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Annotating NGS sequences by assigning taxa labels is a key component for the majority of metagenomic studies, and is often a prerequisite in effectively assessing biodiversity in a given environment. In this work we introduce k-taxatree, an alignment-free machine learning method that enables robust assignment of taxonomic labels to short reads, utilizing a multi-label Random Forest approach as the underlying model. We demonstrate the effectiveness of the method by applying it to data from the V4 hypervariable region of 16S rRNA reads, retrieved from the Earth Microbiome Project, displaying accuracy scores over 95% in the validation set. The workflow has been fully developed in R and is freely available at https://github.com/BiodataAnalysisGroup/k-taxatree.

BackgroundUnderstanding the factors that influence microbes’ environmental distributions is important for deter-mining drivers of microbial community composition. These include environmental variables like temperature and pH, and higher-dimensional variables like geographic distance and host species phylogeny. In microbial ecology, “specificity” is often described in the context of symbiotic or host parasitic interactions, but specificity can be more broadly used to describe the extent to which a species occupies a narrower range of an environmental variable than expected by chance. Using a standardization we describe here, Rao’s (1982, 2010) Quadratic Entropy can be conveniently applied to calculate specificity of a feature, such as a species, to many different environmental variables.ResultsWe present our R package specificity for performing the above analyses, and apply it to four real-life microbial data sets to demonstrate its application. We found that many fungi within the leaves of native Hawaiian plants had strong specificity to rainfall and elevation, even though these variables showed minimal importance in a previous analysis of fungal beta-diversity. In Antarctic cryoconite holes, our tool revealed that many bacteria have specificity to co-occurring algal community composition. Similarly, in the human gut microbiome, many bacteria showed specificity to the composition of bile acids. Finally, our analysis of the Earth Microbiome Project data set showed that most bacteria show strong ontological specificity to sample type. Our software performed as expected on synthetic data as well.Conclusionsspecificity is well-suited to analysis of microbiome data, both in synthetic test cases, and across multiple environment types and experimental designs. The analysis and software we present here can reveal patterns in microbial taxa that may not be evident from a community-level perspective. These insights can also be visualized and interactively shared among researchers using specificity’s companion package, specificity.shiny.

The Earth Microbiome Project (EMP) aided in understanding the role of microbial communities and the influence of collective genetic material (the ‘microbiome’) and microbial diversity patterns across the habitats of our planet. With the evolution of new sequencing technologies, researchers can now investigate the microbiome and map its influence on the environment and human health. Advances in bioinformatics methods for next-generation sequencing (NGS) data analysis have helped researchers to gain an in-depth knowledge about the taxonomic and genetic composition of microbial communities. Metagenomic-based methods have been the most commonly used approaches for microbiome analysis; however, it primarily extracts information about taxonomic composition and genetic potential of the microbiome under study, lacking quantification of the gene products (RNA and proteins). On the other hand, metatranscriptomics, the study of a microbial community’s RNA expression, can reveal the dynamic gene expression of individual microbial populations and the community as a whole, ultimately providing information about the active pathways in the microbiome. In order to address the analysis of NGS data, the ASaiM analysis framework was previously developed and made available via the Galaxy platform. Although developed for both metagenomics and metatranscriptomics, the original publication demonstrated the use of ASaiM only for metagenomics, while thorough testing for metatranscriptomics data was lacking. In the current study, we have focused on validating and optimizing the tools within ASaiM for metatranscriptomics data. As a result, we deliver a robust workflow that will enable researchers to understand dynamic functional response of the microbiome in a wide variety of metatranscriptomics studies. This improved and optimized ASaiM-metatranscriptomics (ASaiM-MT) workflow is publicly available via the ASaiM framework, documented and supported with training material so that users can interrogate and characterize metatranscriptomic data, as part of larger meta-omic studies of microbiomes.

The genomes of mitochondria and chloroplasts contain ribosomal RNA (rRNA) genes, reflecting their evolutionary ancestry as free-living bacteria prior to endosymbiosis. In microbiome studies of animals, plants, or other eukaryotic hosts, these organellar rRNAs are often amplified. If identified, they can be discarded, merely reducing sequencing depth. However, incorrectly annotated mitochondrial reads may compromise statistical analysis by distorting relative abundances of free-living microbes. We quantified this by reanalyzing 7,459 samples from seven 16S rRNA sequencing studies, including the microbiomes of 927 unique animal genera. We find that under-annotation of cryptic mitochondrial reads affects multiple of these large-scale cross-species microbiome comparisons, and can be severe in some samples. It also varies between host species, potentially biasing cross-species microbiome comparisons. We propose a straightforward solution: by supplementing existing taxonomies with diverse mitochondrial rRNA sequences, we resolve up to 97% of unique unclassified sequences in some entire studies as mitochondrial (14% averaged across all studies), without increasing false positive annotations in mitochondria-free mock communities. Overall, improved annotation decreases the proportion of unknown sequences by ≥10-fold in 2,262 of 7,459 samples (30%), including representatives from 5 of 7 studies examined. While standard DADA2 analyses are severely affected, the default positive filter in Deblur run through QIIME2 discards many divergent mitochondrial sequences, preventing bias in analysis, but also making analysis of these sequences more difficult. We recommend leveraging mitochondrial sequence diversity to better identify, remove and analyze mitochondrial rRNA gene sequences in microbiome studies.

