MetaMetaDB: a database and analytic system for investigating microbial habitability

Analysis of the Diversity of Xylophilus ampelinus Strains Held in CIRM-CFBP Reveals a Strongly Homogenous Species

Xylophilus ampelinus is the causal agent of blight and canker on grapevine. Only a few data are available on this species implying that the occurrence of this pathogen may be underestimated, and its actual ecological niche may not be understood. Moreover, its genetic diversity is not well known. To improve our knowledge of this species, an analysis of the complete genome sequences available in NCBI was performed. It appeared that several sequences are misidentified. The complete genome sequence of the type strain was obtained and primers designed in order to sequence gyrB and rpoD genes for the strains held in CIRM-CFBP. The genetic barcoding data were obtained for 93 strains, isolated over 35 years and from several geographical origins. The species revealed to be strongly homogenous, displaying nearly identical sequences for all strains. However, the oldest strains of this collection were isolated in 2001 therefore, a new isolation campaign and epidemiological surveys are necessary, along with the obtention of new complete genome sequences for this species.

Application of Deep Learning in Plant-Microbiota Association Analysis

Unraveling the association between microbiome and plant phenotype can illustrate the effect of microbiome on host and then guide the agriculture management. Adequate identification of species and appropriate choice of models are two challenges in microbiome data analysis. Computational models of microbiome data could help in association analysis between the microbiome and plant host. The deep learning methods have been widely used to learn the microbiome data due to their powerful strength of handling the complex, sparse, noisy, and high-dimensional data. Here, we review the analytic strategies in the microbiome data analysis and describe the applications of deep learning models for plant–microbiome correlation studies. We also introduce the application cases of different models in plant–microbiome correlation analysis and discuss how to adapt the models on the critical steps in data processing. From the aspect of data processing manner, model structure, and operating principle, most deep learning models are suitable for the plant microbiome data analysis. The ability of feature representation and pattern recognition is the advantage of deep learning methods in modeling and interpretation for association analysis. Based on published computational experiments, the convolutional neural network and graph neural networks could be recommended for plant microbiome analysis.

Development of a time-series shotgun metagenomics database for monitoring microbial communities at the Pacific coast of Japan

Although numerous metagenome, amplicon sequencing-based studies have been conducted to date to characterize marine microbial communities, relatively few have employed full metagenome shotgun sequencing to obtain a broader picture of the functional features of these marine microbial communities. Moreover, most of these studies only performed sporadic sampling, which is insufficient to understand an ecosystem comprehensively. In this study, we regularly conducted seawater sampling along the northeastern Pacific coast of Japan between March 2012 and May 2016. We collected 213 seawater samples and prepared size-based fractions to generate 454 subsets of samples for shotgun metagenome sequencing and analysis. We also determined the sequences of 16S rRNA (n = 111) and 18S rRNA (n = 47) gene amplicons from smaller sample subsets. We thereafter developed the Ocean Monitoring Database for time-series metagenomic data ( http://marine-meta.healthscience.sci.waseda.ac.jp/omd/ ), which provides a three-dimensional bird's-eye view of the data. This database includes results of digital DNA chip analysis, a novel method for estimating ocean characteristics such as water temperature from metagenomic data. Furthermore, we developed a novel classification method that includes more information about viruses than that acquired using BLAST. We further report the discovery of a large number of previously overlooked (TAG)n repeat sequences in the genomes of marine microbes. We predict that the availability of this time-series database will lead to major discoveries in marine microbiome research.

Environmental Atlas of Prokaryotes Enables Powerful and Intuitive Habitat-Based Analysis of Community Structures

The recent prevalence of high-throughput sequencing has been producing numerous prokaryotic community structure datasets. Although the trait-based approach is useful to interpret those datasets from ecological perspectives, available trait information is biased toward culturable prokaryotes, especially those of clinical and public health relevance, and thus may not represent the breadth of microbiota found across many of Earth's environments. To facilitate habitat-based analysis free of such bias, here we report a ready-to-use prokaryotic habitat database, ProkAtlas. ProkAtlas comprehensively links 16S rRNA gene sequences to prokaryotic habitats, using public shotgun metagenome datasets. We also developed a computational pipeline for habitat-based analysis of given prokaryotic community structures. After confirmation of the method effectiveness using 16S rRNA gene sequence datasets from individual genomes and the Earth Microbiome Project, we showed its validness and effectiveness in drawing ecological insights by applying it to six empirical prokaryotic community datasets from soil, aquatic, and human gut samples.

