BIOME-Preserve: A novel storage and transport medium for preserving anaerobic microbiota samples for culture recovery.

Sequencing-based protocols for studying the human microbiome have unearthed a wealth of information about the relationship between the microbiome and human health. But these microbes cannot be leveraged as therapeutic targets without culture-based studies to phenotype species of interest and to establish culture collections for use in animal models. Traditional sample collection protocols are focused on preserving nucleic acids and metabolites and are largely inappropriate for preserving sensitive anaerobic bacteria for later culture recovery. Here we introduce a novel microbiome preservation kit (BIOME-Preserve) that facilitates recovery of anaerobic bacteria from human stool. Using a combination of culture recovery and shallow whole-genome shotgun sequencing, we characterized the anaerobes cultured from fresh human stool and from human stool held at room temperature in BIOME-Preserve for up to 120 hours. We recovered several species of interest to microbiome researchers, including Bifidobacterium spp., Bacteroides spp., Blautia spp., Eubacterium halii (now Anaerobutyricum hallii), Akkermansia muciniphila, and Faecalibacterium prausnitzii. We also demonstrated that freezing at -80°C did not adversely affect our ability to culture organisms from BIOME-Preserve, suggesting that it is appropriate both as a transport medium and as a medium for longer-term ultra-cold storage. Together, our results suggest BIOME-Preserve is practical for the collection, transport, and culture of anaerobic bacteria from human samples and can help enable researchers to better understand the link between the microbiome and human health and how to leverage that link through novel microbiome-based therapeutics.

Frequency of Tongue Cleaning Impacts the Human Tongue Microbiome Composition and Enterosalivary Circulation of Nitrate.

The oral microbiome has the potential to provide an important symbiotic function in human blood pressure physiology by contributing to the generation of nitric oxide (NO), an essential cardiovascular signaling molecule. NO is produced by the human body via conversion of arginine to NO by endogenous nitric oxide synthase (eNOS) but eNOS activity varies by subject. Oral microbial communities are proposed to supplement host NO production by reducing dietary nitrate to nitrite via bacterial nitrate reductases. Unreduced dietary nitrate is delivered to the oral cavity in saliva, a physiological process termed the enterosalivary circulation of nitrate. Previous studies demonstrated that disruption of enterosalivary circulation via use of oral antiseptics resulted in increases in systolic blood pressure. These previous studies did not include detailed information on the oral health of enrolled subjects. Using 16S rRNA gene sequencing and analysis, we determined whether introduction of chlorhexidine antiseptic mouthwash for 1 week was associated with changes in tongue bacterial communities and resting systolic blood pressure in healthy normotensive individuals with documented oral hygiene behaviors and free of oral disease. Tongue cleaning frequency was a predictor of chlorhexidine-induced changes in systolic blood pressure and tongue microbiome composition. Twice-daily chlorhexidine usage was associated with a significant increase in systolic blood pressure after 1 week of use and recovery from use resulted in an enrichment in nitrate-reducing bacteria on the tongue. Individuals with relatively high levels of bacterial nitrite reductases had lower resting systolic blood pressure. These results further support the concept of a symbiotic oral microbiome contributing to human health via the enterosalivary nitrate-nitrite-NO pathway. These data suggest that management of the tongue microbiome by regular cleaning together with adequate dietary intake of nitrate provide an opportunity for the improvement of resting systolic blood pressure.

The Microbiome in Posttraumatic Stress Disorder and Trauma-Exposed Controls: An Exploratory Study.