Microbiome studies have recently transitioned from experimental designs with a few hundred samples to designs spanning tens of thousands of samples. Modern studies such as the Earth Microbiome Project (EMP) afford the statistics crucial for untangling the many factors that influence microbial community composition. Analyzing those data used to require access to a compute cluster, making it both expensive and inconvenient. We show that recent improvements in both hardware and software now allow to compute key bioinformatics tasks on EMP-sized data in minutes using a gaming-class laptop, enabling much faster and broader microbiome science insights., Comment: 2 pages, 4 figures, to be published in proceedings of eScience 2021

Comparing the nasal microbiome of healthy individuals and chronic rhinosinusitis (CRS) patients revealed Dolosigranulum pigrum as a species clearly associated with nasal health, although isolates obtained from healthy individuals are scarce. In this study, we explored the properties of this understudied lactic acid bacterium by integrating comparative genomics, habitat mining, cultivation, and functional characterization of interaction capacities. Mining 10.000 samples from the Earth Microbiome Project of 17 habitat types revealed that Dolosigranulum is mainly associated with the human nasal cavity. D. pigrum AMBR11 isolated from the nose of a healthy individual exerted antimicrobial activity against Staphylococcus aureus, decreased proinflammatory cytokine production in airway epithelial cells, and Galleria mellonella larvae mortality induced by this important nasal pathobiont. Furthermore, the strain protected the nasal barrier function in a mouse model using interleukin-4 as disruptive cytokine. Hence, D. pigrum AMBR11 is a mutualist with high potential as topical live biotherapeutic product. ispartof: ISCIENCE vol:24 issue:9 ispartof: location:United States status: published

Comparing the nasal microbiome of healthy individuals and chronic rhinosinusitis (CRS) patients revealed Dolosigranulum pigrum as a species clearly associated with nasal health, although it was not yet cultured from healthy individuals. In this study, we explored the properties of this understudied lactic acid bacterium by integrating comparative genomics, habitat mining, cultivation, and functional characterization of interaction capacities. Mining 10.000 samples from the Earth Microbiome Project of 17 habitat types revealed that Dolosigranulum is mainly associated with the human nasal cavity. D. pigrum AMBR11 isolated from the nose of a healthy individual exerted antimicrobial activity against Staphylococcus aureus , and decreased proinflammatory cytokine production in airway epithelial cells and Galleria mellonella larvae mortality induced by this important nasal pathobiont. Furthermore, the strain protected the nasal barrier function in a mouse model using interleukin-4 as disruptive cytokine. Hence, D. pigrum AMBR11 is a mutualist with high potential as topical live-biotherapeutic product.

Microbes are the most abundant and diverse cellular life forms on Earth and colonize a wide range of environmental niches. However, more than 99% of bacterial and archaeal species have not been obtained in pure culture [1] and we have only glimpsed the surface of this mysterious microbial world. This is so-called Microbial Dark Matter (MDM): the enormous diversity of yet-uncultivated microbes that microbiologists can only study by using cultivation-independent techniques. Recently, a number of international projects have dramatically increased our understanding of the extent and distribution of microbial diversity, including the Global Catalogue of Microorganisms (GCM), the Genomic Encyclopedia of Bacteria and Archaea (GEBA), the Earth Microbiome Project (EMP), the Genomic Encyclopedia of Bacteria and Archaea-Microbial Dark Matter (GEBA-MDM) and several primate microbiome projects; however, the functional diversity of MDM is still mysterious. This perspective addresses why MDM deserves scientific effort and illustrates challenges and opportunities in the future study of these enigmas.

