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Interest in the role of the microbiome in human health has burgeoned over the past decade with the advent of new technologies for interrogating complex microbial communities. The large-scale dynamics of the microbiome can be described by many of the tools and observations used in the study of population ecology. Deciphering the metagenome and its aggregate genetic information also can be used to understand the functional properties of the microbial community. Both the microbiome and metagenome probably have important functions in health and disease; their exploration is a frontier in human genetics.
Until recently, the properties of the microbiota of humans (formerly called ‘the normal flora’) were largely a black box. Cultivation in vitro, which has been the cornerstone of microbiology since the 19th century, cannot be applied to many of the most densely populated microbial communities1. However, DNA-based analyses have expanded our horizon, by generating enormous new data sets that can be mined for information on the composition and functional properties of vastly greater numbers of microbial communities. For example, the Human Microbiome Project (HMP) by the NIH has produced a 2.3 terabyte 16S rRNA metagenomic dataset of over 35 billion reads taken from 690 samples from 300 U.S. subjects, across 15 body sites. Large-scale endeavors (e.g. the HMP2 and also the European project, Metahit3) provide a preliminary understanding of the biology and medical significance of the human microbiome and its collective genes (the metagenome).
The aim of these projects, particularly the HMP, is to characterize the compositional range of the ‘normal’ microbiome of healthy individuals. Important questions concerning the commonalities and differences between healthy individuals in both microbial taxa and functional pathways are being addressed. The presence of major clustering patterns at body sites such as the vagina4 and the gastrointestinal tract5 provide new ways to classify individuals, and possibly, their disease risks. Substantial progress has been made in developing the tools for inquiry, and defining the overarching concepts that advance the field. However, the subject is vast, and the implications for human health and disease are wide-ranging. The study of humans and model animal systems with strong phenotypes is essential for making progress in this field of applied genetics. Although a focus on bacteria is important, inquiries aimed at archaea, viruses, and retroviruses, is also needed.
The purpose of this review is to develop the theoretical basis for investigating how microbiome composition and function affect human health. We provide examples of applying this knowledge to better understand human health, and discuss how microbiome changes could alter host–microbiome interactions to mitigate disease. We also consider the next steps in the development of this field, particularly on the need to focus on the inheritance of the microbiome, and on its involvement in modulating complex traits.
Animals have had residential microbes performing metabolic functions for at least 500 million years, at a conservative estimate6,7. Extensive congruent phylogenies of animal hosts and their microbiota, involving both individual organisms and whole microbial populations1,8,9, suggest the existence of specific selection based on co-adaptation. Cooperative interactions between microbes and their hosts typically involve microbial participation in host functions such as defence, metabolism, and reproduction10. For example, comparing germ-free and conventional mice indicates that microbiota are responsible for most of the metabolites that are detected in plasma11.
Functional variation of host microbiota can be mediated by the introduction or extinction of particular microbial groups or by a change in population structure12–14. Such alterations can in turn be induced by selection by environmental factors10,15 such as dietary changes or exposure to antibiotics10,15. Below we describe the efforts to categorize the composition and complex dynamics of the microbiota.
The taxonomic diversity inherent in complex environmental communities and the task of identifying specific associations with host traits create unique challenges. One approach to metagenome analysis involves assigning unassembled sequences generated by shotgun high-throughput sequencing (HTS)16 to the NCBI non-redundant Clusters of Orthologous Groups (COG) or the Kyoto Encyclopedia of Genes and Genomes (KEGG) databases17. This method facilitates the assessment of interactions that occur within the microbiome, and potentially between microbiome and host18. However, because a substantial fraction of the metagenome (~ 33%) is not well-represented by reference genomes, this strategy provides only a limited understanding of the functional potential of the microbiota. An alternative approach is to use catalogues of known genes to identify functional clusters within a sample; such clusters could correspond to the proposed taxonomic enterotypes5. A catalogue of the microbial genes present in the human gut, for example, is being generated using several approaches including sequencing, assembling and characterizing non-redundant microbial genes from fecal samples19, and whole genome sequencing of reference microbial species20.
