter, Paracoccus, and Phaeomarinomonas were the most abundant cultured representatives, but the majority (78%) was affiliated to uncultured family members. To define them, the 45 most abundant Rhodobacteraceae-OTUs assigned as "uncultured" were phylogenetically assembled in new clusters. Their next relatives particularly belonged to different subgroups other than the Roseobacter group, reflecting a large part of the hidden diversity within the benthic Rhodobacteraceae with unknown functions. The general composition of active Rhodobacteraceae communities was found to be specific for the geographical location, exhibiting a decreasing richness with sediment depth. One-third of the Rhodobacteraceae-OTUs significantly responded to the prevailing redox regime, suggesting an adaption to anoxic conditions. A possible approach to predict their physiological properties is to identify the metabolic capabilities of their nearest relatives. Those need to be proven by physiological experiments, as soon an isolate is available. Because many uncultured members of these subgroups likely thrive under anoxic conditions, in future research, a molecular-guided cultivation strategy can be pursued to isolate novel Rhodobacteraceae from sediments.

The human gut pan-microbiome presents a compositional core formed by discrete phylogenetic units

The complex community of microbes living in the human gut plays an important role in host wellbeing. However, defining a ‘healthy’ gut microbiome in terms of composition has remained an elusive task, despite its anticipated medical and scientific importance. In this regard, a central question has been if there is a ‘core’ microbiome consisting of bacterial groups common to all healthy humans. Recent studies have been able to define a compositional core in human gut microbiome datasets in terms of taxonomic assignments. However, the description of the core microbiome in terms of taxonomic assignments may not be adequate when considering subsequent analyses and applications. Through the implementation of a dynamic clustering approach in the meta-analyisis of comprehensive 16S rRNA marker gene datasets, this study found that the human gut pan-microbiome presents a preeminent compositional core comprised of discrete units of varying phylogenetic depth present in all individuals studied. Since both microbial traits and ecological coherence show signs of phylogenetic conservation, this outcome provides a new conceptual framework in the study of the ecosystem, as well as important practical considerations which should be taken into account in future research.

A standardized bacterial taxonomy based on genome phylogeny substantially revises the tree of life

Taxonomy is an organizing principle of biology and is ideally based on evolutionary relationships among organisms. Development of a robust bacterial taxonomy has been hindered by an inability to obtain most bacteria in pure culture and, to a lesser extent, by the historical use of phenotypes to guide classification. Culture-independent sequencing technologies have matured sufficiently that a comprehensive genome-based taxonomy is now possible. We used a concatenated protein phylogeny as the basis for a bacterial taxonomy that conservatively removes polyphyletic groups and normalizes taxonomic ranks on the basis of relative evolutionary divergence. Under this approach, 58% of the 94,759 genomes comprising the Genome Taxonomy Database had changes to their existing taxonomy. This result includes the description of 99 phyla, including six major monophyletic units from the subdivision of the Proteobacteria, and amalgamation of the Candidate Phyla Radiation into a single phylum. Our taxonomy should enable improved classification of uncultured bacteria and provide a sound basis for ecological and evolutionary studies.

Taxonomic annotation errors incorrectly assign the family Pseudoalteromonadaceae to the order Vibrionales in Greengenes: implications for microbial community assessments

Next-generation sequencing has provided powerful tools to conduct microbial ecology studies. Analysis of community composition relies on annotated databases of curated sequences to provide taxonomic assignments; however, these databases occasionally have errors with implications for downstream analyses. Systemic taxonomic errors were discovered in Greengenes database (v13_5 and 13_8) related to orders Vibrionales and Alteromonadales. These orders have family level annotations that were erroneous at least one taxonomic level, e.g., 100% of sequences assigned to the Pseudoalteromonadaceae family were placed improperly in Vibrionales (rather than Alteromonadales) and >20% of these sequences were indeed Vibrio spp. but were improperly assigned to the Pseudoalteromonadaceae family (rather than to Vibrionaceae). Use of this database is common; we identified 68 peer-reviewed papers since 2013 that likely included erroneous annotations specifically associated with Vibrionales and Pseudoalteromonadaceae, with 20 explicitly stating the incorrect taxonomy. Erroneous assignments using these specific versions of Greengenes can lead to incorrect conclusions, especially in marine systems where these taxa are commonly encountered as conditionally rare organisms and potential pathogens.