OBJECTIVE: Inadequate immunoregulation and elevated inflammation may be risk factors for posttraumatic stress disorder (PTSD), and microbial inputs are important determinants of immunoregulation; however, the association between the gut microbiota and PTSD is unknown. This study investigated the gut microbiome in a South African sample of PTSD-affected individuals and trauma-exposed (TE) controls to identify potential differences in microbial diversity or microbial community structure. METHODS: The Clinician-Administered PTSD Scale for DSM-5 was used to diagnose PTSD according to Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition criteria. Microbial DNA was extracted from stool samples obtained from 18 individuals with PTSD and 12 TE control participants. Bacterial 16S ribosomal RNA gene V3/V4 amplicons were generated and sequenced. Microbial community structure, α-diversity, and ß-diversity were analyzed; random forest analysis was used to identify associations between bacterial taxa and PTSD. RESULTS: There were no differences between PTSD and TE control groups in α- or ß-diversity measures (e.g., α-diversity: Shannon index, t = 0.386, p = .70; ß-diversity, on the basis of analysis of similarities: Bray-Curtis test statistic = -0.033, p = .70); however, random forest analysis highlighted three phyla as important to distinguish PTSD status: Actinobacteria, Lentisphaerae, and Verrucomicrobia. Decreased total abundance of these taxa was associated with higher Clinician-Administered PTSD Scale scores (r = -0.387, p = .035). CONCLUSIONS: In this exploratory study, measures of overall microbial diversity were similar among individuals with PTSD and TE controls; however, decreased total abundance of Actinobacteria, Lentisphaerae, and Verrucomicrobia was associated with PTSD status.

Coupling Targeted and Untargeted Mass Spectrometry for Metabolome-Microbiome-Wide Association Studies of Human Fecal Samples.

Increasing appreciation of the gut microbiome's role in health motivates understanding the molecular composition of human feces. To analyze such complex samples, we developed a platform coupling targeted and untargeted metabolomics. The approach is facilitated through split flow from one UPLC, joint timing triggered by contact closure relays, and a script to retrieve the data. It is designed to detect specific metabolites of interest with high sensitivity, allows for correction of targeted information, enables better quantitation thus providing an advanced analytical tool for exploratory studies. Procrustes analysis revealed that untargeted approach provides a better correlation to microbiome data, associating specific metabolites with microbes that produce or process them. With the subset of over one hundred human fecal samples from the American Gut project, the implementation of the described coupled workflow revealed that targeted analysis using combination of single transition per compound with retention time misidentifies 30% of the targeted data and could lead to incorrect interpretations. At the same time, the targeted analysis extends detection limits and dynamic range, depending on the compounds, by orders of magnitude. A software application has been developed as a part of the workflow to allows for quantitative assessments based on calibration curves. Using this approach, we detect expected microbially modified molecules such as secondary bile acids and unexpected microbial molecules including Pseudomonas-associated quinolones and rhamnolipids in feces, setting the stage for metabolome-microbiome-wide association studies (MMWAS).

Microbiome disruption and recovery in the fish Gambusia affinis following exposure to broad-spectrum antibiotic.

Antibiotics are a relatively common disturbance to the normal microbiota of humans and agricultural animals, sometimes resulting in severe side effects such as antibiotic-associated enterocolitis. Gambusia affinis was used as a vertebrate model for effects of a broad-spectrum antibiotic, rifampicin, on the skin and gut mucosal microbiomes. The fish were exposed to the antibiotic in the water column for 1 week, and then monitored during recovery. As observed via culture, viable counts from the skin microbiome dropped strongly yet returned to pretreatment levels by 1.6 days and became >70% resistant. The gut microbiome counts dropped and took longer to recover (2.6 days), and became >90% drug resistant. The resistance persisted at ~20% of skin counts in the absence of antibiotic selection for 2 weeks. A community biochemical analysis measuring the presence/absence of 31 activities observed a 39% change in results after 3 days of antibiotic treatment. The antibiotic lowered the skin and gut microbiome community diversity and altered taxonomic composition, observed by 16S rRNA profiling. A 1-week recovery period did not return diversity or composition to pretreatment levels. The genus Myroides dominated both the microbiomes during the treatment, but was not stable and declined in abundance over time during recovery. Rifampicin selected for members of the family Comamonadaceae in the skin but not the gut microbiome. Consistent with other studies, this tractable animal model shows lasting effects on mucosal microbiomes following antibiotic exposure, including persistence of drug-resistant organisms in the community.

The Microbiome and Human Biology.

Over the past few years, microbiome research has dramatically reshaped our understanding of human biology. New insights range from an enhanced understanding of how microbes mediate digestion and disease processes (e.g., in inflammatory bowel disease) to surprising associations with Parkinson's disease, autism, and depression. In this review, we describe how new generations of sequencing technology, analytical advances coupled to new software capabilities, and the integration of animal model data have led to these new discoveries. We also discuss the prospects for integrating studies of the microbiome, metabolome, and immune system, with the goal of elucidating mechanisms that govern their interactions. This systems-level understanding will change how we think about ourselves as organisms.