As sequencing and bioinformatics technologies continue to evolve (see ref 21 for a review of the state-of-the-art technologies), scientific priorities will include elucidating the ‘core’ metagenome that occupies a specific human niche, and discerning the differences between normal and diseased hosts.. As an example of the latter, Greenblum et al. applied new tools to understand interhost metagenomic variation in relation to phenotypes such as obesity and inflammatory bowel disease (IBD)22. By categorizing metagenomic sequences based on gene function, they constructed community-level metabolic networks varying in gene abundance, and examined the topological features of these networks in relation to host phenotype. Their analysis identified specific network topologies related to obesity and IBD; skewed topologies chiefly differ in genes related to host interactivity, particularly in relationship to metabolism. Such topological tools can be applied to explore differences in other host disease states.
The composition of the microbiome varies by anatomical site (Figure 1). The primary determinant of community composition is anatomical location: interpersonal variation is substantial23,24 and is higher than the temporal variability seen at most sites in a single individual25. Such relative temporal stability suggests that individuals can be grouped according to the major enterotypes present in the colon5 or the vagina4. However, dietary changes can rapidly cause substantial intestinal metagenomic changes, and enterotypes are known to cluster based on dietary abundance of animal protein vs carbohydrate26. Similarly, nasopharyngeal microbiota in young children varies seasonally24, and vaginal microbiota varies with menses4. In the absence of marked perturbations, the aggregate microbiota of an individual appears to vary relatively narrowly within host-specific boundaries; the basis of such boundaries have not been established, but may represent Nash equilibria13. Because minor microbial populations have the potential to bloom, the temporal variation observed in a host may be mirrored by the interindividual variation at a single time27; that the system is dynamic suggests that there are greater interpersonal similarities than a snap-shot view indicates. However, large perturbations such as antibiotic exposure28 or enteric infections (LA David, personal communication), can lead to transient disequilibrium29, or to the development of a new stable state.
Among all mammals, the microbiota is extensively conserved at high taxonomic levels7, but variation increases at progressively lower taxonomic levels30. Consequently, 85% of the sequences obtained from the distal mouse gut represent genera that are not detected in humans31. Furthermore, intra-species variability of the microbiota within the human population is also substantial5, This degree of variation was unanticipated a priori. However, in retrospect, extensive taxonomic variation is unsurprising: a human harbours a climax population of about 1014bacterial cells, can host 105 – 106 bacterial generations per human generation, is omniverous, and has accumulated genetic and epigenetic diversity as a host species for >1 million years. Indicator organisms such as Helicobacter pylori32 and Streptococcus mutans33 highlight some differences across the microbiota34 and metagenome35 among human ethnic groups; however, the extent of ethnic variation in overall metagenomic composition is unknown. The microbiomes of monozygotic twins are more closely related to one another than those of unrelated individuals36,37 but not strikingly so, indicating important post-natal influences on composition.
The extensive lower-level taxonomic variation and large compositional differences observed even among highly related host organisms (e.g. mice and humans) is counterbalanced by the substantial conservation of metagenomic core functions (Figure 2). This reflects the conservation of core bacterial properties involved in nucleic acid and protein synthesis, and in metabolic and structural requirements. Of the >50 known phyla, most of the human microbiota is composed of <10 (and mostly 6) phyla. Bacteria from other phyla, usually of plant origin, that may be present in skin, nasopharyngeal, or gut samples24,25,38; are generally infrequent (<0.01% of the sequences) and probably represent transient carriage from food- and air-borne exposures. Why did the particular restriction of diversity within a few phyla evolve, not only in humans, but in perhaps all other vertebrates? One possibility is that within the relatively conserved boundaries for the microbiome permitted by the human genome, there exists a large array of contingency organisms and contingency genes. According to this hypothesis, the genes may only be active at some moment in the host’s lifespan, or perhaps at a frequency of less than once per lifespan.
