November 13-14, 2008
Background and Health Rationale
Microbes comprise over 90% of the cells of the human body, forming distinctive communities in different parts of the body. These dynamic microbial communities have a major impact on human health – by promoting proper development of host organs, stimulating development of the immune system, providing nutrients to the human host, and excluding potential pathogens. Alteration of indigenous microbial communities can cause pathological conditions, including periodontal disease, ulcers, and secondary pneumonia. Recent studies indicate that microbial communities can influence many other aspects of human health, from obesity to chronic diseases like atherosclerosis. In fact, the conditions associated with microbial influences reach into every institute at NIH – Tourette Syndrome, sleep cycles, depression, diabetes, colon cancer, asthma, heart disease, and longevity. Rather than the traditional concept of “one microbe, one disease”, it has become clear that some diseases are polymicrobial – caused by particular communities of microbes. The microbial communities of the gut, in particular, have global implications for health through their metabolism of food and drugs. The gut inhabitants transform chemicals we consume into beneficial, harmless, and harmful compounds, thereby influencing the level of human exposure to pharmaceutically active compounds.
Given the key role of indigenous microbial communities in determining human health and disease, personalized medicine, in addition to being based on an individual’s genome sequence, will require an understanding of the microbial communities associated with the individual. One could imagine a time when routine medical screening includes assessment of the microbial health of the patient. Identifying constellations of bacteria that are associated with undesirable conditions could be followed by corrective measures that would alter microbial communities by diet, drugs, or probiotics (live bacterial cultures). Drug choice and dose would be guided by a portrait of the microbial community and an understanding of its ability to transform certain compounds. However, such approaches to personalized medicine are largely beyond our current capacity because, although it is possible to identify the representative microbes in a particular environment, the interactions and processes within microbial communities, and between these communities and the host, are complex and poorly understood. Microbial community ecology, generally, lacks sufficient empirical attention, theory, and principles, so there is little past work to draw from in making predictions about host-associated communities. In fact, many classical community-level questions may be asked most effectively in host-associated communities because they are contained and relatively easy to manipulate in “natural” ways by simply modifying the host’s environment.
The last two decades have provided the tools to begin to identify the microbes in a particular environment – analysis of 16S rRNA gene content provides an initial description of the organisms present, and metagenomics captures their genetic potential. NIH has proposed expanding its definition of the “human genome” to include the genomes of the myriad microorganisms that comprise 90% of the cells in and on the human body. NIH recently funded a Roadmap Initiative, the Human Microbiome Project (HMP), as a series of projects that explore the human microbiome – the communities of microorganisms that reside in and on the human body. The HMP will support the sequencing of the metagenomes of the five major microbiomes: the gut, mouth, skin, vagina, and nose. These projects will generate massive amounts of sequence information that will provide insight into the diversity and stability of these complex communities that play a fundamental role in human health.
Simply identifying the microorganisms and sequencing genes in these microbiomes will not provide the depth of understanding of microbial communities that is needed to design effective preventions or treatments for human diseases. Sequence information will suggest hypotheses to test and will inform the design of experiments needed to elucidate the principles and mechanisms that govern community behavior. However, it will be necessary to understand the physiology and ecology of microbial communities to be able to manipulate these communities to achieve desired human health outcomes.
In November 2008, NIGMS sponsored a colloquium that was intended to evaluate the current status and research needs in the field of host-associated microbial communities. Participants represented leaders in microbial ecology, macroecology, computational biology, microbial physiology, and genomics. The colloquium provoked a creative synthesis about microbial community ecology through which the group identified critical questions that confront the field as well as the research necessary to resolve these questions. A description of the gaps in knowledge and the needed research is described here.
RESEARCH CHALLENGES AND APPROACHES
Natural History to Hypothesis-Driven Research
The biology of microbial communities is a relatively new field and, like most new fields, demands basic research to answer fundamental questions: which microbes comprise the core of the community and which are transient, what are the characteristics of the niches that different microbes fill, how do the microbes in the community interact with each other and with the host, and how do the communities evolve – i.e., the natural history of the microbial community. These characteristics can be learned by observational studies such as the metagenomic analyses supported by the Human Microbiome Project. Metagenomics is a powerful tool for identifying not only what types of microbes are present in a microbial community, but also the presence or absence of genes required for particular metabolic processes. The presence and frequency of certain genes will generate predictions about the physiology of microbial communities that can be tested experimentally. Currently it is not possible to reconstruct the individual genomes from the metagenomic sequences, but improved methods of computational analysis could overcome this barrier, providing testable hypotheses about the metabolic interactions between different types of microorganisms in the community.