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We developed a database, ProkAtlas, denoting the habitat preferences of prokaryotes

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ProkAtlas represents a prokaryotic community using habitat preferences of its members

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The powerfulness of ProkAtlas is showcased by six datasets from various environments

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We provide web interface of ProkAtlas at https://msk33.github.io/prokatlas.html

Comparative genomics of Bacteria commonly identified in the built environment

BACKGROUND

The microbial community of the built environment (BE) can impact the lives of people and has been studied for a variety of indoor, outdoor, underground, and extreme locations. Thus far, these microorganisms have mainly been investigated by culture-based methods or amplicon sequencing. However, both methods have limitations, complicating multi-study comparisons and limiting the knowledge gained regarding in-situ microbial lifestyles. A greater understanding of BE microorganisms can be achieved through basic information derived from the complete genome. Here, we investigate the level of diversity and genomic features (genome size, GC content, replication strand skew, and codon usage bias) from complete genomes of bacteria commonly identified in the BE, providing a first step towards understanding these bacterial lifestyles.

RESULTS

Here, we selected bacterial genera commonly identified in the BE (or "Common BE genomes") and compared them against other prokaryotic genera ("Other genomes"). The "Common BE genomes" were identified in various climates and in indoor, outdoor, underground, or extreme built environments. The diversity level of the 16S rRNA varied greatly between genera. The genome size, GC content and GC skew strength of the "Common BE genomes" were statistically larger than those of the "Other genomes" but were not practically significant. In contrast, the strength of selected codon usage bias (S value) was statistically higher with a large effect size in the "Common BE genomes" compared to the "Other genomes."

CONCLUSION

Of the four genomic features tested, the S value could play a more important role in understanding the lifestyles of bacteria living in the BE. This parameter could be indicative of bacterial growth rates, gene expression, and other factors, potentially affected by BE growth conditions (e.g., temperature, humidity, and nutrients). However, further experimental evidence, species-level BE studies, and classification by BE location is needed to define the relationship between genomic features and the lifestyles of BE bacteria more robustly.

Aquatic Bacterial Communities Associated With Land Use and Environmental Factors in Agricultural Landscapes Using a Metabarcoding Approach

This study applied a 16S rRNA gene metabarcoding approach to characterize bacterial community compositional and functional attributes for surface water samples collected within, primarily, agriculturally dominated watersheds in Ontario and Québec, Canada. Compositional heterogeneity was best explained by stream order, season, and watercourse discharge. Generally, community diversity was higher at agriculturally dominated lower order streams, compared to larger stream order systems such as small to large rivers. However, during times of lower relative water flow and cumulative 2-day rainfall, modestly higher relative diversity was found in the larger watercourses. Bacterial community assemblages were more sensitive to environmental/land use changes in the smaller watercourses, relative to small-to-large river systems, where the proximity of the sampled water column to bacteria reservoirs in the sediments and adjacent terrestrial environment was greater. Stream discharge was the environmental variable most significantly correlated (all positive) with bacterial functional groups, such as C/N cycling and plant pathogens. Comparison of the community structural similarity via network analyses helped to discriminate sources of bacteria in freshwater derived from, for example, wastewater treatment plant effluent and intensity and type of agricultural land uses (e.g., intensive swine production vs. dairy dominated cash/livestock cropping systems). When using metabarcoding approaches, bacterial community composition and coexisting pattern rather than individual taxonomic lineages, were better indicators of environmental/land use conditions (e.g., upstream land use) and bacterial sources in watershed settings. Overall, monitoring changes and differences in aquatic microbial communities at regional and local watershed scales has promise for enhancing environmental footprinting and for better understanding nutrient cycling and ecological function of aquatic systems impacted by a multitude of stressors and land uses.

MicroPheno: predicting environments and host phenotypes from 16S rRNA gene sequencing using a k-mer based representation of shallow sub-samples

Motivation

Microbial communities play important roles in the function and maintenance of various biosystems, ranging from the human body to the environment. A major challenge in microbiome research is the classification of microbial communities of different environments or host phenotypes. The most common and cost-effective approach for such studies to date is 16S rRNA gene sequencing. Recent falls in sequencing costs have increased the demand for simple, efficient and accurate methods for rapid detection or diagnosis with proved applications in medicine, agriculture and forensic science. We describe a reference- and alignment-free approach for predicting environments and host phenotypes from 16S rRNA gene sequencing based on k-mer representations that benefits from a bootstrapping framework for investigating the sufficiency of shallow sub-samples. Deep learning methods as well as classical approaches were explored for predicting environments and host phenotypes.

