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Institute of Medicine (US) Food Forum. The Human Microbiome, Diet, and Health: Workshop Summary. Washington (DC): National Academies Press (US); 2013.

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The Human Microbiome, Diet, and Health: Workshop Summary.

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2Study of the Human Microbiome

While study of what is now known as the human microbiome can be traced as far back as Antonie van Leeuwenhoek (1632–1723), advances in genomics and other areas of microbiology have spurred a resurgence of interest. Much of this interest has been driven by and directed toward genomics, with a major goal of the Human Microbiome Project (HMP) being to characterize the genomic makeup of all microbes inhabiting the human body. However, increasingly, scientists are shifting their attention toward studying not just what microbes are present, but what those microbes are doing. This chapter summarizes the workshop presentations and discussion that revolved around some of this early (contemporary) scientific research on microbiome content and function.


I then most always saw, with great wonder, that in the said matter there were many very little living animalcules, very prettily a-moving.—Antonie van Leeuwenhoek (1632–1723)

While there is no doubt that microbes create some of the world’s greatest disease challenges (malaria, cholera, foodborne illness, and other infectious diseases), in fact 99 percent of microbes do not cause disease. There are many beneficial microbes that contribute to food production (e.g., the production of bread, cheese, yogurt, chocolate, coffee, beer); soil production and regeneration; pollutant and toxin degradation; oxygen production; and plant, animal, and human health. Lita Proctor remarked, “Every living thing on this planet has a microbiome … associated microbes that maintain health and well-being.” She defined the microbiome as the full complement of microbes (bacteria, viruses including bacteriophages, fungi, protozoa) and their genes and genomes in or on the human body.

That there are beneficial microbes living in and on the human body is not a new concept. Proctor traced the notion as far back as van Leeuwenhoek. “Four centuries ago,” she said, “we realized that there are lots of microbes associated with our bodies. But it has taken four centuries for us to really look at these microbial communities in any depth and to consider them not just as pathogens.” While advances in sequencing and other technologies are no doubt contributing to this burgeoning research, Proctor acknowledged the significant contributions of other scientific disciplines. Notably, environmental microbiology and microbial ecology and evolution “really set the conceptual framework for … recognition that the vast majority of microbes that live in and on us are not germs or pathogens but belong there and actually help maintain our health and well-being.”

The Human Microbiome Project

The HMP was initiated by the National Institutes of Health (NIH) in the fall of 2007, with the majority of funding ($153 million of the $173 million to date2) coming from the NIH Common Fund. The Common Fund is designed to catalyze new and emerging areas of science. The HMP used sequencing to examine the microbes associated with the human body. Its main purpose is to create resources for the research community, with a focus on building a “healthy cohort” reference database of human microbiome genome sequences (known as metagenomic sequences), computational tools to analyze complex metagenomic sequences, and clinical protocols for sampling the human microbiome. Other resources include the suite of demonstration projects that provide data on the association of microbiomes with disease. The “healthy cohort” project is a sequencing study of the microbiome based on sampling from 5 major body sites (18 subsites): nasal passages, oral cavities, skin, gastrointestinal (GI) tract, and urogenital tract. The body sites were selected by a panel of experts in human microbiology. The study recruited 300 adults (of whom half were women and half were men) who were clinically verified to be free of overt disease. About 20 percent of the study participants self-identified as a racial minority and 10 percent as Hispanic. Each participant was sampled up to three times over a 2-year period. Two kinds of sequencing data were collected: microbial taxonomic characterization using the 16S ribosomal ribonucleic acid (rRNA) marker gene and sequence data from entire microbial communities (i.e., meta genomic sequences).3

In addition to the healthy cohort project, the HMP is managing a series of demonstration projects to evaluate associations between the microbiome and disease: two skin diseases (eczema and psoriasis), five GI tract diseases (Crohn’s disease, esophageal adenocarcinoma, necrotizing enterocolitis, pediatric inflammatory bowel disease [IBD], and ulcerative colitis), and four urogenital conditions (bacterial vaginosis, circumcision, reproductive history, and sexual history).