The parallel needs of individual bacteria lead to both competition for key substrates and to functional redundancy in the microbiota. Nevertheless, the enormous bacterial biomass also provides many unique or minimally redundant bacterial genes19.
Resilience, the ability to withstand disturbance, is a central concept in ecology. The resilience of the human colonic microbiome is beautifully illustrated by recent studies of twins examined before, during, and after 7-weeks of ingesting a fermented milk product containing a sample composed of ~108 Bifidobacterium, Lactobacillus, Lactococcus, and Streptococccus species36. Despite the daily oral inoculations, the composition of the microbiota at the 16S rDNA level and the metagenome were essentially unaffected. While the microbiome of human adults appears highly resilient, no comparable studies have been performed in children, but because population structures appear more dynamic and developmental39, resilience may be lower. An important natural experiment has been occurring over the past 70 years in which most of the world’s population has been exposed to pharmacological doses of antimicrobial agents. Such usage has been based on the implicit belief that the human microbiome is completely resilient, and returns to the status quo ante after antibiotic-induced perturbation. However, study of indicator organisms such as H. pylori, indicates that extirpations of a bacterial species can occur in individual hosts40. There also may be medium- and long-term selection of resistant organisms, and destabilization of the microbiome with new species compositions, in the absence of further antibiotic exposure28,41. Thus, despite the extensive resilience inherent in a complex eco-system, there may be loss of recovery from continued perturbations29, with important implications for human health42.
Medical scientists are familiar with Koch’s postulates, which are used as criteria to determine whether a microbe causes disease43. However, pathogenetic considerations about the microbiome may better focus on community characteristics, which are largely governed by richness, composition, and interactions among the constituent members7,16,44. Substantial perturbation (community disturbance45) tests the resilience of the community, e.g., it ability to resist invasion by exogenous microbes; stable diverse communities resist pathogens46. At present, 16S rRNA analyses focus on taxonomic differences at or above the species level. However, examinations below the subspecies level, relating to strain, or even allele, ultimately may be more significant, although the technology, importantly the informatics tools, are not yet developed enough.
The human microbiome represents one or more complete ecosystems. The trophic organization of species-rich communities is similar to other complex network topologies, in that it shows extreme heterogeneity, with a relatively small number of highly connected nodes dominating47. Such communities may resist random perturbations, but if keystone species48 are lost, effects may cascade, with secondary extinctions; high biodiversity diminishes this risk12. The substantial non-linear interactions in complex co-evolved systems ensure that ecological networks are robust against random removals49. However, with repeated perturbations, the effects of gene loss can be amplified by downstream effects on co-colonizing microbes (secondary extinctions) and on the host. Because of allelopathy, the effects of extinctions may magnify50. In the short-term, functional redundancy may mask extinction effects, but in the longer-run, extinctions lead to loss of contingency responses that can cause ecological crashes49. Considering the importance of guilds of bacteria exploiting parallel and sequential metabolic pathways, these concepts are germane to the human metagenome. With modernity, horizontal microbial transmission has been diminishing, and there has been unprecedented selection against existing, long-present microbes40. As has occurred in the human stomach with the loss of a dominant species51,52, body sites then can harbor alternative stable states.
In summary, as with other complex ecosystems, the microbiomes that populate specific human anatomical niches are species-rich, but possess particular overall community characteristics at higher organizational levels. All are subject to perturbation in the course of normal development and aging, and especially with disease. As our knowledge of the fundamental characteristics and biology of the human microbiome grows, so will our ability to understand disease-related variation.
As described in the previous section, differences in microbiota composition exist across body sites and between individuals. However, changes are also evident across the human lifespan. Important questions in this field involve determining if such temporal changes are life-stage specific, and whether they are predetermined by host genetic characteristics, or by environmental factor.