Results

A k-mer distribution of shallow sub-samples outperformed Operational Taxonomic Unit (OTU) features in the tasks of body-site identification and Crohn's disease prediction. Aside from being more accurate, using k-mer features in shallow sub-samples allows (i) skipping computationally costly sequence alignments required in OTU-picking and (ii) provided a proof of concept for the sufficiency of shallow and short-length 16S rRNA sequencing for phenotype prediction. In addition, k-mer features predicted representative 16S rRNA gene sequences of 18 ecological environments, and 5 organismal environments with high macro-F1 scores of 0.88 and 0.87. For large datasets, deep learning outperformed classical methods such as Random Forest and Support Vector Machine.

Availability and implementation

The software and datasets are available at https://llp.berkeley.edu/micropheno.

Supplementary information

Supplementary data are available at Bioinformatics online.

Latent environment allocation of microbial community data

As data for microbial community structures found in various environments has increased, studies have examined the relationship between environmental labels given to retrieved microbial samples and their community structures. However, because environments continuously change over time and space, mixed states of some environments and its effects on community formation should be considered, instead of evaluating effects of discrete environmental categories. Here we applied a hierarchical Bayesian model to paired datasets containing more than 30,000 samples of microbial community structures and sample description documents. From the training results, we extracted latent environmental topics that associate co-occurring microbes with co-occurring word sets among samples. Topics are the core elements of environmental mixtures and the visualization of topic-based samples clarifies the connections of various environments. Based on the model training results, we developed a web application, LEA (Latent Environment Allocation), which provides the way to evaluate typicality and heterogeneity of microbial communities in newly obtained samples without confining environmental categories to be compared. Because topics link words and microbes, LEA also enables to search samples semantically related to the query out of 30,000 microbiome samples.

In the past decade, microbiomes from various natural and human symbiotic environments have been thoroughly studied. However, our knowledge is limited as to what types of environments affect the structure of a microbial community. In the first place, how can we define “environments”, in particular, the environmental entities that are often continuously varying and difficult to discretely categorize? We assumed that environments could be represented from microbiome data because the structure of microbial communities reflect the state of the environment. We applied a probabilistic topic model to a dataset containing taxonomic composition data and natural language sample descriptions of >30,000 microbiome samples and extracted “latent environments” of the microbial communities, which are core elements of environmental mixtures. Integrating the training results of the model, we developed a web application to explore the microbiome universe and to place new metagenomic data on this universe like a global positioning system. Our tool shows what kinds of the environment naturally exist and are similar to each other on the perspective of the structural patterns of microbiome, and provides the way to evaluate the commonality and the heterogeneity of users’ microbiome samples.

Bacterial Community in Water and Air of Two Sub-Alpine Lakes in Taiwan

Very few studies have attempted to profile the microbial communities in the air above freshwater bodies, such as lakes, even though freshwater sources are an important part of aquatic ecosystems and airborne bacteria are the most dispersible microorganisms on earth. In the present study, we investigated microbial communities in the waters of two high mountain sub-alpine montane lakes—located 21 km apart and with disparate trophic characteristics—and the air above them. Although bacteria in the lakes had locational differences, their community compositions remained constant over time. However, airborne bacterial communities were diverse and displayed spatial and temporal variance. Proteobacteria, Actinobacteria, Bacteroidetes, and Cyanobacteria were dominant in both lakes, with different relative abundances between lakes, and Parcubacteria (OD1) was dominant in air samples for all sampling times, except two. We also identified certain shared taxa between lake water and the air above it. The results obtained on these communities in the present study provide putative candidates to study how airborne communities shape lake water bacterial compositions and vice versa.

Generalist species drive microbial dispersion and evolution

Microbes form fundamental bases of every Earth ecosystem. As their key survival strategies, some microbes adapt to broad ranges of environments, while others specialize to certain habitats. While ecological roles and properties of such “generalists” and “specialists” had been examined in individual ecosystems, general principles that govern their distribution patterns and evolutionary processes have not been characterized. Here, we thoroughly identified microbial generalists and specialists across 61 environments via meta-analysis of community sequencing data sets and reconstructed their evolutionary histories across diverse microbial groups. This revealed that generalist lineages possess 19-fold higher speciation rates and significant persistence advantage over specialists. Yet, we also detected three-fold more frequent generalist-to-specialist transformations than the reverse transformations. These results support a model of microbial evolution in which generalists play key roles in introducing new species and maintaining taxonomic diversity.

Microbes adapting to broad and specialized ranges of environments (generalists and specialists) have distinct ecological roles and properties. Via meta-analysis of community sequencing datasets, Sriswasdi et al. show that generalists have higher speciation rates and persistence advantage over specialists.

