Strategies to improve reference databases for soil microbiomes

From soil to sequence: filling the critical gap in genome-resolved metagenomics is essential to the future of soil microbial ecology

Soil microbiomes are heterogeneous, complex microbial communities. Metagenomic analysis is generating vast amounts of data, creating immense challenges in sequence assembly and analysis. Although advances in technology have resulted in the ability to easily collect large amounts of sequence data, soil samples containing thousands of unique taxa are often poorly characterized. These challenges reduce the usefulness of genome-resolved metagenomic (GRM) analysis seen in other fields of microbiology, such as the creation of high quality metagenomic assembled genomes and the adoption of genome scale modeling approaches. The absence of these resources restricts the scale of future research, limiting hypothesis generation and the predictive modeling of microbial communities. Creating publicly available databases of soil MAGs, similar to databases produced for other microbiomes, has the potential to transform scientific insights about soil microbiomes without requiring the computational resources and domain expertise for assembly and binning.

Genomic representativeness and chimerism in large collections of SAGs and MAGs of marine prokaryoplankton

BACKGROUND

Single amplified genomes (SAGs) and metagenome-assembled genomes (MAGs) are the predominant sources of information about the coding potential of uncultured microbial lineages, but their strengths and limitations remain poorly understood. Here, we performed a direct comparison of two previously published collections of thousands of SAGs and MAGs obtained from the same, global environment.

RESULTS

We found that SAGs were less prone to chimerism and more accurately reflected the relative abundance and the pangenome content of microbial lineages inhabiting the epipelagic of the tropical and subtropical ocean, as compared to MAGs. SAGs were also better suited to link genome information with taxa discovered through 16S rRNA amplicon analyses. Meanwhile, MAGs had the advantage of more readily recovering genomes of rare lineages.

CONCLUSIONS

Our analyses revealed the relative strengths and weaknesses of the two most commonly used genome recovery approaches in environmental microbiology. These considerations, as well as the need for better tools for genome quality assessment, should be taken into account when designing studies and interpreting data that involve SAGs or MAGs. Video Abstract.

Global soil metagenomics reveals distribution and predominance of Deltaproteobacteria in nitrogen-fixing microbiome

BACKGROUND

Biological nitrogen fixation is a fundamental process sustaining all life on earth. While distribution and diversity of N2-fixing soil microbes have been investigated by numerous PCR amplicon sequencing of nitrogenase genes, their comprehensive understanding has been hindered by lack of de facto standard protocols for amplicon surveys and possible PCR biases. Here, by fully leveraging the planetary collections of soil shotgun metagenomes along with recently expanded culture collections, we evaluated the global distribution and diversity of terrestrial diazotrophic microbiome.

RESULTS

After the extensive analysis of 1,451 soil metagenomic samples, we revealed that the Anaeromyxobacteraceae and Geobacteraceae within Deltaproteobacteria are ubiquitous groups of diazotrophic microbiome in the soils with different geographic origins and land usage types, with particular predominance in anaerobic soils (paddy soils and sediments).

CONCLUSION

Our results indicate that Deltaproteobacteria is a core bacterial taxon in the potential soil nitrogen fixation population, especially in anaerobic environments, which encourages a careful consideration on deltaproteobacterial diazotrophs in understanding terrestrial nitrogen cycling. Video Abstract.

A mixed blessing of influent leachate microbes in downstream biotreatment systems of a full-scale landfill leachate treatment plant

In landfill leachate treatment plants (LLTPs), the microbiome plays a pivotal role in the decomposition of organic compounds, reduction in nutrient levels, and elimination of toxins. However, the effects of microbes in landfill leachate influents on downstream treatment systems remain poorly understood. To address this knowledge gap, we collected 23 metagenomic and 12 metatranscriptomic samples from landfill leachate and activated sludge from various treatment units in a full-scale LLTP. We successfully recovered 1,152 non-redundant metagenome-assembled genomes (MAGs), encompassing a wide taxonomic range, including 48 phyla, 95 classes, 166 orders, 247 families, 238 genera, and 1,152 species. More diverse microbes were observed in the influent leachate than in the downstream biotreatment systems, among which, an unprecedented ∼30 % of microbes with transcriptional expression migrated from the influent to the biological treatment units. Network analysis revealed that 399 shared MAGs across the four units exhibited high node centrality and degree, thus supporting enhanced interactions and increased stability of microbial communities. Functional reconstruction and genome characterization of MAGs indicated that these shared MAGs possessed greater capabilities for carbon, nitrogen, sulfur, and arsenic metabolism compared to non-shared MAGs. We further identified a novel species of Zixibacteria in the leachate influent with discrete lineages from those in other environments that accounted for up to 17 % of the abundance of the shared microbial community and exhibited notable metabolic versatility. Meanwhile, we presented groundbreaking evidence of the involvement of Zixibacteria-encoded genes in the production of harmful gas emissions, such as N2O and H2S, at the transcriptional level, thus suggesting that influent microbes may pose safety risks to downstream treatment systems. In summary, this study revealed the complex impact of the influent microbiome on LLTP and emphasizes the need to consider these microbial characteristics when designing treatment technologies and strategies for landfill leachate management.

Microbial evolution-An under-appreciated driver of soil carbon cycling

Although substantial advances in predicting the ecological impacts of global change have been made, predictions of the evolutionary impacts have lagged behind. In soil ecosystems, microbes act as the primary energetic drivers of carbon cycling; however, microbes are also capable of evolving on timescales comparable to rates of global change. Given the importance of soil ecosystems in global carbon cycling, we assess the potential impact of microbial evolution on carbon-climate feedbacks in this system. We begin by reviewing the current state of knowledge concerning microbial evolution in response to global change and its specific effect on soil carbon dynamics. Through this integration, we synthesize a roadmap detailing how to integrate microbial evolution into ecosystem biogeochemical models. Specifically, we highlight the importance of microscale mechanistic soil carbon models, including choosing an appropriate evolutionary model (e.g., adaptive dynamics, quantitative genetics), validating model predictions with 'omics' and experimental data, scaling microbial adaptations to ecosystem level processes, and validating with ecosystem-scale measurements. The proposed steps will require significant investment of scientific resources and might require 10-20 years to be fully implemented. However, through the application of multi-scale integrated approaches, we will advance the integration of microbial evolution into predictive understanding of ecosystems, providing clarity on its role and impact within the broader context of environmental change.

CONSULT-II: accurate taxonomic identification and profiling using locality-sensitive hashing

MOTIVATION

Taxonomic classification of short reads and taxonomic profiling of metagenomic samples are well-studied yet challenging problems. The presence of species belonging to groups without close representation in a reference dataset is particularly challenging. While k-mer-based methods have performed well in terms of running time and accuracy, they tend to have reduced accuracy for such novel species. Thus, there is a growing need for methods that combine the scalability of k-mers with increased sensitivity.

RESULTS

Here, we show that using locality-sensitive hashing (LSH) can increase the sensitivity of the k-mer-based search. Our method, which combines LSH with several heuristics techniques including soft lowest common ancestor labeling and voting, is more accurate than alternatives in both taxonomic classification of individual reads and abundance profiling.

AVAILABILITY AND IMPLEMENTATION

CONSULT-II is implemented in C++, and the software, together with reference libraries, is publicly available on GitHub https://github.com/bo1929/CONSULT-II.

A Practical Approach to Using the Genomic Standards Consortium MIxS Reporting Standard for Comparative Genomics and Metagenomics

Comparative analysis of (meta)genomes necessitates aggregation, integration, and synthesis of well-annotated data using standards. The Genomic Standards Consortium (GSC) collaborates with the research community to develop and maintain the Minimum Information about any (x) Sequence (MIxS) reporting standard for genomic data. To facilitate the use of the GSC's MIxS reporting standard, we provide a description of the structure and terminology, how to navigate ontologies for required terms in MIxS, and demonstrate practical usage through a soil metagenome example.

