Genomic variation landscape of the human gut microbiome. Nature. 2013;493:45-50. [PMID: 23222524 PMCID: PMC3536929 DOI: 10.1038/nature11711]

A metabolomic perspective on coeliac disease

Metabolomics is an "omic" science that is now emerging with the purpose of elaborating a comprehensive analysis of the metabolome, which is the complete set of metabolites (i.e., small molecules intermediates) in an organism, tissue, cell, or biofluid. In the past decade, metabolomics has already proved to be useful for the characterization of several pathological conditions and offers promises as a clinical tool. A metabolomics investigation of coeliac disease (CD) revealed that a metabolic fingerprint for CD can be defined, which accounts for three different but complementary components: malabsorption, energy metabolism, and alterations in gut microflora and/or intestinal permeability. In this review, we will discuss the major advancements in metabolomics of CD, in particular with respect to the role of gut microbiome and energy metabolism.

Gut microbiome phenotypes driven by host genetics affect arsenic metabolism

Large individual differences in susceptibility to arsenic-induced diseases are well-documented and frequently associated with different patterns of arsenic metabolism. In this context, the role of the gut microbiome in directly metabolizing arsenic and triggering systemic responses in diverse organs raises the possibility that gut microbiome phenotypes affect the spectrum of metabolized arsenic species. However, it remains unclear how host genetics and the gut microbiome interact to affect the biotransformation of arsenic. Using an integrated approach combining 16S rRNA gene sequencing and HPLC-ICP-MS arsenic speciation, we demonstrate that IL-10 gene knockout leads to a significant taxonomic change of the gut microbiome, which in turn substantially affects arsenic metabolism.

Inflammation and colorectal cancer, when microbiota-host mutualism breaks

Structural changes in the gut microbial community have been shown to accompany the progressive development of colorectal cancer. In this review we discuss recent hypotheses on the mechanisms involved in the bacteria-mediated carcinogenesis, as well as the triggering factors favoring the shift of the gut microbiota from a mutualistic to a pro-carcinogenic configuration. The possible role of inflammation, bacterial toxins and toxic microbiota metabolites in colorectal cancer onset is specifically discussed. On the other hand, the strategic role of inflammation as the keystone factor in driving microbiota to become carcinogenic is suggested. As a common outcome of different environmental and endogenous triggers, such as diet, aging, pathogen infection or genetic predisposition, inflammation can compromise the microbiota-host mutualism, forcing the increase of pathobionts at the expense of health-promoting groups, and allowing the microbiota to acquire an overall pro-inflammatory configuration. Consolidating inflammation in the gut, and favoring the bloom of toxigenic bacterial drivers, these changes in the gut microbial ecosystem have been suggested as pivotal in promoting carcinogenesis. In this context, it will become of primary importance to implement dietary or probiotics-based interventions aimed at preserving the microbiota-host mutualism along aging, counteracting deviations that favor a pro-carcinogenic microbiota asset.

An introduction to the analysis of shotgun metagenomic data

Environmental DNA sequencing has revealed the expansive biodiversity of microorganisms and clarified the relationship between host-associated microbial communities and host phenotype. Shotgun metagenomic DNA sequencing is a relatively new and powerful environmental sequencing approach that provides insight into community biodiversity and function. But, the analysis of metagenomic sequences is complicated due to the complex structure of the data. Fortunately, new tools and data resources have been developed to circumvent these complexities and allow researchers to determine which microbes are present in the community and what they might be doing. This review describes the analytical strategies and specific tools that can be applied to metagenomic data and the considerations and caveats associated with their use. Specifically, it documents how metagenomes can be analyzed to quantify community structure and diversity, assemble novel genomes, identify new taxa and genes, and determine which metabolic pathways are encoded in the community. It also discusses several methods that can be used compare metagenomes to identify taxa and functions that differentiate communities.

Interactions in the microbiome: communities of organisms and communities of genes

A central challenge in microbial community ecology is the delineation of appropriate units of biodiversity, which can be taxonomic, phylogenetic, or functional in nature. The term 'community' is applied ambiguously; in some cases, the term refers simply to a set of observed entities, while in other cases, it requires that these entities interact with one another. Microorganisms can rapidly gain and lose genes, potentially decoupling community roles from taxonomic and phylogenetic groupings. Trait-based approaches offer a useful alternative, but many traits can be defined based on gene functions, metabolic modules, and genomic properties, and the optimal set of traits to choose is often not obvious. An analysis that considers taxon assignment and traits in concert may be ideal, with the strengths of each approach offsetting the weaknesses of the other. Individual genes also merit consideration as entities in an ecological analysis, with characteristics such as diversity, turnover, and interactions modeled using genes rather than organisms as entities. We identify some promising avenues of research that are likely to yield a deeper understanding of microbial communities that shift from observation-based questions of 'Who is there?' and 'What are they doing?' to the mechanistically driven question of 'How will they respond?'

Genetic variation of a bacterial pathogen within individuals with cystic fibrosis provides a record of selective pressures

Advances in sequencing technologies have enabled the identification of mutations acquired by bacterial pathogens during infection. However, it remains unclear whether adaptive mutations fix in the population or lead to pathogen diversification within the patient. Here we study the genotypic diversity of Burkholderia dolosa within individuals with cystic fibrosis by resequencing individual colonies and whole populations from single sputum samples. We find extensive intrasample diversity, suggesting that mutations rarely fix in a patient's pathogen population--instead, diversifying lineages coexist for many years. Under strong selection, multiple adaptive mutations arise, but none of these sweep to fixation, generating lasting allele diversity that provides a recorded signature of past selection. Genes involved in outer-membrane components, iron scavenging and antibiotic resistance all showed this signature of within-patient selection. These results offer a general and rapid approach for identifying the selective pressures acting on a pathogen in individual patients based on single clinical samples.

Translating personalized medicine using new genetic technologies in clinical practice: the ethical issues

The integration of new genetic technologies into clinical practice holds great promise for the personalization of medical care, particularly the use of large-scale DNA sequencing for genome-wide genetic testing. However, these technologies also yield unprecedented amounts of information whose clinical implications are not fully understood, and we are still developing technical standards for measuring sequence accuracy. These technical and clinical challenges raise ethical issues that are similar to but qualitatively different from those that we are accustomed to dealing with for traditional medical genetics. The sheer amount of information afforded by genome sequencing requires rethinking of how to implement core ethical principles including, but not limited to: informed consent, privacy and data ownership and sharing, technology regulation, issues of access, particularly as new technology is integrated into clinical practice, and issues of potential stigma and impact on perceptions of disability. In this article, we will review the issues of informed consent, privacy, data ownership and technology regulation as they relate to the emerging field of personalized medicine and genomics.