The congruent phylogenies of mammals and their microbiota8 provide strong evidence for the inheritance of the microbiota7 Although inheritance of the microbiota from the father is presently little studied, increasing evidence supports inheritance from the mother34,53. Until the amniotic sac ruptures, a fetus is considered to be sterile, or essentially sterile. Immediately after vaginal delivery, founding microbial populations in the baby closely resemble that of their mother’s vagina54, with lactobacilli predominating. Since lactic acid-producing bacteria dominate in both vagina and in mother’s milk, the initial gastro-intestinal tract bloom of lactobacilli in the baby cannot be considered accidental. Lactobacilli represent the pioneer community in mice55 as well as humans39, preparing the gastrointestinal tract for subsequent microbial successions, until microbial maturity is reached.
The multiple opportunities for the microbiota to be transferred from a mother to her baby may be disrupted by modern lifestyles. Caesarian section instead of vaginal delivery is an obvious example of the potential impact of medical practice on microbiota composition, with substantial differences in founding population54, that may persist for months56 (Figure 3). In many host species, paternal contributions to offspring traits have been well documented57,58; these observations have been extended to the microbiome, where paternal contributions to offspring H. pylori allele composition have been shown59, In any event, there is evidence for extensive horizontal gene transfer (HGT) within human populations, functional classes, and ecological niches60, indicating the site-specificity and dynamism of selection on the human microbiome. Even so, microbial inheritance can provide important confirmation of human ancestry61.
In Drosophila melanogaster, microbial influences have an effect on mating preference for >30 generations62; can microbiome composition affect mating in humans? Odor is one means to affect mating preference, since human axillary and oral odors are largely influenced by microbial products, especially mercaptans63. In general, the greater the force of mating preference, the more likely those populations become sexually isolated64,65; this could affect tribal differentiation and other ethnic differences in humans. We speculate that metagenome composition may have affected mating preference in humans, representing another phenotype under strong selective pressure.
Over a lifetime, each human develops a densely populated microbiome that is recapitulated in every individual and in every generation. The eruption of teeth is responsible for major successions in the oral microbiota66,67, suggesting that succession may be a general property of microbiome dynamics in humans. In mice, this clearly occurs in the GI tract68. Exposure (or not) to environmental microbes is another important but highly variable reservoir for the resident microbiota. Early life antibiotic use produces major shifts in both microbiota characteristics and in host developmental phenotypes, both on the farm69 and in experimental animals70,71. Whether such precedents are applicable to human children is unknown, but seems likely. If so, then both the timing of microbiome succession and the specific organisms present may affect development. The concept of time-dependent compositional variation affecting host immunological, metabolic, cognitive, and reproductive development is a potentially important and testable hypothesis. We further speculate that nature orchestrates microbiome development so to optimize fecundity, reaching a climax state at or near parturition to maximize success for the next generation. The noted heterozygote advantage for fecundity72 may be an analogue to a genetically diverse microbiota.
Our knowledge of microbiome dynamics, especially age-related changes, during human adulthood is limited. The older literature (predating the use of HTS), clearly shows that the post-menopause vaginal microbiota differs substantially from that during the reproductive period73,74. Similarly, in the stomach,, the age-related progressive development of gastric atrophy (enhanced by H. pylori presence75,76) selects for gastric microbiota that are substantially different from the norm 77. Analogous changes may be occurring in other body sites as senescence advances. In the gut, the ratio of Bacteroidetes to Firmicutes changes with age78.