100 years of anthropogenic impact causes changes in freshwater functional biodiversity

Despite efforts from scientists and regulators, biodiversity is declining at an alarming rate. Unless we find transformative solutions to preserve biodiversity, future generations may not be able to enjoy nature's services. We have developed a conceptual framework that establishes the links between biodiversity dynamics and abiotic change through time and space using artificial intelligence. Here, we apply this framework to a freshwater ecosystem with a known history of human impact and study 100 years of community-level biodiversity, climate change and chemical pollution trends. We apply explainable network models with multimodal learning to community-level functional biodiversity measured with multilocus metabarcoding, to establish correlations with biocides and climate change records. We observed that the freshwater community assemblage and functionality changed over time without returning to its original state, even if the lake partially recovered in recent times. Insecticides and fungicides, combined with extreme temperature events and precipitation, explained up to 90% of the functional biodiversity changes. The community-level biodiversity approach used here reliably explained freshwater ecosystem shifts. These shifts were not observed when using traditional quality indices (e.g. Trophic Diatom Index). Our study advocates the use of high-throughput systemic approaches on long-term trends over species-focused ecological surveys to identify the environmental factors that cause loss of biodiversity and disrupt ecosystem functions.

A Pipeline for Constructing Reference Genomes for Large Cohort-Specific Metagenome Compression

Metagenomic data compression is very important as metagenomic projects are facing the challenges of larger data volumes per sample and more samples nowadays. Reference-based compression is a promising method to obtain a high compression ratio. However, existing microbial reference genome databases are not suitable to be directly used as references for compression due to their large size and redundancy, and different metagenomic cohorts often have various microbial compositions. We present a novel pipeline that generated simplified and tailored reference genomes for large metagenomic cohorts, enabling the reference-based compression of metagenomic data. We constructed customized reference genomes, ranging from 2.4 to 3.9 GB, for 29 real metagenomic datasets and evaluated their compression performance. Reference-based compression achieved an impressive compression ratio of over 20 for human whole-genome data and up to 33.8 for all samples, demonstrating a remarkable 4.5 times improvement than the standard Gzip compression. Our method provides new insights into reference-based metagenomic data compression and has a broad application potential for faster and cheaper data transfer, storage, and analysis.

Environmental DNA Sequencing to Monitor Restoration Practices on Soil Bacterial and Archaeal Communities in Soils Under Desertification in the Brazilian Semiarid

Soils from Brazilian semiarid regions are highly vulnerable to desertification due to their geology, climate, human actions, and intensive land use that contribute to desertification. Therefore, areas under desertification have increased in the Brazilian semiarid region and it has negatively changed the soil bacterial and archaeal communities and their functionality. On the other hand, although restoration strategies are expensive and there are few soils restoration programs, some practices have been applied to restore these soils under desertification. For instance, conservationist practices and grazing exclusion have been strategically implemented, and they created a new altered soil condition for soil microbial communities, boosting soil microbial diversity. Here, we discuss the potential of these restoration strategies to recover the richness and diversity of soil bacterial and archaeal communities that were described through environmental DNA (eDNA) sequencing of soil samples. eDNA sequencing results show that areas where restoration strategies have been applied in regions under desertification in the Brazilian semiarid have increased species richness, diversity, and structure of the bacterial and archaeal community. In addition, network connectivity and functionality of the soil microorganisms have been improved over time. Altogether, we show that management strategies for soil restoration have positive effects on soil microbial communities and these effects can be monitored using the eDNA sequencing approach.

Genomic Features Predict Bacterial Life History Strategies in Soil, as Identified by Metagenomic Stable Isotope Probing

Bacteria catalyze the formation and destruction of soil organic matter, but the bacterial dynamics in soil that govern carbon (C) cycling are not well understood. Life history strategies explain the complex dynamics of bacterial populations and activities based on trade-offs in energy allocation to growth, resource acquisition, and survival. Such trade-offs influence the fate of soil C, but their genomic basis remains poorly characterized. We used multisubstrate metagenomic DNA stable isotope probing to link genomic features of bacteria to their C acquisition and growth dynamics. We identify several genomic features associated with patterns of bacterial C acquisition and growth, notably genomic investment in resource acquisition and regulatory flexibility. Moreover, we identify genomic trade-offs defined by numbers of transcription factors, membrane transporters, and secreted products, which match predictions from life history theory. We further show that genomic investment in resource acquisition and regulatory flexibility can predict bacterial ecological strategies in soil. IMPORTANCE Soil microbes are major players in the global carbon cycle, yet we still have little understanding of how the carbon cycle operates in soil communities. A major limitation is that carbon metabolism lacks discrete functional genes that define carbon transformations. Instead, carbon transformations are governed by anabolic processes associated with growth, resource acquisition, and survival. We use metagenomic stable isotope probing to link genome information to microbial growth and carbon assimilation dynamics as they occur in soil. From these data, we identify genomic traits that can predict bacterial ecological strategies which define bacterial interactions with soil carbon.

Characterizing a subtropical hypereutrophic lake: From physicochemical variables to shotgun metagenomic data

Lake Cajititlán is a subtropical and endorheic lake, which is heavily impacted by nutrient pollution. Agricultural runoff and poorly treated wastewater have entered this reservoir at alarming rates during past rainy seasons, causing the cultural eutrophication of this body of water and resulting in several massive fish kill events. In this study, shotgun metagenomic sequencing was used to examine the taxonomic and functional structure of microbial communities in Lake Cajititlán during the rainy season. Several water quality features and their interactions with microbial communities were also assessed to identify the major factors affecting the water quality and biota, specifically fish species. According to current water quality regulations, most of the physicochemical variables analyzed (dissolved oxygen, pH, Secchi disk, NH4+, NO3−, blue-green algae, total phosphorus, and chlorophyll-a) were outside of the permissible limits. Planktothrix agardhii and Microcystis aeruginosa were the most abundant phytoplankton species, and the dominant bacterial genera were Pseudomonas, Streptomyces, and Flavobacterium, with Pseudomonas fluorescens, Stenotrophomonas maltophilia, and Aeromonas veronii representing the most abundant bacterial species. All of these microorganisms have been reported to be potentially harmful to fish, and the latter three (P. fluorescens, S. maltophilia, A. veronii) also contain genes associated with pathogenicity in fish mortality (fur, luxS, aer, act, aha, exu, lip, ser). Genetic evidence from the microbial communities analyzed herein reveals that anthropogenic sources of nutrients in the lake altered genes involved in nitrogen, phosphorus, sulfur, and carbon metabolism, mainly at the beginning of the rainy season. These findings suggest that abiotic factors influence the structure of the microbial communities, along with the major biogeochemical cycles of Lake Cajititlán, resulting in temporal variations and an excess of microorganisms that can thrive in high-nutrient and low-oxygen environments. After reviewing the literature, this appears to be the first study that focuses on characterizing the water quality of a subtropical hypereutrophic lake through associations between physicochemical variables and shotgun metagenomic data. In addition, there are few studies that have coupled the metabolism of aquatic ecosystems with nutrient cycles.

Beyond Basic Diversity Estimates-Analytical Tools for Mechanistic Interpretations of Amplicon Sequencing Data

Understanding microbial ecology through amplifying short read regions, typically 16S rRNA for prokaryotic species or 18S rRNA for eukaryotic species, remains a popular, economical choice. These methods provide relative abundances of key microbial taxa, which, depending on the experimental design, can be used to infer mechanistic ecological underpinnings. In this review, we discuss recent advancements in in situ analytical tools that have the power to elucidate ecological phenomena, unveil the metabolic potential of microbial communities, identify complex multidimensional interactions between species, and compare stability and complexity under different conditions. Additionally, we highlight methods that incorporate various modalities and additional information, which in combination with abundance data, can help us understand how microbial communities respond to change in a typical ecosystem. Whilst the field of microbial informatics continues to progress substantially, our emphasis is on popular methods that are applicable to a broad range of study designs. The application of these methods can increase our mechanistic understanding of the ongoing dynamics of complex microbial communities.

Incipient Sympatric Speciation and Evolution of Soil Bacteria Revealed by Metagenomic and Structured Non-Coding RNAs Analysis

Simple Summary

The microevolutionary dynamics of soil bacteria under microclimatic differences are largely unexplored in contrast to our improving knowledge of their vast diversity. In this study, we performed a comparative metagenomic analysis of two sharply divergent rocks and soil types at the Evolution Plateau (EP) in eastern Upper Galilee, Israel. We have identified the significant differences in bacterial taxonomic diversity, functions, and patterns of RNA-based gene regulation between the bacteria from two different soil types. Furthermore, we have identified several species with a significant genetic divergence of the same species between the two soil types, highlighting the soil bacteria’s incipient sympatric speciation.