Correlates of gut community composition across an ant species (Cephalotes varians) elucidate causes and consequences of symbiotic variability

Insect guts are often colonized by multispecies microbial communities that play integral roles in nutrition, digestion and defence. Community composition can differ across host species with increasing dietary and genetic divergence, yet gut microbiota can also vary between conspecific hosts and across an individual's lifespan. Through exploration of such intraspecific variation and its correlates, molecular profiling of microbial communities can generate and test hypotheses on the causes and consequences of symbioses. In this study, we used 454 pyrosequencing and TRFLP to achieve these goals in an herbivorous ant, Cephalotes varians, exploring variation in bacterial communities across colonies, populations and workers reared on different diets. C. varians bacterial communities were dominated by 16 core species present in over two-thirds of the sampled colonies. Core species comprised multiple genotypes, or strains and hailed from ant-specific clades containing relatives from other Cephalotes species. Yet three were detected in environmental samples, suggesting the potential for environmental acquisition. In spite of their prevalence and long-standing relationships with Cephalotes ants, the relative abundance and genotypic composition of core species varied across colonies. Diet-induced plasticity is a likely cause, but only pollen-based diets had consistent effects, altering the abundance of two types of bacteria. Additional factors, such as host age, genetics, chance or natural selection, must therefore shape natural variation. Future studies on these possibilities and on bacterial contributions to the use of pollen, a widespread food source across Cephalotes, will be important steps in developing C. varians as a model for studying widespread social insect-bacteria symbioses.

Microbial genes, brain & behaviour - epigenetic regulation of the gut-brain axis

To date, there is rapidly increasing evidence for host-microbe interaction at virtually all levels of complexity, ranging from direct cell-to-cell communication to extensive systemic signalling, and involving various organs and organ systems, including the central nervous system. As such, the discovery that differential microbial composition is associated with alterations in behaviour and cognition has significantly contributed to establishing the microbiota-gut-brain axis as an extension of the well-accepted gut-brain axis concept. Many efforts have been focused on delineating a role for this axis in health and disease, ranging from stress-related disorders such as depression, anxiety and irritable bowel syndrome to neurodevelopmental disorders such as autism. There is also a growing appreciation of the role of epigenetic mechanisms in shaping brain and behaviour. However, the role of epigenetics in informing host-microbe interactions has received little attention to date. This is despite the fact that there are many plausible routes of interaction between epigenetic mechanisms and the host-microbiota dialogue. From this new perspective we put forward novel, yet testable, hypotheses. Firstly, we suggest that gut-microbial products can affect chromatin plasticity within their host's brain that in turn leads to changes in neuronal transcription and eventually alters host behaviour. Secondly, we argue that the microbiota is an important mediator of gene-environment interactions. Finally, we reason that the microbiota itself may be viewed as an epigenetic entity. In conclusion, the fields of (neuro)epigenetics and microbiology are converging at many levels and more interdisciplinary studies are necessary to unravel the full range of this interaction.

Intestinal inflammation and stem cell homeostasis in aging Drosophila melanogaster

As a barrier epithelium, the intestinal epithelium has to coordinate physiological functions like digestion and nutrient resorption with the control of commensal bacteria and the prevention of pathogenic infections. It can therefore mount powerful innate immune and inflammatory responses, while, at the same time, maintaining tissue homeostasis through regenerative processes. How these different functions are coordinated remains unclear, and further insight is required to understand the age-related loss of homeostasis in this system, as well as the etiology of inflammatory and proliferative diseases of the gut. Recent work in Drosophila melanogaster has provided important new insight into the regulation of regenerative activity, innate immune homeostasis, commensal control, as well as age-related dysfunction in the intestine. Interestingly, many of the identified processes and mechanisms mirror similar homeostatic processes in the vertebrate intestine. This review summarized the current understanding of how innate immune responses, changes in commensal bacteria, and other challenges influence regenerative activity in the aging intestinal epithelium of flies and draws parallels to similar processes in mammals.

The role of replicates for error mitigation in next-generation sequencing

Advances in next-generation sequencing (NGS) technologies have rapidly improved sequencing fidelity and substantially decreased sequencing error rates. However, given that there are billions of nucleotides in a human genome, even low experimental error rates yield many errors in variant calls. Erroneous variants can mimic true somatic and rare variants, thus requiring costly confirmatory experiments to minimize the number of false positives. Here, we discuss sources of experimental errors in NGS and how replicates can be used to abate such errors.

MetaID: a novel method for identification and quantification of metagenomic samples

BACKGROUND

Advances in next-generation sequencing (NGS) technology has provided us with an opportunity to analyze and evaluate the rich microbial communities present in all natural environments. The shorter reads obtained from the shortgun technology has paved the way for determining the taxonomic profile of a community by simply aligning the reads against the available reference genomes. While several computational methods are available for taxonomic profiling at the genus- and species-level, none of these methods are effective at the strain-level identification due to the increasing difficulty in detecting variation at that level. Here, we present MetaID, an alignment-free n-gram based approach that can accurately identify microorganisms at the strain level and estimate the abundance of each organism in a sample, given a metagenomic sequencing dataset.

RESULTS

MetaID is an n-gram based method that calculates the profile of unique and common n-grams from the dataset of 2,031 prokaryotic genomes and assigns weights to each n-gram using a scoring function. This scoring function assigns higher weightage to the n-grams that appear in fewer genomes and vice versa; thus, allows for effective use of both unique and common n-grams for species identification. Our 10-fold cross-validation results on a simulated dataset show a remarkable accuracy of 99.7% at the strain-level identification of the organisms in gut microbiome. We also demonstrated that our model shows impressive performance even by using only 25% or 50% of the genome sequences for modeling. In addition to identification of the species, our method can also estimate the relative abundance of each species in the simulated metagenomic samples. The generic approach employed in this method can be applied for accurate identification of a wide variety of microbial species (viruses, prokaryotes and eukaryotes) present in any environmental sample.

CONCLUSIONS

The proposed scoring function and approach is able to accurately identify and estimate the entire taxa in any metagenomic community. The weights assigned to the common n-grams by our scoring function are precisely calibrated to match the reads up to the strain level. Our multipronged validation tests demonstrate that MetaID is sufficiently robust to accurately identify and estimate the abundance of each taxon in any natural environment even when using incomplete or partially sequenced genomes.