Metagenomics is just a beginning, and an expanded description of the communities is needed to produce predictions that can be tested in subsequent mechanistic studies. Metagenomics describes the genetic potential of a community, but does not address what is actually happening. Key among the descriptive methods are the other “omics,” which will generate a portrait of the community’s expressed transcripts, proteins, and metabolites. However, many of the questions cannot be answered with existing methods, demanding the development of new technological advances to probe microbial communities. For example, sophisticated microscopy, which takes advantage of the omics-derived data will make it possible to visualize the architecture of microbial communities at the individual cell level. Determining the proximity of particular microbes to other types of microbes or to unique microniches in the host will provide the basis for elucidating the rules of community assembly and evolution.
The source of nutrients and energy used by microbes may be derived from the host or from other microbes in the community. In some cases the interactions are mutualistic – for example, when one microbe consumes complex carbon sources and secretes short-chain fatty acids which can be catabolized by other microbes or the human host. In other cases, the interactions are antagonistic – for example, when indigenous microbes consume available nutrients or energy sources and thereby limit the colonization of pathogens. The nutrient flow between microbes and the host determines the trophic levels of the microbial community, and ultimately affect the types and stability of microbes in the community, the impact of the microbial community on the host, and the robustness of the community when confronted with antibiotics, changes in diet, or microbial invaders.
Because of the exchange of metabolites and energy sources between microbes and the host, the physiology of the microbial community is essentially a mosaic of metabolic pathways determined by many different factors. That is, the physiology of the microbial community is more than a sum of the physiology of the microorganisms in the community. Although the physiology of microorganisms grown in pure cultures in the laboratory has led to many important discoveries, very little is currently known about the physiology of microbial communities, especially with resolution at the individual cell level. Innovative approaches will be required, which likely will include network analysis, experiments with labeled substrates, and mathematical modeling to contribute to understanding community physiology.
Simple models of microbial interactions may suggest commonalities, but to serve as the basis for general principles, the same phenomena must be evaluated in diverse communities. Bacteria exchange signals that affect gene expression, share metabolites, create nutrient and other chemical gradients, and exchange DNA. Recent metagenomic studies on bacterial viruses indicate that there is a surprising amount of exchange of genetic information between microbes. The flow of information through the community, not just between two members, is one of the least understood processes in microbiology. Just as genetics was the tool that precisely dissected the flow of information within bacterial cells, genetics will provide a parallel approach to dissect interactions in microbial communities. Metagenomics will provide the sequence information to facilitate the construction of specific mutations, allowing direct experimental tests of predictions about the flow of metabolites or genetic information between members of the microbial community. Substituting a mutant for a wild type community member will provide the basis for elucidating networks and the cascade of interactions that ripple through the community based on a single change. New approaches are needed to adapt mutational approaches for the large-scale genetic characterization of microbial communities.
How stable are microbial communities? How do perturbations by antibiotic therapy, changes in diet, disease, surgery, or other assaults change the types and function of a host-associated microbial community? The rules that determine the resistance to and recovery from perturbation are not established. Research is needed to develop these principles so that drugs and probiotics can be designed to make or prevent changes in the community structure. This approach is central to the development of treatments for diseases that are modulated by the community (such as probiotics to prevent obesity) or to keep the community intact in the face of an intervention for another purpose (such as non-target effects of antibiotics on the gut microbiota). Some aspects of these important questions about microbial communities can be addressed by metagenomics. As DNA sequencing technology becomes less expensive, repeated sampling of the metagenome of a community can be used to evaluate changes in the community over time. Using fluorescent in situ hybridization (FISH) approaches with probes specific for particular microorganisms, it is also possible to track changes in the spatial distribution of these organisms within microbial communities.