Abstract

Soil bacteria respond rapidly to changes in new environmental conditions. For adaptation to the new environment, they could mutate their genome, which impacts the alternation of the functional and regulatory landscape. Sometimes, these genetic and ecological changes may drive the bacterial evolution and sympatric speciation. Although sympatric speciation has been controversial since Darwin suggested it in 1859, there are several strong theoretical or empirical evidences to support it. Sympatric speciation associated with soil bacteria remains largely unexplored. Here, we provide potential evidence of sympatric speciation of soil bacteria by comparison of metagenomics from two sharply contrasting abutting divergence rock and soil types (Senonian chalk and its rendzina soil, and abutting Pleistocene basalt rock and basalt soil). We identified several bacterial species with significant genetic differences in the same species between the two soil types and ecologies. We show that the bacterial community composition has significantly diverged between the two soils; correspondingly, their functions were differentiated in order to adapt to the local ecological stresses. The ecologies, such as water availability and pH value, shaped the adaptation and speciation of soil bacteria revealed by the clear-cut genetic divergence. Furthermore, by a novel analysis scheme of riboswitches, we highlight significant differences in structured non-coding RNAs between the soil bacteria from two divergence soil types, which could be an important driver for functional adaptation. Our study provides new insight into the evolutionary divergence and incipient sympatric speciation of soil bacteria under microclimatic ecological differences.

Metabolic Phenotyping of Marine Heterotrophs on Refactored Media Reveals Diverse Metabolic Adaptations and Lifestyle Strategies

Microbial communities, through their metabolism, drive carbon cycling in marine environments. These complex communities are composed of many different microorganisms including heterotrophic bacteria, each with its own nutritional needs and metabolic capabilities. Yet, models of ecosystem processes typically treat heterotrophic bacteria as a “black box,” which does not resolve metabolic heterogeneity nor address ecologically important processes such as the successive modification of different types of organic matter. Here we directly address the heterogeneity of metabolism by characterizing the carbon source utilization preferences of 63 heterotrophic bacteria representative of several major marine clades. By systematically growing these bacteria on 10 media containing specific subsets of carbon sources found in marine biomass, we obtained a phenotypic fingerprint that we used to explore the relationship between metabolic preferences and phylogenetic or genomic features. At the class level, these bacteria display broadly conserved patterns of preference for different carbon sources. Despite these broad taxonomic trends, growth profiles correlate poorly with phylogenetic distance or genome-wide gene content. However, metabolic preferences are strongly predicted by a handful of key enzymes that preferentially belong to a few enriched metabolic pathways, such as those involved in glyoxylate metabolism and biofilm formation. We find that enriched pathways point to enzymes directly involved in the metabolism of the corresponding carbon source and suggest potential associations between metabolic preferences and other ecologically relevant traits. The availability of systematic phenotypes across multiple synthetic media constitutes a valuable resource for future quantitative modeling efforts and systematic studies of interspecies interactions.

IMPORTANCE Half of the Earth’s annual primary production is carried out by phytoplankton in the surface ocean. However, this metabolic activity is heavily impacted by heterotrophic bacteria, which dominate the transformation of organic matter released from phytoplankton. Here, we characterize the diversity of metabolic preferences across many representative heterotrophs by systematically growing them on different fractions of dissolved organic carbon. Our analysis suggests that different clades of bacteria have substantially distinct preferences for specific carbon sources, in a way that cannot be simply mapped onto phylogeny. These preferences are associated with the presence of specific genes and pathways, reflecting an association between metabolic capabilities and ecological lifestyles. In addition to helping understand the importance of heterotrophs under different conditions, the phenotypic fingerprint we obtained can help build higher resolution quantitative models of global microbial activity and biogeochemical cycles in the oceans.

Bacterial Resuscitation from Starvation-Induced Dormancy Results in Phenotypic Diversity Coupled with Translational Activity Depending on Carbon Substrate Availability

Dormancy is a survival strategy of stressed bacteria inhabiting a various environment. Frequent dormant-active transitions owing to environmental changes play an important role in functional redundancy. However, a proper understanding of the phenotypic changes in bacteria during these transitions remains to be clarified. In this study, orthogonal approaches, such as electron microscopy, flow cytometry, and Raman spectroscopy, which can evaluate phenotypic heterogeneity at the single-cell level, were used to observe morphological and molecular phenotypic changes in resuscitated cells, and RNA sequencing (RNASeq) was used to determine the genetic characteristics associated with phenotypes. Within 12 h of the resuscitation process, morphological (cell size and shape) and physiological (growth and viability) characteristics as well as molecular phenotypes (cellular components) were found to be recovered to the extent that they were similar to those in active cells. The recovery rate and detailed phenotypic properties of the resuscitated cells differed significantly depending on the type or concentration of carbon sources. RNASeq analysis revealed that genes related to translation were significantly upregulated under all resuscitation conditions. The simpler the carbon source (e.g., glucose), the higher the expression of genes involved in cellular repair, and the more complex the carbon source (e.g., beef extract), the higher the expression of genes associated with increased energy production associated with cellular aerobic respiration. This study of phenotypic plasticity of resuscitated cells provides fundamental insight into understanding the adaptive fine-tuning of the microbiome in response to environmental changes and the functional redundancy resulting from phenotype heterogeneity.

A Comprehensive Insight of Current and Future Challenges in Large-Scale Soil Microbiome Analyses

In the last decade, various large-scale projects describing soil microbial diversity across large geographical gradients have been undertaken. However, many questions remain unanswered about the best ways to conduct these studies. In this review, we present an overview of the experience gathered during these projects, and of the challenges that future projects will face, such as standardization of protocols and results, considering the temporal variation of microbiomes, and the legal constraints limiting such studies. We also present the arguments for and against the exhaustive description of soil microbiomes. Finally, we look at future developments of soil microbiome studies, notably emphasizing the important role of cultivation techniques.

Evaluating metagenomic assembly approaches for biome-specific gene catalogues

BACKGROUND

For many environments, biome-specific microbial gene catalogues are being recovered using shotgun metagenomics followed by assembly and gene calling on the assembled contigs. The assembly is typically conducted either by individually assembling each sample or by co-assembling reads from all the samples. The co-assembly approach can potentially recover genes that display too low abundance to be assembled from individual samples. On the other hand, combining samples increases the risk of mixing data from closely related strains, which can hamper the assembly process. In this respect, assembly on individual samples followed by clustering of (near) identical genes is preferable. Thus, both approaches have potential pros and cons, but it remains to be evaluated which assembly strategy is most effective. Here, we have evaluated three assembly strategies for generating gene catalogues from metagenomes using a dataset of 124 samples from the Baltic Sea: (1) assembly on individual samples followed by clustering of the resulting genes, (2) co-assembly on all samples, and (3) mix assembly, combining individual and co-assembly.

RESULTS

The mix-assembly approach resulted in a more extensive nonredundant gene set than the other approaches and with more genes predicted to be complete and that could be functionally annotated. The mix assembly consists of 67 million genes (Baltic Sea gene set, BAGS) that have been functionally and taxonomically annotated. The majority of the BAGS genes are dissimilar (< 95% amino acid identity) to the Tara Oceans gene dataset, and hence, BAGS represents a valuable resource for brackish water research.

CONCLUSION

The mix-assembly approach represents a feasible approach to increase the information obtained from metagenomic samples. Video abstract.

Exploration of Soil Functional Microbiomes—A Concept Proposal for Long-Term Fertilized Grasslands

Exploring grassland microbiomes is a challenge in the current context of linking soil microorganism activity with the balance of these ecosystems. Microbiologists are constantly attempting to develop faster and lower-cost methods, and propose new and best-fitted indicators that will provide a more complex data analysis. A different concept was proposed for assessing functional microbiomes by splitting the functional ecological niche into complementary segments. The comparison with the upper and lower limits of the ecological niche provides a clearer image of community alterations due to long-term applied treatments. The method allows the extraction of the most sensitive and stable functional guilds, with the extraction of the most critical dominant–codominant functional groups in every segment of the functional niche. The resulting microbial functional–sociological model is ready to use on community-level physiological profile databases and also can be applied backward for vegetation analysis.

