The human microbiome represents a vastly complex ecosystem that is tightly linked to our development, physiology, and health. Our increased capacity to generate multiple channels of omic data from this system, brought about by recent advances in high throughput molecular technologies, calls for the development of systems-level methods and models that take into account not only the composition of genes and species in a microbiome but also the interactions between these components. Such models should aim to study the microbiome as a community of species whose metabolisms are tightly intertwined with each other and with that of the host, and should be developed with a view towards an integrated, comprehensive, and predictive modeling framework. Here, we review recent work specifically in metabolic modeling of the human microbiome, highlighting both novel methodologies and pressing challenges. We discuss various modeling approaches that lay the foundation for a full-scale predictive model, focusing on models of interactions between microbial species, metagenome-scale models of community-level metabolism, and models of the interaction between the microbiome and the host. Continued development of such models and of their integration into a multi-scale model of the microbiome will lead to a deeper mechanistic understanding of how variation in the microbiome impacts the host, and will promote the discovery of clinically- and ecologically-relevant insights from the rich trove of data now available.
Summary: In the past several years, we have witnessed an increased interest in understanding the structure and function of the indigenous microbiota that inhabits the human body. It is hoped that this will yield novel insight into the role of these complex microbial communities in human health and disease. What is less appreciated is that this recent activity owes a great deal to the pioneering efforts of microbial ecologists who have been studying communities in non-host-associated environments. Interactions between environmental microbiologists and human microbiota researchers have already contributed to advances in our understanding of the human microbiome. We review the work that has led to these recent advances and illustrate some of the possible future directions for continued collaboration between these groups of researchers. We discuss how the application of ecological theory to the human-associated microbiota can lead us past descriptions of community structure and toward an understanding of the functions of the human microbiota. Such an approach may lead to a shift in the prevention and treatment of human diseases that involves conservation or restoration of the normal community structure and function of the host-associated microbiota.
Microbial ecology is flourishing, and in the process, is making contributions to how the ecology and biology of large organisms is understood. Ongoing advances in sequencing technology and computational methods have enabled the collection and analysis of vast amounts of molecular data from diverse biological communities. While early studies focused on cataloguing microbial biodiversity in environments ranging from simple marine ecosystems to complex soil ecologies, more recent research is concerned with community functions and their dynamics over time. Models and concepts from traditional ecology have been used to generate new insight into microbial communities, and novel system-level models developed to explain and predict microbial interactions. The process of moving from molecular inventories to functional understanding is complex and challenging, and never more so than when many thousands of dynamic interactions are the phenomena of interest. We outline the process of how epistemic transitions are made from producing catalogues of molecules to achieving functional and predictive insight, and show how those insights not only revolutionize what is known about biological systems but also about how to do biology itself. Examples will be drawn primarily from analyses of different human microbiota, which are the microbial consortia found in and on areas of the human body, and their associated microbiomes (the genes of those communities). Molecular knowledge of these microbiomes is transforming microbiological knowledge, as well as broader aspects of human biology, health and disease.
Microbiome; Timeseries; Microbial community analysis; Operational taxonomic units
Diversity in the forestomach microbiome is one of the key features of ruminant animals. The diverse microbial community adapts to a wide array of dietary feedstuffs and management strategies. Understanding rumen microbiome composition, adaptation, and function has global implications ranging from climatology to applied animal production. Classical knowledge of rumen microbiology was based on anaerobic, culture-dependent methods. Next-generation sequencing and other molecular techniques have uncovered novel features of the rumen microbiome. For instance, pyrosequencing of the 16S ribosomal RNA gene has revealed the taxonomic identity of bacteria and archaea to the genus level, and when complemented with barcoding adds multiple samples to a single run. Whole genome shotgun sequencing generates true metagenomic sequences to predict the functional capability of a microbiome, and can also be used to construct genomes of isolated organisms. Integration of high-throughput data describing the rumen microbiome with classic fermentation and animal performance parameters has produced meaningful advances and opened additional areas for study. In this review, we highlight recent studies of the rumen microbiome in the context of cattle production focusing on nutrition, rumen development, animal efficiency, and microbial function.
cattle; metabolism; microbiome; nutrition; rumen
Significance: The colonization of wounds by specific microbes or communities of microbes may delay healing and/or lead to infection-related complication. Studies of wound-associated microbial communities (microbiomes) to date have primarily relied upon culture-based methods, which are known to have extreme biases and are not reliable for the characterization of microbiomes. Biofilms are very resistant to culture and are therefore especially difficult to study with techniques that remain standard in clinical settings.