Individual responses of mother sows to a probiotic Enterococcus faecium strain lead to different microbiota composition in their offspring

Pregnant gilts were fed the probiotic Enterococcus faecium NCIMB10415 (SF68) one month before birth of piglets. DNA extracts of sow faeces taken in weekly intervals as well as extracts from the intestine of their offspring during the suckling period at 12 and 26 days of life were analysed by denaturing gradient gel electrophoresis (DGGE) and quantitative PCR. DGGE profiles of faecal bacterial communities from three out of six probiotic-fed sows were distinctly different from the control and other probiotic-fed sows at all time points after probiotic supplementation. The probiotic-fed sows and their offspring were therefore divided into non-responder (n=3) and responder (n=3) groups. The probiotic strain significantly increased faecal lactobacilli cell numbers in mother sows, which could be assigned to a significant increase of Lactobacillus amylovorus and Lactobacillus acidophilus. Responding sows showed a more pronounced increase than non-responding sows. Similarly, suckling piglets from non-responding and responding sows showed numeric and significant differences for different bacterial groups and species. DGGE profiles of suckling piglets from responding sows also grouped more closely than profiles from control animals. Non-metric multiscaling of suckling piglets showed the same tendency for suckling piglets, but not for post-weaning piglets. This study showed that the probiotic E. faecium strain modified the faecal microbiota of sows. This modification is carried over to their offspring, but leads to changes that do not mirror the quantitative composition in the mother sow. Individual variations in the bacterial composition of mother sows before probiotic feed intake may influence the impact of a probiotic in sows and their offspring.

Functional genomic analyses of the gut microbiota for CRC screening

The evidence for a strong correlation between the gut microbiota and colorectal carcinogenesis is quickly gathering pace. This correlation raises important questions, such as whether analysis of the microbiota can be used for screening purposes, and whether targeted intervention can influence the risk of development and progression of neoplasia. The recovery of several pathobionts-such as members of the different bacterial phyla Proteobacteria, Bacteroidetes and Fusobacteria-from the tumour microenvironment of patients with colorectal cancer (CRC) now provides a link between specific microbial colonization and cancer. However, other intestinal bacteria belonging to another major intestinal phylum, Firmicutes, might be effective in the treatment of pathogenic inflammation related to CRC. Future approaches based on the analysis of the gut microbiota of patients with CRC combined with large human cohort studies might open up new possibilities for further prophylactic, screening and treatment strategies.

eggNOG v4.0: nested orthology inference across 3686 organisms

With the increasing availability of various 'omics data, high-quality orthology assignment is crucial for evolutionary and functional genomics studies. We here present the fourth version of the eggNOG database (available at http://eggnog.embl.de) that derives nonsupervised orthologous groups (NOGs) from complete genomes, and then applies a comprehensive characterization and analysis pipeline to the resulting gene families. Compared with the previous version, we have more than tripled the underlying species set to cover 3686 organisms, keeping track with genome project completions while prioritizing the inclusion of high-quality genomes to minimize error propagation from incomplete proteome sets. Major technological advances include (i) a robust and scalable procedure for the identification and inclusion of high-quality genomes, (ii) provision of orthologous groups for 107 different taxonomic levels compared with 41 in eggNOGv3, (iii) identification and annotation of particularly closely related orthologous groups, facilitating analysis of related gene families, (iv) improvements of the clustering and functional annotation approach, (v) adoption of a revised tree building procedure based on the multiple alignments generated during the process and (vi) implementation of quality control procedures throughout the entire pipeline. As in previous versions, eggNOGv4 provides multiple sequence alignments and maximum-likelihood trees, as well as broad functional annotation. Users can access the complete database of orthologous groups via a web interface, as well as through bulk download.

Gut microbiome perturbations induced by bacterial infection affect arsenic biotransformation

Exposure to arsenic affects large human populations worldwide and has been associated with a long list of human diseases, including skin, bladder, lung, and liver cancers, diabetes, and cardiovascular disorders. In addition, there are large individual differences in susceptibility to arsenic-induced diseases, which are frequently associated with different patterns of arsenic metabolism. Several underlying mechanisms, such as genetic polymorphisms and epigenetics, have been proposed, as these factors closely impact the individuals' capacity to metabolize arsenic. In this context, the role of the gut microbiome in directly metabolizing arsenic and triggering systemic responses in diverse organs raises the possibility that perturbations of the gut microbial communities affect the spectrum of metabolized arsenic species and subsequent toxicological effects. In this study, we used an animal model with an altered gut microbiome induced by bacterial infection, 16S rRNA gene sequencing, and inductively coupled plasma mass spectrometry-based arsenic speciation to examine the effect of gut microbiome perturbations on the biotransformation of arsenic. Metagenomics sequencing revealed that bacterial infection significantly perturbed the gut microbiome composition in C57BL/6 mice, which in turn resulted in altered spectra of arsenic metabolites in urine, with inorganic arsenic species and methylated and thiolated arsenic being perturbed. These data clearly illustrated that gut microbiome phenotypes significantly affected arsenic metabolic reactions, including reduction, methylation, and thiolation. These findings improve our understanding of how infectious diseases and environmental exposure interact and may also provide novel insight regarding the gut microbiome composition as a new risk factor of individual susceptibility to environmental chemicals.

Expansion of intestinal Prevotella copri correlates with enhanced susceptibility to arthritis

Rheumatoid arthritis (RA) is a prevalent systemic autoimmune disease, caused by a combination of genetic and environmental factors. Animal models suggest a role for intestinal bacteria in supporting the systemic immune response required for joint inflammation. Here we performed 16S sequencing on 114 stool samples from rheumatoid arthritis patients and controls, and shotgun sequencing on a subset of 44 such samples. We identified the presence of Prevotella copri as strongly correlated with disease in new-onset untreated rheumatoid arthritis (NORA) patients. Increases in Prevotella abundance correlated with a reduction in Bacteroides and a loss of reportedly beneficial microbes in NORA subjects. We also identified unique Prevotella genes that correlated with disease. Further, colonization of mice revealed the ability of P. copri to dominate the intestinal microbiota and resulted in an increased sensitivity to chemically induced colitis. This work identifies a potential role for P. copri in the pathogenesis of RA. DOI: http://dx.doi.org/10.7554/eLife.01202.001.