Additionally, the project is accumulating clinical and phenotype data associated with either the healthy cohort sequencing data or sequencing data from the demonstration projects and is planning to collect nucleic acid extracts and, potentially, cell lines from the healthy cohort. All of the various “moving parts” of the HMP interact through the Data Analysis and Coordination Center and the 200-plus member HMP Consortium.4 Also, the HMP is a founding member of the International Human Microbiome Consortium (IHMC).5

One of the limitations of the HMP is its exclusion of host genetic data. One reason that host genetic data were not collected was subject consent (i.e., subjects participating in the various studies agreed to public release of only certain types of data). Proctor called attention to a 2011 article (Spor et al., 2011) for a review of the scientific literature on the putative relationship of host genetics with the microbiome.

Universal and Personalized Properties of the Human Microbiome

HMP and other recent research on the microbiome have generated plentiful new knowledge, enough to begin to identify “universal” properties of the microbiome. Proctor listed several. First, the human microbiota is acquired anew each generation, at birth. Proctor described newborns as “microbe magnets.” Dominguez-Bello et al. (2010) reported that babies born vaginally acquire a different microbiome than babies born via cesarean section (C-section), with the primary inoculum for vaginally born babies being the mother’s vaginal microbiome and for babies born via C-section, the mother’s skin or the environment. The fact that the microbiome is acquired anew each generation is in stark contrast to the human genome, which is inherited.6

A second universal property is that each adult body part has a distinct microbial community composition. HMP 16S rRNA data reveal a clustering of certain microbial taxa with particular body sites, such as the skin, gut, oral cavity, airways, or urogenital tract, regardless of host gender, age, weight, or any other host metric. Costello et al. (2009) reported a similar finding—that microbial community composition is dictated by body site. Proctor observed that body site clustering is probably driven by the same types of factors that drive microbial colonization and growth in other environments (i.e., pH, temperature, condition of the substrate, other ecological parameters). She said, “The human microbiome is probably like a lot of other microbial ecosystems out there on the planet.”

However, while 16S rRNA data show that microbial composition varies among body sites and even within body sites between individuals, metagenomic data indicate that the major microbial metabolic pathways are effectively the same across body sites. So even though each body site has its own unique microbial assemblage, all of those assemblages, regardless of composition, appear to function similarly with respect to metabolism. This is true for healthy individuals in the HMP study, but it remains to be seen how microbial metabolism compares between healthy and diseased individuals.

A final universal property of the human microbiome is that the gut microbiome changes over a lifetime, with microbiomes in elderly people (aged 65 and over) being very different from microbiomes in middle-aged adults. As part of the ELDERMET project,7 Claesson et al. (2011) reported a greater proportion of Bacteroidetes, more overall microbial taxonomic diversity, and greater individual variation in microbial taxonomic composition among elderly compared to middle-aged individuals. With babies, microbial succession during the first 1 to 2 years of life begins to vary with the transition to a more diverse diet (Yatsunenko et al., 2012), as opposed to the relative stability seen with breast-fed infants. Eventually, by the second year of life, the taxonomic composition of the gut microbiome stabilizes, and the gut develops what appears to be an adult microbiome (Palmer et al., 2007).

On the basis of HMP studies, Proctor noted that evidence to date does not support the notion of a core microbiome, at least not at the species level; the concept of enterotypes; or the classification of microbiomes of any one body site into distinct subsets. None of those properties, in her opinion, is universal. With respect to the notion of a core microbiome, although reproducible subsets of microbes may be found in all individuals at grosser taxonomic levels, such as the phylum level (Backhed et al., 2005) and perhaps at the genus level for some body sites (e.g., the skin and possibly the vaginal microbiomes), Proctor questioned the validity of the notion at the species level. In fact, the finer the taxonomic classification, the more variable the microbial composition is among individuals. She posed the question, Can humans be grouped by enterotype? HMP and other data suggest that the question is still open (Wu et al., 2011). “It is a very attractive concept, and it could still play out,” Proctor said, “but it is not showing up as a reproducible universal property of microbiomes, in our opinion.” Finally, with respect to the notion that a healthy microbiome is defined by the absence of pathogens, she noted that HMP healthy cohort data showed that the sequences of putative pathogens are in fact present in healthy individuals (Zhou et al., unpublished manuscript). The presence of a pathogen sequence does not necessarily mean that the pathogen is actually playing a pathogenic role. Often, it is not the presence of a pathogen that indicates disease, but rather an imbalance in the microbial ecosystem.8

On the basis of the HMP data, Proctor views the microbiome as a personalized property. The vast majority of taxonomic diversity in the microbiome is at the species and strain levels, with the abundance of any one bacterial species varying by up to four orders of magnitude between individuals (Backhed et al., 2005; Qin et al., 2010). In Proctor’s opinion, we each have our own “personal microbes” that “confer particular properties to each one of us,” but it’s not yet clear what those properties are.