Single cell genome sequencing of laboratory mouse microbiota improves taxonomic and functional resolution of this model microbial community

Laboratory mice are widely studied as models of mammalian biology, including the microbiota. However, much of the taxonomic and functional diversity of the mouse gut microbiome is missed in current metagenomic studies, because genome databases have not achieved a balanced representation of the diverse members of this ecosystem. Towards solving this problem, we used flow cytometry and low-coverage sequencing to capture the genomes of 764 single cells from the stool of three laboratory mice. From these, we generated 298 high-coverage microbial genome assemblies, which we annotated for open reading frames and phylogenetic placement. These genomes increase the gene catalog and phylogenetic breadth of the mouse microbiota, adding 135 novel species with the greatest increase in diversity to the Muribaculaceae and Bacteroidaceae families. This new diversity also improves the read mapping rate, taxonomic classifier performance, and gene detection rate of mouse stool metagenomes. The novel microbial functions revealed through our single-cell genomes highlight previously invisible pathways that may be important for life in the murine gastrointestinal tract.

The gut microbiome of exudivorous marmosets in the wild and captivity

Mammalian captive dietary specialists like folivores are prone to gastrointestinal distress and primate dietary specialists suffer the greatest gut microbiome diversity losses in captivity compared to the wild. Marmosets represent another group of dietary specialists, exudivores that eat plant exudates, but whose microbiome remains relatively less studied. The common occurrence of gastrointestinal distress in captive marmosets prompted us to study the Callithrix gut microbiome composition and predictive function through bacterial 16S ribosomal RNA V4 region sequencing. We sampled 59 wild and captive Callithrix across four species and their hybrids. Host environment had a stronger effect on the gut microbiome than host taxon. Wild Callithrix gut microbiomes were enriched for Bifidobacterium, which process host-indigestible carbohydrates. Captive marmoset guts were enriched for Enterobacteriaceae, a family containing pathogenic bacteria. While gut microbiome function was similar across marmosets, Enterobacteriaceae seem to carry out most functional activities in captive host guts. More diverse bacterial taxa seem to perform gut functions in wild marmosets, with Bifidobacterium being important for carbohydrate metabolism. Captive marmosets showed gut microbiome composition aspects seen in human gastrointestinal diseases. Thus, captivity may perturb the exudivore gut microbiome, which raises implications for captive exudivore welfare and calls for husbandry modifications.

Metabolites of soil microorganisms modulate amyloid β production in Alzheimer's neurons

Microbial flora is investigated to be related with neuropathological conditions in Alzheimer’s disease (AD), and is attracting attention as a drug discovery resource. However, the relevance between the soil microbiota and the pathological condition has not been fully clarified due to the difficulty in isolation culture and the component complexity. In this study, we established a library of secondly metabolites produced in microorganism to investigate the potential effect of microorganisms on the production of amyloid β (Aβ), one of the most representative pathogens of AD. We conducted a library screening to quantify Aβ and neuronal toxicity by using cortical neurons from human induced pluripotent stem cells (iPSCs) of AD patients after adding secondary metabolites. Screening results and following assessment of dose-dependency identified Verrucarin A, produced in Myrothecium spp., showed 80% decrease in Aβ production. Furthermore, addition of Mer-A2026A, produced in Streptomyces pactum, showed increase in Aβ42/40 ratio at the low concentration, and decrease in Aβ production at the higher concentration. As a result, established library and iPSC-based phenotyping assay clarified a direct link between Aβ production and soil microorganisms. These results suggest that Aβ-microorganism interaction may provide insight into the AD pathophysiology with potential therapeutics.

Using AnnoTree to Get More Assignments, Faster, in DIAMOND+MEGAN Microbiome Analysis

In microbiome analysis, one main approach is to align metagenomic sequencing reads against a protein reference database, such as NCBI-nr, and then to perform taxonomic and functional binning based on the alignments. This approach is embodied, for example, in the standard DIAMOND+MEGAN analysis pipeline, which first aligns reads against NCBI-nr using DIAMOND and then performs taxonomic and functional binning using MEGAN. Here, we propose the use of the AnnoTree protein database, rather than NCBI-nr, in such alignment-based analyses to determine the prokaryotic content of metagenomic samples. We demonstrate a 2-fold speedup over the usage of the prokaryotic part of NCBI-nr and increased assignment rates, in particular assigning twice as many reads to KEGG. In addition to binning to the NCBI taxonomy, MEGAN now also bins to the GTDB taxonomy.

IMPORTANCE The NCBI-nr database is not explicitly designed for the purpose of microbiome analysis, and its increasing size makes its unwieldy and computationally expensive for this purpose. The AnnoTree protein database is only one-quarter the size of the full NCBI-nr database and is explicitly designed for metagenomic analysis, so it should be supported by alignment-based pipelines.

Inferring microbiota functions from taxonomic genes: a review

Deciphering microbiota functions is crucial to predict ecosystem sustainability in response to global change. High-throughput sequencing at the individual or community level has revolutionized our understanding of microbial ecology, leading to the big data era and improving our ability to link microbial diversity with microbial functions. Recent advances in bioinformatics have been key for developing functional prediction tools based on DNA metabarcoding data and using taxonomic gene information. This cheaper approach in every aspect serves as an alternative to shotgun sequencing. Although these tools are increasingly used by ecologists, an objective evaluation of their modularity, portability, and robustness is lacking. Here, we reviewed 100 scientific papers on functional inference and ecological trait assignment to rank the advantages, specificities, and drawbacks of these tools, using a scientific benchmarking. To date, inference tools have been mainly devoted to bacterial functions, and ecological trait assignment tools, to fungal functions. A major limitation is the lack of reference genomes-compared with the human microbiota-especially for complex ecosystems such as soils. Finally, we explore applied research prospects. These tools are promising and already provide relevant information on ecosystem functioning, but standardized indicators and corresponding repositories are still lacking that would enable them to be used for operational diagnosis.

Considerations for Constructing a Protein Sequence Database for Metaproteomics

Mass spectrometry-based metaproteomics has emerged as a prominent technique for interrogating the functions of specific organisms in microbial communities, in addition to total community function. Identifying proteins by mass spectrometry requires matching mass spectra of fragmented peptide ions to a database of protein sequences corresponding to the proteins in the sample. This sequence database determines which protein sequences can be identified from the measurement, and as such the taxonomic and functional information that can be inferred from a metaproteomics measurement. Thus, the construction of the protein sequence database directly impacts the outcome of any metaproteomics study. Several factors, such as source of sequence information and database curation, need to be considered during database construction to maximize accurate protein identifications traceable to the species of origin. In this review, we provide an overview of existing strategies for database construction and the relevant studies that have sought to test and validate these strategies. Based on this review of the literature and our experience we provide a decision tree and best practices for choosing and implementing database construction strategies.

Multisubstrate DNA stable isotope probing reveals guild structure of bacteria that mediate soil carbon cycling

Soil microorganisms determine the fate of soil organic matter (SOM), and their activities compose a major component of the global carbon (C) cycle. We employed a multisubstrate, DNA-stable isotope probing experiment to track bacterial assimilation of C derived from distinct sources that varied in bioavailability. This approach allowed us to measure microbial contributions to SOM processing by measuring the C assimilation dynamics of diverse microorganisms as they interacted within soil. We identified and tracked 1,286 bacterial taxa that assimilated 13C in an agricultural soil over a period of 48 d. Overall 13C-assimilation dynamics of bacterial taxa, defined by the source and timing of the 13C they assimilated, exhibited low phylogenetic conservation. We identified bacterial guilds composed of taxa that had similar 13C assimilation dynamics. We show that C-source bioavailability explained significant variation in both C mineralization dynamics and guild structure, and that the growth dynamics of bacterial guilds differed significantly in response to C addition. We also demonstrate that the guild structure explains significant variation in the biogeographical distribution of bacteria at continental and global scales. These results suggest that an understanding of in situ growth dynamics is essential for understanding microbial contributions to soil C cycling. We interpret these findings in the context of bacterial life history strategies and their relationship to terrestrial C cycling.

