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In October of 2020, researchers from around the world met online for the sixth annual International Workshop on Microbiome in HIV Pathogenesis, Prevention, and Treatment. New research was presented on the roles of the microbiome on immune response and HIV transmission and pathogenesis and the potential for alterations in the microbiome to decrease transmission and affect comorbidities. This article presents a summary of the findings reported.

Although the COVID-19 pandemic is caused by a single virus, the rest of the human microbiome appears to be involved in the disease and could influence vaccine responses while offering opportunities for microbiome-directed therapeutics. The newly formed Microbiome Centers Consortium (MCC) surveyed its membership and identified four ways to leverage the strengths and experience of microbiome centers in the response to the COVID-19 pandemic. To meet these needs, the MCC will provide a platform to coordinate clinical and environmental research, assist with practical obstacles, and help communicate the connections between the microbiome and COVID-19. We ask that microbiome researchers join us in these efforts to address the ongoing pandemic.

Childhood obesity is a growing problem worldwide. Recent research suggests that the gut microbiota may play an important and potentially causal role in the development of obesity and may be one mechanism that explains the transgenerational transmission of obesity risk. Here we examine the early-life gut microbiota at days 4, 10, 30, 120, 365, and 730 and the association with body mass index (BMI) z-scores at age 12 in a Norwegian prospective cohort (n = 165), and evaluate how these BMI-associated taxa relate to maternal overweight/obesity (Ow/Ob) and excessive gestational weight gain (GWG). We performed 16S rRNA gene sequencing on the gut microbiota samples. Taxonomic phylogeny at days 10 and 730 was significantly associated with childhood BMI, and the gut microbiota taxa at two years of age explained over 50% of the variation in childhood BMI in this cohort. The subset of the early-life taxa within the gut microbiota that best predicted later childhood BMI showed substantial overlap with the maternal taxa most strongly associated with maternal Ow/Ob and excessive GWG. Our results show an association between the infant gut microbiota and later BMI, and they offer preliminary evidence that the infant gut microbiota, particularly at 2 years of age, may have potential to help identify children at risk for obesity.IMPORTANCE Understanding the role of the early-life gut microbiota in obesity is important because there may be opportunities for preventive strategies. We examined the relationships between infant gut microbiota at six times during the first two years of life and BMI at age 12 in a birth cohort of 165 children and their mothers. We found that the gut microbiota from early life to two years shows an increasingly strong association with childhood BMI. This study provides preliminary evidence that the gut microbiome at 2 years of age may offer useful information to help to identify youth who are at risk for obesity, which could facilitate more-targeted early prevention efforts.

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 United States, United Kingdom, and Australia to one another and to environmental samples. Our results show an unexpected range of beta-diversity in human stool microbiomes 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. IMPORTANCE We 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.

BackgroundData 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.ResultsEffects 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 also critically the only method tested that has a good control of false discovery rate.ConclusionsThese findings guide which normalization and differential abundance techniques to use based on the data characteristics of a given study.

Bisphenol A (BPA) accumulates in the maturing gut and liver in utero and is known to alter gut bacterial profiles in offspring. Gut bacterial dysbiosis may contribute to chronic colonic and systemic inflammation. We hypothesized that perinatal BPA exposure-induced intestinal (and liver) inflammation in offspring is due to alterations in the microbiome and colonic metabolome. The 16S rRNA amplicon sequencing analysis revealed differences in beta diversity with a significant reduction in the relative abundances of short-chain fatty acid (SCFA) producers such as Oscillospira and Ruminococcaceae due to BPA exposure. Furthermore, BPA exposure reduced fecal SCFA levels and increased systemic lipopolysaccharide (LPS) levels. BPA exposure-increased intestinal permeability was ameliorated by the addition of SCFA in vitro. Metabolic fingerprints revealed alterations in global metabolism and amino acid metabolism. Thus, our findings indicate that perinatal BPA exposure may cause gut bacterial dysbiosis and altered metabolite profiles, particularly SCFA profiles, leading to chronic colon and liver inflammation. IMPORTANCE Emerging evidence suggests that environmental toxicants may influence inflammation-promoted chronic disease susceptibility during early life. BPA, an environmental endocrine disruptor, can transfer across the placenta and accumulate in fetal gut and liver. However, underlying mechanisms for BPA-induced colonic and liver inflammation are not fully elucidated. In this report, we show how perinatal BPA exposure in rabbits alters gut microbiota and their metabolite profiles, which leads to colonic and liver inflammation as well as to increased gut permeability as measured by elevated serum lipopolysaccharide (LPS) levels in the offspring. Also, perinatal BPA exposure leads to reduced levels of gut bacterial diversity and bacterial metabolites (short-chain fatty acids [SCFA]) and elevated gut permeability-three common early biomarkers of inflammation-promoted chronic diseases. In addition, we showed that SCFA ameliorated BPA-induced intestinal permeability in vitro. Thus, our study results suggest that correcting environmental toxicant-induced bacterial dysbiosis early in life may reduce the risk of chronic diseases later in life.