POGO-DB--a database of pairwise-comparisons of genomes and conserved orthologous genes

POGO-DB (http://pogo.ece.drexel.edu/) provides an easy platform for comparative microbial genomics. POGO-DB allows users to compare genomes using pre-computed metrics that were derived from extensive computationally intensive BLAST comparisons of >2000 microbes. These metrics include (i) average protein sequence identity across all orthologs shared by two genomes, (ii) genomic fluidity (a measure of gene content dissimilarity), (iii) number of ‘orthologs’ shared between two genomes, (iv) pairwise identity of the 16S ribosomal RNA genes and (v) pairwise identity of an additional 73 marker genes present in >90% prokaryotes. Users can visualize these metrics against each other in a 2D plot for exploratory analysis of genome similarity and of how different aspects of genome similarity relate to each other. The results of these comparisons are fully downloadable. In addition, users can download raw BLAST results for all or user-selected comparisons. Therefore, we provide users with full flexibility to carry out their own downstream analyses, by creating easy access to data that would normally require heavy computational resources to generate. POGO-DB should prove highly useful for researchers interested in comparative microbiology and benefit the microbiome/metagenomic communities by providing the information needed to select suitable phylogenetic marker genes within particular lineages.

Expansion of intestinal Prevotella copri correlates with enhanced susceptibility to arthritis

Rheumatoid arthritis (RA) is a prevalent systemic autoimmune disease, caused by a combination of genetic and environmental factors. Animal models suggest a role for intestinal bacteria in supporting the systemic immune response required for joint inflammation. Here we performed 16S sequencing on 114 stool samples from rheumatoid arthritis patients and controls, and shotgun sequencing on a subset of 44 such samples. We identified the presence of Prevotella copri as strongly correlated with disease in new-onset untreated rheumatoid arthritis (NORA) patients. Increases in Prevotella abundance correlated with a reduction in Bacteroides and a loss of reportedly beneficial microbes in NORA subjects. We also identified unique Prevotella genes that correlated with disease. Further, colonization of mice revealed the ability of P. copri to dominate the intestinal microbiota and resulted in an increased sensitivity to chemically induced colitis. This work identifies a potential role for P. copri in the pathogenesis of RA.

DOI:http://dx.doi.org/10.7554/eLife.01202.001

We share our bodies with a diverse set of microorganisms, known collectively as the human microbiome. Indeed, estimates suggest that our bodies contain 10 times as many microbial cells as human cells. Our stomach and intestines alone are home to many hundreds and possibly thousands of microbial species that break down indigestible compounds and help to prevent the growth of harmful bacteria. The immune system must therefore learn to tolerate these microorganisms, while retaining the ability to launch attacks against microorganisms that cause harm. Failure of this process may increase the risk of autoimmune diseases in which the body mistakenly attacks its own cells and tissues.

Rheumatoid arthritis is a chronic autoimmune disease marked by inflammation of the joints. Although the causes of rheumatoid arthritis are unknown, mice with mutations that increase the risk of the disease remain healthy if they are kept under sterile conditions. However, if these mice are exposed to certain species of bacteria sometimes found in the gut, they begin to show signs of joint inflammation.

Here, Scher et al. used genome sequencing to compare gut bacteria from patients with rheumatoid arthritis and healthy controls. A bacterial species called Prevotella copri was more abundant in patients suffering from untreated rheumatoid arthritis than in healthy individuals. Moreover, the presence of P. copri corresponded to a reduction in the abundance of other bacterial groups—including a number of beneficial microbes. In a mouse model of gut inflammation, animals colonized with P. copri had more severe disease than controls, consistent with a pro-inflammatory function of this organism.

Current treatments for rheumatoid arthritis target symptoms. However, by highlighting the role played by gut bacteria, the work of Scher et al. suggests that novel treatment options focused on curbing the spread of P. copri in the gut could delay or prevent the onset of this disease.

DOI:http://dx.doi.org/10.7554/eLife.01202.002

Expansion of intestinal Prevotella copri correlates with enhanced susceptibility to arthritis

Rheumatoid arthritis (RA) is a prevalent systemic autoimmune disease, caused by a combination of genetic and environmental factors. Animal models suggest a role for intestinal bacteria in supporting the systemic immune response required for joint inflammation. Here we performed 16S sequencing on 114 stool samples from rheumatoid arthritis patients and controls, and shotgun sequencing on a subset of 44 such samples. We identified the presence of Prevotella copri as strongly correlated with disease in new-onset untreated rheumatoid arthritis (NORA) patients. Increases in Prevotella abundance correlated with a reduction in Bacteroides and a loss of reportedly beneficial microbes in NORA subjects. We also identified unique Prevotella genes that correlated with disease. Further, colonization of mice revealed the ability of P. copri to dominate the intestinal microbiota and resulted in an increased sensitivity to chemically induced colitis. This work identifies a potential role for P. copri in the pathogenesis of RA. DOI: http://dx.doi.org/10.7554/eLife.01202.001.

The role of the human microbiome in otolaryngology-head and neck surgery: a contemporary review

OBJECTIVES/HYPOTHESIS

The human microbiome represents the collective genomes and gene products of microbes living within and on humans. The objective of this review is to provide a summary of the current microbiome literature pertaining to otolaryngology-head and neck surgery.

DATA SOURCE

Ovid MEDLINE.

METHODS

Scientific publications with clinical correlates.

RESULTS

Human microbiome studies have been facilitated by culture-independent, high-throughput sequencing methods. Data from the Human Microbiome Project has shown that the composition of the human microbiome is specific to each body site and that each individual has a unique microbiome. Alterations in the human microbiome are associated with some disease states; thus, novel therapeutic strategies are being developed based on concepts and findings stemming from microbiome research.

CONCLUSIONS

Although a growing body of research shows potential significance of the human microbiome for human health and disease, there is a paucity of microbiome studies in otolaryngology. More studies are required to increase our understanding of the indigenous microbiota and their effects on diseases of the head and neck.

Metagenomic species profiling using universal phylogenetic marker genes

To quantify known and unknown microorganisms at species-level resolution using shotgun sequencing data, we developed a method that establishes metagenomic operational taxonomic units (mOTUs) based on single-copy phylogenetic marker genes. Applied to 252 human fecal samples, the method revealed that on average 43% of the species abundance and 58% of the richness cannot be captured by current reference genome-based methods. An implementation of the method is available at http://www.bork.embl.de/software/mOTU/.