Functions predict horizontal gene transfer and the emergence of antibiotic resistance

Phylogenetic distance, shared ecology, and genomic constraints are often cited as key drivers governing horizontal gene transfer (HGT), although their relative contributions are unclear. Here, we apply machine learning algorithms to a curated set of diverse bacterial genomes to tease apart the importance of specific functional traits on recent HGT events. We find that functional content accurately predicts the HGT network [area under the receiver operating characteristic curve (AUROC) = 0.983], and performance improves further (AUROC = 0.990) for transfers involving antibiotic resistance genes (ARGs), highlighting the importance of HGT machinery, niche-specific, and metabolic functions. We find that high-probability not-yet detected ARG transfer events are almost exclusive to human-associated bacteria. Our approach is robust at predicting the HGT networks of pathogens, including Acinetobacter baumannii and Escherichia coli, as well as within localized environments, such as an individual’s gut microbiome.

Estimating maximal microbial growth rates from cultures, metagenomes, and single cells via codon usage patterns

Despite the wide perception that microbes have rapid growth rates, many environments like seawater and soil are often dominated by microorganisms that can only grow very slowly. Our knowledge about growth is necessarily biased toward easily culturable organisms, which tend to be those that grow fast, because microbial growth rates have traditionally been measured using laboratory growth experiments. However, how are potential growth rates distributed in nature? Using genomic data, we predicted the growth rates of over 200,000 organisms, including many as yet uncultivated species. These data reveal how current culture collections are strongly biased toward fast-growing organisms. Finally, we noticed a bimodal distribution of maximal growth rates, suggesting a natural division of microbial growth strategies into two classes.

Maximal growth rate is a basic parameter of microbial lifestyle that varies over several orders of magnitude, with doubling times ranging from a matter of minutes to multiple days. Growth rates are typically measured using laboratory culture experiments. Yet, we lack sufficient understanding of the physiology of most microbes to design appropriate culture conditions for them, severely limiting our ability to assess the global diversity of microbial growth rates. Genomic estimators of maximal growth rate provide a practical solution to survey the distribution of microbial growth potential, regardless of cultivation status. We developed an improved maximal growth rate estimator and predicted maximal growth rates from over 200,000 genomes, metagenome-assembled genomes, and single-cell amplified genomes to survey growth potential across the range of prokaryotic diversity; extensions allow estimates from 16S rRNA sequences alone as well as weighted community estimates from metagenomes. We compared the growth rates of cultivated and uncultivated organisms to illustrate how culture collections are strongly biased toward organisms capable of rapid growth. Finally, we found that organisms naturally group into two growth classes and observed a bias in growth predictions for extremely slow-growing organisms. These observations ultimately led us to suggest evolutionary definitions of oligotrophy and copiotrophy based on the selective regime an organism occupies. We found that these growth classes are associated with distinct selective regimes and genomic functional potentials.

CONSULT: accurate contamination removal using locality-sensitive hashing

A fundamental question appears in many bioinformatics applications: Does a sequencing read belong to a large dataset of genomes from some broad taxonomic group, even when the closest match in the set is evolutionarily divergent from the query? For example, low-coverage genome sequencing (skimming) projects either assemble the organelle genome or compute genomic distances directly from unassembled reads. Using unassembled reads needs contamination detection because samples often include reads from unintended groups of species. Similarly, assembling the organelle genome needs distinguishing organelle and nuclear reads. While k-mer-based methods have shown promise in read-matching, prior studies have shown that existing methods are insufficiently sensitive for contamination detection. Here, we introduce a new read-matching tool called CONSULT that tests whether k-mers from a query fall within a user-specified distance of the reference dataset using locality-sensitive hashing. Taking advantage of large memory machines available nowadays, CONSULT libraries accommodate tens of thousands of microbial species. Our results show that CONSULT has higher true-positive and lower false-positive rates of contamination detection than leading methods such as Kraken-II and improves distance calculation from genome skims. We also demonstrate that CONSULT can distinguish organelle reads from nuclear reads, leading to dramatic improvements in skim-based mitochondrial assemblies.

Beyond Taxonomic Identification: Integration of Ecological Responses to a Soil Bacterial 16S rRNA Gene Database

High-throughput sequencing 16S rRNA gene surveys have enabled new insights into the diversity of soil bacteria, and furthered understanding of the ecological drivers of abundances across landscapes. However, current analytical approaches are of limited use in formalizing syntheses of the ecological attributes of taxa discovered, because derived taxonomic units are typically unique to individual studies and sequence identification databases only characterize taxonomy. To address this, we used sequences obtained from a large nationwide soil survey (GB Countryside Survey, henceforth CS) to create a comprehensive soil specific 16S reference database, with coupled ecological information derived from survey metadata. Specifically, we modeled taxon responses to soil pH at the OTU level using hierarchical logistic regression (HOF) models, to provide information on both the shape of landscape scale pH-abundance responses, and pH optima (pH at which OTU abundance is maximal). We identify that most of the soil OTUs examined exhibited a non-flat relationship with soil pH. Further, the pH optima could not be generalized by broad taxonomy, highlighting the need for tools and databases synthesizing ecological traits at finer taxonomic resolution. We further demonstrate the utility of the database by testing against geographically dispersed query 16S datasets; evaluating efficacy by quantifying matches, and accuracy in predicting pH responses of query sequences from a separate large soil survey. We found that the CS database provided good coverage of dominant taxa; and that the taxa indicating soil pH in a query dataset corresponded with the pH classifications of top matches in the CS database. Furthermore we were able to predict query dataset community structure, using predicted abundances of dominant taxa based on query soil pH data and the HOF models of matched CS database taxa. The database with associated HOF model outputs is released as an online portal for querying single sequences of interest (https://shiny-apps.ceh.ac.uk/ID-TaxER/), and flat files are made available for use in bioinformatic pipelines. The further development of advanced informatics infrastructures incorporating modeled ecological attributes along with new functional genomic information will likely facilitate large scale exploration and prediction of soil microbial functional biodiversity under current and future environmental change scenarios.

Longitudinal sampling of external mucosae in farmed European seabass reveals the impact of water temperature on bacterial dynamics

Fish microbiota are intrinsically linked to health and fitness, but they are highly variable and influenced by both biotic and abiotic factors. Water temperature particularly limits bacterial adhesion and growth, impacting microbial diversity and bacterial infections on the skin and gills. Aquaculture is heavily affected by infectious diseases, especially in warmer months, and industry practices often promote stress and microbial dysbiosis, leading to an increased abundance of potentially pathogenic bacteria. In this regard, fish mucosa health is extremely important because it provides a primary barrier against pathogens. We used 16 rRNA V4 metataxonomics to characterize the skin and gill microbiota of the European seabass, Dicentrarchus labrax, and the surrounding water over 12 months, assessing the impact of water temperature on microbial diversity and function. We show that the microbiota of external mucosae are highly dynamic with consistent longitudinal trends in taxon diversity. Several potentially pathogenic genera (Aliivibrio, Photobacterium, Pseudomonas, and Vibrio) were highly abundant, showing complex interactions with other bacterial genera, some of which with recognized probiotic activity, and were also significantly impacted by changes in temperature. The surrounding water temperature influenced fish microbial composition, structure and function over time (days and months). Additionally, dysbiosis was more frequent in warmer months and during transitions between cold/warm months. We also detected a strong seasonal effect in the fish microbiota, which is likely to result from the compound action of several unmeasured environmental factors (e.g., pH, nutrient availability) beyond temperature. Our results highlight the importance of performing longitudinal studies to assess the impact of environmental factors on fish microbiotas.