ObjectivePreterm birth is the main reason for neonatal deaths worldwide. We investigate whether maternal gut microbiota may play a previously overlooked role.MethodsThe Norwegian Microbiota Study (NoMIC) is a case control study on preterm birth (

Disruption of healthy microbial communities has been linked to numerous diseases, yet microbial interactions are little understood. This is due in part to the large number of bacteria, and the much larger number of interactions (easily in the millions), making experimental investigation very difficult at best and necessitating the nascent field of computational exploration through microbial correlation networks. We benchmark the performance of eight correlation techniques on simulated and real data in response to challenges specific to microbiome studies: fractional sampling of ribosomal RNA sequences, uneven sampling depths, rare microbes and a high proportion of zero counts. Also tested is the ability to distinguish signals from noise, and detect a range of ecological and time-series relationships. Finally, we provide specific recommendations for correlation technique usage. Although some methods perform better than others, there is still considerable need for improvement in current techniques.

Long et al. (BMC Bioinformatics 2014, 15(1):278) describe a "discrepancy" in using UniFrac to assess statistical significance of community differences. Specifically, they find that weighted UniFrac results differ between input trees where (a) replicate sequences each have their own tip, or (b) all replicates are assigned to one tip with an associated count. We argue that these are two distinct cases that differ in the probability distribution on which the statistical test is based, because of the differences in tree topology. Further study is needed to understand which randomization procedure best detects different aspects of community dissimilarities.

Differences in the gut microbiota have been reported between individuals with autism spectrum disorders (ASD) and neurotypical controls, although direct evidence that changes in the microbiome contribute to causing ASD has been scarce to date. Here we summarize some considerations of experimental design that can help untangle causality in this complex system. In particular, large cross-sectional studies that can factor out important variables such as diet, prospective longitudinal studies that remove some of the influence of interpersonal variation in the microbiome (which is generally high, especially in children), and studies transferring microbial communities into germ-free mice may be especially useful. Controlling for the effects of technical variables, which have complicated efforts to combine existing studies, is critical when biological effect sizes are small. Large citizen-science studies with thousands of participants such as the American Gut Project have been effective at uncovering subtle microbiome effects in self-collected samples and with self-reported diet and behavior data, and may provide a useful complement to other types of traditionally funded and conducted studies in the case of ASD, especially in the hypothesis generation phase.

Respiratory surfaces are exposed to billions of particulates and pathogens daily. A protective mucus barrier traps and eliminates them through mucociliary clearance (MCC). However, excessive mucus contributes to transient respiratory infections and to the pathogenesis of numerous respiratory diseases. MUC5AC and MUC5B are evolutionarily conserved genes that encode structurally related mucin glycoproteins, the principal macromolecules in airway mucus. Genetic variants are linked to diverse lung diseases, but specific roles for MUC5AC and MUC5B in MCC, and the lasting effects of their inhibition, are unknown. Here we show that mouse Muc5b (but not Muc5ac) is required for MCC, for controlling infections in the airways and middle ear, and for maintaining immune homeostasis in mouse lungs, whereas Muc5ac is dispensable. Muc5b deficiency caused materials to accumulate in upper and lower airways. This defect led to chronic infection by multiple bacterial species, including Staphylococcus aureus, and to inflammation that failed to resolve normally. Apoptotic macrophages accumulated, phagocytosis was impaired, and interleukin-23 (IL-23) production was reduced in Muc5b(-/-) mice. By contrast, in mice that transgenically overexpress Muc5b, macrophage functions improved. Existing dogma defines mucous phenotypes in asthma and chronic obstructive pulmonary disease (COPD) as driven by increased MUC5AC, with MUC5B levels either unaffected or increased in expectorated sputum. However, in many patients, MUC5B production at airway surfaces decreases by as much as 90%. By distinguishing a specific role for Muc5b in MCC, and by determining its impact on bacterial infections and inflammation in mice, our results provide a refined framework for designing targeted therapies to control mucin secretion and restore MCC.