The Virome

While most of the workshop presentations and discussion focused on the bacterial components of the human microbiome, Proctor reminded the workshop attendees of the vast viral world that inhabits the human body. In fact, there are an estimated 10 times more virus-like particles than bacteria in and on the human body. The human “virome” includes bacteriophages, eukaryotic viruses, and endogenous viral elements. Bacteriophage diversity in the human microbiome is greater than in other environments (i.e., mosquito, coral reef, human lung, and free-living environments) (Caporaso et al., 2010).

Very Close Association Between the Human Microbiome and Our Environment

A major overarching theme of the workshop was the very close association that exists between the human microbiome and our external environment. Proctor highlighted three phenomena that reflect this close association over very different timescales: (1) the impact of antibiotics on the microbiome, (2) the high rate of horizontal gene transfer between bacteria in the microbiome and bacteria in the environment, and (3) changes in the microbiome over evolutionary time.

Antibiotic exposure has tremendous consequences for the microbiome. Proctor relayed Jernberg et al.’s (2010) description of the impact of antibiotics on the microbiome and the cascade of events that occur when an individual stops antibiotic treatment. First, antibiotic-resistant microbes increase in number. Second, susceptible bacteria, that is, bacteria that could have been killed by the antibiotic but were not because they picked up resistant genes through horizontal gene transfer with their neighbors, increase in number. Third, bacteria that were never actually exposed to the antibiotic because they were embedded in mucus or otherwise protected from exposure increase in number. The result is an overall increase of resistant and protected bacteria. “We can often cause more problems than we cure in many cases when we take antibiotics,” Proctor said, “especially when we don’t take the full regimen.”

Horizontal gene transfer (the exchange of genes between microbes in the absence of sexual reproduction) between bacteria in the microbiome and bacteria in the environment also has tremendous consequences for the microbiome. Smillie et al. (2011) calculated rates of horizontal gene transfer among more than 2,000 bacterial genomes and reported a greater frequency of horizontal gene transfer in the human microbiome than in other environments, with the most transfer occurring among microbes inhabiting the same body sites (e.g., the microbes of two gut microbial communities are more likely to engage in horizontal gene transfer than a gut microbe and a skin microbe). The researchers concluded that horizontal gene transfer is being driven not by physical proximity, but rather by ecology. Importantly, they also reported that the highest rates of horizontal gene transfer between human-associated and nonhuman microbes were with farm animal microbes.9

Finally, in Proctor’s opinion, understanding the evolutionary context of the microbiome sheds light on the “ultimate connection” between the human microbiome and our external environment. Proctor speculated on the evolutionary history of a group of human immune genes, the human leukocyte antigens, which appear to be derived from other early hominids that interbred with Homo sapiens (Abi-Rached et al., 2011). Proctor said, “It is not really a stretch to also suggest that not only were genes shared, but also the microbiome was shared.” By deriving some of its microbiome from other early hominids, H. sapiens may have been better equipped to deal with novel infectious diseases and other stressors as it migrated out of Africa and into new environments. Several scientists have suggested that contemporary societal practices (e.g., sanitation, clean water, bathing, antibiotic use, cesarean birth, formula feeding, mercury amalgams) are creating an environment in which humans’ microbiomes are no longer exposed to the rich diversity of microbes they used to be exposed to in our evolutionary past. Blaser and Falkow (2009) suggested that if the initial inoculum is coming from the mother, but every next generation of mothers is more microbially impoverished than the previous generation, then fewer and fewer beneficial microbes are being acquired every next generation.

Mutability of the Microbiome: Proposed Microbiome Therapeutics and Diagnostics

NIH is currently examining the possibility of a next phase of this project. However, the HMP is not the only microbiome-related NIH investment. According to Proctor, the rate of funding for microbiome research is accelerating across all of the various NIH institutes. Some of this funding is for applied research on microbiome therapeutics and diagnostics. Proctor listed five categories of proposed therapeutics and diagnostics:

  1. The use of microbiome signatures as biomarkers for disease presence;
  2. The use of enterotypes to classify individuals by disease risk or pharmacokinetics;
  3. The use of antibacterials, anti-inflammatories, and other small molecules produced by microbiome community members for therapeutic purposes;
  4. The engineering of novel microbiome strains to stimulate T-cells, produce pro-inflammatory cytokines, stimulate expression of host antimicrobial factors, deliver drugs, and so forth; and
  5. The development of virome biomarkers or other tools that exploit the virome for therapeutic or diagnostic purposes.