These concepts are particularly relevant to oncogenesis, which is generally age-related. In the multi-step Nordling hypothesis of oncogenesis79, 4–6 somatic cell mutations are needed for cancer development. We propose that age-related microbiota shifts contribute to this multi-step process. Residential microbes can contribute to somatic mutagenesis by causing genotoxicity secondary to inflammation, increased cell proliferation, and production of pro-mutagenic metabolites (e.g. butyrate)80. Genes may have alternative effects at different life stages, illustrating the idea of antagonistic pleiotropy81. We hypothesize that specific human microbiota and their genes that are beneficial early in life may be harmful later in life. The dominant gastric bacterium, H. pylori provides an example:early in life, inflammatory responses to the organism improve control of infection82,83 and allergy84, but later in life promote atrophy and oncogenesis85. A related hypothesis is that co-evolved microbiota are adaptive for the human species both by supporting early-in-life host functions and by leading to later-in-life host demise 86.
How, then, does the microbiome affect human health? Current studies focus on describing the variant microbe populations that appear in specific disease states or the temporal microbial changes over the course of a disease. For many conditions, the challenge is to discover whether there is a causal link between microbiome variation and significant pathology. Limitations in the definitions and stratification of clinical syndromes, including irritable bowel syndrome (IBS) and non-ulcer dyspepsia (NUD), unfortunately reduce the potential of microbiome studies. The diseases listed below review some current recent investigations (Table 1). Investigations are preliminary, but some observations are promising.
The cutaneous microbiome is an obvious target in specific diseases, such as psoriasis, a chronic, idiopathic inflammatory dermatologic condition87. In studies predating HTS, the use of PCR and cloning led to observations that Firmicutes were significantly over-represented and Actinobacteria were significantly under-represented in psoriatic lesions compared to both unaffected skin in psoriasis patients and in normal controls88. Studies to explore these findings using HTS are currently underway89. Atopic dermatitis, another chronic inflammatory condition, has increased in incidence approximately three-fold over the last 30 years in industrialized countries, suggesting a potential role for microbiome alterations. Classic atopic dermatitis occurs in areas, such as the antecubital and popliteal fossae, with similar microbial populations89, suggesting a microbiome role. Similarly, Propionibacterium acnes has been implicated in the common dermatologic condition, acne. Although P. acnes thrives in the cutaneous pilosebaceous units and secretes enzymes that cause local injury and inflammation, and is widely accepted to have a function in acne development90, continuing investigations also are examining other microbes in the development of acne. Chronic skin ulcers, often secondary to venous stasis or diabetes, lead to substantial morbidity. Cutaneous microbiome shifts, such as an increased abundance of Pseudomonadaceae in patients with chronic ulcers treated with antibiotics and an increased abundance of Streptococcaceae in diabetic ulcers have been noted91. Such shifts may interact with aberrantly expressed host cutaneous defense response genes92, thereby increasing disease risk.
The discovery that H. pylori was adapted to survive in the acidic gastric environment overturned the dogma that the stomach is sterile. In H. pylori-negative individuals, gastric microbiota diversity is high; most of the prominent gastric phylotypes (Streptococcus, Actinomyces, Prevotella, Gemella) also are abundant in the oropharynx of these individuals93; indicating that either many constituents are swallowed from more proximal sites, or that close relatives of the oral microbiota colonize more distally. In contrast, among H. pylori-positive persons, H. pylori usually accounts for >90% of sequence reads from the gastric microbiota93, dramatically reducing the overall diversity of this microbiota. The ability of H. pylori to dominate indicates an evolved fitness for that specialized niche. H. pylori is a classical amphibiont; the presence (or absence) of an H. pylori dominated gastric microbiota is strongly associated with particular diseases with important age-related differences85. Its presence increases risk for developing peptic ulcer disease, gastric mucosa-associated lymphoid tissue (MALT) tumors, and gastric adenocarcinoma94 but also is associated with decreased reflux esophagitis95 and childhood-onset asthma96; demonstrating the complex biological interactions with our microbiota.
The colonic microbiota has been suspected for a long time to be involved in the development of colorectal cancers97, possibly by synthesizing short-chain fatty acids (SCFAs) and other metabolites. SCFAs, in particular butyrate, may induce apoptosis, cell cycle arrest, and differentiation, via Wnt signaling98. Microbes may also be genotoxic to colonic epithelial cells, as demonstrated by the induction of aneuploidy and tetraploidy by Enterococcus faecalis99. The colonic microbiota also might promote colorectal cancer by eliciting host responses, for example, by stimulating exaggerated immune responses, potentially via Th17 cells99.