13 pages, 5 figures, supplementary information https://doi.org/10.1038/s41587-020-0718-6.-- Data availability: All available metagenomic data, bins and annotations are available through the IMG/M portal (https://img.jgi.doe.gov/). Bulk download for the 52,515 MAGs is available at https://genome.jgi.doe.gov/GEMs and https://portal.nersc.gov/GEM. Genome-scale metabolic models for the nonredundant, high-quality GEMs are summarized at https://doi.org/10.25982/53247.64/1670777 and available in KBase (https://narrative.kbase.us/#org/jgimags). IMG/M identifiers of all metagenomes binned, including detailed information for each metagenome, are available in Supplementary Table 1.-- The pipeline used to generate the metagenome bins is available at https://bitbucket.org/berkeleylab/metabat/src/master/, Publisher Correction: A genomic catalog of Earth’s microbiomes; Nature Biotechnology 39: 520 (2021); https://doi.org/10.1038/s41587-020-00769-4, The reconstruction of bacterial and archaeal genomes from shotgun metagenomes has enabled insights into the ecology and evolution of environmental and host-associated microbiomes. Here we applied this approach to >10,000 metagenomes collected from diverse habitats covering all of Earth’s continents and oceans, including metagenomes from human and animal hosts, engineered environments, and natural and agricultural soils, to capture extant microbial, metabolic and functional potential. This comprehensive catalog includes 52,515 metagenome-assembled genomes representing 12,556 novel candidate species-level operational taxonomic units spanning 135 phyla. The catalog expands the known phylogenetic diversity of bacteria and archaea by 44% and is broadly available for streamlined comparative analyses, interactive exploration, metabolic modeling and bulk download. We demonstrate the utility of this collection for understanding secondary-metabolite biosynthetic potential and for resolving thousands of new host linkages to uncultivated viruses. This resource underscores the value of genome-centric approaches for revealing genomic properties of uncultivated microorganisms that affect ecosystem processes, This work was conducted by the US DOE Joint Genome Institute, a DOE Office of Science User Facility (contract no. DE-AC02–05CH11231), and used resources of the National Energy Research Scientific Computing Center, which is supported by the Office of Science of the US DOE (contract no. DE-AC02–05CH11231). This work was also supported as part of the Genomic Sciences Program DOE Systems Biology KBase (award nos. DE-AC02-05CH11231, DE-AC02-06CH11357, DE-AC05-00OR22725, and DE-AC02-98CH10886).-- With the funding support of the ‘Severo Ochoa Centre of Excelle

Microbes are embedded in complex communities where they engage in a wide array of intra- and inter-specific interactions. The extent to which these interactions drive or impede microbiome diversity is not well understood. Two contrasting hypotheses have been put forward to explain how species interactions could influence diversity. 9Ecological Controls9 (EC) predicts a negative relationship, where the evolution or migration of novel types is constrained as available niches become filled. In contrast, 9Diversity Begets Diversity9 (DBD) predicts a positive relationship, with existing diversity promoting the accumulation of further diversity via niche construction and other interactions. Using the Earth Microbiome Project, the largest standardized survey of global biodiversity to date, we provide evidence that DBD is strongest in less diverse biomes, such as the animal gut, but weaker in more diverse biomes, such as soil, presumably because most available niches are filled. Also consistent with niche adaptation, resident genera that are strongly specialized for a particular environment show a stronger DBD relationship than do generalists or migrants from another environment. In contrast with the Black Queen Hypothesis that predicts a negative correlation between genome size and community metabolic interactions, we find that genera with larger genomes exhibit a stronger DBD response, which could be due to a higher potential for interactions and niche construction offered by more diverse gene repertoires. Our results highlight the importance of species interactions in shaping microbiome diversity, and the need to account for such interactions in the study of microbial genomes, physiology, and ecological function.

Standard workflows for analyzing microbiomes often include the creation and curation of phylogenetic trees. Here we present EMPress, an interactive tool for visualizing trees in the context of microbiome, metabolome, etc. community data scalable beyond modern large datasets like the Earth Microbiome Project. EMPress provides novel functionality—including ordination integration and animations—alongside many standard tree visualization features, and thus simplifies exploratory analyses of many forms of ‘omic data.

Background Sequencing prokaryotic genomes has revolutionized our understanding of the many roles played by microorganisms. However, the cell and taxon proportions of genome-sequenced bacteria or archaea on earth remain unknown. This study aimed to explore this basic question using large-scale alignment between the sequences released by the Earth Microbiome Project and 155,810 prokaryotic genomes from public databases. Results Our results showed that the median proportions of the genome-sequenced cells and taxa (at 100% identities in the 16S-V4 region) in different biomes reached 38.1% (16.4–86.3%) and 18.8% (9.1–52.6%), respectively. The sequenced proportions of the prokaryotic genomes in biomes were significantly negatively correlated with the alpha diversity indices, and the proportions sequenced in host-associated biomes were significantly higher than those in free-living biomes. Due to a set of cosmopolitan OTUs that are found in multiple samples and preferentially sequenced, only 2.1% of the global prokaryotic taxa are represented by sequenced genomes. Most of the biomes were occupied by a few predominant taxa with a high relative abundance and much higher genome-sequenced proportions than numerous rare taxa. Conclusions These results reveal the current situation of prokaryotic genome sequencing for earth biomes, provide a more reasonable and efficient exploration of prokaryotic genomes, and promote our understanding of microbial ecological functions.