Normalization and microbial differential abundance strategies depend upon data characteristics.

BACKGROUND: Data from 16S ribosomal RNA (rRNA) amplicon sequencing present challenges to ecological and statistical interpretation. In particular, library sizes often vary over several ranges of magnitude, and the data contains many zeros. Although we are typically interested in comparing relative abundance of taxa in the ecosystem of two or more groups, we can only measure the taxon relative abundance in specimens obtained from the ecosystems. Because the comparison of taxon relative abundance in the specimen is not equivalent to the comparison of taxon relative abundance in the ecosystems, this presents a special challenge. Second, because the relative abundance of taxa in the specimen (as well as in the ecosystem) sum to 1, these are compositional data. Because the compositional data are constrained by the simplex (sum to 1) and are not unconstrained in the Euclidean space, many standard methods of analysis are not applicable. Here, we evaluate how these challenges impact the performance of existing normalization methods and differential abundance analyses. RESULTS: Effects on normalization: Most normalization methods enable successful clustering of samples according to biological origin when the groups differ substantially in their overall microbial composition. Rarefying more clearly clusters samples according to biological origin than other normalization techniques do for ordination metrics based on presence or absence. Alternate normalization measures are potentially vulnerable to artifacts due to library size. Effects on differential abundance testing: We build on a previous work to evaluate seven proposed statistical methods using rarefied as well as raw data. Our simulation studies suggest that the false discovery rates of many differential abundance-testing methods are not increased by rarefying itself, although of course rarefying results in a loss of sensitivity due to elimination of a portion of available data. For groups with large (~10×) differences in the average library size, rarefying lowers the false discovery rate. DESeq2, without addition of a constant, increased sensitivity on smaller datasets (<20 samples per group) but tends towards a higher false discovery rate with more samples, very uneven (~10×) library sizes, and/or compositional effects. For drawing inferences regarding taxon abundance in the ecosystem, analysis of composition of microbiomes (ANCOM) is not only very sensitive (for >20 samples per group) but also critically the only method tested that has a good control of false discovery rate. CONCLUSIONS: These findings guide which normalization and differential abundance techniques to use based on the data characteristics of a given study.

Deblur Rapidly Resolves Single-Nucleotide Community Sequence Patterns.

High-throughput sequencing of 16S ribosomal RNA gene amplicons has facilitated understanding of complex microbial communities, but the inherent noise in PCR and DNA sequencing limits differentiation of closely related bacteria. Although many scientific questions can be addressed with broad taxonomic profiles, clinical, food safety, and some ecological applications require higher specificity. Here we introduce a novel sub-operational-taxonomic-unit (sOTU) approach, Deblur, that uses error profiles to obtain putative error-free sequences from Illumina MiSeq and HiSeq sequencing platforms. Deblur substantially reduces computational demands relative to similar sOTU methods and does so with similar or better sensitivity and specificity. Using simulations, mock mixtures, and real data sets, we detected closely related bacterial sequences with single nucleotide differences while removing false positives and maintaining stability in detection, suggesting that Deblur is limited only by read length and diversity within the amplicon sequences. Because Deblur operates on a per-sample level, it scales to modern data sets and meta-analyses. To highlight Deblur's ability to integrate data sets, we include an interactive exploration of its application to multiple distinct sequencing rounds of the American Gut Project. Deblur is open source under the Berkeley Software Distribution (BSD) license, easily installable, and downloadable from https://github.com/biocore/deblur. IMPORTANCE Deblur provides a rapid and sensitive means to assess ecological patterns driven by differentiation of closely related taxa. This algorithm provides a solution to the problem of identifying real ecological differences between taxa whose amplicons differ by a single base pair, is applicable in an automated fashion to large-scale sequencing data sets, and can integrate sequencing runs collected over time.

Balance Trees Reveal Microbial Niche Differentiation.