Recent Advances: Culture-independent approaches employing next-generation DNA sequencing have provided researchers and clinicians a window into wound-associated microbiomes that could not be achieved before and has begun to transform our view of wound-associated biodiversity. Within the past decade, many platforms have arisen for performing this type of sequencing, with various types of applications for microbiome research being possible on each.
Critical Issues: Wound care incorporating knowledge of microbiomes gained from next-generation sequencing could guide clinical management and treatments. The purpose of this review is to outline the current platforms, their applications, and the steps necessary to undertake microbiome studies using next-generation sequencing.
Future Directions: As DNA sequencing technology progresses, platforms will continue to produce longer reads and more reads per run at lower costs. A major future challenge is to implement these technologies in clinical settings for more precise and rapid identification of wound bioburden.
The gut microbiome is the term given to describe the vast collection of symbiotic microorganisms in the human gastrointestinal system and their collective interacting genomes. Recent studies have suggested that the gut microbiome performs numerous important biochemical functions for the host, and disorders of the microbiome are associated with many and diverse human disease processes. Systems biology approaches based on next generation 'omics' technologies are now able to describe the gut microbiome at a detailed genetic and functional (transcriptomic, proteomic and metabolic) level, providing new insights into the importance of the gut microbiome in human health, and they are able to map microbiome variability between species, individuals and populations. This has established the importance of the gut microbiome in the disease pathogenesis for numerous systemic disease states, such as obesity and cardiovascular disease, and in intestinal conditions, such as inflammatory bowel disease. Thus, understanding microbiome activity is essential to the development of future personalized strategies of healthcare, as well as potentially providing new targets for drug development. Here, we review recent metagenomic and metabonomic approaches that have enabled advances in understanding gut microbiome activity in relation to human health, and gut microbial modulation for the treatment of disease. We also describe possible avenues of research in this rapidly growing field with respect to future personalized healthcare strategies.
Between July 18th and 24th 2010, 26 leading microbial ecology, computation, bioinformatics and statistics researchers came together in Snowbird, Utah (USA) to discuss the challenge of how to best characterize the microbial world using next-generation sequencing technologies. The meeting was entitled “Terabase Metagenomics” and was sponsored by the Institute for Computing in Science (ICiS) summer 2010 workshop program. The aim of the workshop was to explore the fundamental questions relating to microbial ecology that could be addressed using advances in sequencing potential. Technological advances in next-generation sequencing platforms such as the Illumina HiSeq 2000 can generate in excess of 250 billion base pairs of genetic information in 8 days. Thus, the generation of a trillion base pairs of genetic information is becoming a routine matter. The main outcome from this meeting was the birth of a concept and practical approach to exploring microbial life on earth, the Earth Microbiome Project (EMP). Here we briefly describe the highlights of this meeting and provide an overview of the EMP concept and how it can be applied to exploration of the microbiome of each ecosystem on this planet.
The gut microbiota is amongst the most densely populated microbial ecosystem on earth. While the microbiome exerts numerous health beneficial functions, the high density of micro-organisms within this ecosystem also facilitates horizontal transfer of antimicrobial resistance (AMR) genes to potential pathogenic bacteria. Over the past decades antibiotic susceptibility testing of specific indicator bacteria from the microbiome, such as Escherichia coli, has been the method of choice in most studies. These studies have greatly enlarged our understanding on the prevalence and distribution of AMR and associated risk factors. Recent studies using (functional) metagenomics, however, highlighted the unappreciated diversity of AMR genes in the human microbiome and identified genes that had not been described previously. Next to metagenomics, more targeted approaches such as polymerase chain reaction for detection and quantification of AMR genes within a population are promising, in particular for large-scale epidemiological screening. Here we present an overview of the indigenous microbiota as a reservoir of AMR genes, the current knowledge on this “resistome” and the recent and upcoming advances in the molecular diagnostic approaches to unravel this reservoir.