A microbial identification framework for risk assessment

Micro-organisms are increasingly used in a variety of products for commercial uses, including cleaning products. Such microbial-based cleaning products (MBCP) are represented as a more environmentally-friendly alternative to chemically based cleaning products. The identity of the micro-organisms formulated into these products is often considered confidential business information and is not revealed or it is only partly revealed (i.e., identification to the genus, not to the species). That paucity of information complicates the evaluation of the risk associated with their use. The accurate taxonomic identification of those micro-organisms is important so that a suitable risk assessment of the products can be conducted. To alleviate difficulties associated with adequate identification of micro-organisms in MBCP and other products containing micro-organisms, a microbial identification framework for risk assessment (MIFRA) has been elaborated. It serves to provide guidance on a polyphasic tiered approach, combining the data obtained from the use of various methods (i.e., polyphasic approach) combined with the sequential selection of the methods (i.e., tiered) to achieve a satisfactory identity of the micro-organism to an acceptable taxonomic level. The MIFRA is suitable in various risk assessment contexts for micro-organisms used in any commercial product.

Ecogenomics and Taxonomy of Cyanobacteria Phylum

Cyanobacteria are major contributors to global biogeochemical cycles. The genetic diversity among Cyanobacteria enables them to thrive across many habitats, although only a few studies have analyzed the association of phylogenomic clades to specific environmental niches. In this study, we adopted an ecogenomics strategy with the aim to delineate ecological niche preferences of Cyanobacteria and integrate them to the genomic taxonomy of these bacteria. First, an appropriate phylogenomic framework was established using a set of genomic taxonomy signatures (including a tree based on conserved gene sequences, genome-to-genome distance, and average amino acid identity) to analyse ninety-nine publicly available cyanobacterial genomes. Next, the relative abundances of these genomes were determined throughout diverse global marine and freshwater ecosystems, using metagenomic data sets. The whole-genome-based taxonomy of the ninety-nine genomes allowed us to identify 57 (of which 28 are new genera) and 87 (of which 32 are new species) different cyanobacterial genera and species, respectively. The ecogenomic analysis allowed the distinction of three major ecological groups of Cyanobacteria (named as i. Low Temperature; ii. Low Temperature Copiotroph; and iii. High Temperature Oligotroph) that were coherently linked to the genomic taxonomy. This work establishes a new taxonomic framework for Cyanobacteria in the light of genomic taxonomy and ecogenomic approaches.

Ananke: temporal clustering reveals ecological dynamics of microbial communities

Taxonomic markers such as the 16S ribosomal RNA gene are widely used in microbial community analysis. A common first step in marker-gene analysis is grouping genes into clusters to reduce data sets to a more manageable size and potentially mitigate the effects of sequencing error. Instead of clustering based on sequence identity, marker-gene data sets collected over time can be clustered based on temporal correlation to reveal ecologically meaningful associations. We present Ananke, a free and open-source algorithm and software package that complements existing sequence-identity-based clustering approaches by clustering marker-gene data based on time-series profiles and provides interactive visualization of clusters, including highlighting of internal OTU inconsistencies. Ananke is able to cluster distinct temporal patterns from simulations of multiple ecological patterns, such as periodic seasonal dynamics and organism appearances/disappearances. We apply our algorithm to two longitudinal marker gene data sets: faecal communities from the human gut of an individual sampled over one year, and communities from a freshwater lake sampled over eleven years. Within the gut, the segregation of the bacterial community around a food-poisoning event was immediately clear. In the freshwater lake, we found that high sequence identity between marker genes does not guarantee similar temporal dynamics, and Ananke time-series clusters revealed patterns obscured by clustering based on sequence identity or taxonomy. Ananke is free and open-source software available at https://github.com/beiko-lab/ananke.