HMP investigators are collecting three types of microbiome sequencing data: 16S rRNA sequences,11 shotgun sequences,12 and reference genome sequences.13 Jennifer Russo Wortman described how HMP Consortium investigators are using this sequencing data to address three key questions: (1) What organisms are present? (2) What do they do? and (3) How do they change in health and disease?

Wortman referred workshop participants to a review article (Kuczynski et al., 2012) for more detail on some of the methodologies she covered (for more information on caveats of the different sequencing technologies, informatics challenges, etc.).

Phylogenetic Analysis: Who Is There?

Investigators are using 16S rRNA sequencing data to address the question, What organisms are present? The initial HMP analysis yielded about 72 million 16s rRNA reads. As Wortman said, “That is a lot of sequenced data to analyze.” The goal was to use those 72 million reads to get a sense of not only which species are present in the various body sites, but also how abundant the various species are. Very generally, using various quality controls, “de-noising” algorithms, and other computational tools (Caporaso et al., 2010), the 72 million reads were clustered into what are known as operational taxonomic units (OTUs). OTUs are proxies for species. OTU data can be used not only to identify how many of which species are present (per-sample OTU counts), but also to infer the evolutionary relationships of those present.

There are two ways to classify OTUs. The first is to use what is already known about 16S rRNA sequences from cultured organisms, that is, data already stored in various reference databases (e.g., the RNA Database Project, or RDP). By comparing 16S rRNA sequences from HMP samples to those reference sequences, in most cases researchers can identify their samples to at least the family or genus level. Reference database comparisons are limited by the fact that not all sampled sequences are covered in these databases, and species-level assignments in particular are hard to find; however, because the method yields very little noise, researchers can be fairly confident of the assignments that are made. The second method is de novo clustering, that is, clustering 16S rRNA sequences on the basis of similarity in sequence (e.g., by allowing only up to 3 percent divergence). De novo clustering yields more granularity, that is, more species-level assignments, but it also generates more noise (e.g., sequencing and amplification artifacts). Because of the different advantages and disadvantages of each method, HMP investigators use both methods to analyze HMP data.

As an example of how OTU classification is being used to analyze the presence and abundance of microbes, HMP researchers analyzed the presence and abundance of bacterial species in stool samples from 200 subjects. While the presence of specific genera was relatively constant among individuals, the relative proportions of those genera were extremely variable. As another, non-HMP example, Wortman mentioned the Kostic et al. (2012) study of the colorectal cancer microbiome. The researchers detected a very clear signal that people with colorectal cancer have an enrichment of Fusobacteria in their tumor tissue. As a final example, HMP researchers used both reference-based and de novo OTU classification to analyze OTU data from all five major body sites among the “healthy cohort” study individuals. Reference-based OTU classification methods were used to analyze genus-level trends, while de novo classification was used to analyze species-level trends. Results of the two methods were consistent for all body sites except for vaginal samples, where researchers found the least amount of genus-level diversity but high levels of species diversity. According to Wortman, previous work by Jacques Ravel and colleagues (2011) has shown that the vaginal microbiome is dominated by the Lactobacillus genus but that there are many different Lactobactilli species present in various abundances.

Metabolic Reconstruction: What Are They Doing?

The goal of metabolic reconstruction is to identify putative pathways by assigning enzymatic functions to sequencing reads wherever possible, based on information in various enzymatic functional databases. As with the phylogenetic analysis, HMP researchers started with a large volume of data, in this case about 3.6 terabases of shotgun sequencing data from 690 samples. Again, they examined both presence (Which pathways are present?) and abundance (How much of each pathway is present?). They used a software program, the HMP Unified Metabolic Analysis Network (HUMAnN),14 to link the reads to known enzymatic functions and putative pathways.