Further evidence of a link between colonic microbiota and colorectal cancer is suggested by the ability of antibiotic administration to alter the composition of the colonic microbiota and to affect the expression of host genes involved in cell cycle regulation, reducing epithelial proliferation100. Early studies evaluating specific microbes were limited to identifying culture-dependent species, such as Streptococcus bovis, but could not adequately assess anaerobic constituents. However, members of the anaerobic genus Fusobacterium have recently been associated with colorectal cancer; whole genome sequences of Fusobacterium species were compared between cases and matched controls using both quantitative PCR analysis and HTS101,102. Fusobacterium nucleatum is a mucosally adherent, pro-inflammatory microbe that was first identified in the mouth103. In colorectal cancer samples, F. nucleatum sequences were significantly enriched compared to samples obtained from control subjects, while both Bacteroidetes and Firmicutes were relatively depleted in those with Fusobacterium-rich malignancies102. The enrichment of Fusobacterium species (not limited to F. nucleatum) was confirmed when evaluating the mucosal microbiome of colorectal cancers compared to adjacent normal tissues from 99 subjects101. However, the causal direction of the association has not yet been ascertained.
The microbiome is essential for the activation of host immune responses 104. For example, Th17 cell differentiation in the mouse lamina propria requires the presence of segmented filamentous bacteria (SFB)105, and polysaccharide A produced by Bacteroides fragilis mediates conversion of CD4+ T cells into regulatory T cells106. The inflammatory bowel diseases have long been considered to reflect interactions between microbes and the host. IBD susceptibility is associated with host polymorphisms in bacterial sensor genes such as nucleotide-binding oligomerization domain-containing protein 2 (NOD2) also known as caspase recruitment domain-containing protein 15 (CARD15)107,108 and toll-like receptor 4 (TLR4)109, and IBD patients sometimes improve following antibiotic treatment110. Early childhood antibiotic exposure has been associated with significantly increased risk for Crohn’s disease111, suggesting that gut microbiome perturbations may be critical for disease risk. Microbial diversity is significantly diminished in Crohn’s disease112, suggesting decreased gut microbiome resilience that could affect immune interactions. Gut microbiome population structures of patients with ulcerative colitis or Crohn’s disease19 depart from normality, but remain clustered by disease within their characteristic deviated patterns. Specific bacteria of the Enterobacteriaceae genus may act together with a disordered microbiome to increase the risk of ulcerative colitis113. Among twins discordant for ulcerative colitis, those affected had significantly reduced bacterial diversity, but increased proportions of Actinobacteria and Proteobacteria114. Patients with Crohn’s disease have over-representation of E. faecium and of several Proteobacteria compared to controls115. The microbial patterns observed for the conditions described above are preliminary and their specificity and causal direction have not been established.
The gut microbiota may be involved in hepatologic conditions, including non-alcoholic fatty liver disease (NAFLD)116, alcoholic steatosis, and hepatocellular carcinoma. The liver is the first solid organ exposed to the metabolic products generated by the gut microbiome, including acetaldehyde, ammonia, and phenols. Compared to germ-free mice, the presence of a microbiome in conventional mice leads to suppression of intestinal epithelium angiopoietin-related protein 4, which inhibits lipoprotein lipase, increasing downstream triglyceride accumulation in the hepatic parenchyma and adipocytes117. Chronic ethanol exposure disturbs the gut microbiome118,119, but roles for the microbiome in steatosis are unresolved. Particular murine colonic commensals (e.g. H. hepaticus) promote the development of hepatocellular carcinoma120. Patients with cirrhosis have a substantially altered microbiome, including community-wide changes at multiple taxonomic levels, with enrichment of Proteobacteria and Fusobacteria (phyla), and Enterobacteriaceae, Veillonellaceae, and Streptococacceae (family) 121. Although many observations suggest links between microbiome composition and liver disease, definitive associations in humans are lacking.