Spatial heterogeneity and co-occurrence patterns of human mucosal-associated intestinal microbiota

Human gut microbiota shows high inter-subject variations, but the actual spatial distribution and co-occurrence patterns of gut mucosa microbiota that occur within a healthy human instestinal tract remain poorly understood. In this study, we illustrated a model of this mucosa bacterial communities' biogeography, based on the largest data set so far, obtained via 454-pyrosequencing of bacterial 16S rDNAs associated with 77 matched biopsy tissue samples taken from terminal ileum, ileocecal valve, ascending colon, transverse colon, descending colon, sigmoid colon and rectum of 11 healthy adult subjects. Borrowing from macro-ecology, we used both Taylor's power law analysis and phylogeny-based beta-diversity metrics to uncover a highly heterogeneous distribution pattern of mucosa microbial inhabitants along the length of the intestinal tract. We then developed a spatial dispersion model with an R-squared value greater than 0.950 to map out the gut mucosa-associated flora's non-linear spatial distribution pattern for 51.60% of the 188 most abundant gut bacterial species. Furthermore, spatial co-occurring network analysis of mucosa microbial inhabitants together with occupancy (that is habitat generalists, specialists and opportunist) analyses implies that ecological relationships (both oppositional and symbiotic) between mucosa microbial inhabitants may be important contributors to the observed spatial heterogeneity of mucosa microbiota along the human intestine and may even potentially be associated with mutual cooperation within and functional stability of the gut ecosystem.

Reconstructing the genomic content of microbiome taxa through shotgun metagenomic deconvolution

Metagenomics has transformed our understanding of the microbial world, allowing researchers to bypass the need to isolate and culture individual taxa and to directly characterize both the taxonomic and gene compositions of environmental samples. However, associating the genes found in a metagenomic sample with the specific taxa of origin remains a critical challenge. Existing binning methods, based on nucleotide composition or alignment to reference genomes allow only a coarse-grained classification and rely heavily on the availability of sequenced genomes from closely related taxa. Here, we introduce a novel computational framework, integrating variation in gene abundances across multiple samples with taxonomic abundance data to deconvolve metagenomic samples into taxa-specific gene profiles and to reconstruct the genomic content of community members. This assembly-free method is not bounded by various factors limiting previously described methods of metagenomic binning or metagenomic assembly and represents a fundamentally different approach to metagenomic-based genome reconstruction. An implementation of this framework is available at http://elbo.gs.washington.edu/software.html. We first describe the mathematical foundations of our framework and discuss considerations for implementing its various components. We demonstrate the ability of this framework to accurately deconvolve a set of metagenomic samples and to recover the gene content of individual taxa using synthetic metagenomic samples. We specifically characterize determinants of prediction accuracy and examine the impact of annotation errors on the reconstructed genomes. We finally apply metagenomic deconvolution to samples from the Human Microbiome Project, successfully reconstructing genus-level genomic content of various microbial genera, based solely on variation in gene count. These reconstructed genera are shown to correctly capture genus-specific properties. With the accumulation of metagenomic data, this deconvolution framework provides an essential tool for characterizing microbial taxa never before seen, laying the foundation for addressing fundamental questions concerning the taxa comprising diverse microbial communities.

Evolving concepts: how diet and the intestinal microbiome act as modulators of breast malignancy

The intestinal microbiome plays an important role in human physiology. Next-generation sequencing technologies, knockout and gnotobiotic mouse models, fecal transplant data and epidemiologic studies have accelerated our understanding of microbiome abnormalities seen in immune diseases and malignancies. Dysbiosis is the disturbed microbiome ecology secondary to external pressures such as host diseases, medications, diet and genetic conditions often leading to abnormalities of the host immune system. Specifically dysbiosis has been shown to lower circulating lymphocytes, and increase neutrophil to lymphocyte ratio, a finding which has been associated with a decreased survival in women with breast cancers. Dysbiosis also plays a role in the recycling of estrogens via the entero-hepatic circulation, increasing estrogenic potency in the host, which is another leading cause of breast malignancy. Non-modifiable factors such as age and genetic mutations disrupt the microbiome, but modifiable factors such as diet may also lead to profound disruptions as well. A better understanding of dietary factors and how they disrupt the microbiome may lead to beneficial nutritional interventions for breast cancer patients.

Exploring variation-aware contig graphs for (comparative) metagenomics using MaryGold

MOTIVATION

Although many tools are available to study variation and its impact in single genomes, there is a lack of algorithms for finding such variation in metagenomes. This hampers the interpretation of metagenomics sequencing datasets, which are increasingly acquired in research on the (human) microbiome, in environmental studies and in the study of processes in the production of foods and beverages. Existing algorithms often depend on the use of reference genomes, which pose a problem when a metagenome of a priori unknown strain composition is studied. In this article, we develop a method to perform reference-free detection and visual exploration of genomic variation, both within a single metagenome and between metagenomes.

RESULTS

We present the MaryGold algorithm and its implementation, which efficiently detects bubble structures in contig graphs using graph decomposition. These bubbles represent variable genomic regions in closely related strains in metagenomic samples. The variation found is presented in a condensed Circos-based visualization, which allows for easy exploration and interpretation of the found variation. We validated the algorithm on two simulated datasets containing three respectively seven Escherichia coli genomes and showed that finding allelic variation in these genomes improves assemblies. Additionally, we applied MaryGold to publicly available real metagenomic datasets, enabling us to find within-sample genomic variation in the metagenomes of a kimchi fermentation process, the microbiome of a premature infant and in microbial communities living on acid mine drainage. Moreover, we used MaryGold for between-sample variation detection and exploration by comparing sequencing data sampled at different time points for both of these datasets.

AVAILABILITY

MaryGold has been written in C++ and Python and can be downloaded from http://bioinformatics.tudelft.nl/software

The uses of race and ethnicity in human microbiome research

Research on the human microbiome is beginning to address factors associated with between-group differences. Recognition of the social and political nature of traditional race and ethnicity categories will allow microbiome research to contribute to reduction of health disparities while avoiding attribution of causal inference to specific race and ethnicity categories.

DNA sequencing methods in human genetics and disease research

DNA sequencing has revolutionized biological and medical research, and is poised to have a similar impact in medicine. This tool is just one of a number of developments in our capability to identify, quantitate and functionally characterize the components of the biological networks keeping us healthy or making us sick, but in many respects it has played the leading role in this process. The new technologies do, however, also provide a bridge between genotype and phenotype, both in man and model (as well as all other) organisms, revolutionize the identification of elements involved in a multitude of human diseases or other phenotypes, and generate a wealth of medically relevant information on every single person, as the basis of a truly personalized medicine of the future.