ROLE OF MODEL SYSTEMS
Developing a Portfolio of Model Systems
Model systems such as worms, flies, and zebrafish have transformed our understanding of human diseases due the conservation of genes across the Tree of Life. Similarly, studies on model systems have had a major impact on both a basic understanding of microbial genetics and physiology, and led to practical applications that impact human health. For example, studies on the nonpathogenic laboratory strain E. coli K-12 and its phages led to the development of molecular biology and recombinant DNA technology; studies on Salmonella enterica led to the discovery of pathogenesis islands and an understanding of the extent of genetic exchange in nature; studies on Bacillus subtilis led to an understanding of the mechanism of sporulation, that is now being applied to thwart anthrax. These discoveries were possible because of the intensive analysis of these model microorganisms, which provided the tools and insights needed to understand more complex biological systems. Although extremely useful, these model systems focused on microorganisms grown in the laboratory. To understand microbial communities, new models are needed that will facilitate the detailed analysis of microbes growing in communities of various levels of complexity.
Diverse model systems are required to decipher the common principles of microbial communities and to develop effective tools for studying microbial communities from a wide variety of natural environments. For example, despite the compelling rationale for large-scale studies on microbial communities that impact human health, the direct characterization of the human microbiome poses experimental limitations. The complexity of the human microbiome makes it impractical to track every microorganism through space and time; small sample sizes that are dictated by availability of volunteers and cost will limit the statistical power needed to detect small, but important differences among communities; and ethical issues will preclude many types of experiments because of the intervention or the sampling needed to answer mechanistic questions. In contrast, model microbial systems provide the opportunity to use a wide variety of experimental approaches, do destructive sampling, and analyze large samples needed to obtain reliable data on the spatial and temporal dynamics of the microbial population within a community. Studies on such model microbial systems will provide an understanding of the fundamental features of microbial communities and will facilitate subsequent characterization of the human microbiome.
Examples of useful model systems include binary host-microbe interactions (e.g., Vibrio colonization of the light organ of the squid), synthetic communities of 2-3 organisms (e.g., artificial biofilms), very simple natural communities (e.g., the leech gut), simple, multispecies communities (e.g., the caterpillar gut), and complex natural communities with genetically tractable hosts (e.g. gut flora of the mouse). It is possible to manipulate model systems in a variety of ways. Animals can be reared without microorganisms present (gnotobiotic) or cleared of their normal microbiota (e.g., via antibiotic treatment) and then colonized with the original community, different subsets of the original microbial community, or another animal’s normal community. These types of experiments will help to determine roles of particular microbes in host health and community structure and function. Introducing the variable of host genetics will take this understanding to another level, which will lead to models that account for the host in microbial community composition and physiology.
Some simple model systems of microbial communities have already led to important insights, including discoveries with direct impact on human health. Model systems for studying bacteria in biofilms have led to an understanding of the role of communication between microbes in the biofilm and the resistance to antimicrobial agents. Studies on microbial communities have led to an understanding of the importance of complex regulatory networks (including positive and negative feedback loops) to adapt to changing environmental conditions. Studies of gnotobiotic mice have provided a link between a number of diseases and the structure of the gut microbial community. Studies on model systems will complement ongoing studies in humans that seek to define variables in the microbial communities associated with human populations and how the differences in the host-associated microbes influence health and disease (e.g., metabolomic studies on particular groups of humans or identical twins).
ROLE OF COMPUTATIONAL BIOLOGY
Mathematical and statistical models have shaped biology at every level. In biochemistry, the standard curve is probably the best known statistical model that makes it clear why it is essential to determine the relationship between two variables. At a population level, use of growth curves has led to enormous insight into bacterial physiology. In macroecology, models that correlate populations of predators and prey produce predictions about animal populations, and in epidemiology, models of viral infectivity and spread can predict the course of an epidemic. Models can both provide predictive ability and establish broad principles. Some are simple correlations between two measurements and some are complex mathematical structures that account for the relationships among many variables. In systems biology, the models run on computers are often sufficiently complex that a human mind cannot decipher the entire data set, but analysis of the data on computers can detect important patterns.
The intention to manipulate microbial communities to achieve outcomes for human health necessitates development of accurate predictive models. If we could predict community robustness or physiological activity based upon models inferred from experimental analysis of microbial communities, it would be possible to design appropriate interventions. Models of community assembly will identify vulnerable points for interventions to change community development or structure.