Summary 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., Graphical Abstract, Highlights • We developed a database, ProkAtlas, denoting the habitat preferences of prokaryotes • ProkAtlas represents a prokaryotic community using habitat preferences of its members • The powerfulness of ProkAtlas is showcased by six datasets from various environments • We provide web interface of ProkAtlas at https://msk33.github.io/prokatlas.html, Environmental Biotechnology; Microbiology; Microbial Genomics; Bioinformatics

Background Microbial interactions shape the structure and function of microbial communities; microbial co-occurrence networks in specific environments have been widely developed to explore these complex systems, but their interconnection pattern across microbiomes in various environments at the global scale remains unexplored. Here, we have inferred an Earth microbial co-occurrence network from a communal catalog with 23,595 samples and 12,646 exact sequence variants from 14 environments in the Earth Microbiome Project dataset. Results This non-random scale-free Earth microbial co-occurrence network consisted of 8 taxonomy distinct modules linked with different environments, which featured environment specific microbial co-occurrence relationships. Different topological features of subnetworks inferred from datasets trimmed into uniform size indicate distinct co-occurrence patterns in the microbiomes of various environments. The high number of specialist edges highlights that environmental specific co-occurrence relationships are essential features across microbiomes. The microbiomes of various environments were clustered into two groups, which were mainly bridged by the microbiomes of plant and animal surface. Acidobacteria Gp2 and Nisaea were identified as hubs in most of subnetworks. Negative edges proportions ranged from 1.9% in the soil subnetwork to 48.9% the non-saline surface subnetwork, suggesting various environments experience distinct intensities of competition or niche differentiation. Conclusion This investigation highlights the interconnection patterns across microbiomes in various environments and emphasizes the importance of understanding co-occurrence feature of microbiomes from a network perspective.

BackgroundHost-associated microbiomes, the microorganisms occurring inside and on host surfaces, influence evolutionary, immunological, and ecological processes. Interactions between host and microbiome affect metabolism and contribute to host adaptation to changing environments. Meta-analyses of host-associated bacterial communities have the potential to elucidate global-scale patterns of microbial community structure and function. It is possible that host surface-associated (external) microbiomes respond more strongly to variations in environmental factors, whereas internal microbiomes are more tightly linked to host factors.ResultsHere, we use the dataset from the Earth Microbiome Project and accumulate data from 50 additional studies totaling 654 host species and over 15,000 samples to examine global-scale patterns of bacterial diversity and function. We analyze microbiomes from non-captive hosts sampled from natural habitats and find patterns with bioclimate and geophysical factors, as well as land use, host phylogeny, and trophic level/diet. Specifically, external microbiomes are best explained by variations in mean daily temperature range and precipitation seasonality. In contrast, internal microbiomes are best explained by host factors such as phylogeny/immune complexity and trophic level/diet, plus climate.ConclusionsInternal microbiomes are predominantly associated with top-down effects, while climatic factors are stronger determinants of microbiomes on host external surfaces. Host immunity may act on microbiome diversity through top-down regulation analogous to predators in non-microbial ecosystems. Noting gaps in geographic and host sampling, this combined dataset represents a global baseline available for interrogation by future microbial ecology studies.

Current methods for detecting, accurately quantifying, and profiling complex microbial communities based on the microbial 16S rRNA marker genes are limited by a number of factors, including inconsistent extraction of microbial nucleic acids, amplification interference from contaminants and host DNA, different coverage of PCR primers utilized for quantification and sequencing, and potentially biases in PCR amplification rates among microbial taxa during amplicon barcoding. Here, we describe a single-step method that enables the quantification of microbial 16S rRNA gene DNA copies with wide dynamic range and broad microbial diversity, and simultaneous amplicon barcoding for quantitative 16S rRNA gene amplicon profiling of microbiota. The method is suitable for a variety of sample types and is robust in samples with low microbial abundance, including samples containing high levels of host mammalian DNA, as is common in human clinical samples. We demonstrate that our modification to the Earth Microbiome Project (EMP) V4 16S rRNA gene primers expands their microbial coverage while dramatically reducing non-specific mammalian mitochondrial DNA amplification, thus achieving wide dynamic range in microbial quantification and broad coverage for capturing high microbial diversity in samples with or without high host DNA background. The approach relies only on broadly available hardware (real-time PCR instruments) and standard reagents utilized for conventional 16S rRNA gene amplicon library preparation both of which make it amenable for immediate and widespread adoption. Simultaneous 16S rRNA gene DNA copy quantification and amplicon barcoding for multiplexed next-generation sequencing from the same analyzed sample, performed in a combined workflow, reduces the amount of sample needed and reduces time and reagent costs. Additionally, we demonstrate that using our modified 16S rRNA gene primers in a digital PCR (dPCR) format enables precise and exact microbial quantification in samples with very high host DNA background levels without the need for quantification standards. Potential future applications of this approach include: (1) quantitative microbiome profiling in human and animal microbiome research; (2) detection of monoinfections and profiling of polymicrobial infections in tissues, stool, and bodily fluids in human and veterinary medicine; (3) environmental sample analyses (e.g., soil and water); and (4) broad-coverage detection of microbial food contamination in products high in mammalian DNA, such as meat products. We predict that utilization of this approach primarily for quantitative microbiome profiling will be invaluable to microbiome studies, which have historically been limited to analysis of relative abundances of microbes.