Genetically obese (ob/ob) mice have decreased Bacteroidetes/Firmicutes ratios compared with lean (ob/+ and +/+ wild-type) siblings31. Transplantation of gut microbiota from the obese (ob/ob) to germ-free mice conferred an obese phenotype, demonstrating transmissibility of metabolic phenotypes17; the transferred microbiomes had increased capacity for energy harvest. In humans, relative Bacteroidetes proportions increase with weight loss122. In mono- and dizygotic twins, obesity was associated with decreased Bacteroidetes and diminished bacterial diversity, with enrichment of genes related to lipid and carbohydrate metabolism. Despite substantial taxonomic variation, functional metagenomic differences were minor37. Changing exposures/selection pressures on the early-life microbiome may contribute to development of obesity. Antibiotic use in human infancy, before age 6 months, was significantly associated with obesity development123. In contrast, perinatal administration of a Lactobacillus rhamnosus GG-based probiotic decreased excessive weight gain during childhood124. These early studies provide support for the concept that microbiota alterations could lead to childhood-onset obesity, which could be modifiable. Alterations in the gut microbiome also occur with interventions used to treat obesity. Roux-en-Y surgery significantly increases levels of Proteobacteria, while altering specific metabolic markers, such as the production of urinary amines and cresols125.
Disregulation of host responses secondary to dysbiosis within the gut lumen could affect distant anatomical sites through activation of host immune responses. This may be a mechanism in rheumatoid arthritis, another chronic idiopathic inflammatory condition. In mice, the presence of SFBs in the gut microbiome causes local expansion of Th17 cells126 that then migrate to peripheral immune compartments and activate B cells into antibody-producing plasma cells. Antibody production leads to immune-mediated destruction of the joints that mirrors rheumatoid arthritis127.
Microbiome analysis in humans has been largely based on observation, with associations of disease phenotypes with particular microbiota constituents. But which is causal: does factor A cause factor B, does factor B cause factor A, or does factor C cause both A and B? Hill developed criteria to address the questions: “In what circumstances can we pass from this observed association to a verdict of causation? Upon what basis should we proceed to do so?”128 The criteria include the strength of association, its consistency, specificity, temporality, and biological plausibility, and whether biological gradients are present, experimental support exists, and support can be extrapolated from known causal relationships. Although these criteria were advanced largely to unravel epidemiological relationships, they are applicable to genetics as well and to metagenomics, in particular. Sometimes successful treatment trials with amelioration or cure of a particular condition provide the critical evidence for a causal relationship. The changed natural history of peptic ulcer disease following elimination of H. pylori129 demonstrated its pathogenic role.
For understanding causation and pathogenesis, model organisms provide an important approach. Animal models approximate some human diseases (e.g. asthma, atherosclerosis), but many diseases are not well reproduced (e.g. psoriasis). For diseases that can be studied in model organisms, microbiota roles can be explored within the constraints of particular model systems (Table 2). Standard models of inbred mice are limited by their uncontrolled microbiome diversity. Certain disease states are well-studied in these models, such as the effects of SFBs on Th17 development or the susceptibility to type I diabetes in non-obese diabetic (NOD) mice. The use of gnotobiotic mice eliminates the above-mentioned microbiome variability, but the animals are expensive and require specialized facilities and expertise limiting their widespread use. The recent availability of gnotobiotic animals from commercial sources permits conventionalization of animals with experimental or control microbiota without needing xenobiotic facilities; such approaches allow for the direct observation of microbiota effects on the host. The extension of this concept to humanized model organisms130 allows better approximation of the effects of the human microbiome on disease processes in tractable animal models.