Outcomes of this Research
In summary, recent studies have clearly demonstrated the importance of microbial communities on human health and disease. Changes in microbial communities can cause rapid or delayed symptoms, and may cause acute or chronic conditions. An understanding of the role of human microbial communities will allow the development of new approaches to manipulate these communities to promote human health and prevent disease. The spread of antibiotic resistance necessitates other approaches to thwart microbial infections, and more nuanced approaches are needed to promote health instead of treating disease. It may be possible to develop effective and long-lasting probiotics that prevent the development of gingivitis or colonization with intestinal pathogens. Such clinical interventions could be used in healthy individuals to prevent disease, in exposed individuals prior to development of symptoms, or to speed recovery after the development of disease.
Understanding the physiology of microbial communities may also enhance the development of new pharmaceuticals. Studying the metabolism of pharmaceuticals by representative microbial communities may provide a more effective approach for the initial analysis of the safety and stability of potential new drugs prior to expensive clinical trials. Moreover, by comparing the microbial communities from hosts who respond favorably to certain drugs with that of hosts who have serious side-effects, it may be possible to manipulate the gut microbial community to prevent the metabolism of certain drugs to toxic products. These studies will be stimulated by improved approaches for mining the data from the Human Microbiome Project to reveal metabolic pathways that modulate particular classes of pharmaceuticals, as well as a better understanding of the metabolomics of microbial communities.
In addition to these impacts on human health, understanding the ecology and physiology of microbial communities will provide critical insights into basic biology. Microbial communities drive numerous aspects of our environment, from health and disease of animals and plants to environmental issues like water quality, bioremediation, CO2, and climate change. However, we currently lack a basic understanding of microbial communities, and did not previously have the tools needed to elucidate important processes within microbial communities such as metabolic flux, genetic exchange, and other factors which determine their composition and robustness.
Many of the basic tenets of ecology learned from macroecological systems can probably be applied to the function of microbial communities, but most have not been tested in microbial systems. For example, the concept of a keystone species and the roles of predator-prey relationships in determining community structure have been developed elegantly with macroorganisms. These concepts provide a model for studying microbial community structure and function, and a framework on which to determine whether similar principles govern micro- and macroorganisms living in communities. In contrast, other fundamental aspects of ecology have been difficult to study at the macroecological level and may be more amenable to study in microbial communities. Studies of biological invasions in larger ecological landscapes are not conducive to the type of controlled, repeatable experiments that are possible with microbial communities. Biological invasion theory attempts to predict invasion patterns, the nature of successful invaders, conditions conducive to invasion, consequences of invasion, and rates and directions of spread. The nature of invasions makes it challenging to test theory with replicated experimental design, as most invasions are unexpected, unwanted, and not studied until after the process has begun. The right model systems will provide an opportunity to dissect invasions at the genetic, genomic, and metabolic levels and develop predictive theory derived from the outcomes of these experiments.
Achieving both the desired health outcomes and the definition of fundamental microbial ecology principles will demand a concerted attack from several new avenues of research. Some of the immediate research needs include:
- Integration of metagenomic analyses with advanced microscopic techniques to investigate the temporal and spatial organization of microbial communities, and the impact of perturbations;
- Computational tools to reconstruct genome sequences and identify metabolic pathways from metagenomic data, coupled with network analysis and mathematical modeling approaches to provide experimental predictions about the physiology and ecology of microbial communities;
- Experimental tools to probe microbial communities in an animal host, including “omics-based” visualization methods to probe the architecture of microbial communities, coupled with nanophysiological approaches to monitor metabolic flux;
- Large-scale mutational and other genetic approaches for testing specific predictions about and elucidating mechanisms of interactions within microbial communities;
- Development of model systems of microbial communities with various levels of complexity that are amenable to experimental analysis and discovery of basic principles.
There is a profound need for a deeper understanding of the role of microbial communities in health and disease, and this understanding has the potential to lead to many innovative interventions that will enhance human health. This need is emphasized by the increasing problems associated with antibiotic- resistant infections, the serious side-effects associated with many new pharmaceuticals, and the growing understanding of host associated microbial communities in chronic health problems. The only risk is not doing it!