OBJECTIVES: The human gastrointestinal microbiota contributes to the relationship between diet and health via microbial metabolism of undigested food components. Thus, it is valuable to understand the relations between gut microorganisms and diet quality. Herein, we examined the associations between the Healthy Eating Index (HEI)-2015 and the fecal microbiota in a subset of healthy adults in the American Gut Project (AGP) cohort. METHODS: This was a cross-sectional study of healthy adults (n = 1162 (759 females); BMI: 18.5–29.9 kg/m(2); 18–65 years of age) from the AGP cohort who provided a stool sample and a time-matched Food Frequency Questionnaire (FFQ; VioScreen). Fecal samples were processed by the AGP lab. Briefly, DNA was extracted from the stool samples, and the V4 region of the 16S rRNA gene was amplified and sequenced. HEI-2015 scores were calculated using VioScreen's report of HEI-2010 scores and nutrient component percentages. The cohort was divided into tertiles based on Total Score, and components Whole Grains, Dairy, Fatty Acids, Sodium, and Saturated Fats. Differential abundance analysis was conducted using ANCOM-BC, comparing the high (T3) tertile to the low (T1) tertile for all categories. Comparisons that were statistically significant following FDR corrections are reported (q

Plants in terrestrial and aquatic environments contain a diverse microbiome. Yet, the chloroplast and mitochondria organelles of the plant eukaryotic cell originate from free-living cyanobacteria and Rickettsiales. This represents a challenge for sequencing the plant microbiome with universal primers, as ~99% of 16S rRNA sequences may consist of chloroplast and mitochondrial sequences. Peptide nucleic acid clamps offer a potential solution by blocking amplification of host-associated sequences. We assessed the efficacy of chloroplast and mitochondria-blocking clamps against a range of microbial taxa from soil, freshwater and marine environments. While we found that the mitochondrial blocking clamps appear to be a robust method for assessing animal-associated microbiota, Proteobacterial 16S rRNA binds to the chloroplast-blocking clamp, resulting in a strong sequencing bias against this group. We attribute this bias to a conserved 14-bp sequence in the Proteobacteria that matches the 17-bp chloroplast-blocking clamp sequence. By scanning the Greengenes database, we provide a reference list of nearly 1500 taxa that contain this 14-bp sequence, including 48 families such as the Rhodobacteraceae, Phyllobacteriaceae, Rhizobiaceae, Kiloniellaceae and Caulobacteraceae. To determine where these taxa are found in nature, we mapped this taxa reference list against the Earth Microbiome Project database. These taxa are abundant in a variety of environments, particularly aquatic and semiaquatic freshwater and marine habitats. To facilitate informed decisions on effective use of organelle-blocking clamps, we provide a searchable database of microbial taxa in the Greengenes and Silva databases matching various n-mer oligonucleotides of each PNA sequence.