Advances in sequencing technologies have enabled novel insights into microbial niche differentiation, from analyzing environmental samples to understanding human diseases and informing dietary studies. However, identifying the microbial taxa that differentiate these samples can be challenging. These issues stem from the compositional nature of 16S rRNA gene data (or, more generally, taxon or functional gene data); the changes in the relative abundance of one taxon influence the apparent abundances of the others. Here we acknowledge that inferring properties of individual bacteria is a difficult problem and instead introduce the concept of balances to infer meaningful properties of subcommunities, rather than properties of individual species. We show that balances can yield insights about niche differentiation across multiple microbial environments, including soil environments and lung sputum. These techniques have the potential to reshape how we carry out future ecological analyses aimed at revealing differences in relative taxonomic abundances across different samples. IMPORTANCE By explicitly accounting for the compositional nature of 16S rRNA gene data through the concept of balances, balance trees yield novel biological insights into niche differentiation. The software to perform this analysis is available under an open-source license and can be obtained at https://github.com/biocore/gneiss. Author Video: An author video summary of this article is available.

The Microbiome and Big Data.

Microbiome datasets have expanded rapidly in recent years. Advances in DNA sequencing, as well as the rise of shotgun metagenomics and metabolomics, are producing datasets that exceed the ability of researchers to analyze them on their personal computers. Here we describe what Big Data is in the context of microbiome research, how this data can be transformed into knowledge about microbes and their functions in their environments, and how the knowledge can be applied to move microbiome research forward. In particular, the development of new high-resolution tools to assess strain-level variability (moving away from OTUs), the advent of cloud computing and centralized analysis resources such as Qiita (for sequences) and GNPS (for mass spectrometry), and better methods for curating and describing "metadata" (contextual information about the sequence or chemical information) are rapidly assisting the use of microbiome data in fields ranging from human health to environmental studies.

Improved Bacterial 16S rRNA Gene (V4 and V4-5) and Fungal Internal Transcribed Spacer Marker Gene Primers for Microbial Community Surveys.

Designing primers for PCR-based taxonomic surveys that amplify a broad range of phylotypes in varied community samples is a difficult challenge, and the comparability of data sets amplified with varied primers requires attention. Here, we examined the performance of modified 16S rRNA gene and internal transcribed spacer (ITS) primers for archaea/bacteria and fungi, respectively, with nonaquatic samples. We moved primer bar codes to the 5' end, allowing for a range of different 3' primer pairings, such as the 515f/926r primer pair, which amplifies variable regions 4 and 5 of the 16S rRNA gene. We additionally demonstrated that modifications to the 515f/806r (variable region 4) 16S primer pair, which improves detection of Thaumarchaeota and clade SAR11 in marine samples, do not degrade performance on taxa already amplified effectively by the original primer set. Alterations to the fungal ITS primers did result in differential but overall improved performance compared to the original primers. In both cases, the improved primers should be widely adopted for amplicon studies. IMPORTANCE We continue to uncover a wealth of information connecting microbes in important ways to human and environmental ecology. As our scientific knowledge and technical abilities improve, the tools used for microbiome surveys can be modified to improve the accuracy of our techniques, ensuring that we can continue to identify groundbreaking connections between microbes and the ecosystems they populate, from ice caps to the human body. It is important to confirm that modifications to these tools do not cause new, detrimental biases that would inhibit the field rather than continue to move it forward. We therefore demonstrated that two recently modified primer pairs that target taxonomically discriminatory regions of bacterial and fungal genomic DNA do not introduce new biases when used on a variety of sample types, from soil to human skin. This confirms the utility of these primers for maintaining currently recommended microbiome research techniques as the state of the art.

From Sample to Multi-Omics Conclusions in under 48 Hours.