Inherent complexities in the composition of the microbiome may preclude investigations of microbe-associated diseases using classical approaches such as Koch’s postulates. Instead of single organisms associated with disease, community characteristics (composition and metagenomic functionality) may be more relevant. The principles of host interaction with pathogens and commensals contain many parallel features, which can help tutor the new field, but the nature of the selection for commensalism is more complex and highly dynamic. The scale of the interface suggests that microbiome–host interactions have important bearings on disease susceptibility, and the microbial effects on the balance of host metabolism and immunity131 provides an excellent model for the broader phenomenon of disease susceptibility. Modifying disease risk by altering metabolic, immunological, or developmental pathways are obvious strategies.
Given the ongoing extinction of our ancient commensal organisms, the future of a healthy human microbiome may include restoration of our ancestral microbial ecology. There are two types of restoration. The first involves restoring ancient organisms (or pathways) in healthy hosts lacking them, as prophylaxis against future imbalances. The second type of restoration could be therapeutic, when the etiologic extinctions and imbalances are recognized. This scientific frontier will require understanding the biology of re-introductions, as well as developing microbial breeding programs. In addition to the technical problems associated with restoring particular organisms to specific hard-to-reach niches, such as the distal ileum, there also will be substantial biological problems related to understanding how reintroductions affect the population structure of the extant organisms, and host interactions.
To better understand the implications of microbiota and metagenome variation in human health and disease, the field needs improved informatics tools, including new approaches for understanding the complexity of the metadata132. The multidimensionality of the human and microbial phenotypes and the dynamic, non-linear interactions challenge deterministic solutions. For example, in analyses of 16S-defined operational taxonomic units (OTU) populations in mice receiving traditional or Western-type diets, Reshef et al., examined top-scoring non-linear abundance relationships133. These often involved “noncoexistence”, sometimes related to known factors (e.g. diet, host gender), but were often unexplained. Although incomplete, such work leads to new approaches to understand the underlying complexity.
We also will need new tools to implement the health implications presented by metagenomic analyses. Although the known principles of evolution and genetics apply, study of the microbiome provides new applications, and will lead to new understandings of complex traits. Important questions to pursue are listed in Box 2. As such, this is a frontier for human preventive medicine, and for medical management of chronic diseases.
* Areas currently under investigation
# Proposed areas for investigation
This work was supported by R01GM63270, R01DK090989, UH2 AR057506, 5 P30 CA016087, and 1UL1RR029893 from the National Institutes of Health, the Diane Belfer Program for Human Microecology, the Michael Saperstein Medical Scholars Fund, and the Levin Fellowship in Gastroenterology.
Ilseung Cho received his MD and MS from New York University and completed his post-graduate training in Internal Medicine and Gastroenterology at New York University (NYU), USA. He is an Assistant Professor of Medicine at the NYU Langone Medical Center, USA. His current research focuses on the role of the gut microbiome in drug metabolism and availability.
Martin J. Blaser received his MD from New York University (NYU), USA. He received post-graduate training at the University of Colorado at Boulder (CO, USA), Centers for Disease Control in Atlanta (Georgia, USA), Rockefeller University (NY, USA), and at the Institut Pasteur in Paris (France). He is a Professor of Medicine and Microbiology at the NYU Langone Medical Center, USA. His current research focuses on antibiotic-induced changes in the human and mouse microbiome and their effects on disease development.
Human Microbiome Project: http://commonfund.nih.gov/hmp
Metagenomics of the Human Intestinal Tract (MetaHIT): http://www.metahit.eu
Metagenomics Rapid Annotation using Subsystem Technology (MG-RAST): http://metagenomics.anl.gov
Quantitative Insights Into Microbial Ecology (QIIME): http://www.qiime.org
QIIME database: http://www.microbio.me/qiime
Ribosomal Database Project (RDP): http://rdp.cme.msu.edu
Nature Reviews Genetics Series on Applications of Next-Generation Sequencing: http://www.nature.com/nrg/series/nextgeneration/index.html