Advances in high-throughput technologies have enhanced our ability to describe microbial communities as they relate to human health and disease. Alongside the growth in sequencing data has come an influx of resources that synthesize knowledge surrounding microbial traits, functions, and metabolic potential with knowledge of how they may impact host pathways to influence disease phenotypes. These knowledge bases can enable the development of mechanistic explanations that may underlie correlations detected between microbial communities and disease. In this review, we survey existing resources and methodologies for the computational integration of broad classes of microbial and host knowledge. We evaluate these knowledge bases in their access methods, content, and source characteristics. We discuss challenges of the creation and utilization of knowledge bases including inconsistency of nomenclature assignment of taxa and metabolites across sources, whether the biological entities represented are rooted in ontologies or taxonomies, and how the structure and accessibility limit the diversity of applications and user types. We make this information available in a code and data repository at: https://github.com/lozuponelab/knowledge-source-mappings. Addressing these challenges will allow for the development of more effective tools for drawing from abundant knowledge to find new insights into microbial mechanisms in disease by fostering a systematic and unbiased exploration of existing information. [ABSTRACT FROM AUTHOR]

Understanding gut microbiota alterations associated with HIV infection and factors that drive these alterations may help explain gut-linked diseases prevalent with HIV. 16S rRNA sequencing of feces from HIV-infected individuals revealed that HIV infection is associated with highly characteristic gut community changes, and antiretroviral therapy does not consistently restore the microbiota to an HIV-negative state. Despite the chronic gut inflammation characteristic of HIV infection, the associated microbiota showed limited similarity with other inflammatory states and instead showed increased, rather than decreased, diversity. Meta-analysis revealed that the microbiota of HIV-infected individuals in the U.S. was most similar to a Prevotella-rich community composition typically observed in healthy individuals in agrarian cultures of Malawi and Venezuela and related to that of U.S. individuals with carbohydrate-rich, protein- and fat-poor diets. By evaluating innate and adaptive immune responses to lysates from bacteria that differ with HIV, we explore the functional drivers of these compositional differences.

RationaleLung infections caused by opportunistic or virulent pathogens are a principal cause of morbidity and mortality in HIV infection. It is unknown whether HIV infection leads to changes in basal lung microflora, which may contribute to chronic pulmonary complications that increasingly are being recognized in individuals infected with HIV.ObjectivesTo determine whether the immunodeficiency associated with HIV infection resulted in alteration of the lung microbiota.MethodsWe used 16S ribosomal RNA targeted pyrosequencing and shotgun metagenomic sequencing to analyze bacterial gene sequences in bronchoalveolar lavage (BAL) and mouths of 82 HIV-positive and 77 HIV-negative subjects.Measurements and main resultsSequences representing Tropheryma whipplei, the etiologic agent of Whipple's disease, were significantly more frequent in BAL of HIV-positive compared with HIV-negative individuals. T. whipplei dominated the community (>50% of sequence reads) in 11 HIV-positive subjects, but only 1 HIV-negative individual (13.4 versus 1.3%; P = 0.0018). In 30 HIV-positive individuals sampled longitudinally, antiretroviral therapy resulted in a significantly reduced relative abundance of T. whipplei in the lung. Shotgun metagenomic sequencing was performed on eight BAL samples dominated by T. whipplei 16S ribosomal RNA. Whole genome assembly of pooled reads showed that uncultured lung-derived T. whipplei had similar gene content to two isolates obtained from subjects with Whipple's disease.ConclusionsAsymptomatic subjects with HIV infection have unexpected colonization of the lung by T. whipplei, which is reduced by effective antiretroviral therapy and merits further study for a potential pathogenic role in chronic pulmonary complications of HIV infection.

Gut microbial communities represent one source of human genetic and metabolic diversity. To examine how gut microbiomes differ among human populations, here we characterize bacterial species in fecal samples from 531 individuals, plus the gene content of 110 of them. The cohort encompassed healthy children and adults from the Amazonas of Venezuela, rural Malawi and US metropolitan areas and included mono- and dizygotic twins. Shared features of the functional maturation of the gut microbiome were identified during the first three years of life in all three populations, including age-associated changes in the genes involved in vitamin biosynthesis and metabolism. Pronounced differences in bacterial assemblages and functional gene repertoires were noted between US residents and those in the other two countries. These distinctive features are evident in early infancy as well as adulthood. Our findings underscore the need to consider the microbiome when evaluating human development, nutritional needs, physiological variations and the impact of westernization.