Wortman echoed what Lita Proctor had emphasized about compositional diversity being greater than functional diversity with respect to variation among different individuals, based on a comparison of phylogenetic analysis and metabolic reconstruction results. In other words, there is a lot more variation in phylotypes than in pathways. “If there is such a thing as a core microbiome,” Wortman said, “it may be at the level of function more than at the level of organism.”


Wortman identified five major challenges to HMP data interpretation:

  1. Meeting data volume and computational requirements. Reiterating what Proctor said, Wortman urged development of an infrastructure for people to access available data. She also cited a need to increase algorithm efficiency and reduce data redundancy.
  2. Linking microbiome function to community composition. How can the two different types of analyses (phylogenetic analysis and metabolic reconstruction) be linked so that more nuanced questions can be addressed? (In other words, Which organisms are responsible for which functions?)
  3. Integrating different types of -omics datasets. All of the shotgun sequencing data are genomic-level data, with very little functional validation that the identified pathways are active. How can genomic data be integrated with transcriptomic, proteomic, and metabolomic data integrated into a systems biology–level approach to studying these communities?
  4. Modeling microbiome community dynamics. How do microbiomes change over time? What are the drivers of those changes? The environment? Health status? How does the prevalence of certain species affect other species in the community?
  5. Correlating microbiome shifts with host phenotype. It can be very difficult to associate shifts in community composition (or functional state) with host phenotype when the phenotype in question is not well defined and when the impact of environmental change on that phenotype is unknown.


Personalized health care solutions demand an integrative, systems-level approach to the understanding of human biocomplexity. Research on the microbiome is a core component of that approach (Nicholson, 2006) (Box 2-1). Genes are just one component of the gene-diet-microbial interactions that make humans the “super system” they are. So while genome-wide association studies (GWAS), for example, are very popular, they are not always, in Jeremy Nicholson’s words, “tremendously revealing,” because statistical significance often has very little to do with biological significance. Speliotes et al. (2010) reported having found 32 statistically significant body mass index (BMI)-associated genes; in fact, the BMI-linked genes accounted for only 1.47 percent of the variance in BMI. Nicholson said, “It is all the other stuff in the world that is really important with the BMI.” He mentioned work by Jeffrey Gordon and colleagues showing a connection between obesity and the microbiome (Ley et al., 2006; Turnbaugh et al., 2006). While the obesity-microbiome connection is controversial, Nicholson pointed out that the mixed results driving the controversy are due to differences in levels of phylogeny and in the way the experiments have been conducted.16

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BOX 2-1

Research on the Microbiome: Major Research Questions. Understanding human biology and the complex interactions that determine individual and population phenotypes, including disease risk factors and etiopathogenesis, demands a systems-level approach, (more...)

This is not to say that systems-level studies of human genome complexity are not generating interesting information. They are. For example, Loscalzo et al. (2007) used a systems-level approach to show that almost all human diseases are genetically connected, with the same gene(s) being implicated in different disorders. However, understanding the human genome itself is not enough if the vision of personalized, or “precision,” health care is to be realized (Mirnezami et al., 2012). The microbiome represents yet another entire level of genetic connectivity. The challenge for the future, Nicholson said, is “to think about layers of networks on top of networks.” That is, how does the human genome network(s) interact with the microbiome genome network(s), across both time and space? “This is really quite a tough problem,” Nicholson said, “probably the toughest problem in 21st-century biology.”

The Metabolic Window on Complex System Activity

Complex interactions between the microbiome and its host generate more than differential disease risks. They also generate differential metabolic phenotypes (Holmes et al., 2008a). In fact, disease risks and metabolic phenotypes are both biologically and statistically linked such that one can assess disease risk by measuring metabolite levels. Metabolite analysis falls under the purview of what Nicholson calls “metabonomics,” which he defined as “the quantitative measurement of the multi-parametric (time-related) metabolic responses of complex systems to a patho-physiological stimulus or genetic modification” (Nicholson et al., 1999). Some scientists, such as Fiehn (2002), use a slightly different term: “metabolomics.”

Regardless of terminology, the idea that changes in metabolic products are an indication of disease is not new. For example, urine wheels were used in the 16th century to diagnose and treat disease (based on the color, smell, and taste of urine). Today, scientists use advanced metabolic profiling tools, namely nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry, that yield a tremendous amount of complex data—a single run generates data on hundreds or thousands of molecules. Researchers use various mathematical modeling tools to extract and convert relevant data into biologically useful information (e.g., information that can be used to identify “normal” versus “abnormal” metabolism). The complexity of the information generated by advanced metabolic profiles is due to the fact that not only are all human cells producing metabolites (with more than 500 functionally distinct cell types), but so too are all microbial cells (Nicholson et al., 2005). Microbes produce short-chain fatty acids, bile acids and related oxysterols, vasoactive (aromatic) amines, cresols and aromatic acids, endocannabinoids, and other molecules.