antimicrobial resistance; resistome; metagenomics; gut microbiota; microbiome
Recent advances in genomic medicine have opened up the possibility of tailored medicine that may eventually replace traditional “one-size-fits all” approaches to the treatment of inflammatory bowel disease (IBD). In addition to exploring the interactions between hosts and microbes, referred to as the microbiome, a variety of strategies that can be tailored to an individual in the coming era of personalized medicine in the treatment of IBD are being investigated. These include prompt genomic screening of patients at risk of developing IBD, the utility of molecular discrimination of IBD subtypes among patients diagnosed with IBD, and the discovery of proteome biomarkers to diagnose or predict cancer risks. Host genetic factors influence the etiology of IBD, as do microbial ecosystems in the human bowel, which are not uniform, but instead represent many different microhabitats that can be influenced by diet and might affect processes essential to bowel metabolism. Further advances in basic research regarding intestinal inflammation may reveal new insights into the role of inflammatory mediators, referred to as the inflammasome, and the macromolecular complex of metabolites formed by intestinal bacteria. Collectively, knowledge of the inflammasome and metagenomics will lead to the development of biomarkers for IBD that target specific pathogenic mechanisms involved in the spontaneous progress of IBD. In this review article, our recent results regarding the discovery of potential proteomic biomarkers using a label-free quantification technique are introduced and on-going projects contributing to either the discrimination of IBD subtypes or to the prediction of cancer risks are accompanied by updated information from IBD biomarker research.
Inflammatory bowel disease; Biomarker; Proteomics; Tailored medicine; Colitic cancer
Establishing the time since death is critical in every death investigation, yet existing techniques are susceptible to a range of errors and biases. For example, forensic entomology is widely used to assess the postmortem interval (PMI), but errors can range from days to months. Microbes may provide a novel method for estimating PMI that avoids many of these limitations. Here we show that postmortem microbial community changes are dramatic, measurable, and repeatable in a mouse model system, allowing PMI to be estimated within approximately 3 days over 48 days. Our results provide a detailed understanding of bacterial and microbial eukaryotic ecology within a decomposing corpse system and suggest that microbial community data can be developed into a forensic tool for estimating PMI.
Our bodies—especially our skin, our saliva, the lining of our mouth and our gastrointestinal tract—are home to a diverse collection of bacteria and other microorganisms called the microbiome. While the roles played by many of these microorganisms have yet to be identified, it is known that they contribute to the health and wellbeing of their host by metabolizing indigestible compounds, producing essential vitamins, and preventing the growth of harmful bacteria. They are important for nutrient and carbon cycling in the environment.
The advent of advanced sequencing techniques has made it feasible to study the composition of this microbial community, and to monitor how it changes over time or how it responds to events such as antibiotic treatment. Sequencing studies have been used to highlight the significant differences between microbial communities found in different parts of the body, and to follow the evolution of the gut microbiome from birth. Most of these studies have focused on live animals, so little is known about what happens to the microbiome after its host dies. In particular, it is not known if the changes that occur after death are similar for all individuals. Moreover, the decomposing animal supplies nutrients and carbon to the surrounding ecosystem, but its influence on the microbial community of its immediate environment is not well understood.
Now Metcalf et al. have used high-throughput sequencing to study the bacteria and other microorganisms (such as nematodes and fungi) in dead and decomposing mice, and also in the soil beneath them, over the course of 48 days. The changes were significant and also consistent across the corpses, with the microbial communities in the corpses influencing those in the soil, and vice versa. Metcalf et al. also showed that these measurements could be used to estimate the postmortem interval (the time since death) to within approximately 3 days, which suggests that the work could have applications in forensic science.
decomposition; microbial succession; time since death; forensics; postmortem interval; microbial ecology; Mouse
Human microbiome research has the potential to transform the practice of medicine, fundamentally shifting the ways in which we think not only about human health, illness, and disease, but also about clinical practice and public health interventions. Drawing from a larger qualitative study on ethical, legal, and social dimensions of human microbiome research, in this article we document perspectives related to the translation of human microbiome research into clinical practice, focusing particularly on implications for health, illness, and disease.
We conducted 60 in-depth, semi-structured interviews (2009–2010) with 63 researchers and National Institutes of Health project leaders (“investigators”) involved with human microbiome research. Interviews explored a range of ethical, legal, and social implications of human microbiome research, including investigators’ perspectives on potential strategies for translating findings to clinical practice. Using thematic content analysis, we identified and analyzed emergent themes and patterns.