Metagenomic detection of viruses in aerosol samples from workers in animal slaughterhouses

Published studies have shown that workers in animal slaughterhouses are at a higher risk of lung cancers as compared to the general population. No specific causal agents have been identified, and exposures to several chemicals have been examined and found to be unrelated. Evidence suggests a biological aetiology as the risk is highest for workers who are exposed to live animals or to biological material containing animal faeces, urine or blood. To investigate possible biological exposures in animal slaughterhouses, we used a metagenomic approach to characterise the profile of organisms present within an aerosol sample. An assessment of aerosol exposures for individual workers was achieved by the collection of personal samples that represent the inhalable fraction of dust/bioaerosol in workplace air in both cattle and sheep slaughterhouses. Two sets of nine personal aerosol samples were pooled for the cattle processing and sheep processing areas respectively, with a total of 332,677,346 sequence reads and 250,144,492 sequence reads of 85 bp in length produced for each. Eukaryotic genome sequence was found in both sampling locations, and bovine, ovine and human sequences were common. Sequences from WU polyomavirus and human papillomavirus 120 were detected in the metagenomic dataset from the cattle processing area, and these sequences were confirmed as being present in the original personal aerosol samples. This study presents the first metagenomic description of personal aerosol exposure and this methodology could be applied to a variety of environments. Also, the detection of two candidate viruses warrants further investigation in the setting of occupational exposures in animal slaughterhouses.

Infection with the carcinogenic liver fluke Opisthorchis viverrini modifies intestinal and biliary microbiome

Opisthorchis viverrini is a fish-borne trematode endemic in East Asia. Following ingestion, the flukes locate to the biliary tre where chronic infection frequently leads to cholangiocarcinoma (CCA). The mechanisms by which O. viverrini infection culminates in CCA remain unknown. An unexplored aspect is its influence on the host microbiome. In the hamster, infection with this pathogen reliably leads to CCA. Genomic DNAs of microbiota from colorectal contents and bile of hamsters and from whole O. viverrini were examined in this model of fluke-induced CCA. Microbial communities were characterized by high-throughput sequencing of variable regions 7-9 of prokaryotic 16S ribosomal DNA. Of ∼1 million sequences, 536,009 with useable reads were assignable to 29,776 operational taxonomy units (OTUs) and, in turn, to 20 phyla and 273 genera of Bacteria or Archaea. Microbial community analyses revealed that fluke infection perturbed the gastrointestinal tract microbiome, increasing Lachnospiraceae, Ruminococcaceae, and Lactobacillaceae, while decreasing Porphyromonadaceae, Erysipelotrichaceae, and Eubacteriaceae (P≤0.05). More than 60 OTUs were detected in the biliary system, which confirmed bacteriobilia and a noteworthy community of microbes associated with the parasites. The fluke-associated microorganisms included potential pathogens from the Enterobacteriaceae and Listeriaceae and others, including Cyanobacteria and Deinococci, usually found in external environments. Given that opisthorchiasis is distinguished from other helminth infections by a robust inflammatory phenotype with conspicuously elevated IL-6, and that inflammation of the biliary system leads to periductal fibrosis, which is a precursor of CCA, the flukes and their microbiota may together drive this distinctive immune response.

Building a genome analysis pipeline to predict disease risk and prevent disease

Reduced costs and increased speed and accuracy of sequencing can bring the genome-based evaluation of individual disease risk to the bedside. While past efforts have identified a number of actionable mutations, the bulk of genetic risk remains hidden in sequence data. The biggest challenge facing genomic medicine today is the development of new techniques to predict the specifics of a given human phenome (set of all expressed phenotypes) encoded by each individual variome (full set of genome variants) in the context of the given environment. Numerous tools exist for the computational identification of the functional effects of a single variant. However, the pipelines taking advantage of full genomic, exomic, transcriptomic (and other) sequences have only recently become a reality. This review looks at the building of methodologies for predicting "variome"-defined disease risk. It also discusses some of the challenges for incorporating such a pipeline into everyday medical practice.

A comprehensive metatranscriptome analysis pipeline and its validation using human small intestine microbiota datasets

Background

Next generation sequencing (NGS) technologies can be applied in complex microbial ecosystems for metatranscriptome analysis by employing direct cDNA sequencing, which is known as RNA sequencing (RNA-seq). RNA-seq generates large datasets of great complexity, the comprehensive interpretation of which requires a reliable bioinformatic pipeline. In this study, we focus on the development of such a metatranscriptome pipeline, which we validate using Illumina RNA-seq datasets derived from the small intestine microbiota of two individuals with an ileostomy.

Results

The metatranscriptome pipeline developed here enabled effective removal of rRNA derived sequences, followed by confident assignment of the predicted function and taxonomic origin of the mRNA reads. Phylogenetic analysis of the small intestine metatranscriptome datasets revealed a strong similarity with the community composition profiles obtained from 16S rDNA and rRNA pyrosequencing, indicating considerable congruency between community composition (rDNA), and the taxonomic distribution of overall (rRNA) and specific (mRNA) activity among its microbial members. Reproducibility of the metatranscriptome sequencing approach was established by independent duplicate experiments. In addition, comparison of metatranscriptome analysis employing single- or paired-end sequencing methods indicated that the latter approach does not provide improved functional or phylogenetic insights. Metatranscriptome functional-mapping allowed the analysis of global, and genus specific activity of the microbiota, and illustrated the potential of these approaches to unravel syntrophic interactions in microbial ecosystems.

Conclusions

A reliable pipeline for metatransciptome data analysis was developed and evaluated using RNA-seq datasets obtained for the human small intestine microbiota. The set-up of the pipeline is very generic and can be applied for (bacterial) metatranscriptome analysis in any chosen niche.