Many microbial metabolites participate in human metabolism in what Nicholson referred to as “combinatorial metabolism.” For example, bile acids, which are critically important host signaling molecules, are co-metabolized by microbes, with significant implications for liver and colonic disease risk (Nicholson and Wilson, 2003). Bile acids are synthesized in the liver on a daily basis and then secreted into the mammalian gut, where they are deconjugated into cholic acid by Lactobacillus and other gut microbiota. The cholic acid, in turn, can be dehydroxylated by yet other microbes into deoxycholic acid. Deoxycholic acid is both hepatoxic and carcinogenic. Microbial co-metabolism of bile acids also impacts lipid bioavailability.

Modifying Host-Microbiome Metabolic Interactions: Mouse and Rat Models

When thinking about how the gut microbiome impacts human metabolism, the tendency is to think about distal colon processing and the production of short-chain fatty acids, but the microbiome plays an important role in the upper gut as well—for example, with lipid bioavailability. Investigators have used both mouse and rat models to study bile acid and other host-microbiome metabolic interactions related to lipid absorption. For example, Martin and colleagues (2007) measured bile acid signaling after transferring human baby microbiomes into gnotobiotic (germ-free) mice and reported an increased emulsification potential and greater lipid bioavailability in the humanized mice compared to the normal mice. Studies with conventional versus germ-free rats have yielded similar findings (Swann et al., 2011).

Also using the mouse model, investigators have demonstrated that introducing probiotics, such as Lactobacillus paracasei or L. rhamnosus, can induce differential metabolic responses (Martin et al., 2008a). Introducing prebiotics in combination with probiotics also induces differential metabolic responses (Martin et al., 2008b).

Another body of evidence indicating that the microbiome plays a key role in human metabolism comes from research on bariatric surgery in both animal models and humans. Roux-en-Y gastric bypass (RYGB), the gold standard for bariatric surgery, has been associated with an 80 percent reduction in diabetes within 24 hours of surgery. The procedure has also been associated with reduced risks of colonic and other cancers. Nicholson explained that because the diabetes is cured immediately (i.e., not after subsequent weight loss), there must be a biochemical explanation. Part of that explanation likely lies in the microbiome. Zhang et al. (2009) reported a massive increase in Gammaproteobacteria in RYGB patients, compared to normal and obese individuals. Using a rat model, Li et al. (2011b) also reported an increase in Gammaproteobacteria as well as a massive change in bile acid metabolism following RYGB. Other microbiome changes have been detected in RYGB rats as well. Nicholson observed that while the microbiome changes may not be “the key” to understanding the connection between bariatric surgery and changes in diabetes, cancer, or other disease risks, “they are certainly part of the gear box” (Holmes et al., 2011). One possible mechanism is the cytotoxic environment created in the gut following bariatric surgery, as evidenced by fecal extract toxicity (Li et al., 2011a).

Gut Microbial Activities Affect Drug Processing in the Host

One of the goals of a systems-level understanding of human bio-complexity is to realize the vision of personalized or, as Nicholson called it, “precision” health care (Mirnezami et al., 2012). Pharmaco metabonomics is one component of that care, in Nicholson’s opinion. He defined pharmacometabonomics as “the prediction of the quantitative outcome or effect of a biomedical intervention based on a pretreatment metabolic model.” The approach is predicated on the concept of “metabolic hyperspace,” where the position of an individual is dependent on a multitude of factors (genes, diet, microbiome) (Nicholson and Wilson, 2003). The closer two individuals are in metabolic hyperspace, the more physiologically similar they are and the more likely they are to behave in the same way when presented with a challenge (e.g., a drug or other therapeutic intervention). As an example of a potential pharmacometabonomic application, Clayton et al. (2006) demonstrated that drug toxicity could be predicted based on pre-intervention metabolic profiles of urine. Other research groups have reported similar findings (Phapale et al., 2010).