We identified three themes: (1) Investigators’ general perspectives on the clinical utility of human microbiome research, (2) Investigators’ perspectives on antibiotic use, overuse, and misuse, and (3) Investigators’ perspectives concerning future challenges of translating data to clinical practice.
The issues discussed by investigators concerning the clinical significance of human microbiome research, including embracing a new paradigm of health and disease, the importance of microbial communities, and clinical utility, will be of critical importance as this research moves forward.
human microbiome; translational genomic research; clinical genomics
Recent advances in molecular technique have now made it possible to interrogate the human microbiome in depth to better understand the interactions with the host organism and its role in diseases. We now report the utility of using Length Heterogeneity Polymerase Chain Reaction (LH-PCR) to survey samples and a proprietary Multitagged Pyrosequencing (MTPS) methodology to interrogate the gut microbiome in healthy and disease states. We present an overview of our studies demonstrating that the application of these molecular biology techniques to an example disease state such as Inflammatory Bowel Disease (IBD). The findings show that there is a core mucosal bacterial microbiome (i.e. a mucosal biofilm) that is distinct from the luminal microbiome in health and that the mucosal microbiome appears to be dysbiotic in IBD. We propose that the mucosal microbiome forms a synergistic and stable interaction with the host immune system, while the lumen microbiome varies based on diet or other environmental factors. We define this composite ecosystem of the human microbiome and human host as the Human Metabiome.
Recent gut microbiome studies in model organisms emphasize the effects of intrinsic and extrinsic factors on the variation of the bacterial composition and its impact on the overall health status of the host. Species occurring in the same habitat might share a similar microbiome, especially if they overlap in ecological and behavioral traits. So far, the natural variation in microbiomes of free-ranging wildlife species has not been thoroughly investigated. The few existing studies exploring microbiomes through 16S rRNA gene reads clustered sequencing reads into operational taxonomic units (OTUs) based on a similarity threshold (e.g., 97%). This approach, in combination with the low resolution of target databases, generally limits the level of taxonomic assignments to the genus level. However, distinguishing natural variation of microbiomes in healthy individuals from “abnormal” microbial compositions that affect host health requires knowledge of the “normal” microbial flora at a high taxonomic resolution. This gap can now be addressed using the recently published oligotyping approach, which can resolve closely related organisms into distinct oligotypes by utilizing subtle nucleotide variation. Here, we used Illumina MiSeq to sequence amplicons generated from the V4 region of the 16S rRNA gene to investigate the gut microbiome of two free-ranging sympatric Namibian carnivore species, the cheetah (Acinonyx jubatus) and the black-backed jackal (Canis mesomelas). Bacterial phyla with proportions >0.2% were identical for both species and included Firmicutes, Fusobacteria, Bacteroidetes, Proteobacteria and Actinobacteria. At a finer taxonomic resolution, black-backed jackals exhibited 69 bacterial taxa with proportions ≥0.1%, whereas cheetahs had only 42. Finally, oligotyping revealed that shared bacterial taxa consisted of distinct oligotype profiles. Thus, in contrast to 3% OTUs, oligotyping can detect fine-scale taxonomic differences between microbiomes.
gut microbiome; bacteria; oligotyping; carnivores; cheetah (Acinonyx jubatus); black-backed jackal (Canis mesomelas); Namibia
Recent advances in next-generation DNA sequencing enable rapid high-throughput quantitation of microbial community composition in human samples, opening up a new field of microbiomics. One of the promises of this field is linking abundances of microbial taxa to phenotypic and physiological states, which can inform development of new diagnostic, personalized medicine, and forensic modalities. Prior research has demonstrated the feasibility of applying machine learning methods to perform body site and subject classification with microbiomic data. However, it is currently unknown which classifiers perform best among the many available alternatives for classification with microbiomic data.
In this work, we performed a systematic comparison of 18 major classification methods, 5 feature selection methods, and 2 accuracy metrics using 8 datasets spanning 1,802 human samples and various classification tasks: body site and subject classification and diagnosis.
We found that random forests, support vector machines, kernel ridge regression, and Bayesian logistic regression with Laplace priors are the most effective machine learning techniques for performing accurate classification from these microbiomic data.