Seasonal Analysis of Microbial Communities in Precipitation in the Greater Tokyo Area, Japan

The presence of microbes in the atmosphere and their transport over long distances across the Earth's surface was recently shown. Precipitation is likely a major path by which aerial microbes fall to the ground surface, affecting its microbial ecosystems and introducing pathogenic microbes. Understanding microbial communities in precipitation is of multidisciplinary interest from the perspectives of microbial ecology and public health; however, community-wide and seasonal analyses have not been conducted. Here, we carried out 16S rRNA amplicon sequencing of 30 precipitation samples that were aseptically collected over 1 year in the Greater Tokyo Area, Japan. The precipitation microbial communities were dominated by Proteobacteria, Firmicutes, Bacteroidetes, and Actinobacteria and were overall consistent with those previously reported in atmospheric aerosols and cloud water. Seasonal variations in composition were observed; specifically, Proteobacteria abundance significantly decreased from summer to winter. Notably, estimated ordinary habitats of precipitation microbes were dominated by animal-associated, soil-related, and marine-related environments, and reasonably consistent with estimated air mass backward trajectories. To our knowledge, this is the first amplicon-sequencing study investigating precipitation microbial communities involving sampling over the duration of a year.

IMNGS: A comprehensive open resource of processed 16S rRNA microbial profiles for ecology and diversity studies

The SRA (Sequence Read Archive) serves as primary depository for massive amounts of Next Generation Sequencing data, and currently host over 100,000 16S rRNA gene amplicon-based microbial profiles from various host habitats and environments. This number is increasing rapidly and there is a dire need for approaches to utilize this pool of knowledge. Here we created IMNGS (Integrated Microbial Next Generation Sequencing), an innovative platform that uniformly and systematically screens for and processes all prokaryotic 16S rRNA gene amplicon datasets available in SRA and uses them to build sample-specific sequence databases and OTU-based profiles. Via a web interface, this integrative sequence resource can easily be queried by users. We show examples of how the approach allows testing the ecological importance of specific microorganisms in different hosts or ecosystems, and performing targeted diversity studies for selected taxonomic groups. The platform also offers a complete workflow for de novo analysis of users’ own raw 16S rRNA gene amplicon datasets for the sake of comparison with existing data. IMNGS can be accessed at www.imngs.org.

Metagenomics and Bioinformatics in Microbial Ecology: Current Status and Beyond

Metagenomic approaches are now commonly used in microbial ecology to study microbial communities in more detail, including many strains that cannot be cultivated in the laboratory. Bioinformatic analyses make it possible to mine huge metagenomic datasets and discover general patterns that govern microbial ecosystems. However, the findings of typical metagenomic and bioinformatic analyses still do not completely describe the ecology and evolution of microbes in their environments. Most analyses still depend on straightforward sequence similarity searches against reference databases. We herein review the current state of metagenomics and bioinformatics in microbial ecology and discuss future directions for the field. New techniques will allow us to go beyond routine analyses and broaden our knowledge of microbial ecosystems. We need to enrich reference databases, promote platforms that enable meta- or comprehensive analyses of diverse metagenomic datasets, devise methods that utilize long-read sequence information, and develop more powerful bioinformatic methods to analyze data from diverse perspectives.

Genomic and metagenomic analysis of microbes in a soil environment affected by the 2011 Great East Japan Earthquake tsunami

BACKGROUND

The Great East Japan Earthquake of 2011 triggered large tsunami waves, which flooded broad areas of land along the Pacific coast of eastern Japan and changed the soil environment drastically. However, the microbial characteristics of tsunami-affected soil at the genomic level remain largely unknown. In this study, we isolated microbes from a soil sample using general low-nutrient and seawater-based media to investigate microbial characteristics in tsunami-affected soil.

RESULTS

As expected, a greater proportion of strains isolated from the tsunami-affected soil than the unaffected soil grew in the seawater-based medium. Cultivable strains in both the general low-nutrient and seawater-based media were distributed in the genus Arthrobacter. Most importantly, whole-genome sequencing of four of the isolated Arthrobacter strains revealed independent losses of siderophore-synthesis genes from their genomes. Siderophores are low-molecular-weight, iron-chelating compounds that are secreted for iron uptake; thus, the loss of siderophore-synthesis genes indicates that these strains have adapted to environments with high-iron concentrations. Indeed, chemical analysis confirmed the investigated soil samples to be rich in iron, and culture experiments confirmed weak cultivability of some of these strains in iron-limited media. Furthermore, metagenomic analyses demonstrated over-representation of denitrification-related genes in the tsunami-affected soil sample, as well as the presence of pathogenic and marine-living genera and genes related to salt-tolerance.

CONCLUSIONS

Collectively, the present results would provide an example of microbial characteristics of soil disturbed by the tsunami, which may give an insight into microbial adaptation to drastic environmental changes. Further analyses on microbial ecology after a tsunami are envisioned to develop a deeper understanding of the recovery processes of terrestrial microbial ecosystems.