Multi-omics methods have greatly advanced our understanding of the biological organism and its microbial associates. However, they are not routinely used in clinical or industrial applications, due to the length of time required to generate and analyze omics data. Here, we applied a novel integrated omics pipeline for the analysis of human and environmental samples in under 48 h. Human subjects that ferment their own foods provided swab samples from skin, feces, oral cavity, fermented foods, and household surfaces to assess the impact of home food fermentation on their microbial and chemical ecology. These samples were analyzed with 16S rRNA gene sequencing, inferred gene function profiles, and liquid chromatography-tandem mass spectrometry (LC-MS/MS) metabolomics through the Qiita, PICRUSt, and GNPS pipelines, respectively. The human sample microbiomes clustered with the corresponding sample types in the American Gut Project (http://www.americangut.org), and the fermented food samples produced a separate cluster. The microbial communities of the household surfaces were primarily sourced from the fermented foods, and their consumption was associated with increased gut microbial diversity. Untargeted metabolomics revealed that human skin and fermented food samples had separate chemical ecologies and that stool was more similar to fermented foods than to other sample types. Metabolites from the fermented foods, including plant products such as procyanidin and pheophytin, were present in the skin and stool samples of the individuals consuming the foods. Some food metabolites were modified during digestion, and others were detected in stool intact. This study represents a first-of-its-kind analysis of multi-omics data that achieved time intervals matching those of classic microbiological culturing. IMPORTANCE Polymicrobial infections are difficult to diagnose due to the challenge in comprehensively cultivating the microbes present. Omics methods, such as 16S rRNA sequencing, metagenomics, and metabolomics, can provide a more complete picture of a microbial community and its metabolite production, without the biases and selectivity of microbial culture. However, these advanced methods have not been applied to clinical or industrial microbiology or other areas where complex microbial dysbioses require immediate intervention. The reason for this is the length of time required to generate and analyze omics data. Here, we describe the development and application of a pipeline for multi-omics data analysis in time frames matching those of the culture-based approaches often used for these applications. This study applied multi-omics methods effectively in clinically relevant time frames and sets a precedent toward their implementation in clinical medicine and industrial microbiology.

The Oral and Skin Microbiomes of Captive Komodo Dragons Are Significantly Shared with Their Habitat.

Examining the way in which animals, including those in captivity, interact with their environment is extremely important for studying ecological processes and developing sophisticated animal husbandry. Here we use the Komodo dragon (Varanus komodoensis) to quantify the degree of sharing of salivary, skin, and fecal microbiota with their environment in captivity. Both species richness and microbial community composition of most surfaces in the Komodo dragon's environment are similar to the Komodo dragon's salivary and skin microbiota but less similar to the stool-associated microbiota. We additionally compared host-environment microbiome sharing between captive Komodo dragons and their enclosures, humans and pets and their homes, and wild amphibians and their environments. We observed similar host-environment microbiome sharing patterns among humans and their pets and Komodo dragons, with high levels of human/pet- and Komodo dragon-associated microbes on home and enclosure surfaces. In contrast, only small amounts of amphibian-associated microbes were detected in the animals' environments. We suggest that the degree of sharing between the Komodo dragon microbiota and its enclosure surfaces has important implications for animal health. These animals evolved in the context of constant exposure to a complex environmental microbiota, which likely shaped their physiological development; in captivity, these animals will not receive significant exposure to microbes not already in their enclosure, with unknown consequences for their health. IMPORTANCE Animals, including humans, have evolved in the context of exposure to a variety of microbial organisms present in the environment. Only recently have humans, and some animals, begun to spend a significant amount of time in enclosed artificial environments, rather than in the more natural spaces in which most of evolution took place. The consequences of this radical change in lifestyle likely extend to the microbes residing in and on our bodies and may have important implications for health and disease. A full characterization of host-microbe sharing in both closed and open environments will provide crucial information that may enable the improvement of health in humans and in captive animals, both of which experience a greater incidence of disease (including chronic illness) than counterparts living under more ecologically natural conditions.

Dog and human inflammatory bowel disease rely on overlapping yet distinct dysbiosis networks.

Inflammatory bowel disease (IBD) is an autoimmune condition that is difficult to diagnose, and animal models of this disease have questionable human relevance1. Here, we show that the dysbiosis network underlying IBD in dogs differs from that in humans, with some bacteria such as Fusobacterium switching roles between the two species (as Bacteroides fragilis switches roles between humans and mice)2. For example, a dysbiosis index trained on humans fails when applied to dogs, but a dog-specific dysbiosis index achieves high correlations with the overall dog microbial community diversity patterns. In addition, a random forest classifier trained on dog-specific samples achieves high discriminatory power, even when using stool samples rather than the mucosal biopsies required for high discriminatory power in humans2. These relationships were not detected in previously published dog IBD data sets due to their limited sample size and statistical power3. Taken together, these results reveal the need to train host-specific dysbiosis networks and point the way towards a generalized understanding of IBD across different mammalian models.

Microbial community assembly and metabolic function during mammalian corpse decomposition.