Our understanding of the vast collection of microbes that live on and inside us (microbiota) and their collective genes (microbiome) has been revolutionized by culture-independent "metagenomic" techniques and DNA sequencing technologies. Most of our microbes live in our gut, where they function as a metabolic organ and provide attributes not encoded in our human genome. Metagenomic studies are revealing shared and distinctive features of microbial communities inhabiting different humans. A central question in psychiatry is the relative role of genes and environment in shaping behavior. The human microbiome serves as the interface between our genes and our history of environmental exposures; explorations of our microbiomes thus offer the possibility of providing new insights into our neurodevelopment and our behavioral phenotypes by affecting complex processes such as inter- and intra personal variations in cognition, personality, mood, sleep, and eating behavior, and perhaps even a variety of neuropsychiatric diseases ranging from affective disorders to autism. Better understanding of microbiome-encoded pathways for xenobiotic metabolism also has important implications for improving the efficacy of pharmacologic interventions with neuromodulatory agents.

High-throughput sequencing methods enable characterization of microbial communities in a wide range of environments on an unprecedented scale. However, insight into microbial community composition is limited by our ability to detect patterns in this flood of sequences. Here we compare the performance of 51 analysis techniques using real and simulated bacterial 16S rRNA pyrosequencing datasets containing either clustered samples or samples arrayed across environmental gradients. We found that many diversity patterns were evident with severely undersampled communities and that methods varied widely in their ability to detect gradients and clusters. Chi-squared distances and Pearson correlation distances performed especially well for detecting gradients, whereas Gower and Canberra distances performed especially well for detecting clusters. These results also provide a basis for understanding tradeoffs between number of samples and depth of coverage, tradeoffs that are important to consider when designing studies to characterize microbial communities.

The mammalian gut is an attractive model for exploring the general question of how habitat impacts the evolution of gene content. Therefore, we have characterized the relationship between 16 S rRNA gene sequence similarity and overall levels of gene conservation in four groups of species: gut specialists and cosmopolitans, each of which can be divided into pathogens and non-pathogens. At short phylogenetic distances, specialist or cosmopolitan bacteria found in the gut share fewer genes than is typical for genomes that come from non-gut environments, but at longer phylogenetic distances gut bacteria are more similar to each other than are genomes at equivalent evolutionary distances from non-gut environments, suggesting a pattern of short-term specialization but long-term convergence. Moreover, this pattern is observed in both pathogens and non-pathogens, and can even be seen in the plasmids carried by gut bacteria. This observation is consistent with the finding that, despite considerable interpersonal variation in species content, there is surprising functional convergence in the microbiome of different humans. Finally, we observe that even within bacterial species or genera 16S rRNA divergence provides useful information about average conservation of gene content. The results described here should be useful for guiding strain selection to maximize novel gene discovery in large-scale genome sequencing projects, while the approach could be applied in studies seeking to understand the effects of habitat adaptation on genome evolution across other body habitats or environment types.

Next-generation sequencing techniques, and PhyloChip, have made simultaneous phylogenetic analyses of hundreds of microbial communities possible. Insight into community structure has been limited by the inability to integrate and visualize such vast datasets. Fast UniFrac overcomes these issues, allowing integration of larger numbers of sequences and samples into a single analysis. Its new array-based implementation offers orders of magnitude improvements over the original version. New 3D visualization of principal coordinates analysis results, with the option to view multiple coordinate axes simultaneously, provides a powerful way to quickly identify patterns that relate vast numbers of microbial communities. We show the potential of Fast UniFrac using examples from three data types: Sanger-sequencing studies of diverse free-living and animal-associated bacterial assemblages and from the gut of obese humans as they diet, pyrosequencing data integrated from studies of the human hand and gut, and PhyloChip data from a study of citrus pathogens. We show that a Fast UniFrac analysis using a reference tree recaptures patterns that could not be detected without considering phylogenetic relationships and that Fast UniFrac, coupled with BLAST-based sequence assignment, can be used to quickly analyze pyrosequencing runs containing hundreds of thousands of sequences, showing patterns relating human and gut samples. Finally, we show that the application of Fast UniFrac to PhyloChip data could identify well-defined subcategories associated with infection. Together, these case studies point the way toward a broad range of applications and show some of the new features of Fast UniFrac.

The PD measure of phylogenetic diversity interprets branch lengths cladistically to make inferences about feature diversity. PD calculations extend conventional species-level ecological indices to the features level. The "phylogenetic beta diversity" framework developed by microbial ecologists calculates PD-dissimilarities between community localities. Interpretation of these PD-dissimilarities at the feature level explains the framework's success in producing ordinations revealing environmental gradients. An example gradients space using PD-dissimilarities illustrates how evolutionary features form unimodal response patterns to gradients. This features model supports new application of existing species-level methods that are robust to unimodal responses, plus novel applications relating to climate change, commercial products discovery, and community assembly.