Towards a predictive systems-level model of the human microbiome: progress, challenges, and opportunities

The human microbiome represents a vastly complex ecosystem that is tightly linked to our development, physiology, and health. Our increased capacity to generate multiple channels of omic data from this system, brought about by recent advances in high throughput molecular technologies, calls for the development of systems-level methods and models that take into account not only the composition of genes and species in a microbiome but also the interactions between these components. Such models should aim to study the microbiome as a community of species whose metabolisms are tightly intertwined with each other and with that of the host, and should be developed with a view towards an integrated, comprehensive, and predictive modeling framework. Here, we review recent work specifically in metabolic modeling of the human microbiome, highlighting both novel methodologies and pressing challenges. We discuss various modeling approaches that lay the foundation for a full-scale predictive model, focusing on models of interactions between microbial species, metagenome-scale models of community-level metabolism, and models of the interaction between the microbiome and the host. Continued development of such models and of their integration into a multi-scale model of the microbiome will lead to a deeper mechanistic understanding of how variation in the microbiome impacts the host, and will promote the discovery of clinically relevant and ecologically relevant insights from the rich trove of data now available.

Fecal microbial metabolism of polyphenols and its effects on human gut microbiota

We investigated the biotransformation of four common dietary polyphenols, rutin, quercetin, chlorogenic acid and caffeic acid, in an in vitro mixed culture model of human intestinal microbiota, to determine effects on human gut bacteria. All four compounds were biotransformed rapidly, disappearing from the medium within 0.5 h and later replaced by known phenolic acid breakdown products, at concentrations up to hundreds of micromolar, much higher than in no-polyphenol control experiments. Quantitative PCR was used to measure effects of the polyphenols on the balance between the major groups of intestinal bacteria that are known to influence gut health, i.e., Bifidobacterium spp., Bacteroidetes, and Firmicutes. Fermentation of polyphenols stimulated proliferation of bifidobacteria and decreased the ratio of Firmicutes to Bacteroidetes, relative to controls. Polyphenols also stimulated short chain fatty acid production by the bacteria. Pure bifidobacterial cultures were treated separately with either fermented media isolated from the incubations, the pure test polyphenols, or the biotransformation products detected in the fermentations. Growth stimulation was observed only with fermented polyphenol media and the pure biotransformation products. It appears that dietary polyphenols may have the ability to modify the gut microbial balance, but this effect is indirect, i.e., it is mediated by biotransformation products, rather than the original plant compounds.

Pharmacometabonomics and personalized medicine

BACKGROUND

Pharmacometabonomics is a new branch of science, first described in 2006 and defined as 'the prediction of the effects of a drug on the basis of a mathematical model of pre-dose metabolite profiles'. Pharmacometabonomics has been used to predict drug metabolism, pharmacokinetics (PK), drug safety and drug efficacy in both animals and humans and is complementary to both pharmacogenomics (PGx) and pharmacoproteomics.

METHODS

A literature review using the search terms pharmacometabonomics, pharmacometabolomics, pharmaco-metabonomics, pharmaco-metabolomics and the singular form of all those terms was conducted in October 2012 using PubMed and Web of Science. The review was updated until mid April 2013.

RESULTS

Since the original description of pharmacometabonomics in 2006, 21 original publications and eight reviews have emerged, covering a broad range of applications from the prediction of PK to the prediction of drug metabolism, efficacy and safety in humans and animals.

CONCLUSIONS

Pharmacometabonomics promises to be an important new approach to the delivery of personalized medicine to improve both drug efficacy and safety for patients in the future. Pharmacometabonomics is particularly powerful as it is sensitive to both genetic and environmental factors such as diet, drug intake and most importantly, a person's microbiome. PGx is now over 50 years old and although it has not achieved as much as some hoped, it is starting to have important applications in personalized medicine. We predict that pharmacometabonomics will be equally important in the next few decades and will be both valuable in its own right and complementary to pharmacoproteomics and PGx.

Omics approaches to study host-microbiota interactions

The intestinal microbiota has profound effects on our physiology and immune system and disturbances in the equilibrium between microbiota and host have been observed in many disorders. Here we discuss the possibilities to further our understanding of how microbiota impacts on human health and disease through the use of large-scale quantifiable tools such as transcriptomics, metagenomics and metabolomics. Reductionist models, including gnotobiotic mouse models have their place in testing hypotheses and elucidating mechanisms by which specific communities or individual species impact on host biology. Network biology approaches can be combined with studies in animal models and cell lines to create iterative cycle of hypotheses and testing, possibly leading to testing in clinical and nutritional intervention studies.

Long-term temporal analysis of the human fecal microbiota revealed a stable core of dominant bacterial species

Next-generation sequencing has greatly contributed to an improved ecological understanding of the human gut microbiota. Nevertheless, questions remain regarding the characteristics of this ecosystem and the ecological processes that shape it, and controversy has arisen regarding the stability of the bacterial populations and the existence of a temporal core. In this study, we have characterized the fecal microbial communities of three human individuals over a one-year period by 454 pyrosequencing of 16S rRNA tags in order to investigate the temporal characteristics of the bacterial communities. The findings revealed a temporal core of 33 to 40 species-level Operational Taxonomic Units (OTUs) within subjects. Although these OTUs accounted only for around 12% of the total OTUs detected, they added up to >75% of the total sequences obtained for each individual. In order to determine the capacity of the sequencing and bioinformatic approaches applied during this study to accurately determine the proportion of a core microbiota, we analyzed the fecal microbiota of nine mice with a defined three-member community. This experiment revealed that the sequencing approach inflated the amount of rare OTUs, which introduced a significant degree of artificial variation across samples, and hence reduced the apparent fraction of shared OTUs. However, when assessing the data quantitatively by focusing on dominant lineages, the sequencing approaches deliver an accurate representation of the community. In conclusion, this study revealed that the human fecal microbiota is dominated by around 40 species that maintain persistent populations over the duration of one year. The findings allow conclusions about the ecological factors that shape the community and support the concept of a homeostatic ecosystem controlled largely by deterministic processes. Our analysis of a three-member community revealed that methodological artifacts of OTU-based approaches complicate core calculations, and these limitations have to be considered in the interpretation of microbiome studies.

The long-term stability of the human gut microbiota

A low-error 16S ribosomal RNA amplicon sequencing method, in combination with whole-genome sequencing of >500 cultured isolates, was used to characterize bacterial strain composition in the fecal microbiota of 37 U.S. adults sampled for up to 5 years. Microbiota stability followed a power-law function, which when extrapolated suggests that most strains in an individual are residents for decades. Shared strains were recovered from family members but not from unrelated individuals. Sampling of individuals who consumed a monotonous liquid diet for up to 32 weeks indicated that changes in strain composition were better predicted by changes in weight than by differences in sampling interval. This combination of stability and responsiveness to physiologic change confirms the potential of the gut microbiota as a diagnostic tool and therapeutic target.