Insights into the taxonomic and functional characterization of agricultural crop core rhizobiomes and their potential microbial drivers

While our understanding of plant-microbe interactions in the rhizosphere microbiome (rhizobiome) has increased, there is still limited information on which taxa and functions drive these rhizobiome interactions. Focusing on the core rhizobiome (members common to two or more microbial assemblages) of crops may reduce the number of targets for determining these interactions, as they are expected to have greater influence on soil nutrient cycling and plant growth than the rest of the rhizobiome. Here, we examined whether the characterization of a core rhizobiome on the basis of only taxonomic or functional traits rather than the combined analysis of taxonomic and functional traits provides a different assessment of the core rhizobiome of agricultural crops. Sequences of the bacterial 16S rRNA gene from six globally important crops were analyzed using two different approaches in order to identify and characterize the taxonomic and functional core rhizobiome. For all crops examined, we found significant differences in the taxonomic and functional composition between the core rhizobiomes, and different phyla, genera, and predicted microbial functions were dominant depending on the core rhizobiome type. Network analysis indicated potentially important taxa were present in both taxonomic and functional core rhizobiomes. A subset of genera and predicted functions were exclusively or predominately present in only one type of core rhizobiome while others were detected in both core rhizobiomes. These results highlight the necessity of including both taxonomy and function when assessing the core rhizobiome, as this will enhance our understanding of the relationships between microbial taxa and soil health, plant growth, and agricultural sustainability.

Decrypting bacterial polyphenol metabolism in an anoxic wetland soil

Microorganisms play vital roles in modulating organic matter decomposition and nutrient cycling in soil ecosystems. The enzyme latch paradigm posits microbial degradation of polyphenols is hindered in anoxic peat leading to polyphenol accumulation, and consequently diminished microbial activity. This model assumes that polyphenols are microbially unavailable under anoxia, a supposition that has not been thoroughly investigated in any soil type. Here, we use anoxic soil reactors amended with and without a chemically defined polyphenol to test this hypothesis, employing metabolomics and genome-resolved metaproteomics to interrogate soil microbial polyphenol metabolism. Challenging the idea that polyphenols are not bioavailable under anoxia, we provide metabolite evidence that polyphenols are depolymerized, resulting in monomer accumulation, followed by the generation of small phenolic degradation products. Further, we show that soil microbiome function is maintained, and possibly enhanced, with polyphenol addition. In summary, this study provides chemical and enzymatic evidence that some soil microbiota can degrade polyphenols under anoxia and subvert the assumed polyphenol lock on soil microbial metabolism.

gapseq: informed prediction of bacterial metabolic pathways and reconstruction of accurate metabolic models

Genome-scale metabolic models of microorganisms are powerful frameworks to predict phenotypes from an organism's genotype. While manual reconstructions are laborious, automated reconstructions often fail to recapitulate known metabolic processes. Here we present gapseq ( https://github.com/jotech/gapseq ), a new tool to predict metabolic pathways and automatically reconstruct microbial metabolic models using a curated reaction database and a novel gap-filling algorithm. On the basis of scientific literature and experimental data for 14,931 bacterial phenotypes, we demonstrate that gapseq outperforms state-of-the-art tools in predicting enzyme activity, carbon source utilisation, fermentation products, and metabolic interactions within microbial communities.

Genome-Resolved Metagenomics Is Essential for Unlocking the Microbial Black Box of the Soil

Despite several efforts to unravel the microbial diversity of soil, most microbes are still unknown. A recent large-scale effort based on genome-resolved metagenomics by Nayfach et al. has demonstrated how this approach can expand our understanding of novel bacterial lineages, including those from soils. Genomic catalogs of soil microbiomes are now enabling a deeper investigation of the evolutionary and functional role of high-complex soil microbiomes, promoting new knowledge from the reuse and sharing of multi-omics data.

Effects of aging on the skin and gill microbiota of farmed seabass and seabream

Background

Important changes in microbial composition related to sexual maturation have been already reported in the gut of several vertebrates including mammals, amphibians and fish. Such changes in fish are linked to reproduction and growth during developmental stages, diet transitions and critical life events. We used amplicon (16S rRNA) high-throughput sequencing to characterize the skin and gill bacterial microbiota of farmed seabass and seabream belonging to three different developmental age groups: early and late juveniles and mature adults. We also assessed the impact of the surrounding estuarine water microbiota in shaping the fish skin and gill microbiota.

Results

Microbial diversity, composition and predicted metabolic functions varied across fish maturity stages. Alpha-diversity in the seabass microbiota varied significantly between age groups and was higher in older fish. Conversely, in the seabream, no significant differences were found in alpha-diversity between age groups. Microbial structure varied significantly across age groups; moreover, high structural variation was also observed within groups. Different bacterial metabolic pathways were predicted to be enriched in the microbiota of both species. Finally, we found that the water microbiota was significantly distinct from the fish microbiota across all the studied age groups, although a high percentage of ASVs was shared with the skin and gill microbiotas.

Conclusions

We report important microbial differences in composition and potential functionality across different ages of farmed seabass and seabream. These differences may be related to somatic growth and the onset of sexual maturation. Importantly, some of the inferred metabolic pathways could enhance the fish coping mechanisms during stressful conditions. Our results provide new evidence suggesting that growth and sexual maturation have an important role in shaping the microbiota of the fish external mucosae and highlight the importance of considering different life stages in microbiota studies.

Supplementary Information

The online version contains supplementary material available at 10.1186/s42523-020-00072-2.

High Culturable Bacterial Diversity From a European Desert: The Tabernas Desert

One of the most diverse ecological niches for microbial bioprospecting is soil, including that of drylands. Drylands are one of the most abundant biomes on Earth, but extreme cases, such as deserts, are considered very rare in Europe. The so-called Tabernas Desert is one of the few examples of a desert area in continental Europe, and although some microbial studies have been performed on this region, a comprehensive strategy to maximize the isolation of environmental bacteria has not been conducted to date. We report here a culturomics approach to study the bacterial diversity of this dryland by using a simple strategy consisting of combining different media, using serial dilutions of the nutrients, and using extended incubation times. With this strategy, we were able to set a large (254 strains) collection of bacteria, the majority of which (93%) were identified through 16S ribosomal RNA (rRNA) gene amplification and sequencing. A significant fraction of the collection consisted of Actinobacteria and Proteobacteria, as well as Firmicutes strains. Among the 254 isolates, 37 different genera were represented, and a high number of possible new taxa were identified (31%), of which, three new Kineococcus species. Moreover, 5 out of the 13 genera represented by one isolate were also possible new species. Specifically, the sequences of 80 isolates held a percentage of identity below the 98.7% threshold considered for potentially new species. These strains belonged to 20 genera. Our results reveal a clear link between medium dilution and isolation of new species, highlight the unexploited bacterial biodiversity of the Tabernas Desert, and evidence the potential of simple strategies to yield surprisingly large numbers of diverse, previously unreported, bacterial strains and species.

Harnessing the plant microbiome to promote the growth of agricultural crops

The rhizosphere microbiome is composed of diverse microbial organisms, including archaea, viruses, fungi, bacteria as well as eukaryotic microorganisms, which occupy a narrow region of soil directly associated with plant roots. The interactions between these microorganisms and the plant can be commensal, beneficial or pathogenic. These microorganisms can also interact with each other, either competitively or synergistically. Promoting plant growth by harnessing the soil microbiome holds tremendous potential for providing an environmentally friendly solution to the increasing food demands of the world's rapidly growing population, while also helping to alleviate the associated environmental and societal issues of large-scale food production. There recently have been many studies on the disease suppression and plant growth promoting abilities of the rhizosphere microbiome; however, these findings largely have not been translated into the field. Therefore, additional research into the dynamic interactions between crop plants, the rhizosphere microbiome and the environment are necessary to better guide the harnessing of the microbiome to increase crop yield and quality. This review explores the biotic and abiotic interactions that occur within the plant's rhizosphere as well as current agricultural practices, and how these biotic and abiotic factors, as well as human practices, impact the plant microbiome. Additionally, some limitations, safety considerations, and future directions to the study of the plant microbiome are discussed.