The vertebrate gut harbors a vast community of bacterial mutualists, the composition of which is modulated by the host immune system. Many gastrointestinal (GI) diseases are expected to be associated with disruptions of host-bacterial interactions, but relatively few comprehensive studies have been reported. We have used the rhesus macaque model to investigate forces shaping GI bacterial communities. We used DNA bar coding and pyrosequencing to characterize 141,000 sequences of 16S rRNA genes obtained from 100 uncultured GI bacterial samples, allowing quantitative analysis of community composition in health and disease. Microbial communities of macaques were distinct from those of mice and humans in both abundance and types of taxa present. The macaque communities differed among samples from intestinal mucosa, colonic contents, and stool, paralleling studies of humans. Communities also differed among animals, over time within individual animals, and between males and females. To investigate changes associated with disease, samples of colonic contents taken at necropsy were compared between healthy animals and animals with colitis and undergoing antibiotic therapy. Communities from diseased and healthy animals also differed significantly in composition. This work provides comprehensive data and improved methods for studying the role of commensal microbiota in macaque models of GI diseases and provides a model for the large-scale screening of the human gut microbiome.

The adult human intestine contains trillions of bacteria, representing hundreds of species and thousands of subspecies. Little is known about the selective pressures that have shaped and are shaping this community's component species, which are dominated by members of the Bacteroidetes and Firmicutes divisions. To examine how the intestinal environment affects microbial genome evolution, we have sequenced the genomes of two members of the normal distal human gut microbiota, Bacteroides vulgatus and Bacteroides distasonis, and by comparison with the few other sequenced gut and non-gut Bacteroidetes, analyzed their niche and habitat adaptations. The results show that lateral gene transfer, mobile elements, and gene amplification have played important roles in affecting the ability of gut-dwelling Bacteroidetes to vary their cell surface, sense their environment, and harvest nutrient resources present in the distal intestine. Our findings show that these processes have been a driving force in the adaptation of Bacteroidetes to the distal gut environment, and emphasize the importance of considering the evolution of humans from an additional perspective, namely the evolution of our microbiomes.

Pyrosequencing technology allows us to characterize microbial communities using 16S ribosomal RNA (rRNA) sequences orders of magnitude faster and more cheaply than has previously been possible. However, results from different studies using pyrosequencing and traditional sequencing are often difficult to compare, because amplicons covering different regions of the rRNA might yield different conclusions. We used sequences from over 200 globally dispersed environments to test whether studies that used similar primers clustered together mistakenly, without regard to environment. We then tested whether primer choice affects sequence-based community analyses using UniFrac, our recently-developed method for comparing microbial communities. We performed three tests of primer effects. We tested whether different simulated amplicons generated the same UniFrac clustering results as near-full-length sequences for three recent large-scale studies of microbial communities in the mouse and human gut, and the Guerrero Negro microbial mat. We then repeated this analysis for short sequences (100-, 150-, 200- and 250-base reads) resembling those produced by pyrosequencing. The results show that sequencing effort is best focused on gathering more short sequences rather than fewer longer ones, provided that the primers are chosen wisely, and that community comparison methods such as UniFrac are surprisingly robust to variation in the region sequenced.

We have implemented in Python the COmparative GENomic Toolkit, a fully integrated and thoroughly tested framework for novel probabilistic analyses of biological sequences, devising workflows, and generating publication quality graphics. PyCogent includes connectors to remote databases, built-in generalized probabilistic techniques for working with biological sequences, and controllers for third-party applications. The toolkit takes advantage of parallel architectures and runs on a range of hardware and operating systems, and is available under the general public license from http://sourceforge.net/projects/pycogent.

BackgroundMoving beyond pairwise significance tests to compare many microbial communities simultaneously is critical for understanding large-scale trends in microbial ecology and community assembly. Techniques that allow microbial communities to be compared in a phylogenetic context are rapidly gaining acceptance, but the widespread application of these techniques has been hindered by the difficulty of performing the analyses.ResultsWe introduce UniFrac, a web application available at http://bmf.colorado.edu/unifrac, that allows several phylogenetic tests for differences among communities to be easily applied and interpreted. We demonstrate the use of UniFrac to cluster multiple environments, and to test which environments are significantly different. We show that analysis of previously published sequences from the Columbia river, its estuary, and the adjacent coastal ocean using the UniFrac interface provided insights that were not apparent from the initial data analysis, which used other commonly employed techniques to compare the communities.ConclusionUniFrac provides easy access to powerful multivariate techniques for comparing microbial communities in a phylogenetic context. We thus expect that it will provide a completely new picture of many microbial interactions and processes in both environmental and medical contexts.