In addition to drug toxicity, pre-intervention metabolic profiling has also been used to predict drug metabolism. For example, Clayton et al. (2009) demonstrated an association between gut microbial metabolites and acetaminophen (Tylenol) metabolism, with microbial excretion patterns partly determining whether an individual is a weak or strong “sulfater.” Sulfation is one of two main pathways of acetaminophen metabolism, with weak sulfaters being poor metabolizers. Nicholson explained that Clostridium and other microbes produce 4-cresol, a structural analog to acetaminophen that saturates the sulfation system, making for a weak sulfater. Moreover, 4-cresol does not compete for sulfation only with acetaminophen, but with all hydroxylated drugs. Nicholson said, “It affects hundreds of different compounds.… One gut microbial enzyme actually has amazing effects on the metabolism disposition and potentially toxicity in a very large number of drugs.”

Interestingly, Nicholson noted, autistic children cannot sulfate acetaminophen (Alberti et al., 1999). In fact, according to Nicholson, the ability to sulfate acetaminophen is one of the most statistically significant tests for autism. Again, there is evidence of a microbial connection. Finegood et al. (2002) demonstrated abnormal Clostridium in children with autism. Altieri et al. (2011) showed that children with autism have much higher levels of microbially produced cresol than normal children. Yap et al. (2010a) reported that even non-autistic siblings of children with autism have higher levels of cresol than non-autistic siblings of children without autism.

The competition between 4-cresol and acetaminophen is just one of many types of microbiome-drug interactions. Other interactions include primary metabolism of orally administered drugs (as the first genomes that oral drugs interact with are microbial, not human, genomes), induction of enzymes that metabolize drugs, and changes in bioavailability (e.g., by changing local pH and the ionization state of drugs).

“There is now an enormous amount of interest in the pharmaceutical industry in the modulated microbiome for changing the way that drugs work,” Nicholson remarked. In fact, there is a great deal of interest in drug-targeting the microbiome itself (Jia et al., 2008). Wallace et al. (2010) showed that drugging the microbiome can alleviate the toxicity associated with the common colon cancer drug CPT-11.

Population Metabolic Phenotyping and Disease Risk

In addition to its potential role in personalized medicine, metabolic phenotyping has potential applicability at the population level. Holmes et al. (2008b) introduced the concept of metabolome-wide association studies (MWAS), the metabolic equivalent of GWAS, and showed significant geographic variation in metabolic phenotypes. The same variation has also been associated with varying risks of cardiovascular and other diseases (Yap et al., 2010b).


The discussion during the question-and-answer period focused on the relevance of using animal models to understand the human microbiome, the relative importance of understanding microbiome composition versus function, and the dietary implications of individual microbiome variation.

Animal Models and the Human Microbiome

Of the mouse model, Nicholson said during his presentation, “This is the first time we have actually had a tool which allows us to measure quantitatively the response of complex organisms to things like probiotic or prebiotic interventions.” During the question-and-answer period, several workshop participants asked questions or commented on the relevance of animal models to understanding the human microbiome. For example, there was a question about the implications of studying the impact of bariatric surgery on bile salt metabolism in rats, given that rats do not have a gall bladder and that human patients that undergo bariatric surgery have an increased incidence of gall bladder disease. Nicholson responded, “The important thing about these rat and mouse models is they help you to develop the tools for studying these complex interactions. We start to get a framework of what sort of pathways are interacting with what sort of bugs.” Nicholson and colleagues are currently finishing a study of 100 bariatric patients, the results of which may indicate whether the rat model is predictive of humans.

Another audience member asked whether there might be a better animal model than mice or rats for studying the human microbiome. Lita Proctor noted that although the HMP was not able to use animal models as a complement to any of the human studies as per NIH Common Fund rules, the door is now open to the development of new animal models. However, it is not clear whether and how the HMP will move in that direction. Meanwhile, the National Institute of General Medical Sciences has been very interested in developing animal models, including some nontraditional animal models (e.g., zebra fish), for microbiome research.

There was also some discussion about the pig model being used to study the human microbiome. Nicholson observed that because of the similarities between pig and human physiology, developing the pig model might be an “important direction” for future research. Regardless of the chosen animal model and regardless of whether the goal is to simulate human disease or a “normal” human microbiome, he emphasized that the metabolic profile generated should approximate a human metabolic profile.