Background Currently, bacterial 16S rRNA gene analyses are based on sequencing of individual variable regions of the 16S rRNA gene (Kozich, et al Appl Environ Microbiol 79:5112–5120, 2013).This short read approach can introduce biases. Thus, full-length bacterial 16S rRNA gene sequencing is needed to reduced biases. A new alternative for full-length bacterial 16S rRNA gene sequencing is offered by PacBio single molecule, real-time (SMRT) technology. The aim of our study was to validate PacBio P6 sequencing chemistry using three approaches: 1) sequencing the full-length bacterial 16S rRNA gene from a single bacterial species Staphylococcus aureus to analyze error modes and to optimize the bioinformatics pipeline; 2) sequencing the full-length bacterial 16S rRNA gene from a pool of 50 different bacterial colonies from human stool samples to compare with full-length bacterial 16S rRNA capillary sequence; and 3) sequencing the full-length bacterial 16S rRNA genes from 11 vaginal microbiome samples and compare with in silico selected bacterial 16S rRNA V1V2 gene region and with bacterial 16S rRNA V1V2 gene regions sequenced using the Illumina MiSeq. Results Our optimized bioinformatics pipeline for PacBio sequence analysis was able to achieve an error rate of 0.007% on the Staphylococcus aureus full-length 16S rRNA gene. Capillary sequencing of the full-length bacterial 16S rRNA gene from the pool of 50 colonies from stool identified 40 bacterial species of which up to 80% could be identified by PacBio full-length bacterial 16S rRNA gene sequencing. Analysis of the human vaginal microbiome using the bacterial 16S rRNA V1V2 gene region on MiSeq generated 129 operational taxonomic units (OTUs) from which 70 species could be identified. For the PacBio, 36,000 sequences from over 58,000 raw reads could be assigned to a barcode, and the in silico selected bacterial 16S rRNA V1V2 gene region generated 154 OTUs grouped into 63 species, of which 62% were shared with the MiSeq dataset. The PacBio full-length bacterial 16S rRNA gene datasets generated 261 OTUs, which were grouped into 52 species, of which 54% were shared with the MiSeq dataset. Alpha diversity index reported a higher diversity in the MiSeq dataset. Conclusion The PacBio sequencing error rate is now in the same range of the previously widely used Roche 454 sequencing platform and current MiSeq platform. Species-level microbiome analysis revealed some inconsistencies between the full-length bacterial 16S rRNA gene capillary sequencing and PacBio sequencing. Electronic supplementary material The online version of this article (doi:10.1186/s12866-016-0891-4) contains supplementary material, which is available to authorized users.

The oral cavity is an important window for the microbial communication between environment and the human body. The oral microbiome plays an important role in human health. Here, we analyzed 447 datasets from human oral samples published by the Earth Microbiome Project (EMP). The microbes in these human oral samples were taxonomically assigned to at least 266 genera of 18 bacterial and archaeal phyla. Among them, 11 genera with the relative abundance more than 1% were identified as 5 different bacterial phyla. Compared with 815 samples from human gut, nose/pharynx and skin, the oral microbiome showed significantly lower diversity and possessed fewer unknown species than those of other body parts, and had distinct differences in species composition from other body parts. In addition, the oral microbiome showed significant differences in the populations of different countries, which may be determined by the living environment and lifestyle/dietary habits. Finally, the correlation analysis showed highly similarity between the oral microbiome and the microbiomes of Aerosol (non-saline) and Surface (non-saline), two types of environmental microbial habitats related closely to human. Together, these findings expand our understanding to the human oral microbiome.

Aim: The high-throughput sequencing methods have revolutionized the study of the microbiota in different matrices including those of the grapevine production chain. DNA extraction is a crucial step in the sample processing. In this study, we compared different DNA purification methods and two primer sets for 16S rRNA gene metabarcoding to evaluate the best protocol to explore the wine microbiota by metabarcoding.Methods and results: We collected a wine from Barbera grapes after malolactic fermentation previously inoculated by Oenococcus oeni starter. The same sample was used to evaluate the best performing protocol to study the wine microbiota. DNA was purified using nine different methods and then amplified for the 16S rRNA gene with two primer sets (according to Illumina or Earth Microbiome Project protocols). The obtained amplicons were then sequenced in a single sequencing session on an Illumina MiSeq. We evaluated the best protocol considering DNA concentration and purity, alpha (Observed species) and beta diversity from metabarcoding analysis.The sequencing generated 36,031,756 reads in total. Although no statistically significant difference was observed between purification methods or primer sets, better results were obtained with phenol-chloroform DNA purification combined to Earth Microbiome Project primers.Metabarcoding was able to highlight the domination of the inoculum, O. oeni, representing the main species of the analyzed wine microbiota.Conclusion: Our data show that, for the tested wine, metabarcoding output is more influenced by the primer set than by the DNA purification method. Moreover, the metabarcoding detected that O. oeni represents the main species, evidencing the domination of the inoculum done with lyophilized commercial preparation of this species. Other lactic acid bacteria are present at a much lower abundance.Significance and impact of the study: This is the first report applying the 16S rRNA gene metabarcoding to study the microbiota of wine. For this reason, the evaluation of alternative methods for DNA processing is essential for future research using this innovative methodology.

In principle, tandem mass spectrometry can be used to detect and quantify the peptides present in a microbiome sample, enabling functional and taxonomic insight into microbiome metabolic activity. However, the phylogenetic diversity constituting a particular microbiome is often unknown, and many of the organisms present may not have assembled genomes. In ocean microbiome samples, with particularly diverse and uncultured bacterial communities, it is difficult to construct protein databases that contain the bulk of the peptides in the sample without losing detection sensitivity due to the overwhelming number of candidate peptides for each tandem mass spectrum. We describe a method for deriving "metapeptides" (short amino acid sequences that may be represented in multiple organisms) from shotgun metagenomic sequencing of microbiome samples. In two ocean microbiome samples, we constructed site-specific metapeptide databases to detect more than one and a half times as many peptides as by searching against predicted genes from an assembled metagenome and roughly three times as many peptides as by searching against the NCBI environmental proteome database. The increased peptide yield has the potential to enrich the taxonomic and functional characterization of sample metaproteomes.