Microbiomic data; Machine learning; Classification; Feature selection
Investigation of the lung microbiome is a relatively new field. Although the lungs were classically believed to be sterile, recently published investigations have identified microbial communities in the lungs of healthy humans. At the present time, there are significant methodologic and technical hurdles that must be addressed in ongoing investigations, including distinguishing the microbiota of the upper and lower respiratory tracts. However, characterization of the lung microbiome is likely to provide important pathogenic insights into cystic fibrosis, respiratory disease of the newborn, chronic obstructive pulmonary disease, and asthma. In addition to characterization of the lung microbiome, the microbiota of the gastrointestinal tract have profound influence on development and maintenance of lung immunity and inflammation. Further study of gastrointestinal-respiratory interactions are likely to yield important insights into the pathogenesis of pulmonary diseases, including asthma. As this field advances over the next several years, we anticipate that studies utilizing larger cohorts, multi-center designs, and longitudinal sampling will add to our knowledge and understanding of the lung microbiome.
The intestinal microbiota is a microbial ecosystem of crucial importance to human health. Understanding how the microbiota confers resistance against enteric pathogens and how antibiotics disrupt that resistance is key to the prevention and cure of intestinal infections. We present a novel method to infer microbial community ecology directly from time-resolved metagenomics. This method extends generalized Lotka–Volterra dynamics to account for external perturbations. Data from recent experiments on antibiotic-mediated Clostridium difficile infection is analyzed to quantify microbial interactions, commensal-pathogen interactions, and the effect of the antibiotic on the community. Stability analysis reveals that the microbiota is intrinsically stable, explaining how antibiotic perturbations and C. difficile inoculation can produce catastrophic shifts that persist even after removal of the perturbations. Importantly, the analysis suggests a subnetwork of bacterial groups implicated in protection against C. difficile. Due to its generality, our method can be applied to any high-resolution ecological time-series data to infer community structure and response to external stimuli.
Recent advances in DNA sequencing and metagenomics are opening a window into the human microbiome revealing novel associations between certain microbial consortia and disease. However, most of these studies are cross-sectional and lack a mechanistic understanding of this ecosystem's structure and its response to external perturbations, therefore not allowing accurate temporal predictions. In this article, we develop a method to analyze temporal community data accounting also for time-dependent external perturbations. In particular, this method combines the classical Lotka–Volterra model of population dynamics with regression techniques to obtain mechanistically descriptive coefficients which can be further used to construct predictive models of ecosystem dynamics. Using then data from a mouse experiment under antibiotic perturbations, we are able to predict and recover the microbiota temporal dynamics and study the concept of alternative stable states and antibiotic-induced transitions. As a result, our method reveals a group of commensal microbes that potentially protect against infection by the pathogen Clostridium difficile and proposes a possible mechanism how the antibiotic makes the host more susceptible to infection.
Primate gastrointestinal microbial communities are becoming increasingly appreciated for their relevance to comparative medicine and conservation, but the factors that structure primate “microbiomes” remain controversial. This study examined a community of primates in Kibale National Park, Uganda, to assess the relative importance of host species and location in structuring gastrointestinal microbiomes. Fecal samples were collected from primates in intact forest and from primates in highly disturbed forest fragments. People and livestock living nearby were also included, as was a geographically distant population of related red colobus in Kenya. A culture-free microbial community fingerprinting technique was used to analyze fecal microbiomes from 124 individual red colobus (Procolobus rufomitratus), 100 individual black-and-white colobus (Colobus guereza), 111 individual red-tailed guenons (Cercopithecus ascanius), 578 human volunteers, and 364 domestic animals, including cattle (Bos indicus and B. indicus × B. taurus crosses), goats (Caprus hircus), sheep (Ovis aries), and pigs (Sus scrofa). Microbiomes sorted strongly by host species, and forest fragmentation did not alter this pattern. Microbiomes of Kenyan red colobus sorted distinctly from microbiomes of Ugandan red colobus, but microbiomes from these two red colobus populations clustered more closely with each other than with any other species. Microbiomes from red colobus and black-and-white colobus were more differentiated than would be predicted by the phylogenetic relatedness of these two species, perhaps reflecting heretofore underappreciated differences in digestive physiology between the species. Within Kibale, social group membership influenced intra-specific variation among microbiomes. However, intra-specific variation was higher among primates in forest fragments than among primates in intact forest, perhaps reflecting the physical separation of fragments. These results suggest that, in this system, species-specific processes such as gastrointestinal physiology strongly structure microbial communities, and that primate microbiomes are relatively resistant to perturbation, even across large geographic distances or in the face of habitat disturbance.