Interrogating Plant-Microbe Interactions with Chemical Tools: Click Chemistry Reagents for Metabolic Labeling and Activity-Based Probes

Continued expansion of the chemical biology toolbox presents many new and diverse opportunities to interrogate the fundamental molecular mechanisms driving complex plant-microbe interactions. This review will examine metabolic labeling with click chemistry reagents and activity-based probes for investigating the impacts of plant-associated microbes on plant growth, metabolism, and immune responses. While the majority of the studies reviewed here used chemical biology approaches to examine the effects of pathogens on plants, chemical biology will also be invaluable in future efforts to investigate mutualistic associations between beneficial microbes and their plant hosts.

Different Arbuscular Mycorrhizal Fungi Cocolonizing on a Single Plant Root System Recruit Distinct Microbiomes

Plant roots are usually colonized by various arbuscular mycorrhizal (AM) fungal species, which vary in morphological, physiological, and genetic traits. This colonization constitutes the mycorrhizal nutrient uptake pathway (MP) and supplements the pathway through roots. Simultaneously, the extraradical hyphae of each AM fungus is associated with a community of bacteria. However, whether the community structure and function of the microbiome on the extraradical hyphae differ between AM fungal species remains unknown. In order to understand the community structure and the predicted functions of the microbiome associated with different AM fungal species, a split-root compartmented rhizobox cultivation system, which allowed us to inoculate two AM fungal species separately in two root compartments, was used. We inoculated two separate AM fungal species combinations, (i) Funneliformis mosseae and Gigaspora margarita and (ii) Rhizophagus intraradices and G. margarita, on a single root system of cotton. The hyphal exudate-fed, active microbiome was measured by combining 13C-DNA stable isotope probing with MiSeq sequencing. We found that different AM fungal species, which were simultaneously colonizing a single root system, hosted active microbiomes that were distinct from one another. Moreover, the predicted potential functions of the different microbiomes were distinct. We conclude that the arbuscular mycorrhizal fungal component of the system is responsible for the recruitment of distinct microbiomes in the hyphosphere. The potential significance of the predicted functions of the microbial ecosystem services is discussed.IMPORTANCE Arbuscular mycorrhizal (AM) fungi form tight symbiotic relationships with the majority of terrestrial plants and play critical roles in plant P acquisition, adding a further dimension of complexity. The plant-AM fungus-bacterium system is considered a continuum, with the bacteria colonizing not only the plant roots, but also the associated mycorrhizal hyphal network, known as the hyphosphere microbiome. Plant roots are usually colonized by different AM fungal species which form an independent phosphorus uptake pathway from the root pathway, i.e., the mycorrhizal pathway. The community structure and function of the hyphosphere microbiome of different AM species are completely unknown. In this novel study, we found that arbuscular mycorrhizal fungi cocolonizing on single plant roots recruit their own specific microbiomes, which should be considered in evaluating plant microbiome form and function. Our findings demonstrate the importance of understanding trophic interactions in order to gain insight into the plant-AM fungus-bacterium symbiosis.

Metagenome assembled-genomes reveal similar functional profiles of CPR/Patescibacteria phyla in soils

Soil microbiome is one of the most heterogeneous biological systems. State-of-the-art molecular approaches such as those based on single-amplified genomes (SAGs) and metagenome assembled-genomes (MAGs) are now improving our capacity for disentailing soil microbial-mediated processes. Here, we analysed publicly available datasets of soil microbial genomes and MAG's reconstructed from the Amazon's tropical soil (primary forest and pasture) and active layer of permafrost, aiming to evaluate their genome size. Our results suggest that the Candidate Phyla Radiation (CPR)/Patescibacteria phyla have genomes with an average size fourfold smaller than the mean identified in the RefSoil database, which lacks any representative of this phylum. Also, by analysing the potential metabolism of 888 soil microbial genomes, we show that CPR/Patescibacteria representatives share similar functional profiles, but different from other microbial phyla and are frequently neglected in the soil microbial surveys. Finally, we argue that the use of MAGs may be a better choice over SAGs to expand the soil microbial databases, like RefSoil.

Towards Unraveling Macroecological Patterns in Rhizosphere Microbiomes

It is generally accepted that plants locally influence the composition and activity of their rhizosphere microbiome, and that rhizosphere community assembly further involves a hierarchy of constraints with varying strengths across spatial and temporal scales. However, our knowledge of rhizosphere microbiomes is largely based on single-location and time-point studies. Consequently, it remains difficult to predict patterns at large landscape scales, and we lack a clear understanding of how the rhizosphere microbiome forms and is maintained by drivers beyond the influence of the plant. By synthesizing recent literature and collating data on rhizosphere microbiomes, we point out the opportunities and challenges offered by advances in molecular biology, bioinformatics, and data availability. Specifically, we highlight the use of exact sequence variants, coupled with existing and newly generated data to decipher the rules of rhizosphere community assembly across large spatial and taxonomic scales.

Development and Analysis of a Stable, Reduced Complexity Model Soil Microbiome

The soil microbiome is central to the cycling of carbon and other nutrients and to the promotion of plant growth. Despite its importance, analysis of the soil microbiome is difficult due to its sheer complexity, with thousands of interacting species. Here, we reduced this complexity by developing model soil microbial consortia that are simpler and more amenable to experimental analysis but still represent important microbial functions of the native soil ecosystem. Samples were collected from an arid grassland soil and microbial communities (consisting mainly of bacterial species) were enriched on agar plates containing chitin as the main carbon source. Chitin was chosen because it is an abundant carbon and nitrogen polymer in soil that often requires the coordinated action of several microorganisms for complete metabolic degradation. Several soil consortia were derived that had tractable richness (30–50 OTUs) with diverse phyla representative of the native soil, including Actinobacteria, Bacteroidetes, Firmicutes, Proteobacteria, and Verrucomicrobia. The resulting consortia could be stored as glycerol or lyophilized stocks at −80°C and revived while retaining community composition, greatly increasing their use as tools for the research community at large. One of the consortia that was particularly stable was chosen as a model soil consortium (MSC-1) for further analysis. MSC-1 species interactions were studied using both pairwise co-cultivation in liquid media and during growth in soil under several perturbations. Co-abundance analyses highlighted interspecies interactions and helped to define keystone species, including Mycobacterium, Rhodococcus, and Rhizobiales taxa. These experiments demonstrate the success of an approach based on naturally enriching a community of interacting species that can be stored, revived, and shared. The knowledge gained from querying these communities and their interactions will enable better understanding of the soil microbiome and the roles these interactions play in this environment.

Resistome analysis of bloodstream infection bacterial genomes reveals a specific set of proteins involved in antibiotic resistance and drug efflux

Bacterial resistance to antibiotics is a global public health problem. Its association with bloodstream infections is even more severe and may easily evolve to sepsis. To improve our response to these bacteria, it is essential to gather thorough knowledge on the main pathogens along with the main mechanisms of resistance they carry. In this paper, we performed a large meta-analysis of 3872 bacterial genomes isolated from blood samples, from which we identified 71 745 antibiotic resistance genes (ARGs). Taxonomic analysis showed that Proteobacteria and Firmicutes phyla, and the species Klebsiella pneumoniae and Staphylococcus aureus were the most represented. Comparison of ARGs with the Resfams database showed that the main mechanism of antibiotic resistance is mediated by efflux pumps. Clustering analysis between resistome of blood and soil-isolated bacteria showed that there is low identity between transport and efflux proteins between bacteria from these environments. Furthermore, a correlation analysis among all features showed that K. pneumoniae and S. aureus formed two well-defined clusters related to the resistance mechanisms, proteins and antibiotics. A retrospective analysis has shown that the average number of ARGs per genome has gradually increased. The results demonstrate the importance of comprehensive studies to understand the antibiotic resistance phenomenon.

metagenomeFeatures: an R package for working with 16S rRNA reference databases and marker-gene survey feature data

SUMMARY

We developed the metagenomeFeatures R Bioconductor package along with annotation packages for three 16S rRNA databases (Greengenes, RDP and SILVA) to facilitate working with 16S rRNA databases and marker-gene survey feature data. The metagenomeFeatures package defines two classes, MgDb for working with 16S rRNA sequence databases, and mgFeatures for marker-gene survey feature data. The associated annotation packages provide a consistent interface to the different databases facilitating database comparison and exploration. The mgFeatures-class represents a crucial step in the development of a common data structure for working with 16S marker-gene survey data in R.