The Role of Soil Microorganisms in Plant Mineral Nutrition-Current Knowledge and Future Directions

In their natural environment, plants are part of a rich ecosystem including numerous and diverse microorganisms in the soil. It has been long recognized that some of these microbes, such as mycorrhizal fungi or nitrogen fixing symbiotic bacteria, play important roles in plant performance by improving mineral nutrition. However, the full range of microbes associated with plants and their potential to replace synthetic agricultural inputs has only recently started to be uncovered. In the last few years, a great progress has been made in the knowledge on composition of rhizospheric microbiomes and their dynamics. There is clear evidence that plants shape microbiome structures, most probably by root exudates, and also that bacteria have developed various adaptations to thrive in the rhizospheric niche. The mechanisms of these interactions and the processes driving the alterations in microbiomes are, however, largely unknown. In this review, we focus on the interaction of plants and root associated bacteria enhancing plant mineral nutrition, summarizing the current knowledge in several research fields that can converge to improve our understanding of the molecular mechanisms underpinning this phenomenon.

SILVA, RDP, Greengenes, NCBI and OTT - how do these taxonomies compare?

Background

A key step in microbiome sequencing analysis is read assignment to taxonomic units. This is often performed using one of four taxonomic classifications, namely SILVA, RDP, Greengenes or NCBI. It is unclear how similar these are and how to compare analysis results that are based on different taxonomies.

Results

We provide a method and software for mapping taxonomic entities from one taxonomy onto another. We use it to compare the four taxonomies and the Open Tree of life Taxonomy (OTT).

Conclusions

While we find that SILVA, RDP and Greengenes map well into NCBI, and all four map well into the OTT, mapping the two larger taxonomies on to the smaller ones is problematic.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-017-3501-4) contains supplementary material, which is available to authorized users.

Collective Phase in Resource Competition in a Highly Diverse Ecosystem

Organisms shape their own environment, which in turn affects their survival. This feedback becomes especially important for communities containing a large number of species; however, few existing approaches allow studying this regime, except in simulations. Here, we use methods of statistical physics to analytically solve a classic ecological model of resource competition introduced by MacArthur in 1969. We show that the nonintuitive phenomenology of highly diverse ecosystems includes a phase where the environment constructed by the community becomes fully decoupled from the outside world.

A new way to contemplate Darwin's tangled bank: how DNA barcodes are reconnecting biodiversity science and biomonitoring

Encompassing the breadth of biodiversity in biomonitoring programmes has been frustrated by an inability to simultaneously identify large numbers of species accurately and in a timely fashion. Biomonitoring infers the state of an ecosystem from samples collected and identified using the best available taxonomic knowledge. The advent of DNA barcoding has now given way to the extraction of bulk DNA from mixed samples of organisms in environmental samples through the development of high-throughput sequencing (HTS). This DNA metabarcoding approach allows an unprecedented view of the true breadth and depth of biodiversity, but its adoption poses two important challenges. First, bioinformatics techniques must simultaneously perform complex analyses of large datasets and translate the results of these analyses to a range of users. Second, the insights gained from HTS need to be amalgamated with concepts such as Linnaean taxonomy and indicator species, which are less comprehensive but more intuitive. It is clear that we are moving beyond proof-of-concept studies to address the challenge of implementation of this new approach for environmental monitoring and regulation. Interpreting Darwin's 'tangled bank' through a DNA lens is now a reality, but the question remains: how can this information be generated and used reliably, and how does it relate to accepted norms in ecosystem study?This article is part of the themed issue 'From DNA barcodes to biomes'.