Culture-independent methods to detect microorganisms have been developed in parallel with traditional culture-based methods ever since the classification of bacteria based on 16S rRNA gene sequences was advocated in the 1970s. The development and the prevalence of culture-independent molecular technologies have provided revolutionary progress in microbial studies. The development of these technologies contributes significantly to the research of microorganisms that cannot be detected by traditional methods such as culture-dependent methods.Many molecular methods targeting the 16S rRNA gene, such as fluorescence in situ hybridization (FISH), quantitative PCR, terminal restriction fragment length polymorphism (T-RFLP), denaturing-gradient gel electrophoresis (DGGE), clone library analysis, and next-generation DNA sequencing (NGS) technologies, have been applied to various microbial studies. Notably, the advent of NGS technologies enabled a large-scale research of the bacterial community. Many recent studies using the NGS technologies have revealed that a larger number of bacteria and taxa than previously thought inhabit various parts of the human body and various places on the earth. The principles and characteristics of each molecular method are different, and each method possesses individual advantages; for example target specificity, comprehensiveness, rapidness, and cost efficiency. Therefore it is important that the methods used in studies are suitable for the objective and materials. Herein, we highlights molecular approaches targeting the 16S rRNA gene in bacterial community analysis, and focuses on the advantages and limitations of each technology.

Identifying bacterial strains in metagenome and microbiome samples using computational analyses of short-read sequences remains a difficult problem. Here, we present an analysis of a human gut microbiome using TruSeq synthetic long reads combined with computational tools for metagenomic long-read assembly, variant calling and haplotyping (Nanoscope and Lens). Our analysis identifies 178 bacterial species, of which 51 were not found using shotgun reads alone. We recover bacterial contigs that comprise multiple operons, including 22 contigs of >1 Mbp. Furthermore, we observe extensive intraspecies variation within microbial strains in the form of haplotypes that span up to hundreds of Kbp. Incorporation of synthetic long-read sequencing technology with standard short-read approaches enables more precise and comprehensive analyses of metagenomic samples.

Microbial ecology has witnessed tremendous progress over the last decade empowered by meta-omics approaches and innovations in DNA/RNA sequencing as well as high-resolution mass spectrometry. In this climate, the rise of meta-omics projects (Raes, 2011) such as MetaHIT and the Human Microbiome Project, Tara Oceans, the Global Ocean Sampling Expedition and the Earth Microbiome Project aiming at unraveling the structure and function of specific microbiomes in different habitats was observed. Now that massive data generation is no longer science fiction, the bottleneck shifts to computational analysis (Falony et al., 2015).

SummaryPhylogenetic comparative methods are powerful but presently under-utilized ways to identify microbial genes underlying differences in community composition. These methods help to identify functionally important genes because they test for associations beyond those expected when related microbes occupy similar environments. We present phylogenize, a pipeline with web, QIIME2, and R interfaces that allows researchers to perform phylogenetic regression on 16S amplicon and shotgun sequencing data and to visualize results. phylogenize applies broadly to both host-associated and environmental microbiomes. Using Human Microbiome Project and Earth Microbiome Project data, we show that phylogenize draws similar conclusions from 16S versus shotgun sequencing and reveals both known and candidate pathways associated with host colonization.Availabilityphylogenize is available at https://phylogenize.org and https://bitbucket.org/pbradz/phylogenize.Contactkpollard@gladstone.ucsf.edu

Hypothesis-driven research has led to many scientific advances, but hypotheses cannot be tested in isolation: rather, they require a framework of aggregated scientific knowledge to allow questions to be posed meaningfully. This framework is largely still lacking in microbiome studies, and the only way to create it is by discovery-driven, tool-driven, and standards-driven research projects. Here we illustrate these issues using several such non-hypothesis-driven projects from our own laboratories, including spatial mapping, the American Gut Project, the Earth Microbiome Project (which is an umbrella project integrating many smaller hypothesis-driven projects), and the knowledgebase-driven tools GNPS and Qiita. We argue that an investment of community resources in infrastructure tasks, and in the controls and standards that underpin them, will greatly enhance the investment in hypothesis-driven research programs.