Vertebrate corpse decomposition provides an important stage in nutrient cycling in most terrestrial habitats, yet microbially mediated processes are poorly understood. Here we combine deep microbial community characterization, community-level metabolic reconstruction, and soil biogeochemical assessment to understand the principles governing microbial community assembly during decomposition of mouse and human corpses on different soil substrates. We find a suite of bacterial and fungal groups that contribute to nitrogen cycling and a reproducible network of decomposers that emerge on predictable time scales. Our results show that this decomposer community is derived primarily from bulk soil, but key decomposers are ubiquitous in low abundance. Soil type was not a dominant factor driving community development, and the process of decomposition is sufficiently reproducible to offer new opportunities for forensic investigations.

Euphorbia plant latex is inhabited by diverse microbial communities.

PREMISE OF THE STUDY: The antimicrobial properties and toxicity of Euphorbia plant latex should make it a hostile environment to microbes. However, when specimens from Euphorbia spp. were propagated in tissue culture, microbial growth was observed routinely, raising the question whether the latex of this diverse plant genus can be a niche for polymicrobial communities. METHODS: Latex from a phylogenetically diverse set of Euphorbia species was collected and genomic microbial DNA extracted. Deep sequencing of bar-coded amplicons from taxonomically informative gene fragments was used to measure bacterial and fungal species richness, evenness, and composition. KEY RESULTS: Euphorbia latex was found to contain unexpectedly complex bacterial (mean: 44.0 species per sample; 9 plants analyzed) and fungal (mean: 20.9 species per sample; 22 plants analyzed) communities using culture-independent methods. Many of the identified taxa are known plant endophytes, but have not been previously found in latex. CONCLUSIONS: Our results suggest that Euphorbia plant latex, a putatively hostile antimicrobial environment, unexpectedly supports diverse bacterial and fungal communities. The ecological roles of these microorganisms and potential interactions with their host plants are unknown and warrant further research.

The host effects of Gambusia affinis with an antibiotic-disrupted microbiome.

While serving as critical tools against bacterial infections, antimicrobial therapies can also result in serious side effects, such as antibiotic-associated entercolitis. Recent studies utilizing next generation sequencing to generate community 16S gene profiles have shown that antibiotics can strongly alter community composition and deplete diversity. However, how these community changes in the microbiota are related to the host side effects is still unclear. We have used the freshwater Western mosquitofish (Gambusia affinis) as a tractable vertebrate model system to study host effects following exposure to a broad spectrum antibiotic, rifampicin. After 3days of exposure, the bacterial communities of the mucosal skin and gut microbiomes lost diversity and shifted composition. Compared to unexposed controls, treated fish were more susceptible to a specific pathogen, Edwardsiella ictaluri, yet displayed no survival differences when subjected to a polymicrobial water challenge of soil or feces. Treated fish were more susceptible to osmotic stress from NaCl, but not to the toxin nitrate. Treated fish failed to gain weight as well as controls over one month when fed a matched diet. Because of small sample sizes, pathogen susceptibility and weight gain differences were not statistically significant. This study provides supporting evidence in an experimental laboratory system that an antibiotic can have significant and persistent negative host effects, and provides for future study into the mechanisms of these effects.

Initial insights into bacterial succession during human decomposition.

Decomposition is a dynamic ecological process dependent upon many factors such as environment, climate, and bacterial, insect, and vertebrate activity in addition to intrinsic properties inherent to individual cadavers. Although largely attributed to microbial metabolism, very little is known about the bacterial basis of human decomposition. To assess the change in bacterial community structure through time, bacterial samples were collected from several sites across two cadavers placed outdoors to decompose and analyzed through 454 pyrosequencing and analysis of variable regions 3-5 of the bacterial 16S ribosomal RNA (16S rRNA) gene. Each cadaver was characterized by a change in bacterial community structure for all sites sampled as time, and decomposition, progressed. Bacteria community structure is variable at placement and before purge for all body sites. At bloat and purge and until tissues began to dehydrate or were removed, bacteria associated with flies, such as Ignatzschineria and Wohlfahrtimonas, were common. After dehydration and skeletonization, bacteria associated with soil, such as Acinetobacter, were common at most body sites sampled. However, more cadavers sampled through multiple seasons are necessary to assess major trends in bacterial succession.