microbiome; non-human primate; Uganda; Colobinae; Cercopithecinae; forest fragmentation
The development of culture-independent techniques and next-generation sequencing has led to a staggering rise in the number of microbiome studies over the last decade. Although it remains important to identify the taxa of microbes present in a variety of environmental samples, including the gut microbiomes of healthy and diseased individuals, the next stage of microbiome research will need to focus on uncovering the role of the microbiome rather than its mere composition. Here, we introduce techniques that go beyond identifying the taxa present within a sample and examine the biological function of the microbiome or the host-microbiome interaction.
The gut microbiome is a complex ecosystem that affects the development, immunological responses and nutritional status of the host. Efforts are being made to unravel the complex interaction between the gut microbiome and host to have a greater understanding about its role in human health. Colonization of the gut by microbes begins at birth, but the succession and composition of the microbial community depends on a number of factors including, but not limited to, the age, diet, genetic composition, gender, geographic location, and health status of an individual. Therefore, inclusion of diverse human subjects in the study of the gut microbiome is indispensable. However, conducting such studies in India presents unique opportunities and challenges. The vast diversity in human genetic composition, dietary habits, and geographic distribution that exists in the Indian population adds to the complexity in understanding the gut microbiome. Gut microbiome-related studies from other parts of the world have reported a possible association of diseases such as obesity and diabetes with the human gut microbiome. In contrast, an in-depth assessment of risk factors associated with altered gut microbiome in such diseases in the Indian population is lacking. Studies including the Indian population may give insights into the association of the gut microbiome with various factors and diseases that may not be possible from studies on western populations. This review briefly discusses the significance of the gut microbiome on human health and the present status of gut microbiome studies in the Indian population. In addition, this review will highlight the unique opportunities and challenges for gut microbiome studies in the Indian population.
Gut microbiome; Indian population; Diversity; Genetics; Diet
The field of palaeomicrobiology is dramatically expanding thanks to recent advances in high-throughput biomolecular sequencing, which allows unprecedented access to the evolutionary history and ecology of human-associated and environmental microbes. Recently, human dental calculus has been shown to be an abundant, nearly ubiquitous, and long-term reservoir of the ancient oral microbiome, preserving not only microbial and host biomolecules but also dietary and environmental debris. Modern investigations of native human microbiota have demonstrated that the human microbiome plays a central role in health and chronic disease, raising questions about changes in microbial ecology, diversity and function through time. This paper explores the current state of ancient oral microbiome research and discusses successful applications, methodological challenges and future possibilities in elucidating the intimate evolutionary relationship between humans and their microbes.
palaeomicrobiology; ancient DNA; oral microbiome; dental calculus; metagenomics; metaproteomics
Next-generation sequencing technologies have revolutionized the analysis of microbial communities in diverse environments, including the human body. This article reviews several aspects of one of these technologies, the pyrosequencing technique, including its principles, applications, and significant contribution to the study of the human microbiome, with especial emphasis on the oral microbiome. The results brought about by pyrosequencing studies have significantly contributed to refining and augmenting the knowledge of the community membership and structure in and on the human body in healthy and diseased conditions. Because most oral infectious diseases are currently regarded as biofilm-related polymicrobial infections, high-throughput sequencing technologies have the potential to disclose specific patterns related to health or disease. Further advances in technology hold the perspective to have important implications in terms of accurate diagnosis and more effective preventive and therapeutic measures for common oral diseases.
next-generation DNA sequencing; pyrosequencing; human microbiome; oral microbiome
Nitrogen (N) cycles have been directly linked to the functional stability of ecosystems because N is an essential element for life. Furthermore, the supply of N to organisms regulates primary productivity in many natural ecosystems. Microbial communities have been shown to significantly contribute to N cycles because many N-cycling processes are microbially mediated. Only particular groups of microbes were implicated in N-cycling processes, such as nitrogen fixation, nitrification, and denitrification, until a few decades ago. However, recent advances in high-throughput sequencing technologies and sophisticated isolation techniques have enabled microbiologists to discover that N-cycling microbes are unexpectedly diverse in their functions and phylogenies. Therefore, elucidating the link between biogeochemical N-cycling processes and microbial community dynamics can provide a more mechanistic understanding of N cycles than the direct observation of N dynamics. In this review, we summarized recent findings that characterized the microbes governing novel N-cycling processes. We also discussed the ecological role of N-cycling microbial community dynamics, which is essential for advancing our understanding of the functional stability of ecosystems.