Single-cell genomics reveals complex carbohydrate degradation patterns in poribacterial symbionts of marine sponges

Many marine sponges are hosts to dense and phylogenetically diverse microbial communities that are located in the extracellular matrix of the animal. The candidate phylum Poribacteria is a predominant member of the sponge microbiome and its representatives are nearly exclusively found in sponges. Here we used single-cell genomics to obtain comprehensive insights into the metabolic potential of individual poribacterial cells representing three distinct phylogenetic groups within Poribacteria. Genome sizes were up to 5.4 Mbp and genome coverage was as high as 98.5%. Common features of the poribacterial genomes indicated that heterotrophy is likely to be of importance for this bacterial candidate phylum. Carbohydrate-active enzyme database screening and further detailed analysis of carbohydrate metabolism suggested the ability to degrade diverse carbohydrate sources likely originating from seawater and from the host itself. The presence of uronic acid degradation pathways as well as several specific sulfatases provides strong support that Poribacteria degrade glycosaminoglycan chains of proteoglycans, which are important components of the sponge host matrix. Dominant glycoside hydrolase families further suggest degradation of other glycoproteins in the host matrix. We therefore propose that Poribacteria are well adapted to an existence in the sponge extracellular matrix. Poribacteria may be viewed as efficient scavengers and recyclers of a particular suite of carbon compounds that are unique to sponges as microbial ecosystems.

NOD2 prevents emergence of disease-predisposing microbiota

The gut flora is composed of a huge number of diverse, well-adapted symbionts that interact with epithelial lining throughout the host's entire life. Not all commensals have the same ability to maintain quiescent, protective inflammation. Importantly, instability in the composition of gut microbial communities (referred to as dysbiosis) has been linked to loss of gut barrier in the context of common human illnesses with increasing socio-economic impacts, such as Crohn disease and colorectal cancer. Our recent findings suggest that disease-predisposing dysbiosis can now be intentionally manipulated by targeting the major Crohn disease-predisposing NOD2 gene. That knowledge will not only add a new dimension to the often overlooked microbiology of Crohn disease and colorectal cancer, but will also have a broad impact on biomedical sciences worldwide.

Gut feelings: bacteria and the brain

The gut-brain axis—an imaginary line between the brain and the gut—is one of the new frontiers of neuroscience. Microbiota in our gut, sometimes referred to as the “second genome” or the “second brain,” may influence our mood in ways that scientists are just now beginning to understand. Unlike with inherited genes, it may be possible to reshape, or even to cultivate, this second genome. As research evolves from mice to people, further understanding of microbiota’s relationship to the human brain could have significant mental health implications.

What is health?

Classical medical research is disease focused and still defines health as absence of disease. Languages, however, associate a positive concept of wholeness with health as does the WHO health definition. Newer medical health definitions emphasize the capacity to adapt to changing external and internal circumstances. The results of the 2010 Global Burden of Disease study provides keys for a quantifiable health metrics by developing statistical tools calculating healthy life expectancy. Of central social and economic importance is the question whether healthy ageing can be achieved. This concept hinges on theories on the biological basis of lifespan determination and whether negligible senescence and the compression of morbidity can be achieved in human societies. Since the health impact of the human gut microbiome is currently a topical research area, microbiologists should be aware of the problems in defining health.

Molecular hyperdiversity defines populations of the nematode Caenorhabditis brenneri

The biology of Sydney Brenner's eponymous species of nematode, Caenorhabditis brenneri, is little known to science, despite its famous sibling Caenorhabditis elegans. Here we demonstrate that C. brenneri harbors the most molecular diversity of any eukaryote, with its 14.1% of polymorphic synonymous sites between individuals being 150-fold greater than humans and most comparable to hyperdiverse bacteria. This diversity is not an artifact of cryptic species divergence but reflects an enormous pan-tropical population, confirmed by fully viable genetic crosses between continents, extensive intralocus recombination, selection on codon use, and only weak geographic genetic structure. These findings in an animal galvanize tests of theory about the evolution of complexity in genomes and phenotypes and enable molecular population genetics methods to finely resolve uncharacterized functional noncoding elements.

From meta-omics to causality: experimental models for human microbiome research

Large-scale 'meta-omic' projects are greatly advancing our knowledge of the human microbiome and its specific role in governing health and disease states. A myriad of ongoing studies aim at identifying links between microbial community disequilibria (dysbiosis) and human diseases. However, due to the inherent complexity and heterogeneity of the human microbiome, cross-sectional, case-control and longitudinal studies may not have enough statistical power to allow causation to be deduced from patterns of association between variables in high-resolution omic datasets. Therefore, to move beyond reliance on the empirical method, experiments are critical. For these, robust experimental models are required that allow the systematic manipulation of variables to test the multitude of hypotheses, which arise from high-throughput molecular studies. Particularly promising in this respect are microfluidics-based in vitro co-culture systems, which allow high-throughput first-pass experiments aimed at proving cause-and-effect relationships prior to testing of hypotheses in animal models. This review focuses on widely used in vivo, in vitro, ex vivo and in silico approaches to study host-microbial community interactions. Such systems, either used in isolation or in a combinatory experimental approach, will allow systematic investigations of the impact of microbes on the health and disease of the human host. All the currently available models present pros and cons, which are described and discussed. Moreover, suggestions are made on how to develop future experimental models that not only allow the study of host-microbiota interactions but are also amenable to high-throughput experimentation.

Holding a grudge: persisting anti-phage CRISPR immunity in multiple human gut microbiomes

The CRISPR (clustered regularly interspaced short palindromic repeats)/Cas (CRISPR-associated) system of bacteria and archaea constitutes a mechanism of acquired adaptive immunity against phages, which is based on genome-encoded markers of previously infecting phage sequences ("spacers"). As a repository of phage sequences, these spacers make the system particularly suitable for elucidating phage-bacteria interactions in metagenomic studies. Recent metagenomic analyses of CRISPRs associated with the human microbiome intriguingly revealed conserved "memory spacers" shared by bacteria in multiple unrelated, geographically separated individuals. Here, we discuss possible avenues for explaining this phenomenon by integrating insights from CRISPR biology and phage-bacteria ecology, with a special focus on the human gut. We further explore the growing body of evidence for the role of CRISPR/Cas in regulating the interplay between bacteria and lysogenic phages, which may be intimately related to the presence of memory spacers and sheds new light on the multifaceted biological and ecological modes of action of CRISPR/Cas.