Background The intestinal microbiota is believed to have a central role in SpA pathogenesis. However, the mechanism through which enteric microbes contribute to peripheral inflammation remains enigmatic. Objectives The primary objective of this study was to determine whether microbial translocation can be observed to extra-intestinal tissues in the HLA-B27/β2m transgenic rat – a foremost translational model of spondyloarthropathy. Moreover, since an arthritis phenotype only presents in less than half of HLA-B27/β2m transgenic animals, we could examine whether microbiota composition between transgenic animals with and without arthritic disease. Methods Intestinal tissue (cecal contents), mesenteric lymph nodes (MLN), spleen, serum, liver, lung, ankle joint and eye were collected from age matched (20–24wk old) HLA-B27/β2m trangenic rats with or without arthritis and WT controls (n=20–45 per group). DNA was extracted and the 16 s rRNA V4 region amplified according to the standard Earth Microbiome Project protocol. Extraction blanks were run with each tissue to control for environmental contamination. Sequencing data (generated by Illumina MiSeq) was first processed using the SourceTracker algorithm to identify and remove contaminant sequences. Remaining reads were run through the DADA2 pipeline implemented in QIIME2. Results Our study of microbial translocation revealed a number of striking observations. Firstly 16 s rRNA was detected at all tissue sites examined. Second, rather than observing a limited number of species, a highly polymicrobial and intestinal DNA signature was observed in all tissue sites examined. This observation was independent of genotype or disease state. The number of total reads in each tissue was highest in cecum as anticipated (approx. 1 00 000 reads) with the yield from other tissues roughly an order of magnitude lower. The most abundant species in joint tissue included Prevotella spp. Prevotella shahii and Prevotella stercorea, Roseburia faecis and Muribaculum intestinale. The microbe Blautia obeum, a close relative of [Ruminococcus] gnavus within the same genus was also found in joint tissue. This of interest since this microbe has recently been associated with disease activity in SpA patients. Interestingly an arthritis phenotype was strongly associated with a loss of intestinal bacterium Eubacterium Oxidoreducens. This is a flavone metabolising bacterium and supports previous metabolomic studies in which we have shown flavone compounds are greatly over-represented in the HLA-B27/s2m transgenic rats vs WT controls. Conclusions We propose translocation of microbes/microbial products from the gut to extra-intestinal tissues may be a contributory mechanism to SpA pathogenesis, although alone is not sufficient to elicit inflammatory disease. Specific changes in microbial community DNA profile in the gut or elsewhere may serve as useful biomarkers of disease state in either patient populations or disease models. This approach may yield useful candidates for further study such as Eubacterium oxidoreducens. Future studies will verify our findings using PCR-independent methods. Acknowledgements JMA and JTR are supported by the Spondylitis Association of America and the Rheumatology Research Foundation. JTR also receives support from the William and Mary Bauman Foundation, the Stan and Madelle Rosenfeld Family Trust and Research to Prevent Blindness. Disclosure of Interest None declared

The views expressed in this Editorial do not necessarily reflect the views of this journal or of ASM. Recently, we published the first large-scale analysis of data from the Earth Microbiome Project (EMP) ([1][1], [2][2]), a truly multidisciplinary research program involving more than 500

Although much work has linked the human microbiome to specific phenotypes and lifestyle variables, data from different projects have been challenging to integrate and the extent of microbial and molecular diversity in human stool remains unknown. Using standardized protocols from the Earth Microbiome Project and sample contributions from over 10,000 citizen-scientists, together with an open research network, we compare human microbiome specimens primarily from the USA, UK, and Australia to one another and to environmental samples. Our results show an unexpected range of beta-diversity in human stool microbiomes as compared to environmental samples, demonstrate the utility of procedures for removing the effects of overgrowth during room-temperature shipping for revealing phenotype correlations, uncover new molecules and kinds of molecular communities in the human stool metabolome, and examine emergent associations among the microbiome, metabolome, and the diversity of plants that are consumed (rather than relying on reductive categorical variables such as veganism, which have little or no explanatory power). We also demonstrate the utility of the living data resource and cross-cohort comparison to confirm existing associations between the microbiome and psychiatric illness, and to reveal the extent of microbiome change within one individual during surgery, providing a paradigm for open microbiome research and education.ImportanceWe show that a citizen-science, self-selected cohort shipping samples through the mail at room temperature recaptures many known microbiome results from clinically collected cohorts and reveals new ones. Of particular interest is integrating n=1 study data with the population data, showing that the extent of microbiome change after events such as surgery can exceed differences between distinct environmental biomes, and the effect of diverse plants in the diet which we confirm with untargeted metabolomics on hundreds of samples.

Phylogenetic profiling and metagenomics of crassulacean acid metabolism (CAM) plants from North America have revealed diverse and structured microbial communities with core microbial taxa in each plant compartment. In silico functional analyses suggest that microorganisms in the rhizosphere and phyllosphere of CAM plants differentially benefit their host plants to succeed in drylands.