nitrogen cycle; ecosystem functions; microbial community dynamics
The human microbial ecosystem plays a diversity of roles in human health and disease. Each individual can be viewed as an island-like “patch” of habitat occupied by microbial assemblages formed by the fundamental processes of community ecology, i.e., dispersal, local diversification, environmental selection, and ecological drift. In this review, we consider how community assembly theory, and as an example, metacommunity theory, could be used to help explain the dynamics of the human microbiome, and in particular, compositional variability within and between hosts. We explore three core scenarios of human microbiome assembly: development in infants, representing assembly in previously unoccupied habitat; recovery from antibiotics, representing assembly after disturbance; and invasion by pathogens, representing assembly in the context of invasive species. Judicious application of ecological theory may lead to improved strategies for maintaining and restoring the microbiota, and the crucial health-associated ecosystem services that it provides.
The oral microbiota survives daily physical and chemical perturbations from the intake of food and personal hygiene measures, resulting in a long-term stable microbiome. Biological properties that confer stability in the microbiome are important for the prevention of dysbiosis—a microbial shift toward a disease, e.g., periodontitis or caries. Although processes that underlie oral diseases have been studied extensively, processes involved in maintaining of a normal, healthy microbiome are poorly understood. In this review we present our hypothesis on how a healthy oral microbiome is acquired and maintained. We introduce our view on the prenatal development of tolerance for the normal oral microbiome: we propose that development of fetal tolerance toward the microbiome of the mother during pregnancy is the major factor for a successful acquisition of a normal microbiome. We describe the processes that influence the establishment of such microbiome, followed by our perspective on the process of sustaining a healthy oral microbiome. We divide microbiome-maintenance factors into host-derived and microbe-derived, while focusing on the host. Finally, we highlight the need and directions for future research.
oral microbiome; placenta; tolerance; mucosal immunity; stability; colonization resistance
Microbial communities are typically large, diverse, and complex, and identifying and understanding the processes driving their structure has implications ranging from ecosystem stability to human health and well-being. Phylogenetic data gives us a new insight into these processes, providing a more informative perspective on functional and trait diversity than taxonomic richness alone. But the sheer scale of high resolution phylogenetic data also presents a new challenge to ecological theory. We bring a sampling theory perspective to microbial communities, considering a local community of co-occuring organisms as a sample from a larger regional pool, and apply our framework to make analytical predictions for local phylogenetic diversity arising from a given metacommunity and community assembly process. We characterize community assembly in terms of quantitative descriptions of clustered, random and overdispersed sampling, which have been associated with hypotheses of environmental filtering and competition. Using our approach, we analyze large microbial communities from the human microbiome, uncovering significant variation in diversity across habitats relative to the null hypothesis of random sampling.
Microbial diversity analyses have revolutionized our knowledge of the microscopic world, from terrestrial and marine to human and urban environments. This growing field rests on the evolutionary relatedness of organisms, and at its frontier is the inference of ecological processes from phylogenetic diversity. However, the rapidly reducing cost of sequencing means that computational analysis of phylogenetic data is becoming increasingly intractable. We develop a new analytical method to address this issue, providing a computationally-efficient way to compare local phylogenetic diversity to a sample from a regional pool of organisms, under a given ecological process. Our approach has both pragmatic and far-reaching applications. Until now investigators have lacked even an analytical method to compare the diversity of unequally-sized communities without throwing data away, while on a deeper level our theory provides a new framework for connecting phylogenetic data to a wide range of ecological processes. As an application of our approach, we use our methods to distinguish between random, clustered and overdispersed sampling for human microbiome habitats. Finally, we identify a new, phylogenetic analogue of the widely used taxonomic measure of diversity, the Species Abundance Distribution, and we find that it has consistent behavior across microbiome habitats.