Microbiome Composition Versus Function

Questions and comments about the relative importance of “Who is there?” and “What are they doing?” were interwoven throughout the discussion. One participant suggested that instead of a “core” microbiome, perhaps there are “core” biological functions of the microbiome. She wondered whether any dietary or other functional microbiomic phenotypes have evolved over time. Another audience member questioned whether it is necessary to change the composition of the microbiome flora in order to induce a biological change, or whether changing the metabolites is sufficient. Nicholson replied, “I see no reason why you should have to do that. What you want to do is change the functional capacity of the microbiome, which means changing the interactions.” He noted that the probiotic and prebiotic studies that he had mentioned during his presentation (Martin et al., 2008a,b) demonstrated significant metabolic changes in the microbiome despite the fact that actual microbial populations were altered only a “little bit.” The same is true of yogurt, he said. When individuals consume yogurt, the few billions of microbes in that yogurt do not change the composition of the few trillions of microbes in your microbiome. Yet, they induce huge metabolic changes. He said, “I think data already exist that probiotics and prebiotics change the existing microbiome function.”

Proctor agreed that inducing a biological change does not require a change in microbiome composition. However, recent evidence suggests that only about half of the gut microbiome is actually active at any given time, so it might be helpful to know which part of the microbiome is active. Nicholson added that another factor to consider is potency. Not only are some microbes active while others are not, but also some are more active than others. He said, “Like in any ecology, there are some very common species that don’t do much, and there are few rare species that actually are linchpins in the ecology.”

Dietary Implications of Individual-Level Microbiome Variation

An audience member asked about the dietary implications of individual variation in microbiome composition and function. As scientists learn more about the microbiome, how it varies among individuals, and how that variation impacts health and disease, will it become necessary to redefine or reestablish nutrient requirements? How long will it take before microbiome variation will have to be considered in a regulatory forum? Proctor agreed that the field could move in that direction. She mentioned recent evidence indicating that a gut microbe is able to produce riboflavin (vitamin B2), raising questions about how much riboflavin our microbiomes supply, as opposed to how much we gain through diet, and what substrates or nutrients stimulate that production. Nicholson added that not only do gut microbes produce vitamins, but they also compete for iron, other nutrients, and calories, especially in infants. He encouraged more research on the interaction between the infant microbiome and diet. “Whatever you put into the diet for the babies,” he said, “the bugs might change what happens to that in quite significant ways.”


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This section summarizes Lita Proctor’s presentation.


As of February 2012 (i.e., at the time of the workshop).


See the next section in this chapter, a summary of Jennifer Russo Wortman’s presentation, for a detailed explanation of how the two different types of data were analyzed and interpreted.


Visit the HMP website for more details: (accessed August 28, 2012).


For more information on the IHMC, visit its website: www​ (accessed August 28, 2012).


See the summary of Josef Neu’s presentation in Chapter 3 for a discussion of microbiota acquired prior to birth, during the third trimester of pregnancy.


Funded through the government of Ireland, ELDERMET is a study of diet, gut bacteria, and health status in elderly (65 years and older) Irish subjects.


For two additional perspectives on the role of an out-of-balance microbiome in human disease, see the summaries of presentations by Richard Darveau and Vincent Young in Chapter 3.


For an in-depth discussion of horizontal gene transfer and its role in spreading antibiotic resistance from livestock farms to the human microbiome, see the Chapter 5 summary of Ellen Silbergeld’s presentation.


This section summarizes Jennifer Russo Wortman’s presentation.


The 16S rRNA gene encodes for a small subunit of the ribosomal RNA. HMP researchers use 16S rRNA sequencing for phylogenetic analysis because the gene has both conserved regions (which are used to develop primers for amplification) and variable regions (which are used to identify specific microbial species).


HMP researchers use shotgun sequencing to sequence all of the DNA that is present within a microbial community. By comparing specific sequence reads to sequences with known functions, they can infer function.


HMP researchers are sequencing as many microbiome reference genomes as possible as part of the “healthy cohort” study that Lita Proctor described (see previous section for a summary of her presentation).


For more information, visit


This section summarizes Jeremy Nicholson’s presentation.


See the summary of Peter Turnbaugh’s presentation in Chapter 3 for a more detailed discussion of evidence suggesting an obesity-microbiome connection.

Copyright © 2013, National Academy of Sciences.
Bookshelf ID: NBK154091


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