AVAILABILITY AND IMPLEMENTATION

https://bioconductor.org/packages/release/bioc/html/metagenomeFeatures.html.

SUPPLEMENTARY INFORMATION

Supplementary material is available at Bioinformatics online.

Inference-based accuracy of metagenome prediction tools varies across sample types and functional categories

BACKGROUND

Despite recent decreases in the cost of sequencing, shotgun metagenome sequencing remains more expensive compared with 16S rRNA amplicon sequencing. Methods have been developed to predict the functional profiles of microbial communities based on their taxonomic composition. In this study, we evaluated the performance of three commonly used metagenome prediction tools (PICRUSt, PICRUSt2, and Tax4Fun) by comparing the significance of the differential abundance of predicted functional gene profiles to those from shotgun metagenome sequencing across different environments.

RESULTS

We selected 7 datasets of human, non-human animal, and environmental (soil) samples that have publicly available 16S rRNA and shotgun metagenome sequences. As we would expect based on previous literature, strong Spearman correlations were observed between predicted gene compositions and gene relative abundance measured with shotgun metagenome sequencing. However, these strong correlations were preserved even when the abundance of genes were permuted across samples. This suggests that simple correlation coefficient is a highly unreliable measure for the performance of metagenome prediction tools. As an alternative, we compared the performance of genes predicted with PICRUSt, PICRUSt2, and Tax4Fun to sequenced metagenome genes in inference models associated with metadata within each dataset. With this approach, we found reasonable performance for human datasets, with the metagenome prediction tools performing better for inference on genes related to "housekeeping" functions. However, their performance degraded sharply outside of human datasets when used for inference.

CONCLUSION

We conclude that the utility of PICRUSt, PICRUSt2, and Tax4Fun for inference with the default database is likely limited outside of human samples and that development of tools for gene prediction specific to different non-human and environmental samples is warranted. Video abstract.

Complementary Metagenomic Approaches Improve Reconstruction of Microbial Diversity in a Forest Soil

Soil ecosystems harbor diverse microorganisms and yet remain only partially characterized as neither single-cell sequencing nor whole-community sequencing offers a complete picture of these complex communities. Thus, the genetic and metabolic potential of this "uncultivated majority" remains underexplored. To address these challenges, we applied a pooled-cell-sorting-based mini-metagenomics approach and compared the results to bulk metagenomics. Informatic binning of these data produced 200 mini-metagenome assembled genomes (sorted-MAGs) and 29 bulk metagenome assembled genomes (MAGs). The sorted and bulk MAGs increased the known phylogenetic diversity of soil taxa by 7.2% with respect to the Joint Genome Institute IMG/M database and showed clade-specific sequence recruitment patterns across diverse terrestrial soil metagenomes. Additionally, sorted-MAGs expanded the rare biosphere not captured through MAGs from bulk sequences, exemplified through phylogenetic and functional analyses of members of the phylum Bacteroidetes Analysis of 67 Bacteroidetes sorted-MAGs showed conserved patterns of carbon metabolism across four clades. These results indicate that mini-metagenomics enables genome-resolved investigation of predicted metabolism and demonstrates the utility of combining metagenomics methods to tap into the diversity of heterogeneous microbial assemblages.IMPORTANCE Microbial ecologists have historically used cultivation-based approaches as well as amplicon sequencing and shotgun metagenomics to characterize microbial diversity in soil. However, challenges persist in the study of microbial diversity, including the recalcitrance of the majority of microorganisms to laboratory cultivation and limited sequence assembly from highly complex samples. The uncultivated majority thus remains a reservoir of untapped genetic diversity. To address some of the challenges associated with bulk metagenomics as well as low throughput of single-cell genomics, we applied flow cytometry-enabled mini-metagenomics to capture expanded microbial diversity from forest soil and compare it to soil bulk metagenomics. Our resulting data from this pooled-cell sorting approach combined with bulk metagenomics revealed increased phylogenetic diversity through novel soil taxa and rare biosphere members. In-depth analysis of genomes within the highly represented Bacteroidetes phylum provided insights into conserved and clade-specific patterns of carbon metabolism.

Charting the Complexity of the Marine Microbiome through Single-Cell Genomics

Marine bacteria and archaea play key roles in global biogeochemistry. To improve our understanding of this complex microbiome, we employed single-cell genomics and a randomized, hypothesis-agnostic cell selection strategy to recover 12,715 partial genomes from the tropical and subtropical euphotic ocean. A substantial fraction of known prokaryoplankton coding potential was recovered from a single, 0.4 mL ocean sample, which indicates that genomic information disperses effectively across the globe. Yet, we found each genome to be unique, implying limited clonality within prokaryoplankton populations. Light harvesting and secondary metabolite biosynthetic pathways were numerous across lineages, highlighting the value of single-cell genomics to advance the identification of ecological roles and biotechnology potential of uncultured microbial groups. This genome collection enabled functional annotation and genus-level taxonomic assignments for >80% of individual metagenome reads from the tropical and subtropical surface ocean, thus offering a model to improve reference genome databases for complex microbiomes.

Structure and variation of root-associated microbiomes of potato grown in alfisol

Root-associated fungi and bacteria play a pivotal role in the plant-soil ecosystem by influencing both plant growth and immunity. The aim of this study was to unravel the biodiversity of the bacterial and fungal rhizosphere (RS) and rhizoplane (RP) microbiota of Zhukovskij rannij potato (Solanum tuberosum L.) cultivar growing in the Alfisol of Tatarstan, Russia. To assess the structure and diversity of microbial communities, we employed the 16S rRNA and internal transcribed spacer gene library technique. Overall, sequence analysis showed the presence of 3982 bacterial and 188 fungal operational taxonomic units (OTUs) in the RP, and 6018 bacterial and 320 fungal OTUs for in the RS. Comparison between microbial community structures in the RS and RP showed significant differences between these compartments. Biodiversity was higher in the RS than in the RP. Although members of Proteobacteria (RS-59.1 ± 4.9%; RP-54.5 ± 9.2%), Bacteroidetes (RS-23.19 ± 10.2%; RP-34.52 ± 10.4%) and Actinobacteria (RS-11.55 ± 4.9%; RP-7.7 ± 5.1%) were the three most dominant phyla, accounting for 94-98% of all bacterial taxa in both compartments, notable variations were observed in the primary dominance of classes and genera in RS and RP samples. In addition, our results demonstrated that the potato rhizoplane was significantly enriched with the genera Flavobacterium, Pseudomonas, Acinetobacter and other potentially beneficial bacteria. The fungal community was predominantly inhabited by members of the Ascomycota phylum (RS-81.4 ± 8.1%; RP-81.7 ± 5.7%), among which the genera Fusarium (RS-10.34 ± 3.41%; RP-9.96 ± 4.79%), Monographella (RS-7.66 ± 4.43%; RP-9.91 ± 5.87%), Verticillium (RS-4.6 ± 1.43%; RP-8.27 ± 3.63%) and Chaetomium (RS-4.95 ± 2.07%; RP-8.33 ± 4.93%) were particularly abundant. Interestingly, potato rhizoplane was significantly enriched with potentially useful fungal genera, such as Mortierella and Metacordiceps. A comparative analysis revealed that the abundance of Fusarium (a cosmopolitan plant pathogen) varied significantly depending on rotation variants, indicating a possible control of phytopathogenic fungi via management-induced shifts through crop rotational methods. Analysis of the core microbiome of bacterial and fungal community structure showed that the formation of bacterial microbiota in the rhizosphere and rhizoplane is dependent on the host plant.

Making the Most of Trait-Based Approaches for Microbial Ecology

There is an increasing interest in applying trait-based approaches to microbial ecology, but the question of how and why to do it is still lagging behind. By anchoring our discussion of these questions in a framework derived from epistemology, we broaden the scope of trait-based approaches to microbial ecology from one oriented mostly around explanation towards one inclusive of the predictive and integrative potential of these approaches. We use case studies from macro-organismal ecology to concretely show how these goals for knowledge development can be fulfilled and propose clear directions, adapted to the biological reality of microbes, to make the most of recent advancements in the measurement of microbial phenotypes and traits.